Spatiotemporal fMRI data processing using generalized canonical correlation analysis

2010 ◽  
Author(s):  
Babak Afshin-Pour ◽  
Gholam-Ali Hossein-Zadeh ◽  
Stephen C. Strother ◽  
Chery Grady ◽  
Hamid Soltanian-Zadeh
Keyword(s):  
Correlation Analysis ◽  
Data Processing ◽  
Canonical Correlation Analysis ◽  
Canonical Correlation ◽  
Fmri Data ◽  
Generalized Canonical Correlation Analysis

scholarly journals Improving the Sensitivity of Task-Related Functional Magnetic Resonance Imaging Data Using Generalized Canonical Correlation Analysis

2021 ◽  
Vol 15 ◽  
Author(s):  
Emmanouela Kosteletou ◽  
Panagiotis G. Simos ◽  
Eleftherios Kavroulakis ◽  
Despina Antypa ◽  
Thomas G. Maris ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Correlation Analysis ◽  
Canonical Correlation Analysis ◽  
Canonical Correlation ◽  
Action Observation ◽  
Fmri Data ◽  
Functional Magnetic Resonance ◽  
Resonance Imaging ◽  
Generalized Canonical Correlation Analysis

General Linear Modeling (GLM) is the most commonly used method for signal detection in Functional Magnetic Resonance Imaging (fMRI) experiments, despite its main limitation of not taking into consideration common spatial dependencies between voxels. Multivariate analysis methods, such as Generalized Canonical Correlation Analysis (gCCA), have been increasingly employed in fMRI data analysis, due to their ability to overcome this limitation. This study, evaluates the improvement of sensitivity of the GLM, by applying gCCA to fMRI data after standard preprocessing steps. Data from a block-design fMRI experiment was used, where 25 healthy volunteers completed two action observation tasks at 1.5T. Whole brain analysis results indicated that the application of gCCA resulted in significantly higher intensity of activation in several regions in both tasks and helped reveal activation in the primary somatosensory and ventral premotor area, theoretically known to become engaged during action observation. In subject-level ROI analyses, gCCA improved the signal to noise ratio in the averaged timeseries in each preselected ROI, and resulted in increased extent of activation, although peak intensity was considerably higher in just two of them. In conclusion, gCCA is a promising method for improving the sensitivity of conventional statistical modeling in task related fMRI experiments.

Efficient and Distributed Generalized Canonical Correlation Analysis for Big Multiview Data

2019 ◽  
Vol 31 (12) ◽  
pp. 2304-2318 ◽  
Author(s):  
Xiao Fu ◽  
Kejun Huang ◽  
Evangelos E. Papalexakis ◽  
Hyun Ah Song ◽  
Partha Talukdar ◽  
...  
Keyword(s):  
Correlation Analysis ◽  
Canonical Correlation Analysis ◽  
Canonical Correlation ◽  
Generalized Canonical Correlation Analysis

scholarly journals Variable selection for generalized canonical correlation analysis

Biostatistics ◽  
2014 ◽  
Vol 15 (3) ◽  
pp. 569-583 ◽  
Author(s):  
A. Tenenhaus ◽  
C. Philippe ◽  
V. Guillemot ◽  
K.-A. Le Cao ◽  
J. Grill ◽  
...  
Keyword(s):  
Correlation Analysis ◽  
Variable Selection ◽  
Canonical Correlation Analysis ◽  
Canonical Correlation ◽  
Generalized Canonical Correlation Analysis ◽  
Selection For

scholarly journals Clustering and variable selection evaluation of 13 unsupervised methods for multi-omics data integration

2019 ◽  
Vol 21 (6) ◽  
pp. 2011-2030 ◽  
Author(s):  
Morgane Pierre-Jean ◽  
Jean-François Deleuze ◽  
Edith Le Floch ◽  
Florence Mauger
Keyword(s):  
Correlation Analysis ◽  
Variable Selection ◽  
Canonical Correlation Analysis ◽  
Canonical Correlation ◽  
Molecular Data ◽  
Heterogeneous Data ◽  
Data Availability ◽  
Omics Data ◽  
Generalized Canonical Correlation Analysis ◽  
Unsupervised Methods

