IJBTT

Artificial Neural Networks for fMRI Data Analysis: A Survey

	International Journal of Engineering Trends and Technology (IJETT)
	© 2017 by IJETT Journal
	Volume-49 Number-8
	Year of Publication : 2017
	Authors : Ashwini S. Savanth, Dr. P.A.Vijaya
	DOI :  10.14445/22315381/IJETT-V49P275

Citation 

Ashwini S. Savanth, Dr. P.A.Vijaya "Artificial Neural Networks for fMRI Data Analysis: A Survey", International Journal of Engineering Trends and Technology (IJETT), V49(8),487-494 July 2017. ISSN:2231-5381. www.ijettjournal.org. published by seventh sense research group

Abstract
fMRI is a valuable experimental and diagnostic tool for assessing the human body especially the brain. It has emerged as a successful tool in the investigation of cognitive functions. fMRI data was traditionally analysed with the univariate method, the popular one being Statistical Parametric Mapping based on the General Linear Model. But lately MVPA has been used to perform multivariate analysis of fMRI data. The multivariate approach originates from a field called as Machine Learning which is a branch of Artificial Intelligence. The Multivariate approaches have several advantages over the univariate approach, in that the Artificial Neural Networks (ANN) have outperformed some of the other classifiers such as Gaussian Naive Bayes, ICA and others. In this paper, an attempt is made to survey MVPA analysis of brain fMRI data using Artificial Neural Networks.

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Keywords
Artificial Neural Networks, Feedforward, MVPA, Self Organizing Map, Univariate.