Lead-DBS

Lead-DBS enables reconstruction and localization of deep brain stimulation (DBS) electrodes and performs imaging-based connectivity and volume-of-tissue-activated analyses to inform DBS placement and outcomes.

Key Features:

• Electrode Placement and Reconstruction: Precise reconstruction and localization of DBS electrodes with multiple reconstruction methods and a specific refinement procedure.
• Multispectral Nonlinear Registration: Co-registration of imaging data across spectral domains using advanced nonlinear registration algorithms.
• Brain Shift Correction: Correction methods for intraoperative brain shift to improve spatial accuracy of electrode localization.
• Connectivity Analyses: Support for structural and functional connectivity analyses linking electrode locations to brain networks.
• Microelectrode Recording Reconstruction: Methods for reconstructing microelectrode recordings for detailed neural mapping during DBS.
• Orientation Detection: Detection of orientation of segmented DBS leads to refine electrode positioning information.
• Volume of Tissue Activated (VTA) Calculation: Calculation of VTA using multiple models and parameters to simulate different DBS settings.
• Whole-brain Tractography: Application of tractography algorithms to preoperative diffusion MRI for comprehensive connectivity mapping.

Scientific Applications:

• High-field Imaging Analysis (3T): Use of 3T imaging with comparative co-registration algorithms and nonlinear warping for high-fidelity spatial alignment.
• Clinical Setting Applications (1.5T): Application to typical clinical 1.5T MRI datasets to support retrospective cohort analyses.
• Volume of Tissue Activated (VTA) Simulation: Simulation of VTA under multiple models and parameter settings to study stimulation effects on brain tissue.
• Whole-brain Tractography and Connectivity Mapping: Whole-brain tractography from preoperative diffusion MRI to map connections between stimulated regions and other brain areas.
• Retrospective Cohort Analysis in Parkinson's Disease (PD): Evaluation of preprocessing strategy impacts on clinical outcomes in retrospective samples, including a cohort of 51 PD patients.

Methodology:

Implemented as a MATLAB toolbox; employs five comparative co-registration algorithms in high-field examples, ten nonlinear warping approaches to template space, and evaluates the impact of preprocessing strategies on clinical outcomes in retrospective cohorts (e.g., 51 PD patients).