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

Tanmayee Samantaray, Utsav Gupta, Jitender Saini, Cota Navin Gupta

Abstract read

In one paragraph

Article in Brain sciences, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

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3citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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2 · The registry

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3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

4 · The record

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5 · Who and what money

Authors and funding

4 authors.

Tanmayee SamantarayNeural Engineering Lab, Department of Biosciences and Bioengineering, Indian Institute of Technology Guwahati, Guwahati 781039, India.

Utsav GuptaNeural Engineering Lab, Department of Biosciences and Bioengineering, Indian Institute of Technology Guwahati, Guwahati 781039, India.

Jitender SainiDepartment of Neuroimaging and Interventional Radiology, National Institute of Mental Health and Neurosciences, Bengaluru 560029, India.

Cota Navin GuptaNeural Engineering Lab, Department of Biosciences and Bioengineering, Indian Institute of Technology Guwahati, Guwahati 781039, India.ORCID 0000-0001-7402-4038

Funding

Government of India Doctoral GrantScheme for Promotion of Academic and Research Collaboration, Govt of India P1073

6 · The paper itself

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 Parkinson's disease (PD) patients and 70 healthy controls (HC) from the National Institute of Mental Health and Neurosciences, India. We parcellated each subject's 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

Indexed as

agebrain connectivityclustering coefficientParkinson’s diseaseUnique Brain Network Identification Number

Identifiers

PMID37759898

DOI10.3390/brainsci13091297

PMCPMC10526827

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