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Individualized rTMS Treatment for Depression using an fMRI-Based Targeting Method
Published on: August 2, 2021
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Functional brain network features specify DBS outcome for patients with treatment resistant depression
Amir Hossein Ghaderi1,2,3, Elliot C Brown2,3,4,5,6,7, Darren Laree Clark2,3,4
1Department of Psychology, University of Calgary, Calgary, AB, Canada.
Molecular Psychiatry
|July 20, 2023
Summary
Brain network features identified via EEG can predict treatment outcomes for deep brain stimulation (DBS) in treatment-resistant depression (TRD). Specific network changes correlate with symptom improvement, suggesting biomarkers for patient selection and therapeutic mechanisms.
Area of Science:
- Neuroscience
- Computational Psychiatry
- Biomarkers
Background:
- Deep brain stimulation (DBS) offers therapeutic potential for treatment-resistant depression (TRD).
- Subcallosal cingulate gyrus (SCG) DBS targets cortical-subcortical dysregulation, but patient selection remains a challenge due to suboptimal response rates.
- Brain network function is influenced by DBS, suggesting network features could serve as predictive biomarkers.
Purpose of the Study:
- To investigate if longitudinal changes in brain network features, assessed using resting-state EEG and graph theoretical analysis, can predict treatment response to SCG-DBS in TRD patients.
- To identify specific network topological and dynamical features associated with SCG-DBS outcomes.
- To explore potential mechanisms underlying the therapeutic effects of SCG-DBS.
Main Methods:
- Longitudinal resting-state EEG data were collected from 10 TRD patients undergoing SCG-DBS at baseline, 1-3 months, and 6 months post-surgery.
- Graph theoretical analysis (clustering coefficient, global efficiency, eigenvector centrality, energy, entropy) was applied to source-localized EEG networks.
- Patients were classified as responders based on a ≥50% reduction in Hamilton Depression Rating Scale (HAM-D) scores at 12 months.
Main Results:
- In the delta band, responders exhibited significantly lower global brain network features (segregation, integration, synchronization, complexity) and higher centrality of the subgenual anterior cingulate cortex (ACC) compared to non-responders.
- SCG-DBS longitudinally increased global network features and decreased subgenual ACC centrality.
- Network features effectively separated patient groups, and network changes correlated with depression symptom severity over time.
Conclusions:
- Pre-treatment brain network topology and dynamics, particularly in the delta band, are associated with SCG-DBS treatment response in TRD.
- SCG-DBS enhances brain network integration, segregation, and synchronizability, potentially leading to faster and more efficient information processing.
- Altered connectivity, especially involving the ACC, may mediate the therapeutic effects of SCG-DBS, highlighting network features as potential biomarkers for patient selection.
