Introspection of UBNIN and Modified-UBNIN Algorithms for Structural MRI. Reply to Kelly et al. A Comparison of Brain-State Representations of Binary Neuroimaging Connectivity Data. Comment on “Samantaray et al. Unique Brain Network Identification Number for Parkinson’s and Healthy Individuals Using Structural MRI. Brain Sci. 2023, 13, 1297”

TL;DR

This paper responds to feedback on a brain network identification method for Parkinson's and healthy individuals using structural MRI data.

Contribution

The authors propose a modified UBNIN algorithm that emphasizes the most connected brain node.

Findings

UBNIN values for Parkinson's and healthy individuals were recalculated with decimal precision.

A new Modified-UBNIN algorithm was introduced to better capture brain network structure.

The authors acknowledge and correct an error in their original rounding methodology.

Abstract

The purpose of this reply is to address the comments given by Kelly et al. on our original paper “Unique Brain Network Identification Number for Parkinson’s and Healthy Individuals using Structural MRI”. We agree to the inadvertent rounding pitfall in our original paper due to the non-inclusion of symbolic math toolbox (MATLAB). We now provide the actual ranges (with decimal values) of the UBNIN values of healthy individuals and those with Parkinson’s disease and further observations. Upon further introspection, we propose another variant, called Modified-UBNIN (UBNIN-MT,MN) which is highly weighted on the node with the highest network degree (i.e., connections). The italicized sentences within inverted commas are statements from Kelly et al.’s comment paper.

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