essay paper on midlife crisis

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Dissecting the midlife crisis: disentangling social, personality and demographic determinants in social brain anatomy

Hannah kiesow.

1 Department of Psychiatry, Psychotherapy, and Psychosomatics, RWTH Aachen University, Aachen, Germany

Lucina Q. Uddin

2 Department of Psychology, University of Miami, Coral Gables, FL USA

Boris C. Bernhardt

3 McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montréal, QC Canada

Joseph Kable

4 Department of Psychology, University of Pennsylvania, Philadelphia, PA USA

Danilo Bzdok

5 Department of Biomedical Engineering, McConnell Brain Imaging Centre, Montreal Neurological Institute (MNI), Faculty of Medicine, McGill University, Montréal, QC Canada

6 Mila – Quebec Artificial Intelligence Institute, Montréal, QC Canada

Associated Data

Source data underlying Figs. ​ Figs.2, 2 , ​ ,4, 4 , and ​ and5, 5 , as well as Supplementary Fig.  4 – 5 , are presented in Supplementary Data  3 . All used data are available to other investigators online (ukbiobank.ac.uk), or available from the corresponding author upon reasonable request.

All analyses conducted for the present study are reproducible and the scripts used for the analysis pipelines are available online ( https://github.com/hannahkiesow/social_brain_aging ).

In any stage of life, humans crave connection with other people. In midlife, transitions in social networks can relate to new leadership roles at work or becoming a caregiver for aging parents. Previous neuroimaging studies have pinpointed the medial prefrontal cortex (mPFC) to undergo structural remodelling during midlife. Social behavior, personality predisposition, and demographic profile all have intimate links to the mPFC according in largely disconnected literatures. Here, we explicitly estimated their unique associations with brain structure using a fully Bayesian framework. We weighed against each other a rich collection of 40 UK Biobank traits with their interindividual variation in social brain morphology in ~10,000 middle-aged participants. Household size and daily routines showed several of the largest effects in explaining variation in social brain regions. We also revealed male-biased effects in the dorsal mPFC and amygdala for job income, and a female-biased effect in the ventral mPFC for health satisfaction.

Hannah Kiesow et al. combine 40 behavioral indicators and neuroimaging data from the UK Biobank to investigate how the transitions in midlife in the domains of social, personality, and demographic determinants impact brain anatomy. Through Bayesian analyses, the authors were able to disentangle which specific traits, relative to other considered candidate traits, contributed the most to explaining differences in social brain volume.

Introduction

Humans are inherently social organisms. As early as the first days of life, infants show signs of distress in the absence of social stimulation 1 . As humans grow older, a thirst for social embeddedness persists and may even intensify 2 , 3 . At midlife, roughly between the ages of 40–70 4 – 7 , adults have gathered decades of social experience that shape how they navigate their daily social encounters. For example, the ability to form accurate social judgments about other individuals is known to mature throughout life 8 . The continuous refinement of social skills well into adulthood may, in turn, mold other factors that impact how we navigate our social environments, such as personality disposition and demographic standing. Midlife is a special life period when many important milestones have often been reached, such as creating a family and establishing oneself in an occupation 6 .

For example, during midlife, one’s overall social network size usually starts to shrink 9 , 10 . When choosing with whom to spend time, middle-aged participants prefer familiar over new social interaction partners, compared with younger people 11 . Similarly, middle-aged men and women preferentially interact with close family members 12 . The consolidation of social circles towards a select core confers a feeling of social embeddedness and promotes well-being 10 , 13 . Overall, there is an age-related tendency to relatively disengage from social ties at the network periphery and prioritize spending time with emotionally close others 10 . As such, midlife can be viewed as a pivotal period in the lifespan when adults transition their focus from exploring new social relationships to fostering existing social connections.

The typical midlife changes in social network configuration are likely accompanied by changes in brain architecture. At this stage of life, hints from the neuroimaging literature suggest that structural alterations in frontal or prefrontal brain regions may occur as a part of normal aging 14 , 15 . Importantly, the loss in brain volume during healthy aging is not evenly distributed across the brain 16 , 17 . In a previous structural brain-imaging study in 547 participants aged 19 to 86, age-related reductions in gray matter volume were observed especially in the frontal and parietal lobes, including regions of the prefrontal cortex 18 . Indeed, the prefrontal cortex, among other frontal cortex regions, has been emphasized to show the strongest age effects in gray matter structure as people grow older, compared with the rest of the brain 5 , 14 , 15 , 17 – 22 (but see these references reporting no such changes: 16 , 23 ). Structural imaging research based on T1-weighted brain scanning has so far struggled to attach unambiguous meaning to findings of more or less gray matter volume in specific brain locations 24 . An increase in gray matter volume could indicate higher density of cell populations, including neurons and their substructures like cell bodies or axons. While an increase of this quantity has repeatedly been observed to be associated with enhanced cognitive performance, various counterexamples have reported reduced functional capacity. In particular, neuronal pruning processes are one candidate mechanism for how less regional volume may allow for computational efficiency gains in a specific cognitive process 24 .

Previous brain-imaging research suggests that the medial prefrontal cortex (mPFC) serves as a common computational resource for separate domains of social cognition 25 , including cognitive processes that implicate self-concept or thinking about other people’s mental states 25 , 26 . In parallel to how brain structure differs as a function of age, these social cognitive processes may also show characteristic changes across the lifespan. For example, a previous functional brain-imaging study investigated differences between younger and older adults in understanding the mental state of other individuals 27 . In a battery of social-cognitive tasks, the authors found older adults to consistently show deficits compared with younger adults in understanding the thoughts and actions of others, accompanied by isolated reductions in mPFC task responses. Moran and colleagues suggested specific involvement of the mPFC in mentalizing skills, and proposed that aging is associated with impairments in processing the intentions and internal states of other people 27 .

In addition to capacities implicated in social interaction, neural activity in the mPFC has also been closely linked to other key domains of everyday life, especially personality and demographics 25 , 28 , 29 . An individual’s personality is an important source of interindividual variability in approaching everyday life and reflects an individual’s disposition in their thought processes and behavior 30 . For example, higher levels of the personality trait neuroticism in early adulthood were associated with overall lower well-being later on in midlife and less positive relations with other individuals 31 . Indeed, individuals with higher levels of neuroticism were shown to have a lower quality of social relationships with others in general and romantic relationships in particular 32 . Moreover, a previous structural brain-imaging study found that higher levels of neuroticism were associated with gray matter loss in the mPFC in adults beginning around the age of 44 28 . However, the same study found that ranking high on the personality trait conscientiousness, a trait associated with positive and satisfying romantic relationships 32 , was linked to larger mPFC volumes and less gray matter decline 28 . Furthermore, another structural brain-imaging study found that extraversion, a trait associated with seeking and engaging in satisfying social interactions, was linked to increased cortical thickness in the prefrontal cortex in adults in late midlife 30 . The collective evidence suggests that personality traits may have an enduring influence on the social behavioral patterns of individuals, and are linked to correlates in brain morphology.

At the broader societal level, interindividual differences in social interaction tendencies also depend on the demographic characteristics of one’s place in society, such as economic resources, occupational prestige and education attainment 33 , 34 . Indeed, a brain-imaging study interrogated the relationship between gray matter volume and socioeconomic disadvantage, a composite measure of household poverty level, public assistance, education level, employment status and household income 35 . The authors reported smaller gray matter volume in the mPFC for middle-aged adults who experience more socioeconomic hardship compared with adults without precarious socioeconomic circumstances. Similarly, a previous structural brain-imaging study ( n  = 431) found that experiencing current financial hardship during midlife was also linked to smaller gray matter volume in the hippocampus and amygdala, two limbic regions with direct axonal connections to the mPFC 36 . Furthermore, Butterworth and colleagues found that middle-aged adults with financial hardship had fewer close social relationships, such as a partner, compared to middle-aged adults more financially stable. The authors speculated that experiencing financial hardship may be a source of stress on an individual, with downstream consequences of reduced brain structure due to corticosteroid exposure 36 . Correspondingly, a previous brain-imaging study ( n  = 359) linked high socioeconomic status to thicker cortical gray matter in brain regions including the prefrontal cortex, in middle-aged adults (ages 35–64) compared to middle-aged adults with a lower socioeconomic standing 37 . This effect was not observed among the younger (ages 20–34) and older (ages 65–89) age groups 37 . Chan and colleagues suggest that higher social status may be protective against age-related brain decline. These structural brain-imaging studies exploring aspects of demographics thus suggest that differences in broader sociocultural experiences may have characteristic imprints in mPFC structure in middle-aged individuals.

In these ways, earlier neuroimaging findings have highlighted the medial prefrontal cortex as a hub that bridges key factors at the individual, interpersonal, and societal level. These disparate domains are usually studied in isolated literature streams, although they may reflect common or distinct manifestations in the mPFC of the human social brain. Factors of personality traits and social behavior dispositions may have inadvertently influenced each other in previous social neuroscience studies, since these indicators were unlikely to be assessed or assessable in the same kind of quantitative analysis (e.g., refs. 38 , 39 ). Additionally, many such neuroscience studies have been based on small participant samples as well as sampled participants of student age 40 .

To supplement these earlier research efforts, we have tailored a probabilistic generative modeling approach to jointly study a rich set of measures from three complementary lifestyle domains, with their extent of similarity and divergence in brain volume associations. Our fully probabilistic modeling framework directly tested against each other 40 traits—tracking everyday experiences in the social, personality, and demographic domains in 10,000 UK Biobank participants in midlife. As the key advantage of this modeling tactic, we could carefully dissect the unique contribution of each examined trait, after accounting for the respective other traits, to explaining regional gray matter variation in the social brain. Given previous findings in the neuroimaging literature, we hypothesized that we will observe the most prominent effects in the medial prefrontal cortex and limbic temporal system in our brain-behavior analyses of brain structure. This expectation reflects the earlier proposition that the medial prefrontal cortex may act as a hub for different aspects of social cognition 25 , including those that relate to personality, interpersonal exchange, and societal dynamics.

By quantitatively mining the UK Biobank resource, we could juxtapose an envelope of 40 lifestyle factors in the context of social brain. Our analyses provide an alternative perspective on the question of how the measured traits are reflected in brain structure in middle age (Table ​ (Table2; 40–69 2 ; 40–69 years at recruitment). The examined traits offered by the UK Biobank cohort can be placed into three main domains: (i) social exchange, (ii) personality profile and (iii) demographic status (Supplementary Data  1 ). By integrating all of these indicators into the same analysis framework, we were able to disentangle which specific traits, relative to the other considered candidate traits, contributed most to explaining social brain volume (i.e., each trait’s marginal association or partial correlation with region variation). We estimated 36 different probabilistic models, one for each target region in the social brain atlas. Henceforth, the term ‘trait effect’ refers to the marginal posterior parameter distributions that were obtained from the estimated probabilistic models. The inferred quantities expose the magnitude, directionality, and model uncertainty in the brain association of the collective analyzed traits (cf. Methods).

List of the 40 examined traits available in the UK Biobank . Each lifestyle indicator of interest from UK Biobank participants is shown alongside its field identification number. Each indicator was analyzed in two groups according to sex. All indicators were divided into one of three categories, defined by traits related to regular social interaction (social domain), traits related to personality (personality domain), and traits related to demographic standing and environment (demographic domain).

Social brain midline: overview of key findings

During midlife, parts of the prefrontal cortex were previously shown to undergo structural reorganization 5 , 14 , 15 , 17 – 22 . We paid special attention to these regions in the prefrontal cortex because they are also known to assist in realizing social cognition (e.g., 25 ), including broader social aspects that may bear relation to personality and demographics. In addition, previous research has shown that the mPFC has direct connectivity inputs to key limbic input regions, especially the amygdala and hippocampus 41 – 44 from the medial-temporal system 45 . Thus, we tested the hypothesis that there would be a medial prefrontal cortex and limbic temporal dominance in age effects. Yet, in disagreement with our primary hypothesis, midline regions did not show the strongest trait effects in comparison to those of the other social brain regions. However, we did observe several noteworthy brain-trait associations in limbic and midline regions.

Within the mPFC and its limbic partner inputs, our study aimed to disentangle trait effects linked to social dynamics from trait effects linked to personality features and demographic status. Our results revealed that several traits related to the richness of one’s daily social encounters contributed to explaining social brain volume in the mPFC and its limbic connections (Fig.  1 ).

Extending previous social neuroscience studies, the richness of the UK Biobank resource allowed uniquely isolating marginal correlations, which prevail over a wide variety of competing explanatory factors. A generative probabilistic model was estimated for each of the 36 social brain regions in our population sample of middle-aged adults. These region-by-region analyses revealed numerous dominant specific (i.e., partial) trait associations in social brain structure. Colors indicate which individual traits have the largest magnitude (i.e., strongest positive or negative association) in explaining its regional gray matter volume, relative to the other 39 out of 40 total examined traits (cf. Supplementary Data 1). Red indicates markers in the social trait category, purple indicates the personality trait category, and blue indicates the demographic trait category. Sharing one’s home with other individuals was the single most frequent trait association to show the largest magnitude in explaining social brain volume for women, in atlas regions including the AI, AM, HC, IFG TPJ and pSTS (left). The personality trait of being a morning person was the most common trait to show the strongest trait association in social brain structure for men, in atlas regions including the AI, HC, FP, IFG, PCC and pMCC (right). Dominant trait associations from the partial correlation analysis are shown in Supplementary Fig.  1 (cf. Table ​ Table3 3 and Supplementary Data  2 for a description of the social brain region abbreviations).

For example, sharing the home environment with other individuals emerged as a top contributor to explaining gray matter volume in the AM_L and bilateral HC (see Supplementary Data  2 for abbreviation list of the social brain regions; women: AM_L: mean of the population trait posterior distribution = 0.035, highest posterior density interval (HPDI) of the population trait posterior distribution covering 95% model uncertainty = 0.005–0.069; HC_L: posterior mean = 0.040, HPDI = 0.012–0.072; HC_R: posterior mean = 0.056, HPDI = 0.019–0.093). In parallel with these dominant trait findings on household size, markers related to interaction with close others, such as friends, showed unique trait associations in our middle-aged population cohort, relative to the other 39 competing traits. For example, feeling satisfied with one’s friendships was a top trait association in the higher associative dmPFC region (women: posterior mean = 0.039, HPDI = 0.000–0.078). Moreover, we also observed that the lifetime number of romantic partners showed a strong trait effect in the AM_R (women: posterior mean = 0.031, HPDI = 0.009–0.056).

