Inferring the Individual Psychopathologic Deficits With Structural Connectivity in a Longitudinal Cohort of Schizophrenia.
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Authors
Sun, Yi
Zhang, Zhe
Kakkos, Ioannis
Matsopoulos, George K
Yuan, Jingjia
Xu, Luoyi
Cao, Shuxia
Chen, Wenjuan
Hu, Xingyue
Li, Tao
Sim, Kang
Qi, Peng
Sun, Yu
Publication Date
2022-06Journal Title
IEEE J Biomed Health Inform
ISSN
2168-2194
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Volume
PP
Issue
99
Pages
1-1
Type
Article
This Version
AM
Physical Medium
Print-Electronic
Metadata
Show full item recordCitation
Sun, Y., Zhang, Z., Kakkos, I., Matsopoulos, G. K., Yuan, J., Suckling, J., Xu, L., et al. (2022). Inferring the Individual Psychopathologic Deficits With Structural Connectivity in a Longitudinal Cohort of Schizophrenia.. IEEE J Biomed Health Inform, PP (99), 1-1. https://doi.org/10.1109/JBHI.2021.3139701
Abstract
The prediction of schizophrenia-related psychopathologic deficits is exceedingly important in the fields of psychiatry and clinical practice. However, objective association of the brain structure alterations to the illness clinical symptoms is challenging. Although, schizophrenia has been characterized as a brain dysconnectivity syndrome, evidence accounting for neuroanatomical network alterations remain scarce. Moreover, the absence of generalized connectome biomarkers for the assessment of illness progression further perplexes the prediction of long-term symptom severity. In this paper, a combination of individualized prediction models with quantitative graph theoretical analysis was adopted, providing a comprehensive appreciation of the extent to which the brain network properties are affected over time in schizophrenia. Specifically, Connectome-based Prediction Models were employed on Structural Connectivity (SC) features, efficiently capturing individual network-related differences, while identifying the anatomical connectivity disturbances contributing to the prediction of psychopathological deficits. Our results demonstrated distinctions among widespread cortical circuits responsible for different domains of symptoms, indicating the complex neural mechanisms underlying schizophrenia. Furthermore, the generated models were able to significantly predict changes of symptoms using SC features at follow-up, while the preserved SC features suggested an association with improved positive and overall symptoms. Moreover, cross-sectional significant deficits were observed in network efficiency and a progressive aberration of global integration in patients compared to healthy controls, representing a group-consensus pathological map, while supporting the dysconnectivity hypothesis.
Identifiers
External DOI: https://doi.org/10.1109/JBHI.2021.3139701
This record's URL: https://www.repository.cam.ac.uk/handle/1810/334687
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