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Intrinsic connectivity, measured using resting-state fMRI, has emerged as a fundamental tool in the study of the human brain. However, due to practical limitations, many studies do not collect enough resting-state data to generate reliable measures of intrinsic connectivity necessary for studying individual differences. Here we present general functional connectivity (GFC) as a method for leveraging shared features across resting-state and task fMRI and demonstrate in the Human Connectome Project and the Dunedin Study that GFC offers better test-retest reliability than intrinsic connectivity estimated from the same amount of resting-state data alone. Furthermore, at equivalent scan lengths, GFC displayed higher estimates of heritability than resting-state functional connectivity. We also found that predictions of cognitive ability from GFC generalized across datasets, performing as well or better than resting-state or task data alone. Collectively, our work suggests that GFC can improve the reliability of intrinsic connectivity estimates in existing datasets and, subsequently, the opportunity to identify meaningful correlates of individual differences in behavior. Given that task and resting-state data are often collected together, many researchers can immediately derive more reliable measures of intrinsic connectivity through the adoption of GFC rather than solely using resting-state data. Moreover, by better capturing heritable variation in intrinsic connectivity, GFC represents a novel endophenotype with broad applications in clinical neuroscience and biomarker discovery.

Attempts to link the Big Five personality traits of Openness-to-Experience, Conscientiousness, Extraversion, Agreeableness, and Neuroticism with variability in trait-like features of brain structure have produced inconsistent results. Small sample sizes and heterogeneous methodology have been suspected in driving these inconsistencies. Here, using data collected from 1,107 university students (636 women, mean age 19.69 ​± ​1.24 years), representing the largest sample to date of unrelated individuals, we tested for associations between the Big Five personality traits and measures of cortical thickness and surface area, subcortical volume, and white matter microstructural integrity. In addition to replication analyses based on a prior study, we conducted exploratory whole-brain analyses. Four supplementary analyses were also conducted to examine 1) possible associations with lower-order facets of personality; 2) modulatory effects of sex; 3) effect of controlling for non-target personality traits; and 4) parcellation scheme effects. Our analyses failed to identify significant associations between the Big Five personality traits and brain morphometry, except for a weak association between greater surface area of the superior temporal gyrus and lower conscientiousness scores. As the latter association is not supported by previous studies, it should be treated with caution. Our supplementary analyses mirrored these predominantly null findings, suggesting they were not substantively biased by our analytic choices. Collectively, these results indicate that if there are associations between the Big Five personality traits and brain structure, they are likely of very small effect size and will require very large samples for reliable detection. •We conducted replication and exploratory analyses of personality-brain morphometry associations.•Analyses failed to replicate previously found associations.•Exploratory analyses primarily did not yield significant associations.•Personality-brain morphometry associations are weak and require large sample sizes for detection.

Pattern classification of human brain activity provides unique insight into the neural underpinnings of diverse mental states. These multivariate tools have recently been used within the field of affective neuroscience to classify distributed patterns of brain activation evoked during emotion induction procedures. Here we assess whether neural models developed to discriminate among distinct emotion categories exhibit predictive validity in the absence of exteroceptive emotional stimulation. In two experiments, we show that spontaneous fluctuations in human resting-state brain activity can be decoded into categories of experience delineating unique emotional states that exhibit spatiotemporal coherence, covary with individual differences in mood and personality traits, and predict on-line, self-reported feelings. These findings validate objective, brain-based models of emotion and show how emotional states dynamically emerge from the activity of separable neural systems.

Neurobiological factors contributing to violence in humans remain poorly understood. One approach to this question is examining allelic variation in the X-linked monoamine oxidase A (MAOA) gene, previously associated with impulsive aggression in animals and humans. Here, we have studied the impact of a common functional polymorphism in MAOA on brain structure and function assessed with MRI in a large sample of healthy human volunteers. We show that the low expression variant, associated with increased risk of violent behavior, predicted pronounced limbic volume reductions and hyperresponsive amygdala during emotional arousal, with diminished reactivity of regulatory prefrontal regions, compared with the high expression allele. In men, the low expression allele is also associated with changes in orbitofrontal volume, amygdala and hippocampus hyperreactivity during aversive recall, and impaired cingulate activation during cognitive inhibition. Our data identify differences in limbic circuitry for emotion regulation and cognitive control that may be involved in the association of MAOA with impulsive aggression, suggest neural systems-level effects of X-inactivation in human brain, and point toward potential targets for a biological approach toward violence.

The ability to control one’s own emotions, thoughts, and behaviors in early life predicts a range of positive outcomes in later life, including longevity. Does it also predict how well people age? We studied the association between self-control and midlife aging in a population-representative cohort of children followed from birth to age 45 y, the Dunedin Study. We measured children’s self-control across their first decade of life using a multi-occasion/multiinformant strategy. We measured their pace of aging and aging preparedness in midlife using measures derived from biological and physiological assessments, structural brain-imaging scans, observer ratings, self-reports, informant reports, and administrative records. As adults, children with better self-control aged more slowly in their bodies and showed fewer signs of aging in their brains. By midlife, these children were also better equipped to manage a range of later-life health, financial, and social demands. Associations with children’s self-control could be separated from their social class origins and intelligence, indicating that self-control might be an active ingredient in healthy aging. Children also shifted naturally in their level of self-control across adult life, suggesting the possibility that self-control may be a malleable target for intervention. Furthermore, individuals’ self-control in adulthood was associated with their aging outcomes after accounting for their self-control in childhood, indicating that midlife might offer another window of opportunity to promote healthy aging.

