Adolescent maturation of cortical excitation-inhibition balance based on individualized biophysical network modeling.

The balance of excitation and inhibition is a key functional property of cortical microcircuits which changes through the lifespan. Adolescence is considered a crucial period for the maturation of excitation-inhibition balance. This has been primarily observed in animal studies, yet human in vivo evidence on adolescent maturation of the excitation-inhibition balance at the individual level is limited. Here, we developed an individualized in vivo marker of regional excitation-inhibition balance in human adolescents, estimated using large-scale simulations of biophysical network models fitted to resting-state functional magnetic resonance imaging data from two independent cross-sectional (N = 752) and longitudinal (N = 149) cohorts. We found a widespread relative increase of inhibition in association cortices paralleled by a relative age-related increase of excitation, or lack of change, in sensorimotor areas across both datasets. This developmental pattern co-aligned with multiscale markers of sensorimotor-association differentiation. The spatial pattern of excitation-inhibition development in adolescence was robust to inter-individual variability of structural connectomes and modeling configurations. Notably, we found that alternative simulation-based markers of excitation-inhibition balance show a variable sensitivity to maturational change. Taken together, our study highlights an increase of inhibition during adolescence in association areas using cross sectional and longitudinal data, and provides a robust computational framework to estimate microcircuit maturation in vivo at the individual level.

Investigating grey matter volumetric trajectories through the lifespan at the individual level.

Adolescents exhibit remarkable heterogeneity in the structural architecture of brain development. However, due to limited large-scale longitudinal neuroimaging studies, existing research has largely focused on population averages, and the neurobiological basis underlying individual heterogeneity remains poorly understood. Here we identify, using the IMAGEN adolescent cohort followed up over 9 years (14-23 y), three groups of adolescents characterized by distinct developmental patterns of whole-brain gray matter volume (GMV). Group 1 show continuously decreasing GMV associated with higher neurocognitive performances than the other two groups during adolescence. Group 2 exhibit a slower rate of GMV decrease and lower neurocognitive performances compared with Group 1, which was associated with epigenetic differences and greater environmental burden. Group 3 show increasing GMV and lower baseline neurocognitive performances due to a genetic variation. Using the UK Biobank, we show these differences may be attenuated in mid-to-late adulthood. Our study reveals clusters of adolescent neurodevelopment based on GMV and the potential long-term impact.

Neural responses to facial emotions and subsequent clinical outcomes in difficult-to-treat depression.

BACKGROUND: Amygdala and dorsal anterior cingulate cortex responses to facial emotions have shown promise in predicting treatment response in medication-free major depressive disorder (MDD). Here, we examined their role in the pathophysiology of clinical outcomes in more chronic, difficult-to-treat forms of MDD. METHODS: Forty-five people with current MDD who had not responded to ⩾2 serotonergic antidepressants (n = 42, meeting pre-defined fMRI minimum quality thresholds) were enrolled and followed up over four months of standard primary care. Prior to medication review, subliminal facial emotion fMRI was used to extract blood-oxygen level-dependent effects for sad v. happy faces from two pre-registered a priori defined regions: bilateral amygdala and dorsal/pregenual anterior cingulate cortex. Clinical outcome was the percentage change on the self-reported Quick Inventory of Depressive Symptomatology (16-item). RESULTS: We corroborated our pre-registered hypothesis (NCT04342299) that lower bilateral amygdala activation for sad v. happy faces predicted favorable clinical outcomes (rs[38] = 0.40, p = 0.01). In contrast, there was no effect for dorsal/pregenual anterior cingulate cortex activation (rs[38] = 0.18, p = 0.29), nor when using voxel-based whole-brain analyses (voxel-based Family-Wise Error-corrected p < 0.05). Predictive effects were mainly driven by the right amygdala whose response to happy faces was reduced in patients with higher anxiety levels. CONCLUSIONS: We confirmed the prediction that a lower amygdala response to negative v. positive facial expressions might be an adaptive neural signature, which predicts subsequent symptom improvement also in difficult-to-treat MDD. Anxiety reduced adaptive amygdala responses.

