MCI Detection using fMRI time series embeddings of Recurrence plots

• URL: http://arxiv.org/abs/2311.18265v1
• Date: Thu, 30 Nov 2023 05:57:50 GMT
• Title: MCI Detection using fMRI time series embeddings of Recurrence plots
• Authors: Ninad Aithal, Chakka Sai Pradeep and Neelam Sinha
• Abstract summary: We study the underlying dynamics at ear-marked Regions of Interest (ROIs) to understand structure or lack thereof. This differential behavior could be key to understanding the neurodegeneration and also to classify between healthy and Mild Cognitive Impairment (MCI) subjects. In this study, we consider 6 brain networks spanning over 160 ROIs derived from Dosenbach template, where each network consists of 25-30 ROIs.
• Score: 5.340644246815989
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: The human brain can be conceptualized as a dynamical system. Utilizing resting state fMRI time series imaging, we can study the underlying dynamics at ear-marked Regions of Interest (ROIs) to understand structure or lack thereof. This differential behavior could be key to understanding the neurodegeneration and also to classify between healthy and Mild Cognitive Impairment (MCI) subjects. In this study, we consider 6 brain networks spanning over 160 ROIs derived from Dosenbach template, where each network consists of 25-30 ROIs. Recurrence plot, extensively used to understand evolution of time series, is employed. Representative time series at each ROI is converted to its corresponding recurrence plot visualization, which is subsequently condensed to low-dimensional feature embeddings through Autoencoders. The performance of the proposed method is shown on fMRI volumes of 100 subjects (balanced data), taken from publicly available ADNI dataset. Results obtained show peak classification accuracy of 93% among the 6 brain networks, mean accuracy of 89.3% thereby illustrating promise in the proposed approach.

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