Classifying schizophrenia using functional MRI and investigating underlying functional phenomena

IF 3.5 3区医学 Q2 NEUROSCIENCES

Brain Research Bulletin Pub Date : 2025-03-07 DOI:10.1016/j.brainresbull.2025.111279

Yangyang Liu , Bi Wan , Zixuan Liu , Shuaiqi Zhang , Pei Liu , Ningning Ding , Yuxin Wang , Jun Dong , Moiz Kabeer Ahmad , Haisan Zhang

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引用次数: 0

Abstract

Background

Existing studies have revealed functional abnormalities in certain brain regions of patients with schizophrenia (SZ), but the relationships between these abnormalities and their impact on disease progression remain unclear.

Methods

Fifty-six patients with SZ and 56 healthy controls were included. Based on resting-state functional magnetic resonance imaging, we analyzed fractional amplitude of low-frequency fluctuations (fALFF), regional homogeneity (ReHo), and degree centrality (DC). Statistically significant metrics were selected as features, and machine learning models were used to distinguish between patients and controls. Analyze the importance of features in the optimal model. The Louvain community detection algorithm and structural equation modeling were used to investigate community relationships and potential causal effects.

Results

The average prediction accuracy of various ML classifiers reached 0.9241 by fALFF, ReHo, and DC values. The SVM model have the highest performance with an accuracy of 0.9464. Abnormal ReHo in the right middle frontal gyrus contributed most to this optimal classifier and participated in the direct impact on SZ. All the features we analyzed ultimately constituted two functional clusters (FClus), which exhibit internal causal influences. FClus1 had a positive influence on SZ, with the cascade starting from abnormal fALFF in the right inferior temporal gyrus. FClus2 had a negative influence on SZ, with the cascade starting from abnormal fALFF in the left fusiform gyrus.Abnormal fALFF in the right caudate nucleus, degree centrality in the right angular gyrus, and ReHo in the right lentiform nucleus do not have a causal impact on the disease.

Conclusions

We identified interactions among features within FClus that potentially influence the onset and progression of schizophrenia, including epicenter phenomenon of FClus, FClus for inhibiting schizophrenia, and abnormal function of brain regions without direct impact. Additionally, we believe that the contribution of features to the disease classification model may indicate the size of their direct impact on the disease, not necessarily their importance in the disease process.

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来源期刊

Brain Research Bulletin 医学-神经科学

CiteScore

6.90

自引率

2.60%

发文量

253

审稿时长

67 days

期刊介绍： The Brain Research Bulletin (BRB) aims to publish novel work that advances our knowledge of molecular and cellular mechanisms that underlie neural network properties associated with behavior, cognition and other brain functions during neurodevelopment and in the adult. Although clinical research is out of the Journal''s scope, the BRB also aims to publish translation research that provides insight into biological mechanisms and processes associated with neurodegeneration mechanisms, neurological diseases and neuropsychiatric disorders. The Journal is especially interested in research using novel methodologies, such as optogenetics, multielectrode array recordings and life imaging in wild-type and genetically-modified animal models, with the goal to advance our understanding of how neurons, glia and networks function in vivo.