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Evaluation of Default Mode Network In Mild Cognitive Impairment and Alzheimer's Disease Individuals
Hichem Metmer, QingHua Zhao, Chong Ji, Junwei Xiao, Jianfeng Lu
Pages - 1 - 12     |    Revised - 31-12-2016     |    Published - 31-01-2017
Volume - 11   Issue - 1    |    Publication Date - February 2017  Table of Contents
Alzheimer's Disease, Mild Cognitive Impairment, Functional Magnetic Resonance Imaging, Independent Component Analysis, Default Mode Network.
Although progressive functional brain network disorders has been one of the indication of Alzheimer's disease, The current research on aging and dementia focus on diagnostics of the cognitive changes of normal aging and Alzheimer Disease (AD), these changes known as Mild Cognitive Impairment (MCI). The default mode network (DMN) is a network of interacting brain regions known to have activity highly correlated with each other and distinct from other networks in the brain, the default mode network is active during passive rest and consists of a set of brain areas that are tightly functionally connected and distinct from other systems within the brain. Anatomically, the DMN includes the posterior cingulated cortex (PCC), dorsal and ventral medial prefrontal cortex, the lateral parietal cortex, and the medial temporal lobes. DMN involves multiple anatomical networks that converge on cortical hubs, such as the PCC, ventral medial prefrontal, and inferior parietal cortices. The aim of this study was to evaluate the default mode network functional connectivity in MCI patients. While no treatments are recommended for MCI currently, Mild Cognitive Impairment is becoming a very important subject for researchers and deserves more recognition and further study, In order to increase the ability to recognize earlier symptoms of Alzheimer's disease.
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Mr. Hichem Metmer
Mr. QingHua Zhao
School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing, 210094, Republic of China - China
Mr. Chong Ji
School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing, 210094, Republic of China - China
Dr. Junwei Xiao
School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing, 210094, Republic of China - China
Professor Jianfeng Lu