Neurophysiological Correlates of Neuroanatomical Dimensions in Major Depressive Disorder: Electroencephalographic Markers of Brain Function and Treatment Outcome
Year: 2026
Type: article
Abstract: Major depressive disorder (MDD) is a heterogeneous disorder with variable treatment responses and no established biomarkers for identification or predictors of treatment response. In the COORDINATE-MDD consortium, a data-driven classification identified two neuroanatomic-based dimensions: Dimension 1 (D1), with preserved grey and white matter volumes, and Dimension 2 (D2), with widespread reductions. Here, we investigated whether resting-state electroencephalography (EEG) features differ between these dimensions and whether such features predict treatment response. Participants were 237 MDD (155 women; mean age 37.47 ± 13.36 year) from two clinical trials: CAN-BIND (escitalopram) and EMBARC (randomized to sertraline or placebo). All were medication-free at baseline, in a current depressive episode of at least moderate severity. Resting-state, eyes-closed EEG was recorded at baseline. EEG features included spectral power, frontal alpha asymmetry (FAA), multiscale sample entropy (MSE), and inter-site phase clustering (ISPC). Analyses examined effects of neuroanatomical dimension (D1, D2) and clinical outcome (responder, non-responder), using ANCOVA and threshold-free cluster enhancement (TFCE). D1 participants showed greater absolute and log-transformed theta, alpha, and beta power, and lower relative delta power compared to D2, particularly in frontal and central regions. Among responders, D1 showed higher alpha and theta power and greater MSE at coarse time scales. Individuals with preserved neuroanatomy in D1 exhibit electrophysiological markers of more flexible and integrated brain function, possibly reflecting efficient top-down regulation and adaptive neural dynamics. In contrast, the D2 dimension, marked by lower complexity and elevated delta activity, may reflect disrupted network integrity, reduced cortical arousal, and impaired information processing.
Authors: Wenyi Xiao, Rachel D. Woodham, Mathilde Antoniades, Dhivya Srinivasan, Yong Fan, Guray Erus, Jose A. Garcia, Stephen R. Arnott, Taolin Chen, Ki Sueng Choi, Cherise R. Chin Fatt, Benicio N. Frey, Faranak Farzan, Vibe G. Frøkjær, Melanie Ganz, Beata R. Godlewska, Stefanie Hassel, Keith Ho, Andrew M. McIntosh, Kun Qin, Susan Rotzinger, Matthew D. Sacchet, Jonathan Savitz, Haochang Shou, Ashish Singh, Aleks Stolicyn, Irina Strigo, Stephen C. Strother, Duygu Tosun, Dongtao Wei, Roland Zahn, Ian M. Anderson, W. Edward Craighead, J.F. William Deakin, Boadie W. Dunlop, Rebecca Elliott, Qiyong Gong, Ian H. Gotlib, Catherine J. Harmer, Sidney H. Kennedy, Gitte M. Knudsen, Helen S. Mayberg, Martin P. Paulus, Jiang Qiu, Madhukar H. Trivedi, Heather C. Whalley, Chao Gan Yan, Allan H. Young, Christos Davatzikos, Cynthia H.Y. Fu
Institutions: University of East London, King's College London, University of Pennsylvania, Indoc Research, Rotman Research Institute, Sichuan University, West China Medical Center of Sichuan University, West China Hospital of Sichuan University, Icahn School of Medicine at Mount Sinai, The University of Texas Southwestern Medical Center, St. Joseph’s Healthcare Hamilton, McMaster University, Simon Fraser University, Rigshospitalet, University of Copenhagen, Copenhagen University Hospital, University of Oxford, University of Calgary, Oxford Health NHS Foundation Trust, Warneford Hospital, University Health Network, Mathison Centre for Mental Health Research and Education, Edinburgh Royal Infirmary, Hubei University of Medicine, Taihe Hospital, Harvard University, Unity Health Toronto, Massachusetts General Hospital, Laureate Institute for Brain Research, University of California, San Francisco, Baycrest Foundation, University of Toronto, Southwest University, University of Manchester, Emory University, Stanford University, University of Edinburgh, Tsinghua University, Imperial College London, National Institute for Health and Care Research, NIHR Maudsley Biomedical Research Centre
Language: English
Subfield: Pharmacology (medical)
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $3,700
Awards: Individualized brain systems and depression, Multimodal MRI Characteristics of Psychotherapy Response in Late Life Depression, Neuroimaging abnormalities in major depressive disorder: effect of inflammation, Disentangling the anatomical, functional and clinical heterogeneity of major depression, using machine learning methods, Mapping Heterogeneity of Neuroanatomical Imaging Signatures of Psychosis via Pattern Analysis, Exploiting genomic approaches to identify the environmental basis of depression, Biosignatures of Treatment Remission in Major Depression, Generation Scotland:NextGenScot, Predictors of Treatment Response Relapse and Recurrence in Major Depression, Predictors of Antidepressant Treatment Response: The Emory CIDAR, Inflammatory Transcripts, Genes and Positive Valence System Function in Anhedonia, Machine Learning and Large-scale Imaging analytics for dimensional representations of brain trajectories in aging and preclinical Alzheimer's Disease: The brain aging chart and the iSTAGING consortium, Establishing Moderators/Biosignatures of Antidepressant Response- Clinical Care, The Center for Neuroscience-based Mental Health Assessment and Prediction (NeuroMAP), Interpretable Deep Learning for Analyzing Brain Development Heterogeneity with Personalized Functional Networks Across Multi-Site Data, Psychobiological Mechanisms Underlying the Association Between Early Life Stress and Depression Across Adolescence, Harmonization of Multi-Site Neuroimaging Data from Complex Study Designs, 5/21 ABCD-USA Consortium: Research Project Site at UC San Diego, Fast and robust deep learning tools for analysis of neuroimaging data of Alzheimer's disease, Ultrascale Machine Learning to Empower Discovery in Alzheimers Disease Biobanks
OpenAlex ID: W4410441240