scholarly journals Independent component analysis of functional MRI: what is signal and what is noise?

2003 ◽  
Vol 13 (5) ◽  
pp. 620-629 ◽  
Author(s):  
M McKeown
Keyword(s):  
Independent Component Analysis ◽  
Functional Mri ◽  
Component Analysis ◽  
Independent Component

scholarly journals Automatic denoising of functional MRI data: Combining independent component analysis and hierarchical fusion of classifiers

NeuroImage ◽  
2014 ◽  
Vol 90 ◽  
pp. 449-468 ◽  
Author(s):  
Gholamreza Salimi-Khorshidi ◽  
Gwenaëlle Douaud ◽  
Christian F. Beckmann ◽  
Matthew F. Glasser ◽  
Ludovica Griffanti ◽  
...  
Keyword(s):  
Independent Component Analysis ◽  
Functional Mri ◽  
Component Analysis ◽  
Independent Component ◽  
Fusion Of Classifiers

scholarly journals Spatial independent component analysis of functional MRI time-series: To what extent do results depend on the algorithm used?

2002 ◽  
Vol 16 (3) ◽  
pp. 146-157 ◽  
Author(s):  
Fabrizio Esposito ◽  
Elia Formisano ◽  
Erich Seifritz ◽  
Rainer Goebel ◽  
Renato Morrone ◽  
...  
Keyword(s):  
Time Series ◽  
Independent Component Analysis ◽  
Functional Mri ◽  
Component Analysis ◽  
Independent Component

scholarly journals Semantic association investigated with functional MRI and independent component analysis

2011 ◽  
Vol 20 (4) ◽  
pp. 613-622 ◽  
Author(s):  
Kwang Ki Kim ◽  
Prasanna Karunanayaka ◽  
Michael D. Privitera ◽  
Scott K. Holland ◽  
Jerzy P. Szaflarski
Keyword(s):  
Independent Component Analysis ◽  
Functional Mri ◽  
Component Analysis ◽  
Independent Component ◽  
Semantic Association

Assessing brain connectivity at rest is clinically relevant in early multiple sclerosis

2012 ◽  
Vol 18 (9) ◽  
pp. 1251-1258 ◽  
Author(s):  
Anthony Faivre ◽  
Audrey Rico ◽  
Wafaa Zaaraoui ◽  
Lydie Crespy ◽  
Françoise Reuter ◽  
...  
Keyword(s):  
Multiple Sclerosis ◽  
Independent Component Analysis ◽  
Functional Mri ◽  
Resting State ◽  
Surrogate Marker ◽  
Component Analysis ◽  
Independent Component ◽  
Resting State Networks ◽  
Frontoparietal Network ◽  
Multiple Sclerosis Patients

Objective: The present study aims to determine the clinical counterpart of brain resting-state networks reorganization recently evidenced in early multiple sclerosis. Methods: Thirteen patients with early relapsing–remitting multiple sclerosis and 14 matched healthy controls were included in a resting state functional MRI study performed at 3 T. Data were analyzed using group spatial Independent Component Analysis using concatenation approach (FSL 4.1.3) and double regression analyses (SPM5) to extract local and global levels of connectivity inside various resting state networks (RSNs). Differences in global levels of connectivity of each network between patients and controls were assessed using Mann–Whitney U-test. In patients, relationship between clinical data (Expanded Disability Status Scale and Multiple Sclerosis Functional Composite Score – MSFC) and global RSN connectivity were assessed using Spearman rank correlation. Results: Independent component analysis provided eight consistent neuronal networks involved in motor, sensory and cognitive processes. For seven RSNs, the global level of connectivity was significantly increased in patients compared with controls. No significant decrease in RSN connectivity was found in early multiple sclerosis patients. MSFC values were negatively correlated with increased RSN connectivity within the dorsal frontoparietal network ( r = −0.811, p = 0.001), the right ventral frontoparietal network ( r = − 0.587, p = 0.045) and the prefronto-insular network ( r = −0.615, p = 0.033). Conclusions: This study demonstrates that resting state networks reorganization is strongly associated with disability in early multiple sclerosis. These findings suggest that resting state functional MRI may represent a promising surrogate marker of disease burden.

scholarly journals A method for making group inferences from functional MRI data using independent component analysis

2002 ◽  
Vol 16 (2) ◽  
pp. 131-131 ◽  
Author(s):  
V. D. Calhoun ◽  
T. Adali ◽  
G. D. Pearlson ◽  
J. J. Pekar
Keyword(s):  
Independent Component Analysis ◽  
Functional Mri ◽  
Component Analysis ◽  
Independent Component
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