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While one can characterize mental health using questionnaires, such tools do not provide direct insight into the underlying biology. By linking approaches that visualize brain activity to questionnaires in the context of individualized prediction, we can gain new insights into the biology and behavioral aspects of brain health. Resting-state fMRI (rs-fMRI) can be used to identify biomarkers of these conditions and study patterns of abnormal connectivity. In this work, we estimate mental health quality for individual participants using static functional network connectivity (sFNC) data from rs-fMRI. The deep learning model uses the sFNC data as input to predict four categories of mental health quality and visualize the neural patterns indicative of each group. We used guided gradient class activation maps (guided Grad-CAM) to identify the most discriminative sFNC patterns. The effectiveness of this model was validated using the UK Biobank dataset, in which we showed that our approach outperformed four alternative models by 4-18% accuracy. The proposed model’s performance evaluation yielded a classification accuracy of 76%, 78%, 88%, and 98% for the excellent, good, fair, and poor mental health categories, with poor mental health accuracy being the highest. The findings show distinct sFNC patterns across each group. The patterns associated with excellent mental health consist of the cerebellar-subcortical regions, whereas the most prominent areas in the poor mental health category are in the sensorimotor and visual domains. Thus the combination of rs-fMRI and deep learning opens a promising path for developing a comprehensive framework to evaluate and measure mental health. Moreover, this approach had the potential to guide the development of personalized interventions and enable the monitoring of treatment response. Overall this highlights the crucial role of advanced imaging modalities and deep learning algorithms in advancing our understanding and management of mental health. 

Abstract ObjectivesBlack older adults have a higher vascular burden compared to non‐Hispanic White (NHW) older adults, which may put them at risk for a form of depression known as vascular depression (VaDep). The literature examining VaDep in Black older adults is sparse. The current study addressed this important gap by examining whether vascular burden was associated with depressive symptoms in Black older adults. MethodsParticipants included 113 Black older adults from the Healthy Brain Project, a substudy of the Health, Aging, and Body Composition Study. In multiple regression analyses, clinical vascular burden (sum of vascular conditions) and white matter hyperintensity (WMH) volume predicted depressive symptoms as measured by the Center for Epidemiologic Studies Depression Scale, controlling for demographic variables. Follow‐up analyses compared the associations in the Black subsample and in 179 NHW older adults. ResultsHigher total WMH volume, but not clinically‐defined vascular burden, predicted higher concurrent depressive symptoms and higher average depressive symptoms over 4 years. Similar associations were found between uncinate fasciculus (UF) WMHs and concurrent depressive symptoms and between superior longitudinal fasciculus WMHs and average depressive symptoms. The association between depressive symptoms and UF WMH was stronger in Black compared to NHW individuals. ConclusionThis research is consistent with the VaDep hypothesis and extends it to Black older adults, a group that has historically been underrepresented in the literature. Results highlight WMH in the UF as particularly relevant to depressive symptoms in Black older adults and suggest this group may be particularly vulnerable to the negative effects of WMH.