The EEG multiverse of schizophrenia

Abstract

Research on schizophrenia typically focuses on one paradigm, for which clear-cut abnormalities between patients and controls are established. Great care is taken to understand the underlying genetical, neurophysiological, and cognitive mechanisms, which eventually may explain the clinical outcome. This approach has led to many important hypotheses about the causes of schizophrenia. One tacit assumption of these deep rooting approaches is that paradigms tap into common and representative characteristics of the disease. Here, we analyzed resting-state electroencephalogram (EEG) of 121 schizophrenia patients and 75 age-matched controls, from which we extracted 194 features. Sixty-nine of these features showed significant group differences with medium to large effect sizes, indicating important abnormalities. To understand to what extent these features tap into the same aspects of the disorder, we computed both Pearson and partial least squares correlations. Surprisingly, correlations were very low, except for very similar features, where correlations were high, suggesting that most features are sensitive to different abnormalities. Using partial least squares regression, we show that combining the 69 features increases predictability of clinical outcomes by more than a factor of 5. We propose that complementing deep with shallow rooting approaches, where many roughly independent features are extracted from one paradigm (or several paradigms), will strongly improve diagnosis and potential treatment of schizophrenia.

Are we really unconscious in “unconscious” states? Common assumptions revisited

“…Further, characterizing states and conditions in this manner allows: (1) large scale contrasting and comparison of the outcome of various neurological measures between different states (e.g., as in Gordillo et al, 2020 ; Nilsen et al, 2020 ); (2) studying memory, confabulation, perception, meta-cognition, etc., within various states in order to build a basis for estimation of reliability of subjective reports and the inferences that can be drawn from such reports (e.g., evidence of at least implicit learning in anesthesia; Ghoneim and Block, 1997 ); and, (3) mapping the heterogenous causes and effects of various states and conditions allowing for more nuanced treatment of patients. For example, Gibson et al (2014) show how individual DOC patients differ in volitional mental imagery ability and where in the cortex such imagery might be detected.…”