Unraveling robust brain-behavior links of depressive complaints through granular network models for understanding heterogeneity.

BACKGROUND: Depressive symptoms are highly prevalent, present in heterogeneous symptom patterns, and share diverse neurobiological underpinnings. Understanding the links between psychopathological symptoms and biological factors is critical in elucidating its etiology and persistence. We aimed to evaluate the utility of using symptom-brain network models to parse the heterogeneity of depressive complaints in a large adolescent sample. METHODS: We used data from the third wave of the IMAGEN study, a multi-center panel cohort study involving 1317 adolescents (52.49 % female, mean ± SD age = 18.5 ± 0.7). Two network models were estimated: one including an overall depressive symptom severity sum score based on the Adolescent Depression Rating Scale (ADRS), and one incorporating individual ADRS item scores. Both networks included measures of cortical thickness in several regions (insula, cingulate, mOFC, fusiform gyrus) and hippocampal volume derived from neuroimaging. RESULTS: The network based on individual item scores revealed associations between cortical thickness measures and specific depressive complaints, obscured when using an aggregate depression severity score. Notably, the insula's cortical thickness showed negative associations with cognitive dysfunction (partial cor. = -0.15); the cingulate's cortical thickness showed negative associations with feelings of worthlessness (partial cor. = -0.10), and mOFC was negatively associated with anhedonia (partial cor. = -0.05). LIMITATIONS: This cross-sectional study relied on the self-reported assessment of depression complaints and used a non-clinical sample with predominantly healthy participants (19 % with depression or sub-threshold depression). CONCLUSIONS: This study showcases the utility of network models in parsing heterogeneity in depressive complaints, linking individual complaints to specific neural substrates. We outline the next steps to integrate neurobiological and cognitive markers to unravel MDD's phenotypic heterogeneity.

The Role of Subgenual Resting-State Connectivity Networks in Predicting Prognosis in Major Depressive Disorder.

Background: A seminal study found higher subgenual frontal cortex resting-state connectivity with 2 left ventral frontal regions and the dorsal midbrain to predict better response to psychotherapy versus medication in individuals with treatment-naïve major depressive disorder (MDD). Here, we examined whether these subgenual networks also play a role in the pathophysiology of clinical outcomes in MDD with early treatment resistance in primary care. Methods: Forty-five people with current MDD who had not responded to ≥2 serotonergic antidepressants (n = 43, meeting predefined functional magnetic resonance imaging minimum quality thresholds) were enrolled and followed over 4 months of standard care. Functional magnetic resonance imaging resting-state connectivity between the preregistered subgenual frontal cortex seed and 3 previously identified left ventromedial, ventrolateral prefrontal/insula, and dorsal midbrain regions was extracted. The clinical outcome was the percentage change on the self-reported 16-item Quick Inventory of Depressive Symptomatology. Results: We observed a reversal of our preregistered hypothesis in that higher resting-state connectivity between the subgenual cortex and the a priori ventrolateral prefrontal/insula region predicted favorable rather than unfavorable clinical outcomes (rs39 = -0.43, p = .006). This generalized to the sample including participants with suboptimal functional magnetic resonance imaging quality (rs43 = -0.35, p = .02). In contrast, no effects (rs39 = 0.12, rs39 = -0.01) were found for connectivity with the other 2 preregistered regions or in a whole-brain analysis (voxel-based familywise error-corrected p < .05). Conclusions: Subgenual connectivity with the ventrolateral prefrontal cortex/insula is relevant for subsequent clinical outcomes in current MDD with early treatment resistance. Its positive association with favorable outcomes could be explained primarily by psychosocial rather than the expected pharmacological changes during the follow-up period.

Evidence has shown that connectivity of the subgenual cortex, a frontal midline brain region, with 3 other brain regions can predict whether people with never-treated depression benefit more from psychological or medication-based treatments. Here, using resting-state fMRI, we show that subgenual connections with one of these regions, the left ventrolateral prefrontal/insula, also predict future average depression levels in people with difficult-to-treat depression. These data suggest that functional connectivity between these regions may be linked to clinical outcomes in major depressive disorder.

