Journal article
A machine learning enhanced mechanistic simulation framework for functional deficit prediction in TBI
- Abstract:
- 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.
- Publication status:
- Published
- Peer review status:
- Peer reviewed
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(Preview, Version of record, pdf, 2.1MB, Terms of use)
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- Publisher copy:
- 10.3389/fbioe.2021.587082
Authors
- Publisher:
- Frontiers Media
- Journal:
- Frontiers in Bioengineering and Biotechnology More from this journal
- Volume:
- 9
- Article number:
- 587082
- Publication date:
- 2021-03-03
- Acceptance date:
- 2021-01-18
- DOI:
- EISSN:
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2296-4185
- Language:
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English
- Keywords:
- Pubs id:
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1156930
- Local pid:
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pubs:1156930
- Deposit date:
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2021-01-18
- ARK identifier:
Terms of use
- Copyright holder:
- Schroder, A et al.
- Copyright date:
- 2021
- Rights statement:
- © 2021 Schroder, Lawrence, Voets, Garcia-Gonzalez, Jones, Peña and Jerusalem. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
- Licence:
- CC Attribution (CC BY)
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