Sone Daichi, Beheshti Iman, Maikusa Norihide, Ota Miho, Kimura Yukio, Sato Noriko, Koepp Matthias, Matsuda Hiroshi
Integrative Brain Imaging Center, National Center of Neurology and Psychiatry, Tokyo, Japan.
Department of Clinical and Experimental Epilepsy, UCL Institute of Neurology, London, UK.
Mol Psychiatry. 2021 Mar;26(3):825-834. doi: 10.1038/s41380-019-0446-9. Epub 2019 Jun 3.
Epilepsy is a diverse brain disorder, and the pathophysiology of its various forms and comorbidities is largely unknown. A recent machine learning method enables us to estimate an individual's "brain-age" from MRI; this brain-age prediction is expected as a novel individual biomarker of neuropsychiatric disorders. The aims of this study were to estimate the brain-age for various categories of epilepsy and to evaluate clinical discrimination by brain-age for (1) the effect of psychosis on temporal lobe epilepsy (TLE), (2) psychogenic nonepileptic seizures (PNESs) from MRI-negative epilepsies, and (3) progressive myoclonic epilepsy (PME) from juvenile myoclonic epilepsy (JME). In total, 1196 T1-weighted MRI scans from healthy controls (HCs) were used to build a brain-age prediction model with support vector regression. Using the model, we calculated the brain-predicted age difference (brain-PAD: predicted age-chronological age) of the HCs and 318 patients with epilepsy. We compared the brain-PAD values based on the research questions. As a result, all categories of patients except for extra-temporal lobe focal epilepsy showed a significant increase in brain-PAD. TLE with hippocampal sclerosis presented a significantly higher brain-PAD than several other categories. The mean brain-PAD in TLE with inter-ictal psychosis was 10.9 years, which was significantly higher than TLE without psychosis (5.3 years). PNES showed a comparable mean brain-PAD (10.6 years) to that of epilepsy patients. PME had a higher brain-PAD than JME (22.0 vs. 9.3 years). In conclusion, neuroimaging-based brain-age prediction can provide novel insight into or clinical usefulness for the diverse symptoms of epilepsy.
癫痫是一种复杂多样的脑部疾病，其各种形式和合并症的病理生理学在很大程度上尚不清楚。最近的一种机器学习方法使我们能够从磁共振成像（MRI）中估计个体的“脑龄”；这种脑龄预测有望成为神经精神疾病的一种新型个体生物标志物。本研究的目的是估计各类癫痫的脑龄，并评估脑龄在以下方面的临床鉴别能力：（1）精神病对颞叶癫痫（TLE）的影响；（2）从MRI阴性癫痫中鉴别出精神性非癫痫发作（PNES）；（3）从青少年肌阵挛癫痫（JME）中鉴别出进行性肌阵挛癫痫（PME）。总共使用了1196例健康对照者（HC）的T1加权MRI扫描数据，通过支持向量回归建立脑龄预测模型。利用该模型，我们计算了HC和318例癫痫患者的脑预测年龄差（脑-PAD：预测年龄-实际年龄）。我们根据研究问题比较了脑-PAD值。结果显示，除颞叶外局灶性癫痫外，所有类型的患者脑-PAD均显著增加。伴有海马硬化的TLE患者的脑-PAD显著高于其他几种类型。伴有发作间期精神病的TLE患者的平均脑-PAD为10.9岁，显著高于无精神病的TLE患者（5.3岁）。PNES的平均脑-PAD（10.6岁）与癫痫患者相当。PME的脑-PAD高于JME（22.0岁对9.3岁）。总之，基于神经影像学的脑龄预测可为癫痫的多样症状提供新的见解或临床应用价值。
