La Rocca Marianna, Garner Rachael, Amoroso Nicola, Lutkenhoff Evan S, Monti Martin M, Vespa Paul, Toga Arthur W, Duncan Dominique
Laboratory of Neuro Imaging, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States.
Dipartimento di Farmacia - Scienze del Farmaco, Università degli Studi di Bari "A. Moro", Bari, Italy.
Front Neurosci. 2020 Nov 30;14:591662. doi: 10.3389/fnins.2020.591662. eCollection 2020.
Traumatic brain injury (TBI) may cause secondary debilitating problems, such as post-traumatic epilepsy (PTE), which occurs with unprovoked recurrent seizures, months or even years after TBI. Currently, the Epilepsy Bioinformatics Study for Antiepileptogenic Therapy (EpiBioS4Rx) has been enrolling moderate-severe TBI patients with the goal to identify biomarkers of epileptogenesis that may help to prevent seizure occurrence and better understand the mechanism underlying PTE. In this work, we used a novel complex network approach based on segmenting T1-weighted Magnetic Resonance Imaging (MRI) scans in patches of the same dimension (network nodes) and measured pairwise patch similarities using Pearson's correlation (network connections). This network model allowed us to obtain a series of single and multiplex network metrics to comprehensively analyze the different interactions between brain components and capture structural MRI alterations related to seizure development. We used these complex network features to train a Random Forest (RF) classifier and predict, with an accuracy of 70 and a 95% confidence interval of [67, 73%], which subjects from EpiBioS4Rx have had at least one seizure after a TBI. This complex network approach also allowed the identification of the most informative scales and brain areas for the discrimination between the two clinical groups: seizure-free and seizure-affected subjects, demonstrating to be a promising pilot study which, in the future, may serve to identify and validate biomarkers of PTE.
创伤性脑损伤（TBI）可能会引发继发性衰弱问题，如创伤后癫痫（PTE），它表现为在TBI数月甚至数年之后出现无端的反复发作性癫痫。目前，抗癫痫发生治疗的癫痫生物信息学研究（EpiBioS4Rx）一直在招募中重度TBI患者，目的是识别癫痫发生的生物标志物，这可能有助于预防癫痫发作，并更好地理解PTE的潜在机制。在这项工作中，我们使用了一种新颖的复杂网络方法，该方法基于将T1加权磁共振成像（MRI）扫描分割成相同维度的小块（网络节点），并使用皮尔逊相关性（网络连接）来测量小块之间的成对相似性。这种网络模型使我们能够获得一系列单重和多重网络指标，以全面分析脑区之间的不同相互作用，并捕捉与癫痫发作发展相关的结构MRI改变。我们使用这些复杂网络特征训练随机森林（RF）分类器，并以70%的准确率和[67, 73%]的95%置信区间预测EpiBioS4Rx研究中的哪些受试者在TBI后至少发生过一次癫痫发作。这种复杂网络方法还能够识别出对区分癫痫未发作和癫痫发作的两个临床组最具信息量的尺度和脑区，这表明它是一项有前景的初步研究，未来可能有助于识别和验证PTE的生物标志物。
