Unique Brain Network Identification Number for Parkinson's and Healthy Individuals Using Structural MRI

Area of Science:

• Neuroimaging
• Computational Neuroscience
• Biophysics

Background:

• Individual brain network variability poses challenges for neuroimaging analysis.
• Understanding age-related changes in brain connectivity is crucial for diagnosing neurological disorders.

Purpose of the Study:

• To introduce a novel algorithm, Unique Brain Network Identification Number (UBNIN), for encoding individual brain networks.
• To investigate age-related variations in brain network topology and connectivity patterns in Parkinson's disease (PD) patients and healthy controls (HC).

Main Methods:

• Structural MRI data from 180 PD patients and 70 HC were analyzed.
• Brain networks were constructed using correlation matrices of gray matter volumes, and unique codes (UBNIN) were derived.
• Connectivity metrics, including the clustering coefficient, were computed across five age cohorts using sparsity thresholds.

Main Results:

• The UBNIN algorithm generated distinct numerical representations for each individual brain network.
• A decreasing trend in the mean clustering coefficient was observed with increasing sparsity across all age cohorts.
• Significant differences in clustering coefficients were found between various age groups in both PD and HC cohorts, indicating age-related changes in network topology.

Conclusions:

• The UBNIN algorithm provides a unique neural signature for individual brain connectivity, applicable across neuroimaging modalities.
• Brain network connectivity patterns demonstrably change with age, suggesting age-related network degeneration and altered information transfer.
• The findings highlight the potential of UBNIN for brainprinting applications and understanding neuropathological changes associated with aging.