Neuroimaging of Epilepsy Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada.
Department of Neurology and Neurosurgery, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada.
Brain. 2022 Apr 29;145(3):897-908. doi: 10.1093/brain/awab425.
In drug-resistant temporal lobe epilepsy, precise predictions of drug response, surgical outcome and cognitive dysfunction at an individual level remain challenging. A possible explanation may lie in the dominant 'one-size-fits-all' group-level analytical approaches that does not allow parsing interindividual variations along the disease spectrum. Conversely, analysing inter-patient heterogeneity is increasingly recognized as a step towards person-centred care. Here, we used unsupervised machine learning to estimate latent relations (or disease factors) from 3 T multimodal MRI features [cortical thickness, hippocampal volume, fluid-attenuated inversion recovery (FLAIR), T1/FLAIR, diffusion parameters] representing whole-brain patterns of structural pathology in 82 patients with temporal lobe epilepsy. We assessed the specificity of our approach against age- and sex-matched healthy individuals and a cohort of frontal lobe epilepsy patients with histologically verified focal cortical dysplasia. We identified four latent disease factors variably co-expressed within each patient and characterized by ipsilateral hippocampal microstructural alterations, loss of myelin and atrophy (Factor 1), bilateral paralimbic and hippocampal gliosis (Factor 2), bilateral neocortical atrophy (Factor 3) and bilateral white matter microstructural alterations (Factor 4). Bootstrap analysis and parameter variations supported high stability and robustness of these factors. Moreover, they were not expressed in healthy controls and only negligibly in disease controls, supporting specificity. Supervised classifiers trained on latent disease factors could predict patient-specific drug response in 76 ± 3% and postsurgical seizure outcome in 88 ± 2%, outperforming classifiers that did not operate on latent factor information. Latent factor models predicted inter-patient variability in cognitive dysfunction (verbal IQ: r = 0.40 ± 0.03; memory: r = 0.35 ± 0.03; sequential motor tapping: r = 0.36 ± 0.04), again outperforming baseline learners. Data-driven analysis of disease factors provides a novel appraisal of the continuum of interindividual variability, which is probably determined by multiple interacting pathological processes. Incorporating interindividual variability is likely to improve clinical prognostics.
在耐药性颞叶癫痫中，精确预测药物反应、手术结果和认知功能障碍仍然具有挑战性。一种可能的解释可能在于主导的“一刀切”的群体分析方法，这种方法不允许沿着疾病谱解析个体间的变化。相反，分析患者间的异质性正逐渐被认为是迈向以患者为中心的护理的一步。在这里，我们使用无监督机器学习从 3T 多模态 MRI 特征[皮质厚度、海马体积、FLAIR、T1/FLAIR、扩散参数]中估计潜在的关系（或疾病因素），这些特征代表了 82 例颞叶癫痫患者的全脑结构病理学模式。我们评估了我们的方法对年龄和性别匹配的健康个体和一组经组织学证实的局灶性皮质发育不良的额叶癫痫患者的特异性。我们确定了四个潜在的疾病因素，这些因素在每个患者中以不同的方式共同表达，并由同侧海马微观结构改变、髓鞘丢失和萎缩（因素 1）、双侧边缘系统和海马神经胶质增生（因素 2）、双侧新皮质萎缩（因素 3）和双侧白质微观结构改变（因素 4）特征化。引导分析和参数变化支持这些因素的高度稳定性和鲁棒性。此外，它们在健康对照组中没有表达，在疾病对照组中也只有微不足道的表达，支持了其特异性。基于潜在疾病因素训练的监督分类器可以预测 76±3%的患者特定药物反应和 88±2%的术后癫痫发作结果，优于不基于潜在因子信息的分类器。潜在因子模型预测了认知功能障碍的患者间变异性（言语智商：r=0.40±0.03；记忆：r=0.35±0.03；连续运动敲击：r=0.36±0.04），再次优于基线学习者。疾病因素的数据驱动分析提供了个体间变异性连续体的新评价，这可能是由多种相互作用的病理过程决定的。纳入个体间变异性可能会改善临床预后。
