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Treatment response prediction and individualized identification of first-episode drug-naive schizophrenia using brain functional connectivity
Cao, Bo1,2; Cho, Raymond Y.3; Chen, Dachun4; Xiu, Meihong4; Wang, Li5; Soares, Jair C.2; Zhang, Xiang Yang5
First AuthorCao, Bo
Correspondent Emailxiang yang zhang zhangxy@psych.ac.cn
Contribution Rank5
Abstract

Identifying biomarkers in schizophrenia during the first episode without the confounding effects of treatment has been challenging. Leveraging these biomarkers to establish diagnosis and make individualized predictions of future treatment responses to antipsychotics would be of great value, but there has been limited progress. In this study, by using machine learning algorithms and the functional connections of the superior temporal cortex, we successfully identified the first-episode drug-naive (FEDN) schizophrenia patients (accuracy 78.6%) and predict their responses to antipsychotic treatment (accuracy 82.5%) at an individual level. The functional connections (FC) were derived using the mutual information and the correlations, between the blood-oxygen-level dependent signals of the superior temporal cortex and other cortical regions acquired with the resting-state functional magnetic resonance imaging. We also found that the mutual information and correlation FC was informative in identifying individual FEDN schizophrenia and prediction of treatment response, respectively. The methods and findings in this paper could provide a critical step toward individualized identification and treatment response prediction in first-episode drug-naive schizophrenia, which could complement other biomarkers in the development of precision medicine approaches for this severe mental disorder.

Keywordsupport vector machines SVM
2020-04-01
Language英语
DOI10.1038/s41380-018-0106-5
Source PublicationMOLECULAR PSYCHIATRY
ISSN1359-4184
Volume25Issue:4Pages:906-913
Subtypearticle
Indexed BySCI
Funding ProjectNARSAD Young Investigator Grant of the Brain & Behavior Research Foundation ; Michael E. Debakey VA Medical Center ; Beth K. and Stuart C. Yudofsky Division of Neuropsychiatry, Department of Psychiatry and Behavioral Sciences, Baylor College of Medicine in Houston, TX ; NIMH[R01 085667] ; Dunn Research Foundation ; Pat Rutherford, Jr. Endowed Chair in Psychiatry
PublisherNATURE PUBLISHING GROUP
WOS KeywordSUPERIOR TEMPORAL GYRUS ; ANTIPSYCHOTIC TREATMENT ; STRUCTURAL MRI ; SEGMENTATION ; PSYCHOSIS ; DISEASE ; PATTERN ; SYSTEM ; MATTER
WOS Research AreaBiochemistry & Molecular Biology ; Neurosciences & Neurology ; Psychiatry
WOS SubjectBiochemistry & Molecular Biology ; Neurosciences ; Psychiatry
WOS IDWOS:000525957300020
QuartileQ1
Citation statistics
Cited Times:22[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.psych.ac.cn/handle/311026/31556
Collection健康与遗传心理学研究室
Corresponding AuthorZhang, Xiang Yang
Affiliation1.Univ Alberta, Fac Med & Dent, Dept Psychiat, Edmonton, AB, Canada
2.Univ Texas Hlth Sci Ctr Houston, McGovern Med Sch, Dept Psychiat & Behav Sci, Houston, TX 77030 USA
3.Baylor Coll Med, Dept Psychiat & Behav Sci, Houston, TX 77030 USA
4.Peking Univ, Beijing HuiLongGuan Hosp, Beijing 100096, Peoples R China
5.Chinese Acad Sci, Inst Psychol, Beijing 100101, Peoples R China
Corresponding Author AffilicationInstitute of Psychology, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Cao, Bo,Cho, Raymond Y.,Chen, Dachun,et al. Treatment response prediction and individualized identification of first-episode drug-naive schizophrenia using brain functional connectivity[J]. MOLECULAR PSYCHIATRY,2020,25(4):906-913.
APA Cao, Bo.,Cho, Raymond Y..,Chen, Dachun.,Xiu, Meihong.,Wang, Li.,...&Zhang, Xiang Yang.(2020).Treatment response prediction and individualized identification of first-episode drug-naive schizophrenia using brain functional connectivity.MOLECULAR PSYCHIATRY,25(4),906-913.
MLA Cao, Bo,et al."Treatment response prediction and individualized identification of first-episode drug-naive schizophrenia using brain functional connectivity".MOLECULAR PSYCHIATRY 25.4(2020):906-913.
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