how to do research in neuroscience

What Is A Neuroscientist? Responsibilities, Education And Qualifications

Updated: Jan 8, 2024, 12:56pm

Neuroscience, or the study of the brain, is a complex, diverse and interdisciplinary field that draws from biology, psychology, medicine, computer science and many other disciplines. Neuroscientists are medical scientists who research the inner workings of the nervous system and brain to better understand how they develop and function.

These insights can lead to better diagnoses and treatments for people with various neurological disorders and diseases, including Alzheimer’s disease, autism spectrum disorders and depression. Neuroscience can also give us a better understanding of the brain overall and what it means to be human.

Keep reading to learn more about how to become a neuroscientist, including education requirements, the salary and job outlook for this career and potential specializations.

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What Is a Neuroscientist?

A neuroscientist is a medical scientist who studies the brain and the rest of the nervous system, including the spinal cord and nerves. Many specializations exist within neuroscience, and neuroscientists focus on diverse issues and topics.

To become a neuroscientist, you’ll need at least a master’s degree in neuroscience. However, depending on your specialty area and the type of work you want to do, you may also need a Ph.D. in neuroscience. If you want to practice clinical neuroscience and work with patients, you’ll also need to go to medical school and earn your M.D.

Typical Responsibilities and Job Tasks

Because neuroscience incorporates so many subdisciplines, neuroscientists typically concentrate their research on a specific area, like clinical neuroscience, molecular neuroscience or neurogenetics. Their responsibilities vary depending on their specialty and research focus.

The list below includes some common responsibilities for neuroscientists:

• Plan and oversee research projects
• Write grant proposals to get funding for research projects
• Analyze data
• Conduct neurological clinical trials
• Publish and present research findings
• University-level teaching
• Diagnosing and creating treatment plans for patients
• Use laboratory equipment to monitor brain activity

Work Environment

A neuroscientist’s work environment depends on various factors, particularly specialization. For example, you likely will not find yourself working in a hospital or clinic unless you’re a clinical neuroscientist or neurologist. Other common work environments may include:

• Research and development
• Medical and diagnostic laboratories
• Universities
• Pharmaceutical companies
• Government agencies

How To Become a Neuroscientist

Neuroscience involves a long career path. Here’s how to become a neuroscientist:

Earn a Bachelor’s Degree

Some colleges offer undergraduate programs in neuroscience, but biology or another scientific major can also prepare you to pursue a career in the field. Some biology programs offer a concentration in neuroscience.

Neuroscience bachelor’s programs provide a foundation in the theory and practice of neuroscience. A typical curriculum covers the functioning and structure of the nervous system and brain, including classes like principles of genetics, molecular biology and introduction to statistics.

A typical bachelor’s in neuroscience takes full-time students four years to complete. Graduates must pursue further education in the field to become professional neuroscientists.

Earn a Master’s Degree in Neuroscience

Neuroscience master’s programs cover more advanced topics, providing students with more experience conducting laboratory research and specialized training in an area of expertise.

A typical curriculum includes classes like disorders of the developing nervous system, neurobiology of behavioral disorders, lab techniques and biostatistics. Students must often complete a research thesis or capstone project to graduate. Full-time neuroscience master’s students can typically graduate in two years.

Earn a Ph.D. in Neuroscience

Though you can qualify for some neuroscience jobs with a master’s degree, most advanced research careers in the field require a Ph.D. in neuroscience. Some students complete a master’s degree before applying to a Ph.D. program. Others come straight from undergrad.

Neuroscience Ph.D. programs include advanced coursework and lab work. Students must design and complete a research dissertation or thesis and defense. Ph.D. students choose a narrow research area, and a typical neuroscience Ph.D. takes four to six years to complete.

Besides taking classes, conducting research and working on a dissertation, neuroscience doctoral students may also teach undergraduate courses.

Consider Going to Medical School

Clinical neuroscientists, who work with patients, must earn an M.D. Medical students learn about anatomy, pharmacology, diagnosis and treatment of patients, and medical ethics. Coursework takes place both in the classroom and through hands-on experience with patients. After completing medical school, graduates must earn a physician’s license.

Some schools offer dual Ph.D./M.D. programs for prospective neuroscientists and neurologists who want to do academic work. These programs typically take seven to eight years to complete.

Complete Postdoctoral Training or a Medical Residency

Depending on whether you earn a Ph.D. or a medical degree, you can consider starting your career with either a postdoctoral appointment or a residency.

Ph.D. graduates often pursue postdoc research training positions, which let them work with more experienced professionals in their focus areas and hone their research skills.

After earning their medical degree, graduates typically complete a residency to gain hands-on experience in their specialty. Residencies can last three to nine years.

Neuroscientist Salary and Job Outlook

Neuroscientist salaries vary depending on position, specialty, research area, education and experience. However, neuroscientists can generally expect a promising job outlook and high salaries in the coming years.

The U.S. Bureau of Labor Statistics (BLS) doesn’t collect data on neuroscientists specifically, but it lists information for medical scientists, which includes neuroscientists. The BLS projects a 10% growth in employment for medical scientists between 2022 and 2032—much faster than the national average growth rate for all occupations.

The BLS reports that medical scientists earned a median annual salary of $99,930 as of May 2022. The top 10% of medical scientists earned more than $170,260.

Neuroscientists who earn an M.D. and become neurologists can make significantly higher wages. Neurologists made a median annual salary of $224,260 in May 2022, according to the BLS.

Neuroscientist Specializations

Neuroscience is a large and diverse field, so neuroscientists can choose from many different specialty areas that require advanced knowledge and skills. Explore several neuroscientist specializations below.

• Behavioral neuroscience: This specialization researches the ways the brain impacts and influences behavior.
• Clinical neuroscience: Clinical neuroscientists focus on research and treatments related to brain and nervous system diseases and disorders.
• Cognitive neuroscience: This specialty focuses on cognitive processes and functions with the goal of understanding cognition at a neural level.
• Developmental neuroscience: This specialty explores the way the brain develops and changes over time.
• Molecular neuroscience: This area of neuroscience studies neurological functioning at the molecular level.

Professional Organizations for Neuroscientists

The following organizations provide professional support and resources for neuroscientists:

Society for Neuroscience (SfN): Founded in 1969, SfN is the largest professional organization for neuroscientists in the world with more than 35,000 members. It publishes two journals, offers professional development opportunities, educates the public and performs advocacy work on behalf of the field. The organization also hosts the world’s largest collection of neuroscience job listings.

American Neurological Association (ANA): ANA brings together academic neurologists and neuroscientists to advocate for a world free from neurological disease. Members access ANA’s annual meeting, professional journals, mentoring opportunities, and scholarships and awards. They can also use the ANA career center to learn about job openings.

Cognitive Neuroscience Society (CNS): CNS is a membership organization that focuses on neuroscience research related to cognition. The group includes 2,000 students, faculty and postdocs around the world. It publishes a journal and monthly newsletters, hosts an annual meeting and presents awards to notable professionals in the field.

Frequently Asked Questions (FAQs) About Becoming a Neuroscientist

What does a neuroscientist do.

Neuroscientists study the brain and the rest of the nervous system. Their focus areas and day-to-day tasks vary significantly depending on work environment and specialization. Typical tasks include writing grants, conducting experiments and publishing research.

How many years does it take to become a neuroscientist?

Becoming a neuroscientist takes anywhere from six to 12 years or longer depending on your specialty and career goals. Some neuroscience jobs require only a master’s degree in neuroscience. However, if you want to work in advanced research or clinical neuroscience, you should plan to spend an extra five to eight years completing a Ph.D., and potentially an M.D.

What qualifies you to be a neuroscientist?

Required qualifications vary by neuroscience subspecialty, but in general, you’ll need a master’s degree or Ph.D. in neuroscience to become a neuroscientist. If you want to work in clinical settings with patients, you’ll also need to complete medical school.

Is it hard being a neuroscientist?

Most people would say yes. It takes significant time and hard work to get into competitive graduate programs and complete the advanced degrees needed to become a neuroscientist.

Do you go to medical school to be a neuroscientist?

Not necessarily, as many neuroscience specialties do not require a medical degree. However, if you want to become a clinical neuroscientist who works with patients, you’ll need to go to medical school and earn your M.D.

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A Beginner’s Guide to Neuroscience

Bryn Farnsworth

Neuroscience is the study of the brain. The brain is perhaps the most complicated and intricate system that exists – it processes and creates almost every aspect of our conscious experience. The brain isn’t merely another organ in our body – we are our brains. This all goes to say – it’s pretty important.

Over the years, neuroscientists have attempted to clarify the complications and smooth out the intricacies of the brain in order to better understand it, and as a result, better understand ourselves.

Modern neuroscience began around the end of the 19th Century with the discovery of neurons (more on that below), but the first documented study of the brain can be traced back to a hieroglyphic in ancient Egypt. A physician at that time noted down a list of head injuries, listed potential remedies, and created the first instance of the word for “brain”.

While it took a while to go from “brain” to “neuron”, the leaps in knowledge have been faster and more frequent since. Below, we will go through the main components of modern neuroscience.

Table of Contents

What is neuroscience.

Neuroscience is an amalgamation of medical, evolutionary, and computational disciplines, fostering a deeper understanding of how the more than the approximately 85 billion nerve cells in the human brain are born, how they grow, and interconnect to form human thoughts and actions.

Basic Brain Facts

The brain consists of several discrete parts: most notably the cerebrum, the brain stem, and the cerebellum. The cerebrum consists of six brain areas that span two hemispheres. Four of these areas can be seen from the external view of the brain (shown in the image above) – the frontal, parietal, occipital, and temporal lobes. The two other lobes, the limbic and insular lobes, are found within the cerebrum.

The outermost part of the cerebrum is called the cortex (or sometimes the neocortex). This contains a sheet of neurons that wraps around all the lobes of the brain, and is roughly 1.5 – 3mm thick.

Neurons are the principal communicators of the brain – they are involved in sending signals from one region to another, and ultimately triggering actions, encoding and retrieving memories, and creating the experience of being, well, alive. A dense collection of neurons are found within the cortex, and other areas within the limbic and insular lobes. Neurons send the messages, and a variety of other cells – called glial cells – support and facilitate this communication.

Some of the most crucial and well-studied parts of the brain are:

• The frontal cortex (located at the very front of the frontal lobe) – plays role in a range of cognitive functions, including attention, decision making, planning complex behaviors, and regulating social actions. This is typically termed executive function.
• The middle of the brain (in the parietal lobe, roughly just after the border of the frontal cortex) is involved in both motor processing (the feeling of touch) and in motor coordination (movement) . These aren’t the only brain areas involved in these processes, but they are the principal actors.
• The occipital cortex is involved insight – it features many different layers, each of which processes a different component involved in perceiving the world visually.
• The temporal lobes are largely discussed for their relevance in language – the left side of the brain features Wernicke’s and Broca’s areas, involved in speech comprehension and speech production, respectively (there is a bit more complex than this seems to state, but the general concept holds true).
• Within the brain , there are several other regions that are of note, including the basal ganglia (a collection of regions involved in action selection), the hippocampi (involved in-memory processing), and the amygdalae (involved in fear processing).

There are many other critical and fascinating areas of the brain that appear increasingly specialized, depending on how closely you look at them (for example, the “ Grandmother / Jennifer Aniston” neuron ), but going through each is beyond the scope of what we can cover here. The important thing to note is not so much where each action appears to arise from, but that each will be driven by multiple components of the brain – no thought or action is an island.

Branches of Neuroscience

There are many different branches of neuroscience – everything from computational, to pharmacological, to molecular neuroscience and well beyond. Below we will explain two of the more common branches: cognitive and behavioral neuroscience.

Cognitive neuroscience

Cognitive neuroscience is concerned with the scientific study of biological substrates underlying cognition and mental processes and addresses questions such as how psychological/cognitive functions are reflected by neural activity in the brain.

Typical data collection methods employed by cognitive neuroscientists are functional neuroimaging ( fMRI , PET), electroencephalography (EEG), behavioral genetics, and lesion studies.

Behavioral neuroscience

In contrast, behavioral neuroscience (also known as biopsychology), addresses the impact of the nervous system on attention, perception, motivation, performance, learning, and memory and their manifestations in human behavior . Studies in behavioral neuroscience focus on the interaction of brain and behavior in real or simulated environments.

Check out: What is Neuropsychology?

The Nervous System and the Brain

The body is connected to the brain through a complex conglomerate of cells and nerves that transmits data back and forth between the brain, spinal cord, organs, and limbs. The brain and spinal cord are considered to be the Central Nervous System (CNS), as they integrate all incoming information from sensors and effectors, and modulate the activity of the body.

By contrast, the Peripheral Nervous System (PNS) comprises somatic and autonomous systems, responsible for voluntary control of skeletal muscles as well as involuntary regulation of bodily functions such as heart rate, digestion, respiration, pupillary response, urination, and sexual arousal.

Read more: An Introduction To The Sympathetic and Parasympathetic Nervous System

While the PNS is distinct from the CNS, there is a large amount of crosstalk between the two systems. It can therefore be informative to study the action of one of the systems in order to learn more about the other.

Neuroscience Core Concepts

According to the Society for Neuroscience , the following “Neuroscience Core Concepts” are the essential principles of this fascinating discipline:

• The brain is the body’s most complex organ.
• Neurons communicate using both electrical and chemical signals.
• Genetically determined circuits are the foundation of the nervous system.
• Life experiences change the nervous system.
• Intelligence arises as the brain reasons, plans, and solves problems.
• The brain makes it possible to communicate knowledge through language.
• The human brain endows us with a natural curiosity to understand how the world works.
• Fundamental discoveries promote healthy living and treatment of disease.

Top research areas that apply Neuroscience

The application of neuroscience has seen a large growth in different research areas. According to Frontiers in Neuroscience, the top 10 research areas are:

• Behavioral sciences
• Pharmacology, pharmacy
• Biochemistry, molecular biology
• PsychologyEndocrinology, metabolism
• Radiology, nuclear medicine, medical imaging
• Ophthalmology
• Geriatrics, gerontology

That’s not to say neuroscience hasn’t made an impact in the commercial applications; in areas like UX, Machine-human interactions, consumer behavior and neuromarketing. More and more commercial use cases are utilizing the power of neuroscience to gain a competitive advantage.

Read more: Consumer Neuroscience [Introduction & Examples] Understanding Consumer behavior

In summary, neuroscience tackles all questions concerned with the actions and operations of the brain – both before, during, and afterthought or behavior is completed.

Neuroscientists all over the world are constantly striving towards an increased understanding and knowledge of brain structures and function in conjunction with more reliable and valid research methods. The ultimate goal of which is to both provide a better understanding of who we are, while also enabling healthcare and medical science to develop new techniques and treatments to attenuate, counteract or even cure brain diseases such as Alzheimer’s or Parkinson’s.

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While there are more neuroscience studies being completed today than ever before, we’re still at the very beginning of fully uncovering the magnitude of what the human brain is actually capable of.

At iMotions , we provide a framework for collecting multi-dimensional behavioral and cognitive biosignals in order to help decode the fascinating complexity of the human nervous system and allow you to answer the research questions of today and tomorrow.

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Like a lot of people, Joy Franco gets in to work around 8 a.m. and starts her day checking on her projects, making lists of things to do and getting down to work. But unlike most people, Franco’s work is worms. She is a graduate student in the lab of neuroscientist Miriam Goodman , a professor of molecular and cellular physiology and a member of the Wu Tsai Neurosciences Institute  and Stanford Bio-X who studies the sense of touch in a tiny worm called C. elegans . Their goal is to better understand how the sense of touch works and why sometimes it doesn’t.

The work is enlightening, but often tedious. There are solutions to mix, experiments to run and worms to feed, a repetitive task that involves transferring worm food by pipette into hundreds of individual dishes. There is also the issue of failure – equipment breaks, reagents go bad or experiments produce results that make little sense.

Yet scientific work also brings its own special kinds of rewards. There is, for example, the hope that studying touch in worms will one day lead to treatments or therapies for people undergoing chemotherapy, which sometimes robs patients of their sense of touch. There is the pleasure that some get simply from making something, whether it’s a new piece of lab equipment or a scientific figure. And there is the unique if intermittent joy of discovering something new.

Here, Franco, Goodman, postdoctoral fellows  Dail Chapman  and Alakananda Das and graduate student Adam Nekimken talk about what it’s like to be a neuroscientist, what keeps them motivated and what other paths they might have taken – or might still take.

Versions of this story originally appeared in the Spring 2019 issue of Stanford Medicine magazine and on the Scope blog .

Miriam Goodman

Goodman first joined the Stanford faculty in 2002 and became chair of her department in 2017. She spoke about the joy of making, the necessity of failure and the particular kind of excitement that comes from scientific discovery.

“It’s that hope that your prediction will be correct and the openness to the possibility of seeing something you didn’t expect. Discoveries are literally intermittent reinforcement. Their timing is completely unpredictable.”

Read the interview

Franco is a third-year PhD student in the lab. In an audio story recorded for Stanford Medicine , she talks about the beauty of microscopic worms, how bikes got her into engineering and neuroscience, and the sometimes painful realities that drive her.

“I look at that and think, ‘Well, I have to do something in this world. I might as well help try to figure this out.’ And that is definitely wishful thinking, because it takes a lot of work from a lot of people, but it is what motivates me.”

Listen to the interview

Alakananda Das

Das is a postdoctoral fellow who joined Goodman’s lab in 2016. She spoke about the struggles of getting ideas to work, what keeps her going and the pleasure she finds in the sometimes repetitive work of laboratory science.

“There is a sense of accomplishment that you get when you finally get something. But the thing is, even with the ones that didn’t work out, we still learned something. It’s just not what we were expecting.”

Adam Nekimken

Nekimken is a fourth-year mechanical engineering graduate student now finishing up his doctorate. He talked about using engineering to further neuroscience, his interest in industry work and the value of basic science research.

“I’m working on new devices for studying the worms. Those in themselves are not going to cure any diseases, but you have to keep in mind the bigger picture: Maybe someone will see that and have an idea that could end up helping people.”

Dail Chapman

Chapman is a postdoctoral fellow in Goodman’s lab. She spoke about what makes science fun, how to deal with boredom and how she went about telling Goodman that she wanted to pursue a career in teaching.

“When she asked what I saw myself doing, I totally let my guard down and let my passion for teaching come through, and it was a nerve-wracking moment. You would think that would be a problem. And instead, it wasn’t.”