Abstract Recent advances in NGS sequencing, microarrays and mass spectrometry for omics data production have enabled the generation and collection of different modalities of high-dimensional molecular data. The integration of multiple omics datasets is a statistical challenge, due to the limited number of individuals, the high number of variables and the heterogeneity of the datasets to integrate. Recently, a lot of tools have been developed to solve the problem of integrating omics data including canonical correlation analysis, matrix factorization and SM. These commonly used techniques aim to analyze simultaneously two or more types of omics. In this article, we compare a panel of 13 unsupervised methods based on these different approaches to integrate various types of multi-omics datasets: iClusterPlus, regularized generalized canonical correlation analysis, sparse generalized canonical correlation analysis, multiple co-inertia analysis (MCIA), integrative-NMF (intNMF), SNF, MoCluster, mixKernel, CIMLR, LRAcluster, ConsensusClustering, PINSPlus and multi-omics factor analysis (MOFA). We evaluate the ability of the methods to recover the subgroups and the variables that drive the clustering on eight benchmarks of simulation. MOFA does not provide any results on these benchmarks. For clustering, SNF, MoCluster, CIMLR, LRAcluster, ConsensusClustering and intNMF provide the best results. For variable selection, MoCluster outperforms the others. However, the performance of the methods seems to depend on the heterogeneity of the datasets (especially for MCIA, intNMF and iClusterPlus). Finally, we apply the methods on three real studies with heterogeneous data and various phenotypes. We conclude that MoCluster is the best method to analyze these omics data. Availability: An R package named CrIMMix is available on GitHub at https://github.com/CNRGH/crimmix to reproduce all the results of this article.

scholarly journals Multivariate group-level analysis for task fMRI data with canonical correlation analysis

NeuroImage ◽  
2019 ◽  
Vol 194 ◽  
pp. 25-41 ◽  
Author(s):  
Xiaowei Zhuang ◽  
Zhengshi Yang ◽  
Karthik R. Sreenivasan ◽  
Virendra R. Mishra ◽  
Tim Curran ◽  
...  
Keyword(s):  
Correlation Analysis ◽  
Canonical Correlation Analysis ◽  
Canonical Correlation ◽  
Fmri Data ◽  
Group Level ◽  
Level Analysis

scholarly journals Exploration of Neural Activity under Cognitive Reappraisal Using Simultaneous EEG-fMRI Data and Kernel Canonical Correlation Analysis

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Biao Yang ◽  
Jinmeng Cao ◽  
Tiantong Zhou ◽  
Li Dong ◽  
Ling Zou ◽  
...  
Keyword(s):  
Correlation Analysis ◽  
Canonical Correlation Analysis ◽  
Neural Activity ◽  
Canonical Correlation ◽  
Cognitive Reappraisal ◽  
Fmri Data ◽  
Spatiotemporal Resolution ◽  
Neutral State ◽  
Negative State ◽  
Mode Decomposition

Background. Neural activity under cognitive reappraisal can be more accurately investigated using simultaneous EEG- (electroencephalography) fMRI (functional magnetic resonance imaging) than using EEG or fMRI only. Complementary spatiotemporal information can be found from simultaneous EEG-fMRI data to study brain function. Method. An effective EEG-fMRI fusion framework is proposed in this work. EEG-fMRI data is simultaneously sampled on fifteen visually stimulated healthy adult participants. Net-station toolbox and empirical mode decomposition are employed for EEG denoising. Sparse spectral clustering is used to construct fMRI masks that are used to constrain fMRI activated regions. A kernel-based canonical correlation analysis is utilized to fuse nonlinear EEG-fMRI data. Results. The experimental results show a distinct late positive potential (LPP, latency 200-700ms) from the correlated EEG components that are reconstructed from nonlinear EEG-fMRI data. Peak value of LPP under reappraisal state is smaller than that under negative state, however, larger than that under neutral state. For correlated fMRI components, obvious activation can be observed in cerebral regions, e.g., the amygdala, temporal lobe, cingulate gyrus, hippocampus, and frontal lobe. Meanwhile, in these regions, activated intensity under reappraisal state is obviously smaller than that under negative state and larger than that under neutral state. Conclusions. The proposed EEG-fMRI fusion approach provides an effective way to study the neural activities of cognitive reappraisal with high spatiotemporal resolution. It is also suitable for other neuroimaging technologies using simultaneous EEG-fMRI data.

Sign in / Sign up

Export Citation Format

Share Document