- Comprehensive pipeline combining dMRI probabilistic tractography with dynamical-systems modelling to simulate EEG-like dynamics from individual structural connectomes and extract multi-level connectivity features.
- Novel joint two-brain simulation generates and ranks candidate inter-brain coupling features within a single system for hypothesis-driven testing.
- Simulated intra-individual synchrony added discriminative information, boosting ASD-control classification from 71.2% accuracy (AUC 0.843) to 77.6% (AUC 0.871).
J Neurosci Methods. 2026 Nov 5:110943. doi: 10.1016/j.jneumeth.2026.110943. Online ahead of print.
ABSTRACT
BACKGROUND: Computational models can link anatomical structure to neural dynamics. However, there is no validated, reproducible pipeline that simulates individual and inter-brain EEG-like dynamics from an individual’s own structural connectome.
NEW METHOD: To address this, we developed a comprehensive computational pipeline that combines probabilistic tractography from Diffusion Magnetic Resonance Imaging (dMRI) with dynamical systems modeling to simulate EEG-like neural dynamics from individual connectomes. It extracts anatomical connectivity, intra-individual functional connectivity, and inter-individual (two-brain) functional coupling features. It then evaluates their group-discriminative information with random-forest classification under repeated nested cross-validation. Simulated phase-locking values are calibrated against empirical resting-state EEG.
RESULTS: In a test sample of individuals with autism spectrum disorder (ASD) and matched controls (16 each), simulated intra-individual synchrony added classification information beyond anatomy, raising accuracy from 71.2% (AUC 0.843) to 77.6% (AUC 0.871), in a sample where groups did not differ in mean structural connectivity.
COMPARISON WITH EXISTING METHODS: Unlike current single-brain simulations, this pipeline generates and jointly simulates two individual brain models within a single system, and reports the contribution of each feature level explicitly.
CONCLUSIONS: The pipeline provides a reproducible, extensible framework for deriving informative individual-level dynamic features from anatomy. The two-brain extension generates and ranks candidate inter-brain features, providing a principled way to identify between-brain measures for hypothesis-driven testing.
PMID:42988068 | DOI:10.1016/j.jneumeth.2026.110943
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