Literature DB >> 12030831

Exploratory fMRI analysis by autocorrelation maximization.

Ola Friman1, Magnus Borga, Peter Lundberg, Hans Knutsson.   

Abstract

A novel and computationally efficient method for exploratory analysis of functional MRI data is presented. The basic idea is to reveal underlying components in the fMRI data that have maximum autocorrelation. The tool for accomplishing this task is Canonical Correlation Analysis. The relation to Principal Component Analysis and Independent Component Analysis is discussed and the performance of the methods is compared using both simulated and real data. 2002 Elsevier Science (USA)

Mesh：

Year:  2002        PMID： 12030831     DOI： 10.1006/nimg.2002.1067

Source DB:  PubMed          Journal:  Neuroimage        ISSN： 1053-8119            Impact factor:   6.556


  13 in total

1.  Complementary activation of the ipsilateral primary motor cortex during a sustained handgrip task.

Authors:  Kenichi Shibuya; Naomi Kuboyama; Seigo Yamada
Journal:  Eur J Appl Physiol       Date:  2015-09-16       Impact factor: 3.078

2.  Transient visual perturbations boost short-term balance learning in virtual reality by modulating electrocortical activity.

Authors:  Steven M Peterson; Estefania Rios; Daniel P Ferris
Journal:  J Neurophysiol       Date:  2018-07-25       Impact factor: 2.714

Review 3.  Independent component analysis of functional MRI: what is signal and what is noise?

Authors:  Martin J McKeown; Lars Kai Hansen; Terrence J Sejnowsk
Journal:  Curr Opin Neurobiol       Date:  2003-10       Impact factor: 6.627

4.  Dimensionality reduction impedes the extraction of dynamic functional connectivity states from fMRI recordings of resting wakefulness.

Authors:  MohammadMehdi Kafashan; Ben Julian A Palanca; ShiNung Ching
Journal:  J Neurosci Methods       Date:  2017-09-22       Impact factor: 2.390

5.  Canonical Correlation Analysis for Data Fusion and Group Inferences: Examining applications of medical imaging data.

Authors:  Nicolle M Correa; Tülay Adali; Yi-Ou Li; Vince D Calhoun
Journal:  IEEE Signal Process Mag       Date:  2010       Impact factor: 12.551

6.  Joint Blind Source Separation by Multi-set Canonical Correlation Analysis.

Authors:  Yi-Ou Li; Tülay Adalı; Wei Wang; Vince D Calhoun
Journal:  IEEE Trans Signal Process       Date:  2009-10-01       Impact factor: 4.931

7.  Multi-set canonical correlation analysis for the fusion of concurrent single trial ERP and functional MRI.

Authors:  Nicolle M Correa; Tom Eichele; Tülay Adali; Yi-Ou Li; Vince D Calhoun
Journal:  Neuroimage       Date:  2010-01-25       Impact factor: 6.556

8.  Canonical Correlation Analysis for Feature-Based Fusion of Biomedical Imaging Modalities and Its Application to Detection of Associative Networks in Schizophrenia.

Authors:  Nicolle M Correa; Yi-Ou Li; Tülay Adalı; Vince D Calhoun
Journal:  IEEE J Sel Top Signal Process       Date:  2008-12-01       Impact factor: 6.856

9.  Group Study of Simulated Driving fMRI Data by Multiset Canonical Correlation Analysis.

Authors:  Yi-Ou Li; Tulay Adalı; Vince D Calhoun
Journal:  J Signal Process Syst       Date:  2012-07-01

10.  A Compound fault diagnosis for rolling bearings method based on blind source separation and ensemble empirical mode decomposition.

Authors:  Huaqing Wang; Ruitong Li; Gang Tang; Hongfang Yuan; Qingliang Zhao; Xi Cao
Journal:  PLoS One       Date:  2014-10-07       Impact factor: 3.240
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