Objective

Focal cortical dysplasia (FCD) is the most common epileptogenic developmental malformation and a prevalent cause of surgically amenable epilepsy. While cellular and molecular biology data suggest that FCD lesional characteristics lie along a spectrum, this notion remains to be verified in vivo. We tested the hypothesis that machine learning applied to MRI captures FCD lesional variability at a mesoscopic scale.

Methods

We studied 46 patients with histologically verified FCD Type II and 35 age- and sex-matched healthy controls. We applied consensus clustering, an unsupervised learning technique that identifies stable clusters based on bootstrap-aggregation, to 3 T multicontrast MRI (T1-weighted MRI and FLAIR) features of FCD normalized with respect to distributions in controls.

Results

Lesions were parcellated into four classes with distinct structural profiles variably expressed within and across patients: Class-1 with isolated white matter (WM) damage; Class-2 combining grey matter (GM) and WM alterations; Class-3 with isolated GM damage; Class-4 with GM-WM interface anomalies. Class membership was replicated in two independent datasets. Classes with GM anomalies impacted local function (resting-state fMRI derived ALFF), while those with abnormal WM affected large-scale connectivity (assessed by degree centrality). Overall, MRI classes reflected typical histopathological FCD characteristics: Class-1 was associated with severe WM gliosis and interface blurring, Class-2 with severe GM dyslamination and moderate WM gliosis, Class-3 with moderate GM gliosis, Class-4 with mild interface blurring. A detection algorithm trained on class-informed data outperformed a class-naïve paradigm.

Significance

Machine learning applied to widely available MRI contrasts uncovers FCD Type II variability at a mesoscopic scale and identifies tissue classes with distinct structural dimensions, functional and histopathological profiles. Integrating in vivo staging of FCD traits with automated lesion detection is likely to inform the development of novel personalized treatments.

中文翻译：

---

无监督机器学习揭示了介观尺度下局灶性皮质发育不良的病变变异性

目的

局灶性皮质发育不良（FCD）是最常见的致癫痫性发育畸形，是外科手术性癫痫的普遍原因。尽管细胞和分子生物学数据表明FCD病变特征沿谱分布，但这一概念仍有待在体内得到验证。我们测试了一种假设，即机器学习应用于MRI可以在介观范围内捕获FCD病变变异性。

方法

我们研究了46例经组织学证实为II型FCD的患者和35例年龄和性别相匹配的健康对照者。我们将共识聚类（一种无监督的学习技术，该技术基于引导聚合来识别稳定聚类）应用于FCD的3 T多层对比MRI（T1加权MRI和FLAIR）特征，这些特征相对于对照中的分布进行了归一化。

结果

病变被分为四类，具有明显的结构特征，在患者内和患者之间可变表达：具有孤立白质（WM）损伤的1类；结合灰质（GM）和WM变更的2类；3级，具有孤立的GM损坏；具有GM-WM接口异常的Class-4。类成员资格被复制到两个独立的数据集中。具有GM异常的类别会影响局部功能（静止状态fMRI得出的ALFF），而具有WM异常的类别会影响大规模连接（通过程度中心性进行评估）。总体而言，MRI级别反映了典型的组织病理学FCD特征：1级伴有严重的WM神经胶质化和界面模糊，2级伴有严重的GM骨化和中度WM神经胶质，3级伴有中度GM神经胶质增生，4级伴有轻度界面模糊。

意义

应用于广泛可用的MRI的机器学习对比度在介观范围内揭示了FCD II型变异性，并识别出具有不同结构尺寸，功能和组织病理学特征的组织类别。将FCD性状的体内分期与自动病变检测相结合可能会为新型个性化治疗的发展提供信息。