Miriam Goodman (Image credit: Timothy Archibald)

Now that she runs her own lab, and especially since she became chair of her department in 2017, Goodman spends more time in meetings and responding to email, taking her away from the work she most enjoys: making things – tools and equipment for the lab, or beautiful scientific figures for a publication. Running a lab is “analogous to being a coach,” she said. “I’m working to help everyone see where they can expand their knowledge, deepen their technical skills, overcome the fears that we all have.” Here, she talks about what she might have done had she not pursued science, the scientific necessity of failure and the idea that scientific research is a kind of intermittent reinforcement – a lab technique in which researchers dole out rewards on an unpredictable basis to train lab animals.

“When I first started the lab here, I was doing experiments myself recording from touch receptor neurons with Bob O’Hagan, who was then a graduate student at Columbia visiting Stanford so the two of us could work together on these neurons. Late in the day, near the end of Bob’s visit, we got the first data, and we’re sitting looking at the computer going, ‘Huh.’ The cell is activated when you push it, which was something I expected, and it was activated when you stop pushing, which is something I did not anticipate, and I was like, ‘That’s really cool.’ I’m sitting next to Bob, I’m whacking him on the shoulder. I don’t know what the heck it means, but wow, isn’t that cool?

“If you’re really lucky, that kind of discovery happens maybe two or three times a year. It’s that hope that your prediction will be correct and the openness to the possibility of seeing something you didn’t expect that I think keeps most scientists going. Discoveries are literally intermittent reinforcement. Their timing is completely unpredictable.

“Failure is a part of doing science too, because there’s uncertainty. You can’t make progress in an uncertain path if you don’t have failures. That doesn’t mean that you as a person are a failure. Everything that you’ve done up until this point – you spent the week tending your worms, getting them to the right stage. You learned how to use the microscope you didn’t know how to use before, and you found out the worms didn’t look like you expected them to look, but you still did those things. So now you just have to deal with this piece that maybe didn’t end up with the data you were seeking. But, in any given week, the data aren’t the only outcome that matters. The journey and the discovery and the data are intertwined.

“The other piece of it is, I do like to make stuff, and being in a research lab there’s always something you need to be making. If I had known that industrial design was a thing that you could do, maybe I would have done that. If I had known you could be a software engineer without debugging code endlessly, I might have done that. I’m not unhappy with what I’m doing. I wish I had more time to spend in the lab than I do.

“So let me tell one little anecdote that relates to the lab that I worked in as a college student writing code. During the last summer I was in the lab, the lab head was dying of lung cancer. The lab was actually physically in the hospital. He would come from his hospital bed – when he felt well enough to walk – to the lab to find out what we were doing because it made him happy, because it gave him joy, even though he was in enormous pain. There was something really powerful to see about that passion and connection that many scientists have to their work.

“I don’t know if I’ve got that deep a connection as he did, but if I can rebuild that connection with the experimental work through my own hands or those of the folks in my lab and in my collaborators’ labs, I can imagine that.”

Go to the web site to hear the audio.

Joy Franco (Image credit: Timothy Archibald)

Franco hopes to open up new ways of studying touch. She is a graduate student in mechanical engineering and focuses on finding better ways to grow touch-sensitive neurons in a dish. But she did not always imagine this life for herself – in fact, she did not always think she’d go to college. In the audio story above, Franco explains how her love of bicycles set her on a path to graduate school, what it’s like to be a neuroscientist and the experiences that keep her motivated to continue in scientific research.

(A transcript of the interview follows.)

Joy Franco  To me riding bikes was everything, right, and like it, it – riding bikes changed my life for the better in every way possible. And it made me so happy to see that another person could experience that.

Nathan Collins Hi, I’m Nathan Collins, and that was Joy Franco. Franco is a graduate student here at Stanford University, and I had the chance to interview her for a piece I did for Stanford Medicine magazine. I found out that Franco is not your typical graduate student. For one thing, she’s a mechanical engineer working in a neuroscience lab to study the sense of touch. For another she’s a competitive cyclist. And here’s the third thing: When she graduated from high school she didn’t think she was ever going to go to college. Here’s Franco.

Joy Franco So when I graduated from high school I didn’t really have any college prospects, so I moved out of the house, and I started working full time and living on my own. Over the years, you know, I really struggled to pay bills, struggled to be, you know, an early 20-something year old trying to find her own way in the Bay Area and make ends meet. So I went in and out of community college for a while and had been out of community college for a while when I was talking to some of my cyclist friends about bike fit.

Nathan Collins Specifically the biomechanics of bike fit, that is, how your position on a bike affects your speed and efficiency. It was that subject that propelled Franco back to school and on her way to a bachelor’s degree. But it was another experience with bikes that set her on the path to neuroscience.

Joy Franco So I went back to community college and was just in it, like really in it. And that first, first semester I was at community college, Cañada College in Redwood City, I was also racing mountain bikes still. And during a mountain bike race this guy with a prosthetic lower leg passed me. And it was pouring down rain and mud, and I just was really blown away by the rider – his spirit, but also that someone could engineer a tool that would enable him to do something that he wouldn’t normally be able to do. It kind of got me really interested in you know adaptive technologies for people who are mobility limited. I learned about neurally interfaced prosthetics, which is just so awesome, right?

Nathan Collins The prosthetics Franco is talking about are pretty awesome. They include robotic arms and computer keyboards that people with paralysis can control via electrodes implanted directly into their brains.

Joy Franco And that sort of got me interested in neuroscience, and I learned about Stanford being an amazing place to study neuroscience. And so my first semester back at community college, I immediately was like I want to go get a PhD at Stanford, you know. And so that was, you know, spring of 2011 and then everything from there on out was just, “what do I need to do to get a PhD at Stanford?”

Nathan Collins Of course, she did get into Stanford, and by the time she had, she had developed an interest in using mechanical engineering as a tool for understanding neuroscience. She had also gotten four equations tattooed on the inside of her right forearm. One of those equations describes the shape of a heart.

Joy Franco So it’s a very long equation that has one two three four components. I wanted to really commemorate that moment in my life because I had given up racing bicycles. I’d given up hanging out with my friends, and trying to get into Stanford PhD program meant that like I was working all day Saturday, all day Sunday – like I never took any time off, you know? And so it was just three years of really, really hard work and – and sacrifice.

Nathan Collins Today Franco works in a lab of neuroscientist Miriam Goodman. The goal of her work is to better understand our sense of touch and why sometimes people lose their sense of touch. But because it’s hard to study touch directly in humans the lab instead focuses on minuscule worms called Caenorhabditis elegans , or C. elegans for short. They’re about a millimeter long, and a lot of the lab’s time is spent developing techniques and equipment for gently stroking the worms to see how they’ll react. Some of their time goes to breeding different kinds of worms so that they can figure out how different genes affect the sense of touch. And a great deal of their time goes to simply feeding and caring for their worms. It is not always glamorous.

Joy Franco So the worms – they live on these agar plates. It’s kind of like jello, and on top of the jello is E. coli. There’s bacteria. You know, it’s not dangerous E. coli, but we have to actually put the E. coli on the plates in order to feed the worms. We call it seeding plates, and you seed probably like 200, two to three hundred of them at a time. And that is definitely the most boring task.

Joy Franco My favorite part of the job is looking at worms through the microscope. When you’re – you know you’re on the microscope and they’re moving around it is – it is just beautiful. And because the worms that we use we have fluorescent florophores in them, it’s really a glow worm that’s moving around. And I just can’t get past how awesome that is and how awesome it is that that’s my job.

Nathan Collins But why does she do it? Because touch is so important to our lives.

Joy Franco Loss of touch sensation is horrible. You know if you know someone who’s gone through chemotherapy it’s you know pretty easy to connect with them about how chemotherapy induced peripheral neuropathy is just a horrible thing.

Nathan Collins Usually people think of chemotherapy as the hard part of getting cancer treatment. But many who go through chemotherapy lose their sense of touch, sometimes permanently, and among Franco’s goals is to help those people. Another goal is to help combat neurodegenerative diseases including dementia and Alzheimer’s.

Joy Franco Neurodegeneration is horrible. It is absolutely my worst fear in life to end up with dementia. My, my grandmother, she had dementia right before she passed away. And I can tell you from firsthand observation it’s – it’s really not something that you want. It’s very, very sad and painful for everyone involved. And I sort of look at that and think, “well, I have to do something in this world. I might as well help try to figure this out.” Or, you know, who knows, maybe one day we can figure out a therapy or maybe one day figure out a cure. And that is definitely wishful thinking because it takes a lot of work from a lot of people, but it is what motivates me to keep coming back.

Nathan Collins  To find out more about Franco and Miriam Goodman’s Wormsense lab, read my story, “Life in a Lab,” in the latest issue of Stanford Medicine magazine.

Das grew up in India and came to the U.S. for graduate school before joining the Goodman lab, where she is now a third-year postdoctoral fellow. Her work focuses on the molecular basis of touch and other forms of mechanosensation, or how cells detect and respond to pressure. Day to day, that means she spends a lot of time editing worm genomes. Here, she talks about the struggles she’s faced getting a project to work, what keeps her going and the pleasure she finds in the sometimes repetitive work of laboratory science.

“In the past two years I have been working toward one goal, and everything that I’d tried had failed, and then just in the last month I got a success. I had these four methods lined up initially, and I went through one by one. It’s just the last one that worked. What got me through it is knowing it can be done, and then it’s just a matter of finding the right way to do it. There is a sense of accomplishment that you get when you finally get something, and the longer the frustration has been, the greater the sense of achievement. But the thing is, even with the ones that didn’t work out, that doesn’t mean it didn’t tell us something about the system. We still learned something. It’s just not what we were expecting.

“The work that I mostly do, the gene editing part, the designing of these changes that I’m going to put into the genome, I find that part really exciting. When I look at the process, it’s basically taking blocks of modules and putting them in different configurations and seeing what the result would be. So it kind of reminds me of building a machine and switching out different parts and seeing how the machine would function.

“I want to continue to do research. I don’t particularly prefer academia or industry or any such thing, but whatever would allow me to continue doing research in some form, I would be happy with it. I also enjoy teaching. So far I have mentored several undergrads, and I really enjoy that part, working with students, showing them things and training them in what I do. I really enjoy doing bench work. The repetitive motions of it, I find it actually calming. Being a Principal Investigator I find a little bit daunting, just because when you are a PI you are not involved in bench research that much. And I would really like to be involved in bench science.”

Image credit: L.A. Cicero

Nekimken originally planned to be a mechanical engineer and started working in the lab through a collaboration between Goodman and Beth Pruitt, a former professor of mechanical engineering. He is developing tiny mechanical devices that help researchers touch their worms in more controlled ways. Now at the end of his graduate training, Nekimken is considering his next steps.

“I knew when I started grad school I wanted to do micro-fabrication, making these little micro-mechanical devices. I did an internship after my junior year of undergrad and found that I really liked this stuff, but I didn’t want to work on the typical application of that, which is mostly making smartphones incrementally better. That was why I joined Beth Pruitt’s lab, because the theme of her lab is using these micro-fabricated technologies to study biology. I wasn’t looking in particular for biology, but it sort of ended up that was the application that she was working on, and one of the projects in lab was collaborating with Miriam to study touch in worms.

“I do like the interdisciplinarity of it. I’ve learned a ton of biology, and I think that’s interesting to learn a new field entirely. And I like working with the micro-devices because it’s something that almost feels out of reach. With modern manufacturing technology we can make devices that are very small and do what you want them to do. That’s sort of the engineer in me, designing new things and building them up and then actually using a thing that you built.

“My plan is to go into industry, in part because I want to work on something that has an impact at a larger scale than doing the research. For me, the biggest result of doing my PhD – other than publishing some papers that may have some impact – is learning the process of thinking through this research and learning how to formulate experiments and perform them.

“We do very basic biology research. Maybe with the exception of what Lingxin Wang [a postdoctoral fellow in the lab] is doing studying chemotherapy drugs, not much of the work we do has direct applications for humans. I’m working on new devices and new methods for studying the worms, so those on their own are not going to cure any diseases or anything, but you have to keep in mind the bigger picture: Maybe someone will see that and have an idea that leads them to some other insight that could end up helping people.

“You’ll hear people talk about, ‘Why are we funding this and that stupid research when you could have just done this one that leads to some big thing?’ But the problem is, we just don’t know that ahead of time, and so we have to do all of this research and just try and understand the natural world, with the foreknowledge that someone, somewhere, will have an important insight.”

Dail Chapman (Image credit: Timothy Archibald)

Unlike many of her colleagues, Chapman came to the lab planning one day to teach science at a liberal arts college. A mug above her desk reads, “I became a teacher for the money and fame.” She had some trepidation about revealing her long-term plan when Goodman interviewed her for her current position in the lab.

“I was super apprehensive about even bringing up teaching because I think, as scientists, the expectation is that you want to become a renowned researcher. So I was talking to my boss about how I should conduct the interview, and whether I should be honest about my career pursuits, and he told me, ‘No.’

“But it just didn’t feel right to essentially lie to someone who may become my future boss. And it’s not in my character. So when she asked what I saw myself doing, I totally let my guard down and let my passion for teaching come through, and it was a nerve-wracking moment. You would think that would be a problem. And instead, it wasn’t. She said, ‘No, I love teaching and I like to teach every chance I can get. I went to a small liberal arts college too, and I know what it’s like to be in that environment where teaching is at the center of the institution’s goals.’

“I do the research partly because I find it very interesting. I love to think about how we work as human beings, and in this lab particularly, how our nervous system works and our neurons work. It’s also fun. I think it’s fun because the people make it fun. I wouldn’t consider pipetting all day fun. But when you’re working with really talented individuals, and those days when something does work, that’s really fun. But I also do it because it’s a step in my career path and I think to be a good teacher you have to have experience with good science. And the only way to do that is to do the science.

“Anything that bores me? I think the days where you’re just in the middle of three different experiments that you’ve done before, so you’re not working yourself mentally. It’s a little bit of a battle as a scientist, because I think what happens in those moments is you have an inner dialogue with yourself and you start to predict, OK, what’s going to go wrong and how can I fix it? And if you spend your days thinking about what could go wrong, it’s not really mentally healthy. So finding ways to keep that conversation at a minimum and just do the task you need to do without making mistakes, I think that’s how you combat that boredom.

“Failure is just part of science, and for me it’s just a test of persistence and a test of using my resources and using my own brain to figure out what’s going wrong in order to get it right. But that’s been a huge struggle for me with science and something that’s really turned me on to teaching, because you can work for years and years on a science project and it’s nothing in your control, it’s just not going to work. And so I think teaching offers that kind of daily sense of reward and that I’ve spent my time well.”

Harvard University COVID-19 updates

Undergraduate Neuroscience Research and Thesis Neuro Research Guide

Neuro research guide, guide to choosing a neuroscience research lab, part 1: what factors to consider when joining a lab..

There are hundreds of Harvard affiliated labs, and all of them are different. Below, we have distilled those differences into several important categories to consider as you make your choice. The overarching goal is to help you choose a lab that will foster your development as a scientist.

Research Topic. Students often feel that the research topic is the decisive factor. For example, you may have a topic that you are already passionate about ( e.g., Alzheimer’s Disease, Traumatic Brain Injury, or Free Will). While this pre-existing interest can be a great motivator, consider the following: over three semesters and one summer, you will spend > 750 hours in the lab! For that much time, we think the priority should be finding a great lab environment with supportive mentors.

• False! A topic isn’t usually exciting until you learn more about it. We find that once students realize what questions are being asked, what the debates are in this area, and what real-world implications the topic has, what methods are being used/developed, they quickly become passionate about their work.
• False! Medical and graduate schools are evaluating your lab experiences in two major ways: First, they want evidence of your scientific development in the lab, including your independence in designing experiments/analyzing data and your understanding of your research topic. Second, they want to see an authentic, personalized, positive letter from your faculty mentor detailing your drive, independence, attention to detail, collegiality, etc .

Size of the lab. The size of the lab is not always, but very often, a predictor of student success. Higher visibility labs tend to be big (>20 people). Although they are publishing great papers, the environment may not be ideal for undergrads. Why? In bigger labs, it is harder to get face time with the faculty mentor. Moreover, each daily supervisor (post doc or grad student) may have several undergraduates working with them or be consumed with their own work. Students can feel lost when they do not get enough mentoring and attention. Another peril of working for ‘rockstar’ faculty is that projects may be aiming for publication in Nature, Science, or Cell, which can take more than 5 years of full-time teamwork … when things go right. As such, students are often a small part of a bigger, longer project (a ‘cog in the machine’). Their role is often more of a technical one with less control, thought, or creativity in the experimental design and therefore less scientific development and growth.

In spite of the drawbacks, some students prefer to work on high visibility projects in big labs. That is fine but be aware that these projects can lead to lower thesis evaluation grades because faculty evaluators look for evidence that the student has put independent thought and individual work into the thesis.

Independence and Project Type. It is daunting to be responsible for your own research project’s success or failure, especially when you are just starting out in science. Yet, this really is the best way to learn how to do science. Having to make decisions about what experiment to do or how to analyze your work requires a deep understanding of your research area. Large, team projects can be fun, but students often grow and learn more from small projects where they can make decisions.

Typically, projects that are small in scope (short term) work best, so you can learn from your mistakes and get feedback on your results within weeks to reconfigure if need be. Working more independently on a project also gives you more control of your data, rather than being handed data from someone else and not having any influence on how or why the experiments were done.

Mentoring. Arguably the most important aspect of your lab experience is your direct mentor. Try to meet your direct mentor before signing up with a lab. You want to make sure that they are invested in your success: meaning, 1) they has time to meet with your regularly, they have reasonable demands on your time (15 hours or less per week during the term), and they can communicate clearly with you.

Whatever lab you’re in, be sure to schedule face time with the faculty mentor (alone or with your daily supervisor) at least once per month. This will help you forge a connection to the lab head and be part of conversations that can influence your study design and color the interpretation of your results. You should also make an effort to attend lab meeting to learn about other projects and develop your critical thinking/questioning skills.

Commute/ Location. It might seem harrowing, but commuting to a lab is very possible. The free M2 shuttle can get you to the Longwood/hospital area in about 30 minutes (outside of rush hour times and extreme weather). The MBTA can get you to MGH, MIT, Broad Institute just as quickly. As long as you can arrange your schedule to have big blocks of consecutive lab time (3 hours), the commute will only be a fraction of your dedicated lab time.