Some humans age faster than others. Variation in biological aging can be measured in midlife, but the implications of this variation are poorly understood. We tested associations between midlife biological aging and indicators of future frailty-risk in the Dunedin cohort of 1037 infants born the same year and followed to age 45. Participants' Pace of Aging was quantified by tracking declining function in 19 biomarkers indexing the cardiovascular, metabolic, renal, immune, dental, and pulmonary systems across ages 26, 32, 38, and 45 years. At age 45 in 2019, participants with faster Pace of Aging had more cognitive difficulties, signs of advanced brain aging, diminished sensory-motor functions, older appearance, and more pessimistic perceptions of aging. People who are aging more rapidly than same-age peers in midlife may prematurely need supports to sustain independence that are usually reserved for older adults. Chronological age does not adequately identify need for such supports.

Mapping individual differences in brain function has been hampered by poor reliability as well as limited interpretability. Leveraging patterns of brain‐wide functional connectivity (FC) offers some promise in this endeavor. In particular, a macroscale principal FC gradient that recapitulates a hierarchical organization spanning molecular, cellular, and circuit level features along a sensory‐to‐association cortical axis has emerged as both a parsimonious and interpretable measure of individual differences in behavior. However, the measurement reliabilities of this FC gradient have not been fully evaluated. Here, we assess the reliabilities of both global and regional principal FC gradient measures using test–retest data from the young adult Human Connectome Project (HCP‐YA) and the Dunedin Study. Analyses revealed that the reliabilities of principal FC gradient measures were (1) consistently higher than those for traditional edge‐wise FC measures, (2) higher for FC measures derived from general FC (GFC) in comparison with resting‐state FC, and (3) higher for longer scan lengths. We additionally examined the relative utility of these principal FC gradient measures in predicting cognition and aging in both datasets as well as the HCP‐aging dataset. These analyses revealed that regional FC gradient measures and global gradient range were significantly associated with aging in all three datasets, and moderately associated with cognition in the HCP‐YA and Dunedin Study datasets, reflecting contractions and expansions of the cortical hierarchy, respectively. Collectively, these results demonstrate that measures of the principal FC gradient, especially derived using GFC, effectively capture a reliable feature of the human brain subject to interpretable and biologically meaningful individual variation, offering some advantages over traditional edge‐wise FC measures in the search for brain–behavior associations. We demonstrate that a macroscale hierarchical functional connectivity gradient that recapitulates fundamental biological features of the human cortex has higher test–retest reliability than traditional edge‐wise measures, especially when derived from combined resting‐state and task‐based data. Further, gradient measures capture individual differences in aging and cognition.

Carriers of the short allele of a functional 5′ promoter polymorphism of the serotonin transporter gene have increased anxiety-related temperamental traits, increased amygdala reactivity and elevated risk of depression. Here, we used multimodal neuroimaging in a large sample of healthy human subjects to elucidate neural mechanisms underlying this complex genetic association. Morphometrical analyses showed reduced gray matter volume in short-allele carriers in limbic regions critical for processing of negative emotion, particularly perigenual cingulate and amygdala. Functional analysis of those regions during perceptual processing of fearful stimuli demonstrated tight coupling as a feedback circuit implicated in the extinction of negative affect. Short-allele carriers showed relative uncoupling of this circuit. Furthermore, the magnitude of coupling inversely predicted almost 30% of variation in temperamental anxiety. These genotype-related alterations in anatomy and function of an amygdala-cingulate feedback circuit critical for emotion regulation implicate a developmental, systems-level mechanism underlying normal emotional reactivity and genetic susceptibility for depression.

Stress exposure is associated with individual differences in corticolimbic structure and function that often mirror patterns observed in psychopathology. Gene x environment interaction research suggests that genetic variation moderates the impact of stress on risk for psychopathology. On the basis of these findings, imaging genetics, which attempts to link variability in DNA sequence and structure to neural phenotypes, has begun to incorporate measures of the environment. This research paradigm, known as imaging gene x environment interaction (iGxE), is beginning to contribute to our understanding of the neural mechanisms through which genetic variation and stress increase psychopathology risk. Although awaiting replication, evidence suggests that genetic variation within the canonical neuroendocrine stress hormone system, the hypothalamic-pituitary-adrenal axis, contributes to variability in stress-related corticolimbic structure and function, which, in turn, confers risk for psychopathology. For iGxE research to reach its full potential it will have to address many challenges, of which we discuss: (i) small effects, (ii) measuring the environment and neural phenotypes, (iii) the absence of detailed mechanisms, and (iv) incorporating development. By actively addressing these challenges, iGxE research is poised to help identify the neural mechanisms underlying genetic and environmental associations with psychopathology.

A functional polymorphism in the promoter region of the human serotonin transporter gene (SLC6A4) has been associated with several dimensions of neuroticism and psychopathology, especially anxiety traits, but the predictive value of this genotype against these complex behaviors has been inconsistent. Serotonin [5-hydroxytryptamine, (5-HT)] function influences normal fear as well as pathological anxiety, behaviors critically dependent on the amygdala in animal models and in clinical studies. We now report that individuals with one or two copies of the short allele of the serotonin transporter (5-HTT) promoter polymorphism, which has been associated with reduced 5-HTT expression and function and increased fear and anxiety-related behaviors, exhibit greater amygdala neuronal activity, as assessed by BOLD functional magnetic resonance imaging, in response to fearful stimuli compared with individuals homozygous for the long allele. These results demonstrate genetically driven variation in the response of brain regions underlying human emotional behavior and suggest that differential excitability of the amygdala to emotional stimuli may contribute to the increased fear and anxiety typically associated with the short SLC6A4 allele.