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

• URL: http://arxiv.org/abs/2306.01689v2
• Date: Tue, 19 Sep 2023 09:20:48 GMT
• Title: Unique Brain Network Identification Number for Parkinson's Individuals Using Structural MRI
• Authors: Tanmayee Samantaray, Utsav Gupta, Jitender Saini, and Cota Navin Gupta
• Abstract summary: We propose a novel algorithm called Unique Brain Network Identification Number, UBNIN for encoding the brain networks of individual subjects. We parcellated each subjects brain volume and constructed an individual adjacency matrix using the correlation between the gray matter volumes of every pair of regions. The numerical representation UBNIN was observed to be distinct for each individual brain network, which may also be applied to other neuroimaging modalities.
• Score: 0.060379119983736775
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We propose a novel algorithm called Unique Brain Network Identification Number, UBNIN for encoding the brain networks of individual subjects. To realize this objective, we employed structural MRI on 180 Parkinsons disease PD patients and 70 healthy controls HC from the National Institute of Mental Health and Neurosciences, India. We parcellated each subjects brain volume and constructed an individual adjacency matrix using the correlation between the gray matter volumes of every pair of regions. The unique code is derived from values representing connections for every node i, weighted by a factor of 2^1-i. The numerical representation UBNIN was observed to be distinct for each individual brain network, which may also be applied to other neuroimaging modalities. This model may be implemented as a neural signature of a persons unique brain connectivity, thereby making it useful for brainprinting applications. Additionally, we segregated the above datasets into five age cohorts to study the variation in network topology over age. Sparsity was adopted as the threshold estimate to binarize each age-based correlation matrix. For each age cohort, a decreasing trend was observed in the mean clustering coefficient with increasing sparsity. Significantly different clustering coefficients were noted in PD between age cohort B and C, C and E, and in HC between E and B, E and C, E and D, and C and D. Our findings suggest network connectivity patterns change with age, indicating network disruption may be due to the underlying neuropathology. Varying clustering coefficients for different cohorts indicate that information transfer between neighboring nodes changes with age. This provides evidence of age related brain shrinkage and network degeneration. We also discuss limitations and provide an open-access link to software codes and a help file for the entire study.

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