Childhood adversities characterize the heterogeneity in the brain pattern of individuals during neurodevelopment.

BACKGROUND: Several factors shape the neurodevelopmental trajectory. A key area of focus in neurodevelopmental research is to estimate the factors that have maximal influence on the brain and can tip the balance from typical to atypical development. METHODS: Utilizing a dissimilarity maximization algorithm on the dynamic mode decomposition (DMD) of the resting state functional MRI data, we classified subjects from the cVEDA neurodevelopmental cohort (n = 987, aged 6-23 years) into homogeneously patterned DMD (representing typical development in 809 subjects) and heterogeneously patterned DMD (indicative of atypical development in 178 subjects). RESULTS: Significant DMD differences were primarily identified in the default mode network (DMN) regions across these groups (p < 0.05, Bonferroni corrected). While the groups were comparable in cognitive performance, the atypical group had more frequent exposure to adversities and faced higher abuses (p < 0.05, Bonferroni corrected). Upon evaluating brain-behavior correlations, we found that correlation patterns between adversity and DMN dynamic modes exhibited age-dependent variations for atypical subjects, hinting at differential utilization of the DMN due to chronic adversities. CONCLUSION: Adversities (particularly abuse) maximally influence the DMN during neurodevelopment and lead to the failure in the development of a coherent DMN system. While DMN's integrity is preserved in typical development, the age-dependent variability in atypically developing individuals is contrasting. The flexibility of DMN might be a compensatory mechanism to protect an individual in an abusive environment. However, such adaptability might deprive the neural system of the faculties of normal functioning and may incur long-term effects on the psyche.

Optimising brain age estimation through transfer learning: A suite of pre-trained foundation models for improved performance and generalisability in a clinical setting.

Estimated age from brain MRI data has emerged as a promising biomarker of neurological health. However, the absence of large, diverse, and clinically representative training datasets, along with the complexity of managing heterogeneous MRI data, presents significant barriers to the development of accurate and generalisable models appropriate for clinical use. Here, we present a deep learning framework trained on routine clinical data (N up to 18,890, age range 18-96 years). We trained five separate models for accurate brain age prediction (all with mean absolute error ≤4.0 years, R2 ≥ .86) across five different MRI sequences (T2 -weighted, T2 -FLAIR, T1 -weighted, diffusion-weighted, and gradient-recalled echo T2 *-weighted). Our trained models offer dual functionality. First, they have the potential to be directly employed on clinical data. Second, they can be used as foundation models for further refinement to accommodate a range of other MRI sequences (and therefore a range of clinical scenarios which employ such sequences). This adaptation process, enabled by transfer learning, proved effective in our study across a range of MRI sequences and scan orientations, including those which differed considerably from the original training datasets. Crucially, our findings suggest that this approach remains viable even with limited data availability (as low as N = 25 for fine-tuning), thus broadening the application of brain age estimation to more diverse clinical contexts and patient populations. By making these models publicly available, we aim to provide the scientific community with a versatile toolkit, promoting further research in brain age prediction and related areas.

The relationship between negative life events and cortical structural connectivity in adolescents.

Adolescence is a crucial period for physical and psychological development. The impact of negative life events represents a risk factor for the onset of neuropsychiatric disorders. This study aims to investigate the relationship between negative life events and structural brain connectivity, considering both graph theory and connectivity strength. A group (n = 487) of adolescents from the IMAGEN Consortium was divided into Low and High Stress groups. Brain networks were extracted at an individual level, based on morphological similarity between grey matter regions with regions defined using an atlas-based region of interest (ROI) approach. Between-group comparisons were performed with global and local graph theory measures in a range of sparsity levels. The analysis was also performed in a larger sample of adolescents (n = 976) to examine linear correlations between stress level and network measures. Connectivity strength differences were investigated with network-based statistics. Negative life events were not found to be a factor influencing global network measures at any sparsity level. At local network level, between-group differences were found in centrality measures of the left somato-motor network (a decrease of betweenness centrality was seen at sparsity 5%), of the bilateral central visual and the left dorsal attention network (increase of degree at sparsity 10% at sparsity 30% respectively). Network-based statistics analysis showed an increase in connectivity strength in the High stress group in edges connecting the dorsal attention, limbic and salience networks. This study suggests negative life events alone do not alter structural connectivity globally, but they are associated to connectivity properties in areas involved in emotion and attention.

