Regional patterns of human cortex development correlate with underlying neurobiology.

Human brain morphology undergoes complex changes over the lifespan. Despite recent progress in tracking brain development via normative models, current knowledge of underlying biological mechanisms is highly limited. We demonstrate that human cortical thickness development and aging trajectories unfold along patterns of molecular and cellular brain organization, traceable from population-level to individual developmental trajectories. During childhood and adolescence, cortex-wide spatial distributions of dopaminergic receptors, inhibitory neurons, glial cell populations, and brain-metabolic features explain up to 50% of the variance associated with a lifespan model of regional cortical thickness trajectories. In contrast, modeled cortical thickness change patterns during adulthood are best explained by cholinergic and glutamatergic neurotransmitter receptor and transporter distributions. These relationships are supported by developmental gene expression trajectories and translate to individual longitudinal data from over 8000 adolescents, explaining up to 59% of developmental change at cohort- and 18% at single-subject level. Integrating neurobiological brain atlases with normative modeling and population neuroimaging provides a biologically meaningful path to understand brain development and aging in living humans.

Genetic-Dependent Brain Signatures of Resilience: Interactions among Childhood Abuse, Genetic Risks and Brain Function.

Resilience to emotional disorders is critical for adolescent mental health, especially following childhood abuse. Yet, brain signatures of resilience remain undetermined due to the differential susceptibility of the brain's emotion processing system to environmental stresses. Analyzing brain's responses to angry faces in a longitudinally large-scale adolescent cohort (IMAGEN), we identified two functional networks related to the orbitofrontal and occipital regions as candidate brain signatures of resilience. In girls, but not boys, higher activation in the orbitofrontal-related network was associated with fewer emotional symptoms following childhood abuse, but only when the polygenic burden for depression was high. This finding defined a genetic-dependent brain (GDB) signature of resilience. Notably, this GDB signature predicted subsequent emotional disorders in late adolescence, extending into early adulthood and generalizable to another independent prospective cohort (ABCD). Our findings underscore the genetic modulation of resilience-brain connections, laying the foundation for enhancing adolescent mental health through resilience promotion.

Variation in moment-to-moment brain state engagement changes across development and contributes to individual differences in executive function.

Neural variability, or variation in brain signals, facilitates dynamic brain responses to ongoing demands. This flexibility is important during development from childhood to young adulthood, a period characterized by rapid changes in experience. However, little is known about how variability in the engagement of recurring brain states changes during development. Such investigations would require the continuous assessment of multiple brain states concurrently. Here, we leverage a new computational framework to study state engagement variability (SEV) during development. A consistent pattern of SEV changing with age was identified across cross-sectional and longitudinal datasets (N>3000). SEV developmental trajectories stabilize around mid-adolescence, with timing varying by sex and brain state. SEV successfully predicts executive function (EF) in youths from an independent dataset. Worse EF is further linked to alterations in SEV development. These converging findings suggest SEV changes over development, allowing individuals to flexibly recruit various brain states to meet evolving needs.

Amygdala Reactivity, Antidepressant Discontinuation, and Relapse.

