ArticleMolecular psychiatry2021
Resolving heterogeneity in schizophrenia through a novel systems approach to brain structure: individualized structural covariance network analysis.
Article in Molecular psychiatry, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 70 papers, 2 of them syntheses that pooled it.
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Who cites it
70 citing papers in PubMed, 2 syntheses or guidelines pooled it, 145 citations in OpenAlex.
- Structural covariance networks in schizophrenia: A systematic review Part I.Schizophrenia research · 2022Pooled it
- Structural covariance networks in schizophrenia: A systematic review Part II.Schizophrenia research · 2022Pooled it
- Neuroimaging-Based Subgroups in Schizophrenia: A Critical Appraisal of Clustering Studies.Biological psychiatry global open science · 2026Review
- Brain Structural Covariance Networks in Long-Term First-Person Shooter and Multiplayer Online Battle Arena Players: Cross-Sectional Study.JMIR serious games · 2026Article
- Individualized cortical gradient and network topology reveal symptom-linked disruptions and neurobiological subtypes in schizophrenia.medRxiv : the preprint server for health sciences · 2026Article
- Personalized mapping of body homeostasis using whole-body PET connectomics and routine FDG PET imaging.Communications medicine · 2026Article
- Normative age-related structural brain deviations underlying psychopathology, cognitive impairment and neurological soft signs in schizophrenia spectrum disorders.Translational psychiatry · 2026Article
- Orbitofrontal Thickness and Network Associations as Transdiagnostic Signature of Amotivation Along the Bipolar-Schizophrenia Spectrum.Schizophrenia bulletin · 2026Article
- Mapping neurophysiological biotypes of postpartum depression and underlying neural and molecular basis.Communications medicine · 2026Article
- Tracking early cognitive decline in preclinical AD with brain MRI similarity.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2026Article
- Data-driven glycolipid network phenotypes reveal a graded risk spectrum for cardiometabolic multimorbidity: a prospective study in middle-aged and older Chinese adults.Lipids in health and disease · 2026Article
- Similar minds age alike: an MRI similarity approach for predicting age-related cognitive decline.npj aging · 2026Article
- GABAMolecular psychiatry · 2026Article
- Deciphering the Heterogeneity of Schizophrenia: A Multimodal and Multivariate Neuroimaging Framework for Unveiling Brain-Symptom Relationships and Underlying Subtypes.Schizophrenia bulletin · 2026Article
- Screen, Sample, Stratify: Biomarkers and Machine Learning Compress Dementia Pathways.Biomedicines · 2026Article
- Disrupted intrathalamic and thalamocortical structural covariance networks in posttraumatic stress disorder.Network neuroscience (Cambridge, Mass.) · 2026Article
- Early diagnosis of Alzheimer's Disease: Graph theoretical analysis of cerebellar network features based on 18F-AV45 PET.PloS one · 2026Article
- The impact of intention to adopt generative AI for exercise information on exercise adherence among Chinese college students: the mediating role of autonomous motivation and network analysis.Frontiers in public health · 2026Article
- Subtyping insomnia disorder with a population graph attention autoencoder: revealing two distinct biotypes.Frontiers in neuroscience · 2026Article
- Toward neuroimaging-based diagnostic support: a deep learning approach with a closed-loop system for psychiatric classification.iScience · 2025Article
10 more citing papers are in PubMed but not listed here.
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Authors and funding
21 authors at 9 institutions in 7 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Reliable mapping of system-level individual differences is a critical first step toward precision medicine for complex disorders such as schizophrenia. Disrupted structural covariance indicates a system-level brain maturational disruption in schizophrenia. However, most studies examine structural covariance at the group level. This prevents subject-level inferences. Here, we introduce a Network Template Perturbation approach to construct individual differential structural covariance network (IDSCN) using regional gray-matter volume. IDSCN quantifies how structural covariance between two nodes in a patient deviates from the normative covariance in healthy subjects. We analyzed T1 images from 1287 subjects, including 107 first-episode (drug-naive) patients and 71 controls in the discovery datasets and established robustness in 213 first-episode (drug-naive), 294 chronic, 99 clinical high-risk patients, and 494 controls from the replication datasets. Patients with schizophrenia were highly variable in their altered structural covariance edges; the number of altered edges was related to severity of hallucinations. Despite this variability, a subset of covariance edges, including the left hippocampus-bilateral putamen/globus pallidus edges, clustered patients into two distinct subgroups with opposing changes in covariance compared to controls, and significant differences in their anxiety and depression scores. These subgroup differences were stable across all seven datasets with meaningful genetic associations and functional annotation for the affected edges. We conclude that the underlying physiology of affective symptoms in schizophrenia involves the hippocampus and putamen/pallidum, predates disease onset, and is sufficiently consistent to resolve morphological heterogeneity throughout the illness course. The two schizophrenia subgroups identified thus have implications for the nosology and clinical treatment.
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