Machine learning models for diagnosis and risk prediction in eating disorders, depression, and alcohol use disorder.

This study uses machine learning models to uncover diagnostic and risk prediction markers for eating disorders (EDs), major depressive disorder (MDD), and alcohol use disorder (AUD). Utilizing case-control samples (ages 18-25 years) and a longitudinal population-based sample (n=1,851), the models, incorporating diverse data domains, achieved high accuracy in classifying EDs, MDD, and AUD from healthy controls. The area under the receiver operating characteristic curves (AUC-ROC [95% CI]) reached 0.92 [0.86-0.97] for AN and 0.91 [0.85-0.96] for BN, without relying on body mass index as a predictor. The classification accuracies for MDD (0.91 [0.88-0.94]) and AUD (0.80 [0.74-0.85]) were also high. Each data domain emerged as accurate classifiers individually, with personality distinguishing AN, BN, and their controls with AUC-ROCs ranging from 0.77 to 0.89. The models demonstrated high transdiagnostic potential, as those trained for EDs were also accurate in classifying AUD and MDD from healthy controls, and vice versa (AUC-ROCs, 0.75-0.93). Shared predictors, such as neuroticism, hopelessness, and symptoms of attention-deficit/hyperactivity disorder, were identified as reliable classifiers. For risk prediction in the longitudinal population sample, the models exhibited moderate performance (AUC-ROCs, 0.64-0.71), highlighting the potential of combining multi-domain data for precise diagnostic and risk prediction applications in psychiatry.

Adolescent to young adult longitudinal development of subcortical volumes in two European sites with four waves.

Adolescent subcortical structural brain development might underlie psychopathological symptoms, which often emerge in adolescence. At the same time, sex differences exist in psychopathology, which might be mirrored in underlying sex differences in structural development. However, previous studies showed inconsistencies in subcortical trajectories and potential sex differences. Therefore, we aimed to investigate the subcortical structural trajectories and their sex differences across adolescence using for the first time a single cohort design, the same quality control procedure, software, and a general additive mixed modeling approach. We investigated two large European sites from ages 14 to 24 with 503 participants and 1408 total scans from France and Germany as part of the IMAGEN project including four waves of data acquisition. We found significantly larger volumes in males versus females in both sites and across all seven subcortical regions. Sex differences in age-related trajectories were observed across all regions in both sites. Our findings provide further evidence of sex differences in longitudinal adolescent brain development of subcortical regions and thus might eventually support the relationship of underlying brain development and different adolescent psychopathology in boys and girls.

The genetic architecture of the human hypothalamus and its involvement in neuropsychiatric behaviours and disorders.

Despite its crucial role in the regulation of vital metabolic and neurological functions, the genetic architecture of the hypothalamus remains unknown. Here we conducted multivariate genome-wide association studies (GWAS) using hypothalamic imaging data from 32,956 individuals to uncover the genetic underpinnings of the hypothalamus and its involvement in neuropsychiatric traits. There were 23 significant loci associated with the whole hypothalamus and its subunits, with functional enrichment for genes involved in intracellular trafficking systems and metabolic processes of steroid-related compounds. The hypothalamus exhibited substantial genetic associations with limbic system structures and neuropsychiatric traits including chronotype, risky behaviour, cognition, satiety and sympathetic-parasympathetic activity. The strongest signal in the primary GWAS, the ADAMTS8 locus, was replicated in three independent datasets (N = 1,685-4,321) and was strengthened after meta-analysis. Exome-wide association analyses added evidence to the association for ADAMTS8, and Mendelian randomization showed lower ADAMTS8 expression with larger hypothalamic volumes. The current study advances our understanding of complex structure-function relationships of the hypothalamus and provides insights into the molecular mechanisms that underlie hypothalamic formation.

