ArticleFrontiers in behavioral neuroscience2020
Individualized Prediction of PTSD Symptom Severity in Trauma Survivors From Whole-Brain Resting-State Functional Connectivity.
Article in Frontiers in behavioral neuroscience, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed, 25 citations in OpenAlex.
- Re-experiencing trauma: Occipital lobe resting-state functional connectivity in posttraumatic stress disorder.Psychiatry research. Neuroimaging · 2026Article
- Gray Matter Morphological Networks are Associated with Neurobiological Features, Cognitive Status and Clinical Recovery in Traumatic Brain Injury.medRxiv : the preprint server for health sciences · 2026Article
- Connectome-Based Predictive Modeling of PTSD Development Among Recent Trauma Survivors.JAMA network open · 2025Article
- Affective Visual Circuit Dysfunction in Trauma and Stress-Related Disorders.Biological psychiatry · 2025Review
- Brain dynamics reflecting an intra-network brain state is associated with increased posttraumatic stress symptoms in the early aftermath of trauma.Nature. Mental health · 2025Article
- Exploring the impact of biological sex on intrinsic connectivity networks in PTSD: A data-driven approach.Progress in neuro-psychopharmacology & biological psychiatry · 2025Article
- A systematic review of machine learning findings in PTSD and their relationships with theoretical models.Nature. Mental health · 2025Article
- Machine learning applied to fMRI patterns of brain activation in response to mutilation pictures predicts PTSD symptoms.BMC psychiatry · 2023Article
- A tale of two targets: examining the differential effects of posterior cingulate cortex- and amygdala-targeted fMRI-neurofeedback in a PTSD pilot study.Frontiers in neuroscience · 2023Article
- Connectome-based predictive models using resting-state fMRI for studying brain aging.Experimental brain research · 2022Article
- Article
- The cerebellum and psychological trauma: A systematic review of neuroimaging studies.Neurobiology of stress · 2022Review
- Hippocampal Resting-State Functional Connectivity Forecasts Individual Posttraumatic Stress Disorder Symptoms: A Data-Driven Approach.Biological psychiatry. Cognitive neuroscience and neuroimaging · 2022Article
- Flights and Perchings of the BrainMind: A Temporospatial Approach to Psychotherapy.Frontiers in psychology · 2022Article
Corrections and comments
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Authors and funding
7 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Previous studies have demonstrated relations between spontaneous neural activity evaluated by resting-state functional magnetic resonance imaging (fMRI) and symptom severity in post-traumatic stress disorder. However, few studies have used brain-based measures to identify imaging associations with illness severity at the level of individual patients. This study applied connectome-based predictive modeling (CPM), a recently developed data-driven and subject-level method, to identify brain function features that are related to symptom severity of trauma survivors. Resting-state fMRI scans and clinical ratings were obtained 10-15 months after the earthquake from 122 earthquake survivors. Symptom severity of post-traumatic stress disorder features for each survivor was evaluated using the Clinician Administered Post-traumatic Stress Disorder Scale (CAPS-IV). A functionally pre-defined atlas was applied to divide the human brain into 268 regions. Each individual's functional connectivity 268 × 268 matrix was created to reflect correlations of functional time series data across each pair of nodes. The relationship between CAPS-IV scores and brain functional connectivity was explored in a CPM linear model. Using a leave-one-out cross-validation (LOOCV) procedure, findings showed that the positive network model predicted the left-out individual's CAPS-IV scores from resting-state functional connectivity. CPM predicted CAPS-IV scores, as indicated by a significant correspondence between predicted and actual values (
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