The good news, if you don’t want to commute, is that labs closer to Cambridge typically have more experience working with undergrads. This often translates into a better mentoring culture for students. All things being equal, we recommend you start looking for a lab on campus (Biolabs, NW Building, William James). If you don’t find a good fit there, consider labs at the Medical School that are affiliated with the Program for Neuroscience . If you still aren’t satisfied, you can extend your search to other Harvard-affiliated hospitals or centers (MGH, Children’s Hospital, Beth Israel, Brigham and Women’s, McLean, etc .)

Part 2: Questions to ask before joining a lab.

Here is a list of potential questions to ask the lab director when you meet to talk about joining a lab:

• Typically, students meet with the faculty mentor two or three times per semester. Its great if it is more frequently, but it should not be less.
• Typically, students work with a grad student or a post-doc. They often meet every time the student comes to lab (at least at the beginning) and communicate informally by email. Since they play a big role in mentoring you, it is always a good idea to meet them before joining.
• Student projects are most rewarding when students are involved in experimental design and all aspects of data analysis. It gives the student more ownership and control of the project, which very often creates a better environment to learn to do science.
• Longer term experiments (more than a year) are usually team-projects where students don’t have much influence or control of the project.
• While every student is different, other undergrads in the lab can usually tell you what kind of experience to expect. (Laura and Ryan can give you feedback on labs students have worked in as well in case you want an additional opinion.)
• Lab meetings can be a great way to assess the group dynamics and lab culture to make sure it feels like a comfortable and stimulating environment for you.
• Typically, students should expect to spend 5-10 hrs/week if they are volunteering in the lab during the semester, or 10-15 hrs/week if they are enrolled in research for credit (Neuro 91). Most labs expect students spend one summer working full-time in the lab (often before senior year) if they are serious about a thesis or a career in research after graduation.
• This varies by lab: sometimes students will choose among several options. Sometimes there might only be one project that needs additional help (or has an available mentor). Occasionally, faculty mentors want students to develop their own project idea! You just don’t know until you ask.

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• Published: 23 November 2020

Focus on neuroscience methods

Nature Neuroscience volume  23 ,  page 1455 ( 2020 ) Cite this article

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In this special issue, we present a series of reviews, perspectives and commentaries that highlight advances in methods and analytical approaches and provide guidelines and best practices in various areas of neuroscience.

Neuroscience is continuously evolving, as technologies and analytical approaches—from the molecular and cellular levels to the systems and behavioral levels—open up previously inaccessible research questions and strategies. Understanding the transformative aspects of new approaches, as well as their limitations, is central to the progress that these techniques enable. Nature Neuroscience presents a special focus issue that highlights advances in methods, analyses and practices across scales of investigation and subfields of neuroscience.

Single-cell transcriptomics has enabled detailed profiling of cellular heterogeneity in the brain, but it is equally important to arrive at principled approaches to organize and interpret those data. Rafael Yuste, Michael Hawrylycz, Hongkui Zeng, Ed Lein and colleagues discuss the systematic classification of cortical cell types based on transcriptomics and the orthogonal modalities of morphology, physiology and connectivity, and they propose the implementation of a community platform for aggregating and updating the taxonomies in a standardized manner.

The analysis of cell types illustrates the complexity of brain organization. Three-dimensional tissue culture has propelled the modeling of human brain development in healthy and disease states. Ilaria Chiaradia and Madeline Lancaster provide an overview of brain organoids as a research tool and discuss their use in the study of brain development and disease. The authors further highlight technical innovations and advances in protocols used to generate brain organoids, as well as future avenues for modeling with organoids.

Human-derived induced pluripotent stem cells (iPSC) have led to the development of improved models of brain disorders, but functionally characterizing disease-associated genomic loci remains a challenge. Kayla Townsley, Laura Huckins and Kristen Brennand discuss approaches for assessing the functional effects of psychiatric and neurodevelopmental disease candidate risk variants, including how they may be localized to specific cell types and how they may affect gene expression.

The analysis of brain function also calls for ways to probe neural activity in vivo. Extending the scales of measurement and types of questions that can be addressed in behaving animals has necessitated the development of new technologies. John Rogers and colleagues review advances in electrical, optical and microfluidic devices and sensors for recording and manipulating neural signals. The authors highlight cutting-edge engineering and novel systems that can be implemented wirelessly in freely moving animals.

An invaluable window through which to view the brain is the analysis of behavior. Recent developments have transformed the ways in which behavioral measurement can be achieved. Talmo Pereira, Joshua Shaevitz and Mala Murthy provide an overview of techniques for automated quantification of behavior in animals, including deep-learning-based pose estimation methods and approaches for extracting and classifying behavioral dynamics. The authors discuss ways to relate quantitative behavioral analyses to recordings of neural activity, a strategy that can provide insights into neural coding.

The noninvasive imaging of spontaneous neural activity has yielded insights into functional brain network organization. Janine Bijsterbosch, Eugene Duff and colleagues discuss analytical approaches to represent functional connectivity data and, importantly, consider how the choice of representation can shape the interpretation of functional brain organization. The authors provide guidelines aimed at improving generalizability and reproducibility in the field.

Data acquisition, analysis and reporting can all influence research reproducibility and replicability. In their perspective, Cyril Pernet, Aina Puce and their colleagues from the COBIDAS MEEG committee discuss issues related to electroencephalography (EEG) and magnetoencephalography (MEG) studies, and they provide best practice recommendations to promote interpretability, sharing and reuse of these data.

We assembled this special issue to highlight techniques and analytical approaches that facilitate research across diverse areas of neuroscience, together with recommendations and guidelines for best practices. This issue also contains several Technical Reports that describe novel methods and approaches, including genetic tools for selective labeling and manipulation of cells, imaging for neuronal circuit reconstruction, and analysis of electrophysiological recordings and network functional connectivity. With this, we aim to signal our continued interest in publishing transformative technologies and methods in neuroscience and our commitment to enhancing data and analytical reporting and reproducibility. This collection represents only a small sample of the broad array of approaches that are enabling new discoveries in neuroscience. Exciting advances in genetic strategies for targeting cell populations, optical methods for recording and manipulating neuronal ensembles in behaving animals, and computational and analytical approaches for interpreting large-scale brain activity are among the many types of developments that we anticipate will facilitate new insights in the near future. We look forward to hearing about these and other novel advances as they continue to push neuroscience into interesting new directions.

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Fundamental Neuroscience Research

For well over a century, discoveries in basic neuroscience research have been the basis for our understanding of the nervous system and the foundation for developing treatments for neurological disorders. Insights into fundamental neuroscience (FN) have advanced at an ever-faster pace in the 21 st  century, with remarkable novel discoveries in areas ranging from subcellular mechanisms of action to whole brain activities. FN generates key insights, drives innovation, and underlies many therapeutic breakthroughs that benefit humanity.

Background:

Neurology’s Stake in Foundational Neuroscience Research

Back to Basics: A call for fundamental neuroscience research

Discussion of Present and Future Plans For NINDS Support of Fundamental Neuroscience

The National Institute of Neurological Disorders and Stroke ( NINDS ) is planning a critical effort focused on advancing research in Fundamental Neuroscience (FN). To address this important and foundational aspect of neuroscience research, NINDS convened a  Fundamental Neuroscience Working Group (FNWG) . FNWG held a series of meetings to discuss key issues and prepared a report to the NANDS Council with recommendations to inform NINDS approaches and plans to support and foster FN research. The report will be presented to NANDS Council on Wednesday September 6, 2023: NIH VideoCast - NANDS Council - September 2023 .

FNWG activities and materials can be found here: Fundamental Neuroscience Working Group (FNWG) . The FNWG's report on Advancing Fundamental Neuroscience Research (pdf, 915 KB) can be found here. Members of the public are encouraged to submit comments on the report, council presentation or any points related to promoting FN research to  [email protected] .

Do you have a cool image related to FN that you would like share?

If so, please include your image with the following information and submit to: [email protected]

• Description of image
• Credit of image

NOTE: Image should be in a jpg, jpeg, gif, png or pdf format.

Resources and Tools

Animal Models Resources available to neuroscience researchers interested in utilizing animals as models for nervous system function.

Gene Expression The resources listed below are for gene expression-related information relevant to neuroscience research.

Strategic Plan NINDS supports and performs a broad array of rigorous and important neuroscience research from fundamental studies of basic nervous system function to studies to improve treatments and prevent neurological disorders.

NINDS Funding Strategy Current NINDS funding guidelines and payline.

NIH BRAIN Initiative The  Brain Research Through Advancing Innovative Neurotechnologies®  ( BRAIN ) Initiative is aimed at revolutionizing our understanding of the human brain.

Building Up The Nerve Neuroscience trainees are taken through the life cycle of a grant from idea to award at NINDS with the people who make it happen.

Find an NINDS Program Director Please reach out to individual Program Directors or Program Managers for more information about specific opportunities or visit  Find Your Program Director  to learn more.

Contact Us We would love to hear from you!

Related FN Articles Inviting your input: fostering research in fundamental neuroscience Request for Information (RFI) on Advancing Research in Fundamental Neuroscience   (Expired)

Research Topics & Ideas: Neuroscience

50 Topic Ideas To Kickstart Your Research Project

If you’re just starting out exploring neuroscience-related topics for your dissertation, thesis or research project, you’ve come to the right place. In this post, we’ll help kickstart your research by providing a hearty list of neuroscience-related research ideas , including examples from recent studies.

PS – This is just the start…

We know it’s exciting to run through a list of research topics, but please keep in mind that this list is just a starting point . These topic ideas provided here are intentionally broad and generic , so keep in mind that you will need to develop them further. Nevertheless, they should inspire some ideas for your project.

To develop a suitable research topic, you’ll need to identify a clear and convincing research gap , and a viable plan to fill that gap. If this sounds foreign to you, check out our free research topic webinar that explores how to find and refine a high-quality research topic, from scratch. Alternatively, consider our 1-on-1 coaching service .

Neuroscience-Related Research Topics

• Investigating the neural mechanisms underlying memory consolidation during sleep.
• The role of neuroplasticity in recovery from traumatic brain injury.
• Analyzing the impact of chronic stress on hippocampal function.
• The neural correlates of anxiety disorders: A functional MRI study.
• Investigating the effects of meditation on brain structure and function in mindfulness practitioners.
• The role of the gut-brain axis in the development of neurodegenerative diseases.
• Analyzing the neurobiological basis of addiction and its implications for treatment.
• The impact of prenatal exposure to environmental toxins on neurodevelopment.
• Investigating gender differences in brain aging and the risk of Alzheimer’s disease.
• The neural mechanisms of pain perception and its modulation by psychological factors.
• Analyzing the effects of bilingualism on cognitive flexibility and brain aging.
• The role of the endocannabinoid system in regulating mood and emotional responses.
• Investigating the neurobiological underpinnings of obsessive-compulsive disorder.
• The impact of virtual reality technology on cognitive rehabilitation in stroke patients.
• Analyzing the neural basis of social cognition deficits in autism spectrum disorders.
• The role of neuroinflammation in the progression of multiple sclerosis.
• Investigating the effects of dietary interventions on brain health and cognitive function.
• The neural substrates of decision-making under risk and uncertainty.
• Analyzing the impact of early life stress on brain development and mental health outcomes.
• The role of dopamine in motivation and reward processing in the human brain.
• Investigating neural circuitry changes in depression and response to antidepressants.
• The impact of sleep deprivation on cognitive performance and neural function.
• Analyzing the brain mechanisms involved in empathy and moral reasoning.
• The role of the prefrontal cortex in executive function and impulse control.
• Investigating the neurophysiological basis of schizophrenia.

Neuroscience Research Ideas (Continued)

• The impact of chronic pain on brain structure and connectivity.
• Analyzing the effects of physical exercise on neurogenesis and cognitive aging.
• The neural mechanisms underlying hallucinations in psychiatric and neurological disorders.
• Investigating the impact of music therapy on brain recovery post-stroke.
• The role of astrocytes in neural communication and brain homeostasis.
• Analyzing the effect of hormone fluctuations on mood and cognition in women.
• The impact of neurofeedback training on attention deficit hyperactivity disorder (ADHD).
• Investigating the neural basis of resilience to stress and trauma.
• The role of the cerebellum in non-motor cognitive and affective functions.
• Analyzing the contribution of genetics to individual differences in brain structure and function.
• The impact of air pollution on neurodevelopment and cognitive decline.
• Investigating the neural mechanisms of visual perception and visual illusions.
• The role of mirror neurons in empathy and social understanding.
• Analyzing the neural correlates of language development and language disorders.
• The impact of social isolation on neurocognitive health in the elderly.
• Investigating the brain mechanisms involved in chronic fatigue syndrome.
• The role of serotonin in mood regulation and its implications for antidepressant therapies.
• Analyzing the neural basis of impulsivity and its relation to risky behaviors.
• The impact of mobile technology usage on attention and brain function.
• Investigating the neural substrates of fear and anxiety-related disorders.
• The role of the olfactory system in memory and emotional processing.
• Analyzing the impact of gut microbiome alterations on central nervous system diseases.
• The neural mechanisms of placebo and nocebo effects.
• Investigating cortical reorganization following limb amputation and phantom limb pain.
• The role of epigenetics in neural development and neurodevelopmental disorders.

Recent Neuroscience Studies

While the ideas we’ve presented above are a decent starting point for finding a research topic, they are fairly generic and non-specific. So, it helps to look at actual studies in the neuroscience space to see how this all comes together in practice.

Below, we’ve included a selection of recent studies to help refine your thinking. These are actual studies,  so they can provide some useful insight as to what a research topic looks like in practice.

• The Neurodata Without Borders ecosystem for neurophysiological data science (Rübel et al., 2022)
• Genetic regulation of central synapse formation and organization in Drosophila melanogaster (Duhart & Mosca, 2022)
• Embracing brain and behaviour: Designing programs of complementary neurophysiological and behavioural studies (Kirwan et al., 2022).
• Neuroscience and Education (Georgieva, 2022)
• Why Wait? Neuroscience Is for Everyone! (Myslinski, 2022)
• Neuroscience Knowledge and Endorsement of Neuromyths among Educators: What Is the Scenario in Brazil? (Simoes et al., 2022)
• Design of Clinical Trials and Ethical Concerns in Neurosciences (Mehanna, 2022) Methodological Approaches and Considerations for Generating Evidence that Informs the Science of Learning (Anderson, 2022)
• Exploring the research on neuroscience as a basis to understand work-based outcomes and to formulate new insights into the effective management of human resources in the workplace: A review study (Menon & Bhagat, 2022)
• Neuroimaging Applications for Diagnosis and Therapy of Pathologies in the Central and Peripheral Nervous System (Middei, 2022)
• The Role of Human Communicative Competence in Post-Industrial Society (Ilishova et al., 2022)
• Gold nanostructures: synthesis, properties, and neurological applications (Zare et al., 2022)
• Interpretable Graph Neural Networks for Connectome-Based Brain Disorder Analysis (Cui et al., 2022)

As you can see, these research topics are a lot more focused than the generic topic ideas we presented earlier. So, for you to develop a high-quality research topic, you’ll need to get specific and laser-focused on a specific context with specific variables of interest.  In the video below, we explore some other important things you’ll need to consider when crafting your research topic.

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What Is Neuroscience?

Reviewed by Psychology Today Staff

Neuroscience examines the structure and function of the human brain and nervous system. Neuroscientists use cellular and molecular biology, anatomy and physiology, human behavior and cognition , and other disciplines, to map the brain at a mechanistic level.

Humans have an estimated hundred billion neurons, or brain cells, each with about a thousand connections to other cells. One of the great challenges of modern neuroscience is to map out all the networks of cell-to-cell communication—the brain circuits that process all thoughts, feelings, and behaviors. The resulting picture, emerging bit by bit, is known as "the connectome." The ability of the brain to elaborate new connections and neuronal circuits—neuroplasticity—underlies all learning.

Biology and psychology unite in the field of neuroscience, to tackle questions such as the brain’s role in pain perception or the underlying cause of Parkinson’s disease. Computer simulations, imaging, and other tools give researchers and medical experts new insight into the physical anatomy of the brain, its five million kilometers of wiring, and its relationship to the rest of the mind and body.

Just as computers are hard-wired with electrical connections, the brain is hard-wired with neural connections. These connections link together its various lobes and also link sensory input and motor output with the brain’s message centers, allowing information to come in and be sent back out.

One major aim of current neuroscience research, then, is to study how this wiring works and what happens when it's damaged. New developments in brain scanning allow researchers to see more detailed images and determine not only where there may be damage but also how that damage affects, for instance, motor skills and cognitive behavior in conditions like multiple sclerosis and dementia .

A rapidly expanding discipline, neuroscience findings have grown by leaps and bounds over the past half-century. More work, however, will always be needed to fully understand the neural roots of human behavior, consciousness, and memory .

We are intimately familiar with them but defining them is harder than it seems. Understanding how emotions give rise to subjective feelings is key to understanding consciousness.

Viewing art is more complex than it appears. Find out how neuroaesthetics can affect your well-being and outlook on life.

What if bullying was addressed from a stress response perspective? Looking through this lens opens up the possibilities to remedy bullying conduct.

To save your brain from neurobiological boredom, stand up and be squatted every now and then, and start to think of sitting as part of your interruption of standing.

Are studies of music therapy’s impact reliable?

Over the years therapists have largely overlooked dreamwork as a form of therapy. The research suggests we should bring it back.

August 13, 2024, is International Left-Handers Day. Here are 5 scientific facts about left-handedness and musicality.

One of the most debated areas of intent is decoding whether or not someone is lying. An enormous amount of human effort has gone into increasing our ability to detect liars.

A multicenter trial shows that transcranial magnetic stimulation is significantly more effective than successive medication trials for patients with treatment-resistant depression.

Pregnancy and postpartum are for some women associated with a temporary reduction in eating disorder symptoms. Changes in hormones and neurotransmitters may mediate these effects.

