Connectomic Predictive Modeling Guides Selective Perturbation of Tracts in the Subcallosal Cingulate White Matter

Autor: Ki Sueng Choi, Helen S. Mayberg, Bryan Howell, Cameron C. McIntyre, Allison C. Waters, Ashan Veerakumar, Mosadoluwa Obatusin
Rok vydání: 2021
Předmět:
Zdroj: NER
DOI: 10.1109/ner49283.2021.9441379
Popis: Deep brain stimulation (DBS) of the subcallosal cingulate (SCC) is an emerging experimental therapy for treatment-resistant depression. Successful outcomes are critically dependent on placing the active contact within the confluence of white matter adjacent to the SCC using patient-specific connectomic guidance, but the relative contribution of each putative target fiber bundle to the clinical response is still unknown. This study's goal was to assess the feasibility of selective target activation of two candidate SCC targets, forceps minor and the cingulum bundle, using biophysical modeling to guide parameter selection in individual patients. We tested two complementary use cases, isolated (or preferential) activation of each target, and activation of the same targets as the clinical setting but with minimal theoretical battery depletion. One selective setting per fiber bundle and one energy-efficient settings per lead were selected from 774 settings, and cortical responses to model settings were evaluated with the left lead at 2 Hz using high-density EEG. Optimal settings differed by patient, hemisphere, and use case. Isolated activation of the left cingulum bundle generated an ipsilateral cortical response with peak activity near 16 ms, whereas preferential activation of forceps minor produced a relatively slower bilateral response in the frontal sensors. A key feature of concomitant target activation was a midline sweep from 40–90 ms. Efficient setting generated a topographically similar response as the monopolar clinical setting but with some differences in the spatial extent of frontal polar activity. The results demonstrate the feasibility of selective perturbation of SCC white matter with model-based guidance.
Databáze: OpenAIRE
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