ArticleSchizophrenia bulletin2024
Functional Connectivity-Based Searchlight Multivariate Pattern Analysis for Discriminating Schizophrenia Patients and Predicting Clinical Variables.
Article in Schizophrenia bulletin, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Infratentorial cerebral microbleeds and brain age gap in stroke patients: a cross-sectional neuroimaging study.GeroScience · 2026Article
- Classification of schizophrenia spectrum disorder using machine learning and functional connectivity: reconsidering the clinical application.BMC psychiatry · 2025Article
- The Impact of Atlas Parcellation on Functional Connectivity Analysis Across Six Psychiatric Disorders.Human brain mapping · 2025Article
- The efficacy of meditation on insula dysfunction in patients with chronic schizophrenia.Frontiers in psychiatry · 2025Article
- Multidimensional EEG features integration with feature selection strategy for precision diagnosis of depressive disorders.Frontiers in psychiatry · 2025Article
Corrections and comments
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Authors and funding
9 authors.
Funding
Abstract
backgroundSchizophrenia, a multifaceted psychiatric disorder characterized by functional dysconnectivity, poses significant challenges in clinical practice. This study explores the potential of functional connectivity (FC)-based searchlight multivariate pattern analysis (CBS-MVPA) to discriminate between schizophrenia patients and healthy controls while also predicting clinical variables. STUDY
designWe enrolled 112 schizophrenia patients and 119 demographically matched healthy controls. Resting-state functional magnetic resonance imaging data were collected, and whole-brain FC subnetworks were constructed. Additionally, clinical assessments and cognitive evaluations yielded a dataset comprising 36 clinical variables. Finally, CBS-MVPA was utilized to identify subnetworks capable of effectively distinguishing between the patient and control groups and predicting clinical scores. STUDY
resultsThe CBS-MVPA approach identified 63 brain subnetworks exhibiting significantly high classification accuracies, ranging from 62.2% to 75.6%, in distinguishing individuals with schizophrenia from healthy controls. Among them, 5 specific subnetworks centered on the dorsolateral superior frontal gyrus, orbital part of inferior frontal gyrus, superior occipital gyrus, hippocampus, and parahippocampal gyrus showed predictive capabilities for clinical variables within the schizophrenia cohort.
conclusionThis study highlights the potential of CBS-MVPA as a valuable tool for localizing the information related to schizophrenia in terms of brain network abnormalities and capturing the relationship between these abnormalities and clinical variables, and thus, deepens our understanding of the neurological mechanisms of schizophrenia.
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