Effect of interoception on intra- and inter-network connectivity of human brain — An independent component analysis of fMRI data

2015 ◽  
Author(s):  
Behnaz Jarrahi ◽  
Dante Mantini ◽  
Spyros Kollias
Keyword(s):  
Independent Component Analysis ◽  
Human Brain ◽  
Network Connectivity ◽  
Component Analysis ◽  
Independent Component ◽  
Fmri Data

scholarly journals Modular and state-relevant connectivity in high-frequency resting-state BOLD fMRI data: An independent component analysis

2020 ◽  
Author(s):  
Thomas DeRamus ◽  
Ashkan Faghiri ◽  
Armin Iraji ◽  
Oktay Agcaoglu ◽  
Victor Vergara ◽  
...  
Keyword(s):  
Independent Component Analysis ◽  
Resting State ◽  
Network Connectivity ◽  
Resting State Fmri ◽  
Component Analysis ◽  
Independent Component ◽  
Fmri Data ◽  
Bold Signal ◽  
Drift Motion ◽  
Eyes Closed

AbstractResting-state fMRI (rs-fMRI) data are typically filtered at different frequency bins between 0.008∼0.2 Hz (varies across the literature) prior to analysis to mitigate nuisance variables (e.g., drift, motion, cardiac, and respiratory) and maximize the sensitivity to neuronal-mediated BOLD signal. However, multiple lines of evidence suggest meaningful BOLD signal may also be parsed at higher frequencies. To test this notion, a functional network connectivity (FNC) analysis based on a spatially informed independent component analysis (ICA) was performed at seven different bandpass frequency bins to examine FNC matrices across spectra. Further, eyes open (EO) vs. eyes closed (EC) resting-state acquisitions from the same participants were compared across frequency bins to examine if EO vs. EC FNC matrices and randomness estimations of FNC matrices are distinguishable at different frequencies.Results show that FNCs in higher-frequency bins display modular FNC similar to the lowest frequency bin, while r-to-z FNC and FNC-based measures indicating matrix non-randomness were highest in the 0.31-0.46 Hz range relative to all frequency bins above and below this range. As such, the FNC within this range appears to be the most temporally correlated, but the mechanisms facilitating this coherence require further analyses. Compared to EO, EC displayed greater FNC (involved in visual, cognitive control, somatomotor, and auditory domains) and randomness values at lower frequency bins, but this phenomenon flipped (EO > EC) at frequency bins greater than 0.46 Hz, particularly within visual regions.While the effect sizes range from small to large specific to frequency range and resting state (EO vs. EC), with little influence from common artifacts. These differences indicate that unique information can be derived from FNC between BOLD signals at different frequencies relative to a given restingstate acquisition and support the hypothesis meaningful BOLD signal is present at higher frequency ranges.

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 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
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