ArticleBehavioural brain research2022
Resting-state neural signal variability in women with depressive disorders.
Article in Behavioural brain research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.
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Who cites it
8 citing papers in PubMed, 1 synthesis or guideline pooled it, 11 citations in OpenAlex.
- Neural correlates of peripartum depression: a systematic review, meta-analysis and comparison to major depressive disorder.Molecular psychiatry · 2025Pooled it
- Effects of Cortisol Administration on Resting-State Functional Connectivity in Women with Depression.Psychiatry research. Neuroimaging · 2024Trial
- The mediating effect of neuroticism on the association between brain signal variability in the prefrontal cortex and depression among healthy individuals.Brain imaging and behavior · 2026Article
- Subtype-specific enhancement of implicit statistical learning in migraine: insights from BOLD signal variability.The journal of headache and pain · 2026Article
- Spontaneous brain regional dynamics contribute to generalizable brain-behaviour associations.Nature human behaviour · 2026Article
- Computational cognitive mechanisms of visual working memory in major depressive disorder and sex differences.BMC psychology · 2025Article
- Neural signal variability relates to maladaptive rumination in depression.Journal of psychiatric research · 2022Article
- Effects of repetitive transcranial magnetic stimulation on individual variability of resting-state functional connectivity in major depressive disorder.Journal of psychiatry & neuroscience : JPNArticle
Corrections and comments
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Authors and funding
6 authors at 3 institutions in 1 country.
Funding
Abstract
Aberrant activity and connectivity in default mode (DMN), frontoparietal (FPN), and salience (SN) network regions is well-documented in depression. Recent neuroimaging research suggests that altered variability in the blood oxygen level-dependent (BOLD) signal may disrupt normal network integration and be an important novel predictor of psychopathology. However, no studies have yet determined the relationship between resting-state BOLD signal variability and depressive disorders nor applied BOLD signal variability features to the classification of depression history using machine learning (ML). We collected resting-state fMRI data for 79 women with different depression histories: no history, past history, and current depressive disorder. We tested voxelwise differences in BOLD signal variability related to depression group and severity. We also investigated whether BOLD signal variability of DMN, FPN, and SN regions could predict depression history group using a supervised random forest ML model. Results indicated that individuals with any history of depression had significantly decreased BOLD signal variability in the left and right cerebellum and right parietal cortex (p
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.