Lucas Alfredo, Mouchtaris Sofia, Tranquille Ashley, Sinha Nishant, Gallagher Ryan, Mojena Marissa, Stein Joel M, Das Sandhitsu, Davis Kathryn A
Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
Department of Bioengineering, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
Epilepsia. 2024 Apr;65(4):1092-1106. doi: 10.1111/epi.17908. Epub 2024 Feb 12.
Epilepsy patients are often grouped together by clinical variables. Quantitative neuroimaging metrics can provide a data-driven alternative for grouping of patients. In this work, we leverage ultra-high-field 7-T structural magnetic resonance imaging (MRI) to characterize volumetric atrophy patterns across hippocampal subfields and thalamic nuclei in drug-resistant focal epilepsy.
Forty-two drug-resistant epilepsy patients and 13 controls with 7-T structural neuroimaging were included in this study. We measured hippocampal subfield and thalamic nuclei volumetry, and applied an unsupervised machine learning algorithm, Latent Dirichlet Allocation (LDA), to estimate atrophy patterns across the hippocampal subfields and thalamic nuclei of patients. We studied the association between predefined clinical groups and the estimated atrophy patterns. Additionally, we used hierarchical clustering on the LDA factors to group patients in a data-driven approach.
In patients with mesial temporal sclerosis (MTS), we found a significant decrease in volume across all ipsilateral hippocampal subfields (false discovery rate-corrected p [pFDR] < .01) as well as in some ipsilateral (pFDR < .05) and contralateral (pFDR < .01) thalamic nuclei. In left temporal lobe epilepsy (L-TLE) we saw ipsilateral hippocampal and some bilateral thalamic atrophy (pFDR < .05), whereas in right temporal lobe epilepsy (R-TLE) extensive bilateral hippocampal and thalamic atrophy was observed (pFDR < .05). Atrophy factors demonstrated that our MTS cohort had two atrophy phenotypes: one that affected the ipsilateral hippocampus and one that affected the ipsilateral hippocampus and bilateral anterior thalamus. Atrophy factors demonstrated posterior thalamic atrophy in R-TLE, whereas an anterior thalamic atrophy pattern was more common in L-TLE. Finally, hierarchical clustering of atrophy patterns recapitulated clusters with homogeneous clinical properties.
Leveraging 7-T MRI, we demonstrate widespread hippocampal and thalamic atrophy in epilepsy. Through unsupervised machine learning, we demonstrate patterns of volumetric atrophy that vary depending on disease subtype. Incorporating these atrophy patterns into clinical practice could help better stratify patients to surgical treatments and specific device implantation strategies.
癫痫患者常根据临床变量进行分组。定量神经影像学指标可为患者分组提供一种数据驱动的替代方法。在本研究中，我们利用超高场7-T结构磁共振成像（MRI）来表征药物难治性局灶性癫痫患者海马亚区和丘脑核团的体积萎缩模式。
本研究纳入了42例药物难治性癫痫患者和13例接受7-T结构神经影像学检查的对照者。我们测量了海马亚区和丘脑核团的体积，并应用无监督机器学习算法潜在狄利克雷分配（LDA）来估计患者海马亚区和丘脑核团的萎缩模式。我们研究了预定义临床组与估计的萎缩模式之间的关联。此外，我们对LDA因子进行层次聚类，以数据驱动的方式对患者进行分组。
在颞叶内侧硬化（MTS）患者中，我们发现同侧所有海马亚区的体积均显著减小（错误发现率校正p值[pFDR]<0.01），以及一些同侧（pFDR<0.05）和对侧（pFDR<0.01）丘脑核团体积减小。在左侧颞叶癫痫（L-TLE）患者中，我们观察到同侧海马和一些双侧丘脑萎缩（pFDR<0.05），而在右侧颞叶癫痫（R-TLE）患者中，观察到广泛的双侧海马和丘脑萎缩（pFDR<0.05）。萎缩因子表明我们的MTS队列有两种萎缩表型：一种影响同侧海马，另一种影响同侧海马和双侧前丘脑。萎缩因子表明R-TLE患者存在丘脑后部萎缩，而L-TLE患者中丘脑前部萎缩模式更为常见。最后，萎缩模式的层次聚类概括出了具有同质临床特征的聚类。
利用7-T MRI，我们证明了癫痫患者存在广泛的海马和丘脑萎缩。通过无监督机器学习，我们证明了体积萎缩模式因疾病亚型而异。将这些萎缩模式纳入临床实践有助于更好地对患者进行分层，以确定手术治疗和特定的设备植入策略。
