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Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

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

Tanmayee Samantaray¹, Utsav Gupta¹, Jitender Saini²

¹Neural Engineering Lab, Department of Biosciences and Bioengineering, Indian Institute of Technology Guwahati, Guwahati 781039, India.

|September 28, 2023

Summary

A new algorithm, Unique Brain Network Identification Number (UBNIN), uniquely encodes individual brain networks using structural MRI. This method reveals age-related changes in brain connectivity, offering insights into network degeneration.

Keywords:

Parkinson’s disease Unique Brain Network Identification Number age brain connectivity clustering coefficient

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Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease

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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.