Schroder Anna, Lawrence Tim, Voets Natalie, Garcia-Gonzalez Daniel, Jones Mike, Peña Jose-Maria, Jerusalem Antoine
Department of Engineering Science, University of Oxford, Oxford, United Kingdom.
Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom.
Front Bioeng Biotechnol. 2021 Mar 3;9:587082. doi: 10.3389/fbioe.2021.587082. eCollection 2021.
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 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.
静息态功能磁共振成像（rsfMRI）及其所识别的潜在脑网络，最近已成为评估功能缺陷的一种有前景的途径，而无需患者主动参与。我们在此假设，这种改变可以从网络内的组织损伤推断出来。从工程学角度来看，撞击后组织机械损伤的数值预测在计算上仍然成本高昂。为此，我们提出了一个数值框架，旨在预测任意头部撞击后的静息态网络破坏情况，具体描述为头部速度、撞击位置和角度以及撞击器形状。所提出的方法使用了一个预先计算案例的库，并借助机器学习层进行高效快速的预测。通过一个虚拟跌倒案例说明了机器学习层的准确性，其中机器学习预测结果与完整模拟结果非常匹配。最终，使用九名创伤性脑损伤（TBI）患者在受伤后24小时内扫描的rsfMRI数据对所得框架进行了测试，利用辅助医疗信息重建了事故。虽然全面验证需要更多临床数据，但这种方法为（i）即时预测rsfMRI改变（可根据辅助医疗数据在临床场所轻松测量）以及（ii）通过rsfMRI测量进行逆向工程事故重建打开了大门。
