Laboratoire Traitement du Signal et de l'Image (LTSI, INSERM UMR 1099), Université de Rennes, Rennes, France.
Frontal Functions and Pathology Laboratory (FrontLab), Institut du Cerveau, Paris, France.
Int J Comput Assist Radiol Surg. 2024 Dec;19(12):2339-2349. doi: 10.1007/s11548-024-03197-w. Epub 2024 Jun 14.
Frontotemporal lobe dementia (FTD) results from the degeneration of the frontal and temporal lobes. It can manifest in several different ways, leading to the definition of variants characterised by their distinctive symptomatologies. As these variants are detected based on their symptoms, it can be unclear if they represent different types of FTD or different symptomatological axes. The goal of this paper is to investigate this question with a constrained cohort of FTD patients in order to see if the heterogeneity within this cohort can be inferred from medical images rather than symptom severity measurements.
An ensemble of convolutional neural networks (CNNs) is used to classify diffusion tensor images collected from two databases consisting of 72 patients with behavioural variant FTD and 120 healthy controls. FTD biomarkers were found using voxel-based analysis on the sensitivities of these CNNs. Sparse principal components analysis (sPCA) is then applied on the sensitivities arising from the patient cohort in order to identify the axes along which the patients express these biomarkers. Finally, this is correlated with their symptom severity measurements in order to interpret the clinical presentation of each axis.
The CNNs result in sensitivities and specificities between 83 and 92%. As expected, our analysis determines that all the robust biomarkers arise from the frontal and temporal lobes. sPCA identified four axes in terms of biomarker expression which are correlated with symptom severity measurements.
Our analysis confirms that behavioural variant FTD is not a singular type or spectrum of FTD, but rather that it has multiple symptomatological axes that relate to distinct regions of the frontal and temporal lobes. This analysis suggests that medical images can be used to understand the heterogeneity of FTD patients and the underlying anatomical changes that lead to their different clinical presentations.
额颞叶痴呆（FTD）是由于额颞叶的退化导致的。它可以以几种不同的方式表现出来，导致以其独特的症状为特征的变体的定义。由于这些变体是根据其症状来检测的，所以不清楚它们是否代表不同类型的 FTD 或不同的症状轴。本文的目的是用一个受限制的 FTD 患者队列来研究这个问题，以了解这个队列中的异质性是否可以从医学图像而不是症状严重程度的测量中推断出来。
使用卷积神经网络（CNN）的集合来对来自两个数据库的扩散张量图像进行分类，这两个数据库包括 72 名行为变体 FTD 患者和 120 名健康对照者。通过对这些 CNN 的敏感性进行基于体素的分析，找到了 FTD 的生物标志物。然后对来自患者队列的敏感性应用稀疏主成分分析（sPCA），以确定患者表达这些生物标志物的轴。最后，将其与他们的症状严重程度的测量进行相关，以解释每个轴的临床表现。
CNN 的敏感性和特异性在 83%至 92%之间。正如预期的那样，我们的分析确定了所有的稳健的生物标志物都来自于额叶和颞叶。sPCA 在生物标志物表达方面确定了四个轴，与症状严重程度的测量相关。
我们的分析证实，行为变体 FTD 不是一种单一的 FTD 类型或谱系，而是有多个症状轴，与额叶和颞叶的不同区域有关。这种分析表明，医学图像可以用于了解 FTD 患者的异质性以及导致他们不同临床表现的潜在解剖变化。
