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We propose a novel algorithm called Unique Brain Network IdentificationNumber, UBNIN for encoding the brain networks of individual subjects. Torealize this objective, we employed structural MRI on 180 Parkinsons disease PDpatients and 70 healthy controls HC from the National Institute of MentalHealth and Neurosciences, India. We parcellated each subjects brain volume andconstructed an individual adjacency matrix using the correlation between thegray matter volumes of every pair of regions. The unique code is derived fromvalues representing connections for every node i, weighted by a factor of2^1-i. The numerical representation UBNIN was observed to be distinct for eachindividual brain network, which may also be applied to other neuroimagingmodalities. This model may be implemented as a neural signature of a personsunique brain connectivity, thereby making it useful for brainprintingapplications. Additionally, we segregated the above datasets into five agecohorts to study the variation in network topology over age. Sparsity wasadopted as the threshold estimate to binarize each age-based correlationmatrix. For each age cohort, a decreasing trend was observed in the meanclustering coefficient with increasing sparsity. Significantly differentclustering 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 suggestnetwork connectivity patterns change with age, indicating network disruptionmay be due to the underlying neuropathology. Varying clustering coefficientsfor different cohorts indicate that information transfer between neighboringnodes changes with age. This provides evidence of age related brain shrinkageand network degeneration. We also discuss limitations and provide anopen-access link to software codes and a help file for the entire study.

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