Neurology Department, Department of Clinical Neuroscience, HUG, University Hospitals and Faculty of Medicine Geneva, Geneva, Switzerland.
Department of Clinical Neurosciences, LREN, CHUV, University of Lausanne, Lausanne, Switzerland.
Brain Behav. 2020 Nov;10(11):e01825. doi: 10.1002/brb3.1825. Epub 2020 Sep 17.
Mesial temporal lobe epilepsy (TLE) is one of the most widespread neurological network disorders. Computational anatomy MRI studies demonstrate a robust pattern of cortical volume loss. Most statistical analyses provide information about localization of significant focal differences in a segregationist way. Multivariate Bayesian modeling provides a framework allowing inferences about inter-regional dependencies. We adopt this approach to answer following questions: Which structures within a pattern of dynamic epilepsy-associated brain anatomy reorganization best predict TLE pathology. Do these structures differ between TLE subtypes?
We acquire clinical and MRI data from TLE patients with and without hippocampus sclerosis (n = 128) additional to healthy volunteers (n = 120). MRI data were analyzed in the computational anatomy framework of SPM12 using classical mass-univariate analysis followed by multivariate Bayesian modeling.
After obtaining TLE-associated brain anatomy pattern, we estimate predictive power for disease and TLE subtypes using Bayesian model selection and comparison. We show that ipsilateral para-/hippocampal regions contribute most to disease-related differences between TLE and healthy controls independent of TLE laterality and subtype. Prefrontal cortical changes are more discriminative for left-sided TLE, whereas thalamus and temporal pole for right-sided TLE. The presence of hippocampus sclerosis was linked to stronger involvement of thalamus and temporal lobe regions; frontoparietal involvement was predominant in absence of sclerosis.
Our topology inferences on brain anatomy demonstrate a differential contribution of structures within limbic and extralimbic circuits linked to main effects of TLE and hippocampal sclerosis. We interpret our results as evidence for TLE-related spatial modulation of anatomical networks.
颞叶内侧癫痫（TLE）是最广泛的神经网络疾病之一。计算解剖学 MRI 研究表明存在皮质体积损失的明显模式。大多数统计分析以分离主义的方式提供关于焦点差异定位的信息。多元贝叶斯建模提供了一种允许对区域间相关性进行推断的框架。我们采用这种方法来回答以下问题：在动态癫痫相关脑解剖结构重排模式中，哪些结构最能预测 TLE 病理学。这些结构在 TLE 亚型之间是否存在差异？
我们从伴有和不伴有海马硬化（n=128）的 TLE 患者以及健康志愿者（n=120）中获取临床和 MRI 数据。使用 SPM12 中的计算解剖框架分析 MRI 数据，采用经典的整体多元分析，然后采用多元贝叶斯建模。
获得 TLE 相关脑解剖模式后，我们使用贝叶斯模型选择和比较来估计疾病和 TLE 亚型的预测能力。我们表明，同侧旁-/海马区域对 TLE 与健康对照组之间的疾病相关差异的贡献最大，与 TLE 的偏侧性和亚型无关。额皮质变化对左侧 TLE 的区分度更高，而丘脑和颞极对右侧 TLE 的区分度更高。海马硬化的存在与丘脑和颞叶区域的更强参与有关；无硬化时，额顶叶参与更为突出。
我们对脑解剖结构的拓扑推断表明，与 TLE 和海马硬化的主要影响相关的边缘和边缘外回路中的结构有不同的贡献。我们将我们的结果解释为 TLE 相关的解剖网络空间调制的证据。
