Expansion of a frontostriatal salience network in individuals with depression

Abstract

Hundreds of neuroimaging studies spanning two decades have revealed only modest differences in brain structure and functional connectivity in depression, complicating efforts to derive mechanistic pathophysiologic insights or develop biomarkers. 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, 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 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.

One size does not fit all: notable individual variation in brain activity correlates of antidepressant treatment response

“…In the field of neuroimaging, select studies have already shown that stable individual-specific fMRI characteristics are associated with cognitive functioning and clinical symptoms ( 76 – 78 ). A recent preprint further indicates that individual characteristics of the salience network can be related to the development, presence and fluctuation of MD symptoms ( 79 ). Research on the use of EEG as a biometric to identify individuals suggests that similar stable individual-specific EEG characteristics exist (e.g., 80 , 81 ).…”

Framewise multi-echo distortion correction for superior functional MRI

“…Recent neuroimaging breakthroughs, such as the discovery of the somato-cognitive action network (SCAN), in the central sulcus, which was previously thought to be the exclusive domain of effector-specific primary motor cortex [16], utilized ME-fMRI data. ME-fMRI was also used to discover that the ventromedial prefrontal cortex (vmPFC), a region plagued by massive distortions, includes an enlarged salience network node in depression patients [28]. Similarly, ME-fMRI was able to identify individual-specific persistent brain changes after a single dose of the psychedelic psilocybin [29].…”

“…Therefore, group-averaging partially obscures the greater spatial precision obtainable with ME-fMRI and MEDIC. Hence, it may not be a coincidence that several strong proponents of ME-fMRI have been using it for PFM, through which greater confidence in spatial details can be directly converted into neuroscientific insights [15, 16,28,29,38]. If the goal is individual-specific PFM, then ME-fMRI and MEDIC improve SNR and distortion correction, with the minor cost of slightly longer data processing times and increase in TR.…”

Large Individual Differences in Functional Connectivity in the Context of Major Depression and Antidepressant Pharmacotherapy

“…In neurotypical individuals, common and stable individual features were the largest contributors to the variance in fMRI functional connectivity (FC) across sessions and tasks ( Gratton et al, 2018 ). In addition, such stable individual features may relate to cognition and clinical symptoms ( Finn et al, 2015 ; Gordon et al, 2018 ; D. Wang et al, 2020 ; Lynch et al, 2023 ). These findings have led to calls for nuanced examinations of individual differences and commonalities in psychiatric populations like MD ( Etkin, 2019 ; Gratton et al, 2020 ).…”

“…The only other deep phenotyping study examining MD suggests that individual differences in salience network topology can be linked to depressive symptomatology and may confer depression risk ( Lynch et al, 2023 , preprint). Deep phenotyping in neurotypical samples also highlights stable individual differences that are greater in association networks such as the salience network and less in primary sensorimotor networks ( Mueller et al, 2013 ; Chen et al, 2015 ; Gratton et al, 2018 ; Kong et al, 2019 ; Seitzman et al, 2019 ).…”