Optimized Design and Analysis of Sparse-Sampling fMRI Experiments
Sparse-sampling is an important methodological advance in functional magnetic resonance imaging (fMRI), in which silent delays are introduced between MR volume acquisitions, allowing for the presentation of auditory stimuli without contamination by acoustic scanner noise and for overt vocal response...
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| Формат: | Стаття |
| Мова: | en_US |
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Frontiers Research Foundation
2013
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| Онлайн доступ: | http://hdl.handle.net/1721.1/79614 https://orcid.org/0000-0002-5312-6729 https://orcid.org/0000-0002-9149-1815 |
| _version_ | 1826192067463741440 |
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| author | Perrachione, Tyler Kent Ghosh, Satrajit S. |
| author2 | Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences |
| author_facet | Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences Perrachione, Tyler Kent Ghosh, Satrajit S. |
| author_sort | Perrachione, Tyler Kent |
| collection | MIT |
| description | Sparse-sampling is an important methodological advance in functional magnetic resonance imaging (fMRI), in which silent delays are introduced between MR volume acquisitions, allowing for the presentation of auditory stimuli without contamination by acoustic scanner noise and for overt vocal responses without motion-induced artifacts in the functional time series. As such, the sparse-sampling technique has become a mainstay of principled fMRI research into the cognitive and systems neuroscience of speech, language, hearing, and music. Despite being in use for over a decade, there has been little systematic investigation of the acquisition parameters, experimental design considerations, and statistical analysis approaches that bear on the results and interpretation of sparse-sampling fMRI experiments. In this report, we examined how design and analysis choices related to the duration of repetition time (TR) delay (an acquisition parameter), stimulation rate (an experimental design parameter), and model basis function (an analysis parameter) act independently and interactively to affect the neural activation profiles observed in fMRI. First, we conducted a series of computational simulations to explore the parameter space of sparse design and analysis with respect to these variables; second, we validated the results of these simulations in a series of sparse-sampling fMRI experiments. Overall, these experiments suggest the employment of three methodological approaches that can, in many situations, substantially improve the detection of neurophysiological response in sparse fMRI: (1) Sparse analyses should utilize a physiologically informed model that incorporates hemodynamic response convolution to reduce model error. (2) The design of sparse fMRI experiments should maintain a high rate of stimulus presentation to maximize effect size. (3) TR delays of short to intermediate length can be used between acquisitions of sparse-sampled functional image volumes to increase the number of samples and improve statistical power. |
| first_indexed | 2024-09-23T09:05:46Z |
| format | Article |
| id | mit-1721.1/79614 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T09:05:46Z |
| publishDate | 2013 |
| publisher | Frontiers Research Foundation |
| record_format | dspace |
| spelling | mit-1721.1/796142022-09-26T10:24:42Z Optimized Design and Analysis of Sparse-Sampling fMRI Experiments Perrachione, Tyler Kent Ghosh, Satrajit S. Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences McGovern Institute for Brain Research at MIT Perrachione, Tyler Kent Ghosh, Satrajit S. Sparse-sampling is an important methodological advance in functional magnetic resonance imaging (fMRI), in which silent delays are introduced between MR volume acquisitions, allowing for the presentation of auditory stimuli without contamination by acoustic scanner noise and for overt vocal responses without motion-induced artifacts in the functional time series. As such, the sparse-sampling technique has become a mainstay of principled fMRI research into the cognitive and systems neuroscience of speech, language, hearing, and music. Despite being in use for over a decade, there has been little systematic investigation of the acquisition parameters, experimental design considerations, and statistical analysis approaches that bear on the results and interpretation of sparse-sampling fMRI experiments. In this report, we examined how design and analysis choices related to the duration of repetition time (TR) delay (an acquisition parameter), stimulation rate (an experimental design parameter), and model basis function (an analysis parameter) act independently and interactively to affect the neural activation profiles observed in fMRI. First, we conducted a series of computational simulations to explore the parameter space of sparse design and analysis with respect to these variables; second, we validated the results of these simulations in a series of sparse-sampling fMRI experiments. Overall, these experiments suggest the employment of three methodological approaches that can, in many situations, substantially improve the detection of neurophysiological response in sparse fMRI: (1) Sparse analyses should utilize a physiologically informed model that incorporates hemodynamic response convolution to reduce model error. (2) The design of sparse fMRI experiments should maintain a high rate of stimulus presentation to maximize effect size. (3) TR delays of short to intermediate length can be used between acquisitions of sparse-sampled functional image volumes to increase the number of samples and improve statistical power. Ellison Medical Foundation National Science Foundation (U.S.) Graduate Research Fellowship Program National Institutes of Health (U.S.) (Grant R03-EB008673) 2013-07-18T15:08:18Z 2013-07-18T15:08:18Z 2013-04 2012-12 Article http://purl.org/eprint/type/JournalArticle 1662-4548 1662-453X http://hdl.handle.net/1721.1/79614 Perrachione, Tyler K., and Satrajit S. Ghosh. “Optimized Design and Analysis of Sparse-Sampling fMRI Experiments.” Frontiers in Neuroscience 7 (2013). https://orcid.org/0000-0002-5312-6729 https://orcid.org/0000-0002-9149-1815 en_US http://dx.doi.org/10.3389/fnins.2013.00055 Frontiers in Neuroscience Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use. application/pdf Frontiers Research Foundation Frontiers Research Foundation |
| spellingShingle | Perrachione, Tyler Kent Ghosh, Satrajit S. Optimized Design and Analysis of Sparse-Sampling fMRI Experiments |
| title | Optimized Design and Analysis of Sparse-Sampling fMRI Experiments |
| title_full | Optimized Design and Analysis of Sparse-Sampling fMRI Experiments |
| title_fullStr | Optimized Design and Analysis of Sparse-Sampling fMRI Experiments |
| title_full_unstemmed | Optimized Design and Analysis of Sparse-Sampling fMRI Experiments |
| title_short | Optimized Design and Analysis of Sparse-Sampling fMRI Experiments |
| title_sort | optimized design and analysis of sparse sampling fmri experiments |
| url | http://hdl.handle.net/1721.1/79614 https://orcid.org/0000-0002-5312-6729 https://orcid.org/0000-0002-9149-1815 |
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