This research also indicates that, with age, human brain connectivity changes, as illustrated by obtained network metrics
Researchers at Indian Institute of Technology, Guwahati seem to have developed a novel algorithm, Unique Brain Network Identification Number (UBNIN), designed to encode the intricate brain networks of healthy humans and patients with Parkinson’s disease (PD).
This study involved analysis of structural brain MRI scans of 180 PD patients and 70 healthy individuals from National Institute of Mental Health and Neurosciences (NIMHANS), India. The researchers adopted a network perspective, representing different brain regions as nodes and establishing connection values of the network based on regional grey matter volume. Further, connection values for every node was weighted to capture the significance of each link by following a series of algorithmic steps. The hence obtained numerical representation (UBNIN) was observed to be distinct for each individual brain network, also applicable to other neuroimaging brain modalities. This innovative research holds immense potential in the realm of brain printing and emerges as a promising biomarker with numerical value for tracking mental illness progression over time.
Also, Parkinson’s disease, a neurodegenerative disorder, with clinical symptoms such as tremors, stiffness, and slow movement, worsens with age. However, neurodegeneration begins long before these symptoms appear, making early detection imperative for effective PD management. Addressing this critical gap, in a first-of-its-kind study, IIT Guwahati and NIMHANS researchers used non-invasive structural MRI scans during rest. Categorizing PD patients into five age groups (A: ≤ 32 years, B: 33–42 years, C: 43–52 years, D: 53–62 years, and E: ≥ 63 years), this study delved into how age impacts the brain connectivity over varying sparsities. For each age cohort, clustering coefficient presented a decreasing trend with increasing sparsity.
Explaining the research findings, Dr Cota Navin Gupta, Assistant Professor, Neural Engineering Lab, Department of Biosciences and Bioengineering, IIT Guwahati said, “UBNIN is a special number representing unique characteristics of each human brain from a network perspective. Interestingly, we can also reverse engineer any human’s UBNIN value to reconstruct the original brain network. This UBNIN algorithm will enable us to identify and characterize (encode-decode) brain networks of every human beings efficiently.”
PhD Scholar Tanmayee Samantaray further added, “Applying UBNIN algorithm on longitudinal neuroimaging data (i.e. over time) holds promise for elucidating the dynamics of brain plasticity (i.e. changes in human brain). This insight is crucial to understand how the human brain degenerates, and copes with damage due to underlying neurological diseases.”
The developed UBNIN algorithm makes MRI data interpretable and holds a potential to transform neurodegenerative disorder diagnosis and treatment. This may be used as a biomarker to complement other diagnostic tests recommended by neurologists. UBNIN’s applications range from brainprinting to optimizing storage for structural MRI brain networks. This could open avenues for low-bandwidth, high-speed information transfer of human brain networks for Telemedicine and related applications. UBNIN’s adaptability could also be extended to other neuroimaging modalities like electroencephalogram (EEG), functional MRI (both resting and task based), etc. It can also be applied to other neurological conditions like Schizophrenia, Alzheimer’s, Depression, etc. Furthermore, it may be implemented on various datasets such as protein, social and traffic networks, making it a versatile tool for understanding complex system dynamics.
Further, Dr Gupta added “We are now looking into the possibilities of using UBNIN as a potential biomarker to distinguish healthy and Parkinson’s at group level.”
The current findings have been published in the journal Brain Sciences and was co-authored by Tanmayee Samantaray, Utsav Gupta, Dr Jitender Saini and Dr Cota Navin Gupta.
This research has been funded by the Ministry of Education (MoE) doctoral scholarship, Government of India and supported by the Scheme for Promotion of Academic and Research Collaboration (SPARC Grant), Government of India.