[P1-261]: TRACKING ALZHEIMER's DISEASE PROGRESSION BY NON-LINEAR DIMENSION REDUCTION OF BRAIN MRI FEATURES

2017 ◽  
Vol 13 (7S_Part_7) ◽  
pp. P349-P350
Author(s):  
Kangwon Seo ◽  
Rong Pan ◽  
Kewei Chen ◽  
Pradeep Thiyyagura
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Dimension Reduction ◽  
Disease Progression ◽  
Linear Dimension ◽  
Brain Mri ◽  
Mri Features ◽  
Non Linear

scholarly journals Group Guided Fused Laplacian Sparse Group Lasso for Modeling Alzheimer’s Disease Progression

2020 ◽  
Vol 2020 ◽  
pp. 1-23
Author(s):  
Xiaoli Liu ◽  
Jianzhong Wang ◽  
Fulong Ren ◽  
Jun Kong
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Disease Progression ◽  
Group Structure ◽  
Group Lasso ◽  
Imaging Biomarkers ◽  
Compact Sets ◽  
Time Points ◽  
Sparse Group Lasso ◽  
Mri Features

As the largest cause of dementia, Alzheimer’s disease (AD) has brought serious burdens to patients and their families, mostly in the financial, psychological, and emotional aspects. In order to assess the progression of AD and develop new treatment methods for the disease, it is essential to infer the trajectories of patients’ cognitive performance over time to identify biomarkers that connect the patterns of brain atrophy and AD progression. In this article, a structured regularized regression approach termed group guided fused Laplacian sparse group Lasso (GFL-SGL) is proposed to infer disease progression by considering multiple prediction of the same cognitive scores at different time points (longitudinal analysis). The proposed GFL-SGL simultaneously exploits the interrelated structures within the MRI features and among the tasks with sparse group Lasso (SGL) norm and presents a novel group guided fused Laplacian (GFL) regularization. This combination effectively incorporates both the relatedness among multiple longitudinal time points with a general weighted (undirected) dependency graphs and useful inherent group structure in features. Furthermore, an alternating direction method of multipliers- (ADMM-) based algorithm is also derived to optimize the nonsmooth objective function of the proposed approach. Experiments on the dataset from Alzheimer’s Disease Neuroimaging Initiative (ADNI) show that the proposed GFL-SGL outperformed some other state-of-the-art algorithms and effectively fused the multimodality data. The compact sets of cognition-relevant imaging biomarkers identified by our approach are consistent with the results of clinical studies.

scholarly journals Improved Alzheimer’s Disease Detection by MRI Using Multimodal Machine Learning Algorithms

Diagnostics ◽  
2021 ◽  
Vol 11 (11) ◽  
pp. 2103
Author(s):  
Gopi Battineni ◽  
Mohmmad Amran Hossain ◽  
Nalini Chintalapudi ◽  
Enea Traini ◽  
Venkata Rao Dhulipalla ◽  
...  
Keyword(s):  
Machine Learning ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Brain Mri ◽  
Modern Medicine ◽  
Machine Learning Algorithms ◽  
Gradient Boosting ◽  
Adult Onset ◽  
Mri Features ◽  
Supervised Classifiers

Adult-onset dementia disorders represent a challenge for modern medicine. Alzheimer’s disease (AD) represents the most diffused form of adult-onset dementias. For half a century, the diagnosis of AD was based on clinical and exclusion criteria, with an accuracy of 85%, which did not allow for a definitive diagnosis, which could only be confirmed by post-mortem evaluation. Machine learning research applied to Magnetic Resonance Imaging (MRI) techniques can contribute to a faster diagnosis of AD and may contribute to predicting the evolution of the disease. It was also possible to predict individual dementia of older adults with AD screening data and ML classifiers. To predict the AD subject status, the MRI demographic information and pre-existing conditions of the patient can help to enhance the classifier performance. In this work, we proposed a framework based on supervised learning classifiers in the dementia subject categorization as either AD or non-AD based on longitudinal brain MRI features. Six different supervised classifiers are incorporated for the classification of AD subjects and results mentioned that the gradient boosting algorithm outperforms other models with 97.58% of accuracy.

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.

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