An evolutionary computation approach to examine functional brain plasticity
One common research goal in systems neurosciences is to understand how the functional relationship between a pair of regions of interest (ROIs) evolves over time. Examining neural connectivity in this way is well-suited for the study of developmental processes, learning, and even in recovery or trea...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2016-04-01
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| Series: | Frontiers in Neuroscience |
| Subjects: | |
| Online Access: | http://journal.frontiersin.org/Journal/10.3389/fnins.2016.00146/full |
| _version_ | 1818693960227356672 |
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| author | Arnab eRoy Arnab eRoy Colin eCampbell Rachel A Bernier Rachel A Bernier Frank eHillary Frank eHillary Frank eHillary |
| author_facet | Arnab eRoy Arnab eRoy Colin eCampbell Rachel A Bernier Rachel A Bernier Frank eHillary Frank eHillary Frank eHillary |
| author_sort | Arnab eRoy |
| collection | DOAJ |
| description | One common research goal in systems neurosciences is to understand how the functional relationship between a pair of regions of interest (ROIs) evolves over time. Examining neural connectivity in this way is well-suited for the study of developmental processes, learning, and even in recovery or treatment designs in response to injury. For most fMRI based studies, the strength of the functional relationship between two ROIs is defined as the correlation between the average signal representing each region. The drawback to this approach is that much information is lost due to averaging heterogeneous voxels, and therefore, the functional relationship between a ROI-pair that evolve at a spatial scale much finer than the ROIs remain undetected. To address this shortcoming, we introduce a novel evolutionary computation (EC) based voxel-level procedure to examine functional plasticity between an investigator defined ROI-pair by simultaneously using subject-specific BOLD-fMRI data collected from two sessions seperated by finite duration of time. This data-driven procedure detects a sub-region composed of spatially connected voxels from each ROI (a so-called sub-regional-pair) such that the pair shows a significant gain/loss of functional relationship strength across the two time points. The procedure is recursive and iteratively finds all statistically significant sub-regional-pairs within the ROIs. Using this approach, we examine functional plasticity between the default mode network (DMN) and the executive control network (ECN) during recovery from traumatic brain injury (TBI); the study includes 14 TBI and 12 healthy control subjects. We demonstrate that the EC based procedure is able to detect functional plasticity where a traditional averaging based approach fails. The subject-specific plasticity estimates obtained using the EC-procedure are highly consistent across multiple runs. Group-level analyses using these plasticity estimates showed an increase in the strength of functional relationship between DMN and ECN for TBI subjects, which is consistent with prior findings in the TBI-literature. The EC-approach also allowed us to separate sub-regional-pairs contributing to positive and negative plasticity; the detected sub-regional-pairs significantly overlap across runs thus highlighting the reliability of the EC-approach. These sub-regional-pairs may be useful in performing nuanced analyses of brain-behavior relationships during recovery from TBI. |
| first_indexed | 2024-12-17T13:21:59Z |
| format | Article |
| id | doaj.art-112ce07f0d7f405f9f1b25b5c63280f4 |
| institution | Directory Open Access Journal |
| issn | 1662-453X |
| language | English |
| last_indexed | 2024-12-17T13:21:59Z |
| publishDate | 2016-04-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Neuroscience |
| spelling | doaj.art-112ce07f0d7f405f9f1b25b5c63280f42022-12-21T21:46:50ZengFrontiers Media S.A.Frontiers in Neuroscience1662-453X2016-04-011010.3389/fnins.2016.00146171998An evolutionary computation approach to examine functional brain plasticityArnab eRoy0Arnab eRoy1Colin eCampbell2Rachel A Bernier3Rachel A Bernier4Frank eHillary5Frank eHillary6Frank eHillary7Pennsylvania State UniversityPennsylvania State UniversityWashington CollegePennsylvania State UniversityPennsylvania State UniversityPennsylvania State UniversityHershey Medical CenterPennsylvania State UniversityOne common research goal in systems neurosciences is to understand how the functional relationship between a pair of regions of interest (ROIs) evolves over time. Examining neural connectivity in this way is well-suited for the study of developmental processes, learning, and even in recovery or treatment designs in response to injury. For most fMRI based studies, the strength of the functional relationship between two ROIs is defined as the correlation between the average signal representing each region. The drawback to this approach is that much information is lost due to averaging heterogeneous voxels, and therefore, the functional relationship between a ROI-pair that evolve at a spatial scale much finer than the ROIs remain undetected. To address this shortcoming, we introduce a novel evolutionary computation (EC) based voxel-level procedure to examine functional plasticity between an investigator defined ROI-pair by simultaneously using subject-specific BOLD-fMRI data collected from two sessions seperated by finite duration of time. This data-driven procedure detects a sub-region composed of spatially connected voxels from each ROI (a so-called sub-regional-pair) such that the pair shows a significant gain/loss of functional relationship strength across the two time points. The procedure is recursive and iteratively finds all statistically significant sub-regional-pairs within the ROIs. Using this approach, we examine functional plasticity between the default mode network (DMN) and the executive control network (ECN) during recovery from traumatic brain injury (TBI); the study includes 14 TBI and 12 healthy control subjects. We demonstrate that the EC based procedure is able to detect functional plasticity where a traditional averaging based approach fails. The subject-specific plasticity estimates obtained using the EC-procedure are highly consistent across multiple runs. Group-level analyses using these plasticity estimates showed an increase in the strength of functional relationship between DMN and ECN for TBI subjects, which is consistent with prior findings in the TBI-literature. The EC-approach also allowed us to separate sub-regional-pairs contributing to positive and negative plasticity; the detected sub-regional-pairs significantly overlap across runs thus highlighting the reliability of the EC-approach. These sub-regional-pairs may be useful in performing nuanced analyses of brain-behavior relationships during recovery from TBI.http://journal.frontiersin.org/Journal/10.3389/fnins.2016.00146/fullfMRIEvolutionary computationnetwork plasticityTraumatic brain injury (TBI)Voxel-based approach |
| spellingShingle | Arnab eRoy Arnab eRoy Colin eCampbell Rachel A Bernier Rachel A Bernier Frank eHillary Frank eHillary Frank eHillary An evolutionary computation approach to examine functional brain plasticity Frontiers in Neuroscience fMRI Evolutionary computation network plasticity Traumatic brain injury (TBI) Voxel-based approach |
| title | An evolutionary computation approach to examine functional brain plasticity |
| title_full | An evolutionary computation approach to examine functional brain plasticity |
| title_fullStr | An evolutionary computation approach to examine functional brain plasticity |
| title_full_unstemmed | An evolutionary computation approach to examine functional brain plasticity |
| title_short | An evolutionary computation approach to examine functional brain plasticity |
| title_sort | evolutionary computation approach to examine functional brain plasticity |
| topic | fMRI Evolutionary computation network plasticity Traumatic brain injury (TBI) Voxel-based approach |
| url | http://journal.frontiersin.org/Journal/10.3389/fnins.2016.00146/full |
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