In addition to traits characterizing aspects of one’s social lifestyle, we also found personality effects in regions of our social brain atlas (Fig.  1 ). Specifically, being a morning versus evening person showed the largest magnitude in explaining gray matter volume in the FP and bilateral HC, compared to the other candidate traits (men: FP: posterior mean = 0.052, 95% HPDI = 0.002–0.104; HC_L: posterior mean = 0.051, HPDI = 0.020–0.084; HC_R: posterior mean = 0.054, HPDI = 0.022–0.087).

Regarding traits capturing the societal level, we observed several demographic indicators to show unique trait effects in midline social brain regions and limbic inputs (Figs.  1 and ​ and2). 2 ). Our posterior parameter distributions showed that earning a high yearly income explained the biggest fraction of region volume in the vmPFC and bilateral AM, compared with the other considered traits (men: vmPFC: posterior mean = 0.053, 95% HPDI = 0.011–0.100; AM_L: posterior mean = 0.060, HPDI = 0.022–0.104; AM_R: posterior mean = 0.048, HPDI = 0.017–0.084). In the vmPFC, a similar strong trait effect was observed (Fig.  2 ). Health satisfaction, a trait plausibly linked to socioeconomic status, was the top contributor to vmPFC gray matter volume compared with the other examined traits (women: posterior mean = 0.067, HPDI = 0.030–0.105). In addition, working a manual, as opposed to a knowledge-based job, showed a dominant trait effect in the FP (women: posterior mean = 0.059, HPDI = 0.007–0.115). Taken together, our probabilistic evidence revealed specific trait effects that were mostly linked to social experience and demographics in social brain midline and limbic regions.

During midlife, regions of the mPFC have been found to show an accelerated decline in gray matter structure (cf. introduction). For the sake of illustration, the marginal posterior population distributions from our vmPFC analysis are depicted using raincloud plots (1 of the 36 region-by-region analyses that we have conducted on the UK Biobank). We reveal to what extent vmPFC region volume is specifically explained by the 40 examined lifestyle traits at the population level (cf. Supplementary Data  1 ). The half violin plots show the posterior parameter distributions of the specific contributions of each single trait to vmPFC volume, not explained by the other traits, in middle-aged men and women. The boxplots and scatterplots underneath depict the probabilistic parameter guesses that together form the marginal posterior distributions. Each brain-behavior HPDI communicates three kinds of information: direction (e.g., a positive parameter value indicates a tendency for a higher brain volume in the presence of that particular trait), magnitude (e.g., a parameter mean further above or below zero indicates a bigger dependence of region volume on that particular trait), and certainty (e.g., a narrower interval indicates that the model is more sure about the estimated direction and magnitude of a particular brain-behavior effect). For middle-aged women, satisfaction with health contributed most to explaining vmPFC region volume. In contrast, for middle-aged men, earning a higher job income explained most vmPFC gray matter volume. Error bars/dispersion shows uncertainty of Bayesian posterior parameter distributions. Source data are provided in Supplementary Data  3 .

Moreover, the reiteration of our analyses based on partial region volumes of the social brain (cf. Methods) shed a different light on the dominant trait associations in social brain midline and limbic regions. The partial correlation analysis also revealed a richer variety of dominant trait effects across atlas regions, mostly in the category of demographic traits (Supplementary Fig.  1 ; cf. Supplementary Note for a full description of the partial correlation analysis results). For example, having an occupation that requires working more than 40 hours a week showed a dominant trait association in the vmPFC (men: posterior mean = 0.005, 95% HPDI = −0.006–0.016). However, completing full-time education, another trait indexing an aspect of demographic profile, showed dominant trait associations in the limbic AM, HC and FP (men: AM_R: posterior mean = 0.009, HPDI = −0.002–0.020; women: FP: posterior mean = 0.039, HPDI = 0.008–0.075; HC_L: posterior mean = −0.006, HPDI = −0.016–0.003). Taken together, the partial correlation analysis findings for the midline social brain regions revealed a wider assortment of dominant trait associations mostly linked to demographic profile.

Social markers: household size is consistently linked to social brain structure

Our fully probabilistic region-by-region analyses revealed at the interpersonal level that social traits associated with social network composition and quality of interpersonal exchange showed consistently strong trait effects across social brain regions (Fig.  1 ). In our middle-aged population cohort, sharing the home environment with other individuals was the most common trait to explain the most region volume in about half of our 36 region analyses.

Living with others in the same household, as opposed to living alone, showed trait effects in regions of the higher associative network of our social brain atlas, including the bilateral TPJ (women: TPJ_L: posterior mean = 0.070, 95% HPDI = 0.015–0.127; TPJ_R: posterior mean = 0.059, HPDI = 0.010–0.109). Compared with the other candidate traits, sharing the home environment with other individuals also explained the largest fraction of volume variation in regions of the intermediate network, including the bilateral AI and bilateral IFG (men: AI_L: posterior mean = −0.045, HPDI = −0.107–0.011; women: AI_L: posterior mean = 0.056, HPDI = 0.012–0.101; AI_R: posterior mean = 0.058, HPDI = 0.024–0.100; aMCC: posterior mean = 0.070, HPDI = 0.021–0.120; IFG_L: posterior mean = 0.039, HPDI = −0.005–0.083; IFG_R: posterior mean = 0.065, HPDI = 0.014–0.118; SMG_R: posterior mean = 0.040, HPDI = −0.005–0.087). Furthermore, this social trait showed dominant population trait effects in regions of the visual sensory network of the social brain, including the bilateral pSTS and MT/V5_L (women: MT/V5_L: posterior mean = 0.050, HPDI = 0.004–0.097; pSTS_L: posterior mean = 0.058, HPDI = 0.010–0.110; pSTS_R: posterior mean = 0.049, HPDI = 0.000–0.096).

Additionally, social markers related to close interpersonal relationships also revealed large magnitudes in explaining gray matter volume in regions of the visual sensory, limbic, and higher associative networks. In particular, the lifetime number of romantic partners showed dominant trait effects in several visual sensory, limbic, and higher associative social brain regions including the FG, rACC, and TP (women: FG_L: posterior mean = 0.032, 95% HPDI = 0.005–0.060; MTV5_R: posterior mean = 0.054, HPDI = 0.012–0.094; TP_L: posterior mean = 0.037, HPDI = 0.010–0.067; TP_R: posterior mean = 0.033, HPDI = 0.000–0.065; rACC posterior mean = 0.057, HPDI = 0.018–0.099; men: FG_L: posterior mean = 0.048, HPDI = 0.009–0.086). This pattern of trait effects linked to social interaction with close family and friends extended to a region of the visual sensory network. Specifically, being a member of a sports club, a social trait related to regular involvement in social groups, showed the largest trait effect in the FG_R (men: posterior mean = 0.045, HPDI = 0.002–0.086).

In interindividual differences of social support, regular exchange with emotionally close others explained the most region volume in the bilateral NAC, compared with the other analyzed traits (women: NAC_L: posterior mean = 0.076, 95% HPDI = 0.018–0.133; NAC_R: posterior mean = 0.040, HPDI = −0.010–0.091). In a related social marker indexing the quality of close relationships, feelings of loneliness showed a unique trait association in several limbic and intermediate network brain regions, including the reward-related NAC (men: NAC_R: posterior mean = −0.051, HPDI = −0.128–0.020; aMCC: posterior mean = −0.051, HPDI = −0.114–0.011). Collectively, dimensions on the strength of social closeness to close family and friends were associated most with gray matter volume in a majority of social brain regions. Notably, sharing a home with other individuals was the most frequently observed dominant trait association in our participant sample.

Our reanalyses based on partial volume correlations revealed dominant trait associations similar to that of the main analysis (cf. Supplementary Fig.  1 ; cf. Supplementary Note for a full description of the partial correlation analysis results). For example, having a job that requires much social interaction was a most frequent trait to contribute to social brain volume in regions of the visual sensory, limbic, and intermediate brain networks (women: FG_R: posterior mean = −0.015, 95% HPDI = −0.036–0.004; IFG_L: posterior mean = −0.004, HPDI = −0.020–0.008; NAC_R: posterior mean = −0.008, HPDI = −0.025–0.007; SMA_L: posterior mean = −0.010, HPDI = −0.031–0.009). As such, our social trait findings from the partial correlation analysis here identified a similar set of dominant trait associations to that of the main analysis.

Personality markers: top brain associations are explained by daily routine and well-being

Focusing on trait effects from the personality category, markers associated with daily routines and psychological well-being showed large magnitudes in explaining gray matter volume (Fig.  1 ). In general, the personality trait of being a morning versus evening person explained the largest fraction of volume variation in 11 different social brain regions, compared to the other candidate traits (Fig.  1 ). This biorhythm indicator showed dominant trait effects in regions of the intermediate network of our social brain atlas, including the AI_R (men: posterior mean = 0.050, 95% HPDI = 0.015–0.089), bilateral CB (men: CB_L: posterior mean = 0.069, HPDI = 0.016–0.123; CB_R: posterior mean = 0.083, HPDI = 0.028–0.140), SMA_L (men: posterior mean = 0.056, HPDI = 0.014–0.101), and IFG_R (men: posterior mean = 0.058, HPDI = 0.015–0.106). Morning versus evening chronotype also contributed to gray matter volume in regions of the higher associative network, such as the MTG_L (men: posterior mean = 0.043, HPDI = 0.006–0.081), PCC (men: posterior mean = 0.051, HPDI = 0.013–0.088), and pMCC (men: posterior mean = 0.044, HPDI = 0.012–0.075).

Additionally, our region-by-region analyses identified different personality traits related to long-term well-being to show dominant trait effects in several social brain regions belonging to the limbic, intermediate, and higher associative networks. For example, for our middle-aged participants, having a happy mood showed the largest trait effect in the limbic rACC (men: posterior mean = 0.059, 95% HPDI = 0.017–0.100). However, neuroticism explained the most variation in several regions of the intermediate and higher associative networks, including the TPJ_R, compared to the other analyzed traits (men: TPJ_R: posterior mean = −0.044, HPDI = −0.106–0.015; SMG_L: posterior mean = −0.050, HPDI = −0.113–0.009). Taken together, the personality traits associated with daily routine schedules and personal well-being emerged top traits to explain gray matter volume in a number of social brain regions.

The supplementary partial correlation analysis revealed a wider variety of personality traits to explain social brain region volume in our middle-aged population cohort (Supplementary Fig.  1 ; cf. Supplementary Note for a full description of the partial correlation analysis results). For example, the feeling of miserableness revealed dominant trait associations in several intermediate and higher associative social brain regions including the TP_R (men: posterior mean = 0.006, 95% HPDI = −0.007–0.020) and SMG_L (women: posterior mean = 0.014, HPDI = −0.010–0.041). Similarly, we observed the risk-taking indicator to show dominant trait associations in the limbic HC_R (men: posterior mean = −0.011, HPDI = −0.024–0.001) and intermediate SMG_R region (women: posterior mean = 0.008, HPDI = −0.011–0.029). As such, results from the partial correlation analysis revealed a larger range of personality traits to contribute to social brain gray matter volume, compared with the main analysis.

Demographic markers: traits related to income and occupation are associated with limbic and higher associative brain regions

At the broader societal level, we observed a variety of demographic traits related to social status and occupation to explain social brain gray matter volume in our middle-aged population sample (Fig.  1 ). In particular, earning a high yearly wage showed dominant trait effects in several higher associative and visual sensory regions of our social brain atlas (men: MTG_R: posterior mean = 0.069, 95% HPDI = 0.027–0.111; pSTS_L: posterior mean = 0.065, HPDI = 0.016–0.113; women: MTG_R: posterior mean = 0.047, HPDI = 0.006–0.090).

Our region-by-region probabilistic results further revealed granularity in aspects related to one’s occupational environment. For example, working a manual job explained the most variation in four social brain regions of the visual sensory, intermediate, and higher associative brain networks (men: MTV5_R: posterior mean = 0.042, 95% HPDI = −0.007–0.091; SMG_R: posterior mean = 0.058, HPDI = 0.011–0.108; women: PCC: posterior mean = 0.042, HPDI = 0.009–0.074; SMA_R: posterior mean = 0.070, HPDI = 0.025–0.120). Similarly, having a job that requires walking or standing for most of the workday showed a dominant trait effect in the higher associative TP_L (men: posterior mean = 0.045, HPDI = 0.009–0.078). In the context of the work environment, feeling satisfaction with one’s occupation contributed to explaining region volume in regions of the visual sensory and intermediate networks (men: MTV5_L: posterior mean = 0.033, HPDI = −0.003–0.075; SMA_R: posterior mean = 0.042, HPDI = −0.002–0.086; pSTS_R: posterior mean = 0.059, HPDI = 0.018–0.102). Taken together, our dominant demographic trait findings at the general societal level revealed that during midlife, several aspects of one’s job environment contributed most to explaining gray matter volume variation in a variety of social brain regions.

