Department of Neuroscience, Clinical Imaging Sciences Centre, Brighton and Sussex Medical School, University of Sussex, Brighton, East Sussex, United Kingdom.
Department of Neuroscience, Trafford Centre, Brighton and Sussex Medical School, University of Sussex, Brighton, East Sussex, United Kingdom.
Ann Clin Transl Neurol. 2020 May;7(5):722-732. doi: 10.1002/acn3.51035. Epub 2020 May 4.
To characterize disease evolution in amyotrophic lateral sclerosis using an event-based model designed to extract temporal information from cross-sectional data. Conventional methods for understanding mechanisms of rapidly progressive neurodegenerative disorders are limited by the subjectivity inherent in the selection of a limited range of measurements, and the need to acquire longitudinal data.
The event-based model characterizes a disease as a series of events, each comprising a significant change in subject state. The model was applied to data from 154 patients and 128 healthy controls selected from five independent diffusion MRI datasets acquired in four different imaging laboratories between 1999 and 2016. The biomarkers modeled were mean fractional anisotropy values of white matter tracts implicated in amyotrophic lateral sclerosis. The cerebral portion of the corticospinal tract was divided into three segments.
Application of the model to the pooled datasets revealed that the corticospinal tracts were involved before other white matter tracts. Distal corticospinal tract segments were involved earlier than more proximal (i.e., cephalad) segments. In addition, the model revealed early ordering of fractional anisotropy change in the corpus callosum and subsequently in long association fibers.
These findings represent data-driven evidence for early involvement of the corticospinal tracts and body of the corpus callosum in keeping with conventional approaches to image analysis, while providing new evidence to inform directional degeneration of the corticospinal tracts. This data-driven model provides new insight into the dynamics of neuronal damage in amyotrophic lateral sclerosis.
利用基于事件的模型来描述肌萎缩侧索硬化症的疾病演变,该模型旨在从横断面数据中提取时间信息。理解快速进展性神经退行性疾病机制的传统方法受到从有限范围内选择测量值所固有的主观性以及获取纵向数据的需要的限制。
基于事件的模型将疾病描述为一系列事件,每个事件都包含受试者状态的重大变化。该模型应用于从 1999 年至 2016 年在四个不同的成像实验室中获取的五个独立扩散 MRI 数据集中选择的 154 名患者和 128 名健康对照者的数据。所建模的生物标志物是肌萎缩侧索硬化症相关的白质束的平均分数各向异性值。皮质脊髓束的大脑部分分为三个节段。
将模型应用于汇总数据集揭示,皮质脊髓束在其他白质束之前受到影响。远端皮质脊髓束节段比更靠近头部(即颅侧)的节段更早受累。此外,该模型还揭示了胼胝体的各向异性变化的早期顺序,随后是长的联合纤维。
这些发现代表了基于数据的证据,证明了皮质脊髓束和胼胝体的主体在图像分析的传统方法中早期受累,同时提供了新的证据来告知皮质脊髓束的定向变性。这种数据驱动的模型为肌萎缩侧索硬化症中神经元损伤的动态提供了新的见解。