scholarly journals Experimental Comparisons of Sparse Dictionary Learning and Independent Component Analysis for Brain Network Inference From fMRI Data

2019 ◽  
Vol 66 (1) ◽  
pp. 289-299 ◽  
Author(s):  
Wei Zhang ◽  
Jinglei Lv ◽  
Xiang Li ◽  
Dajiang Zhu ◽  
Xi Jiang ◽  
...  
Keyword(s):  
Independent Component Analysis ◽  
Dictionary Learning ◽  
Network Inference ◽  
Component Analysis ◽  
Brain Network ◽  
Independent Component ◽  
Fmri Data ◽  
Sparse Dictionary Learning ◽  
Experimental Comparisons

scholarly journals NeuroMark: a fully automated ICA method to identify effective fMRI markers of brain disorders

2019 ◽  
Author(s):  
Yuhui Du ◽  
Zening Fu ◽  
Jing Sui ◽  
Shuang Gao ◽  
Ying Xing ◽  
...  
Keyword(s):  
Independent Component Analysis ◽  
Component Analysis ◽  
Autism Spectrum ◽  
Brain Network ◽  
Independent Component ◽  
Mental Illnesses ◽  
Fmri Data ◽  
Brain Disorders ◽  
Automated Method ◽  
Functional Brain Network

SummaryIncreasing sharing initiatives on neuroimaging data have provided unprecedented opportunities to study brain disorders. Standardized approaches for capturing reproducible and comparable biomarkers are greatly needed. Here, we propose a method, NeuroMark, which leverages a priori-driven independent component analysis to effectively extract functional brain network biomarkers from functional magnetic resonance imaging (fMRI) data. NeuroMark automatically estimates features adaptable to each individual and comparable across subjects by taking advantage of the replicated brain network templates extracted from 1828 healthy controls as guidance to initialize the individual-level networks. Four studies including 2454 subjects were conducted spanning six brain disorders (schizophrenia, autism spectrum disorder, depression, bipolar disorder, mild cognitive impairment and Alzheimer’s disease) to evaluate the proposed method from different perspectives (replication, cross-study comparison, subtle difference identification, and multi-disorder classification). Results demonstrate the great potential of NeuroMark in its feasibility to link different datasets/studies/disorders and enhance sensitivity in identifying biomarkers for patients with challenging mental illnesses.Significance StatementIncreasing evidence highlights that features extracted from resting fMRI data can be leveraged as potential biomarkers of brain disorders. However, it has been difficult to replicate results using different datasets, translate findings across studies, and differentiate brain disorders sharing similar clinical symptoms. It is important to systematically characterize the degree to which unique and similar impaired patterns are reflective of brain disorders. We propose a fully automated method (called NeuroMark) that leverages priori-driven independent component analysis (ICA) using replicated brain network templates to estimate individual-subject network features. Evaluated by four studies involving six different brain disorders, we show that NeuroMark can effectively link the comparison of biomarkers across different studies/datasets/disorders and enable classification between complex brain disorders, while also providing information about relevant aspects of whole brain functional connectivity.

scholarly journals Independent Component Analysis of Instantaneous Power-Based fMRI

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Yuan Zhong ◽  
Gang Zheng ◽  
Yijun Liu ◽  
Guangming Lu
Keyword(s):  
Independent Component Analysis ◽  
Latent Variables ◽  
Human Subjects ◽  
Hand Movement ◽  
Simulated Data ◽  
Component Analysis ◽  
Brain Network ◽  
Independent Component ◽  
Fmri Data ◽  
Instantaneous Power

In functional magnetic resonance imaging (fMRI) studies using spatial independent component analysis (sICA) method, a model of “latent variables” is often employed, which is based on the assumption that fMRI data are linear mixtures of statistically independent signals. However, actual fMRI signals are nonlinear and do not automatically meet with the requirement of sICA. To provide a better solution to this problem, we proposed a novel approach termed instantaneous power based fMRI (ip-fMRI) for regularization of fMRI data. Given that the instantaneous power of fMRI signals is a scalar value, it should be a linear mixture that naturally satisfies the “latent variables” model. Based on our simulated data, the curves of accuracy and resulting receiver-operating characteristic curves indicate that the proposed approach is superior to the traditional fMRI in terms of accuracy and specificity by using sICA. Experimental results from human subjects have shown that spatial components of a hand movement task-induced activation reveal a brain network more specific to motor function by ip-fMRI than that by the traditional fMRI. We conclude that ICA decomposition of ip-fMRI may be used to localize energy signal changes in the brain and may have a potential to be applied to detection of brain activity.

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
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