不同的神经退行性模式:[18F]氟脱氧葡萄糖- pet和基于mri的阿尔茨海默病亚型的比较

IF 4.1 Q1 CLINICAL NEUROLOGY Brain communications Pub Date : 2024-11-23 eCollection Date: 2024-01-01 DOI:10.1093/braincomms/fcae426
Sophia H Wheatley, Rosaleena Mohanty, Konstantinos Poulakis, Fedor Levin, J Sebastian Muehlboeck, Agneta Nordberg, Michel J Grothe, Daniel Ferreira, Eric Westman
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引用次数: 0

摘要

[18F]氟脱氧葡萄糖(FDG)-PET和MRI是阿尔茨海默病神经退行性变的关键影像学指标。FDG-PET显示的顶叶颞叶代谢低下与MRI显示的阿尔茨海默病内侧颞叶萎缩密切相关。大量的生物学异质性,表现为不同的低代谢或萎缩模式亚型,先前已在阿尔茨海默病中使用数据驱动和假设驱动的方法进行了描述。然而,在阿尔茨海默病亚型的背景下,这两种成像方式之间的联系尚未被探索。为了研究这种联系,目前的研究使用了fpg - pet和MRI扫描,这些扫描来自180名β淀粉样蛋白阳性的阿尔茨海默病痴呆患者,339名β淀粉样蛋白阳性的轻度认知障碍患者和176名β淀粉样蛋白阴性的认知正常对照。随机森林分层聚类是一种用于识别亚型的数据驱动模型,以两种方式实施:一种是标准摄取值比率,另一种是灰质体积。鉴定出五种以代谢低下和萎缩为基础的亚型,表现出皮层为主和边缘为主的模式,尽管百分比和临床表现不同。发现3种以皮质为主的低代谢亚型为皮质为主(32%)、皮质+为主(11%)和皮质后为主(8%),2种以边缘为主的低代谢亚型为边缘为主(36%)和边缘额为主(13%)。此外,从MRI数据中观察到少量萎缩(最小)和广泛(弥漫性)神经变性亚型。发现的5种萎缩亚型为皮质型(19%)、边缘型(27%)、弥漫性(29%)、弥漫性+型(6%)和轻度(19%)。模态间比较显示,所有FDG-PET亚型均显示内侧颞叶萎缩,而不同的MRI亚型显示地形相似的低代谢模式。此外,FDG-PET和MRI亚型的分配在个体水平上并不一致。对数据驱动聚类模型与先验假设驱动方法的比较分析表明,这些亚型方法之间只有部分一致。FDG-PET亚型在边缘主导和皮层主导模式之间存在较大差异,MRI亚型在萎缩的严重程度上存在较大差异。总之,本研究强调,使用FDG-PET和MRI识别的阿尔茨海默病亚型捕获不同的通路,显示皮层与边缘神经变性优势。然而,这些亚型在模式之间没有双向关系,因此不能互换。
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Divergent neurodegenerative patterns: Comparison of [18F] fluorodeoxyglucose-PET- and MRI-based Alzheimer's disease subtypes.

[18F] fluorodeoxyglucose (FDG)-PET and MRI are key imaging markers for neurodegeneration in Alzheimer's disease. It has been well established that parieto-temporal hypometabolism on FDG-PET is closely associated with medial temporal atrophy on MRI in Alzheimer's disease. Substantial biological heterogeneity, expressed as distinct subtypes of hypometabolism or atrophy patterns, has been previously described in Alzheimer's disease using data-driven and hypothesis-driven methods. However, the link between these two imaging modalities has not yet been explored in the context of Alzheimer's disease subtypes. To investigate this link, the current study utilized FDG-PET and MRI scans from 180 amyloid-beta positive Alzheimer's disease dementia patients, 339 amyloid-beta positive mild cognitive impairment and 176 amyloid-beta negative cognitively normal controls from the Alzheimer's Disease Neuroimaging Initiative. Random forest hierarchical clustering, a data-driven model for identifying subtypes, was implemented in the two modalities: one with standard uptake value ratios and the other with grey matter volumes. Five hypometabolism- and atrophy-based subtypes were identified, exhibiting both cortical-predominant and limbic-predominant patterns although with differing percentages and clinical presentations. Three cortical-predominant hypometabolism subtypes found were Cortical Predominant (32%), Cortical Predominant+ (11%) and Cortical Predominant posterior (8%), and two limbic-predominant hypometabolism subtypes found were Limbic Predominant (36%) and Limbic Predominant frontal (13%). In addition, little atrophy (minimal) and widespread (diffuse) neurodegeneration subtypes were observed from the MRI data. The five atrophy subtypes found were Cortical Predominant (19%), Limbic Predominant (27%), Diffuse (29%), Diffuse+ (6%) and Minimal (19%). Inter-modality comparisons showed that all FDG-PET subtypes displayed medial temporal atrophy, whereas the distinct MRI subtypes showed topographically similar hypometabolic patterns. Further, allocations of FDG-PET and MRI subtypes were not consistent when compared at an individual level. Additional analysis comparing the data-driven clustering model with prior hypothesis-driven methods showed only partial agreement between these subtyping methods. FDG-PET subtypes had greater differences between limbic-predominant and cortical-predominant patterns, and MRI subtypes had greater differences in severity of atrophy. In conclusion, this study highlighted that Alzheimer's disease subtypes identified using both FDG-PET and MRI capture distinct pathways showing cortical versus limbic predominance of neurodegeneration. However, the subtypes do not share a bidirectional relationship between modalities and are thus not interchangeable.

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