Robust ordering of independent components in functional magnetic resonance imaging time series data using canonical correlation analysis

2003 ◽  
Author(s):  
Tamer Youssef ◽  
Abou-Bakr M. Youssef ◽  
Stephen M. LaConte ◽  
Xiaoping P. Hu ◽  
Yasser M. Kadah
Keyword(s):  
Magnetic Resonance Imaging ◽  
Time Series ◽  
Magnetic Resonance ◽  
Correlation Analysis ◽  
Canonical Correlation ◽  
Time Series Data ◽  
Series Data ◽  
Functional Magnetic Resonance ◽  
Resonance Imaging ◽  
Independent Components

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.

scholarly journals Estimates of locus coeruleus function with functional magnetic resonance imaging are influenced by localization approaches and the use of multi-echo data

2019 ◽  
Author(s):  
Hamid B. Turker ◽  
Elizabeth Riley ◽  
Wen-Ming Luh ◽  
Stan J. Colcombe ◽  
Khena M. Swallow
Keyword(s):  
Magnetic Resonance Imaging ◽  
Time Series ◽  
Magnetic Resonance ◽  
Functional Magnetic Resonance Imaging ◽  
Locus Coeruleus ◽  
Signal To Noise Ratio ◽  
Fmri Data ◽  
Functional Magnetic Resonance ◽  
Resonance Imaging ◽  
Signal To Noise

AbstractThe locus coeruleus (LC) plays a central role in regulating human cognition, arousal, and autonomic states. Efforts to characterize the LC’s function in humans using functional magnetic resonance imaging have been hampered by its small size and location near a large source of noise, the fourth ventricle. We tested whether the ability to characterize LC function is improved by employing neuromelanin-T1 weighted images (nmT1) for LC localization and multi-echo functional magnetic resonance imaging (ME-fMRI) for estimating intrinsic functional connectivity (iFC). Analyses indicated that, relative to a probabilistic atlas, utilizing nmT1 images to individually localize the LC increases the specificity of seed time series and clusters in the iFC maps. When combined with independent components analysis (ME-ICA), ME-fMRI data provided significant gains in the temporal signal to noise ratio relative to denoised single-echo (1E) data. The effects of acquiring nmT1 images and ME-fMRI data did not appear to only reflect increases in power: iFC maps for each approach only moderately overlapped. This is consistent with findings that ME-fMRI offers substantial advantages over 1E data acquisition and denoising. It also suggests that individually identifying LC with nmT1 scans is likely to reduce the influence of other nearby brainstem regions on estimates of LC function.HighlightsManual tracing of locus coeruleus increased specificity of seed time seriesManual tracing of locus coeruleus increased specificity of intrinsic connectivityMulti-echo fMRI increased temporal signal-to-noise ratio compared to single-echo fMRIConnectivity maps across methodologies overlapped only moderatelyMeasurement of LC function benefits from multi-echo fMRI and tracing ROIs

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