Diffusion MRI connectometry automatically reveals affected fiber pathways in individuals with chronic stroke

Area of Science:

• Neuroimaging
• Neuroscience
• Medical Imaging

Background:

• Human connectome databases are emerging but lack individual application.
• Diffusion spectrum imaging (DSI) provides detailed brain connectivity data.
• Individualized analysis of brain pathways is crucial for understanding neurological conditions.

Purpose of the Study:

• To develop and validate diffusion MRI connectometry for analyzing individual brain connectivity.
• To establish a normative dataset for comparison with individual patient data.
• To assess the utility of connectometry in identifying affected brain pathways in stroke patients.

Main Methods:

• Acquired DSI data from 90 subjects to create a normative database.
• Reconstructed patient DSI data to a common stereotaxic space.
• Calculated percentile ranks of diffusion quantities to identify deviant connectivity.
• Used deterministic tractography with low percentile rank seeds to reconstruct affected tracks.

Main Results:

• Diffusion MRI connectometry successfully identified affected brain pathways in 7 chronic stroke patients.
• Connectometry results correlated well with stroke lesions visualized on T2-weighted images.
• Connectometry revealed entire affected tracks, surpassing the lesion-only visualization of T2-weighted, ADC, and FA maps.

Conclusions:

• Diffusion MRI connectometry is a valuable tool for examining deviant brain connectivity in individuals.
• This technique provides a more comprehensive view of affected brain pathways than conventional imaging.
• Connectometry offers personalized insights into structural connectivity relevant for diagnosis and prognosis in neurological diseases.