scholarly journals Brain network of semantic integration in sentence reading: Insights from independent component analysis and graph theoretical analysis

2012 ◽  
Vol 35 (2) ◽  
pp. 367-376 ◽  
Author(s):  
Zheng Ye ◽  
Nuria Doñamayor ◽  
Thomas F. Münte
Keyword(s):  
Theoretical Analysis ◽  
Independent Component Analysis ◽  
Component Analysis ◽  
Brain Network ◽  
Semantic Integration ◽  
Independent Component ◽  
Graph Theoretical Analysis ◽  
Sentence Reading

scholarly journals Independent Component Analysis and Graph Theoretical Analysis in Patients with Narcolepsy

2018 ◽  
Vol 35 (4) ◽  
pp. 743-755 ◽  
Author(s):  
Fulong Xiao ◽  
Chao Lu ◽  
Dianjiang Zhao ◽  
Qihong Zou ◽  
Liyue Xu ◽  
...  
Keyword(s):  
Theoretical Analysis ◽  
Independent Component Analysis ◽  
Component Analysis ◽  
Independent Component ◽  
Graph Theoretical Analysis

Differential recruitment of theory of mind brain network across three tasks: An independent component analysis

2018 ◽  
Vol 347 ◽  
pp. 385-393 ◽  
Author(s):  
Melissa D. Thye ◽  
Carla J. Ammons ◽  
Donna L. Murdaugh ◽  
Rajesh K. Kana
Keyword(s):  
Theory Of Mind ◽  
Independent Component Analysis ◽  
Component Analysis ◽  
Brain Network ◽  
Independent Component

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.

A Detection Technology for Weak Vibration Signal Based on Independent Component Analysis

2012 ◽  
Vol 226-228 ◽  
pp. 312-315
Author(s):  
Hai Dong Guo ◽  
Shun Ming Li ◽  
Yuan Yuan Zhang ◽  
Xing Xing Wang ◽  
Sai Ma
Keyword(s):  
Theoretical Analysis ◽  
Independent Component Analysis ◽  
Blind Source Separation ◽  
Detection Method ◽  
Vibration Signal ◽  
Component Analysis ◽  
Independent Component ◽  
Independent Source ◽  
Detection Technology ◽  
Source Signals

For weak vibration signal with strong noise, a new kind of weak vibration signal detection method was proposed in this paper. Based on the redundancy reducing capability and the uncertain amplitude of independent component analysis, virtual noise was introduced to extend the dimension of original observed signal after we analyzed the prior features of noises in observed signal. Then extended signals were processed to get the independent source signals by applying to blind source separation (BSS). Thus, the noise embedded in observed signal was removed and characteristics of weak vibration signal were obtained successfully. Through the theoretical analysis and the simulation, the introduced method of this paper was checked to be available and then it was applied to faults analysis of rotor misalignment successfully. Finally, we made a conclusion that this method had great application value for the extraction of weak vibration signal.

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.

Sign in / Sign up

Export Citation Format

Share Document