Finding maximally disconnected subnetworks with shortest path tractography.

Area of Science:

• Neuroscience
• Medical Imaging
• Computational Biology

Background:

• Connectome-based lesion symptom mapping (CLSM) links brain network disruptions to clinical outcomes.
• Current CLSM methods require improvement for accurate disconnectome computation.

Purpose of the Study:

• To introduce a novel, fast, and accurate method for computing disconnectomes.
• To extend CLSM by quantifying connectivity loss due to lesions.

Main Methods:

• Developed a greedy algorithm to find maximally disconnected subgraphs.
• Constructed probability-weighted structural connectivity matrices from Human Connectome Project data.
• Calculated percent connectivity loss based on lesion intersection with white matter pathways.

Main Results:

• Identified major disconnections in the thalamus, basal ganglia, and inferior parietal cortex in stroke patients.
• The size of the maximally disconnected subgraph correlated strongly with clinical measures.
• Demonstrated visualization and quantification of cortical disconnection without diffusion imaging.

Conclusions:

• The novel method provides a fast and reliable way to visualize and quantify brain disconnections from routine MRI.
• This approach can be applied to large patient databases for diseases like stroke and multiple sclerosis.
• Potential for identifying biomarkers for patient recovery potential.