Results from the partial correlation analysis revealed that demographic traits related to occupation were also the top contributing traits in a number of social brain regions (cf. Supplementary Fig.  1 ; cf. Supplementary Note for a full description of the partial correlation analysis results). For example, feeling satisfied with one’s occupation was the most frequent trait to contribute to social brain volume in several limbic, intermediate, and higher associative network brain regions (men: AI_R: posterior mean = −0.011, 95% HPDI = −0.032–0.002; IFG_R: posterior mean = 0.010, HPDI = −0.011–0.034; NAC_L: posterior mean = −0.025, HPDI = −0.052–0.000; NAC_R: posterior mean = 0.007, HPDI = −0.006–0.021; Prec: posterior mean = −0.007, HPDI = −0.023–0.008; women: pSTS_R: posterior mean = 0.010, HPDI = −0.004–0.030). Similarly, the partial correlation analysis showed that working more than 40 h a week contributed to social brain gray matter volume (men: CB_L: posterior mean = −0.007, HPDI = −0.020–0.004; MTG_R: posterior mean = 0.007, HPDI = −0.008–0.024; MT/V5_L: posterior mean = −0.010, HPDI = −0.032–0.009; vmPFC: posterior mean = 0.005, HPDI = −0.006–0.016). As such, the demographic indicator related findings from the partial correlation analysis revealed that the strongest trait associations with social brain gray matter volume were related to occupation.

In summary, our region-by-region analyses on a diverse selection of social, personality, and demographic traits together showed manifestations in social brain gray matter structure in a population cohort of adults. Compared with the social and demographic domains, traits from the personality category showed less dominant trait associations for our participant sample. Instead, markers indexing social exchange at the interpersonal and broader societal level contributed more to explaining volume variation in social brain regions during midlife.

Participant age drives trait associations dominant in mPFC and limbic regions

Our analyses also revealed substantial contributions of age to jointly explaining gray matter variation together with other lifestyle traits (Fig.  3 and Supplementary Fig.  2 ; cf. Supplementary Fig.  3 for results from the partial correlation analysis). Furthermore, most of the joint age-trait effects in the medial prefrontal cortex and its interaction partners from the limbic system were manifested differently in our sample of men and women.

The co-relationship between age and a trait association in explaining region variation is quantified by the joint posterior parameter distribution for one particular social brain region (black arrow) for men (blue) and women (pink). This summary visualization exposes the traits with top effects in the full analysis (cf. Fig.  1 ; cf. Supplementary Data  2 for a description of the social brain region abbreviations). The left column shows these brain-trait associations for women and the right column shows the top trait contributions for men. Middle-aged men and women showed diverging age-trait associations in dmPFC volume in the context of social interaction quality and social status. However, in the context of two socioeconomic status measures, men and women were more similar in age-trait associations with vmPFC volume. The joint posteriors of trait associations in the FP revealed socioeconomic status as measured by job type and personality to show incongruent population parameter distributions. The limbic AM and HC regions additionally showed non-overlapping posterior distributions between middle-aged individuals for social network size and social lifestyle. Supplementary Fig.  3 showcases the age-trait associations in the midline and limbic regions from the partial correlation analysis. Error bars/dispersion shows uncertainty of Bayesian posterior parameter distributions.

For example, in the dmPFC, age and high friendship satisfaction (the top trait association in the dmPFC for women) were together associated with largely divergent manifestations of dmPFC region volume for men and women across midlife (Fig.  3 ). Similarly, age and earning a higher income (the top trait association in the dmPFC for men) was differentially linked to dmPFC region volume for men and women. However, as a function of age, working a manual job was related to FP variation. For women, working a manual job was the top FP trait association (cf. Fig.  1 ). Similarly, age and the personality disposition of being a morning person showed only slightly overlapping posterior parameter distributions in the FP for men and women (Fig.  3 ). For the top female trait association in the vmPFC, high health satisfaction and age jointly explained region variation with largely incongruent posterior distributions for men and women. In conjunction with age, having a high yearly job income (the top male trait association in the vmPFC) showed overlapping model posteriors in explaining vmPFC volume variation for men and women.

Furthermore, we also observed age to jointly drive the strong trait effects in several limbic regions of the social brain that are known to have connections to the mPFC. For example, age and the number of lifetime romantic partners (the top female trait association in the AM_R) showed largely divergent posterior distributions for men and women. In a similar social context, age and sharing one’s home with other individuals (the top female trait association in the AM_L) showed largely incongruent posterior distributions for men and women (Supplementary Fig.  2 ). Moreover, high job income (the top male trait association in the bilateral amygdala) showed large posterior divergences in explaining gray matter volume for men and women (Fig.  3 and Supplementary Fig.  2 ). In the memory-related region of the social brain, age and sharing a home with other individuals influenced bilateral hippocampal architecture differently for men and women. In conjunction with age, morning chronotype (the top male trait association in the bilateral hippocampus) showed an opposite trend for hippocampal region volume in men and women (Fig.  3 and Supplementary Fig.  2 ).

Taken together, the joint age-trait effects revealed mostly divergent manifestations of gray matter volume in midline and limbic brain regions for men and women. For women, these population volume effects were more apparent in sociodemographic and social indicators, as a function of age. For men, co-relationships between age and sociodemographic or personality indicators were more prominent.

Network-by-network summary: volume variation of specific regions is better explained by the collective traits

In each of the four networks of the social brain atlas (cf. Methods), we calculated posterior predictive checks for each region model. This diagnostic assessment is based on simulation of new replicated data using our previously inferred probabilistic models 46 . The simulated model outcome could then be compared to the actual observed outcomes to get a sense of our already estimated model parameter distributions. For each of the 36 examined regions from the social brain atlas, we computed the amount of explained variance from the posterior predictive checks (coefficient of determination, R 2 ) (Fig.  4A ). In this way, we interrogated which of the four networks were best explained from the 40 examined lifestyle traits for the total population sample and for men and women separately (Fig.  4B ).

In each of the four networks of our social brain atlas (cf. Methods; cf. Supplementary Data  2 for a description of the social brain region abbreviations), we computed posterior predictive checks for every analysis of a given target region. These model-based simulations of replicated data were then compared to the actually observed data 47 to compute the overall explained variance (coefficient of determination, R 2 ) for A the whole population cohort and B men and women, separately. Posterior predictive checks thus safeguarded against several important issues related to model fit by evaluating model-simulated empirical expectations of target region volumes. Intuitively, we asked the Bayesian model: “Based on drawing examples from the previously inferred model posterior, what should the region volume in each particular participant be given his or her 40 trait indicators?”. We thus evaluated model-predicted data that could have been observed or will potentially be observed in the future. This practical check of model-based predictions of observations is a well-recognized approximation to external validation given the actual data at hand 48 . The collective population-level results suggest that in each of the four subnetworks of the social brain atlas, at least one region showed an explained variance of >10% in our middle-aged participant cohort. Source data are provided in Supplementary Data  3 .

The total explained variance was highest for the bilateral pSTS (pSTS_R: ~16%; pSTS_L: ~15%), two regions of the visual-sensory network (Fig.  4A ). In the intermediate network, aMCC and AI_L volumes showed the highest total explained variance (aMCC: ~16%; AI_L: ~12%). Instead, the TPJ_L (~16%) and TPJ_R (~12%) were the most explanatory parts of the higher associative network. In the limbic network, the HC_R (~11%) and NAC_L (~11%) showed the best R 2 scores. Taken together, our results suggest that, in each of the canonical networks of the social brain atlas, at least one region was able to achieve an explained variance of >10% in our population sample.

Sex specific trait effects are found for health satisfaction and income in midline and limbic regions

Finally, we directly quantified the extent of sex differentiation in our brain-trait associations by calculating the difference between the marginal posterior distributions. We carried out the subtraction (female–male) of the model posterior parameter distributions for each trait in each social brain region analysis. The obtained difference contrasts of the  marginal posterior parameter distributions could reveal relatively more male- or more female-driven effects for a trait at hand. In the medial prefrontal regions and its limbic partners we observed more male-driven population trait effects (Fig.  5 and Supplementary Fig.  4 ; cf. Supplementary Fig.  5 for results from the partial correlation analysis). However, results in the FP showed more female-biased trait effects. Compared to the other midline social brain regions, the posterior distributions for the FP also showed much more uncertainty in the difference contrasts of the model posteriors.

Left/Right: In each of the 40 examined traits (cf. Supplementary Data  1 ), boxplots show the difference contrasts between the marginal posterior population distributions of each sex (female–male). Means of posterior parameter distribution above zero indicate a relatively female-biased effect for a specific trait association (pink). For means below zero, there is a relatively male-biased effect for that specific trait (blue). Middle: To provide a summary visualization, we counted across the 40 trait associations, for each brain region, to see how many traits were biased predominantly towards males (blue) or females (pink). Purple shows an equal number of male- and female-biased trait associations. Transparency indicates the strength of the sex divergence. Overall, a male bias in volume effects becomes apparent in almost all examined medial prefrontal and limbic regions. In the dmPFC and AM_R, yearly job income showed a stronger effect in men compared to women. However, women drive the trait associations in the FP of the higher-associative social brain, especially with regards to several demographic traits, such as the age of full-time education completion and working a manual job (cf. Supplementary Fig.  5 for sex differentiation in lifestyle trait associations from the partial correlation analysis; cf. Supplementary Data  2 for a description of the social brain region abbreviations). Error bars/dispersion shows uncertainty of Bayesian posterior parameter distributions. Source data are provided in Supplementary Data  3 .

In the vmPFC, a large female-biased trait effect became apparent. Health satisfaction contributed more to explaining volume variation in the vmPFC for women than for men. Further, in the dmPFC and bilateral amygdala regions, we observed a few specific, strong male-biased population trait effects. Notably, in the dmPFC, AM_L and AM_R, earning a higher job income showed a much larger effect for men, compared to that for women (Fig.  5 and Supplementary Fig.  4 ). In addition, the feeling of embarrassment and the personality trait of being a morning person showed larger trait effects in the AM_R for men compared with women. In the memory-related region of the social brain, male-specific trait effects were apparent in the bilateral hippocampus. Regular weekly contact with close individuals contributed more to explaining volume variation in the bilateral hippocampus for men than for women (Supplementary Fig.  4 ). However, sharing a home with other individuals showed stronger female-specific trait effects in the HC_L and HC_R. In sum, the findings from the female vs. male contrast analyses revealed degrees of sex differentiation in population associations in the limbic and higher associative regions across trait domains.

During midlife, many aspects of social life are subject to transition 6 . By designing a fully probabilistic modeling approach, our study interrogated social brain architecture through the prism of 40 indicators that describe the (i) social, (ii) personality, and (iii) demographic profile of ~10,000 UK Biobank participants. Many previous social neuroscience studies were limited in juxtaposing a wide breadth of lifestyle markers, partly because of the scarcity of datasets that provide deep phenotyping with a diversity of behavioral assessments. The present study confronts such a rich collection of lifestyle traits, which covers the individual, interpersonal, and broader societal level in one coherent analysis framework.

Previous neuroimaging research has reported that during this stage of life, the brain undergoes concomitant structural changes, especially in regions of the cortical midline 14 , 15 , 18 . More generally, the medial prefrontal cortex has been recognized to be a common denominator for disparate fields of neuroscience that are usually studied in isolation 25 , 26 , especially social interaction 27 , personality 28 , and demographics 29 . Integrating these separate results points to the medial prefrontal cortex to be a common neurocomputational resource for different processes related to daily experiences and social identity. As the hypothesis that motivated the present study, we expected to observe dominant trait effects in medial prefrontal and medial temporal limbic regions of the social brain, compared to the other atlas regions. Our collective findings did not confirm that these midline regions show the strongest volume effects for the examined target traits. In particular, our structural brain-imaging results do not fall in line with the idea that the medial prefrontal cortex acts as a singular hub for different domains of social experience. While we do find relevant brain-behavior effects in the hypothesized medial prefrontal and limbic regions, notable trait effects became apparent in several other regions of our atlas. Our middle-aged population results thus showcase in ~10,000 individuals that interindividual variation in gray matter volume in social brain atlas regions is linked to important determinants of day-to-day experience. Here, we highlight that at midlife, aspects of social support and social status may contribute most to explaining variation in the examined brain region volumes.

Key elements of daily experience include interacting with close friends and family members. Previous behavioral studies have highlighted that during midlife, adults tend to interact especially with individuals they consider to have a close, social and emotional bond with 10 . Maintaining close social bonds depends on mentalizing, or understanding the mental states of other individuals 49 . As part of the higher associative network of the social brain 50 , the dmPFC is widely acknowledged for its role in Theory-of-Mind processes 25 , which involve taking the perspective of other individuals to understand their emotions, beliefs, and motivations 51 . Here, we observed that, comparing 40 traits against each other, friendship satisfaction emerged as a dominant trait effect in the dmPFC, perhaps related to the tendency that adults prioritize maintaining close friendships during midlife.

A previous behavioral study reported that feeling unsatisfied with personal relationships is associated with the experience of loneliness 2 . Although loneliness is a subjective perception, it can have wide ranging consequences, including decreased mental and psychological well-being 52 , and even increased mortality 53 . Indeed, a cohort study on loneliness in ~900 middle-aged participants 7 found that loneliness was linked to higher levels of stress and systemic inflammation. The authors suggest these observations to be linked to poor health outcomes, with increased risk for morbidity and mortality. In our population-level results, we observed the feeling of loneliness to be associated with gray matter volume in the NAC and aMCC. Commonly considered to be a key node of the reward circuitry, the NAC has also been found to be a neural correlate of social reward 54 . A previous structural imaging study found that individuals with a higher disposition towards social relationships displayed larger NAC volumes 54 , which may reflect the rewarding aspects of social attachment or social interaction. Our results suggest that middle-aged adults may show a sensitivity to social reward in the NAC, especially during midlife when social circles are selectively smaller and more intimate 10 .

Furthermore, Rotge and colleagues found across 46 different studies that areas of the midcingulate cortex, including the aMCC, were consistently linked to social pain, which involves the subjective feelings of loss, social disconnection, rejection or exclusion from other individuals 55 . The authors also found that being exposed to social pain for longer periods of time was linked to aMCC activity. These findings suggest a link between social brain anatomy and a role for social pain processing. These previous findings on social exclusion may relate to our finding that the trait loneliness contributed most to aMCC social brain volume. Indeed, using multimodal brain-imaging, Spreng and colleagues found that perceived isolation, or loneliness, was associated with gray matter volume, white matter integrity and functional connectivity of regions of the default mode network, including the aMCC 56 . Together, this constellation of findings on interpersonal social relations adds support to the idea that the amount of regular investments in social networks is closely linked to our structural measurements of atlas regions in the reward circuitry and intermediate network.

