Identification of Alzheimer’s Disease using Functional Connectivity Measures
Chaitra N1, Chethana L2, Menaka Shankar3, P.A. Vijaya4

1Chaitra N*, Assistant Professor, Department of ECE, BNM Institute of Technology, Bangalore, India.
2Chethana L, Department of ECE, BNM Institute of Technology, Bangalore, India.
3Menaka Shankar, Department of ECE, BNM Institute of Technology, Bangalore, India.
4Dr. P.A. Vijaya, Professor and Head of the Department, Department of ECE, B N M Institute of Technology, Bangalore, India.

Manuscript received on November 15, 2019. | Revised Manuscript received on 20 November, 2019. | Manuscript published on December 10, 2019. | PP: 1452-1456 | Volume-9 Issue-2, December 2019. | Retrieval Number: B6214129219/2019©BEIESP | DOI: 10.35940/ijitee.B6214.129219
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Abstract: Alzheimer’s disease (AD) is a gradual neuro cognitive disorder caused by the damage of brain cells over a certain period of time. One non-invasive and efficient technique to investigate AD is to use functional magnetic resonance imaging (fMRI). Functional connectivity is a change in the functional connections between brain regions when an activity takes place. The correlation value gives the strength of functional connectivity. Pearson’s correlation method was used to calculate the correlation coefficient between two time series. Mutual information which denotes the information successfully transmitted through a channel was also considered. In this paper, these two measures are compared and their performance and suitability is assessed for fMRI connectivity modelling based on the classification accuracy. Machine learning techniques such as support vector machine (SVM) is employed for connectivity analysis and classification of Alzheimer’s from control population. 
Keywords: Alzheimer’s Disease, Functional Magnetic Resonance Imaging, Functional Connectivity, Pearson’s Correlation Coefficient, Mutual Information, Machine Learning.
Scope of the Article: Machine Learning.