Paper Info
Title
HOW MANY FMRI SCANS ARE NECESSARY AND SUFFICIENT FOR RESTING BRAIN CONNECTIVITY ANALYSIS?
Abstract
Functional connectivity analysis by detecting neuronal co-activation in the brain can be efficiently done using Resting State Functional Magnetic Resonance Imaging (rs-fMRI) analysis. Most of the existing research in this area employ correlation-based group averaging strategies of spatial smoothing and temporal normalization of fMRI scans, whose reliability of results heavily depends on the voxel resolution of fMRI scan as well as scanning duration. Scanning period from 5 to 11 minutes has been chosen by most of the studies while estimating the connectivity of brain networks. In this study, we have demonstrated that Dictionary Learning (DL) based rs-fMRI analysis, due to its inherent sparsity constraint-based matrix factorization property, can identify all the functional networks efficiently even with a limited number of scans. Preliminary results demonstrate that a scan of 1 minute is sufficient for DL to produce all the networks with better accuracy, computation, and reliability than existing methods.
Year
DOI
Venue
2018
10.1109/GlobalSIP.2018.8646415
2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP)
Keywords
Field
DocType
Dictionary Learning,fMRI,sparse,ICC
Voxel,Normalization (statistics),Functional magnetic resonance imaging,Pattern recognition,Computer science,Resting state fMRI,Matrix decomposition,Correlation,Smoothing,Artificial intelligence,Computation
Conference
ISSN
ISBN
Citations 
2376-4066
978-1-7281-1295-4
0
PageRank 
References 
Authors
0.34
0
4
Name
Order
Citations
PageRank
Debadatta Dash121.42
Anil Kumar Sao212919.67
Jun Wang314415.26
Bharat B Biswal450336.32