Visualization of the Dynamic Brain Activation Pattern during a Decision-Making Task
Decision making is a complex process involving various parts of the brain which are active during different times. It is challenging to measure externally the exact instant when any given region becomes active during the decision-making process. Here, we propose the development and validation of an...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-10-01
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| Series: | Brain Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3425/12/11/1468 |
| _version_ | 1797468915071188992 |
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| author | Harshit Parmar Eric Walden |
| author_facet | Harshit Parmar Eric Walden |
| author_sort | Harshit Parmar |
| collection | DOAJ |
| description | Decision making is a complex process involving various parts of the brain which are active during different times. It is challenging to measure externally the exact instant when any given region becomes active during the decision-making process. Here, we propose the development and validation of an algorithm to extract and visualize the dynamic functional brain activation information from the observed fMRI data. We propose the use of a regularized deconvolution model to simultaneously map various activation regions within the brain and track how different activation regions changes with time, thus providing both spatial and temporal brain activation information. The proposed technique was validated using simulated data and then applied to a simple decision-making task for identification of various brain regions involved in different stages of decision making. Using the results of the dynamic activation for the decision-making task, we were able to identify key brain regions involved in some of the phases of decision making. The visualization aspect of the algorithm allows us to actually see the flow of activation (and deactivation) in the form of a motion picture. The dynamic estimate may aid in understanding the causality of activation between various brain regions in a better way in future fMRI brain studies. |
| first_indexed | 2024-03-09T19:14:04Z |
| format | Article |
| id | doaj.art-4a03717ead1a4bd0bf8719bd13109af0 |
| institution | Directory Open Access Journal |
| issn | 2076-3425 |
| language | English |
| last_indexed | 2024-03-09T19:14:04Z |
| publishDate | 2022-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Brain Sciences |
| spelling | doaj.art-4a03717ead1a4bd0bf8719bd13109af02023-11-24T03:56:25ZengMDPI AGBrain Sciences2076-34252022-10-011211146810.3390/brainsci12111468Visualization of the Dynamic Brain Activation Pattern during a Decision-Making TaskHarshit Parmar0Eric Walden1Texas Tech Neuroimaging Institute, Texas Tech University, 2500 Broadway, Lubbock, TX 79409, USATexas Tech Neuroimaging Institute, Texas Tech University, 2500 Broadway, Lubbock, TX 79409, USADecision making is a complex process involving various parts of the brain which are active during different times. It is challenging to measure externally the exact instant when any given region becomes active during the decision-making process. Here, we propose the development and validation of an algorithm to extract and visualize the dynamic functional brain activation information from the observed fMRI data. We propose the use of a regularized deconvolution model to simultaneously map various activation regions within the brain and track how different activation regions changes with time, thus providing both spatial and temporal brain activation information. The proposed technique was validated using simulated data and then applied to a simple decision-making task for identification of various brain regions involved in different stages of decision making. Using the results of the dynamic activation for the decision-making task, we were able to identify key brain regions involved in some of the phases of decision making. The visualization aspect of the algorithm allows us to actually see the flow of activation (and deactivation) in the form of a motion picture. The dynamic estimate may aid in understanding the causality of activation between various brain regions in a better way in future fMRI brain studies.https://www.mdpi.com/2076-3425/12/11/1468decision makingdynamic activationfMRIvisualizationdeconvolution |
| spellingShingle | Harshit Parmar Eric Walden Visualization of the Dynamic Brain Activation Pattern during a Decision-Making Task Brain Sciences decision making dynamic activation fMRI visualization deconvolution |
| title | Visualization of the Dynamic Brain Activation Pattern during a Decision-Making Task |
| title_full | Visualization of the Dynamic Brain Activation Pattern during a Decision-Making Task |
| title_fullStr | Visualization of the Dynamic Brain Activation Pattern during a Decision-Making Task |
| title_full_unstemmed | Visualization of the Dynamic Brain Activation Pattern during a Decision-Making Task |
| title_short | Visualization of the Dynamic Brain Activation Pattern during a Decision-Making Task |
| title_sort | visualization of the dynamic brain activation pattern during a decision making task |
| topic | decision making dynamic activation fMRI visualization deconvolution |
| url | https://www.mdpi.com/2076-3425/12/11/1468 |
| work_keys_str_mv | AT harshitparmar visualizationofthedynamicbrainactivationpatternduringadecisionmakingtask AT ericwalden visualizationofthedynamicbrainactivationpatternduringadecisionmakingtask |