Consistent with the perspective that adults characteristically place their social investments on people already in their social circles during midlife, we found markers of social closeness to also relate to the AM and HC—two limbic brain regions with immediate anatomical connections to the mPFC, as evidenced in humans and monkeys 41 , 42 , 44 . Gauging 40 candidate traits, our probabilistic modeling results isolated the lifetime number of romantic partners as a top interpersonal trait to explain variation in AM volume. Consistently, previous structural brain-imaging studies have also linked the AM to variation in social group size in several age groups 57 , 58 . In particular, a relationship was reported between larger gray matter volume in the AM and increasing size of social networks 57 . According to these investigators’ interpretation, interindividual variation in amygdala volume is linked to differences in the complexity of one’s social life 57 . Furthermore, the lifetime number of romantic partners showed dominant trait manifestations in the limbic rACC and visual-sensory FG_L regions of our social brain atlas. These two brain regions were previously found to share functional connectivity or similar functional associations with the amygdala in the context of integrating affective and social scenes 59 , 60 . Together, our observed volumetric trends at population scale show that the amygdala and its close connections may have coherent links to social indices at an interpersonal level.

In a similar vein, our results from a middle-aged cohort revealed that sharing one’s home environment with others emerged as the most frequent dominant trait association across analyses in over half of the 36 target regions. Notably, these dominant trait associations were found across levels of the processing hierarchy 50 , from the pSTS and AM to HC and AI to TPJ. Previous neuroimaging studies have also shown some of these brain-trait effects based on the richness and frequency of their social interactions 57 , 61 . The prominence of these volumetric findings in a majority of the social brain regions may underlie middle-aged individuals’ propensity to selectively interact with close family and friends 12 . Indeed, middle-aged adults living alone often report feeling less satisfied with their personal relationships, suggesting an unmet need for belonging 2 . Together, these considerations support the perspective that during midlife, individuals typically invest resources in close relationships with friends and family.

In addition to consolidating social circles, other milestones are accomplished during midlife, such as establishing oneself in an occupation 6 . Social status is an abstract construct of individuals’ standing in society compared with others 62 , 63 . In our middle-aged population results, we observed that having a job that earns a high yearly income was a top contributor to gray matter region volume across visual sensory, limbic and higher associative atlas regions including the pSTS, AM, MTG, dmPFC, and vmPFC, compared with the other examined traits. As a marker of socioeconomic affluence, the amount of yearly income a person earns aids in tracking one’s own place in the hierarchical layers of society as well as the social status of other individuals 64 , 65 . A previous functional MRI study explored the neural processing of social hierarchies and found that participants generally tend to focus more on superior versus inferior individuals 63 . In an unstable social hierarchy, brain regions of the occipital and parietal cortex extending into the pSTS, AM and mPFC were recruited when a participant viewed a superior individual 63 . The authors interpret their findings in the pSTS, AM and mPFC in the context of emotional processing and impression formation of other people’s behavior. Hence, our findings on income as a dominant trait manifestation in several social brain regions are consistent with the possibility that social status may have volume adaptations in the frontal and temporal cortices at different levels of the processing hierarchy during midlife.

At the individual level, being more of a morning person is associated with behavioral tendencies that promote prosociality, social connection and cooperation, as well as personality traits of conscientiousness and agreeableness 66 , 67 . Conscientiousness has been linked to career success and overall physical and psychological well-being across the lifespan 68 , 69 . Compared against the other examined traits, being a morning versus evening person here showed a dominant trait association in the FP. Located in the mPFC, the FP is a region that is not only closely linked to perspective-taking capacity 70 , but also future- and goal-oriented thinking 71 . In line with our social brain-trait associations, a previous voxel-based morphometry study found morning chronotype to be associated with gray matter volume in several brain regions, including the AI, IFG, and mPFC 66 . Thus, having an early-riser biorhythm may perhaps provide far-reaching benefits during midlife, such as achieving certain career milestones.

Relatedly, our population-level results revealed that the early-bird chronotype trait from the UK Biobank also emerged as a dominant trait association in different atlas regions of the frontal, parietal and temporal cortex, including the AI, HC, IFG, MTG, PCC and pMCC. Several of these social brain regions have previously exhibited functional coactivation in meta-analytic connectivity modeling or resting state functional connectivity, such as the IFG and PCC 50 . The HC serves critical roles in memory processes, and has been thought to be involved in social monitoring, such as processes related to adhering to social norms 72 . Indeed, previous research has shown that being a morning, rather than evening, person is related to better adherence to social norms, self-control, cooperation, respecting authority, and the social desire to give off a positive impression 73 . Additionally, being more of a morning person may sometimes require overriding one’s natural sleep-wake cycle to conform to prevailing societal norms, even if one may be more of an evening person 73 . Conversely, the night-owl chronotype, as captured in the UK Biobank, has been linked to measures related to risk-taking, creativity, and resistance to acting in conventional manners 73 . The conjunction of these previous behavioral findings and our present population-level results support several parts of our social brain atlas to relate to morning versus evening orientation.

As a caveat to conclusions from our study, we used an atlas of the social brain that was derived from functional MRI data as a basis for structural MRI analyses. In a review, Suarez and colleagues describe how functional and structural MRI data do not share all properties from a signal processing perspective and expose certain differences in global organization, despite much overlap 74 . For example, the authors emphasize that, although the structural connectivity and functional connectivity of brain regions are correlated, this correspondence is imperfect. A degree of misalignment becomes especially evident in cases where brain regions are functionally connected, but not structurally connected 74 . As another example, from a mesoscopic perspective, resting-state functional connectivity networks are characterized mostly by spatially distributed systems related to cognition and perception. However, these functional networks may not be identified in brain structure, perhaps because the functional networks do not share anatomical connections 74 .

Despite these considerations on the discordance between functional and structural MRI data, we sought to link indices of personality, social interaction and demographics to gray matter brain structure. One key advantage of investigating brain structure, as opposed to investigating neural activity, is that structural measurements capture information about stable states of the individual, such as personality, that are less affected by factors such as time of day 66 . Furthermore, the regions of our social brain atlas derived using fMRI 50 have consistently been associated with social and affective processes in brain structure in the neuroimaging literature (e.g., 57 , 61 , 70 ). In our previous work, this atlas has also served as a starting point to identify links between brain structure and social cognitive processes (e.g., 58 , 75 , 76 ). More broadly, neuroimaging studies using both functional and structural MRI data are mostly correlational in nature, by showing associative relationships between the brain and a behavioral trait, and they are typically impotent in establishing causal relationships 24 . Hence, future studies should use different techniques for intervention on the brain  such as TMS to supplement these efforts and make steps towards causal relationships between behavior and the brain 24 .

We also acknowledge that manual quality control is a challenge, and has been argued to become infeasible at the scale of several thousand participants 77 , 78 . Due to these constraints to manual quality control, our study relied on the automated expert pipelines for quality control and assurance from FMRIB Oxford 77 , 78 . This widely trusted data pre-processing workflow was specifically designed for the UK Biobank based on the first 10,000 participant data release, which is the same 10,000 participant data release on which the present study focused. However, we acknowledge that some sources of inaccuracy may have played a role in our investigation, which is challenging to exhaustively exclude given our large sample of participants.

As another limitation to the present study, the UK Biobank is a prospective epidemiological study. This initiative collected a vast portfolio of behavioral and demographic assessments, medical and cognitive measures, and biological samples. However, UK Biobank traits do not necessarily concur with measurements of classical psychological constructs that are traditionally studied in behavioral research on personality or demographics. Furthermore, it has been explicitly acknowledged that a complete picture of demographics, for example, is difficult to obtain 79 . Additionally, the usage of a limited scope of indicators may obscure a comprehensive understanding of how different experiences in an environment contribute to behavior or brain structure 33 . In this sense, we encourage the readers to interpret our results with appropriate caution. Nevertheless, the UK Biobank does provide widely-used measures of demographic standing, including household income, education attainment and occupation 79 .

As a final limitation, we admit that our approach is impotent to isolating causal relationships or ground-truth directionality in the examined effects (cf. above). However, our study illuminates behavioral factors that co-occur with brain volume manifestations in middle-aged men and women. On the one hand, our quantitative findings can annotate how the decades-long accumulation of social skills and experience in societal roles may resonate in inter-individual variation in morphological measures of the circuitry implicated in navigating social environments. As an alternative interpretation, on the other hand, distinct individual, interpersonal and societal dimensions feed into life choices and life experience well into midlife. Our population-level insights provide a glimpse into how different lifestyles may be reflected in long-term effects in social brain circuitry.

By elevating a Bayesian modeling framework to population scale, we simultaneously put to the test 40 lifestyle traits with their correspondences in the social brain. This analytical strategy allowed pinpointing some of the leading sources of population variation with imprints in brain anatomy. In offering neurobiological evidence in ~10,000 middle-aged individuals, we began to shed light on potential intersections between three disparate fields of neuroscientific inquiry: social interaction, personality disposition, and demographic constitution.

Population data resource

The UK Biobank initiative (UKBB) is a prospective epidemiology resource that contains a vast portfolio of behavioral and demographic assessments, medical and cognitive measures, as well as biological samples from a large cohort. ~500,000 participants were recruited across the United Kingdom 80 . This openly accessible population dataset aims to provide multimodal brain-imaging for ~100,000 individuals to be completed in 2022 78 . The present study focused on 9,939 participants who provided T1-weighted structural brain magnetic resonance imaging (MRI), comprising 48% males and 52% females. These individuals were all in middle-age, ages 40 to 69 years at the time of recruitment (mean = 55 years, SD = 7.5 years). All participants were uniformly assessed and brain-scanned at the same scanning facility (i.e., Cheadle). To ensure comparability and reproducibility with other and future UKBB studies, we relied on the data preprocessing pipelines from FMRIB Oxford 77 , 78 . The present analyses were conducted under UKBB application number 23827. All participants provided informed consent to participate ( http://biobank.ctsu.ox.ac.uk/crystal/field.cgi?id=200 ).

Our present study co-analyzed a set of 40 behavioral indicators (Supplementary Data  1 ) provided by the UKBB resource (Table  1 ). The 40 summary measures belonged to three different domains: (i) social (12 items), (ii) personality (15 items), and (iii) demographic (13 items). All UKBB participants were administered questions for the particular trait measures (see here for further details: https://www.ukbiobank.ac.uk/ ). For example, to obtain a measure of the risk-taking trait, participants were asked “Would you describe yourself as someone who takes risks?”. According to the responses given by the UKBB participants for each interview question, participants were split into two evenly sized groups that reflect the presence or absence of the particular trait: (a) not a risk-taker and (b) risk-taker. To achieve direct comparability of the equal variable encoding in the rich collection of lifestyle indices, all target items were represented with two-choice encoding 58 .

UK Biobank demographic information.

Brain-imaging preprocessing procedures

MRI measurements were acquired with a 3 T Siemens Skyra scanner at the same dedicated recruitment center (i.e., Cheadle), with the same acquisition protocols and same standard Siemens 32-channel radiofrequency receiver head coils. To protect the anonymity of the study participants, brain scans were defaced and any sensitive information from the header was removed. Automated processing and quality control pipelines were deployed 77 . To improve homogeneity of the imaging data, noise was removed by means of 190 sensitivity features. This approach allowed the reliable identification and exclusion of problematic brain scans, such as scans with excessive head motion.

High-resolution T1-weighted images of brain anatomy were acquired using a 3D MPRAGE T1-weighted sequence at 1 mm isotropic resolution. Preprocessing included gradient distortion correction, field of view reduction using the Brain Extraction Tool 81 and FLIRT 82 , 83 , as well as non-linear registration to MNI152 standard space at 1 mm resolution using FNIRT 84 . To avoid unnecessary interpolation, all image transformations were estimated, combined, and applied by a single interpolation step. Tissue-type segmentation into cerebrospinal fluid, gray matter, and white matter was applied using FAST (FMRIB’s Automated Segmentation Tool 85 ) to generate full bias-field-corrected images. In turn, SIENAX 86 was used to derive volumetric measures normalized for head sizes. The ensuing adjusted volume measurements represented the amount of gray matter corrected for individual brain sizes.

Social brain atlas definition

Our study benefited from a recently available atlas of the social brain 50 , which provides a current best estimate of social brain topography in humans. This atlas resulted from quantitatively synthesizing ~4000 experimental functional MRI studies, involving thousands of participants 50 . Thirty-six convergence locations of interest (Table ​ (Table3) were 3 ) were derived in a data-led fashion that were consistently involved in a wide assortment of social and affective tasks (see Supplementary Data  2 for stereotaxic MNI coordinates for each of the social brain atlas regions).

Social brain atlas regions and their MNI coordinates. Social brain regions and their respective functional network 51 .

The 36 data-derived social brain regions are connectionally and functionally segregated into four major brain networks (cf. Supplementary Data  2 ): (i) a visual-sensory network (fusiform gyrus, posterior superior temporal sulcus, MT/V5), (ii) a limbic network (amygdala, ventromedial prefrontal cortex, rostral anterior cingulate cortex, hippocampus, nucleus accumbens), (iii) an intermediate network (inferior frontal gyrus, anterior insula, anterior mid-cingulate cortex, cerebellum, supplementary motor area, supramarginal gyrus), and (iv) a higher-associative network (dorsomedial prefrontal cortex, frontal pole, posterior mid-cingulate cortex, posterior cingulate cortex, precuneus, temporo-parietal junction, middle-temporal gyrus, temporal pole).

The topographical specificity of the present quantitative analyses was thus enhanced by guiding brain volume extraction of the 36 known regions of interest. Neurobiologically interpretable measures of gray matter volume were thus extracted in the ~10,000 participants. This was achieved by summarizing the whole-brain anatomical maps guided by the topographical compartments of the social brain. In particular, we applied a smoothing filter of 5 mm FWHM to the participants’ structural brain maps to homogenize local neuroanatomical differences 58 , 76 .

