The EEG multiverse of schizophrenia

Abstract

Research on schizophrenia typically focuses on one paradigm for which clear-cut differences between patients and controls are established. Great efforts are made to understand the underlying genetical, neurophysiological, and cognitive mechanisms, which eventually may explain the clinical outcome. One tacit assumption of these "deep rooting" approaches is that paradigms tap into common and representative aspects of the disorder. Here, we analyzed the resting-state electroencephalogram (EEG) of 121 schizophrenia patients and 75 controls. Using multiple signal processing methods, we extracted 194 EEG features. Sixty-nine out of the 194 EEG features showed a significant difference between patients and controls, indicating that these features detect an important aspect of schizophrenia. Surprisingly, the correlations between these features were very low. We discuss several explanations to our results and propose that complementing "deep" with "shallow" rooting approaches might help in understanding the underlying mechanisms of the disorder.

Keywords: electroencephalography; neuroimaging; psychiatry; psychosis; resting-state; schizophrenia.

Fig. 1

Effect size (Cohen’s d) of the group differences between patients and controls for each of the 194 EEG features. We took the values of the electrode, brain region, or microstate parameter, with the largest effect size according to Cohen’s d ( values were converted to Cohen’s d) to be the representative variable for each feature. Significant group differences, after correction for multiple comparisons (using FDR), are depicted in red, with dotted red horizontal lines serving as a guide to their labels. > and < were added to the feature labels to indicate if patients had significantly higher or lower values than controls, respectively. The non-significant effects are shown in blue. Error bars represent 95% confidence intervals. A list with the abbreviations and the corresponding name of each feature is presented in Supplementary Table 1.

Fig. 2

Pairwise correlations between the 69 EEG features which showed significant group differences between patients and controls. Patients’ -values are presented in the upper triangle and controls’ -values are shown in the lower triangle. Strong negative and positive -values are depicted in red and blue, respectively, and -values around 0 in yellow. For each feature, we used the values of the electrode, brain region, or microstate parameter which showed the largest effect size as the representative variable for the correlations. A list with the abbreviations and corresponding name of each feature is shown in Supplementary Table 1.

Fig. 3

Shared information between the 69 EEG features which showed significant group differences, as measured by the relative inertia () computed with PLSC. The relative inertia ranges from 0 (the two features are completely unrelated) to 1 (the two features’ values move together by the exact same percentage). Patients’ relative inertias are presented in the upper triangle, and controls’ relative inertias are shown in the lower triangle. A list with the abbreviations and corresponding name of each feature is shown in Supplementary Table 1.