Neurocraft

Neurocraft analyzes microscale brain network dynamics at millisecond timescales to track evolving network states with high temporal and frequency resolution.

Key Features:

• Dynamic Connectivity Analysis: Generates dynamic connectivity maps for investigating transient pathophysiological dynamics in brain networks.
• High-resolution Signal Processing: Employs a signal-processing engine that provides superior temporal and frequency resolution for millisecond-scale analyses.
• Force-Directed Representations and Propagation Models: Uses force-directed visualizations and propagation models to interrogate structural and functional connectivity patterns and network state propagation.
• Machine Learning Integration: Incorporates unsupervised machine-learning algorithms to identify and select key network features at individual and group levels.

Scientific Applications:

• Focal Epilepsy Network Characterization: Applied to intracranial electrode recordings (50 electrodes) during epileptiform discharges lasting less than one second to identify network differences between seizure-free and non-seizure-free patients after resective surgery.
• Real-time Adaptive Neuromodulation Research: Supports decoding transitions from normal to abnormal states relevant to patients with cardiac and neurostimulation devices to inform device-based adaptive neuromodulation strategies.

Methodology:

Uses a high-resolution signal-processing engine to generate dynamic connectivity maps, applies force-directed representations and propagation models, and employs unsupervised machine-learning algorithms to analyze intracranial electrode recordings during epileptiform discharges.