Gunning, F. | bioRxiv Science

bioRxiv2023

Expansion of a frontostriatal salience network in individuals with depression

Hundreds of neuroimaging studies spanning two decades have revealed differences in brain structure and functional connectivity in depression, but with modest effect sizes, complicating efforts to derive mechanistic pathophysiologic insights or develop biomarkers.1 Furthermore, although depression is a fundamentally episodic condition, few neuroimaging studies have taken a longitudinal approach, which is critical for understanding cause and effect and delineating mechanisms that drive mood state transitions over time. The emerging field of precision functional mapping using densely-sampled longitudinal neuroimaging data has revealed unexpected, functionally meaningful individual differences in brain network topology in healthy individuals,2-5 but these approaches have never been applied to individuals with depression. Here, using precision functional mapping techniques and 11 datasets comprising n=187 repeatedly sampled individuals and >21,000 minutes of fMRI data, we show that the frontostriatal salience network is expanded two-fold in most individuals with depression. This effect was replicable in multiple samples, including large-scale, group-average data (N=1,231 subjects), and caused primarily by network border shifts affecting specific functional systems, with three distinct modes of encroachment occurring in different individuals. Salience network expansion was unexpectedly stable over time, unaffected by changes in mood state, and detectable in children before the subsequent onset of depressive symptoms in adolescence. Longitudinal analyses of individuals scanned up to 62 times over 1.5 years identified connectivity changes in specific frontostriatal circuits that tracked fluctuations in specific symptom domains and predicted future anhedonia symptoms before they emerged. Together, these findings identify a stable trait-like brain network topology that may confer risk for depression and mood-state dependent connectivity changes in frontostriatal circuits that predict the emergence and remission of depressive symptoms over time.

neuroscience↗

bioRxiv2018

Functional and Optogenetic Approaches to Discovering Stable Subtype-Specific Circuit Mechanisms in Depression

BackgroundUsing canonical correlation analysis (CCA), hierarchical clustering, and machine learning methods, we recently identified four subtypes of depression defined by distinct patterns of abnormal functional connectivity in depression-related brain networks, which in turn predicted differing clinical symptom profiles and individual differences in treatment response. However, whether and how dysfunction in specific circuits may give rise to specific depressive symptoms and behaviors remains unclear. Furthermore, this approach assumes that there are robust and stable canonical correlations between functional connectivity and depressive symptoms--an assumption that was not extensively tested in our earlier work.\n\nMethodsFirst, we comprehensively re-evaluate the stability of canonical correlations between functional connectivity and symptoms, using optimized approaches for large-scale statistical testing, and we validate methods for improving stability. Next, we illustrate one approach to formulating hypotheses regarding subtype-specific circuit mechanisms driving depressive symptoms and behaviors and then testing them in animal models using optogenetic fMRI. We review recent work in this field and describe one example of this approach.\n\nResultsCorrelations between connectivity features and clinical symptoms are robustly significant, and CCA solutions tested repeatedly on held-out data generalize, but they are sensitive to data quality, preprocessing decisions, and clinical sample heterogeneity, which can reduce effect sizes. Generalization can be markedly improved by adding L2-regularization to CCA, which decreases variance, increases canonical correlations in left-out data, and stabilizes feature selection. This approach, in turn, can be used to identify candidate circuits for optogenetic interrogation in rodent models.\n\nConclusionsMulti-view approaches like CCA are a conceptually useful framework for discovering stable patient subtypes by synthesizing multiple clinical and functional measures. Optogenetic fMRI holds substantial promise for testing hypotheses regarding subtype-specific mechanisms driving specific symptoms and behaviors in depression.

neuroscience↗