Next, gray matter volume was extracted in spheres of 5 mm diameter around the consensus location from the atlas, averaging the MRI signal across the voxels belonging to a given target region. We would like to note that using a smaller sphere diameter of 2.5 mm or a bigger one of 7.5 mm yielded virtually identical results, which led to the same conclusions. This way of engineering morphological brain features yielded 36 volume brain variables per participant, that is, as many as the total number of social brain regions. Each of the 36 brain volume variables was subsequently z -scored across participants by centering to zero mean and unit-variance scaling to one. These commonly employed estimates of population brain volume variability 78 , 87 in social brain anatomy served as the basis for all subsequent analysis steps.

All of the regions of interest used in this study are available online for transparency and reuse at the open-data sharing platform NeuroVault ( http://neurovault.org/collections/2462/ ).

Probabilistic multiple regression of region variation on lifestyle traits

To explicitly interrogate the unique contribution of the 40 lifestyle traits to explaining variation in a given social brain region, we implemented a generative probabilistic multiple regression approach 58 , 88 , 89 . By adopting this Bayesian modeling framework, we could directly learn from data the specific relevance of traits spanning three different domains taken from the UK Biobank (cf. Supplementary Data  1 ): social behavior, personality, and demographics. In this way, we were also able to estimate the Bayesian posterior uncertainty intervals of trait effects, rather than restricting attention to rigid categorical differences. Before implementation of the region-specific probabilistic analyses, we performed a de-confounding procedure on all 36 target region volumes to remove variation due to head size and body mass index. This data cleaning step was performed in Python using nilearn ( http://nilearn.github.io/ , version 0.6.2). The probability models were specified as follows. For the j th social brain region ( j  = 1, …, 36):

where y j is the volume of the j th atlas region; x i is a considered social lifestyle trait and β ( j ) i is the corresponding coefficient ( i  = 1,…, 40). The priors endow the model parameters β ( j ) i with a Normal(0, 1) distribution for the location component and with a HalfCauchy(1) distribution for the dispersion component, while ϵ j is the model error which is assumed to be normally distributed with a variance component defined by HalfCauchy(5) distribution. Preliminary sensitivity analysis confirmed that small changes to our choices of prior led to virtually the same results, which was expected given the sample size of our UK Biobank cohort 89 .

Approximate posterior inference was achieved by Markov Chain Monte Carlo (MCMC) using PyMC3 in Python ( https://github.com/pymc-devs/pymc3 , version 3.7), which sampled in a random walk towards the target posterior distribution. In 5000 draws, the approximate parameter distributions were improved across MCMC steps in the sense of converging to the target distribution. For the partial correlation analysis (cf. below), we carried out the random walk using a number of 10,000 draws to ensure a more stable estimate of the parameter space. At each step of the MCMC chain, the entire set of parameter values were estimated to be jointly credible given the population data. A range of possible explanations for the data or parameter configurations for the relation between the social lifestyle traits and social brain volume were browsed through by obtaining multiple plausible settings of model parameters that could have led to the observed data. We searched through possible configurations of parameters as an efficient way of exploring the important part of the parameter posterior space. In particular, we dropped the first 4000 samples from the chain because (1) the chain had probably not yet fully reached stationarity and (2) this step reduced dependence on the starting parameter values. Proper convergence was assessed by ensuring the R-hat metrics stayed below 1.02 47 .

In particular, for the purpose of posterior predictive checks, in each brain-behavior analysis 500 candidate models were spawned. Each instance in that collection of possible modeling solutions embodied a different realization of the collective model parameters. In an empirical approach, the predictions generated from each of the 500 distinct possible models were then examined for plausibility against the real data at hand. We thus evaluated model-predicted data that could have been observed or will potentially be observed in the future. Any quantitative model predicts well in some situations but not in others. The used framework allowed quantifying the limits of our Bayesian models based on the cases in which they succeeded or failed.

Reanalysis after partial correlation analysis among social brain regions

In a subanalysis of our main analysis approach (cf. previous paragraph), we introduced a preceding partial correlation step to separate out the unique portion of variation in the 36 region volumes of the social brain atlas using a linear de-correlation procedure. For one specific region among the 36 total regions, this initial processing step partialed out the volume variation shared with any of the other respective 35 social brain regions, before carrying out the actual probabilistic Bayesian analysis of interest (cf. previous paragraph).

Each of the new de-correlated residual region volumes ϵ j for the social brain atlas was obtained by regressing that region’s ( z -scored) measures against the ( z -scored) measures from all remaining regions:

where the residuals ϵ j represent the orthogonalized variance component of region j across the UK Biobank participants that could not be linearly explained by any of the 35 remaining brain region volumes. Note that the estimated values of the beta parameters were not of scientific interest in this data preprocessing step. The partialed region volumes ϵ j served as the basis for the otherwise identical brain-trait analyses.

In other words, the partial correlation subanalysis goes one step further and accounts for the unique variance of each of the traits in addition to accounting for the companion effects from the other social brain regions. Hence, any linear variation shared with other brain regions was removed for a particular brain region, such that the unique contribution of each trait is shown for that one specific social brain region only. In this way, we were able to provide a complementary perspective on how the 40 candidate traits are coherently associated with volume variation in the social brain in our population cohort. As such, in this variant of our original analysis, we revisited the interrogated social brain-trait associations while disregarding volume variation that was shared between any pair of social brain regions in our UKBB participants.

Technical note on neuroscientific interpretation of model coefficients

From a quantitative perspective, compared to other imaging neuroscience work, our study departs in important ways from linear latent factor modeling approaches, such as partial least squares, reduced-rank regression, and canonical correlation analysis. Such dimensionality-reducing tools would necessarily have considered all our 40 traits in conjunction, that is, operating exclusively on the totality of input variables: how a linear combination of the entire set of trait variables explains variation in a region volume at hand. These latent factor models naturally ask for shared variation among the collective trait variables that have a joint relationship with region variation. Instead of aiming at correlated variable effects on a volumetric outcome, our elected multiple regression approach asked for the marginal relationship of each individual trait variable with the region volume, in direct competition with the 39 remaining traits examined in our study (i.e., conditioning effects). In doing so, the 40 trait variables could be weighed against each other to dissociate each trait’s separate role in explaining region variation. For example, if a fraction of volume variation in the frontal pole was jointly explained by personality trait A and demographic trait B, then the one of the two corresponding trait parameters with the bigger magnitude of linear association would be attributed the dominating role; since it explains more region volume compared to the competing other trait. Thus, our quantitative approach aimed at singling out each trait’s unique role for tracking interindividual differences in a social brain region, which is uncorrelated from the collection of candidate traits.

Statistics and reproducibility

To verify if our Bayesian regression analyses generalize to other datasets with the same trait indicators, we have implemented the identical data analysis pipeline (cf. above) in several new, independent participant samples. For this purpose, we used the recently available 40,000 participant release from the UK Biobank (Data Access Application: 25163). To assess replicability, the unseen ~30,000 participants were randomly divided into three new samples of ~10,000 participants each. The regression modeling workflow from the main analysis was carried out again in each of the three data splits. Subsequently, Pearson correlation coefficients were computed between the original analysis and the three new analyses based on the mean of posterior parameter distributions (Supplementary Fig.  6 ). This re-assessment confirmed the replicability of the constellation of findings obtained in our original analysis in unseen participant samples.

Reporting summary

Further information on research design is available in the  Nature Research Reporting Summary linked to this article.

Supplementary information

Acknowledgements.

D.B. has been funded by the Brain Canada Foundation, through the Canada Brain Research Fund, with the financial support of Health Canada, National Institutes of Health (NIH R01 AG068563A), the Canadian Institute of Health Research (CIHR 438531), the Healthy Brains Healthy Lives initiative (Canada First Research Excellence fund), Google (Research Award, Teaching Award), and by the CIFAR Artificial Intelligence Chairs program (Canada Institute for Advanced Research).

Author contributions

H.K. and D.B. contributed to the conception, design, and analysis of the work. H.K., D.B., L.U., B.B., and J.K. contributed to the interpretation of the data as well as drafting and revising the manuscript. D.B. led data analysis.

Open Access funding enabled and organized by Projekt DEAL.

Data availability

Code availability, competing interests.

The authors declare no competing interests.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

The online version contains supplementary material available at 10.1038/s42003-021-02206-x.

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The Midlife Unraveling

In my late thirties, my intuition had tried to warn me about the possibility of a midlife struggle. I experienced internal rumblings about the meaning and purpose of my life. I was incredibly busy proving myself in all of my different roles (mother, professor, researcher, writer, friend, sister, daughter, wife), so much so that it was difficult for any emotion other than fear to grab my attention. However, I do remember flashes of wondering if I’d always be too afraid to let myself be truly seen and known.

But intuition is a heart thing, and until recently I had steamrolled over most of my heart’s caution signs with intellectualizing. In my head, I had always responded to the idea of “midlife angst” by scoffing and coming up with some politically and therapeutically correct way of saying that midlife whining is pathetic. The entire concept of the midlife crisis is bullshit. If you’re struggling at midlife, it’s because you haven’t suffered or sacrificed enough. Quit pissing and moaning, work harder, and suck it up.

As it turns out, I was right about one thing—to call what happens at midlife “a crisis” is bullshit. A crisis is an intense, short-lived, acute, easily identifiable, and defining event that can be controlled and managed. Midlife is not a crisis. Midlife is an unraveling.

By definition, you can’t control or manage an unraveling. You can’t cure the midlife unraveling with control any more than the acquisitions, accomplishments, and alpha-parenting of our thirties cured our deep longing for permission to slow down and be imperfect.

Midlife is when the universe gently places her hands upon your shoulders, pulls you close, and whispers in your ear:

I’m not screwing around. All of this pretending and performing—these coping mechanisms that you’ve developed to protect yourself from feeling inadequate and getting hurt—has to go. Your armor is preventing you from growing into your gifts. I understand that you needed these protections when you were small. I understand that you believed your armor could help you secure all of the things you needed to feel worthy and lovable, but you’re still searching and you’re more lost than ever. Time is growing short. There are unexplored adventures ahead of you. You can’t live the rest of your life worried about what other people think. You were born worthy of love and belonging. Courage and daring are coursing through your veins. You were made to live and love with your whole heart. It’s time to show up and be seen.

If you look at each midlife “event” as a random, stand-alone struggle, you might be lured into believing you’re only up against a small constellation of “crises.” The truth is that the midlife unraveling is a series of painful nudges strung together by low-grade anxiety and depression, quiet desperation, and an insidious loss of control. By low-grade, quiet, and insidious, I mean it’s enough to make you crazy, but seldom enough for people on the outside to validate the struggle or offer you help and respite. It’s the dangerous kind of suffering—the kind that allows you to pretend that everything is OK.

We go to work and unload the dishwasher and love our families and get our hair cut. Everything looks pretty normal on the outside. But on the inside we’re barely holding it together. We want to reach out, but judgment (the currency of the midlife realm) holds us back. It’s a terrible case of cognitive dissonance—the psychologically painful process of trying to hold two competing truths in a mind that was engineered to constantly reduce conflict and minimize dissension (e.g., I’m falling apart and need to slow down and ask for help. Only needy, flaky, unstable people fall apart and ask for help).

It’s human nature and brain biology to do whatever it takes to resolve cognitive dissonance—lie, cheat, rationalize, justify, ignore. For most of us, this is where our expertise in managing perception bites us on the ass. We are torn between desperately wanting everyone to see our struggle so that we can stop pretending and desperately doing whatever it takes to make sure no one ever sees anything except what we’ve edited and approved for posting.

What bubbles up from this internal turmoil is fantasy. We might glance over at a cheap motel while we’re driving down the highway and think, I’ll just check in and stay there until they come looking for me. Then they’ll know I’m losing my mind. Or maybe we’re standing in the kitchen unloading the dishwasher when we suddenly find ourselves holding up a glass and wondering, Would my family take this struggle more seriously if I just started hurling all this shit through the window?

Most of us opt out of these choices. We’d have to arrange to let the dog out and have the kids picked up before we checked into the lonely roadside motel. We’d spend hours cleaning up glass and apologizing for our “bad choices” to our temper tantrum–prone toddlers. It just wouldn’t be worth it, so most of us just push through until “losing it” is no longer a voluntary fantasy.

Midlife or Midlove

Many scholars have proposed that the struggle at midlife is about the fear that comes with our first true glimpse of mortality. Again, wishful thinking. Midlife is not about the fear of death. Midlife is death. Tearing down the walls that we spent our entire life building is death. Like it or not, at some point during midlife, you’re going down, and after that there are only two choices: staying down or enduring rebirth.

It’s a painful irony that the very things that may have kept us safe growing up ultimately get in the way of our becoming the parents, partners, and/or people we want to be.

Maybe, like me, you are the perfect pleaser and performer, and now all of that perfection and rule-following is suffocating. Or maybe you work hard to keep people at a safe distance and now the distance has turned into intolerable loneliness. There are also the folks who grew up taking care of everyone else because they had no choice. Their death is having to let go of the caretaking, and their rebirth is learning how to take care of themselves (and work through the pushback that always comes with setting new boundaries).

Whatever the issue, it seems as if we spend the first half of our lives shutting down feelings to stop the hurt and the second half trying to open everything back up to heal the hurt.

Sometimes when the “tear the walls down and submit to death” thing overwhelms me, I find it easier to think about midlife as midlove. After two decades of research on shame, authenticity, and belonging, I’m convinced that loving ourselves is the most difficult and courageous thing we’ll ever do. Maybe we’ve been given a finite amount of time to find that self-love, and midlife is the halfway mark. It’s time to let go of the shame and fear and embrace love. Time to fish or cut bait.

I don’t think midlife/midlove is on a schedule. I was forty-one when it hit, but I have friends and I’ve interviewed people who found themselves smack-dab in the middle of the unraveling as early as their mid-thirties and as late as their fifties. The only firm timing for midlife/midlove is that it ends only when we physically die. This is not something you can treat then dismiss. The search for self-love and acceptance is like most of the new ailments that hit at midlife—it’s a chronic condition. It may start in midlife, but we have to deal with it for the rest of our lives.

