The use of a priori information in ICA-based techniques for real-time fMRI: an evaluation of static/dynamic and spatial/temporal characteristics
Real-time brain functional MRI (rt-fMRI) allows in-vivo non-invasive monitoring of neural networks. The use of multivariate data-driven analysis methods such as independent component analysis (ICA) offers an attractive trade-off between data interpretability and information extraction, and can be us...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2013-03-01
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| Series: | Frontiers in Human Neuroscience |
| Subjects: | |
| Online Access: | http://journal.frontiersin.org/Journal/10.3389/fnhum.2013.00064/full |
| _version_ | 1818113612742393856 |
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| author | Nicola eSoldati Vince D Calhoun Lorenzo eBruzzone Jorge eJovicich Jorge eJovicich |
| author_facet | Nicola eSoldati Vince D Calhoun Lorenzo eBruzzone Jorge eJovicich Jorge eJovicich |
| author_sort | Nicola eSoldati |
| collection | DOAJ |
| description | Real-time brain functional MRI (rt-fMRI) allows in-vivo non-invasive monitoring of neural networks. The use of multivariate data-driven analysis methods such as independent component analysis (ICA) offers an attractive trade-off between data interpretability and information extraction, and can be used during both task-based and rest experiments. The purpose of this study was to assess the effectiveness of different ICA-based procedures to monitor in real-time a target IC defined from a functional localizer which also used ICA. Four novel methods were implemented to monitor ongoing brain activity in a sliding window approach. The methods differed in the ways in which a priori information, derived from ICA algorithms, was used to monitora target independent component (IC). We implemented four different algorithms, all based on ICA. One Back-projection method used ICA to derive static spatial information from the functional localizer, off line, which was then back-projected dynamically during the real-time acquisition. The other three methods used real-time ICA algorithms that dynamically exploited temporal, spatial, or spatial-temporal priors during the real-time acquisition. The methods were evaluated by simulating a rt-fMRI experiment that used real fMRI data. The performance of each method was characterized by the spatial and/or temporal correlation with the target IC component monitored, computation time and intrinsic stochastic variability of the algorithms. In this study the Back-projection method, which could monitor more than one IC of interest, outperformed the other methods. These results are consistent with a functional task that gives stable target ICs over time. The dynamic adaptation possibilities offered by the other ICA methods proposed may offer better performance than the Back-projection in conditions where the functional activation shows higher spatial and/or temporal variability. |
| first_indexed | 2024-12-11T03:37:36Z |
| format | Article |
| id | doaj.art-362238336e9e478fb714f9c6adaf3e5c |
| institution | Directory Open Access Journal |
| issn | 1662-5161 |
| language | English |
| last_indexed | 2024-12-11T03:37:36Z |
| publishDate | 2013-03-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Human Neuroscience |
| spelling | doaj.art-362238336e9e478fb714f9c6adaf3e5c2022-12-22T01:22:12ZengFrontiers Media S.A.Frontiers in Human Neuroscience1662-51612013-03-01710.3389/fnhum.2013.0006437120The use of a priori information in ICA-based techniques for real-time fMRI: an evaluation of static/dynamic and spatial/temporal characteristicsNicola eSoldati0Vince D Calhoun1Lorenzo eBruzzone2Jorge eJovicich3Jorge eJovicich4CIMeC- Center for Mind-Brain SciencesUniversity of New MexicoUniversity of TrentoCIMeC- Center for Mind-Brain SciencesUniversity of TrentoReal-time brain functional MRI (rt-fMRI) allows in-vivo non-invasive monitoring of neural networks. The use of multivariate data-driven analysis methods such as independent component analysis (ICA) offers an attractive trade-off between data interpretability and information extraction, and can be used during both task-based and rest experiments. The purpose of this study was to assess the effectiveness of different ICA-based procedures to monitor in real-time a target IC defined from a functional localizer which also used ICA. Four novel methods were implemented to monitor ongoing brain activity in a sliding window approach. The methods differed in the ways in which a priori information, derived from ICA algorithms, was used to monitora target independent component (IC). We implemented four different algorithms, all based on ICA. One Back-projection method used ICA to derive static spatial information from the functional localizer, off line, which was then back-projected dynamically during the real-time acquisition. The other three methods used real-time ICA algorithms that dynamically exploited temporal, spatial, or spatial-temporal priors during the real-time acquisition. The methods were evaluated by simulating a rt-fMRI experiment that used real fMRI data. The performance of each method was characterized by the spatial and/or temporal correlation with the target IC component monitored, computation time and intrinsic stochastic variability of the algorithms. In this study the Back-projection method, which could monitor more than one IC of interest, outperformed the other methods. These results are consistent with a functional task that gives stable target ICs over time. The dynamic adaptation possibilities offered by the other ICA methods proposed may offer better performance than the Back-projection in conditions where the functional activation shows higher spatial and/or temporal variability.http://journal.frontiersin.org/Journal/10.3389/fnhum.2013.00064/fullICAadaptive algorithmsreal-time fMRIa priori knowledgedynamic monitoring |
| spellingShingle | Nicola eSoldati Vince D Calhoun Lorenzo eBruzzone Jorge eJovicich Jorge eJovicich The use of a priori information in ICA-based techniques for real-time fMRI: an evaluation of static/dynamic and spatial/temporal characteristics Frontiers in Human Neuroscience ICA adaptive algorithms real-time fMRI a priori knowledge dynamic monitoring |
| title | The use of a priori information in ICA-based techniques for real-time fMRI: an evaluation of static/dynamic and spatial/temporal characteristics |
| title_full | The use of a priori information in ICA-based techniques for real-time fMRI: an evaluation of static/dynamic and spatial/temporal characteristics |
| title_fullStr | The use of a priori information in ICA-based techniques for real-time fMRI: an evaluation of static/dynamic and spatial/temporal characteristics |
| title_full_unstemmed | The use of a priori information in ICA-based techniques for real-time fMRI: an evaluation of static/dynamic and spatial/temporal characteristics |
| title_short | The use of a priori information in ICA-based techniques for real-time fMRI: an evaluation of static/dynamic and spatial/temporal characteristics |
| title_sort | use of a priori information in ica based techniques for real time fmri an evaluation of static dynamic and spatial temporal characteristics |
| topic | ICA adaptive algorithms real-time fMRI a priori knowledge dynamic monitoring |
| url | http://journal.frontiersin.org/Journal/10.3389/fnhum.2013.00064/full |
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