HINEC Documentation
Complete reference for the HIgh-order NEural Connectivity pipeline. From installation to validation, everything you need to process diffusion MRI data into structural connectomes.
What is HINEC?
HINEC is a MATLAB-based neuroscience pipeline that transforms raw diffusion-weighted MRI scans into white matter fiber tract reconstructions. It implements a complete workflow from data preprocessing through diffusion tensor estimation, fractional anisotropy computation, atlas-based parcellation, deterministic fiber tracking, and publication-quality visualization.
The pipeline supports three tracking algorithms — standard FACT (Euler), fourth-order Runge-Kutta (RK4), and adaptive Runge-Kutta-Fehlberg (RKF45) — with YAML-based configuration for reproducible experiments.
Documentation
Getting Started
Prerequisites, installation, and your first pipeline run.
Pipeline
The complete 10-step preprocessing and processing workflow.
Mathematical Foundations
Diffusion tensors, FA, eigenvectors, and the signal model.
Tractography
FACT, RK4, and RKF45 fiber tracking algorithms.
Visualization
3D tractography, slices, connectivity matrices, and export.
YAML Configuration
Presets, parameters, and reproducible experiment setup.
Function Reference
Complete API for every public MATLAB function.
Run Directory System
Timestamped, reproducible run organization.
Validation
ISMRM 2015 challenge scoring and benchmarking.
Troubleshooting
Common issues, diagnostics, and solutions.
Quick Start
# Clone and enter the repository
git clone https://github.com/uicneuro/hinec && cd hinec
# Run with default configuration
./bin/run_hinec.sh data/parcellation_sample/sample sample.mat
# Or use MATLAB directly
main('data/parcellation_sample/sample', 'output/sample.mat');
runTractography('output/sample.mat');See Getting Started for detailed installation and configuration instructions.