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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.

SUBMITTER: Taylor JJ

PROVIDER: S-EPMC8032870 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature

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

Taylor Joseph J JJ Kurt Hatice Guncu HG Anand Amit A

Frontiers in psychiatry 20210326

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 hypothesiz ...[more]

PMID: 33841196

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Project description:Resting-state functional connectivity (RSFC) is altered across various psychiatric disorders. Brain network modeling (BNM) has the potential to reveal the neurobiological underpinnings of such abnormalities by dynamically modeling the structure-function relationship and examining biologically relevant parameters after fitting the models with real data. Although innovative BNM approaches have been developed, two main issues need to be further addressed. First, previous BNM approaches are primarily limited to simulating noise-driven dynamics near a chosen attractor (or a stable brain state). An alternative approach is to examine multi(or cross)-attractor dynamics, which can be used to better capture non-stationarity and switching between states in the resting brain. Second, previous BNM work is limited to characterizing one disorder at a time. Given the large degree of co-morbidity across psychiatric disorders, comparing BNMs across disorders might provide a novel avenue to generate insights regarding the dynamical features that are common across (vs. specific to) disorders. Here, we address these issues by (1) examining the layout of the attractor repertoire over the entire multi-attractor landscape using a recently developed cross-attractor BNM approach; and (2) characterizing and comparing multiple disorders (schizophrenia, bipolar, and ADHD) with healthy controls using an openly available and moderately large multimodal dataset from the UCLA Consortium for Neuropsychiatric Phenomics. Both global and local differences were observed across disorders. Specifically, the global coupling between regions was significantly decreased in schizophrenia patients relative to healthy controls. At the same time, the ratio between local excitation and inhibition was significantly higher in the schizophrenia group than the ADHD group. In line with these results, the schizophrenia group had the lowest switching costs (energy gaps) across groups for several networks including the default mode network. Paired comparison also showed that schizophrenia patients had significantly lower energy gaps than healthy controls for the somatomotor and visual networks. Overall, this study provides preliminary evidence supporting transdiagnostic multi-attractor BNM approaches to better understand psychiatric disorders' pathophysiology.

Project description:Anxiety disorders (AD) are associated with altered connectivity in large-scale intrinsic brain networks. It remains uncertain how much these signatures overlap across different phenotypes due to a lack of well-powered cross-disorder comparisons. We used resting-state functional magnetic resonance imaging (rsfMRI) to investigate differences in functional connectivity (FC) in a cross-disorder sample of AD patients and healthy controls (HC). Before treatment, 439 patients from two German multicenter clinical trials at eight different sites fulfilling a primary diagnosis of panic disorder and/or agoraphobia (PD/AG, N = 154), social anxiety disorder (SAD, N = 95), or specific phobia (SP, N = 190) and 105 HC underwent an 8 min rsfMRI assessment. We performed categorical and dimensional regions of interest (ROI)-to-ROI analyses focusing on connectivity between regions of the defensive system and prefrontal regulation areas. AD patients showed increased connectivity between the insula and the thalamus compared to controls. This was mainly driven by PD/AG patients who showed increased (insula/hippocampus/amygdala-thalamus) and decreased (dorsomedial prefrontal cortex/periaqueductal gray-anterior cingulate cortex) positive connectivity between subcortical and cortical areas. In contrast, SAD patients showed decreased negative connectivity exclusively in cortical areas (insula-orbitofrontal cortex), whereas no differences were found in SP patients. State anxiety associated with the scanner environment did not explain the FC between these regions. Only PD/AG patients showed pronounced connectivity changes along a widespread subcortical-cortical network, including the midbrain. Dimensional analyses yielded no significant results. The results highlighting categorical differences between ADs at a systems neuroscience level are discussed within the context of personalized neuroscience-informed treatments. PROTECT-AD's registration at NIMH Protocol Registration System: 01EE1402A and German Register of Clinical Studies: DRKS00008743. SpiderVR's registration at ClinicalTrials.gov: NCT03208400.

Dataset Information

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

Publications

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

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