Department of Neurology, University of Pennsylvania, Philadelphia, PA, USA.
Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA.
Ann Neurol. 2021 Nov;90(5):751-762. doi: 10.1002/ana.26233. Epub 2021 Oct 15.
Tau neurofibrillary tangles (T) are the primary driver of downstream neurodegeneration (N) and subsequent cognitive impairment in Alzheimer's disease (AD). However, there is substantial variability in the T-N relationship - manifested in higher or lower atrophy than expected for level of tau in a given brain region. The goal of this study was to determine if region-based quantitation of this variability allows for identification of underlying modulatory factors, including polypathology.
Cortical thickness (N) and F-Flortaucipir SUVR (T) were computed in 104 gray matter regions from a cohort of cognitively-impaired, amyloid-positive (A+) individuals. Region-specific residuals from a robust linear fit between SUVR and cortical thickness were computed as a surrogate for T-N mismatch. A summary T-N mismatch metric defined using residuals were correlated with demographic and imaging-based modulatory factors, and to partition the cohort into data-driven subgroups.
The summary T-N mismatch metric correlated with underlying factors such as age and burden of white matter hyperintensity lesions. Data-driven subgroups based on clustering of residuals appear to represent different biologically relevant phenotypes, with groups showing distinct spatial patterns of higher or lower atrophy than expected.
These data support the notion that a measure of deviation from a normative relationship between tau burden and neurodegeneration across brain regions in individuals on the AD continuum captures variability due to multiple underlying factors, and can reveal phenotypes, which if validated, may help identify possible contributors to neurodegeneration in addition to tau, which may ultimately be useful for cohort selection in clinical trials. ANN NEUROL 2021;90:751-762.
tau 神经原纤维缠结(T)是阿尔茨海默病(AD)下游神经退行性变(N)和随后认知障碍的主要驱动因素。然而,T-N 关系存在很大的可变性——表现在给定脑区 tau 水平下,萎缩程度高于或低于预期。本研究的目的是确定基于区域的这种变异性定量分析是否可以识别潜在的调节因素,包括多病理学。
从认知障碍、淀粉样蛋白阳性(A+)个体的队列中计算了 104 个灰质区域的皮质厚度(N)和 F-Flortaucipir SUVR(T)。使用 SUVR 和皮质厚度之间稳健线性拟合的区域特异性残差作为 T-N 不匹配的替代指标进行计算。使用残差定义的综合 T-N 不匹配指标与人口统计学和基于成像的调节因素相关,并将队列分为数据驱动亚组。
综合 T-N 不匹配指标与年龄和白质高信号病变负担等潜在因素相关。基于残差聚类的基于数据的亚组似乎代表了不同的生物学上相关的表型,这些组显示出比预期更高或更低的萎缩的不同空间模式。
这些数据支持这样一种观点,即个体 AD 连续体中大脑区域 tau 负担与神经退行性变之间的正常关系的偏差度量可以捕捉到多种潜在因素引起的变异性,并且可以揭示表型,如果得到验证,可能有助于除 tau 之外,确定神经退行性变的可能贡献者,这对于临床试验中的队列选择可能最终是有用的。