Resting State Functional Connectivity Biomarkers of Treatment Response in Mood Disorders: A Review

Abstract

There are currently no validated treatment biomarkers in psychiatry. Resting State Functional Connectivity (RSFC) is a popular method for investigating the neural correlates of mood disorders, but the breadth of the field makes it difficult to assess progress toward treatment response biomarkers. In this review, we followed general PRISMA guidelines to evaluate the evidence base for mood disorder treatment biomarkers across diagnoses, brain network models, and treatment modalities. We hypothesized that no treatment biomarker would be validated across these domains or with independent datasets. Results are organized, interpreted, and discussed in the context of four popular analytic techniques: (1) reference region (seed-based) analysis, (2) independent component analysis, (3) graph theory analysis, and (4) other methods. Cortico-limbic connectivity is implicated across studies, but there is no single biomarker that spans analyses or that has been replicated in multiple independent datasets. We discuss RSFC limitations and future directions in biomarker development.

Synaptic plasticity and mental health: methods, challenges and opportunities

“…Epigenetic markers are also useful to predict drug efficacy such as the absence of methylated exon 4 of BDNF which is associated with reduced response to anti-depressants [ 89 ]. Not only would it be informative to have biomarkers of treatment response, but also of treatment progression [ 90 ]. To address some of the concerns with regards to disease heterogeneity and causes of dysregulation, it is crucial to have a better biological understanding of healthy neuroplasticity and how it can be dysregulated.…”

Computational approaches to treatment response prediction in major depression using brain activity and behavioral data: A systematic review

“…Importantly, changes in each of these networks have been related to different symptoms (e.g., anhedonia being more associated with the reward network, and rumination with the DMN) and thus might differentially respond to treatments (Li et al, 2018;Chahal et al, 2020). Despite the increasing understanding of the neural mechanisms underlying MDD, recent review articles on regional activation and functional connectivity measures suggest however a lack of reliable neuromarkers for treatment response prediction (Fonseka et al, 2018;Taylor et al, 2021), which highlights the need for methodological advancements.…”

“…We restrict our review to studies that performed explicit analysis of treatment response prediction and incorporated validation techniques (e.g., cross validation or hold-out validation) to increase the generalizability of findings. Studies that investigated associations between various neuromarkers and treatment response but did not use an out-of-sample validation to assess their predictive power are not discussed (for these studies, see recent reviews: Taylor et al, 2021;Kang and Cho, 2020;Fonseka et al, 2018;Dichter et al, 2015;Phillips et al, 2015;Olbrich and Arns, 2013). Additionally, in the interest of brevity, studies with sample sizes of 30 and below will not be discussed due to their limited generalizability (see Tables 1 and 2 for study summaries).…”

Connectomic disturbances underlying insomnia disorder and predictors of treatment response

“…Additionally, ID is associated with mood disorders (e.g., anxiety, depression) and neurodegenerative disorders (e.g., Parkinson’s disease and dementia). Even if sufferers experience insomnia alone, they have an increased risk of developing both physical and mental health problems ( Taylor et al, 2021 ). Furthermore, compared to good sleepers, ID sufferers experience poor daytime performance and poor cognitive function, with subsequent impairment in quality of life and well-being ( Fortier-Brochu et al, 2012 ).…”