dimst23/tdcs-anatomy-montage-optimization: tDCS Anatomy-Based Montage Analysis Pipeline v1.1.0

Computational Pipeline for Anatomy-Informed tDCS Electrode Optimization This repository contains a complete computational pipeline for investigating relationships between anatomical features and electric field distribution in transcranial direct current stimulation (tDCS). The code supports the publication "Anatomy-informed recommendations for electrode montage and shape in electrical stimulation methods: A tDCS case study" (Stoupis & Assecondi, 2026). Full documentation and reproduction guides available in the GitHub wiki. Key Features Analysis of N=590 subjects from the HCP-Aging dataset Evaluation of 7 anatomical predictors across 12 electrode configurations (4 montages × 3 shapes) Identification of CSF thickness as the dominant predictor of field distribution PCA analysis revealing 3 components explaining 95.9% of E-field variability Pipeline Components Segmentation: CHARM-based head tissue segmentation and finite element modelingAnalysis: Feature extraction, atlas registration, and SimNIBS-based tDCS simulationStatistics: Mixed-effects regression (GLM), parcellation-based network analysis, and PCA Version 1.1.0 Highlights This release expands statistical analysis capabilities with enhanced spatial and network analysis, improved PCA visualizations, addition of circular 5 cm electrode configurations, and various improvements to plotting and data processing workflows. Software Packages Used SimNIBS 4.1, FreeSurfer 6.0, Python 3.11, R 4.5.2 GitHub Repository: https://github.com/dimst23/tdcs-anatomy-montage-optimization Full Changelog: https://github.com/dimst23/tdcs-anatomy-montage-optimization/compare/v1.0.1...v1.1.0