A machine learning enhanced mechanistic simulation framework for functional deficit prediction in TBI
Resting state functional magnetic resonance imaging (rsfMRI), and the underlying brain networks identified with it, have recently appeared as a promising avenue for the evaluation of functional deficits without the need for active patient participation. We hypothesize here that such alteration can b...
| Main Authors: | , , , , , , |
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| Format: | Journal article |
| Language: | English |
| Published: |
Frontiers Media
2021
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| _version_ | 1797089736810037248 |
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| author | Schroder, A Lawrence, T Voets, N Garcia-Gonzalez, D Jones, M Pena, J-M Jerusalem, A |
| author_facet | Schroder, A Lawrence, T Voets, N Garcia-Gonzalez, D Jones, M Pena, J-M Jerusalem, A |
| author_sort | Schroder, A |
| collection | OXFORD |
| description | Resting state functional magnetic resonance imaging (rsfMRI), and the underlying brain networks identified with it, have recently appeared as a promising avenue for the evaluation of functional deficits without the need for active patient participation. We hypothesize here that such alteration can be inferred from tissue damage within the network. From an engineering perspective, the numerical prediction of tissue mechanical damage following an impact remains computationally expensive. To this end, we propose a numerical framework aimed at predicting resting state network disruption for an arbitrary head impact, as described by the head velocity, location and angle of impact, and impactor shape. The proposed method uses a library of precalculated cases leveraged by a machine learning layer for efficient and quick prediction. The accuracy of the machine learning layer is illustrated with a dummy fall case, where the machine learning prediction is shown to closely match the full simulation results. The resulting framework is finally tested against the rsfMRI data of nine TBI patients scanned within 24 h of injury, for which paramedical information was used to reconstruct in silico the accident. While more clinical data are required for full validation, this approach opens the door to (i) on-the-fly prediction of rsfMRI alterations, readily measurable on clinical premises from paramedical data, and (ii) reverse-engineered accident reconstruction through rsfMRI measurements. |
| first_indexed | 2024-03-07T03:08:22Z |
| format | Journal article |
| id | oxford-uuid:b3589caf-8bb6-441e-82c5-ce1e899556c5 |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-07T03:08:22Z |
| publishDate | 2021 |
| publisher | Frontiers Media |
| record_format | dspace |
| spelling | oxford-uuid:b3589caf-8bb6-441e-82c5-ce1e899556c52022-03-27T04:18:20ZA machine learning enhanced mechanistic simulation framework for functional deficit prediction in TBIJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:b3589caf-8bb6-441e-82c5-ce1e899556c5EnglishSymplectic ElementsFrontiers Media2021Schroder, ALawrence, TVoets, NGarcia-Gonzalez, DJones, MPena, J-MJerusalem, AResting state functional magnetic resonance imaging (rsfMRI), and the underlying brain networks identified with it, have recently appeared as a promising avenue for the evaluation of functional deficits without the need for active patient participation. We hypothesize here that such alteration can be inferred from tissue damage within the network. From an engineering perspective, the numerical prediction of tissue mechanical damage following an impact remains computationally expensive. To this end, we propose a numerical framework aimed at predicting resting state network disruption for an arbitrary head impact, as described by the head velocity, location and angle of impact, and impactor shape. The proposed method uses a library of precalculated cases leveraged by a machine learning layer for efficient and quick prediction. The accuracy of the machine learning layer is illustrated with a dummy fall case, where the machine learning prediction is shown to closely match the full simulation results. The resulting framework is finally tested against the rsfMRI data of nine TBI patients scanned within 24 h of injury, for which paramedical information was used to reconstruct in silico the accident. While more clinical data are required for full validation, this approach opens the door to (i) on-the-fly prediction of rsfMRI alterations, readily measurable on clinical premises from paramedical data, and (ii) reverse-engineered accident reconstruction through rsfMRI measurements. |
| spellingShingle | Schroder, A Lawrence, T Voets, N Garcia-Gonzalez, D Jones, M Pena, J-M Jerusalem, A A machine learning enhanced mechanistic simulation framework for functional deficit prediction in TBI |
| title | A machine learning enhanced mechanistic simulation framework for functional deficit prediction in TBI |
| title_full | A machine learning enhanced mechanistic simulation framework for functional deficit prediction in TBI |
| title_fullStr | A machine learning enhanced mechanistic simulation framework for functional deficit prediction in TBI |
| title_full_unstemmed | A machine learning enhanced mechanistic simulation framework for functional deficit prediction in TBI |
| title_short | A machine learning enhanced mechanistic simulation framework for functional deficit prediction in TBI |
| title_sort | machine learning enhanced mechanistic simulation framework for functional deficit prediction in tbi |
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