Aberrant large-scale brain modules in deficit and non-deficit schizophrenia

Linlin Fan; Miao Yu; Amy Pinkham; Yiyi Zhu; Xiaowei Tang; Xiang Wang; Xiaobin Zhang; Junji Ma; Jinbo Zhang; Xiangrong Zhang; Zhengjia Dai

doi:10.1016/j.pnpbp.2021.110461

Aberrant large-scale brain modules in deficit and non-deficit schizophrenia

Prog Neuropsychopharmacol Biol Psychiatry. 2022 Mar 8:113:110461. doi: 10.1016/j.pnpbp.2021.110461. Epub 2021 Oct 21.

Authors

Linlin Fan¹, Miao Yu², Amy Pinkham³, Yiyi Zhu³, Xiaowei Tang⁴, Xiang Wang⁵, Xiaobin Zhang⁶, Junji Ma⁷, Jinbo Zhang⁷, Xiangrong Zhang⁸, Zhengjia Dai⁹

Affiliations

¹ Department of Psychology, Sun Yat-sen University, Guangzhou, Guangdong, China; School of Behavioral and Brain Sciences, The University of Texas at Dallas, Richardson, TX, United States.
² Department of Neurology, Affiliated Nanjing Brain Hospital, Nanjing Medical University, No. 264 Guangzhou Road, Nanjing, Jiangsu, China.
³ School of Behavioral and Brain Sciences, The University of Texas at Dallas, Richardson, TX, United States.
⁴ Department of Geriatric Psychiatry, Affiliated Nanjing Brain Hospital, Nanjing Medical University, Nanjing, Jiangsu, China; Department of Psychiatry, Affiliated WuTaiShan Hospital of Medical College of Yangzhou University, Yangzhou, Jiangsu, China.
⁵ Medical Psychological Institute of the Second Xiangya Hospital, Central South University, Changsha, Hunan, China.
⁶ Department of Psychiatry, Affiliated WuTaiShan Hospital of Medical College of Yangzhou University, Yangzhou, Jiangsu, China.
⁷ Department of Psychology, Sun Yat-sen University, Guangzhou, Guangdong, China.
⁸ Department of Geriatric Psychiatry, Affiliated Nanjing Brain Hospital, Nanjing Medical University, Nanjing, Jiangsu, China. Electronic address: drxrz@hotmail.com.
⁹ Department of Psychology, Sun Yat-sen University, Guangzhou, Guangdong, China. Electronic address: daizhengj@mail.sysu.edu.cn.

PMID: 34688810
DOI: 10.1016/j.pnpbp.2021.110461

Abstract

Objective: Schizophrenia is a heterogenous psychiatric disease, and deficit schizophrenia (DS) is a clinical subgroup with primary and enduring negative symptoms. Although previous neuroimaging studies have identified functional connectome alterations in schizophrenia, the modular organizations in DS and nondeficit schizophrenia (NDS) remain poorly understood. Therefore, this study aimed to investigate the modular-level alterations in DS patients compared with the NDS and healthy control (HC) groups.

Methods: A previously collected dataset was re-analyzed, in which 74 chronic male schizophrenia patients (33 DS and 41 NDS) and 40 HC underwent resting-state functional magnetic resonance imaging with eyes closed in a Siemens 3 T scanner (scanning duration = 8 min). Modular- (intramodule and intermodule connectivity) and nodal- [normalized within-module degree (Z_i) and participation coefficient (PC_i)] level graph theory properties were computed and compared among the three groups. Receiver operating characteristic curve (ROC) analyses were performed to examine the classification ability of these measures, and partial correlations were conducted between network measures and symptom severity. Validation analyses on head motion, network sparsity, and parcellation scheme were also performed.

Results: Both schizophrenia subgroups showed decreased intramodule connectivity in salience network (SN), somatosensory-motor network (SMN), and visual network (VN), and increased intermodule connectivity in SMN-default mode network (DMN) and SMN-frontoparietal network (FPN). Compared with NDS patients, DS patients showed weaker intramodule connectivity in SN and stronger intermodule connectivity in SMN-FPN and SMN-VN. At the nodal level, the schizophrenia-related alterations were distributed in SN, SMN, VN, and DMN, and 7 DS-specific nodal alterations were identified. Intramodule connectivity of SN, intermodule connectivity of SMN-VN, and Z_i of left precuneus successfully distinguished the three groups. Partial correlational analyses revealed that these measures were related to negative symptoms, general psychiatric symptoms, and neurocognitive function.

Conclusion: Our findings suggest that functional connectomes, especially SN, SMN, and VN, may capture the distinct and common disruptions of DS and NDS. These findings may help to understand the neuropathology of negative symptoms of schizophrenia and inform targets for treating different schizophrenia subtypes.

Keywords: Deficit schizophrenia; Graph theory; Heterogeneity; Modularity; Resting-state fMRI.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Brain / physiopathology*
Connectome*
Datasets as Topic
Default Mode Network*
Humans
Magnetic Resonance Imaging
Male
Middle Aged
Parietal Lobe
Schizophrenia* / classification
Schizophrenia* / physiopathology