Importance: Antidepressant discontinuation substantially increases the risk of a depression relapse, but the neurobiological mechanisms through which this happens are not known. Amygdala reactivity to negative information is a marker of negative affective processes in depression that is reduced by antidepressant medication, but it is unknown whether amygdala reactivity is sensitive to antidepressant discontinuation or whether any change is related to the risk of relapse after antidepressant discontinuation. Objective: To investigate whether amygdala reactivity to negative facial emotions changes with antidepressant discontinuation and is associated with subsequent relapse. Design, Setting, and Participants: The Antidepressiva Absetzstudie (AIDA) study was a longitudinal, observational study in which adult patients with remitted major depressive disorder (MDD) and currently taking antidepressants underwent 2 task-based functional magnetic resonance imaging (fMRI) measurements of amygdala reactivity. Patients were randomized to discontinuing antidepressants either before or after the second fMRI measurement. Relapse was monitored over a 6-month follow-up period. Study recruitment took place from June 2015 to January 2018. Data were collected between July 1, 2015, and January 31, 2019, and statistical analyses were conducted between June 2021 and December 2023. The study took place in a university setting in Zurich, Switzerland, and Berlin, Germany. Of 123 recruited patients, 83 were included in analyses. Of 66 recruited healthy control individuals matched for age, sex, and education, 53 were included in analyses. Exposure: Discontinuation of antidepressant medication. Outcomes: Task-based fMRI measurement of amygdala reactivity and MDD relapse within 6 months after discontinuation. Results: Among patients with MDD, the mean (SD) age was 35.42 (11.41) years, and 62 (75%) were women. Among control individuals, the mean (SD) age was 33.57 (10.70) years, and 37 (70%) were women. Amygdala reactivity of patients with remitted MDD and taking medication did not initially differ from that of control individuals (t125.136 = 0.33; P = .74). An increase in amygdala reactivity after antidepressant discontinuation was associated with depression relapse (3-way interaction between group [12W (waited) vs 1W2 (discontinued)], time point [MA1 (first scan) vs MA2 (second scan)], and relapse: ß, 18.9; 95% CI, 0.8-37.1; P = .04). Amygdala reactivity change was associated with shorter times to relapse (hazard ratio, 1.05; 95% CI, 1.01-1.09; P = .01) and predictive of relapse (leave-one-out cross-validation balanced accuracy, 67%; 95% posterior predictive interval, 53-80; P = .02). Conclusions and Relevance: An increase in amygdala reactivity was associated with risk of relapse after antidepressant discontinuation and may represent a functional neuroimaging marker that could inform clinical decisions around antidepressant discontinuation.

A robust brain network for sustained attention from adolescence to adulthood that predicts later substance use.

Substance use, including cigarettes and cannabis, is associated with poorer sustained attention in late adolescence and early adulthood. Previous studies were predominantly cross-sectional or under-powered and could not indicate if impairment in sustained attention was a predictor of substance use or a marker of the inclination to engage in such behavior. This study explored the relationship between sustained attention and substance use across a longitudinal span from ages 14 to 23 in over 1000 participants. Behaviors and brain connectivity associated with diminished sustained attention at age 14 predicted subsequent increases in cannabis and cigarette smoking, establishing sustained attention as a robust biomarker for vulnerability to substance use. Individual differences in network strength relevant to sustained attention were preserved across developmental stages and sustained attention networks generalized to participants in an external dataset. In summary, brain networks of sustained attention are robust, consistent, and able to predict aspects of later substance use.

Polygenic risk scores for nicotine use and family history of smoking are associated with smoking behaviour.

INTRODUCTION: Formal genetics studies show that smoking is influenced by genetic factors; exploring this on the molecular level can offer deeper insight into the etiology of smoking behaviours. METHODS: Summary statistics from the latest wave of the GWAS and Sequencing Consortium of Alcohol and Nicotine (GSCAN) were used to calculate polygenic risk scores (PRS) in a sample of ~2200 individuals who smoke/individuals who never smoked. The associations of smoking status with PRS for Smoking Initiation (i.e., Lifetime Smoking; SI-PRS), and Fagerström Test for Nicotine Dependence (FTND) score with PRS for Cigarettes per Day (CpD-PRS) were examined, as were distinct/additive effects of parental smoking on smoking status. RESULTS: SI-PRS explained 10.56% of variance (Nagelkerke-R2) in smoking status (p=6.45x10-30). In individuals who smoke, CpD-PRS was associated with FTND score (R2=5.03%, p=1.88x10-12). Parental smoking alone explained R2=3.06% (p=2.43×10-12) of smoking status, and 0.96% when added to the most informative SI-PRS model (total R²=11.52%). CONCLUSION: These results show the potential utility of molecular genetic data for research investigating smoking prevention. The fact that PRS explains more variance than family history highlights progress from formal to molecular genetics; the partial overlap and increased predictive value when using both suggests the importance of combining these approaches.

Automatic rating of incomplete hippocampal inversions evaluated across multiple cohorts.