And just in case you think you can blow off the universe the way you did when you were in your twenties and she whispered, “Pay attention,” or when you were in your early thirties and she whispered, “Slow down,” I assure you that she’s much more dogged in midlife. When I tried to ignore her, she made herself very clear: “There are consequences for squandering your gifts. There are penalties for leaving big pieces of your life unlived. You’re halfway to dead. Get a move on.”

Once the shock of the universe’s visits wears off—and you get over thinking, Oh my God! I’d prefer a crisis! —there are several ways to respond:

I hear tell that there are actually people who pull the universe closer, embrace her wisdom, thank her for the opportunity to grow, and calmly walk into the unraveling. I try to spend limited time with these people, so I can’t tell you much about how this works.

Another option is to deny that any of this ever happened. Of course, denial is not so easy at this level—it is the universe that we’re talking about here. Pretending that midlife is not happening requires active denial, like putting your fingers in your ears and singing, “La-la-la-la-la.” As sweet and childlike as that may sound, these folks are normally not so sweet and childlike.

After the ear-plugging and humming, the only way to maintain your denial of the midlife unraveling is to become even more perfect, more certain, and more judgmental. For these folks, allowing just one ounce of uncertainty or doubt or questioning to bubble up could cause rapid, involuntary unraveling. They can’t be wrong—their lives could spin out of control. They march through life, teeth and butt cheeks clenched, without flinching and, often, without feeling.

There’s also the numbing option. If there’s one thing that we’ve mastered by midlife, it’s how to take the edge off of feeling pain and discomfort. We are so good at numbing—eating, drinking, spending, planning, playing online, perfecting, staying really, really busy. If every midlifer who “only drinks a good glass of wine with dinner” stopped drinking, there wouldn’t be a vineyard left in business. Unfortunately, what makes midlife different from the other stages that we’ve managed to survive is that the symptoms don’t improve over time. Choosing to numb the midlife unraveling is choosing to numb for the rest of your life.

Last, there’s the “no-holds-barred” resistance response. I liken it to existential cage fighting. You and the universe go into the ring and only one person comes out. This, of course, was my option.

When the universe came to me, I listened. And when she was done whispering, I pulled back, looked into her eyes, and spit in her face.

How dare she ask anything of me! I had worked and sacrificed and paid enough. I had spent my life saying “yes” when I wanted to scream, “Hell no! Do it yourself!” I had met every deadline, expectation, and request possible. I had earned every bit of my armor, and I was enraged by the idea of giving it up.

I expected her to walk away like the dejected mother of an angry teenager, but she simply stood in front of me, wiping the spit off her cheek.

We stared at each other for a minute, then I said, “I’m not afraid of you. I know what you’re asking and the answer is no. I’ve spent my entire life building these walls and digging these moats. Do you really think a little whisper is going to intimidate me? Do I strike you as the unraveling type?”

I’m not ornery or rebellious by nature; it’s just that I spent thirty years trying to outrun and outsmart vulnerability and uncertainty. The fact that the almighty universe had descended and asked me to turn myself over to her custody didn’t mean a damn thing to me. I’m not the surrendering type.

She was quiet.

I didn’t back down. I was my own little emotional militia. I put on my most serious game face and said, “I know what you’re trying to do and it’s not going to work. I’m prepared. I’ve spent a decade researching and writing on shame and vulnerability and all of the hard shit that you throw around to scare people. I’m ready.”

She looked back at me with loving eyes, then said, “I’m sorry it has to be this way, but clearly this is how you want to do it. You leave me no choice.”

Her calmness was unsettling. I was afraid. She wasn’t backing down. So in this moment of sheer terror, I did the only thing I knew how to do when confronted with fear—I bullied her. I gave her a small shove and said, “Then bring it!”

Her loving eyes didn’t change one bit. She just looked at me and said, “I will.”

When the Universe Brings It

I put up the fight of my life, but I was totally outmatched. The universe knew exactly how to use vulnerability and uncertainty to bring down this perfectionistic shame researcher: a huge, unexpected wallop of professional failure, one devastating and public humiliation after the next, a showdown with God, strained connections with my family, anxiety so severe that I started having dizzy spells, depression, fear, and the thing that pissed me off the most—grace. No matter how hard or far I fell, grace was there to pick me up, dust me off, and shove me back in for some more.

It was an ugly street fight, and even though I got my ass kicked, it was the best thing that ever happened to me. There was a significant amount of pain and loss, but something amazing happened along the way—I discovered me. The real me. The messy, imperfect, brave, scared, creative, loving, compassionate, wholehearted me.

Maya Angelou writes, “There is no greater agony than bearing an untold story inside you.” I’ve always honored the power of story. In fact, I believe so strongly in its power that I’ve dedicated my career to excavating untold stories and bringing them up to the light. In some miraculous way, I feel as if this midlife unraveling has taught me—in my head and my heart—how to be brave. I’m still not good at surrendering or “living in the question,” but I am getting better. I guess you could say I’ve graduated to “writhing in the question.” Not exactly Zen, but it is progress.

As far as my relationship with the universe . . . well, we’ve actually become very good friends. I even came to love and trust her when, in a quiet moment, I looked deeply into her eyes and realized that she, the universe, was me.

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The Midlife Crisis in Developmental Psychology Essay

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The midlife crisis exists, and it is associated with an awareness of the limitations of youth and the transience of the time allotted for life. As Pashkow (2018) notes, a midlife crisis is a gap between what there is and how it is perceived. A person in this period realizes that the achievements of his pr her life may not correspond to what he or she still would like to accomplish, but the time available is already short. First of all, it is associated with cognitive changes, as functions such as memory, abstract reasoning, and processing speed are reduced. Physical abilities also decline as the first significant signs of aging appear. Psychosocially, in middle adulthood, people begin to take an active role in caring about their heritage and impact on the community as well as future generations.

The cause of a midlife crisis may be the stress of mismatching reality and expectations, which makes a person look for some transformation. Perhaps the midlife crisis is one of the results of social pressure and comparison, so people not affected by these factors may not experience it. I think the myth of the midlife crisis is spreading in society because it is an easily explained form of more complex transitions in human development. It is likely that such developmental changes as the stage of generativity or stagnation are mistaken for a midlife crisis.

I think that a midlife crisis can occur not only in people who have not achieved their goals but also in those who are quite successful by social standards. For example, a person may be disappointed in his achievements, which is caused by a shift in the system of values ​​and views. Probably during this period, many people seek to try radically new activities and experiences for themselves in order to change their lives completely. With regard to poorly educated people, I think that they most often have low wages and, as a result, a lot of difficulties and concerns. These obstacles probably prevent them from reflecting on their lives and experiencing a midlife crisis.

Pashkow, P. (2018). Midlife crisis needs a rebrand [Video]. YouTube. Web.

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IvyPanda. (2023, March 15). The Midlife Crisis in Developmental Psychology. https://ivypanda.com/essays/the-midlife-crisis-in-developmental-psychology/

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IvyPanda . "The Midlife Crisis in Developmental Psychology." March 15, 2023. https://ivypanda.com/essays/the-midlife-crisis-in-developmental-psychology/.

Economics CAGE Research Centre

The midlife crisis.

641/2022 Osea Giuntella, Sally McManus, Redzo Mujcic, Andrew J. Oswald, Nattavudh Powdthavee and Ahmed Tohamy

This paper documents a longitudinal crisis of midlife among the inhabitants of rich nations. Yet middle-aged citizens in our data sets are close to their peak earnings, have typically experienced little or no illness, reside in some of the safest countries in the world, and live in the most prosperous era in human history. This is paradoxical and troubling. The finding is consistent, however, with the prediction -- one little-known to economists -- of Elliott Jaques (1965). Our analysis does not rest on elementary cross-sectional analysis. Instead the paper uses panel and through-time data on, in total, approximately 500,000 individuals. It checks that the key results are not due to cohort effects. Nor do we rely on simple life-satisfaction measures. The paper shows that there are approximately quadratic hill-shaped patterns in data on midlife suicide , sleeping problems , alcohol dependence , concentration difficulties , memory problems , intense job strain , disabling headaches , suicidal feelings , and extreme depression . We believe the seriousness of this societal problem has not been grasped by the affluent world’s policy-makers.

Culture, Behaviour and Development

National Bureau of Economic Research

https://dx.doi.org/10.3386/w30442

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• Essay on Crisis

Midlife Crisis Essay Example

Type of paper: Essay

Topic: Crisis , Life , Study , Psychology , Age , Empirical , Ritter , Exist

Words: 1250

Published: 03/30/2023

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The definition of midlife is literally a point at the middle of a person’s life. ‘The concept of ‘crisis’, in mid-life and at other times, implies a rapid or substantial change in personality, which is dislocating with respect to one’s sense of identity (Freund & Ritter, 2009, p. 583). Thus, the best definition of midlife crisis is a sense of crisis situation that one experiences around middle age driven by a sense of diminishing youth and the loss that accompanies this. The question really arises about the validity and existence of the Midlife Crisis. The essay will delve into whether or not midlife crisis affects us at a certain point in our lives by first examining a theoretical work and then critically evaluating the empirical viewpoint ultimately concluding that Midlife Crisis does not exist to the extent believed or does not exist at all. As per Lemme, the earliest mentions of Life Stages commences from Freud, Jung and Erikson. Both Erikson and Jung, identified the ages of 40 and 55 as a time of transition for most people. (Lemme, 2006, p. 48) Much later, it was Levinson, who structured the Life Stages to the age of 45 and 60. Within these, he identified the ages of 40 - 45 as the era of middle adulthood and 45 -60 as later adulthood. (Lemme, 2006, p. 58). It was Levinson who probably gave impetus to the probable existence of the Midlife Crisis, although his work doesn’t explicitly mention the topic. On the subject of whether Midlife Crisis exists, Lemme prefers to term this phase as midlife transition or a correction rather than a Crisis. (p. 72). In essence, Lemme accepts that a certain transition phase does exist at Midlife, but does not term this phase as a Crisis. With this theoretical background, this essay proceeds to examine some empirical works on the subject. As per Margie Lachman, there exists a largely dichotomous view of the Midlife as a Life Stage since popular perception indicates midlife to be one of crisis, while other viewpoints also indicate mid-life as the peak of one’s life since it is a stage wherein an individual becomes a supporting system for multiple people within the family and the Society. (Lachman, 2004, p. 313). Accordingly, it follows that the period of Midlife Crisis is individual dependant, but is characterized by a sense of achievement and the attainment of goals. A study conducted by David Karp (1988) strongly supported the beliefs put forth by Lachman, while additionally reinforcing the approximate commencement of the Midlife occurs between the ages of 50 to 60, but is not necessarily characterized by a Crisis Situation. (Karp, 1988, p. 728). Karp also mentions that most of his respondents felt elated to be at that particular time of their life and not everything was as if in a crisis situation. (p.737). Similarly, Teshale and others (2015), postulated that as popularly held, Midlife is a period in time when one can compensate for one’s deficits to ensure a happier and more stable future life. (Teshale et.al, 2015, p. 25). In essence, Teshale et.al, have portrayed midlife as a critical juncture in one’s life given the all-important point at which the period lies. If one now takes a critical view, Freund and Ritter (2009) define Midlife Crisis and set out marking a critical view of the same. In this case, they support the fact that both theoretical and empirical evidence do not point to the existence of a strong Mid Life Crisis phenomena. (Freund & Ritter, p. 585). This is primarily because goals are dynamic and relatively abstract and, as such, the assessment of goals becomes difficult. (Freund & Ritter, 2009, p. 586).Also, it is difficult as one gets older to set similar goals and work towards attaining those goals. (Freund & Ritter, 2009, p. 587).The fact that both these points rally extensively against traditional beliefs on Midlife Crisis, indicate that the concept is almost non-existent. Likewise, empirical evidence gathered by Susan Heidrich and Carol Ryff point to three main theoretical aspects: Social Integration, Social Comparisons and Self-Discrepancy. (p. 328). Heidrich and Ryff postulated that these three self-evaluative processes mediated the relationship between physical and mental health in case of older women (50 years+). The outcome of this empirical study clearly indicated that elderly women efficiently managed to report high levels of psychological well-being, despite common old age problems such as various mental and physical health issues. (p. 333). In essence, this implies that the existence of MidLife Crisis ranges from weak to non-existent. The view has been supported by Hanna Drimalla in her article, where she says that too much is being about the midlife crisis phenomena. She says that midlife, in itself, is a nebulous concept since while midlife is associated with certain irreversible changes, it does not lead to a crisis situation as is commonly believed by most people.(Drimalla, 2015). If one considers a study outside the US, Minakshi Tikoo (1996) conducted a study in India with both middle aged men and women as respondents to broaden the scope of the study. The most important outcome of this study was that it could not find effects of age, visible gender differences in perception, clearly indicating that the concept of midlife crisis did not even exist in India or in the Indian psyche. (Tikoo, 1996, p. 887). This potential gap existing in most pro-Midlife Crisis studies should be reviewed since it ideally means that these studies have committed some grave error to reach an erroneous conclusion. In conclusion, one can understand that while the theoretical view on the matter does not support the existence of midlife crisis much, empirical view supports the stand strongly. Most studies on the subject have yielded weak to strong contrary results and have only gone to show that the phenomena is not as widely existent as formerly believed. In some countries outside the US, due to cultural or other reasons the concept of Midlife Crisis does not even exist amongst individuals.

