Background: Schizophrenia (SZ) is a highly heritable and heterogeneous disorder that is often associated with widespread structural brain abnormalities. However, the causes of interindividual differences in genetic susceptibility remain largely unknown. This study attempted to address this important issue by utilizing a prospective study in which unaffected first-degree relatives of SZ (FH+) were recruited.
Methods: A total of 198 participants (143 FH + and 55 healthy control participants) were recruited and completed diffusion tensor imaging scans, graph theory analysis and semiannual standardized clinical evaluations within the first three years.
Results: FH + participants who developed SZ (SZ/FH+) had similar but pronounced structural network changes at baseline compared to FH + participants who did not (HC/FH+). Additionally, among network properties, rich-club connections showed a good correlation with the severity of SZ, which was the most significant and stable effect. Logistic regression analyses showed that rich-club connections at baseline had high predictive accuracy for the subsequent occurrence of SZ.
Conclusions: Among healthy people with a familial history of SZ, those who exhibit decreased rich-club connections are susceptible to developing this disease. Our findings may aid in the development of timely interventions to prevent SZ and possibly assist researchers and clinicians in evaluating the efficacy of interventions.
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http://dx.doi.org/10.1186/s12888-024-06411-w | DOI Listing |
Comput Biol Med
December 2024
Center for Brain and Brain-Inspired Computing Research, School of Computer Science, Northwestern Polytechnical University, Xi'an, China. Electronic address:
Background: Studying influential nodes (I-nodes) in brain networks is of great significance in the field of brain imaging. Most existing studies consider brain connectivity hubs as I-nodes such as the regions of high centrality or rich-club organization. However, this approach relies heavily on prior knowledge from graph theory, which may overlook the intrinsic characteristics of the brain network, especially when its architecture is not fully understood.
View Article and Find Full Text PDFBMC Psychiatry
December 2024
The Affiliated People's Hospital of Jiangsu University, Zhenjiang First People's Hospital, No.8, Dianli Road, Zhenjiang, 212002, Jiangsu, China.
Background: Schizophrenia (SZ) is a highly heritable and heterogeneous disorder that is often associated with widespread structural brain abnormalities. However, the causes of interindividual differences in genetic susceptibility remain largely unknown. This study attempted to address this important issue by utilizing a prospective study in which unaffected first-degree relatives of SZ (FH+) were recruited.
View Article and Find Full Text PDFMed Phys
December 2024
Research Center for Medical AI, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.
Purposes: Positron emission tomography (PET) imaging is widely used to detect focal lesions or diseases and to study metabolic abnormalities between organs. However, analyzing organ correlations alone does not fully capture the characteristics of the metabolic network. Our work proposes a graph-based analysis method for quantifying the topological properties of the network, both globally and at the nodal level, to detect systemic or single-organ metabolic abnormalities caused by diseases such as lung cancer.
View Article and Find Full Text PDFFront Aging Neurosci
November 2024
Department of Radiology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, Jiangsu, China.
Neuroimage Clin
November 2024
Department of Diagnostic Radiology, University of Hong Kong, Hong Kong, China. Electronic address:
Objective: To explore topological alterations of white matter (WM) structural connectome, and their associations with clinical characteristics in type 1 narcolepsy (NT1).
Methods: 46 NT1 patients and 34 age- and sex-matched healthy controls were recruited for clinical data and diffusion tensor imaging collection. Using graph theory analysis, the topology metrics of structural connectome, rich club organization, and connectivity properties were compared between two groups.
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