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Updated: Aug 5, 2026

Targeting Neuronal Fiber Tracts for Deep Brain Stimulation Therapy Using Interactive, Patient-Specific Models
Published on: August 12, 2018
Optimizing ECT in a complex neuroanatomical context: A case-based approach combining clinical outcome and electric
Julie Gosez1, Tom Le Tutour2, Anish Sarkar3
1Pierre-Deniker Clinical Research Unit, Henri Laborit Hospital Centre, Poitiers, France; Pprime Institut, UPR 3346, University of Poitiers, Poitiers, France; Ansys France, Villeurbanne, France.
Abstract:
Electroconvulsive therapy (ECT) is an effective intervention for treatment-resistant schizophrenia, but failure to induce adequate seizures remains a clinical challenge, especially in patients with high seizure thresholds. We report the case of a 55-year-old woman with chronic treatment-resistant schizophrenia and a left temporal meningioma, who initially responded to bifrontal ECT but later developed resistance to induced seizures. A switch to right unilateral (RUL) stimulation successfully re-induced therapeutic seizures, with sustained clinical improvement. To understand this outcome, we conducted electric field modeling using patient-specific MRI data and finite-element simulations with SimNIBS. The analysis compared bifrontal and RUL montages, with and without modeling of the meningioma. Results showed no significant impact of the meningioma on current distribution. However, the RUL montage consistently generated higher mean electric fields in cortical regions compared to the bifrontal configuration. These findings offer a mechanistic explanation for the clinical efficacy of the RUL approach in this case and highlight the potential of electric field modeling to optimize stimulation parameters, especially in anatomically complex cases. This case supports the utility of switching electrode montages as a simple, safe, and effective strategy when seizure thresholds rise, and it suggests a promising role for computational modeling in guiding personalized ECT planning. Further research is needed to assess the broader applicability of this approach and to explore its cognitive and functional implications.