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Conflicts of Interest

• Sepahvand, T.; Power, K.D.; Qin, T.; Yuan, Q. The Basolateral Amygdala: The Core of a Network for Threat Conditioning, Extinction, and Second-Order Threat Conditioning. Biology 2023 , 12 , 1274. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Skolariki, K.; Vrahatis, A.G.; Krokidis, M.G.; Exarchos, T.P.; Vlamos, P. Assessing and Modelling of Post-Traumatic Stress Disorder Using Molecular and Functional Biomarkers. Biology 2023 , 12 , 1050. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Lawson, L.; Spivak, S.; Webber, H.; Yasin, S.; Goncalves, B.; Tarrio, O.; Ash, S.; Ferrol, M.; Ibragimov, A.; Olivares, A.G.; et al. Alterations in Brain Activity Induced by Transcranial Magnetic Stimulation and Their Relation to Decision Making. Biology 2023 , 12 , 1366. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Nguyen, G.H.; Oh, S.; Schneider, C.; Teoh, J.Y.; Engstrom, M.; Santana-Gonzalez, C.; Porter, D.; Quevedo, K. Neurofeedback and Affect Regulation Circuitry in Depressed and Healthy Adolescents. Biology 2023 , 12 , 1399. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Pham, T.Q.; Matsui, T.; Chikazoe, J. Evaluation of the Hierarchical Correspondence between the Human Brain and Artificial Neural Networks: A Review. Biology 2023 , 12 , 1330. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Zhang, R.; Zeng, Y.; Tong, L.; Yan, B. Specific Neural Mechanisms of Self-Cognition and the Application of Brainprint Recognition. Biology 2023 , 12 , 486. [ Google Scholar ] [ CrossRef ] [ PubMed ]

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Keenan, J.P. The Science and Philosophy of the Brain and the Future of Neuroscience. Biology 2024 , 13 , 607. https://doi.org/10.3390/biology13080607

Keenan JP. The Science and Philosophy of the Brain and the Future of Neuroscience. Biology . 2024; 13(8):607. https://doi.org/10.3390/biology13080607

Keenan, Julian Paul. 2024. "The Science and Philosophy of the Brain and the Future of Neuroscience" Biology 13, no. 8: 607. https://doi.org/10.3390/biology13080607

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How the Brain Learns to Make Inferences

Summary: Researchers have uncovered how the brain processes inferential reasoning by recording neuron activity in individuals as they learned through trial and error. The study revealed that specific brain regions, particularly the hippocampus, create geometric neural representations when people successfully infer new rules from prior knowledge.

These findings provide insight into how conceptual knowledge is encoded in the brain and could inform future treatments for neurological and psychiatric disorders. The research also showed that similar neural patterns form whether learning occurs through experience or verbal instruction.

• The hippocampus, known for spatial memory, also encodes cognitive maps for inferential reasoning.
• Neuron activity was visualized as high-dimensional geometric shapes during successful reasoning.
• Verbal instruction and trial-and-error learning produce similar neural representations.

Source: Columbia University

It takes brains to infer how any two things in the world relate to each other, whether it’s the way bad weather links to commuting delays or how environmental conditions lead to the evolution of species.

A new study based on recordings in the brains of people has yielded a pathbreaking trove of data that researchers now have used to reveal, with more clarity than ever, the neural incarnations of inferential reasoning.

”We are beginning to understand how the brain learns and how we extract knowledge from what we experience,” said Ueli Rutishauser, PhD, a co-corresponding author on the study and a professor of neuroscience, neurosurgery and biomedical science at Cedars-Sinai Medical Center. 

The study, conducted as part of a multi-institutional consortium funded by the National Institutes of Health’s The Brain Research Through Advancing Innovative Neurotechnologies ® Initiative, or The BRAIN Initiative ®, was published online today in  Nature . 

Using electrical recordings from more than 3,000 neurons in 17 volunteers with epilepsy who were undergoing invasive monitoring in the hospital to locate the sources of their seizures, the researchers accrued a “uniquely revealing dataset that is letting us for the first time monitor how the brain’s cells represent a learning process critical for inferential reasoning,” said Stefano Fusi, PhD, a principal investigator at Columbia’s Zuckerman Mind Brain Behavior Institute and the paper’s other co-corresponding author. 

As the researchers recorded from the neurons, the scientists challenged the participants with a simple inferential reasoning task. In this task, subjects discovered by trial-and-error the correct, money-rewarded associations between images, like pictures of a car or a piece of fruit, and a left or right button press.

Once the participants learned these associations for a set of images, the researchers pivoted and then switched which button was the correct association for each image.

Initially, volunteers made incorrect choices, as they did not realize that the previously learned associations had changed. However, these errors enabled the volunteers to quickly infer that a new image-button rule had become operative and they could further infer that all of the new image-button rules had switched, even those they had yet to experience. The scientists liken this experimental task to real-life inferences, such as those overseas travelers often need to make. 

“If you live both in New York and in London, and you fly to the UK, you know that you have to look right when you want to cross a road. You’ve switched to a different mental state that represents the traffic rule you have learned by living in London,” said Dr. Fusi, also a professor of neuroscience at Columbia’s Vagelos College of Physicians and Surgeons and a member of Columbia’s Center for Theoretical Neuroscience. 

“Even if you visit places you have never been to in the UK, like the countryside in Wales, you infer that the new rules still apply there,” he added. “You still have to look right instead of left when crossing a road.”

“This work elucidates a neural basis for conceptual knowledge, which is essential for reasoning, making inferences, planning and even regulating emotions,” said Daniel Salzman, MD, PhD, a coauthor of the  Nature  paper, a principal investigator at the Zuckerman Institute, and a professor of psychiatry and neuroscience at Columbia’s Vagelos College of Physicians and Surgeons.

But how are these kinds of thinking physically expressed in the activity of neurons? Using mathematical tools that Dr. Fusi honed to integrate recordings from thousands of neurons, the researchers recast the volunteers’ brain activity into geometric representations – into shapes, that is – albeit ones occupying thousands of dimensions instead of the familiar three dimensions that we routinely visualize.

“These are high-dimensional geometrical shapes that we cannot imagine or visualize on a computer monitor,” said Dr. Fusi. “But we can use mathematical techniques to visualize much simplified renditions of them in 3D.” 

When the researchers compared shapes of brain activity between instances when the subjects made successful inferences with those when their inferences were unsuccessful, stark differences emerged.

“In certain neuronal populations during learning, we saw transitions from disordered representations to these beautiful geometric structures that were correlated with the ability to reason inferentially,” said Dr. Fusi.

What’s more, the researchers observed these structures only in recordings from the hippocampus and not in the other brain regions the scientists monitored, such as the amygdala and frontal lobe cortical areas. It’s a surprising finding, the researchers said, because the hippocampus has long been viewed as the brain’s locus for embodying neural maps of physical spaces.

The new findings show that it also can construct cognitive maps linked to brain functions like making inferences and learning.  

Another head-turning result of the research, Dr. Rutishauser said, is that volunteers who learn the associative rules between images and buttons only via verbal instruction, and not by virtue of trial-and-error experience, nonetheless forge the same “beautifully structured neural representations in the hippocampus.”

This is an important observation, he said, because while human beings often learn from each other through verbal exchanges, very little is known about how verbal information changes neural representations.

“Verbal instruction is how we build knowledge about things that we have never actually experienced,” added Dr. Rutishauser. “Our work now shows that verbal instructions result in very similar structured neural representations compared to those that result from experiential learning.”

The researchers emphasize that none of these discoveries would have been possible without the collaboration and voluntary participation of patients who suffer from drug-resistant epilepsy and who were in the hospital following surgery.

The electrodes for collecting the neural data were temporarily implanted by the patients’ doctors for the sole purpose of locating the source of each person’s seizures, with the ultimate goal of using that information for further surgical or neuromodulation-based treatment.

“These individuals gave us the precious opportunity to learn something new about how all of our brains work,” Dr. Rutishauser said.

Collaborator Dr. Taufik Valiante at the Krembil Research Institute and Division of Neurosurgery at the University of Toronto contributed to this study by enrolling patients. Graduate student Hristos Courellis and postdoctoral researcher Juri Minxha, PhD, at Cedar-Sinai Medical Center and the California Institute of Technology, performed much of the study’s data collection and analysis.

“This study provides new insights into how our brains allow us to learn and carry out tasks flexibly and in response to changing conditions and experiences,” said Dr. Merav Sabri, program director for The BRAIN Initiative.

“These insights build on the body of knowledge that could one day lead us toward interventions for neurologic and psychiatric conditions that involve deficits in memory and decision-making.”

About this neuroscience research news

Author: Ivan Amato Source: Columbia University Contact: Ivan Amato – Columbia University Image: The image is credited to Neuroscience News

Original Research: Open access. “ Abstract representations emerge in human hippocampal neurons during inference ” by Ueli Rutishauser et al. Nature

Abstract representations emerge in human hippocampal neurons during inference

Humans have the remarkable cognitive capacity to rapidly adapt to changing environments. Central to this capacity is the ability to form high-level, abstract representations that take advantage of regularities in the world to support generalization.

However, little is known about how these representations are encoded in populations of neurons, how they emerge through learning and how they relate to behaviour.

Here we characterized the representational geometry of populations of neurons (single units) recorded in the hippocampus, amygdala, medial frontal cortex and ventral temporal cortex of neurosurgical patients performing an inferential reasoning task.

We found that only the neural representations formed in the hippocampus simultaneously encode several task variables in an abstract, or disentangled, format.

This representational geometry is uniquely observed after patients learn to perform inference, and consists of disentangled directly observable and discovered latent task variables.

Learning to perform inference by trial and error or through verbal instructions led to the formation of hippocampal representations with similar geometric properties.

The observed relation between representational format and inference behaviour suggests that abstract and disentangled representational geometries are important for complex cognition.

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Consider this before declaring a neuroscience major

If you’re wondering what a neuroscience major involves, possible careers in the field and what you should consider before declaring, here are some tips.

What is neuroscience, and why should I major in it?

Neuroscience is the study of the biology behind thoughts, perceptions, emotions, motivations, decisions and actions. You explore the amazing neurological connections between the brain and behavior. 

Although the major is based in Augustana’s psychology department , you’ll also have the opportunity to take classes in areas like biology, philosophy, plus the liberal arts. This is an interdisciplinary major where you don’t have to choose only the sciences or the humanities; you get to take classes in both.

In neuroscience as well as psychology, you’ll find plenty of opportunities for hands-on experiences and data collection/assessment. If you like doing research or think you might like doing research, neuroscience is a good way to find out for sure.

Having a strong background in high school biology or chemistry is a bonus when studying neuroscience in college, though not critical. In addition, high school courses in electronics, biology, chemistry, geology, geography, human biology, physics, mathematics, computer science or psychology give you an advantage. 

How do I choose a neuroscience school?

If you know you want to study neuroscience, make sure the schools you consider will offer the support you need to be successful while you're a student and after you graduate. Here are some things to look for:

• Faculty attention and mentorship

Augustana has an 11:1 student-faculty ratio, and professors are able to truly get to know their students, regardless of major. The ability to form relationships with professors gives Augustana students opportunities to find mentors and advisors in their field, and many serve as research assistants or co-authors as undergraduates. These close relationships can result in grad school reference letters that will help you stand out!

Because professors are focused on student success, teaching is their first priority. There are no teaching assistants at Augustana; 100% of classes are taught by professors.

At Augustana, three full-time neuroscience faculty have a Ph.D. in psychology or neuroscience, with expertise in the areas of learning and memory, pain and drug-seeking behavior, communication, cognitive and affective neuropsychology, and sensation and perception.

Faculty from the biology, computer science, communication sciences and disorders, philosophy and religion departments also teach courses in the major. As is common in most Augustana majors, neuroscience majors complete a Senior Inquiry capstone experience, which involves research on or off campus with a project faculty mentor.

• Career and internship support

The career mentors and advisors at CORE (Careers, Opportunities, Research and Exploration) , Augustana's career and exploration center, help an average of 150 students find health care-related internships or job shadows each year.

Students go to CORE for career coaching, help with graduate school applications and résumés, internships, study abroad and more. Also through CORE, every student has access to $2,000 Augie Choice funding to support a standout learning experience such as research, an internship or study abroad.

If you major in neuroscience at Augustana, you can apply to participate in the prestigious Texas Medical Center Summer Research Internship Program, as well as other high-quality internships and opportunities to practice empirical research.

• Research opportunities

Look for a college that encourages research and participation in national and international conferences to present your research. 

Augustana students often travel to present their research as part of their professional development. Examples for neuroscience majors include:  Midbrains: The Undergraduate Neuroscience Conference of the Upper Midwest , the Chicago Society for Neuroscience  and the Undergraduate Research Symposium in the Biological Sciences and Psychology sponsored by the Midstates Consortium for Math and Science.

Conducting research and presenting your results before peers in your field looks great on a job or grad school application.

So, what can I do with my neuroscience degree when I graduate?

A challenging yet rewarding major, neuroscience can be an excellent starting point to a career in medicine, psychology or research science. 

Undergraduate neuroscience majors typically earn advanced degrees in neuroscience or a related field like psychology, and many choose to go to medical school and pursue a career as a physician, surgeon, psychiatrist, psychologist, neuroscientist*, genetic counselor, substance abuse and behavioral disorder counselor, industrial organizational psychologist and college professor.

*Neuroscientists work in both offices and laboratories, often as part of a multi-function research team. Common workplaces include universities, hospitals, government agencies and private industry settings. In research-oriented careers, neuroscientists typically spend their time designing and carrying out scientific experiments that contribute to the understanding of the nervous system and its function.

Keep in mind you don't need a graduate degree to have a great career. With only a B.A. in neuroscience, you may qualify for many positions. A partial list includes:

• Pharmaceutical sales representative
• Laboratory technician 
• Medical technician
• Pharmacy technician
• Regulatory affairs specialist
• Psychometrist
• Science writer or editor
• Clinical research assistant
• Patient care assistant
• Health educator
• EEG technologist
• Medical and health care manager
• Natural sciences manager
• Advertising or marketing representative

How much do neuroscientists make?

Earning an advanced degree in neuroscience will help you find higher-paying jobs in management positions and careers in policy work, in addition to science and medicine.

Qualifications for a neuroscientist include extensive laboratory experience; strong research, analytical and communication skills; and a familiarity with common neurological disorders. 

The average annual salary for a neuroscientist is $73,500 , according to payscale.com in October 2020. 

If you're a high school student still searching for the right college, our admissions team can help you decide if Augustana is a good fit for you. Start by exploring our admissions information , including visits and financial aid, or request more information .

If you're already in college and wish to transfer to a school that offers a neuroscience major, start by exploring our options for transfer students .

• What is environmental studies, and why should you major in it?
• Want to be a public health major? Here are some tips.

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How to prep for finals

5 Ways Neuroscience is Impacting the Classroom

May 12, 2016

By Quannah Parker-McGowan.

Parker-McGowan teaches Learning and the Brain: Translating Research into Practice, a course in Northeastern University’s Master’s in Education program.

With new discoveries in neuroscience and education, it’s time to challenge the way we approach both teaching and learning. 

The prospect of using brain-based research to inform our classroom practices is exciting, since we’re making strides in understanding individual students’ needs and connecting each student with the most effective practices. 

Here are some ways that neuroscience is finding its way into our classrooms: 

1. Rethinking the way we view students.

Research is showing us how to look at students in terms of individual strengths and how we can customize teaching strategies to meet their needs.

For example, when a student is labeled as “learning disabled,” we tend to focus on what they can’t do. But neuroscience’s contribution on how to increase someone’s long-term memory provides us insight that can help teachers form strategies that instead play to a student’s strengths.

2. Emphasizing the importance of emotion in learning.

We know that experience shapes the brain tremendously. Focusing on creating a positive and stimulating learning environment for students can enhance their learning and go a long way in helping students retain new material.

3. Challenging us to expand our methods.

Brain development data is giving teachers insight into how students learn best, so we can create curriculum to reach a variety of students. We know that one teaching approach won’t work for all students, and sometimes we need to broaden our methods to connect with different kinds of learners.

4. Seeing how learning experiences impact the brain.

The concept of neural plasticity has vast educational implications. From learning a new language to making connections between subject matters, the brain responds to and adapts to new experiences. Neuroscience sheds light on when and how to go about creating these experiences and making the most of students’ windows of opportunity.

5. Providing new kinds of feedback.

Applying neuroscience in the classroom can help teachers hone their ability to engage students—a result of using the four insights above. And this results in more meaningful and long-lasting learning. 

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121 Original Neuroscience Research Topics

Now, wouldn’t it be great if you had a list of awesome neuroscience research topics to choose from? Our PhD dissertation help would definitely make writing a thesis or dissertation a lot easier. Well, the good news is that we have a long list of neuroscience paper topics for you right here.

The list of topics is updated periodically, so you will surely be able to find a unique topic; something that nobody has though of yet. And yes, you can use any of our topics for free.

Writing a Neuroscience Dissertation

To write a good dissertation, you need more than just our interesting neuroscience topics. Your supervisor expects you to make some progress pretty quickly, so you really need all the help you can get. You can get all the assistance you need to get started quickly from our dissertation experts and you’ll also find the following guide useful:

Set up your project and conduct the necessary research and data analysis. Don’t forget to think about an interesting, captivating thesis statement. Start by writing the first chapter of the dissertation, the introduction. This will provide your readers with comprehensive background information about your study. Write the Literature Review chapter. This will take some time, especially if you are dealing with a popular subject. Write the Methodology chapter. This is basically an iteration and in-depth description of each and every method you have used to collect the data. Write the Results chapter. In this chapter, you will present your readers the results of your research. You don’t need to provide your own take on the data yet. Next comes the Discussion (or Analysis) chapter. This is where you are free to discuss your results and show your readers how they support your thesis. Finally, the Conclusion chapter wraps everything up. You can summarize your methods, results and analysis and make it clear that your paper has answered all the relevant research questions. Write the References section and the Appendices section. Edit and proofread your work thoroughly to make sure you don’t lose points over some minor mistakes – or have our expert proofreaders and editors do it for you.

This step-by-step guide applies to any thesis or dissertation. However, before you even get this far, you need a great topic to start with. Fortunately, we have 121 brand new topics for you right here on this page.

