NeuralCipher Research overview
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Abstract Precise targeting in deep brain stimulation (DBS) is challenged by individual neuroanatomical variability and postoperative brain shift, often compromising therapeutic efficacy in movement disorders like Parkinson's disease. Conventional atlas-based approaches lack patient-specific models to predict stimulation field interactions with target nuclei (e.g., STN, GPi). Here we present an integrative computational pipeline combining multi-modal imaging with biophysical simulation to enable personalized DBS planning. Our framework leverages: 1) multi-modal registration (advanced normalization tools, ANTs; or statistical parametric mapping, SPM) with subcortical brain shift correction, significantly reducing electrode placement error; 2) AI-driven electrode reconstruction (PaCER) achieving 0.4 ± 0.1 mm contact localization accuracy; and 3) patient-specific finite element modelling (iso2mesh/TetGen) predicting confined stimulation fields. Validated on clinical imaging data (pre-op T1/T2 MRI; post-op CT), the pipeline generated anatomically grounded electrophysiological models in
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