Individualized Texture Similarity Network in Schizophrenia

Hao Ding; Yu Zhang; Yingying Xie; Xiaotong Du; Yi Ji; Liyuan Lin; Zhongyu Chang; Bin Zhang; Meng Liang; Chunshui Yu; Wen Qin

doi:10.1016/j.biopsych.2023.12.025

Individualized Texture Similarity Network in Schizophrenia

Biol Psychiatry. 2024 Jan 12:S0006-3223(24)00029-5. doi: 10.1016/j.biopsych.2023.12.025. Online ahead of print.

Authors

Hao Ding¹, Yu Zhang², Yingying Xie², Xiaotong Du², Yi Ji², Liyuan Lin², Zhongyu Chang², Bin Zhang³, Meng Liang⁴, Chunshui Yu⁵, Wen Qin⁶

Affiliations

¹ Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China; School of Medical 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 Tianjin Anding Hospital, Tianjin Medical University, Tianjin, China; Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, China.
⁴ School of Medical Imaging, Tianjin Medical University, Tianjin, China.
⁵ Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China; School of Medical Imaging, Tianjin Medical University, Tianjin, China. Electronic address: chunshuiyu@tmu.edu.cn.
⁶ Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China. Electronic address: wayne.wenqin@gmail.com.

PMID: 38218309
DOI: 10.1016/j.biopsych.2023.12.025

Abstract

Background: Structural covariance network disruption has been considered an important pathophysiological indicator for schizophrenia. Here, we introduced a novel individualized structural covariance network measure, referred to as a texture similarity network (TSN), and hypothesized that the TSN could reliably reveal unique intersubject heterogeneity and complex dysconnectivity patterns in schizophrenia.

Methods: The TSN was constructed by measuring the covariance of 180 three-dimensional voxelwise gray-level co-occurrence matrix feature maps between brain areas in each participant. We first tested the validity and reproducibility of the TSN in characterizing the intersubject variability in 2 longitudinal test-retest healthy cohorts. The TSN was further applied to elucidate intersubject variability and dysconnectivity patterns in 10 schizophrenia case-control datasets (609 schizophrenia cases vs. 579 controls) as well as in a first-episode depression dataset (69 patients with depression vs. 69 control participants).

Results: The test-retest analysis demonstrated higher TSN intersubject than intrasubject variability. Moreover, the TSN reliably revealed higher intersubject variability in both chronic and first-episode schizophrenia, but not in depression. The TSN also reproducibly detected coexistent increased and decreased TSN strength in widespread brain areas, increased global small-worldness, and the coexistence of both structural hyposynchronization in the central networks and hypersynchronization in peripheral networks in patients with schizophrenia but not in patients with depression. Finally, aberrant intersubject variability and covariance strength patterns revealed by the TSN showed a missing or weak correlation with other individualized structural covariance network measures, functional connectivity, and regional volume changes.

Conclusions: These findings support the reliability of a TSN in revealing unique structural heterogeneity and complex dysconnectivity in patients with schizophrenia.

Keywords: Gray-level co-occurrence matrix; Intersubject variability; Schizophrenia; Structural covariance; Subject-level similarity network; Texture analysis.