Incomplete Hippocampal Inversion (IHI), sometimes called hippocampal malrotation, is an atypical anatomical pattern of the hippocampus found in about 20% of the general population. IHI can be visually assessed on coronal slices of T1 weighted MR images, using a composite score that combines four anatomical criteria. IHI has been associated with several brain disorders (epilepsy, schizophrenia). However, these studies were based on small samples. Furthermore, the factors (genetic or environmental) that contribute to the genesis of IHI are largely unknown. Large-scale studies are thus needed to further understand IHI and their potential relationships to neurological and psychiatric disorders. However, visual evaluation is long and tedious, justifying the need for an automatic method. In this paper, we propose, for the first time, to automatically rate IHI. We proceed by predicting four anatomical criteria, which are then summed up to form the IHI score, providing the advantage of an interpretable score. We provided an extensive experimental investigation of different machine learning methods and training strategies. We performed automatic rating using a variety of deep learning models ("conv5-FC3", ResNet and "SECNN") as well as a ridge regression. We studied the generalization of our models using different cohorts and performed multi-cohort learning. We relied on a large population of 2,008 participants from the IMAGEN study, 993 and 403 participants from the QTIM and QTAB studies as well as 985 subjects from the UKBiobank. We showed that deep learning models outperformed a ridge regression. We demonstrated that the performances of the "conv5-FC3" network were at least as good as more complex networks while maintaining a low complexity and computation time. We showed that training on a single cohort may lack in variability while training on several cohorts improves generalization (acceptable performances on all tested cohorts including some that are not included in training). The trained models will be made publicly available should the manuscript be accepted.

Personality, Social Factors, Brain Functioning, Familial Risk, and Trajectories of Alcohol Misuse in Adolescence.

Importance: The development of an alcohol use disorder in adolescence is associated with increased risk of future alcohol dependence. The differential associations of risk factors with alcohol use over the course of 8 years are important for preventive measures. Objective: To determine the differential associations of risk-taking aspects of personality, social factors, brain functioning, and familial risk with hazardous alcohol use in adolescents over the course of 8 years. Design, Setting, and Participants: The IMAGEN multicenter longitudinal cohort study included adolescents recruited from European schools in Germany, the UK, France, and Ireland from January 2008 to January 2019. Eligible participants included those with available neuropsychological, self-report, imaging, and genetic data at baseline. Adolescents who were ineligible for magnetic resonance imaging or had serious medical conditions were excluded. Data analysis was conducted from July 2021 to September 2022. Exposure: Personality testing, psychosocial factors, brain functioning, and familial risk of alcohol misuse. Main Outcome and Measures: Hazardous alcohol use as measured with the Alcohol Use Disorders Identification Test scores, a main planned outcome of the IMAGEN study. Alcohol misuse trajectories at ages 14, 16, 19, and 22 years were modeled using latent growth curve models. Results: A total of 2240 adolescents (1110 female [49.6%] and 1130 male [50.4%]) were included in the study. There was a significant negative association of psychosocial resources (ß = -0.29; SE = 0.03; P < .001) with the general risk of alcohol misuse as well as a significant positive association of the risk-taking aspects of personality with the intercept (ß = 0.19; SE = 0.04; P < .001). Furthermore, there were significant positive associations of the social domain (ß = 0.13; SE = 0.02; P < .001) and the personality domain (ß = 0.07; SE = 0.02; P < .001) with trajectories of alcohol misuse development over time (slope). Family history of substance misuse was negatively associated with general risk of alcohol misuse (ß = -0.04; SE = 0.02; P = .045) and its development over time (ß = -0.03; SE = 0.01; P = .01). Brain functioning showed no significant association with intercept or slope of alcohol misuse in the model. Conclusions and Relevance: The findings of this cohort study suggest known risk factors of adolescent drinking may contribute differentially to future alcohol misuse. This approach may inform more individualized preventive interventions.

Influences of heart rate feedback and autistic traits on affective mindreading.

Although mindreading is an important prerequisite for successful social interactions, the underlying mechanisms are still matter of debate. It is unclear, for example, if inferring others' and own mental states are distinct processes or are based on a common mechanism. Using an affect-induction experimental set-up with an acoustic heart rate feedback that addresses affective mindreading in self and others, we investigated if non-autistic study participants relied on similar information for self- and other-directed mindreading. We assumed that due to altered mindreading capacities in autism, mainly individuals with low autistic traits would focus on additional sensory cues, such as heart rate, to infer their own and their gambling partner's affective states. Our analyses showed that the interpretation of a heart rate signal differed in self- and other-directed mindreading trials. This effect was modulated by autistic traits suggesting that individuals with higher autistic traits might not have interpreted the heart rate feedback for gambling partner ratings and differentiated less between self- and other-directed mindreading trials. We discuss these results in the context of a common mechanism underlying self- and other-directed mindreading and hypothesize that the weighting of internal and external sensory information might contribute to how we make sense of our and others' mental states.