Faculty, Staff and Student Publications

Language

English

Publication Date

1-1-2025

Journal

NeuroImage: Clinical

DOI

10.1016/j.nicl.2024.103726

PMID

39700898

PMCID

PMC11721508

PubMedCentral® Posted Date

12-12-2024

PubMedCentral® Full Text Version

Post-print

Abstract

Background and hypothesis: Treatment-resistant schizophrenia (TR-SZ) and non-treatment-resistant schizophrenia (NTR-SZ) lack specific biomarkers to distinguish from each other. This investigation aims to identify consistent dysfunctional brain connections with different atlases, multiple feature selection strategies, and several classifiers in distinguishing TR-SZ and NTR-SZ.

Study design: 55 TR-SZs, 239 NTR-SZs, and 87 healthy controls (HCs) were recruited from the Affiliated Brain Hospital of Nanjing Medical University. Resting-state functional connection (FC) matrices were constructed from automated anatomical labeling (AAL), Yeo-Networks (YEO) and Brainnetome (BNA) atlases. Two feature selection methods (Select From Model and Recursive Feature Elimination) and four classifiers (Adaptive Boost, Bernoulli Naïve Bayes, Gradient Boosting and Random Forest) were combined to identify the consistent FCs in distinguishing TR-SZ and HC, NTR-SZ and HC, TR-SZ and NTR-SZ.

Study results: The whole brain FCs, except the temporal-occipital FC, were consistent in distinguishing SZ and HC. Abnormal frontal-limbic, frontal-parietal and occipital-temporal FCs were consistent in distinguishing TR-SZ and NTR-SZ, that were further correlated with disease progression, symptoms and medication dosage. Moreover, the frontal-limbic and frontal-parietal FCs were highly consistent for the diagnosis of SZ (TR-SZ vs. HC, NTR-SZ vs. HC and TR-SZ vs. NTR-SZ). The BNA atlas achieved the highest classification accuracy (>90 %) comparing with AAL and YEO in the most diagnostic tasks.

Conclusions: These results indicate that the frontal-limbic and the frontal-parietal FCs are the robust neural pathways in the diagnosis of SZ, whereas the frontal-limbic, frontal-parietal and occipital-temporal FCs may be informative in recognizing those TR-SZ in the clinical practice.

Keywords

Humans, Male, Female, Adult, Schizophrenia, Magnetic Resonance Imaging, Schizophrenia, Treatment-Resistant, Occipital Lobe, Frontal Lobe, Young Adult, Middle Aged, Neural Pathways, Limbic System, Limbic Lobe, Nerve Net, Treatment-resistant schizophrenia, Resting-state fMRI, Functional connection, Brain atlas, Machine learning, Brain network

Published Open-Access

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