Machine Learning-Based Electroencephalographic Phenotypes of Schizophrenia and Major Depressive Disorder

Kuk-In Jang; Sungkean Kim; Soo Young Kim; Chany Lee; Jeong-Ho Chae

doi:10.3389/fpsyt.2021.745458

Machine Learning-Based Electroencephalographic Phenotypes of Schizophrenia and Major Depressive Disorder

Front Psychiatry. 2021 Oct 13:12:745458. doi: 10.3389/fpsyt.2021.745458. eCollection 2021.

Authors

Kuk-In Jang¹, Sungkean Kim², Soo Young Kim³, Chany Lee¹, Jeong-Ho Chae³

Affiliations

¹ Department of Cognitive Science Research, Korea Brain Research Institute (KBRI), Daegu, South Korea.
² Department of Human-Computer Interaction, Hanyang University, Ansan, South Korea.
³ Department of Psychiatry, College of Medicine, The Catholic University of Korea, Seoul, South Korea.

Abstract

Background: Psychiatric diagnosis is formulated by symptomatic classification; disease-specific neurophysiological phenotyping could help with its fundamental treatment. Here, we investigated brain phenotyping in patients with schizophrenia (SZ) and major depressive disorder (MDD) by using electroencephalography (EEG) and conducted machine-learning-based classification of the two diseases by using EEG components. Materials and Methods: We enrolled healthy controls (HCs) (n = 30) and patients with SZ (n = 34) and MDD (n = 33). An auditory P300 (AP300) task was performed, and the N1 and P3 components were extracted. Two-group classification was conducted using linear discriminant analysis (LDA) and support vector machine (SVM) classifiers. Positive and negative symptoms and depression and/or anxiety symptoms were evaluated. Results: Considering both the results of statistical comparisons and machine learning-based classifications, patients and HCs showed significant differences in AP300, with SZ and MDD showing lower N1 and P3 than HCs. In the sum of amplitudes and cortical sources, the findings for LDA with classification accuracy (SZ vs. HCs: 71.31%, MDD vs. HCs: 74.55%), sensitivity (SZ vs. HCs: 77.67%, MDD vs. HCs: 79.00%), and specificity (SZ vs. HCs: 64.00%, MDD vs. HCs: 69.67%) supported these results. The SVM classifier showed reasonable scores between SZ and HCs and/or MDD and HCs. The comparison between SZ and MDD showed low classification accuracy (59.71%), sensitivity (65.08%), and specificity (54.83%). Conclusions: Patients with SZ and MDD showed deficiencies in N1 and P3 components in the sum of amplitudes and cortical sources, indicating attentional dysfunction in both early and late sensory/cognitive gating input. The LDA and SVM classifiers in the AP300 are useful to distinguish patients with SZ and HCs and/or MDD and HCs.

Keywords: auditory P300; electroencephalographic phenotyping; machine learning; major depressive disorder; schizophrenia.