TY  - CONF
Y1  - 2022///
SN  - 9798350334548
PB  - Institute of Electrical and Electronics Engineers Inc.
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85149108259&doi=10.1109%2fICFTSC57269.2022.10040043&partnerID=40&md5=b30dc9e17a93785ba32307251f379fd5
A1  - Sadiq, A.
A1  - Yahya, N.
A1  - Tang, T.B.
A1  - Hashim, H.
A1  - Saad, M.N.M.
EP  - 228
AV  - none
N1  - cited By 0; Conference of 2022 International Conference on Future Trends in Smart Communities, ICFTSC 2022 ; Conference Date: 1 December 2022 Through 2 December 2022; Conference Code:186671
N2  - The assessment of the brain network is a powerful tool for determining brain topological organisation, and it is been employed extensively in the study of mental disorders. Observing the brain's connectivity patterns is one of the most effective approaches for analysing brain functionality. According to recent research, Alzheimer's disease is significantly linked to the changes in network connection among distinct brain regions. In this work, five brain regions are chosen to be analyzed in showing the variation in functional connectivity. These regions are hippocampus, parahippocampus, olfactory, superior parietal gyrus and anterior cingulate gyrus. Pearson's Correlation is used for determining the functional connection between the five brain regions. When comparing Alzheimer's disease (AD) patients to Normal Controls, the findings indicate that there are higher variations of functional connectivity in AD patients. © 2022 IEEE.
KW  - Correlation methods; Functional neuroimaging; Neurodegenerative diseases
KW  -  Alzheimers disease; Brain lobe; Brain networks; Brain regions; Disruption; Functional connectivity; Mental disorders; Resting state; Variance; Variance analysis
KW  -  Brain
TI  - Variance Analysis on Functional Connectivity of Resting-state fMRI Signals in Alzheimer's Disease Patients
ID  - scholars17262
SP  - 223
ER  -