4.7 Article

Longitudinal predictive modeling of tau progression along the structural connectome

期刊

NEUROIMAGE
卷 237, 期 -, 页码 -

出版社

ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.neuroimage.2021.118126

关键词

Alzheimer's disease; Tau tangles; Positron emission tomography; Structural connectome; Diffusion tensor imaging

资金

  1. NIH [K01AG050711, R03AG070750]

向作者/读者索取更多资源

The study suggests that tau neurofibrillary tangles in the brain of AD patients exhibit a stereotypical spatiotemporal trajectory strongly correlated with disease progression, with evidence of tau transmission along preexisting neural connectivity conduits. An analytic graph diffusion framework is proposed for individualized predictive modeling of tau progression in the structural connectome. Using longitudinal imaging data, the model shows high consistency between predicted and observed tau measures, indicating potential as a personalizable predictive framework for AD.
Tau neurofibrillary tangles, a pathophysiological hallmark of Alzheimer's disease (AD), exhibit a stereotypical spatiotemporal trajectory that is strongly correlated with disease progression and cognitive decline. Personal-ized prediction of tau progression is, therefore, vital for the early diagnosis and prognosis of AD. Evidence from both animal and human studies is suggestive of tau transmission along the brains preexisting neural connectivity conduits. We present here an analytic graph diffusion framework for individualized predictive modeling of tau progression along the structural connectome. To account for physiological processes that lead to active generation and clearance of tau alongside passive diffusion, our model uses an inhomogenous graph diffusion equation with a source term and provides closed-form solutions to this equation for linear and exponential source functionals. Longitudinal imaging data from two cohorts, the Harvard Aging Brain Study (HABS) and the Alzheimer's Disease Neuroimaging Initiative (ADNI), were used to validate the model. The clinical data used for developing and vali-dating the model include regional tau measures extracted from longitudinal positron emission tomography (PET) scans based on the F-18-Flortaucipir radiotracer and individual structural connectivity maps computed from diffu-sion tensor imaging (DTI) by means of tractography and streamline counting. Two-timepoint tau PET scans were used to assess the goodness of model fit. Three-timepoint tau PET scans were used to assess predictive accuracy via comparison of predicted and observed tau measures at the third timepoint. Our results show high consistency between predicted and observed tau and differential tau from region-based analysis. While the prognostic value of this approach needs to be validated in a larger cohort, our preliminary results suggest that our longitudinal predictive model, which offers an in vivo macroscopic perspective on tau progression in the brain, is potentially promising as a personalizable predictive framework for AD.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.7
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据