scholarly journals Functional ensemble survival tree: Dynamic prediction of Alzheimer’s disease progression accommodating multiple time‐varying covariates

2020 ◽  
Author(s):  
Shu Jiang ◽  
Yijun Xie ◽  
Graham A. Colditz
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Disease Progression ◽  
Multiple Time ◽  
Time Varying ◽  
Dynamic Prediction ◽  
Time Varying Covariates ◽  
Survival Tree

scholarly journals Functional Ensemble Survival Tree: Dynamic Prediction of Alzheimer’s Disease Progression Accommodating Multiple Time-Varying Covariates

2020 ◽  
Author(s):  
By Shu Jiang ◽  
Yijun Xie ◽  
Graham A. Colditz
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Disease Progression ◽  
Functional Principal Component Analysis ◽  
Multiple Time ◽  
Time Varying ◽  
Dynamic Prediction ◽  
Time Varying Covariates ◽  
Baseline Covariates ◽  
Survival Tree

With the exponential growth in data collection, multiple time-varying biomarkers are commonly encountered in clinical studies, along with rich set of baseline covariates. This paper is motivated by addressing a critical issue in the field of Alzheimer’s disease (AD) in which we aim to predict the time for AD conversion in people with mild cognitive impairment to inform prevention and early treatment decisions. Conventional joint models of biomarker trajectory with time-to-event data rely heavily on model assumptions and may not be applicable when the number of covariates is large. This thus motivated us to consider a functional ensemble survival tree framework to characterize the joint effects of both functional and baseline covariates in predicting disease progression. The proposed framework incorporates multivariate functional principal component analysis to characterize the changing patterns of multiple time-varying neurocognitive biomarker trajectories and then nest these features within an ensemble survival tree in predicting the progression of AD. We provide a fast implementation of the algorithm that accommodates personalized dynamic prediction that can be updated as new observations are gathered to reflect the patient’s latest prognosis. The algorithm is empirically shown to perform well in simulation studies and is illustrated through the analysis of data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). We provide implementation of our proposed method in R package funest.

Identifying Configurational Abnormalities in Alzheimer’S Disease Progression Using Multi-View Structure Connectome

2019 ◽  
Author(s):  
Lei Guo ◽  
Haoteng Tang ◽  
Qi Wang ◽  
Emily Dennis ◽  
Dajiang Zhu ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Disease Progression

Simultaneous modeling of Alzheimer's disease progression via multiple cognitive scales

2021 ◽  
Author(s):  
Line Kühnel ◽  
Anna‐Karin Berger ◽  
Bo Markussen ◽  
Lars L. Raket
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Disease Progression ◽  
Simultaneous Modeling

scholarly journals Disease progression modelling from preclinical Alzheimer’s disease (AD) to AD dementia

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Soo Hyun Cho ◽  
Sookyoung Woo ◽  
Changsoo Kim ◽  
Hee Jin Kim ◽  
Hyemin Jang ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Disease Progression ◽  
Disease Assessment ◽  
Time Interval ◽  
Disease Course ◽  
Preclinical Alzheimer’S Disease ◽  
Apoe Ε4 ◽  
Preclinical Ad ◽  
Cognitive Subscale

AbstractTo characterize the course of Alzheimer’s disease (AD) over a longer time interval, we aimed to construct a disease course model for the entire span of the disease using two separate cohorts ranging from preclinical AD to AD dementia. We modelled the progression course of 436 patients with AD continuum and investigated the effects of apolipoprotein E ε4 (APOE ε4) and sex on disease progression. To develop a model of progression from preclinical AD to AD dementia, we estimated Alzheimer’s Disease Assessment Scale-Cognitive Subscale 13 (ADAS-cog 13) scores. When calculated as the median of ADAS-cog 13 scores for each cohort, the estimated time from preclinical AD to MCI due to AD was 7.8 years and preclinical AD to AD dementia was 15.2 years. ADAS-cog 13 scores deteriorated most rapidly in women APOE ε4 carriers and most slowly in men APOE ε4 non-carriers (p < 0.001). Our results suggest that disease progression modelling from preclinical AD to AD dementia may help clinicians to estimate where patients are in the disease course and provide information on variation in the disease course by sex and APOE ε4 status.

scholarly journals The extracellular chaperone Clusterin enhances Tau aggregate seeding in a cellular model

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Patricia Yuste-Checa ◽  
Victoria A. Trinkaus ◽  
Irene Riera-Tur ◽  
Rahmi Imamoglu ◽  
Theresa F. Schaller ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Disease Progression ◽  
Frontotemporal Dementia ◽  
Brain Regions ◽  
Model System ◽  
Cellular Model ◽  
Tau Pathology ◽  
Extracellular Chaperone ◽  
Tau Aggregate

AbstractSpreading of aggregate pathology across brain regions acts as a driver of disease progression in Tau-related neurodegeneration, including Alzheimer’s disease (AD) and frontotemporal dementia. Aggregate seeds released from affected cells are internalized by naïve cells and induce the prion-like templating of soluble Tau into neurotoxic aggregates. Here we show in a cellular model system and in neurons that Clusterin, an abundant extracellular chaperone, strongly enhances Tau aggregate seeding. Upon interaction with Tau aggregates, Clusterin stabilizes highly potent, soluble seed species. Tau/Clusterin complexes enter recipient cells via endocytosis and compromise the endolysosomal compartment, allowing transfer to the cytosol where they propagate aggregation of endogenous Tau. Thus, upregulation of Clusterin, as observed in AD patients, may enhance Tau seeding and possibly accelerate the spreading of Tau pathology.

scholarly journals Deep Recurrent Model for Individualized Prediction of Alzheimer’s Disease Progression

NeuroImage ◽  
2021 ◽  
pp. 118143
Author(s):  
Wonsik Jung ◽  
Eunji Jun ◽  
Heung-Il Suk ◽  
Alzheimer’s Disease Neuroimaging Initiative
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Disease Progression ◽  
Individualized Prediction

scholarly journals Discriminating Alzheimer's disease progression using a new hippocampal marker from T1-weighted MRI: The local surface roughness

2018 ◽  
Vol 40 (5) ◽  
pp. 1666-1676 ◽  
Author(s):  
Carlos Platero ◽  
María Eugenia López ◽  
María del Carmen Tobar ◽  
Miguel Yus ◽  
Fernando Maestu
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Surface Roughness ◽  
Disease Progression ◽  
Local Surface
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