Multimodal EEG-fNIRS classification as a clinical tool for bipolar disorder diagnosis.

Bipolar disorder (BD) is a complex mood disorder characterized by recurrent depressive and manic/hypomanic episodes, accompanied by significant cognitive dysfunction and emotional dysregulation. Accurate and timely diagnosis, especially the differentiation between subtypes, remains a challenge due to overlapping symptoms, variable onset times for more specific symptoms (e.g., psychotic features), and the reliance on subjective assessments. This study examines the use of a multimodal approach combining electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) to identify patterns of BD emotional dysregulation, aiming to enhance its diagnosis and subtype differentiation. The protocol employed an emotional visual task to evaluate the interference of emotional content on cognitive function. EEG data were collected using a whole-head cap, while fNIRS focused on hemodynamic changes in the frontal cortex. Furthermore, the feasibility of using a potential simplified, portable EEG-fNIRS system was explored by focusing the analysis on frontal regions. The cohort included BD patients [BP] of two main subtypes, and healthy controls [HC]. Behavioral analysis revealed significant performance differences between BP and HC groups. While EEG alone enabled groups' classification, integrating EEG and fNIRS improved accuracy by reducing misclassification rates. Although classification using only frontal EEG regions was slightly less accurate than the full-head cap, fNIRS integration ensured robust results, supporting the feasibility for a potential simplified system. These findings underscore the complementary strengths of EEG and fNIRS in capturing neural and vascular markers of emotional dysregulation in BD and support the development of multimodal diagnostic tools for BD.