Drimalla, H. (2015). Science Debunks Midlife Myths. Scientific American.Retrieved from http://www.scientificamerican.com/article/science-debunks­midlife­myths/ Freund, A & Ritter, J. (2009). Midlife Crisis: A Debate. Gerontology, 55, 582 – 591. Retrieved from http://www.karger.com/ger Heidrich, S. &Ryff, D.C. (1993). Physical and Mental Health in Later Life: The Self-System as Mediator. Psychology and Aging Journal 8.3, 327 – 338. Retrieved from psycnet.apa.org/journals/pag/8/3/327.pdf Karp, A. D. (1988). A Decade of Reminders: Changing Age Consciousness Between Fifty and Sixty Years Old. The Gerontologist Journal28.6, 727 – 738. Retrieved from http://gerontologist.oxfordjournals.org/content/28/6/727.full.pdf+html Lachman, M.E. (2004). Development in Midlife.Annual Review of Psychology. 55, 305 – 331. Retrieved from www.ncbi.nlm.nih.gov/pubmed/14744218 Lemme, B.H. (2006). Development in Adulthood. Boston, MA: Pearson Publishing. Teshale, S. et.al. (2015). Midlife as a pivotal period in the life course: Balancing growth and decline at the crossroads of youth and old age. International Journal of Behavioral Development 39.1, 20 -31. Retrieved from https://uic.pure.elsevier.com/en/publications/midlife-as-a-pivotal-period-in-the-life- course-balancing-growth-a Tikoo, M. (1996).An Exploratory Study of Differences in Developmental Concerns of Middle- Aged Men and Women in India.Psychological Reports Journal. 78, 883 – 887.

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Midlife Crisis

Midlife crisis is a concept that has been widely used to explain the middle adulthood. The stage has been characterized by people taking very drastic measures. It is also at this point that the human self-experiences a conflict due to inability to realize one’s own goals in life. This is what causes crisis in the life of an individual. The major casualties of this crisis are the youths who are just transiting into adulthood. Though the midlife crisis is majorly caused by the external factors, research has shown that it can also be an effect of one’s own personality conflict. It is, therefore, necessary that one understands and accepts his one personality. This write up will highlight a number of the midlife aspects.

According to Doheny (2008), midlife crisis can be defined as the moment in life during which an individual does outrageous and impractical things. During this period, those faced with limitations in life pause from pursuing their goals and start reviewing their achievements by taking stock of what they have been able to do and those that they are yet to accomplish. Even though the specific concept of midlife crisis seems unattainable on empirical and theoretical grounds, a moderate concept has potential to stimulate new research directions to explain the relationship between the process of social expectations and personal goals.

Jones (2007) argues that a midlife crisis age varies depending on the individuals and the kind of external factors which they face. According to him, this crisis or transition tends to occur around significant life events. He proceeds by giving an example of a situation in which one has just completed college and is wondering where to begin from. Jones observes that both men and women are equally subjected to the midlife crisis with variation occurring only in regard to the areas of concern. He gives examples of such people who are in the midlife crisis with men who usually want to gauge their might by their job performance to prove something to the world. Another good example is that of women who get validity through relationship, that is, as a wife, a woman or even both.

Despite the fact that many people associate midlife crisis with men, Meyer (2008) refutes this and goes ahead to explain the major reasons that drive both men and women to distinctively experience midlife crisis. She argues that irrespective of gender, midlife crisis will always affect people who put little thoughts into what they want out of their lives and more in taking care of others. Mayer (2008) also identifies a number of factors which according to her are the major reasons that drive men to start feeling the midlife crisis. These include fear of age, ill health, and failure to attain the goals they set in life. On the other hand, she notes that women are driven to experience midlife crisis when they are in menopause and their sexuality changes or when they feel contented with their sacrifices. This paper will discuss various aspects of midlife crisis.

Midlife Crisis as a Normal Stage in Life

Macko & Rubin (2004) noted that Levinson in his theory of adult development had stated that all adults go through series of stages. They state that at the centre of his theory, Levinson used the concept of life structure which, according to him, is the underlying pattern of a person’s life at any particular time. According to them, Levinson had continued that for many people, the structure of life involves mainly their family and work, though he adds that religion and economic status can also be inclusive.On the other hand, Macko & Rubin (2004) note that midlife crisis ranges on average from two to seven years. They explain that this crisis usually begins slowly and may thus not be immediately detected by others. They also identified three stages of midlife transition namely separation, liminality, and reintegration. Macko & Rubin (2004) note that since a midlife transition has catastrophic levels, the transitional stages must be a part of the crisis stages. They simplified the description of midlife transition as the bridge between the life before midlife, which they call the accommodation stage and the individuation which, according to them, is the path following midlife.

Basing Herns’ work on Carl Jung’s scheme of Myers Briggs Model of Personality, Herns (2008) made an assumption that people’s preferences are innate. This means that individuals acquire and exhibit them right from the birth. Herns, therefore, locks out the possibility of the influenced of the environment and instead argues that it is only people’s behaviors that are influenced by the environment. Her study elaborates that in most cases, young children adopt to those around them for acceptance. By this adaptation, these children’s behaviors and perception are modified in order to fit in the larger society.

Accommodation is the name Herns (2008) gave this stage of life. According to her, the crisis during the midlife makes people to, just like children; present themselves as different people in different situations. She calls this plastic self the personae. She, further, notes that whenever there is a conflict between the personae and the real self, lots of energy is used. For such people, midlife transition can sometimes be a difficult and painful process.

Herns (2008) explains that in the process of development towards self-realization, an individual begins to question his identity. She notes that this questioning of the self leads to an individual separating the true self from the personae, hence the stage of separation in life. According to her, the act of question and separation of personae from the real self leads an individual to a degree of uncertainty, the stage he calls liminality. The person then rejects the old personae and reintegrates the real self, hence the whole process of midlife crisis.

Indicators/Symptoms of the Midlife Crisis

Freund & Ritter (2008) argued that the midlife transition can be delighting for some people but also tough for others. However, this depends on a number of factors including the support from the partners and the individual’s loved ones. Jones (2007) agreed with these scholars noting loneliness during this period leads many people to deep depression. Jones added that in some cases a person may even find himself/herself developing depression symptoms without knowing. Such symptoms may include changes in the eating or sleeping habits. He observes that in the wave of the midlife crisis, many people find themselves losing appetite for food or experience discomfort with their sleeps.

Another symptom discussed by Jones (2007) is the feeling of restlessness, anxiety and irritability. An individual faced with the midlife crisis will always feel unsettled, since he has many issues that he feels are unresolved. Such an individual becomes impatient to examine and takes an evaluation of the steps he has to make. Freund & Ritter (2008) identified the loss of interest in activities that one had once enjoyed as another symptom of the crisis in midlife. They further observed that such individuals find themselves not enjoying their previous hobbies.

Jones (2007) took this debate further arguing that midlife crisis has made some people to either commit or attempt suicide when faced with no solutions to their problems. He, therefore, emphasizes a need for different stakeholders such as the governments and the therapists to come up with ways of preventing such occurrence and the further worsening of such state. With midlife crisis leading to depression, Clayton (2009) suggests that the most effective solution to it could be the behavior or talk therapy. In his argument, he gives an example of the findings of a study by the Stanford University researchers who had compared the outcomes of situation where there was medication alone, talk therapy alone, and that which involved a combination of both. The study, which involved 656 persons with chronic depressions, found out that the combination of the two approaches produces faster and more effective remission of chronic depression.

Avoidance of Midlife Crisis

Altbach & Umakoshi (2004) defined that midlife crisis occurs when we find that the convictions that had supported our world view during the earlier stages of life are no longer admissible and so an individual no longer knows what to think. They see a crisis as occurring when a need to shake off and express oneself individually becomes overwhelming. They, thus, describe it as a desperation moment of one’s life. Thus, the real way to overcome the challenges which come with midlife is pursuing real things instead of those which are perceived. They propose that the best solution to avoid the midlife crisis is by living a simple life. Altbach & Umakoshi (2004) further advised that any individual should only focus on those things that really matter. They then proceed and identify them as health, family, spirituality, and search of true passions despite disapprovals from others.

Altbach & Umakoshi (2004) further added that one should be able to accept dissatisfaction as another acceptable choice in life. According to them, this can help curb the results of disappointment. They also noted that people should seek to keep their relationships fresh to help them in evaluating their actions. This will help to avoid taking drastic measures that may lead to a crisis resulting from loneliness. Moreover, they underlined the need for people to begin evaluating their own personality early enough in life to enable them become aware of whom they are and avoid conflicting images that may reappear in the future.

Finally, Gallivan (2010) proposes that people should deal with the factors associated with the crisis rather than emphasizing on the crisis itself. According to him, these factors include sadness, fear, anger, and identity crisis. He also proposes that the people in this crisis should put emphasis on resolving their midlife cognitive, environmental, and relationship complaints.

It is thus clear that the consequences of the midlife crisis depend on the way in which it is managed. With a positive and healthy approach to problem solving mechanism, the midlife crisis can be accepted just like any other transitional stage of life. However, people must be able to apart from living simple lives, accept the choices they have made in life. Additionally, individuals must learn to understand their real selves in terms of preferences no matter what objections they receive.

