Numerous genetic and environmental factors contribute to psychiatric disorders and other brain disorders. Common risk factors likely converge on biological pathways regulating the optimization of brain structure and function across the lifespan. Here, using structural magnetic resonance imaging and machine learning, we estimated the gap between brain age and chronological age in 36,891 individuals aged 3 to 96 years, including individuals with different brain disorders. We show that several disorders are associated with accentuated brain aging, with strongest effects in schizophrenia, multiple sclerosis and dementia, and document differential regional patterns of brain age gaps between disorders. In 16,269 healthy adult individuals, we show that brain age gap is heritable with a polygenic architecture overlapping those observed in common brain disorders. Our results identify brain age gap as a genetically modulated trait that offers a window into shared and distinct mechanisms in different brain disorders.

Cognitive abilities and mental disorders are complex traits sharing a largely unknown neuronal basis and aetiology. Their genetic architectures are highly polygenic and overlapping, which is supported by heterogeneous phenotypic expression and substantial clinical overlap. Brain network analysis provides a non-invasive means of dissecting biological heterogeneity yet its sensitivity, specificity and validity in clinical applications remains a major challenge. We used machine learning on static and dynamic temporal synchronization between all brain network nodes in 10,343 healthy individuals from the UK Biobank to predict (i) cognitive and mental health traits and (ii) their genetic underpinnings.We predicted age and sex to serve as our reference point. The traits of interest included individual level educational attainment and fluid intelligence (cognitive) and dimensional measures of depression, anxiety, and neuroticism (mental health). We predicted polygenic scores for educational attainment, fluid intelligence, depression, anxiety, and different neuroticism traits, in addition to schizophrenia. Beyond high accuracy for age and sex, permutation tests revealed above chance-level prediction accuracy for educational attainment and fluid intelligence. Educational attainment and fluid intelligence were mainly negatively associated with static brain connectivity in frontal and default mode networks, whereas age showed positive correlations with a more widespread pattern. In comparison, prediction accuracy for polygenic scores was at chance level across traits, which may serve as a benchmark for future studies aiming to link genetic factors and fMRI-based brain connectomics. SignificanceAlthough cognitive abilities and susceptibility to mental disorders reflect individual differences in brain function, neuroimaging is yet to provide a coherent account of the neuronal underpinnings. Here, we aimed to map the brain functional connectome of (i) cognitive and mental health traits and (ii) their polygenic architecture in a large populationbased sample. We discovered high prediction accuracy for age and sex, and above-chance accuracy for educational attainment and intelligence (cognitive). In contrast, accuracies for dimensional measures of depression, anxiety and neuroticism (mental health), and polygenic scores across traits, were at chance level. These findings support the link between cognitive abilities and brain connectomics and provide a reference for studies mapping the brain connectomics of mental disorders and their genetic architectures.

Fatigue and emotional distress rank high among self-reported unmet needs in stroke survivors. Currently, few treatment options exist for post stroke fatigue, a condition frequently associated with depression. Non-invasive brain stimulation techniques such as transcranial direct current stimulation (tDCS) have shown promise in alleviating fatigue and depression in other patient groups, but the acceptability and effects for chronic phase stroke survivors are not established. Here, we used a randomized sham-controlled design to evaluate the added effect of tDCS combined with computerized cognitive training to alleviate symptoms of fatigue and depression. 74 patients were enrolled at baseline (mean time since stroke = 26 months) and 54 patients completed the intervention. Self-report measures of fatigue and depression were collected at five consecutive timepoints, spanning a period of two months. While fatigue and depression severity were reduced during the course of the intervention, Bayesian analyses provided evidence for no added effect of tDCS. Less severe symptoms of fatigue and depression were associated with higher improvement rate in select tasks, and study withdrawal was higher in patients with more severe fatigue and younger age. Time-resolved analyses of individual symptoms by a network-approach suggested overall higher centrality of fatigue symptoms (except item 1 and 2) than depression symptoms. In conclusion, the results support the notion of fatigue as a significant stroke sequela with possible implications for treatment adherence and response, but reveal no effect of tDCS on fatigue or depression.