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Functional brain network features specify DBS outcome for patients with treatment resistant depression

Abstract

Deep brain stimulation (DBS) has shown therapeutic benefits for treatment resistant depression (TRD). Stimulation of the subcallosal cingulate gyrus (SCG) aims to alter dysregulation between subcortical and cortex. However, the 50% response rates for SCG-DBS indicates that selection of appropriate patients is challenging. Since stimulation influences large-scale network function, we hypothesized that network features can be used as biomarkers to inform outcome. In this pilot project, we used resting-state EEG recorded longitudinally from 10 TRD patients with SCG-DBS (11 at baseline). EEGs were recorded before DBS-surgery, 1–3 months, and 6 months post surgery. We used graph theoretical analysis to calculate clustering coefficient, global efficiency, eigenvector centrality, energy, and entropy of source-localized EEG networks to determine their topological/dynamical features. Patients were classified as responders based on achieving a 50% or greater reduction in Hamilton Depression (HAM-D) scores from baseline to 12 months post surgery. In the delta band, false discovery rate analysis revealed that global brain network features (segregation, integration, synchronization, and complexity) were significantly lower and centrality of subgenual anterior cingulate cortex (ACC) was higher in responders than in non-responders. Accordingly, longitudinal analysis showed SCG-DBS increased global network features and decreased centrality of subgenual ACC. Similarly, a clustering method separated two groups by network features and significant correlations were identified longitudinally between network changes and depression symptoms. Despite recent speculation that certain subtypes of TRD are more likely to respond to DBS, in the SCG it seems that underlying brain network features are associated with ability to respond to DBS. SCG-DBS increased segregation, integration, and synchronizability of brain networks, suggesting that information processing became faster and more efficient, in those patients in whom it was lower at baseline. Centrality results suggest these changes may occur via altered connectivity in specific brain regions especially ACC. We highlight potential mechanisms of therapeutic effect for SCG-DBS.

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Fig. 1: Study design and methodological approaches.
Fig. 2: Differences between responders and non-responders.
Fig. 3: Correlation analysis between percentage of change in HAM-D (\(\left[ {\frac{{{{{{{\rm{HAMD}}}}}}_{12\,{{{{{\rm{months}}}}}}} - {{{{{\rm{HAMD}}}}}}_{{{{{{\rm{baseline}}}}}}}}}{{{{{{{\rm{HAMD}}}}}}_{{{{{{\rm{baseline}}}}}}}}}} \right] \times 100\)) and graph measures.
Fig. 4: Longitudinal analysis in the DBS Off state.
Fig. 5: Longitudinal analysis in the DBS On state.
Fig. 6: Participants who show lower segregation, integration, and synchronization; and higher ACC-SCG hubness at baseline benefit from SCG-DBS.

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Code availability

CC and EF, EigC were computed using the Brainconnectivity toolbox (https://sites.google.com/site/bctnet/) [32]. H was calculated using the eig function and S was calculated using the entropy function in MATLAB (R2019b). The permutation t-test and FDR analysis codes are available at https://github.com/AHGhaderi/Amir-Hossein-Ghaderi/blob/main/permu.m and https://www.mathworks.com/matlabcentral/fileexchange/27418-fdr_bh respectively. The ensemble KNN approach was implemented using the fitcensemble function with a subspace of knn in MATLAB 2022b (https://www.mathworks.com/products/matlab.html) and the cross-validation approach was carried out using the crossval function. MATLAB code for this classifier is available at https://github.com/AHGhaderi/AHGhaderi/blob/main/trainClassifierEnsemble.m.

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Acknowledgements

The authors thank all participants and acknowledge the funders. Funding was provided by Alberta Innovates Health Solutions (AIHS) and Natural Sciences and Engineering Research Council of Canada (NSERC) to ZHTK (04126-2017) and ABP (05299-2020). AHG was funded by an Eyes-High Postdoctoral Award from the University of Calgary. ECB and DLC were both post-doctoral fellows with AIHS and received additional funding from NSERC-CREATE. RR has received honorarium for serving on the advisory committee of AstraZeneca, Lundbeck, Janssen, Otsuka, and received an investigator-initiated grant from AstraZeneca and Pfizer.

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AHG (conceptualization, methodology, software, formal analysis, writing-original draft, writing – review & editing, visualization, supervision, project administration), ECB (data curation, project administration), DLC (Data curation, Project administration), RR (resources, supervision, project administration), ZHTK (resources, methodology, writing – review & editing, supervision, project administration, funding acquisition), ABP (resources, methodology, writing – review & editing, supervision, project administration, funding acquisition).

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Correspondence to Zelma H. T. Kiss or Andrea B. Protzner.

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Ghaderi, A.H., Brown, E.C., Clark, D.L. et al. Functional brain network features specify DBS outcome for patients with treatment resistant depression. Mol Psychiatry 28, 3888–3899 (2023). https://doi.org/10.1038/s41380-023-02181-1

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