TY  - JOUR
T1  - Functional Mribold Interpretation of Brain Functions Using
Independent Component Analysis
AU - Suresh, P. AU - Bommannaraja, K. 
JO  - Asian Journal of Information Technology
VL  - 15
IS  - 10
SP  - 1607
EP  - 1620
PY  - 2016
DA  - 2001/08/19
SN  - 1682-3915
DO  - ajit.2016.1607.1620
UR  - https://makhillpublications.co/view-article.php?doi=ajit.2016.1607.1620
KW  - Functional neuroimaging
KW  -Functional magnetic resonance imaging (fMRI)
KW  -Independent Component Analysis (ICA)
KW  -inter-task fMRI analysis
KW  -Principal Component Analysis (PCA)
AB  - Functional MRI (fMRI) is a widely used technique to study about the brain activation and its
implications through Blood Oxygenation Level Dependent (BOLD) interpretations. Data-driven analysis
methods, in particular Independent Component Analysis (ICA) have proven quite useful for the analysis of
fMRI data. A promising approach to multi-subject analysis is Group Independent Component Analysis (GICA),
which identifies group components and reconstructs activations at the individual level. In this research, a
robust model-free technique is proposed for detecting the fMRI activations during the tasks activating the
language areas of the Brain. We evaluated the performance of the proposed method on a moderate size real time
fMRI data acquired under three different tasks. This results in a set of spatial maps and time courses which are
common to the whole group, together with an individual response activation map for each of the subjects in
the group. The results show that, ICA components are involved in the direct correlation between language
based tasks and their spatial/time course maps.
ER  -