Whole-brain gray matter maturation trajectories associated with autistic traits from adolescence to early adulthood.

A growing number of evidence supports a continued distribution of autistic traits in the general population. However, brain maturation trajectories of autistic traits as well as the influence of sex on these trajectories remain largely unknown. We investigated the association of autistic traits in the general population, with longitudinal gray matter (GM) maturation trajectories during the critical period of adolescence. We assessed 709 community-based adolescents (54.7% women) at age 14 and 22. After testing the effect of sex, we used whole-brain voxel-based morphometry to measure longitudinal GM volumes changes associated with autistic traits measured by the Social Responsiveness Scale (SRS) total and sub-scores. In women, we observed that the SRS was associated with slower GM volume decrease globally and in the left parahippocampus and middle temporal gyrus. The social communication sub-score correlated with slower GM volume decrease in the left parahippocampal, superior temporal gyrus, and pallidum; and the social cognition sub-score correlated with slower GM volume decrease in the left middle temporal gyrus, the right ventromedial prefrontal and orbitofrontal cortex. No longitudinal association was found in men. Autistic traits in young women were found to be associated with specific brain trajectories in regions of the social brain and the reward circuit known to be involved in Autism Spectrum Disorder. These findings support both the hypothesis of an earlier GM maturation associated with autistic traits in adolescence and of protective mechanisms in women. They advocate for further studies on brain trajectories associated with autistic traits in women.

Genetic architectures of cerebral ventricles and their overlap with neuropsychiatric traits.

The cerebral ventricles are recognized as windows into brain development and disease, yet their genetic architectures, underlying neural mechanisms and utility in maintaining brain health remain elusive. Here we aggregated genetic and neuroimaging data from 61,974 participants (age range, 9 to 98 years) in five cohorts to elucidate the genetic basis of ventricular morphology and examined their overlap with neuropsychiatric traits. Genome-wide association analysis in a discovery sample of 31,880 individuals identified 62 unique loci and 785 candidate genes associated with ventricular morphology. We replicated over 80% of loci in a well-matched cohort of lateral ventricular volume. Gene set analysis revealed enrichment of ventricular-trait-associated genes in biological processes and disease pathogenesis during both early brain development and degeneration. We explored the age-dependent genetic associations in cohorts of different age groups to investigate the possible roles of ventricular-trait-associated loci in neurodevelopmental and neurodegenerative processes. We describe the genetic overlap between ventricular and neuropsychiatric traits through comprehensive integrative approaches under correlative and causal assumptions. We propose the volume of the inferior lateral ventricles as a heritable endophenotype to predict the risk of Alzheimer's disease, which might be a consequence of prodromal Alzheimer's disease. Our study provides an advance in understanding the genetics of the cerebral ventricles and demonstrates the potential utility of ventricular measurements in tracking brain disorders and maintaining brain health across the lifespan.

Unravelling Robust Brain-Behavior Links of Depressive Symptoms Through Granular Network Models: Understanding Heterogeneity and Clinical Implications.

Background: Depressive symptoms are highly prevalent, present in heterogeneous symptom patterns, and share diverse neurobiological underpinnings. Understanding the links between psychopathological symptoms and biological factors is critical in elucidating its etiology and persistence. We aimed to evaluate the utility of using symptom-brain networks to parse the heterogeneity of depressive symptomatology in a large adolescent sample. Methods: We used data from the third wave of the IMAGEN study, a multi-center panel cohort study involving 1,317 adolescents (52.49% female, mean±SD age=18.5±0.72). Two network models were estimated: one including an overall depressive symptom severity sum score based on the Adolescent Depression Rating Scale (ADRS), and one incorporating individual ADRS symptom/item scores. Both networks included measures of cortical thickness in several regions (insula, cingulate, mOFC, fusiform gyrus) and hippocampal volume derived from neuroimaging. Results: The network based on individual symptom scores revealed associations between cortical thickness measures and specific symptoms, obscured when using an aggregate depression severity score. Notably, the insula's cortical thickness showed negative associations with cognitive dysfunction (partial cor.=-0.15); the cingulate's cortical thickness showed negative associations with feelings of worthlessness (partial cor. = -0.10), and mOFC was negatively associated with anhedonia (partial cor. = -0.05). Limitations: This cross-sectional study included participants who were relatively healthy and relied on the self-reported assessment of depression symptoms. Conclusions: This study showcases the utility of network models in parsing heterogeneity in depressive symptoms, linking individual symptoms to specific neural substrates. We outline the next steps to integrate neurobiological and cognitive markers to unravel MDD's phenotypic heterogeneity.