Interesting Neuroscience Topics

If you are looking for some of the most interesting neuroscience topics, you have definitely arrived at the right place. Our experts have put together the best list of ideas for you:

• Research the occurrence of cerebrovascular disease in the United States
• What causes a headache?
• An in-depth look at muscular dystrophy
• The causes of multiple sclerosis
• Talk about neuroregeneration
• Define cognitive neuroscience
• Everything about dementia
• Study brain development from birth to age 2
• What causes Parkinson’s disease?
• The function of peripheral nerves
• What are vestibular disorders?
• Pain and the science behind it
• An in-depth analysis of stem cells

Engaging Topics in Neuroscience

Are you looking for some engaging topics in neuroscience? If you want the best ideas, all you have to do is take a look at the following list and take your pick:

• Research the Down syndrome
• A closer look at ADHD
• What causes brain tumors?
• What causes epilepsy episodes?
• Research the occurrence of schizophrenia in the UK
• An in-depth look at brain stimulation
• Treating severe depression in young adults
• Improving memory in the adult population
• The importance of sleep for brain health
• Mapping the human brain

Comprehensive Neuroscience Topic for Every Student

The nice thing about our blog is that we have a comprehensive neuroscience topic for every student. Even better, all our topics are relatively simple, so you don’t have to spend a lot of time doing research:

• The future of brain implants
• The processes behind depression
• The role of dopamine
• How are emotions created?
• Love starts in your brain, not your heart
• ADHD behavior and brain activity
• Effects of illegal drugs on dopamine production
• How does dyslexia manifest itself?
• Early stages of Schizophrenia
• The link between gut bacteria and the brain
• Studying the brains of people with a high IQ

Neuroscience Research Questions

The best way to get ideas for your next paper is to take a look at some original neuroscience research questions. Here are some that should get you started right away:

• How do brain tumors cause damage?
• What causes substance addiction?
• What role does the brain play in autistic spectrum disorders?
• Does being a vegetarian influence your brain?
• What causes chronic migraines?
• Why is Pierre Paul Broca’s work important?
• Why is stress so dangerous for the brain?
• How do genes influence the onset of Alzheimer’s disease?
• What can cause a brain tumor?
• Does music affect the human brain?
• Can repeated head injuries damage the brain? (think about modern sports)
• What does being Bipolar I mean?

Easy Neuroscience Paper Topics

Our experts have created a list of easy neuroscience paper topics for you. You could start writing your thesis in no time if you choose one of these great ideas:

• What causes epilepsy?
• A closer look at Alzheimer’s disease
• What can cause a loss of feeling?
• The effects of dementia on the brain
• The symptoms of Parkinson’s disease
• What can cause memory loss?
• Mitigating headaches without medication
• The effects of a mild stroke
• Talk about Amyotrophic Lateral Sclerosis
• What can cause a lack of coordination?

Neuroscience Research Topics for College Students

We have a list of awesome neuroscience research topics for college students and you can use any one of them for free. Take a look at our best ideas yet:

• Can the brain be linked to substance abuse?
• How does the brain recognize people?
• Latest development in brain surgery
• An in-depth look at neuroplasticity
• Innovative medication for treating brain disorders
• Treating Alzheimer’s in 2023
• How damaging is Cannabis for the brain?

Cognitive Neuroscience Research Topics

If you want to talk about something in cognitive neuroscience, we have put together the best and most interesting cognitive neuroscience research topics:

• The role played by neurons in our body
• What is Magnetoencephalography?
• How difficult is it to map the entire brain?
• Define consciousness from a neurological POV
• How does our brain affect our perception?
• Discuss Transcranial Magnetic Stimulation procedures
• Latest advancements in Functional magnetic resonance imaging

Brain Research Topics

Brain research is a very interesting thing to talk about, especially since we are still struggling to understand how certain things work. Take a look at some amazing brain research topics:

• Study the brain development of an infant
• Brain tumor stages
• The effect of social media on the human brain
• Multiple sclerosis treatment options
• What can cause muscular dystrophy?
• Discuss 3 cerebrovascular diseases
• Interesting breakthroughs in cellular neuroscience
• Talk about our brain’s problem-solving abilities
• The effects of sugar on brain chemistry

Neurobiology Topics

We agree, researching a topic in neurobiology is not easy. However, with the right neurobiology topics, you could write an awesome thesis without spending years working on it:

• Research the role of the amygdala
• What are brain neurotransmitters?
• The causes of posttraumatic stress disorder
• How do we recognize a bipolar disorder?
• The importance of hormones
• Talk about experimental psychology

Behavioral Neuroscience Research Topics

Do you want to write your dissertation on a behavioral neuroscience topic? Our experts have compiled a list of the most interesting behavioral neuroscience research topics for you:

• The processes behind sensation
• How does the brain control our movement?
• An in-depth look at motivated behavior
• Best way to diagnose a sleep disorder
• Improving success at academic activities
• How does your brain perceive the environment?

Cool Neuroscience Topics

We have some very cool neuroscience topics right here and the good news is that they’re all relatively easy. The list has been updated recently and new topics have been added:

• Effects of plant-based diets
• The life and work of Cornelia Bargmann
• Discuss a breakthrough in neurotech
• 3D brain function mapping
• Discuss the importance of brain implants
• The life and work of Róbert Bárány

Controversial Topics in Neuroscience

Just like any other field, neuroscience has its controversies. And what better way to start a dissertation than finding the most controversial topics in neuroscience:

• Discuss the Bayesian brain theory
• Ethics behind wearable brain gadgets
• Discuss postnatal neurogenesis
• Can our brain “deep learn”?
• Invasive brain imaging procedures
• How do we differentiate between good and bad?

Hot Topics in Neuroscience

Did you know that getting hot topics in neuroscience is not overly difficult? This section of our list of topics is updated periodically, so you can definitely find an original idea right here:

• Electrical brain stimulation methods
• Define the concept of Free Will
• Talk about hereditary brain disorders
• How is speech formed?
• Can our brain hibernate?
• What causes aggressive behavior?

Current Topics in Neuroscience

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• Cerebellar Neurons that can help you lose weight
• Effects of a meat-based diet
• Latest brain mapping technology
• CT scans in 2023
• Brain implants that can control a computer
• An in-depth look at super-agers

Complex Neurological Research Topics

Are you looking for some complex neurological research topics? If you want to give a difficult topic a try, don’t hesitate to choose one of these excellent ideas:

• An in-depth look at the Demyelinating disease
• The effects of a cerebrovascular stroke
• Bioterrorism in 2023
• Legal issues in neurology
• Dopamine’s link to aggressiveness
• Brain changes that lead to alcohol addiction

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State-of-the-Art Brain Recordings Reveal How Neurons Resonate

Findings shed light on how the human brain turns words into thoughts

• Miles Martin - [email protected]

Published Date

Topics covered:.

• Neuroscience

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For decades, scientists have focused on how the brain processes information in a hierarchical manner, with different brain areas specialized for different tasks. However, how these areas communicate and integrate information to form a coherent whole has remained a mystery. Now, researchers at University of California San Diego School of Medicine have brought us closer to solving it by observing how neurons synchronize across the human brain while reading. The findings are published in Nature Human Behavior and are also the basis of a thesis by UC San Diego School of Medicine doctoral candidate Jacob Garrett.

“How the activity of the brain relates to the subjective experience of consciousness is one of the fundamental unanswered questions in modern neuroscience,” said study senior author Eric Halgren, Ph.D., professor in the Departments of Neurosciences and Radiology at UC San Diego School of Medicine. “If you think about what happens when you read text, something in the brain has to turn that series of lines into a word and then associate it with an idea or an object. Our findings support the theory that this is accomplished by many different areas of the brain activating in sync.”

This synchronization of different brain areas, called “co-rippling” is thought to be essential for binding different pieces of information together to form a coherent whole. In rodents, co-rippling has been observed in the hippocampus, the part of the brain that encodes memories. In humans, Halgren and his colleagues previously observed that co-rippling also occurs across the entire cerebral cortex.

To examine co-rippling at the mechanistic level, Ilya Verzhbinsky, an M.D./Ph.D. candidate in UC San Diego School of Medicine’s Medical Scientist Training Program completing his research in Halgren’s lab, led a study published in the Proceedings of the National Academy of Sciences that looked at what happens to single neurons firing in different cortical areas during ripples. The present study looks at the phenomenon with a wider lens, asking how the many billions of neurons in the cortex are able to coordinate this firing to process information.

“There are 16 billion neurons in the cortex – double the number of people on Earth,” said Halgren. “In the same way a large chorus needs to be organized to sound as a single entity, our brain neurons need to be coordinated to produce a single thought or action. Co-rippling is like neurons singing on pitch and in rhythm, allowing us to integrate information and make sense of the world. Unless they’re co-rippling, these neurons have virtually no effect on the other, but once ripples are present about two thirds of neuron pairs in the cortex become synchronized. We were surprised by how powerful the effect was.”

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The lines on this diagram of the brain represent connections between various areas of the cerebral cortex involved in language processing. When we read, the neurons in these areas fire in precise synchronicity, a phenomenon known as “co-rippling.” Photo credit: UC San Diego Health Sciences

Co-rippling in the cortex has been difficult to observe in humans due to limitations of noninvasive brain scanning. To work around this problem, the researchers used an approach called intracranial electroencephalography (EEG) scanning, which measures the electrical activity of the brain from inside the skull. The team studied a group of 13 patients with drug-resistant epilepsy who were already undergoing EEG monitoring as part of their care. This provided an opportunity to study the activity of the brain in more depth than typical brain scans using noninvasive approaches.

Participants were shown a series of animal names interspersed with strings of random consonants or nonsense fonts and then asked to press a button to indicate the animal whose name they saw. The researchers observed three stages of cognition during these tests: an initial hierarchical phase in visual areas of the cortex in which the participant could see the word without conscious understanding of it; a second stage in which this information was “seeded” with co-ripples into other areas of the cortex involved in more complex cognitive functions; and a final phase, again with co-ripples, where the information across the cortex is integrated into conscious knowledge and a behavioral response – pressing the button.

The researchers found that throughout the exercise, co-rippling occurred between the various parts of the brain engaged in these cognitive stages, but the rippling was stronger when the participants were reading real words.

The study's findings have potential long-term implications for the treatment of neurological and psychiatric disorders, such as schizophrenia, which are characterized by disruptions in these information integration processes.

"It will be easier to find ways to reintegrate the mind in people with these disorders if we can better understand how minds are integrated in typical, healthy cases,” added Halgren.

More broadly, the study's findings have significant implications for our understanding of the link between brain function and human experience.

"This is a fundamental question of human existence and gets at the heart of the relationship between mind and brain,” said Halgren. “By understanding how our brain's neurons work together, we can gain new insights into the nature of consciousness itself."

Additional co-authors on the study include Erik Kaestner at UC San Diego School of Medicine, Chad Carlson at Medical College of Wisconsin, Werner K. Doyle and Orrin Devinsky at New York University Langone School of Medicine, and Thomas Thesen at Geisel School of Medicine.

The study was funded, in part, by National Institutes of Health (grants MH117155, T32MH020002) and the Office of Naval Research (grant N00014-16-1-2829).

Disclosures: The authors declare no competing interests.

"This is a fundamental question of human existence and gets at the heart of the relationship between mind and brain. By understanding how our brain's neurons work together, we can gain new insights into the nature of consciousness itself."

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August 13, 2024

How psychedelic drugs alter the brain

At a glance.

• Researchers found that psilocybin temporarily disrupts a brain network involved in creating a person’s sense of self.
• The findings help explain the neurobiology of psychedelic experiences and give insight into harnessing the potential therapeutic effects of psychedelic drugs.

Psychedelic drugs such as psilocybin cause acute changes in how people perceive time, space, and the self. In ongoing experimental research, a single psilocybin dose under controlled conditions is showing promise for relieving mental health symptoms such as depression. These therapeutic effects can last long after the acute effects of the drug wear off. But it’s not clear how these drugs affect the workings of the brain and lead to the drugs’ therapeutic effects.

A research team led by Dr. Joshua Siegel at Washington University in St. Louis used functional magnetic resonance imaging (fMRI) to track changes in brain activity related to use of psilocybin. Seven healthy young adults participated in the study, which involved regular fMRI sessions before, during, and after a carefully controlled dose of psilocybin. Results of the study, which was funded in part by NIH, appeared in Nature on July 17, 2024.

Psilocybin caused major changes in functional connectivity, or FC—a measure of how activity in different regions of the brain is correlated—throughout the brain. These regions included most of the cerebral cortex, thalamus, hippocampus, and cerebellum. The changes were more than 3 times greater than those caused by a control compound, methylphenidate (a stimulant used to treat attention deficit hyperactivity disorder). Psilocybin induced the largest changes in areas involved in the default mode network. This network is usually most active when the brain isn’t focused on a specific task. It is thought to govern people’s sense of space, time, and self.

Study participants also completed a questionnaire designed to measure the intensity of their subjective psychedelic experience. The questionnaire scores correlated with FC changes in their brains. The greater the FC changes, the more intense the person’s psychedelic experience.

Psilocybin caused activity within brain networks to become less synchronized. It also led to less distinction between brain networks that normally show distinct activity. Notably, the psilocybin-associated FC changes were reduced when participants performed a task that involved concentrating on matching spoken words with images.

Most brain activity returned to normal within days of taking psilocybin. But a reduction in FC between the default mode network and part of the hippocampus lasted for at least three weeks. This may reflect lasting changes in hippocampus circuits involved with the perception of self.

The findings shed light on how psychedelic drugs may affect brain function and alter perceptions of self. “The idea is that you’re taking this system that’s fundamental to the brain’s ability to think about the self in relation to the world, and you’re totally desynchronizing it temporarily,” Siegel explains.

This research provides important information to inform scientists as they seek to harness these drugs’ therapeutic potential. However, the researchers strongly caution against self-medicating with psilocybin, as there are serious risks to taking it without supervision by trained mental health experts.

—by Brian Doctrow, Ph.D.

Related Links

• Research in Context: Treating Depression
• How Psychedelic Drugs May Help with Depression
• How Anesthetics and Benzodiazepine Affect the Brain Differently
• How Ketamine Relieves Symptoms of Depression
• Protein Structure Reveals How LSD Affects the Brain
• When Sadness Lingers: Understanding and Treating Depression
• Psychedelic and Dissociative Drugs

References:  Psilocybin desynchronizes the human brain. Siegel JS, Subramanian S, Perry D, Kay BP, Gordon EM, Laumann TO, Reneau TR, Metcalf NV, Chacko RV, Gratton C, Horan C, Krimmel SR, Shimony JS, Schweiger JA, Wong DF, Bender DA, Scheidter KM, Whiting FI, Padawer-Curry JA, Shinohara RT, Chen Y, Moser J, Yacoub E, Nelson SM, Vizioli L, Fair DA, Lenze EJ, Carhart-Harris R, Raison CL, Raichle ME, Snyder AZ, Nicol GE, Dosenbach NUF. Nature . 2024 Jul 17. doi: 10.1038/s41586-024-07624-5. Online ahead of print. PMID: 39020167.

Funding:  NIH’s National Institute of Mental Health (NIMH), National Institute of Neurological Disorders and Stroke (NINDS), and National Institute on Drug Abuse (NIDA); Washington University in St. Louis’s Taylor Family Institute for Innovative Psychiatric Research, McDonnell Center for Systems Neuroscience, Institute of Clinical and Translational Sciences, Intellectual and Developmental Disabilities Research Center, Hope Center for Neurological Disorders, and Mallinckrodt Institute of Radiology; Dysphonia International; Tianqiao and Chrissy Chen Institute; Kiwanis International.

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Using Cognitive Neuroscience to Improve Mental Health Treatment: A Comprehensive Review

Jessica a. wojtalik.

Doctoral candidate at the University of Pittsburgh School of Social Work

Shaun M. Eack

Professor at the University of Pittsburgh School of Social Work and Department of Psychiatry

Matthew J. Smith

Associate professor at the University of Michigan School of Social Work

Matcheri S. Keshavan

Professor in the Harvard Medical School Department of Psychiatry

Mental health interventions do not yet offer complete, client-defined functional recovery, and novel directions in treatment research are needed to improve the efficacy of available interventions. One promising direction is the integration of social work and cognitive neuroscience methods, which provides new opportunities for clinical intervention research that will guide development of more effective mental health treatments that holistically attend to the biological, social, and environmental contributors to disability and recovery. This article reviews emerging trends in cognitive neuroscience and provides examples of how these advances can be used by social workers and allied professions to improve mental health treatment. We discuss neuroplasticity, which is the dynamic and malleable nature of the brain. We also review the use of risk and resiliency biomarkers and novel treatment targets based on neuroimaging findings to prevent disability, personalize treatment, and make interventions more targeted and effective. The potential of treatment research to contribute to neuroscience discoveries regarding brain change is considered from the experimental-medicine approach adopted by the National Institute of Mental Health. Finally, we provide resources and recommendations to facilitate the integration of cognitive neuroscience into mental health research in social work.

Mental health is an essential component of overall health and well-being ( World Health Organization, 2013 ), and mental illnesses—such as schizophrenia, depression, bipolar disorder, post-traumatic stress disorder (PTSD), and autism—are leading causes of disability in the U.S. ( U.S. Burden of Disease Collaborators, 2013 ). These mental health conditions are too often functionally disabling, impacting individuals’ ability to think clearly, live independently in the community, maintain meaningful interpersonal relationships, and achieve personal goals. Social workers are the largest group of mental health care providers in the U.S. ( Heisler & Bagalman, 2015 ) and have contributed to the development and testing of psychosocial interventions for people living with some of the most disabling mental health conditions (e.g., Anderson, Reiss, & Hogarty, 1986 ; Garland, 2013 ; Hogarty et al., 2004 ; Rapp, 1998 ; Stein & Test, 1980 ). Despite decades of focused research on pharmacological and psychosocial interventions to improve mental health—and despite numerous significant advances—the effectiveness of these interventions remains only moderately successful ( Bishop-Fitzpatrick, Minshew, & Eack, 2014 ; Mueser, Deavers, Penn, & Cassisi, 2013 ; Newby, McKinnon, Kuyken, Gilbody, & Dalgleish, 2015 ). Very few people with disabling mental illness achieve personally acceptable clinical and functional recovery, and even fewer return to levels of psychosocial functioning experienced prior to the onset of their condition ( Harvey & Bellack, 2009 ; Judd et al., 2008 ; McIntyre & O’Donovan, 2004 ). Recent advances in cognitive neuroscience research offer unique opportunities to improve the effectiveness of both pharmacological and psychosocial interventions for those confronted with mental health issues.

