Unraveling multi‐scale neuroimaging biomarkers and molecular foundations for schizophrenia: A combined multivariate pattern analysis and transcriptome‐neuroimaging association study

Author:

Peng Yanmin1ORCID,Chai Chao23ORCID,Xue Kaizhong4,Tang Jie2,Wang Sijia2,Su Qian5,Liao Chongjian1,Zhao Guoshu2,Wang Shaoying2,Zhang Nannan2,Zhang Zhihui2,Lei Minghuan2,Liu Feng2ORCID,Liang Meng1

Affiliation:

1. School of Medical Imaging and Tianjin Key Laboratory of Functional Imaging Tianjin Medical University Tianjin China

2. Department of Radiology and Tianjin Key Laboratory of Functional Imaging Tianjin Medical University General Hospital Tianjin China

3. Department of Radiology, School of Medicine, Tianjin First Central Hospital Nankai University Tianjin China

4. Department of Radiology and Nuclear Medicine, Xuanwu Hospital Capital Medical University Beijing China

5. Department of Molecular Imaging and Nuclear Medicine Tianjin Medical University Cancer Institute and Hospital Tianjin China

Abstract

AbstractAimsSchizophrenia is characterized by alterations in resting‐state spontaneous brain activity; however, it remains uncertain whether variations at diverse spatial scales are capable of effectively distinguishing patients from healthy controls. Additionally, the genetic underpinnings of these alterations remain poorly elucidated. We aimed to address these questions in this study to gain better understanding of brain alterations and their underlying genetic factors in schizophrenia.MethodsA cohort of 103 individuals with diagnosed schizophrenia and 110 healthy controls underwent resting‐state functional MRI scans. Spontaneous brain activity was assessed using the regional homogeneity (ReHo) metric at four spatial scales: voxel‐level (Scale 1) and regional‐level (Scales 2–4: 272, 53, 17 regions, respectively). For each spatial scale, multivariate pattern analysis was performed to classify schizophrenia patients from healthy controls, and a transcriptome‐neuroimaging association analysis was performed to establish connections between gene expression data and ReHo alterations in schizophrenia.ResultsThe ReHo metrics at all spatial scales effectively discriminated schizophrenia from healthy controls. Scale 2 showed the highest classification accuracy at 84.6%, followed by Scale 1 (83.1%) and Scale 3 (78.5%), while Scale 4 exhibited the lowest accuracy (74.2%). Furthermore, the transcriptome‐neuroimaging association analysis showed that there were not only shared but also unique enriched biological processes across the four spatial scales. These related biological processes were mainly linked to immune responses, inflammation, synaptic signaling, ion channels, cellular development, myelination, and transporter activity.ConclusionsThis study highlights the potential of multi‐scale ReHo as a valuable neuroimaging biomarker in the diagnosis of schizophrenia. By elucidating the complex molecular basis underlying the ReHo alterations of this disorder, this study not only enhances our understanding of its pathophysiology, but also pave the way for future advancements in genetic diagnosis and treatment of schizophrenia.

Funder

National Natural Science Foundation of China

Publisher

Wiley

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