Mandal, Pravat K and Shukla, Deepika (2018) Brain Metabolic, Structural, and Behavioral Pattern Learning for Early Predictive Diagnosis of Alzheimer's Disease. Journal of Alzheimer’s Disease, 63 (3). pp. 935-939.

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Abstract

Alzheimer's disease (AD) is a devastating neurodegenerative disorder affecting millions of people worldwide. Laboratory research and longitudinal clinical studies have helped to reveal various information about the disease but the exact causal process is not known yet. Patterns from alteration of neurochemicals (e.g., glutathione depletion, etc.), hippocampal atrophy, and brain effective connectivity loss as well as associated behavioral changes have generated important characteristic features. These imaging-based readouts and neuropsychological outcomes along with supervised clinical review are critical for developing a comprehensive artificial intelligence strategy for early predictive AD diagnosis and therapeutic development.

Item Type:	Article
Subjects:	Neurodegenerative Disorders Neuro-Oncological Disorders Neurocognitive Processes Neuronal Development and Regeneration Informatics and Imaging Genetics and Molecular Biology
Depositing User:	Dr. D.D. Lal
Date Deposited:	09 May 2018 09:44
Last Modified:	13 Dec 2021 09:51
URI:	http://nbrc.sciencecentral.in/id/eprint/348

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