The integration of cognitive neuroscience and mental health research to increase the effectiveness of interventions that promote recovery from mental illness is a research priority ( National Institutes of Health, 2015 ), and social workers have a central role in such translational research efforts ( Brekke, Ell, & Palinkas, 2007 ). National Institute of Mental Health (NIMH) research priorities include investigating the neurobiological mechanisms and trajectories of mental health conditions to identify optimal therapeutic windows for intervention and possibly prevent illness onset. (For more information about the NIMH’s research priorities and associated funding announcements, see https://www.nimh.nih.gov/about/strategic-planning-reports/strategic-research-priorities/index.shtml .) In the current article, we review the emerging trends from cognitive neuroscience and brain plasticity research and provide examples of how these advances can be used by social workers and allied professions to improve mental health treatment (see Table 1 ). We encourage social work researchers, practitioners, and mental health educators to facilitate the integration of cognitive neuroscience by using this article to become familiar with the cognitive neuroscience vocabulary, introducing it into social work courses, and beginning to incorporate neuroimaging measures into their own research.

Key Points on Using Cognitive Neuroscience to Improve Mental Health Treatment

Note . NIMH 5 National Institute of Mental Health.

In 1990, U.S. President George H. W. Bush signed a proclamation designating 1990– 2000 as the “Decade of the Brain” ( Bush, 1990 ) to promote considerable neuroscience research efforts and raise public awareness of neurobiologically based conditions, such as schizophrenia and Alzheimer’s disease ( Goldstein, 1990 ). After nearly three decades, incredible progress has been made in the fundamental understanding of the human brain ( Insel & Landis, 2013 ; Jones & Mendell, 1999 ) and the neurobiological etiology of mental illness ( Charney, Buxbaum, Sklar, & Nestler, 2013 ; Goodkind et al., 2015 ). Indeed, many mental health conditions are now understood to involve numerous aspects of the brain and to follow a neurodevelopmental trajectory ( Ansorge, Hen, & Gingrich, 2007 ; Faludi & Mirnics, 2011 ; Keshavan, Anderson, & Pettergrew, 1994 ). Some of the most disabling mental health symptoms emerge in late adolescence and early adulthood ( Paus, Keshavan, & Giedd, 2008 ), which is a critical period of brain development ( Purves, White, & Riddle, 1996 ); the emergence of these symptoms has been shown to correspond with abnormal synaptic pruning and proliferation ( Faludi & Mirnics, 2011 ; Keshavan et al., 1994 ). This neurodevelopmental insult results in abnormal brain function ( Pantelis et al., 2003 ) and significant loss of gray matter volume ( Cannon et al., 2015 ), which underlie many of the signs and symptoms of mental illness (e.g., substantial cognitive challenges observed in individuals with schizophrenia; Fusar-Poli, Radua, McGuire, & Borgwardt, 2011 ; Rapoport, Giedd, & Gogtay, 2012 ).

Along with the evident neural basis and impact of many mental health conditions, the brain is also known to be “plastic.” Studies of neuroplasticity—the ability of the brain to adapt and change—suggest that the adult human brain has a remarkable capacity for strengthening and generating new neuronal connections to enhance daily functioning ( Bruel-Jungerman, Davis, & Laroche, 2007 ; Buonomano & Merzenich, 1998 ). Such findings have generated renewed therapeutic optimism for the possibility of greater recovery from mental health conditions that were previously thought to be characterized by static encephalopathy ( Goldberg, Hyde, Kleinman, & Weinberger, 1993 ). Thus, social workers have begun to integrate cognitive neuroscience methods into their research to understand and enhance the efficacy of their interventions (e.g., Eack et al., 2010 ; Garland, Froeliger, & Howard, 2015 ; Matto et al., 2013 ). For example, Eack, Newhill, and Keshavan (2016) demonstrated that individuals with schizophrenia who received a psychosocial treatment to improve their thinking skills had increased communication between frontal and temporal brain areas, which was associated with enhanced emotion processing. Such results suggest that frontotemporal neural communication may be an important treatment target for improving recovery. Moreover, one could conclude that cognitive training might be more effective if these interventions engaged clients in activities that would support enhanced communication between frontotemporal brain regions. Numerous social work investigators have written about the value of integrating cognitive neuroscience and clinical intervention research ( Farmer, 2008 ; Matto & Strolin-Goltzman, 2010 ; Matto, Strolin-Goltzman, & Ballan, 2014 ; Shapiro & Applegate, 2000 ).

Cognitive Neuroscience Basics

To understand the implications that cognitive neuroscience holds for informing mental health treatment, it is first essential to understand some basic knowledge about neuroscience and the brain. The field of cognitive neuroscience is directed toward the study of the neurobiological mechanisms that underlie mental processes and behaviors ( Frackowiak, 2004 ), and for the purposes of this article, we broadly include cognitive, affective, and clinical neuroscience domains under the cognitive neuroscience rubric. The brain is an incredible organ that can generally be defined as an integrated and complex information processing system that generates thoughts, emotions, and behaviors. The adult human brain is estimated to contain 100 billion neurons, which is comparable to the number of stars in the Milky Way galaxy ( Fischbach, 1992 ). Remarkably, all this processing power is packed into a mere 3 pounds of tissue (about 1300 cubic centimeters), and much of the brain’s function is being actively investigated and discovered.

Brain anatomy

The anatomy of the brain is broadly made up of gray matter, white matter, and cerebrospinal fluid. Gray matter comprises the cerebral cortex (i.e., the top and outer portions of the brain) and contains neuronal and glial cell bodies, dendrites, and unmyelinated axons (i.e., axons that do not have myelin containing sheaths). Unmyelinated axons are largely responsible for conscious and effortful mental processes ( Rosenzweig, Breedlove, & Watson, 2005 ), including memory and problem-solving. The cerebral cortex has a folded appearance, with the peaks known as gyri and the valleys referred to as sulci . White matter is located deeper in the brain and contains bundles of neuronal tracts that connect different areas of gray matter. These tracts encompass axons that are wrapped in myelin , which is an insulating fatty material that increases neuronal communication. White matter tracts interconnect and organize neuronal transactions in gray matter ( Rosenzweig et al., 2005 ). Among other functions, cerebrospinal fluid cushions the brain and fills the ventricles (i.e., fluid filled cavities) throughout the brain to protect it from injury during head movement.

The cerebral cortex is a primary focus of cognitive neuroscientists and is organized into five broad regions, or lobes—frontal, parietal, temporal, occipital, and limbic (see Figure 1 ). The frontal lobe is vital to a person’s ability to function within society, facilitating executive function (i.e., working memory), higher order social cognitive abilities (i.e., emotion regulation, theory of mind), self-awareness, moral reasoning, language, and voluntary movement ( Adolphs, 2001 ; Chayer & Freedman, 2001 ). The parietal lobe has a role in tactile and sensory processing. For example, spatial awareness, visuomotor actions (i.e., grasping an object), mathematical operations, imitation, and understanding intentions of others are cognitive abilities associated with the parietal lobe ( Dehaene, Piazza, Pinel, & Cohen, 2003 ; Fogassi & Luppino, 2005 ; Rizzolatti, Fogassi, & Gallese, 2001 ). The temporal lobe contains the primary auditory cortex involved in speech processing and the hippocampus involved in memory ( Squire & Zola-Morgan, 1991 ). Lower-order social cognition ( Adolphs, 2001 ; LaBar, Crupain, Voyvodic, & McCarthy, 2003 )—such as perceiving socially relevant information and recognizing emotions in faces—is also associated with the temporal lobe. The occipital lobe is the location of the primary visual cortex and is involved in visual processing. Finally, the limbic system and insular cortices ( Figure 1 ) are evolutionally older regions located deep in the brain and are central to basic human functioning. Physical and social pain are processed in the insula ( Craig & Craig, 2009 ; Kross, Berman, Mischel, Smith, & Wager, 2011 ), and impulsivity, reward, and emotions are associated with the limbic system ( McClure, Laibson, Loewenstein, & Cohen, 2004 ; Morgane, Galler, & Mokler, 2005 ). A basic knowledge of the brain’s functional anatomy is important for understanding how various neuroimaging methods assess brain structure and function. It is also important to remember that the brain is a vast and highly connected network, and that cognitive and behavioral functions are largely the result of the coordination of numerous brain regions that can span lobes.

Visualization of the broad regional organization of the human cerebral cortex.

Neuroimaging approaches

Neuroimaging methodologies are noninvasive techniques used in cognitive neuroscience to assess the structure and function of the human brain. Magnetic resonance imaging (MRI) is a technique for assessing brain structure that produces a high-spatial-resolution image of brain anatomy, including gray matter, white matter, and cerebrospinal fluid. MRI is one of the most commonly used neuroimaging techniques for assessing neuroanatomy. Common measures of brain function (or activity) include positron emission tomography (PET) and functional magnetic resonance imaging (fMRI). Both measure brain activation over a period of time by indirectly assessing blood flow while a participant is performing a cognitive task in the scanner. More specifically, fMRI measures the blood oxygenation level dependent (BOLD) signal and reflects changes in blood flow ( hemodynamic response ) that correspond to neural activity ( Huettel, Song, & McCarthy, 2008 ). PET scanning requires the injection of a radioactive tracer, which is then measured (as opposed to intrinsic blow flow) to obtain an assessment of brain function ( Huettel et al., 2008 ); PET is used less frequently due to the need to inject the radioactive tracer. Other neuroimaging techniques index neural communication patterns and can reflect either functional or structural connectivity throughout the brain ( Friston, 1994 , 2011 ; Hermundstad et al., 2013 ). For example, resting-state fMRI assesses functionally linked brain areas while the person is resting (i.e., not performing a task in the scanner; Van Den Heuvel & Pol, 2010 ). Similarly, task-based functional imaging studies measure correlated patterns of brain activity when a person is performing a task in the scanner ( Barch et al., 2013 ). In contrast, diffusion tensor imaging (DTI) is used to examine structural connectivity, which is a measure of how well white matter tracts connect with gray matter ( Sporns, Tononi, & Kötter, 2005 ). Together, these different neuroimaging methodologies provide powerful strategies for understanding how the brain is organized, how it functions when processing information, and how various regions communicate to support cognition and behavior. Perhaps most importantly, all of these measured parameters can be modified by mental health interventions ( Barsaglini, Sartori, Benetti, Pettersson-Yeo, & Mechelli, 2014 ; Messina, Sambin, Palmieri, & Viviani, 2013 ; Ramsay & MacDonald, 2015 ).

The Dynamic, Neuroplastic Brain

As neuroimaging methods rapidly advance, data increasingly indicate that the brain is malleable, neuroplastic, and reciprocally influenced by the social environment, leading to entirely new opportunities for social work interventions to enhance psychosocial functioning and recovery. For many years, neuroscientists steadfastly believed that once the brain reached its mature state in adulthood, the number of neural connections were fixed. However, early cognitive neuroscience research in rodents demonstrated that environmental enrichment increased neuronal connections, providing the first evidence of neuroplasticity ( Diamond, Krech, & Rosenzweig, 1964 ). Neuroplasticity is the capability of the brain to be flexible, dynamic, and adjust to the environment by creating, rebuilding, or strengthening neuronal connections, especially in response to learning or injury ( Bruel-Jungerman et al., 2007 ; Buonomano & Merzenich, 1998 ). In the 1980s, Taub (1980) advanced the understanding of neuroplasticity ( Schwartz & Begley, 2003 ) by revealing that neural connections could be regenerated in the somatosensory cortex of monkeys after the connections controlling one arm were severed. Taub and colleagues (1993) later applied these findings to rehabilitating neural connections important for daily living skills after a stroke using constraint-induced movement therapy. This therapy forces the reorganization of neural connections—often motor neurons—in the brain area damaged by stroke. Individuals treated with constraint-induced movement therapy practice movements using the impacted limb by constraining the unimpacted, contralateral appendage ( Taub, Uswatte, & Mark, 2014 ). Taub’s original findings provided impetus to study adult brain plasticity, and the last 40 years of research in this area has demonstrated convincing evidence that the adult brain is, in fact, capable of remarkable changes through the creation and adaptation of neural connections based on environmental experience ( Fuchs & Flügge, 2014 ).

People exist in an ever-changing physical and social environment that influences brain development and function, which has implications for mental health treatment research. Given this plasticity feature, the brain is always changing. For instance, individuals who develop a mental illness often significantly withdraw to an impoverished and isolated social life ( Hooley, 2010 ). Thus, exposure to social deprivation, neglect, and stress negatively influences the brain by modifying circuits (i.e., preventing the creation of new neuronal connections) responsible for cognitive function ( Lu et al., 2003 ). For example, experiencing chronic stress can be neurotoxic and inhibit factors important for neuronal health ( Pittenger & Duman, 2008 ). This effect of stress contributes to abnormal brain functioning and gray matter loss, and it has been observed to lead to cognitive impairment in people with depression ( Fossati, Radtchenko, & Boyer, 2004 ), schizophrenia ( Cannon et al., 2015 ), and autism ( Berger, Rohn, & Oxford, 2013 ). Within this special section on social work and neuroscience, the article “The Neuroscience of Resilience” by Hunter, Gray, and McEwan (in press) discusses the effects of early adversity and stress on the brain. As Hunter et al. note, it is increasingly clear that negative social environments and experiences induce adverse neuroplasticity, changing the brain for the worse (i.e., gray matter loss). These findings are particularly relevant for social work because they provide evidence of the powerful role that the social environment plays in brain development and function—an important assumption of the biopsychosocial model and prevention efforts.

Fortunately, supportive and enriched social environments can facilitate adaptive brain plasticity ( Davidson & McEwen, 2012 ; Keshavan, Mehta, Padmanabhan, & Shah, 2015 ) by strengthening or generating novel neural connections reflected in increased gray matter and/or brain activation. Research in healthy volunteers has provided some of the first evidence of adaptive neuroplasticity in adulthood through learning or training ( Driemeyer, Boyke, Gaser, Büchel, & May, 2008 ; Maguire et al., 2000 ). For example, participants who learned to juggle over a 3-month period displayed increased gray matter in the middle temporal gyrus and intra-parietal sulcus—which are involved in visuomotor and visuospatial processing— compared to a nonjuggling group ( Draganski et al., 2004 ). Regarding mental health, the principles of neuroplasticity are the driving force for cognitive remediation interventions ( Keshavan, Vinogradov, Rumsey, Sherrill, & Wagner, 2014 ; Morimoto, Wexler, & Alexopoulos, 2012 ), such as cognitive enhancement therapy (CET) for schizophrenia ( Hogarty et al., 2004 ) and autism ( Eack et al., 2013 ), and neuroplasticity-based computerized cognitive remediation for older adults with depression ( Morimoto et al., 2012 ). Participation in such interventions is thought to capitalize on neurobiological plasticity reserves to restore or enhance neural connections through repeated practice of cognitive exercises in an enriched social environment ( Keshavan & Hogarty, 1999 ; Morimoto et al., 2014 ). Unfortunately, outside of cognitive remediation, few discoveries surrounding brain plasticity have been translated to the development or refinement of other clinical interventions for mental illness ( Cramer et al., 2011 ), although notable examples are emerging ( Matto et al., 2013 ).

Overall, the neuroplasticity literature validates the long-held biopsychosocial perspective of social workers that socioenvironmental and genetic factors interact to influence neurobiology ( Garland & Howard, 2009 ). In the context of mental health treatment research, the principles of neuroplasticity are also reminiscent of the foundational framework of person-in-environment ( Bartlett, 1970 ) that postulates a bidirectional relationship between neurobiology and environment ( Green & McDermott, 2010 ). The dynamic, neuroplastic nature of the brain affords many opportunities for social workers to use cognitive neuroscience approaches in their translational research, thereby facilitating the development of more targeted and effective interventions that could directly address core pathophysiological processes to promote mental health recovery.

How Can Cognitive Neuroscience Research Improve Mental Health Treatment?

Early detection of people at risk for mental illness.

The current system for diagnosing mental health conditions is the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5; American Psychiatric Association, 2013 ). In this system, a diagnosis is made after the onset of mental illness based on presenting behavioral symptoms that meet a set of criteria. However, neural signs of mental health symptoms are often present years before the onset of the condition, opening the window for very early intervention or possible prevention. These neural signs are known as biomarkers —biological indicators of a medical condition or disease state. In the context of cognitive neuroscience research, a biomarker is a measurable neurobiological feature indicating that a person is at risk of developing certain mental health symptoms ( Singh & Rose, 2009 ). Abnormal patterns in brain activation while performing a task and/or reduced gray matter volume in regions supporting cognitive functioning are simple but frequently reported examples of neural biomarkers that may signal mental illness vulnerability ( Chang et al., 2017 ; Wackerhagen et al., 2017 ; Walter et al., 2016 ). Biomarkers are an ongoing area of investigation, and a valid and reliable marker for diagnosis does not yet exist for any major mental health condition. However, the use of neuroimaging-based biomarkers has the exciting potential to make mental health screening and diagnostic models more accurate, thereby increasing scientific opportunities for applied clinical research in very early intervention and preventive treatments.

Multivariate statistics—methods for analyzing multiple dependent variables at once (i.e., partial least squares)—are a common and robust approach to mental health biomarker research ( Haxby, Connolly, & Guntupalli, 2014 ). In the neuroscience literature, this statistical approach is generally known as multivariate pattern analysis or multivariate pattern recognition. Simply put, multivariate pattern analysis uses data on gray matter volume from MRI or brain activity from fMRI (the dependent variables) to classify people with and without a mental illness with high sensitivity and specificity ( Borgwardt & Fusar-Poli, 2012 ; Krishnan, Williams, McIntosh, & Abdi, 2011 ). Sensitivity indicates the ability of multivariate pattern analysis to correctly identify the proportion of individuals with a mental illness in a given sample, and specificity is the ability to correctly identify the proportion of individuals without a mental illness. For example, two recent meta-analyses were conducted across all neuroimaging studies using multivariate pattern recognition in major depression ( Kambeitz et al., 2016 ) and schizophrenia ( Kambeitz et al., 2015 ). Across 33 included neuroimaging studies, differential patterns in brain structure and activity correctly classified individuals with major depression and healthy volunteers at 77% sensitivity and 78% specificity. The meta-analytic findings among 38 neuroimaging studies in schizophrenia were similar, with high sensitivity (80%) and specificity (80%) in correctly classifying individuals with schizophrenia and healthy volunteers based on patterns in brain structure and function ( Kambeitz et al., 2015 ). Multivariate pattern analysis has also demonstrated capabilities in deciphering between different mental health diagnoses. Using resting-state fMRI data, multivariate pattern analysis correctly classified individuals with schizophrenia at 92% sensitivity and individuals with bipolar disorder at 83% sensitivity ( Calhoun, Maciejewski, Pearlson, & Kiehl, 2008 ). These findings have important implications for the use of neuroimaging biomarkers in diagnostic models that can accurately detect individuals very early in the course of illness to avoid long durations of untreated illness, inappropriate treatments, and disability.

