scholarly journals A Linear Structural Equation Model for Covert Verb Generation Based on Independent Component Analysis of fMRI Data from Children and Adolescents

2011 ◽  
Vol 5 ◽  
Author(s):  
Prasanna Karunanayaka ◽  
Vincent J. Schmithorst ◽  
Jennifer Vannest ◽  
Jerzy P. Szaflarski ◽  
Elena Plante ◽  
...  
Keyword(s):  
Independent Component Analysis ◽  
Children And Adolescents ◽  
Structural Equation Model ◽  
Structural Equation ◽  
Component Analysis ◽  
Equation Model ◽  
Independent Component ◽  
Fmri Data ◽  
Verb Generation

Strategies for reducing large fMRI data sets for independent component analysis

2006 ◽  
Vol 24 (5) ◽  
pp. 591-596 ◽  
Author(s):  
Ze Wang ◽  
Jiongjiong Wang ◽  
Vince Calhoun ◽  
Hengyi Rao ◽  
John A. Detre ◽  
...  
Keyword(s):  
Independent Component Analysis ◽  
Component Analysis ◽  
Independent Component ◽  
Fmri Data ◽  
Data Sets

scholarly journals Parallel group independent component analysis for massive fMRI data sets

PLoS ONE ◽  
2017 ◽  
Vol 12 (3) ◽  
pp. e0173496 ◽  
Author(s):  
Shaojie Chen ◽  
Lei Huang ◽  
Huitong Qiu ◽  
Mary Beth Nebel ◽  
Stewart H. Mostofsky ◽  
...  
Keyword(s):  
Independent Component Analysis ◽  
Component Analysis ◽  
Independent Component ◽  
Fmri Data ◽  
Data Sets ◽  
Parallel Group ◽  
Group Independent Component Analysis

Localization of Latent Epileptic Activities Using Spatio-Temporal Independent Component Analysis of fMRI Data

2006 ◽  
Vol 19 (1-2) ◽  
pp. 21-28 ◽  
Author(s):  
Huafu Chen ◽  
Dezhong Yao ◽  
Guangming Lu ◽  
Zhiqiang Zhang ◽  
Qiaoli Hu
Keyword(s):  
Independent Component Analysis ◽  
Component Analysis ◽  
Independent Component ◽  
Fmri Data ◽  
Spatio Temporal

scholarly journals Independent component analysis of fMRI data: Examining the assumptions

1998 ◽  
Vol 6 (5-6) ◽  
pp. 368-372 ◽  
Author(s):  
Martin J. McKeown ◽  
Terrence J. Sejnowski
Keyword(s):  
Independent Component Analysis ◽  
Component Analysis ◽  
Independent Component ◽  
Fmri Data

scholarly journals Localization of Latent Epileptic Activities Using Spatio-Temporal Independent Component Analysis of fMRI Data

2007 ◽  
Vol 19 (4) ◽  
pp. 223-223
Author(s):  
Huafu Chen ◽  
Dezhong Yao ◽  
Guangming Lu ◽  
Zhiqiang Zhang ◽  
Qiaoli Hu
Keyword(s):  
Independent Component Analysis ◽  
Component Analysis ◽  
Independent Component ◽  
Fmri Data ◽  
Spatio Temporal

scholarly journals Independent component analysis of fMRI data in the complex domain

2002 ◽  
Vol 48 (1) ◽  
pp. 180-192 ◽  
Author(s):  
V.D. Calhoun ◽  
T. Adal? ◽  
G.D. Pearlson ◽  
P.C.M. van Zijl ◽  
J.J. Pekar
Keyword(s):  
Independent Component Analysis ◽  
Component Analysis ◽  
Independent Component ◽  
Complex Domain ◽  
Fmri Data

Comparison of independent component analysis algorithms for EEG-fMRI data fusion

2012 ◽  
Author(s):  
Vahab Youssofzadeh ◽  
Ibrahima Faye ◽  
Aamir Saeed Malik ◽  
Faruque Reza ◽  
Nidal Kamel ◽  
...  
Keyword(s):  
Independent Component Analysis ◽  
Data Fusion ◽  
Component Analysis ◽  
Independent Component ◽  
Fmri Data

scholarly journals Investigations into resting-state connectivity using independent component analysis

2005 ◽  
Vol 360 (1457) ◽  
pp. 1001-1013 ◽  
Author(s):  
Christian F Beckmann ◽  
Marilena DeLuca ◽  
Joseph T Devlin ◽  
Stephen M Smith
Keyword(s):  
Independent Component Analysis ◽  
Resting State ◽  
Temporal Structure ◽  
Low Frequency ◽  
Component Analysis ◽  
Independent Component ◽  
Fmri Data ◽  
Analytical Technique ◽  
Spatio Temporal ◽  
Resting State Connectivity

Inferring resting-state connectivity patterns from functional magnetic resonance imaging (fMRI) data is a challenging task for any analytical technique. In this paper, we review a probabilistic independent component analysis (PICA) approach, optimized for the analysis of fMRI data, and discuss the role which this exploratory technique can take in scientific investigations into the structure of these effects. We apply PICA to fMRI data acquired at rest, in order to characterize the spatio-temporal structure of such data, and demonstrate that this is an effective and robust tool for the identification of low-frequency resting-state patterns from data acquired at various different spatial and temporal resolutions. We show that these networks exhibit high spatial consistency across subjects and closely resemble discrete cortical functional networks such as visual cortical areas or sensory–motor cortex.

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