Reduced subgenual cingulate–dorsolateral prefrontal connectivity as an electrophysiological marker for depression

• Published in: Scientific reports Vol. 12; no. 1; p. 16903
• Main Authors: Benschop, Lars, Vanhollebeke, Gert, Li, Jian, Leahy, Richard M., Vanderhasselt, Marie-Anne, Baeken, Chris
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 07.10.2022; Nature Publishing Group; Nature Portfolio
• Subjects: 631/477; 692/53/2423; 692/699/476/1414; Biomarkers; Brain mapping; Depression; Depressive Disorder, Major - diagnostic imaging; EEG; Electroencephalography; Functional magnetic resonance imaging; Gyrus Cinguli - diagnostic imaging; Humanities and Social Sciences; Humans; Magnetic Resonance Imaging; Mental depression; Mental disorders; multidisciplinary; Neural networks; Neuroimaging; Patients; Prefrontal cortex; Prefrontal Cortex - physiology; Science; Science (multidisciplinary); Support vector machines
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-022-20274-9

Major Depressive Disorder (MDD) is a widespread mental illness that causes considerable suffering, and neuroimaging studies are trying to reduce this burden by developing biomarkers that can facilitate detection. Prior fMRI- and neurostimulation studies suggest that aberrant subgenual Anterior Cingulate (sgACC)—dorsolateral Prefrontal Cortex (DLPFC) functional connectivity is consistently present within MDD. Combining the need for reliable depression markers with the electroencephalogram’s (EEG) high clinical utility, we investigated whether aberrant EEG sgACC–DLPFC functional connectivity could serve as a marker for depression. Source-space Amplitude Envelope Correlations (AEC) of 20 MDD patients and 20 matched controls were contrasted using non-parametric permutation tests. In addition, extracted AEC values were used to (a) correlate with characteristics of depression and (b) train a Support Vector Machine (SVM) to determine sgACC–DLPFC connectivity’s discriminative power. FDR-thresholded statistical maps showed reduced sgACC–DLPFC AEC connectivity in MDD patients relative to controls. This diminished AEC connectivity is located in the beta-1 (13–17 Hz) band and is associated with patients’ lifetime number of depressive episodes. Using extracted sgACC–DLPFC AEC values, the SVM achieved a classification accuracy of 84.6% (80% sensitivity and 89.5% specificity) indicating that EEG sgACC–DLPFC connectivity has promise as a biomarker for MDD.