From a more preventive perspective, multivariate pattern analysis has shown success in classifying individuals at ultra-high risk of developing psychosis. With a 92% sensitivity rate, structural MRI patterns differentiated individuals who developed psychosis from healthy volunteers ( Koutsouleris et al., 2012 ). The classification accuracy rate for the ultra-high risk individuals who did not transition to psychosis from those who did was 84% based on structural brain patterns. In trauma-related conditions, gray matter pattern differences were used to accurately classify 67% of trauma survivors who developed PTSD compared to trauma survivors who did not develop PTSD ( Gong et al., 2013 ). Such results have exciting implications for identifying and personalizing trauma-related interventions based on biomarkers that can predict those at risk of developing PTSD after a traumatic experience. Using diagnostic models from structural and functional brain scans has significant clinical utility for identifying phenotypes of mental illness risk and recovery. As such, the use of cognitive neuroscience research to detect neuroimaging-based biomarkers that are predictive of several forms of mental illness can improve mental health treatment by moving it from a tertiary form of prevention to a practice of primary prevention.

It should be noted, however, that there are several ethical concerns in neuroimaging-based biomarker research that are worthy of discussion ( Singh & Rose, 2009 ). First and foremost, before biomarker research can be translated to clinical practice, there must be a careful, cautious approach to developing therapeutic practice methods for when and how to inform people about their risks and/or resiliencies to mental illness based on neurobiology. This leads to the second ethical dilemma of biomarker research: that identifying a client at risk for mental illness can be potentially traumatizing and stigmatizing, especially considering that multivariate pattern analysis does not yet have 100% classification sensitivity. Third, biomarkers have only been studied in the context of research labs, making generalizability to clinical practice low ( Cook, 2008 ). Lastly, the use of biomarkers is costly, as MRI scanners are expensive and may not be accessible in rural communities. Trained experts are also required to operate the scanner, analyze the data, and interpret the findings ( Lakhan, Vieira, & Hamlat, 2010 ). Despite these considerations and the need for increased accuracy in multivariate models, cognitive neuroscience research aimed at identifying biomarkers of mental illness holds promise for improving mental health treatment by directing interventions to those who are most vulnerable. Pattern-recognition methods show particular promise for paving the way for effective early intervention strategies to prevent disability ( Woo, Chang, Lindquist, & Wager, 2017 ).

Identifying those who are resilient to mental illness

Neuroimaging-based biomarker research is also a rich area for identifying the brain signatures of resiliency. By understanding who is exposed to an adverse social environment and does not develop a mental health condition, we can learn about how the brain can protect itself. The study of the neural substrates that protect people from developing a mental health condition is a new area of investigation. However, themes are emerging suggesting that better regulated and organized neural networks for reward processing, emotion regulation, and stress management may be features of sustained mental health, even in the context of significant stress and trauma ( van der Werff, van den Berg, Pannekoek, Elzinga, & Van Der Wee, 2013 ). For example, a recent study by Swartz, Knodt, Radtke, and Hariri (2015) demonstrated that lower amygdala activation (implicated in emotion regulation) while viewing angry and fearful faces during a baseline scan in 340 healthy young adults predicted lower psychiatric vulnerability to life stressors up to 4 years later.

Neurobiological differences between people who develop or do not develop post-traumatic sequelae—such as PTSD ( Horn, Charney, & Feder, 2016 ) and major depression ( Han & Nestler, 2017 )—have provided additional clues for resiliency biomarkers of mental illness. Lower gray matter or reduced fear-related activation in the amygdala is a common protective biomarker of PTSD, as it may signal healthier regulation of emotional responses to stressful experiences ( Gupta et al., 2017 ; Kuo, Kaloupek, & Woodward, 2012 ; Morey, Haswell, Hooper, & De Bellis, 2016 ). Greater gray matter volume in the hippocampus and dorsolateral prefrontal cortex appear to be features of resiliency against developing major depression, especially for individuals with a family history of the condition ( Amico et al., 2011 ; Arnone, McIntosh, Ebmeier, Munafò, & Anderson, 2012 ; Rao et al., 2010 ). Explicit memory, which is the conscious retrieval of a thought, relies on communication (connectivity) between the dorsolateral prefrontal cortex and hippocampus and is a prominent cognitive challenge for people with major depression ( Pittenger & Duman, 2008 ). Therefore, having more and stronger neural connections from the dorsolateral prefrontal cortex to the hippocampus (e.g., larger gray matter volumes) underpinning effective explicit memory may be a neuroprotective factor against depression.

The neuro-resiliency field is growing, and common themes across mental health conditions have started to emerge. The amygdala has been prominently implicated in many studies, and this small limbic lobe brain structure is thought to be responsible, in part, for the generation of fear and other emotions ( Costafreda, Brammer, David, & Fu, 2008 ). A frequent finding is that an overactive amygdala places a person at greater risk for mental health challenges ( McLaughlin et al., 2014 ; Olsavsky et al., 2012 ; Zhong et al., 2011 ). In contrast, those whose amygdala appears to be under better control during stress are often less likely to experience trauma and other mental health-related symptoms ( Swartz et al., 2015 ). At the same time, increased integrity of prefrontal structures, which are involved in regulating amygdalar activity, also portend increased resiliency, even in the face of significant social and environmental adversity that would often contribute to the onset of a mental health condition ( Herringa et al., 2016 ; Tottenham & Galván, 2016 ). Such findings highlight the complex interplay between brain networks in supporting healthy psychological adjustment and begin to indicate the optimal states of the brain that can support resiliency and adjustment during the experience of life stressors.

Having a greater number of whole-brain neuronal connections (known as “brain reserve”) is another promising neural marker for resiliency ( Satz, 1993 ; Stern, 2002 ). Historically, the protective effects of brain reserve were discovered in postmortem brain studies of Alzheimer’s disease. Neuroscientists observed that, despite the presence of neuropathic plaques and tangles of Alzheimer’s disease in the postmortem brains, certain individuals did not experience the cognitive signs or symptoms of the disease as they aged ( Katzman et al., 1988 ). Such findings indicate that pathophysiology can exist in the brain, but protective mechanisms such as brain reserve can intervene in the clinical manifestation of the condition ( Mortimer, 1997 ). According to Mortimer (1997) , the brain reserve mechanism of action is thought to be a redundancy or larger number of neural connections that are readily available to compensate for the occurrence of lesions or disease insult to the brain ( Keshavan et al., 2011 ).

To summarize, neuroimaging-based biomarkers of resiliency have obvious clinical utility for screening, prevention, and personalization of mental health treatment through an understanding of how the brain can protect itself. Neuro-resiliency research will aid in the identification of which neural circuits must be strengthened to prevent mental illness and promote healthy psychosocial functioning and adjustment. In the social work context, neuro-resiliency research also raises new person-in-environment questions around why some people do not acquire neurodevelopmental impairments or harm in the face of extreme stress. For example, given the malleable nature of the brain, do resilient individuals who experience adversity, oppression, or discrimination have a more neuroplastic brain that is capable of rapidly adapting to change in the social environment, compared to less resilient people? How can psychosocial interventions facilitate a neurobiological foundation that is resilient to stress in some of the most underprivileged and oppressed populations?

Identifying neural treatment targets for mental health interventions

Cognitive neuroscience has perhaps gained the most traction in promoting mental health treatment through the identification of neural targets for intervention. By understanding the neurobiological mechanisms of mental illness, cognitive neuroscience research has generated a significant amount of evidence indicating where to target medications, brain stimulation, and psychosocial interventions. By providing in vivo measurements of brain function and structure during mental health treatment, cognitive neuroscience provides a window to observe the relationship between treatment parameters and distinct clinical and functional improvements. Such research will increase opportunities to make mental health treatments more targeted and precise. For example, suppose a new cognitive training intervention was observed to increase prefrontal brain activity associated with improved attention and ability to maintain employment. This observation would indicate that it may be optimal to emphasize certain training exercises associated with prefrontal cognitive control and attention for functional recovery in the employment domain. Intervention developers can then use this information to modify their cognitive training program to focus on the attention-training elements to provide a more targeted, less lengthy treatment experience that could be increasingly efficacious for improving vocational functioning. This target-engagement approach is an essential component of experimental medicine (see Insel, 2012 , p. 24), an approach to developing mental health interventions adopted by NIMH ( Insel, 2015 ). Clearly, such knowledge would help intervention research to prioritize novel and refined treatment strategies to improve mental health interventions.

Cognition, daily functioning, and motivation are all behavioral domains that are frequently impacted by mental health conditions. The identification and understanding of the neural underpinnings driving these behavioral domains can serve as mental health treatment targets. For example, in people with schizophrenia, lower gray matter volume in frontal, temporal, and limbic regions is associated with challenges in social and nonsocial cognition ( Wolf, Höse, Frasch, Walter, & Vasic, 2008 ; Yamada et al., 2007 ). Meta-analytic evidence has similarly demonstrated that lower gray matter volume in frontal and limbic regions is a neural correlate of functional abilities in people with schizophrenia ( Wojtalik, Smith, Keshavan, & Eack, 2017 ). More specifically, better social functioning in people with schizophrenia is predicted by greater empathy-related activation in the middle cingulate cortex of the limbic lobe ( Smith et al., 2015 ) and increased connectivity between the medial prefrontal cortex and posterior cingulate cortex ( Fox et al., 2017 ). In people with schizophrenia, there is substantial regional overlap among the frontal, limbic, and temporal lobes, which support cognitive and functional outcomes. These brain regions may be important neurobiological substrates that could be targeted through pharmacological and/or psychosocial interventions to promote a more complete recovery from schizophrenia.

Regarding motivation, the striatum is an important limbic lobe region affecting motivational outcomes across people with schizophrenia, major depression, and bipolar disorder ( Whitton, Treadway, & Pizzagalli, 2015 ). This transdiagnostic neural target is associated with underactivation in people with major depression and schizophrenia, which translates to a lack of motivation (i.e., negative symptoms, anhedonia). In bipolar disorder, hyperactivation in the striatum is linked to oversensitivity to reward and high motivation for rewarding experiences (i.e., mania; Whitton et al., 2015 ). Compared to healthy volunteers, individuals with bipolar disorder recruit significantly more striatal activity during the anticipation of monetary rewards, possibly representing a neural determinant of elevated mood ( Nusslock et al., 2012 ). Others have observed lower striatal activation during reward processing in major depression and schizophrenia. For example, Subramaniam et al. (2015) observed significantly less activation in the striatum during monetary reward anticipation in people with schizophrenia when compared to healthy individuals. In healthy volunteers, greater activation in the striatum was associated with an increased sense of pleasure and motivation after receiving a monetary reward, but similar activation was not observed in individuals with schizophrenia.

For interventionists, these results suggest that increasing recruitment of the striatum during anticipatory reward processing in clients with major depression and schizophrenia (and decreasing striatal activity in bipolar disorder) is a potentially promising treatment target to improve motivational difficulties. For example, behavioral treatments that encourage increased motivation by positively reinforcing rewarding behaviors ( Favrod, Giuliani, Ernst, & Bonsack, 2010 ) such as exercising ( Dauwan, Begemann, Heringa, & Sommer, 2016 ; Firth, Cotter, Elliott, French, & Yung, 2015 ) appear to activate the striatum ( Robertson et al., 2016 ). Repeated reinforcement of a rewarding behavior may alter the striatum so that over time it will become more intrinsically activated by the anticipation of reward, which may generalize to improved motivational outcomes in clients outside of the clinic. In this way, knowledge of the neural contributors to different domains of behavioral outcomes in people with mental health conditions can improve the precision of mental health treatments by providing direct neurobiological targets for intervention. Based on growing data on the plasticity of the brain and its impact by the social environment, it is increasingly plausible to address these neural targets with non-pharmacological interventions ( Keshavan et al., 2014 ).

Using cognitive neuroscience research to personalize mental health treatment

An emerging theme in the earlier sections of this paper is the strong potential for biomarkers and treatment targets to help make mental health treatment more personalized. Because mental illness has a substantial brain component, and even single conditions are highly heterogeneous, understanding a person’s neurobiology will help select the treatment protocols most likely to work the first time, or most quickly, rather than having clients endure repeated treatment failures. The complex interplay between genetics, environment, and neurobiology—along with the uniqueness of each person—makes true personalization of mental health treatment quite challenging. However, neuroimaging-based biomarkers provide information about neurobiological features that could accurately determine which pharmacological or psychosocial treatments would be the best fit for specific clients, at least from a neurobiological perspective ( Holsboer, 2008 ). The NIMH promotes the utility of cognitive neuroscience tools for identifying neural substrates that can be used to personalize mental health treatment ( Cuthbert, 2014 ). The Research Domain Criteria (RDoC) is an NIMH initiative that encourages a dimensional approach to understanding mental illness as an alternative to the current categorical diagnostic system ( Insel et al., 2010 ; NIMH, n.d. ). The strategic goal of RDoC is to fund research that will identify the neural basis of differential dimensions of observable behavior and functioning in order to inform treatment personalization based on neurobiological features ( Morris & Cuthbert, 2012 ).

The use of cognitive neuroscience to predict responsiveness to treatment has been a particularly important avenue for determining the neural mechanisms of personalizing mental health treatment. For example, an early study by Bryant et al. (2008) observed that optimal response to cognitive–behavioral therapy (CBT) 6 months after completion was related to lower fear-related activity in the amygdala and anterior cingulate cortex of the limbic lobe in clients with PTSD. Lower amygdala reactivity to fearful faces is likely reflective of a lower fear response ( Bryant et al., 2008 ). A more recent study in PTSD found that having larger hippocampal volume at the start of prolonged exposure was predictive of better treatment response ( Rubin et al., 2016 ). The capacity to extinguish traumatic memories may be a capability associated with larger hippocampal volume ( Rubin et al., 2016 ). In the context of treatment personalization, it can be inferred that the absence of these neurobiological features (i.e., lower amygdala reactivity and larger hippocampus) in clients with PTSD is rate-limiting. These clients may require emotion regulation and memory training prior to the start of CBT or prolonged exposure to optimize response.

In the case of early course schizophrenia, having a greater amount of cortical gray matter at the start of cognitive remediation is associated with an accelerated improvement in social cognition. People with lower cortical gray matter benefit from treatment, but the rate of social cognitive improvement is slower ( Keshavan et al., 2011 ). Such results indicate that cortical gray matter volume can be assessed at the start of cognitive remediation to personalize the length of treatment and which cognitive domains to target for optimizing recovery. Finally, Dunlop et al. (2017) used multivariate pattern analysis to examine connectivity signatures of clients with major depression who responded to CBT and antidepressant medication compared to clients who were not responsive to treatment. Differential resting-state connectivity patterns in the cingulate cortex accurately classified clients who failed to respond to treatment against clients who did respond at an average of 82%. These results again demonstrate the clinical utility of using cognitive neuroscience to aid in the identification of effective first-line treatment. This is particularly important given that treatment failures and the revolving-door experience can be demoralizing for clients and a significant barrier to treatment engagement ( Andrade et al., 2014 ).

How Can Mental Health Treatment Identify Brain Mechanisms Underlying Cognition, Affect, and Behavior?

Although much can be learned about improving mental health treatment by incorporating data and discoveries from cognitive neuroscience, intervention research also provides a powerful platform for understanding the brain and how it changes when exposed to treatment (see Table 1 ). The longitudinal context of treatment research, as well as the experimental manipulation of interventions, goes far beyond the cross-sectional studies that characterize much of cognitive neuroscience. Clinical trials afford unique opportunities for understanding the plasticity of specific brain regions, how the brain changes in response to psychosocial intervention, and the connection between brain changes and meaningful treatment outcomes. The capacity for intervention research to yield significant findings for cognitive neuroscience has been particularly represented by the experimental-medicine approach adopted by NIMH ( Insel, 2015 ).

Experimental medicine

Experimental medicine is a novel approach adopted by NIMH to, in part, integrate biological and treatment research, and to use the experimental and longitudinal context of clinical trials to yield new cognitive neuroscience discoveries in a more robust context than cross-sectional research. The basic premise of the approach is that interventions to improve mental health usually have underlying targets that mediate treatment outcomes. More specifically, this approach uses neural markers and their change by treatments as proxy outcome measures in clinical trials, thereby making treatment research more efficient and leading to quicker answers ( Lewandowski, Ongur, & Keshavan, 2018 ). These targets often implicate cognitive neuroscience constructs, such as increased brain communication and better outcomes in autism ( Plitt, Barnes, Wallace, Kenworthy, & Martin, 2015 ) or lower prefrontal activity predicting social disability in schizophrenia ( Wojtalik et al., 2017 ). The experimental-medicine approach makes these intervention targets explicit foci of treatment. The experimental context of clinical trials is used to intervene on these targets and assess the impact on meaningful mental health outcomes ( Insel, 2012 ). It is hypothesized that two important advances will result from this approach. First, mental health treatment research will accelerate by focusing on target engagement and a “fast-fail” approach to intervention development. In such an approach, researchers perform smaller and more rapid clinical trials to identify treatment targets that show malleability and connection to favorable outcomes and quickly abandon those targets that do not ( Insel & Gogtay, 2014 ). Second, the underlying mechanisms contributing to mental illness will be discovered as researchers identify which targets contribute to mental health outcomes. As such, experimental medicine views treatment research not only as a tool for building better interventions, but also for discovering the targets—often neurobiological in nature—that do and do not contribute to mental health ( Insel, 2012 , 2015 ).

The experimental-medicine paradigm comes from the broader biomedical treatment literature for physical illnesses, including oncology. Experimental medicine is an attempt by NIMH and the scientific community to align mental health intervention research with medical research in other fields. Major advances in cancer research have been accomplished by increasing the focus on potential targets for tumor development and proliferation, ruling out those targets that show no signs of contributing to pathophysiology and noting targets that are less tractable to intervention ( Jones & Price, 2012 ). Many of the most significant treatment advances in mental health have serendipitously taken this route, such as when dopamine antagonists were discovered to treat schizophrenia and serotonin reuptake inhibitors were identified for the treatment of depression. The neural contributors to these conditions were largely formulated around observations that such medications affected a neural target, and when that neural target changed, mental health symptoms improved substantially. This is why schizophrenia is known to involve dopaminergic hyperactivity and why depression is known to involve serotonin neurotransmission. However, not all mental health issues are amenable to such an approach ( Markowitz, 2016 ). Experimental medicine is an attempt to learn more from treatment research—not only about what works, but about the mechanisms that contribute to and ultimately cause mental illness. As such, treatment research can significantly inform research on the cognitive neuroscience of mental health, particularly in understanding where and how the brain can change, as well as the changes that are most needed to support improved recovery and psychosocial functioning.

