Journal of affective disorders · 2026
Connectome-based graph metrics predict electroconvulsive therapy response in adults with depression.
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Ramon François, Attali David, Paolillo Cécile, Le Berre Alice, Uszynski Ivy, Charron Sylvain, Lui Maliesse, Debacker Clement, Haroche Alexandre, Dadi Ghita, Moyal Mylène, Henensal Adèle, Mekaoui Lila, Gorwood Philip, Poupon Cyril, Oppenheim Catherine, Plaze Marion, Cachia Arnaud
Аннотация
INTRODUCTION: Electroconvulsive therapy (ECT) is the most effective intervention for depression, yet no validated biomarkers reliably predict which patients will remit. Large-scale structural and functional dysconnectivity is well-established in major depressive disorder, motivating the use of graph-theoretical metrics to capture network architecture and identify treatment-sensitive markers.
METHODS: In a prospective longitudinal study, 41 adult patients with a major depressive episode undergoing ECT and 24 healthy controls underwent anatomical, diffusion and resting-state functional MRI at baseline (V1). Patients were reassessed after five ECT sessions (V2, n = 31) and two weeks following treatment completion (V3, n = 29). Structural networks were constructed from multi-shell diffusion tractography and functional networks from rs-fMRI. Longitudinal change of global and local efficiency was assessed using linear mixed-effects models, and baseline predictors of remission (MADRS ≤10 at V3) were evaluated using ANOVAs and penalized binomial regression.
RESULTS: Graph-theory metrics remained stable across timepoints, indicating no large-scale network reorganization during ECT. At baseline, patients showed reduced structural local efficiency relative to controls. Future remitters exhibited lower structural local and global efficiency in fractional anisotropy and neurite density index (NDI)-weighted networks at baseline compared with non-remitters, whereas non-remitters showed reduced functional local efficiency at baseline relative to controls. Penalized regression identified baseline NDI-weighted global efficiency as a significant predictor of remission.
CONCLUSION: Baseline structural and functional network profiles distinguished ECT remitters from non-remitters. These findings suggest that non-remission is characterized by reduced functional integration with relatively preserved structural connectivity, while remission is associated with preserved functional organization but lower baseline structural efficiency.
METHODS: In a prospective longitudinal study, 41 adult patients with a major depressive episode undergoing ECT and 24 healthy controls underwent anatomical, diffusion and resting-state functional MRI at baseline (V1). Patients were reassessed after five ECT sessions (V2, n = 31) and two weeks following treatment completion (V3, n = 29). Structural networks were constructed from multi-shell diffusion tractography and functional networks from rs-fMRI. Longitudinal change of global and local efficiency was assessed using linear mixed-effects models, and baseline predictors of remission (MADRS ≤10 at V3) were evaluated using ANOVAs and penalized binomial regression.
RESULTS: Graph-theory metrics remained stable across timepoints, indicating no large-scale network reorganization during ECT. At baseline, patients showed reduced structural local efficiency relative to controls. Future remitters exhibited lower structural local and global efficiency in fractional anisotropy and neurite density index (NDI)-weighted networks at baseline compared with non-remitters, whereas non-remitters showed reduced functional local efficiency at baseline relative to controls. Penalized regression identified baseline NDI-weighted global efficiency as a significant predictor of remission.
CONCLUSION: Baseline structural and functional network profiles distinguished ECT remitters from non-remitters. These findings suggest that non-remission is characterized by reduced functional integration with relatively preserved structural connectivity, while remission is associated with preserved functional organization but lower baseline structural efficiency.