6Resting-State fMRI: Large Data Analysis in Neuroimaging
M. Menagadevi1*, S. Mangai2, S. Sudha2 and D. Thiyagarajan3
1Department of Biomedical Engineering, Dr. NGP Institute of Technology, Coimbatore, TN, India
2Department of Biomedical Engineering,Velalar College of Engineering and Technology, Erode, TN, India
3Department of Artificial Intelligence and Machine Learning, School of Engineering, Malla Reddy University, Hyderabad, Telangana, India
Abstract
Recent advancements in brain imaging have considerably improved to detect pathophysiological variation in the brain network. Structural and functional characteristics of the brain is understood using different imaging techniques such as angiography, midline ultrasonography, skull radiography, Computed Tomography (CT), Positron Emission Tomography (PET), Magnetic Resonance Imaging (MRI) etc. All the imaging modalities have their unique advantages and limitations. However, among these modalities, resting-state functional Magnetic Resonance Imaging (rfMRI) is the best technique to find brain disorders by measuring the brain connectivity. A huge amount of data is required to explore this complex connectivity network. Recent innovations in imaging techniques, the rfMRI have a unique methodological approach to analyze the big data related to neurological disorders. To find the brain of complex cognitive operations, large set of rfMRI data is required. The development of fMRI in medical applications provides an opportunity to use this ...
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