Akbar Md Navid, Ruf Sebastian, La Rocca Marianna, Garner Rachael, Barisano Giuseppe, Cua Ruskin, Vespa Paul, Erdoğmuş Deniz, Duncan Dominique
Department of Electrical and Computer Engineering, College of Engineering, Northeastern University, Boston, MA 02115, USA.
USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA.
Comput Diffus MRI. 2021 Oct;13006:133-143. doi: 10.1007/978-3-030-87615-9_12. Epub 2021 Sep 25.
Traumatic brain injury (TBI) is a serious condition, potentially causing seizures and other lifelong disabilities. Patients who experience at least one seizure one week after TBI (late seizure) are at high risk for lifelong complications of TBI, such as post-traumatic epilepsy (PTE). Identifying which TBI patients are at risk of developing seizures remains a challenge. Although magnetic resonance imaging (MRI) methods that probe structural and functional alterations after TBI are promising for biomarker detection, physical deformations following moderate-severe TBI present problems for standard processing of neuroimaging data, complicating the search for biomarkers. In this work, we consider a prediction task to identify which TBI patients will develop late seizures, using fractional anisotropy (FA) features from white matter tracts in diffusion-weighted MRI (dMRI). To understand how best to account for brain lesions and deformations, four preprocessing strategies are applied to dMRI, including the novel application of a lesion normalization technique to dMRI. The pipeline involving the lesion normalization technique provides the best prediction performance, with a mean accuracy of 0.819 and a mean area under the curve of 0.785. Finally, following statistical analyses of selected features, we recommend the dMRI alterations of a certain white matter tract as a potential biomarker.
创伤性脑损伤（TBI）是一种严重的病症，可能会引发癫痫和其他终身残疾。在创伤性脑损伤后一周内经历至少一次癫痫发作（迟发性癫痫）的患者，面临创伤性脑损伤终身并发症的高风险，例如创伤后癫痫（PTE）。确定哪些创伤性脑损伤患者有癫痫发作风险仍然是一项挑战。尽管用于探测创伤性脑损伤后结构和功能改变的磁共振成像（MRI）方法在生物标志物检测方面很有前景，但中度至重度创伤性脑损伤后的身体变形给神经影像数据的标准处理带来了问题，使得寻找生物标志物变得复杂。在这项工作中，我们考虑一项预测任务，即使用扩散加权磁共振成像（dMRI）中白质束的分数各向异性（FA）特征来确定哪些创伤性脑损伤患者会发生迟发性癫痫。为了了解如何最好地处理脑损伤和变形，我们对dMRI应用了四种预处理策略，包括将一种病变归一化技术新颖地应用于dMRI。涉及病变归一化技术的流程提供了最佳预测性能，平均准确率为0.819，平均曲线下面积为0.785。最后，在对选定特征进行统计分析后，我们推荐某一白质束的dMRI改变作为一种潜在的生物标志物。
