Individualized identification of first-episode bipolar disorder using machine learning and cognitive tests

Jeffrey Sawalha, Liping Cao, Jianshan Chen, Alessandro Selvitella, Yang Liu, Chanjuan Yang, Xuan Li, Xiaofei Zhang, Jiaqi Sun, Yamin Zhang, Liansheng Zhao, Liqian Cui, Yizhi Zhang, Jie Sui, Russell Greiner, Xin min Li, Andrew Greenshaw, Tao Li, Bo Cao*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

9 Citations (Scopus)

Abstract

Identifying cognitive dysfunction in the early stages of Bipolar Disorder (BD) can allow for early intervention. Previous studies have shown a strong correlation between cognitive dysfunction and number of manic episodes. The objective of this study was to apply machine learning (ML) techniques on a battery of cognitive tests to identify first-episode BD patients (FE-BD). Two cohorts of participants were used for this study. Cohort #1 included 74 chronic BD patients (CHR-BD) and 53 healthy controls (HC), while the Cohort #2 included 37 FE-BD and 18 age- and sex-matched HC. Cognitive functioning was assessed using the Cambridge Neuropsychological Test Automated Battery (CANTAB). The tests examined domains of visual processing, spatial memory, attention and executive function. We trained an ML model to distinguish between chronic BD patients (CHR-BD) and HC at the individual level. We used linear Support Vector Machines (SVM) and were able to identify individual CHR-BD patients at 77% accuracy. We then applied the model to Cohort #2 (FE-BD patients) and achieved an accuracy of 76% (AUC = 0.77). These results reveal that cognitive impairments may appear in early stages of BD and persist into later stages. This suggests that the same deficits may exist for both CHR-BD and FE-BD. These cognitive deficits may serve as markers for early BD. Our study provides a tool that these early markers can be used for detection of BD.

Original languageEnglish
Pages (from-to)662-668
Number of pages7
JournalJournal of Affective Disorders
Volume282
Early online date18 Dec 2020
DOIs
Publication statusPublished - 1 Mar 2021

Bibliographical note

Funding Information:
This research was supported in part by NARSAD Young Investigator Grants of The Brain & Behavior Research Foundation, Natural Sciences and Engineering Research Council of Canada (NSERC) and the Alberta Synergies in Alzheimer's and Related Disorders (SynAD) program; The National Key R&D Program of China [grant number 2016YFC1306804 ]; the National Natural Science Foundation of China [grant number 81771466 ]; the Natural Science Foundation of Guangdong Province, China [grant number 2018A030313283]; Guangzhou Municipal Key Discipline in Medicine [2017-2019]; Guangzhou Municipal Psychiatric Disease Clinical Transformation Laboratory, Guangzhou, China [grant number 201805010009]; Key Laboratory for Innovation Platform Plan, Science, and Technology Program of Guangzhou, China; University of Alberta , Department of Psychiatry [grant number RES0041720 ].

Funding Information:
The CRC and NARSAD grants provided part of honorary support for Dr. Cao and SynAD provided support in part for the small computational equipment. The National Key R&D Program of China, the National Natural Science Foundation of China, the Natural Science Foundation of Guangdong Province, Guangzhou Municipal Key Discipline in Medicine, Guangzhou Municipal Psychiatric Disease Clinical Transformation Laboratory, Key Laboratory for Innovation Platform Plan, Science, and Technology Program of Guangzhou supported the data collection, curation and analysis, and relevant key personnels.

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