Home DocumentationSeeing Connectivity

Seeing Connectivity

HINEC provides tools to inspect, compare, and export white matter pathways — from quick checks to publication-quality figures.

Overview

HINEC does not stop at computation. It provides a suite of visualization tools optimized for different tasks — from instant DTI quality checks to interactive 3D tractography exploration and publication-quality exports.

Tool	Type	Speed	Use Case
nim_plot	DTI eigenvectors	Instant	Quick DTI quality check
visualizeTractography	3D tractography	5-30s	Comprehensive 3D exploration
visualizeTractographySlices	2D slices	5-30s/slice	Detailed slice-by-slice inspection
generateSlices + FastTractographyViewer.py	Pre-computed 2D	<100ms/slice	Fast daily navigation
nim_plot_tractography	Basic 3D	2-10s	Quick track visualization
nim_plot_connectivity_matrix	2D heatmap	1-5s	Connectivity analysis
nim_plot_vector_field	2D quiver	1-2s	Direction field inspection

Which Tool to Use

What do you need?

├── Quick DTI quality check → nim_plot

├── 3D whole-brain tractography → visualizeTractography('mode', 'whole')

├── Specific brain region → visualizeTractography('mode', 'region')

├── All regions at once → visualizeTractography('mode', 'grid')

├── Step through regions → visualizeTractography('mode', 'sequential')

├── Slice-by-slice inspection → visualizeTractographySlices

├── Fast daily navigation → generateSlices + FastTractographyViewer.py

├── Connectivity between regions → nim_plot_connectivity_matrix

├── Direction field → nim_plot_vector_field

└── Publication figures → visualizeTractography + 'export' option

DTI Eigenvector Visualization

nim_plot displays principal eigenvectors color-coded by direction. This is the primary tool for verifying DTI processing quality.

matlab

nim_plot(nim, 'mode', 'single');              % Whole volume
nim_plot(nim, 'mode', 'parcel', 'region_id', 5); % Single region
nim_plot(nim, 'mode', 'parcels');              % All parcellation regions
nim_plot(nim, 'mode', 'single', 'downsample', 3); % Every 3rd voxel

3D Tractography Visualization

visualizeTractography is the primary tool for comprehensive 3D exploration. It supports four modes, multiple color schemes, track filtering, and publication export.

matlab

% Whole brain view
visualizeTractography('tracks.mat', 'nim.mat', 'mode', 'whole');
 
% Single region with FA coloring
visualizeTractography('tracks.mat', 'nim.mat', ...
    'mode', 'region', 'regions', [5], 'color_mode', 'fa');
 
% Grid view of all regions
visualizeTractography('tracks.mat', 'nim.mat', 'mode', 'grid');
 
% From run directory (auto-detects latest tracks)
visualizeTractography('hinec_runs/run_2025-01-15/', 'nim.mat');

Track Coloring

Mode	Description	Best For
'direction'	RGB from eigenvector (R=L/R, G=A/P, B=S/I)	Anatomical orientation
'fa'	Hot colormap based on mean FA along track	White matter integrity
'uniform'	Single color for all tracks	Simplicity
'region'	Random color per parcellation region	Region comparison

Slice-by-Slice Inspection

For detailed investigation of individual slices, visualizeTractographySlices renders 2D cross-sections with track overlays. For fast daily navigation, pre-compute slices and use the Python-based viewer.

matlab

% Interactive slice viewer
visualizeTractographySlices('tracks.mat', 'nim.mat');
 
% Pre-compute slices for fast viewing
generateSlices('tracks.mat', 'nim.mat', 'output_dir', 'slices/');
% Then in terminal:
python FastTractographyViewer.py slices/

Connectivity Matrix

nim_plot_connectivity_matrix generates a heatmap showing the number of tracks connecting each pair of brain regions. This is the primary output for structural connectivity analysis.

matlab

nim_plot_connectivity_matrix(nim, tracks);

DTI Color Convention

HINEC follows the standard neuroimaging color convention for eigenvector visualization:

Red

Left / Right (X axis)

Green

Anterior / Posterior (Y axis)

Blue

Superior / Inferior (Z axis)

Note

Colors are derived from the absolute value of the primary eigenvector components using the vector_to_color() function.

Tractography

YAML Configuration