1. An electroencephalography connectome predictive model of major depressive disorder severity
- Author
-
Kabbara, Aya, Robert, Gabriel, Khalil, Mohamad, Verin, Marc, Benquet, Pascal, Hassan, Mahmoud, Lebanese Association for Scientific Research [Tripoli] (LASeR), MINDig, Centre Hospitalier Guillaume Régnier [Rennes], Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT), Comportement et noyaux gris centraux - EA 4712, Université de Rennes (UR)-Institut des Neurosciences Cliniques de Rennes (INCR), emlyon business school (EM), Lebanese University [Beirut] (LU), Ecole Doctorale des Sciences et de la Technologie (EDST), Azm Center for research in biotechnology and its applications [TRIPOLI], Université Libanaise, CHU Pontchaillou [Rennes], Laboratoire Traitement du Signal et de l'Image (LTSI), Université de Rennes (UR)-Institut National de la Santé et de la Recherche Médicale (INSERM), Reykjavík University, robert, gabriel, Comportement et noyaux gris centraux = Behavior and Basal Ganglia [Rennes], Université de Rennes (UR)-Université européenne de Bretagne - European University of Brittany (UEB)-CHU Pontchaillou [Rennes]-Institut des Neurosciences Cliniques de Rennes (INCR), This work was financed by the Rennes University, the Institute of Clinical Neuroscience of Rennes (Projects named EEGCog and EEGNET3). This work was also financed by the AZM and SAADE Association, Tripoli, Lebanon and by the National Council for Scientific Research (CNRS) in Lebanon. Authors would like to thank Campus France, Programme Hubert Curien CEDRE (PROJET N° 42257YA) and the Lebanese Association for Scientific Research (LASER) for their support, HAL UR1, Admin, Université de Rennes (UR)-Institut des Neurosciences Cliniques de Rennes = Institute of Clinical Neurosciences of Rennes (INCR), and Université de Rennes (UR)-Université européenne de Bretagne - European University of Brittany (UEB)-CHU Pontchaillou [Rennes]-Institut des Neurosciences Cliniques de Rennes = Institute of Clinical Neurosciences of Rennes (INCR)
- Subjects
[SDV.IB] Life Sciences [q-bio]/Bioengineering ,Depressive Disorder, Major ,Multidisciplinary ,[SDV.NEU.PC]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC]/Psychology and behavior ,[SDV]Life Sciences [q-bio] ,[SDV.MHEP.PSM] Life Sciences [q-bio]/Human health and pathology/Psychiatrics and mental health ,[SDV.NEU.PC] Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC]/Psychology and behavior ,Brain ,Electroencephalography ,[STAT] Statistics [stat] ,Machine Learning ,[STAT]Statistics [stat] ,[SDV] Life Sciences [q-bio] ,FOS: Biological sciences ,Quantitative Biology - Neurons and Cognition ,[SDV.MHEP.PSM]Life Sciences [q-bio]/Human health and pathology/Psychiatrics and mental health ,Connectome ,Humans ,Neurons and Cognition (q-bio.NC) ,[SDV.IB]Life Sciences [q-bio]/Bioengineering ,[SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing ,[SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processing - Abstract
Emerging evidence showed that major depressive disorder (MDD) is associated with disruptions of brain structural and functional networks, rather than impairment of isolated brain region. Thus, connectome-based models capable of predicting the depression severity at the individual level can be clinically useful. Here, we applied a machine-learning approach to predict the severity of depression using resting-state networks derived from source-reconstructed Electroencephalography (EEG) signals. Using regression models and three independent EEG datasets (N = 328), we tested whether resting state functional connectivity could predict individual depression score. On the first dataset, results showed that individuals scores could be reasonably predicted (r = 0.6, p = 4 × 10–18) using intrinsic functional connectivity in the EEG alpha band (8–13 Hz). In particular, the brain regions which contributed the most to the predictive network belong to the default mode network. We further tested the predictive potential of the established model by conducting two external validations on (N1 = 53, N2 = 154). Results showed statistically significant correlations between the predicted and the measured depression scale scores (r1 = 0.52, r2 = 0.44, p
- Published
- 2022
- Full Text
- View/download PDF