Major Depressive Disorder Recognition and Cognitive Analysis Based on Multi-layer Brain Functional Connectivity Networks
This work addresses MDD recognition and cognitive analysis for medical diagnosis, but it appears incremental as it builds on existing connectivity methods with multi-layer enhancements.
The paper tackled the problem of recognizing Major Depressive Disorder (MDD) by proposing a multi-layer brain functional connectivity networks (MBFCN) method, achieving results that identified the Alpha-Beta1 frequency band as key for recognition and found deficient connections in specific brain regions for extremely depressed disorders.
On the increase of major depressive disorders (MDD), many researchers paid attention to their recognition and treatment. Existing MDD recognition algorithms always use a single time-frequency domain method method, but the single time-frequency domain method is too simple and is not conducive to simulating the complex link relationship between brain functions. To solve this problem, this paper proposes a recognition method based on multi-layer brain functional connectivity networks (MBFCN) for major depressive disorder and conducts cognitive analysis. Cognitive analysis based on the proposed MBFCN finds that the Alpha-Beta1 frequency band is the key sub-band for recognizing MDD. The connections between the right prefrontal lobe and the temporal lobe of the extremely depressed disorders (EDD) are deficient in the brain functional connectivity networks (BFCN) based on phase lag index (PLI). Furthermore, potential biomarkers by the significance analysis of depression features and PHQ-9 can be found.