Factors affecting the labelling accuracy of brain MRI studies relevant for deep learning abnormality detection.

Unlocking the vast potential of deep learning-based computer vision classification systems necessitates large data sets for model training. Natural Language Processing (NLP)-involving automation of dataset labelling-represents a potential avenue to achieve this. However, many aspects of NLP for dataset labelling remain unvalidated. Expert radiologists manually labelled over 5,000 MRI head reports in order to develop a deep learning-based neuroradiology NLP report classifier. Our results demonstrate that binary labels (normal vs. abnormal) showed high rates of accuracy, even when only two MRI sequences (T2-weighted and those based on diffusion weighted imaging) were employed as opposed to all sequences in an examination. Meanwhile, the accuracy of more specific labelling for multiple disease categories was variable and dependent on the category. Finally, resultant model performance was shown to be dependent on the expertise of the original labeller, with worse performance seen with non-expert vs. expert labellers.

Structural neurodevelopment at the individual level - a life-course investigation using ABCD, IMAGEN and UK Biobank data.

Adolescents exhibit remarkable heterogeneity in the structural architecture of brain development. However, due to the lack of large-scale longitudinal neuroimaging studies, existing research has largely focused on population averages and the neurobiological basis underlying individual heterogeneity remains poorly understood. Using structural magnetic resonance imaging from the IMAGEN cohort (n=1,543), we show that adolescents can be clustered into three groups defined by distinct developmental patterns of whole-brain gray matter volume (GMV). Genetic and epigenetic determinants of group clustering and long-term impacts of neurodevelopment in mid-to-late adulthood were investigated using data from the ABCD, IMAGEN and UK Biobank cohorts. Group 1, characterized by continuously decreasing GMV, showed generally the best neurocognitive performances during adolescence. Compared to Group 1, Group 2 exhibited a slower rate of GMV decrease and worsened neurocognitive development, which was associated with epigenetic changes and greater environmental burden. Further, Group 3 showed increasing GMV and delayed neurocognitive development during adolescence due to a genetic variation, while these disadvantages were attenuated in mid-to-late adulthood. In summary, our study revealed novel clusters of adolescent structural neurodevelopment and suggested that genetically-predicted delayed neurodevelopment has limited long-term effects on mental well-being and socio-economic outcomes later in life. Our results could inform future research on policy interventions aimed at reducing the financial and emotional burden of mental illness.

Treatment resistance NMDA receptor pathway polygenic score is associated with brain glutamate in schizophrenia.

Dysfunction of glutamate neurotransmission has been implicated in the pathophysiology of schizophrenia and may be particularly relevant in severe, treatment-resistant symptoms. The underlying mechanism may involve hypofunction of the NMDA receptor. We investigated whether schizophrenia-related pathway polygenic scores, composed of genetic variants within NMDA receptor encoding genes, are associated with cortical glutamate in schizophrenia. Anterior cingulate cortex (ACC) glutamate was measured in 70 participants across 4 research sites using Proton Magnetic Resonance Spectroscopy (1H-MRS). Two NMDA receptor gene sets were sourced from the Molecular Signatories Database and NMDA receptor pathway polygenic scores were constructed using PRSet. The NMDA receptor pathway polygenic scores were weighted by single nucleotide polymorphism (SNP) associations with treatment-resistant schizophrenia, and associations with ACC glutamate were tested. We then tested whether NMDA receptor pathway polygenic scores with SNPs weighted by associations with non-treatment-resistant schizophrenia were associated with ACC glutamate. A higher NMDA receptor complex pathway polygenic score was significantly associated with lower ACC glutamate (ß = -0.25, 95 % CI = -0.49, -0.02, competitive p = 0.03). When SNPs were weighted by associations with non-treatment-resistant schizophrenia, there was no association between the NMDA receptor complex pathway polygenic score and ACC glutamate (ß = 0.05, 95 % CI = -0.18, 0.27, competitive p = 0.79). These results provide initial evidence of an association between common genetic variation implicated in NMDA receptor function and ACC glutamate levels in schizophrenia. This association was specific to when the NMDA receptor complex pathway polygenic score was weighted by SNP associations with treatment-resistant schizophrenia.