Identifying where the brain changes in response to mental health treatment

The discovery of brain plasticity and the significant interplay between the social environment and neural change has ushered in a new era of understanding the capabilities of human neurobiology. It is now widely recognized that the brain is malleable and continuously shaped beyond early development by learning, interaction, and the environment ( Bruel-Jungerman et al., 2007 ; Buonomano & Merzenich, 1998 ). What is less known is where the brain can change, and treatment research is well poised to contribute knowledge to this area. Like many phenomena, brain plasticity is expected to be unevenly distributed ( Buonomano & Merzenich, 1998 ). Perhaps the most “neuroplastic” area discovered to date is the hippocampus, a region in the middle of the brain that is heavily involved in memory storage. This finding is largely drawn from experimental animal studies ( Soya et al., 2007 ; Van Praag, Shubert, Zhao, & Gage, 2005 ), and human exercise-physiology studies ( Erickson et al., 2011 ; Fuss et al., 2014 ). Researchers have conducted numerous studies on the impact of aerobic activity on hippocampal volume and memory across many different population groups. For example, Erickson and colleagues (2011) completed a randomized trial of a 40-minute walking intervention versus a stretching control in older adults and found that hippocampal volume was significantly increased in the moderate exercise condition relative to control. Furthermore, these volumetric changes were associated with significant improvements in memory, perhaps reversing age-related memory decline by several years ( Erickson et al., 2011 ).

For some time it was thought that only the hippocampus might possess this remarkable ability to self-repair and generate new neurons ( Deng, Aimone, & Gage, 2010 ), but additional studies are emerging indicating that plasticity is possible in other areas of the brain to support improved function in different domains. For example, Wang and colleagues (2016) examined the effects of CBT on brain communication patterns in adults with attention deficit hyperactivity disorder and found significant increases in the communication between frontal and parietal regions among participants treated with CBT. These neural changes were associated with reduced symptoms of attention deficit hyperactivity disorder ( Wang et al., 2016 ). Another study of CBT by Shou and colleagues (2017) found increased frontoparietal connectivity with the amygdala in adults with stress-related conditions who were receiving treatment, again suggesting that CBT can impact neural communication patterns across diverse areas of the brain in support of improved mental health outcomes ( Shou et al., 2017 ). A recent meta-analytic review of the impact of cognitive remediation interventions on brain function in schizophrenia found widespread increases in brain activity throughout the prefrontal cortex associated with treatment ( Ramsay & MacDonald, 2015 ). Although these intervention studies focus on brain function and communication, and it remains unclear the degree to which the generation of new neurons outside of the hippocampus is possible, it is increasing evident that many brain regions have greater functional plasticity than previously recognized.

Much of what is known about the capacity of different regions of the human brain to change has been generated from mental health intervention research. Whether it is aerobic interventions to protect against memory loss or cognitive exercises to enhance attention and problem-solving, these studies are a driving force behind what is known about where the brain can change. The longitudinal and often experimental nature of mental health treatment research provides a powerful platform for probing and understanding brain plasticity. Although a great deal remains to be learned and the distribution of functional and structural plasticity throughout the human brain is only beginning to be mapped, there is considerable optimism that much of our neural make-up could be amenable to positive change and influence ( Cramer et al., 2011 ; Garland & Howard, 2009 ). Mental health treatment research will be an essential contributor to building knowledge in this area.

Discovering how the brain changes in response to mental health treatment

In addition to understanding where the brain can change, research on mental health interventions can also provide important information on how the brain changes. As the field is learning more about the impact of mental health interventions on different regions of the brain, new findings indicate that multiple avenues exist for improving mental health outcomes through neural change. A primary question for the field at this time is whether brain parameters need to be restored to those equivalent to healthy or unaffected individuals, or whether the brain might compensate and chart completely novel avenues of function and structure to improve outcomes ( Penadés et al., 2017 ). There is evidence from the stroke and rehabilitation literature that compensation may be a common way for the brain to improve function ( Murphy & Corbett, 2009 ). For example, a study by Small, Hlustik, Noll, Genovese, and Solodkin (2002) observed that participants who regained limb function after a motor-impacting stroke had significant increases in motor cortex activity in the hemisphere opposite of the affected area of the brain. This suggested neural compensation: that the brain was rerouting motor activity from the damaged hemisphere to the functional one to improve movement ( Small et al., 2002 ).

In the mental health field, evidence is emerging that both compensatory and restorative changes are possible. For example, in a study of cognitive remediation in people living with schizophrenia, Penadés and colleagues (2013) found that pre-frontal brain function became more normalized and similar to healthy individuals during the course of treatment, although the implications for behavior were less clear. Another study of neural feedback training in children with attention deficit hyperactivity disorder found that individuals could up-regulate a key region of the prefrontal cortex similar to healthy individuals, with significant improvements in hyperactivity and inattentive symptoms ( Alegria et al., 2017 ). Further, a study of age-related memory loss found that strategy-based memory training produced increased brain activity in novel regions not active prior to treatment, suggesting the compensatory recruitment of brain regions to enhance memory function in older adults ( Belleville et al., 2015 ). In addition, a study of children with abuse-related PTSD observed a normalization of frontotemporal activation during the course of CBT, which was associated with reduced emotional arousal; this indicated possible normalization of some aspects of brain function contributing to clinical recovery ( Thomaes et al., 2012 ).

These neuroimaging studies of mental health interventions provide important insights into how the brain changes to improve cognition, psychosocial functioning, and recovery. Although this area of research is in its infancy, evidence exists across a wide range of conditions for the possibility of both restorative and compensatory mechanisms involved in brain change associated with mental health improvement. Understanding whether a neuroimaging effect represents compensation or restoration is heavily dependent upon a firm grasp of neurotypical brain function and abnormalities associated with disorder. Both cognitive and clinical neuroscience are rapidly generating such data, and research from mental health interventions can help determine not only where the brain changes, but also whether normalization results from treatment or whether functional improvements can be gained through compensatory processes.

Linking treatment-related brain change to meaningful behavioral outcomes

From the discussion of how mental health research can inform cognitive neuroscience, it should be increasingly clear that the brain can change as a result of mental health intervention. However, not all changes are meaningful or helpful, and simply discovering that a region is amenable to change will do little to help those living with a disability if such changes are not linked to meaningful outcomes. The integration of mental health treatment research with cognitive neuroscience can reveal how treatment-related brain changes support real-world functioning and recovery, which is likely most pertinent to the underserved populations social workers serve in community mental health clinics.

The use of cognitive neuroscience techniques in mental health treatment research can demonstrate specific locations in the brain altered by intervention that subsequently had a positive influence on symptoms, cognition, and/or functioning. For example, individuals with major depression have difficulty processing emotional information. Such challenges reflect underactivation of frontolimbic regions, which Ritchey, Dolcos, Eddington, Strauman, and Cabeza (2011) demonstrated could be reversed by participating in CBT. After completing an average of 21 CBT sessions, individuals with major depression had significant pre- to posttreatment increases in brain activation in the ventromedial prefrontal cortex, amygdala, caudate, hippocampus, and the anterior temporal lobe while evaluating emotional faces in the scanner. However, only the ventromedial prefrontal cortex and anterior temporal lobe predicted meaningful improvement in depression symptomatology ( Ritchey et al., 2011 ). CBT is a comprehensive program that attempts to address several aspects of thinking (i.e., evaluating automatic thoughts, identifying feelings, and modifying beliefs). Therefore, such results indicate that although other brain areas were alerted by CBT, the ventromedial prefrontal cortex and anterior temporal lobe may have a meaningful link to emotional processing abilities and relieving symptoms of depression—a marked advance in understanding the functional significance of these brain changes.

In individuals with autism, difficulty recognizing emotions in others is a prominent feature of the condition ( Lozier, Vanmeter, & Marsh, 2014 ; Uljarevic & Hamilton, 2013 ). Reduced activation in the fusiform gyrus, a temporal lobe region implicated in facial-affect recognition, is a commonly observed neural correlate of social cognitive impairment in autism ( Corbett et al., 2009 ; Schultz, 2005 ). Bölte et al. (2006) hypothesized that after 5 weeks of facial-affect recognition training, individuals with autism would show significant behavioral improvement in facial-affect recognition associated with increased activity in the fusiform gyrus. However, increased activation in the superior parietal lobule and right medial occipital lobe, not the fusiform gyrus, were significantly correlated with improved behavioral facial-affect recognition skills after training ( Bölte et al., 2006 ). Similarly, a more recent study observed that improved interpersonal communication scores were significantly related to medial prefrontal cortex activation—rather than the assumed fusiform gyrus—following facial-affect recognition training ( Bölte et al., 2015 ). These results further demonstrate the importance of integrating neuroimaging measures into clinical trials to advance our understanding of where change in the brain matters for improving psychosocial functioning and recovery.

Repetitive transcranial magnetic stimulation (rTMS) is a noninvasive treatment that uses targeted brain stimulation by sending a small but strong electrical current into the cerebral cortex to alter brain functioning. rTMS has treated several mental health conditions ( Slotema, Dirk Blom, Hoek, & Sommer, 2010 ). Given that this treatment is a focused approach for stimulating selected brain regions with the goal to reduce behavioral symptoms, rTMS exemplifies the importance of linking brain change to meaningful outcomes. This treatment has been applied to the positive and negative symptoms of schizophrenia, albeit with mixed findings for efficacy ( Hasan et al., 2017 ; Kimura et al., 2016 ). rTMS treatments traditionally target the left dorsolateral prefrontal cortex to address negative symptoms and the left temporoparietal cortex to reduce positive symptoms in the condition ( Freitas, Fregni, & Pascual-Leone, 2009 ; Shi, Yu, Cheung, Shum, & Chan, 2014 ). Despite target engagement of these theorized regions by rTMS, there often is no observed effect on clinical outcome (i.e., reduced hallucinations). For example, in a double-blind, randomized controlled trial, Novak et al. (2006) applied rTMS to the left dorsolateral prefrontal cortex for 10 days and observed no significant improvements in negative symptom scores. The variability of efficacy findings in rTMS for schizophrenia likely reflects a lack of knowledge of where to target rTMS to optimize the effects on meaningful behavioral change. Such rTMS studies underscore the importance of knowing the exact brain regions that need to be altered by an intervention in order to observe tangible improvements in clinical and functional outcomes in clients.

To truly optimize mental health treatment protocols, however, neuroimaging-based research will need go a step further and examine the degree to which brain changes associated with treatment impact more distal, real-world functioning goals. Preliminary cognitive neuroscience findings indicate that there is a neural signal associated with better social functioning in society ( Wojtalik et al., 2017 ), including financial stability in old age ( Han et al., 2014 ), income level ( Hanson, Chandra, Wolfe, & Pollak, 2011 ), socioeconomic status ( Noble, Houston, Kan, & Sowell, 2012 ), and marriage ( Petrican, Rosenbaum, & Grady, 2015 ). Knowledge of the reciprocal link between changes in the brain and meaningful progress in the ability to hold a competitive job, finish college, and live independently has vital implications for maximizing and personalizing pharmacological, brain stimulation, and psychosocial interventions. All of these outcomes are prominent goals for most clients working with social workers and are pertinent to clients’ overall quality of life ( Eack & Newhill, 2007 ). Conclusively, examining treatment-related brain alterations and their mediating/moderating role in psychosocial recovery is an imperative direction for improving the specificity and precision of mental health treatments to have a real and meaningful influence on clients’ ability to achieve personal goals.

Summary and Conclusions

Mental health conditions are severely and persistently disabling, placing significant barriers on individuals’ ability to recover and achieve personal goals, such as maintaining competitive employment and living independently. Despite significant advances in mental health treatment in both pharmacological and psychosocial arenas, functional recovery from mental health conditions remains inadequate. Such limits in mental health treatment are evidence of the need for novel methods to improve interventions. Recent advances in cognitive neuroscience offer unique opportunities to improve the effectiveness of mental health treatment. This paper reviewed how incorporating cognitive neuroscience into clinical research conducted by social workers and allied mental health professions may improve mental health interventions and promote greater functional recovery ( National Institutes of Health, 2015 ).

We reviewed several developing avenues that demonstrate the clinical utility of combining cognitive neuroscience and mental health treatment research (see Table 1 ). Cognitive neuroscience can improve mental health treatments so they are more preventive, early intervening, and targeted at core pathophysiology. Neuroimaging-based biomarker and treatment-target research can indicate brain signatures of risk and resiliency for mental health conditions or symptoms. Findings from such research directs interventionists to optimal locations in the brain to prevent onset or reduce long-term disability by intervening early in the course of an illness. The goal of linking clients’ neurobiology to treatment protocols is to increase personalization and decrease the lengthy process of finding the right treatment. Additionally, mental health treatment research provides a powerful context for cognitive neuroscience research. The longitudinal nature of clinical trials using neuroimaging measures provides additional information about where , how , and what in the brain can be altered by treatment to promote the greatest opportunities for complete, client-defined recovery from mental illness. Such knowledge will support the field in prioritizing novel and refined treatment strategies to improve interventions for people burdened by some of the most disabling effects of mental illness.

Of course, the brain is not the ultimate solution for developing and optimizing mental health treatments. Readers should be aware of the cautions, limitations, and reductionist nature of cognitive neuroscience methodologies (for a full review see Satel & Lilenfeld, 2013 ). Current neuroimaging tools capture an oversimplified and reduced picture of the intricate complexities of the physiology of the human brain and the black box of the mind. For example, fMRI provides a correlational link between changes in blood flow (i.e., a lighted blob on an image) and a single cognitive ability, such as working memory or empathy. Neurobiological changes during a cognitive paradigm measured with cognitive neuroscience techniques are not a direct representation of what a person is truly thinking or feeling, and it would be premature for researchers to make such claims ( Satel & Lilienfeld, 2013 ). Neuroimaging methods are merely a new and increasingly available tool for social workers to use in their research to address the biopsychosocial contributors to mental health. Our goal with this article is to encourage equal distribution of importance across all three domains of the biopsychosocial framework. Social workers often use psychosocial approaches in mental health treatment, but biology also requires attention to facilitate a comprehensive, holistic view of client circumstances. The integration of social work and cognitive neuroscience has considerable potential to improve the lives of people suffering from mental health conditions by attending to biology, the social environment, and their reciprocal interplay.

The use of cognitive neuroscience to improve mental health treatment also contributes to several of the 12 Grand Challenges for Social Work ( American Academy of Social Work and Social Welfare, n.d. ), such as (a) harnessing technology for social good, (b) advancing long and productive lives, (c) eradicating social isolation, and (d) achieving equal opportunity and justice. For example, DeVylder (2016) proposed that social workers lead collaborative efforts to develop innovative psychosocial treatments to prevent the onset of psychosis aligned with the grand challenge to ensure healthy development for all youth. The application of cognitive neuroscience findings indicating brain areas linked to the conversion of psychosis ( Dazzan et al., 2011 ; Smieskova et al., 2010 ; Thermenos et al., 2016 ) directly informs the development of prevention treatments, is consistent with the biopsychosocial framework, and is essential to the success of this grand challenge.

The integration of cognitive neuroscience and mental health treatment research is an exciting and innovative opportunity for improving intervention effectiveness. However, implementing neuroimaging measures can be intimidating and overwhelming. Social workers may be surprised by the richness of resources and the feasibility of incorporating cognitive neuroscience research. A first step to integrate cognitive neuroscience and mental health treatment research is the inclusion of neuroimaging findings from clinical trials in social work courses, which is already becoming a practice in psychiatry ( Etkin & Cuthbert, 2014 ). For general teaching resources, see Egan, Neely-Barnes, and Combs-Orme (2011) ; Farmer (2008) ; Matto et al. (2014) ; and Shapiro and Applegate (2000) . Additionally, cognitive neuroscience findings can be used as a clinical platform to destigmatize diagnoses and symptoms by educating clients and their family members about the neurobiology of mental health conditions and the ability of treatments to change the brain for the better.

Regarding methods and analysis, software for processing and analyzing neuroimaging data is freely available, such as Statistical Parametric Mapping (SPM; http://www.fil.ion.ucl.ac.uk/spm/resources ) and FSL ( https://fsl.fmrib.ox.ac.uk/fsl/fslwiki ). SPM requires MATLAB to operate, but it provides an easy-to-use graphical user interface and a detailed, step-by-step manual ( http://www.fil.ion.ucl.ac.uk/spm/doc/ ) with downloadable practice data across several neuroimaging modalities ( http://www.fil.ion.ucl.ac.uk/spm/data/ ). Statistical Analysis of fMRI Data ( Ashby, 2011 ) is a helpful, low-cost textbook that provides a straightforward and easy-to-read foundation for neuroimaging methods and analysis. Cognitive neuroscience training courses also are available for scientists without a traditional neuroscience background (see, for example, Neurometrika at http://neurometrika.org/ ).

Overall, by summarizing the cognitive neuroscience literature related to mental health treatment and providing resources, we hope that social workers and allied professions feel encouraged and capable of beginning to include cognitive neuroscience measures in their intervention studies. The next generation of mental health treatment advances will require such an integration, and a true biopsychosocial understanding of client strengths, challenges, and opportunities for intervention. The integration of cognitive neuroscience and mental health treatment research has the potential to eradicate lifelong disability by increasing the precision of client-driven, first-line treatment decisions.

Acknowledgments

This research was supported by National Institutes of Health grants MH 92440 (Matcheri S. Keshavan and Shaun M. Eack, principal investigators), MH 106450 (Shaun M. Eack, principal investigator), and MH 113277 (Jessica A. Wojtalik, principal investigator).

Contributor Information

Jessica A. Wojtalik, Doctoral candidate at the University of Pittsburgh School of Social Work.

Shaun M. Eack, Professor at the University of Pittsburgh School of Social Work and Department of Psychiatry.

Matthew J. Smith, Associate professor at the University of Michigan School of Social Work.

Matcheri S. Keshavan, Professor in the Harvard Medical School Department of Psychiatry.

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