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			, , , , and Additional contact information Sally McManus: National Centre for Social Research, London Redzo Mujcic: Warwick Business School, University of Warwick Andrew J Oswald: Department of Economics, University of Warwick, and CAGE Centre, IZA Institute, Bonn, Nattavudh Powthavee: Department of Economics, Nanyang Technological University, Singapore & IZA Institute, Bonn from This paper documents a longitudinal crisis of midlife among the inhabitants of rich nations. Yet middle-aged citizens in our data sets are close to their peak earnings, have typically experienced little or no illness, reside in some of the safest countries in the world, and live in the most prosperous era in human history. This is paradoxical and troubling. The finding is consistent, however, with the prediction -- one little-known to economists -- of Elliott Jaques (1965). Our analysis does not rest on elementary cross-sectional analysis. Instead the paper uses panel and through-time data on, in total, approximately 500,000 individuals. It checks that the key results are not due to cohort effects. Nor do we rely on simple life-satisfaction measures. The paper shows that there are approximately quadratic hill-shaped patterns in data on midlife suicide, sleeping problems, alcohol dependence, concentration difficulties, memory problems, intense job strain, disabling headaches, suicidal feelings, and extreme depression. We believe the seriousness of this societal problem has not been grasped by the affluent world’s policy-makers. JEL Codes: I31 ; I14 ; I12 ; ; ; ; ; ; (search for similar items in EconPapers) 2022 this item is included in , and (2) (external link) Journal Article: (2023) (2022) (2022) (2022) for items with the same title. BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text for this paper in The Warwick Economics Research Paper Series (TWERPS) from Contact information at . Bibliographic data for series maintained by Margaret Nash ( ). Advanced Search All new items Journal articles Manuscripts All Categories Metaphysics and Epistemology Epistemology Metaphilosophy Metaphysics Philosophy of Action Philosophy of Language Philosophy of Mind Philosophy of Religion Value Theory Applied Ethics Meta-Ethics Normative Ethics Philosophy of Gender, Race, and Sexuality Philosophy of Law Social and Political Philosophy Value Theory, Miscellaneous Science, Logic, and Mathematics Logic and Philosophy of Logic Philosophy of Biology Philosophy of Cognitive Science Philosophy of Computing and Information Philosophy of Mathematics Philosophy of Physical Science Philosophy of Social Science Philosophy of Probability General Philosophy of Science Philosophy of Science, Misc History of Western Philosophy Ancient Greek and Roman Philosophy Medieval and Renaissance Philosophy 17th/18th Century Philosophy 19th Century Philosophy 20th Century Philosophy History of Western Philosophy, Misc Philosophical Traditions African/Africana Philosophy Asian Philosophy Continental Philosophy European Philosophy Philosophy of the Americas Philosophical Traditions, Miscellaneous Philosophy, Misc Philosophy, Introductions and Anthologies Philosophy, General Works Teaching Philosophy Philosophy, Miscellaneous Other Academic Areas Natural Sciences Social Sciences Cognitive Sciences Formal Sciences Arts and Humanities Professional Areas Other Academic Areas, Misc Submit a book or article Upload a bibliography Personal page tracking Archives we track Information for publishers Introduction Submitting to PhilPapers Frequently Asked Questions Subscriptions Editor's Guide The Categorization Project For Publishers For Archive Admins PhilPapers Surveys Bargain Finder About PhilPapers Create an account The Midlife Crisis Author's profile. Reprint years Other versions. No versions found PhilArchive External links. Through your library Sign in / register and customize your OpenURL resolver Configure custom resolver Similar books and articles Citations of this work, references found in this work. Crisis in Middle Adulthood Most recent studies agree that a significant proportion of adults aged between 45 and 65 experience psychological problems as they handle life crises associated with middle adulthood. In the present case, I will assume the role of a counselor and provide psychological support and advice to a patient in his middle adulthood who insists on experiencing mental complications and is unhappy and unsatisfied with his life accomplishments. Using the information provided by the patient, it will be straightforward to identify the main problem and propose an ideal solution. Potential Problem Evaluating the information provided by Henok indicates that the patient is likely going through a mid-life crisis, and the condition is adversely affecting his mental health. Most people develop health complications as they age, and in many cases, aging is associated with a decline in the functioning of body organs. For instance, Aman et al. (2021) show that with age, people are likely to experience physical health challenges such as digestive issues, heart diseases, and sight and hearing problems, among others. In the case of Henok, I think the main problem is mental health due to chronic stress resulting from worries and denial as the patient goes through the mid-life crisis experience. During mid-life crises, most people in middle adulthood are likely to develop stress as they realize the gap between their dreams of an ideal life and their current lifestyles. As Lally, M., & Valentine-French (2019) highlight, a mid-life crisis defines a time when people in middle adulthood reevaluate past commitments, make necessary dramatic changes, give an expression to aspirations and talents previously ignored, and develop a sense of urgency concerning life and its significance. The decision of Henock to live a youthful lifestyle by engaging in activities such as skydiving and motorcycling indicates a midlife crisis since the victim is trying to reassert his masculinity as he races against time. Also, the unexplained change of behavior resulting in Henock’s anxious and uneasy feelings despite his love for his wife and sons suggests that the patient is worried that he lacks the capacity and opportunity to live his desired lifestyle of choice and that the remaining time is not enough to make necessary adjustments. The lack of sleep and chest pains experienced by Henock result from too much stress that affects the functioning of the lungs, brain, and digestive system, among other organs. Overcoming the Problem Setting new goals is a suitable solution to help Henock solve the problems he is experiencing during his mid-life crisis. I think Henock is chasing unrealistic dreams and, after realizing they will never be accurate, makes him stressed and has a sense of invalidism. For example, the decision to buy a beach house without engaging his wife and the desire to date young women shows that Henock is trying to achieve what he failed during his youth. In that life, instead of sticking to previous goals, Henock should readjust his life ambitions and start appreciating and enjoying his age. For instance, I advise Henock to commit to guiding his sons to a life-satisfying career and help them make good decisions. Performing a life audit is another recommendable solution that Henock can apply to solve the mental problems caused by stress and worries of failing in life. The patient should perceive and judge his life positively by focusing on past and current achievements. For example, instead of Henock falling into the temptation of dating younger women, he can reflect on the good moments and the success his wife has brought into his life, and as a result, he will highly value her. I refer Henock to mental health providers to attend full therapy sessions, mainly featuring family counseling and mid-life crisis problems. From the complaints recorded by Henock, much of his stress results from his negative attitude toward his impact and significance to the family’s well-being and happiness. Therefore, family counseling will help the patient identify his vital role in his family and thus develop a positive attitude and feelings toward life. In addition to family therapy, I advise Henock to gain membership to social networks that focus on topics revolving around mid-life crises. For example, joining a social network for couples in the prime of middle adulthood can provide Henock with a platform to talk to victims of similar issues and collectively identify a working solution. Encouragement My main encouragement for Henock is that the psychological problems he is currently going through are temporal, and it is possible to regain the momentum of life. Like most people during middle adulthood, Henock is undergoing a period of transition that requires an evaluation of achievements compared to expectations. From medical records of previous patients with mid-life crises, I have observed a pattern of successful recovery from mental issues after patients accept their new identities, personalities, and life goals. Also, I encourage Henock to change his negative mentality of himself and life since that will only harm the existing problems. As Eriksen (2021) explained, mid-life crisis victims should create a supportive environment to help reduce instances of triggering negative thoughts. Generally, Henock should remain positive and commit to getting help; eventually, he will have his life and mental health back. Aman, Y., Schmauck-Medina, T., Hansen, M., Morimoto, R. I., Simon, A. K., Bjedov, I., … & Fang, E. F. (2021). Autophagy in healthy aging and disease.  Nature aging ,  1 (8), 634-650. Eriksen, C. B. (2021). Men in/and crisis: The cultural narrative of men’s midlife crises.  Journal of Aging Studies ,  57 , 100926. Lally, M., & Valentine-French, S. (2019). Lifespan Development: A Psychological Perspective Second Edition. 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Yet, even as U.S. military policy reforms during the “War on Terror” appear to reflect greater equality, violent patterns of abuse and misogyny continued within military workplaces. This author of this report found that sexual assault prevalence in the military is likely two to four times higher than official government estimations. Based on a comparison of available data collected by the U.S. Department of Defense to independent data, the research estimates there were 75,569 cases of sexual assault in 2021 and 73,695 cases in 2023. On average, over the course of the war in Afghanistan, 24 percent of active-duty women and 1.9 percent of active-duty men experienced sexual assault. The report highlights how experiences of gender inequality are most pronounced for women of color, who experience intersecting forms of racism and sexism and are one of the fastest-growing populations within the military. Independent data also confirm queer and trans service members’ disproportionately greater risk for sexual assault. The report notes that during the post-9/11 wars, the prioritization of force readiness above all else allowed the problem of sexual assault to fester, papering over internal violence and gender inequalities within military institutions. READ FULL PAPER > Executive Summary > Advertisement Supported by W.H.O. Declares Global Emergency Over New Mpox Outbreak The epidemic is concentrated in the Democratic Republic of Congo, but the virus has now appeared in a dozen other African countries. Share full article By Apoorva Mandavilli The rapid spread of mpox, formerly called monkeypox, in African countries constitutes a global health emergency, the World Health Organization declared on Wednesday. This is the second time in three years that the W.H.O. has designated an mpox epidemic as a global emergency. It previously did so in July 2022. That outbreak went on to affect nearly 100,000 people , primarily gay and bisexual men, in 116 countries, and killed about 200 people. The threat this time is deadlier. Since the beginning of this year, the Democratic Republic of Congo alone has reported 15,600 mpox cases and 537 deaths. Those most at risk include women and children under 15. “The detection and rapid spread of a new clade of mpox in eastern D.R.C., its detection in neighboring countries that had not previously reported mpox, and the potential for further spread within Africa and beyond is very worrying,” said Dr. Tedros Adhanom Ghebreyesus, the W.H.O.’s director general. The outbreak has spread through 13 countries in Africa, including a few that had never reported mpox cases before. On Tuesday, the Africa Centers for Disease Control and Prevention declared a “public health emergency of continental security,” the first time the organization has taken that step since the African Union granted it the power to do so last year. “It’s in the interests of the countries, of the continent and of the world to get our arms around this and stop transmission as soon as we can,” said Dr. Nicole Lurie, the executive director for preparedness and response at the Coalition for Epidemic Preparedness Innovations, a nonprofit that finances vaccine development. We are having trouble retrieving the article content. Please enable JavaScript in your browser settings. Thank you for your patience while we verify access. If you are in Reader mode please exit and  log into  your Times account, or  subscribe  for all of The Times. Thank you for your patience while we verify access. Already a subscriber?  Log in . Want all of The Times?  Subscribe . 330 IMAGES (PDF) The Midlife Crisis (PDF) The Midlife Crisis Edexcel Politics A Level: Model Essay on Participation Crisis The Midlife Crisis in Developmental Psychology What Are the Signs of a Midlife Crisis? What Are the Signs of a Midlife Crisis? COMMENTS The Mid-life Crisis Essay examples Best Essays. 2258 Words. 10 Pages. 9 Works Cited. Open Document. Mid-life Crisis 1. Mid-life transition can be seen as a difficult process because it is the point of our lives where we start to accept the end of our youth and begin the process of aging. For those who do not have a smooth emotional transition can suffer from a mid-life crisis. Life begins at 40: the demographic and cultural roots of the midlife crisis When Jaques first presented his paper on 'death and the midlife crisis' to the British Psychoanalytical Society in 1957, ... in an essay on Donald McGill's postcards: One of the few authentic class-differences, as opposed to class-distinctions, still existing in England is that the working classes age very much earlier. They do not live ... (PDF) Midlife Crisis: A Debate This paper critically discusses the concept of a midlife crisis and the relevant empirical evidence, presenting arguments for and against a strict, a moderate, and a lenient conceptualization of ... Dissecting the midlife crisis: disentangling social, personality and Introduction. Humans are inherently social organisms. As early as the first days of life, infants show signs of distress in the absence of social stimulation 1.As humans grow older, a thirst for social embeddedness persists and may even intensify 2,3.At midlife, roughly between the ages of 40-70 4-7, adults have gathered decades of social experience that shape how they navigate their daily ... The Midlife Unraveling The truth is that the midlife unraveling is a series of painful nudges strung together by low-grade anxiety and depression, quiet desperation, and an insidious loss of control. By low-grade, quiet, and insidious, I mean it's enough to make you crazy, but seldom enough for people on the outside to validate the struggle or offer you help and ... Mid-life crisis Essay Example [2105 Words] Mid-life crisis essay example for your inspiration. ️ 2105 words. Read and download unique samples from our free paper database. ... The term midlife crisis was invented in the year 1965 by Elliott Jaques to represent the time adults' start acknowledging that they have limited time to live and they will die someday (Oliver and Wright 2013: ... Midlife Crisis in a 55-Year-Old Man Essay Midlife crises are often experienced by people who have achieved success in their careers but do not feel like they have accomplished anything emotionally or spiritually (Schmidt, 2020). Many people experiencing this tend to turn to alcohol or drugs to cope with this dissatisfaction. Get a custom Essay on Midlife Crisis in a 55-Year-Old Man. The Midlife Crisis The Midlife Crisis. Osea Giuntella, Sally McManus, Redzo Mujcic, Andrew J. Oswald, Nattavudh Powdthavee & Ahmed Tohamy. Working Paper 30442. DOI 10.3386/w30442. Issue Date September 2022. This paper documents a longitudinal crisis of midlife among the inhabitants of rich nations. Yet middle-aged citizens in our data sets are close to their peak ... Midlife Crisis Essay Examples The process of behavioral and psychological transformations that people go through throughout middle adulthood is known as the Midlife Crisis/Transitional Model. A midlife crisis is a transition when a person has identity and confidence issues.[1] The midlife crisis affects both men and women, but recent research has shown that women suffer ... The Midlife Crisis This paper documents a longitudinal crisis of midlife among the inhabitants of rich nations. Yet middle-aged citizens in our datasets are close to their peak earnings, have typically experienced little or no illness, reside in some of the safest countries in the world, and live in the most prosperous era in human history. The Midlife Crisis in Developmental Psychology Essay The midlife crisis exists, and it is associated with an awareness of the limitations of youth and the transience of the time allotted for life. As Pashkow (2018) notes, a midlife crisis is a gap between what there is and how it is perceived. A person in this period realizes that the achievements of his pr her life may not correspond to what he ... The midlife crisis 641/2022 Osea Giuntella, Sally McManus, Redzo Mujcic, Andrew J. Oswald, Nattavudh Powdthavee and Ahmed Tohamy, This paper documents a longitudinal crisis of midlife among the inhabitants of rich nations. Yet middle-aged citizens in our data sets are close to their peak earnings, have typically experienced little or no illness, reside in some of the safest countries in the world, and live in ... Midlife Crisis/ Transitional Model This paper will focus on the midlife crisis/transition model. In light of this, the paper will discuss the physical, cognitive, and psychosocial changes during middle adulthood's midlife crisis. The idea that people in their middle years go through a crisis of self-worth and doubt as they become more conscious of their death and the passing ... Midlife Crisis Essay 859 Words. 4 Pages. Open Document. Mid-life crisis explain as an extremity in a middle age, in people's lives. Where middle ages may face a disappearance of identity and reduce their self-confidence. Midlife crisis has been known around the world for many years, but has not always been believed by everyone because not everyone face it. PDF The Midlife Crisis The paper offers new evidence that midlife is a time when people disproportionately. take their own lives, have trouble sleeping, are clinically depressed, spend time thinking about. suicide, feel life is not worth living, find it hard to concentrate, forget things, feel overwhelmed. Free Essay About Midlife Crisis The definition of midlife is literally a point at the middle of a person's life. 'The concept of 'crisis', in mid-life and at other times, implies a rapid or substantial change in personality, which is dislocating with respect to one's sense of identity (Freund & Ritter, 2009, p. 583). Thus, the best definition of midlife crisis is a ... Midlife Crisis From Crisis To Positive Transition Paper Type: Free Essay: Subject: Sociology: Wordcount: 1715 words: Published: 1st Jan 2015: ... "Midlife crisis" is the most common that takes attention when we talk about middle adulthood. Papalia et al. states that changes in personality and lifestyle come together to attribute to the crisis, however, whether or not these changes lead to ... Midlife Crisis Midlife Crisis. Midlife crisis is a concept that has been widely used to explain the middle adulthood. The stage has been characterized by people taking very drastic measures. It is also at this point that the human self-experiences a conflict due to inability to realize one's own goals in life. This is what causes crisis in the life of an ... EconPapers: The Midlife Crisis The Midlife Crisis. Osea Giuntella, Sally McManus, Redzo Mujcic, Andrew J. Oswald, Nattavudh Powdthavee and Ahmed Tohamy. No 30442, NBER Working Papers from National Bureau of Economic Research, Inc Abstract: This paper documents a longitudinal crisis of midlife among the inhabitants of rich nations. Yet middle-aged citizens in our data sets are close to their peak earnings, have typically ... EconPapers: The midlife crisis The midlife crisis. Osea Giuntella, Sally McManus, Redzo Mujcic, Andrew J Oswald, Nattavudh Powthavee and Ahmed Tohamy. Additional contact information. The Warwick Economics Research Paper Series (TWERPS) from University of Warwick, Department of Economics. Abstract: This paper documents a longitudinal crisis of midlife among the inhabitants of ... Kieran Setiya, The Midlife Crisis Abstract. Argues that philosophy can solve the midlife crisis, at least in one of its forms. This crisis turns on the exhaustibility of our ends. The solution is to value ends that are 'atelic,' so inexhaustible. Topics include: John Stuart Mill's nervous breakdown; Aristotle on the finality of the highest good; and Schopenhauer on the ... Essay On Midlife Crisis Essay On Midlife Crisis. Middle adulthood, commonly referred to as "mid-life," is a normal development life stage. As adults, we go through many changes throughout our life, including physical, psychological, emotional and social changes. Regardless of age, change is inevitable. More than 25 percent of individuals over the age of 35 believe ... Crisis in Middle Adulthood As Lally, M., & Valentine-French (2019) highlight, a mid-life crisis defines a time when people in middle adulthood reevaluate past commitments, make necessary dramatic changes, give an expression to aspirations and talents previously ignored, and develop a sense of urgency concerning life and its significance. Deserted: The U.S. Military's Sexual Assault Crisis as a Cost of War Papers. Deserted: The U.S. Military's Sexual Assault Crisis as a Cost of War. Over the past decade, the U.S. military has implemented policies to promote gender equality, notably lifting the ban on women in combat roles in 2013 and opening all military jobs to women by 2016. ... READ FULL PAPER > Executive Summary > WATSON INSTITUTE FOR ... Here's What's In Kamala Harris's Economic Plan Key Takeaways Vice President Kamala Harris laid out her economic agenda Friday, promising tax breaks for middle-class families and to crack down on companies raising rent and grocery prices.Many ... Elon Musk's Twitter Takeover Is Now the Worst Buyout for Banks Since The $13 billion that Elon Musk borrowed to buy Twitter has turned into the worst merger-finance deal for banks since the 2008-09 financial crisis. The seven banks involved in the deal, including ... W.H.O. Declares Global Emergency Over New Mpox Outbreak The outbreak that year stayed mostly within tight sexual networks, among gay and bisexual men. A combination of behavioral changes and vaccination tamped down the spread.. In the United States ... assignment essay homework paper phd report resume review thesis