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Tractography-based classification in distinguishing patients with first-episode schizophrenia from healthy individuals
Deng, Yi1,2,3; Hung, Karen S. Y.1; Lui, Simon S. Y.1,2; Chui, William W. H.2; Lee, Joe C. W.1; Wang, Yi2,4; Li, Zhi2,4; Mak, Henry K. F.5; Sham, Pak C.6; Chan, Raymond C. K.2,4; Cheung, Eric F. C.1,2
First AuthorDeng, Yi
2019-01-10
Source PublicationPROGRESS IN NEURO-PSYCHOPHARMACOLOGY & BIOLOGICAL PSYCHIATRY
Correspondent Emailcheungfc@ha.org.hk (e.f.c. cheung).
ISSN0278-5846
Subtypearticle
Volume88Pages:66-73
Contribution Rank2
Abstract

Background: Schizophrenia has been characterized as a neurodevelopmental disorder of brain disconnectivity. However, whether disrupted integrity of white matter tracts in schizophrenia can potentially serve as individual discriminative biomarkers remains unclear. Methods: A random forest algorithm was applied to tractography-based diffusion properties obtained from a cohort of 65 patients with first-episode schizophrenia (FES) and 60 healthy individuals to investigate the machine-learning discriminative power of white matter disconnectivity. Recursive feature elimination was used to select the ultimate white matter features in the classification. Relationships between algorithm-predicted probabilities and clinical characteristics were also examined in the FES group. Results: The classifier was trained by 80% of the sample. Patients were distinguished from healthy individuals with an overall accuracy of 71.0% (95% confident interval: 61.1%, 79.6%), a sensitivity of 67.3%, a specificity of 75.0%, and the area under receiver operating characteristic curve (AUC) was 79.3% (chi(2) p < 0.001). In validation using the held-up 20% of the sample, patients were distinguished from healthy individuals with an overall accuracy of 76.0% (95% confident interval: 54.9%, 90.6%), a sensitivity of 76.9%, a specificity of 75.0%, and an AUC of 73.1% (chi(2) p = 0.012). Diffusion properties of inter-hemispheric fibres, the cerebello-thalamo-cortical circuits and the long association fibres were identified to be the most discriminative in the classification. Higher predicted probability scores were found in younger patients. Conclusions: Our findings suggest that the widespread connectivity disruption observed in FES patients, especially in younger patients, might be considered potential individual discriminating biomarkers.

KeywordCorpus Callosum Diffusion Tensor Imaging Discriminant Analysis Psychosis Machine Learning Random Forest
DOI10.1016/j.pnpbp.2018.06.010
Language英语
Funding OrganizationPhilip KH Wong Foundation ; Beijing Training Project for Leading Talents in ST ; Beijing Municipal Science & Technology Commission Grant
Funding ProjectPhilip KH Wong Foundation ; Beijing Training Project for Leading Talents in ST[Z151100000315020] ; Beijing Municipal Science & Technology Commission Grant[Z161100000216138]
WOS Research AreaNeurosciences & Neurology ; Pharmacology & Pharmacy ; Psychiatry
WOS SubjectClinical Neurology ; Neurosciences ; Pharmacology & Pharmacy ; Psychiatry
WOS IDWOS:000445634300007
PublisherPERGAMON-ELSEVIER SCIENCE LTD
WOS KeywordBrain ; Abnormalities ; Disease ; Antipsychotics ; Challenges ; Biomarkers ; Fasciculus ; Accuracy ; Images
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Document Type期刊论文
Identifierhttp://ir.psych.ac.cn/handle/311026/27121
Collection中国科学院心理健康重点实验室
Corresponding AuthorCheung, Eric F. C.
Affiliation1.Castle Peak Hosp, Tuen Mun, Hong Kong, Peoples R China
2.Chinese Acad Sci, Inst Psychol, Neuropsychol & Appl Cognit Neurosci Lab, CAS Key Lab Mental Hlth, Beijing, Peoples R China
3.Univ Calif Davis, MIND Inst, Cognit Anal & Brain Imaging Lab, Davis, CA 95616 USA
4.Univ Chinese Acad Sci, Dept Psychol, Beijing, Peoples R China
5.Univ Hong Kong, Dept Radiol, Hong Kong, Hong Kong, Peoples R China
6.Univ Hong Kong, Ctr Genom Sci, Hong Kong, Hong Kong, Peoples R China
Recommended Citation
GB/T 7714
Deng, Yi,Hung, Karen S. Y.,Lui, Simon S. Y.,et al. Tractography-based classification in distinguishing patients with first-episode schizophrenia from healthy individuals[J]. PROGRESS IN NEURO-PSYCHOPHARMACOLOGY & BIOLOGICAL PSYCHIATRY,2019,88:66-73.
APA Deng, Yi.,Hung, Karen S. Y..,Lui, Simon S. Y..,Chui, William W. H..,Lee, Joe C. W..,...&Cheung, Eric F. C..(2019).Tractography-based classification in distinguishing patients with first-episode schizophrenia from healthy individuals.PROGRESS IN NEURO-PSYCHOPHARMACOLOGY & BIOLOGICAL PSYCHIATRY,88,66-73.
MLA Deng, Yi,et al."Tractography-based classification in distinguishing patients with first-episode schizophrenia from healthy individuals".PROGRESS IN NEURO-PSYCHOPHARMACOLOGY & BIOLOGICAL PSYCHIATRY 88(2019):66-73.
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