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Cohen-Gilbert, J. E.

Publications and source records attributed to Cohen-Gilbert, J. E..

2 recordsLinked to original sources

Cognition-centric brain activity across diverse imaging tasks constrains the representation of mental health in task-evoked brain function

Large-scale neuroimaging studies best powered to investigate mental health biomarkers continue to administer a set of in-scanner tasks that, to date, yield weak brain-mental health associations. Triangulating the source of these weak associations is critical to directing the focus of future work. Using these tasks strong brain-cognition associations as a positive control, we analyzed potential sources of weak brain-mental health associations across five popular fMRI tasks and dozens of task-evoked brain networks. Across tasks and networks, latent mental health factors showed substantially weaker brain associations than cognition despite comparable measurement reliability and greater interindividual variability. Moreover, increasing brain-feature dimensionality improved cognitive but not mental health signal, and task-evoked network profiles were 1.8-2.3 times more homogeneous and 3.5 times lower-dimensional than mental health profiles. Findings implicate a signal evocation bottleneck: common fMRI tasks elicit little interindividual variation in mental health-related brain activity relative to the diversity of mental health itself. Overall, these findings motivate a shift in focus to the development and use of tasks in large-scale studies more explicitly designed to evoke mental health-relevant brain activity. Significance StatementIdentification of robust individual-specific mental health-related brain signals from fMRI remains elusive, limiting the clinical utility of brain imaging. To investigate why, we systematically evaluated brain signals evoked by tasks used by the Human Connectome Project-Young Adult study, which are now commonly included in many large-scale neuroimaging studies. Our findings suggest that, more so than analytical or measurement constraints on detecting these signals, a primary limitation is a failure of these prevalent fMRI tasks to evoke mental health-relevant brain signals in the first place. These findings suggest that progress in psychiatric neuroimaging may require tasks more explicitly designed to evoke mental health-relevant brain activity.

neuroscience↗

Concurrent large-scale brain dynamics during the emotional face matching task and their relation to behavior and mental health

Prior investigations of emotion processings neural underpinnings rely on a priori models of brain response, obscuring detection of task-relevant neurobiological processes with complex temporal dynamics. To overcome this limitation, we applied unsupervised machine learning to functional magnetic resonance imaging data acquired during the emotional face matching task (EFMT) in healthy young adults from the Human Connectome Project (n=413; n=416 replication). Tensorial independent component analysis showed that the EFMT engages 10 large-scale brain networks - each recruiting visual association cortex in distinct temporal fashions and in tandem with diverse non-visual regions - that collectively recruit 74% of cortex, posterior cerebellum, and amygdala. Despite prominent use of the EFMT to probe negative affect and related psychopathology, EFMT-recruited networks strongly reflected individual differences in cognition but not internalizing/negative affect. Overall, we characterize a richer-than-expected tapestry of concurrent EFMT-recruited brain processes, their diverse activation dynamics, and their relations to task performance and latent mental health phenotypes.

neuroscience↗