Covariation of preadult environmental exposures, adult brain imaging phenotypes, and adult personality traits.

Exposure to preadult environmental exposures may have long-lasting effects on mental health by affecting the maturation of the brain and personality, two traits that interact throughout the developmental process. However, environment-brain-personality covariation patterns and their mediation relationships remain unclear. In 4297 healthy participants (aged 18-30 years), we combined sparse multiple canonical correlation analysis with independent component analysis to identify the three-way covariation patterns of 59 preadult environmental exposures, 760 adult brain imaging phenotypes, and five personality traits, and found two robust environment-brain-personality covariation models with sex specificity. One model linked greater stress and less support to weaker functional connectivity and activity in the default mode network, stronger activity in subcortical nuclei, greater thickness and volume in the occipital, parietal and temporal cortices, and lower agreeableness, consciousness and extraversion as well as higher neuroticism. The other model linked higher urbanicity and better socioeconomic status to stronger functional connectivity and activity in the sensorimotor network, smaller volume and surface area and weaker functional connectivity and activity in the medial prefrontal cortex, lower white matter integrity, and higher openness to experience. We also conducted mediation analyses to explore the potential bidirectional mediation relationships between adult brain imaging phenotypes and personality traits with the influence of preadult environmental exposures and found both environment-brain-personality and environment-personality-brain pathways. We finally performed moderated mediation analyses to test the potential interactions between macro- and microenvironmental exposures and found that one category of exposure moderated the mediation pathways of another category of exposure. These results improve our understanding of the effects of preadult environmental exposures on the adult brain and personality traits and may facilitate the design of targeted interventions to improve mental health by reducing the impact of adverse environmental exposures.

A prospective 5-year longitudinal study detects neurocognitive and imaging correlates of seizure remission in self-limiting Rolandic epilepsy.

OBJECTIVE: Self-limiting Rolandic epilepsy (RE) is the most common epilepsy in school-age children. Seizures are generally infrequent, but cognitive, language, and motor coordination problems can significantly impact the child's life. To better understand brain structure and function changes in RE, we longitudinally assessed neurocognition, cortical thickness, and subcortical volumes. METHODS: At baseline, we recruited 30 participants diagnosed with RE and 24-healthy controls and followed up for 4.94 ± 0.8 years when the participants with RE were in seizure remission. Measures included were as follows: T1-weighted magnetic resonance brain imaging (MRI) with FreeSurfer analysis and detailed neuropsychological assessments. MRI and neuropsychological data were compared between baseline and follow-up in seizure remission. RESULTS: Longitudinal MRI revealed excess cortical thinning in the left-orbitofrontal (p = 0.0001) and pre-central gyrus (p = 0.044). There is a significant association (p = 0.003) between a reduction in cortical thickness in the left-orbitofrontal cluster and improved processing of filtered words. Longitudinal neuropsychology revealed significant improvements in the symptoms of developmental coordination disorder (DCD, p = 0.005) in seizure remission. CONCLUSIONS: There is evidence for altered development of neocortical regions between active seizure state and seizure remission in RE within two clusters maximal in the left-orbitofrontal and pre-central gyrus. There is significant evidence for improvement in motor coordination between active seizures and seizure remission and suggestive evidence for a decline in fluid intelligence and gains in auditory processing.