Cargando…

Fully bayesian longitudinal unsupervised learning for the assessment and visualization of AD heterogeneity and progression

Tau pathology and brain atrophy are the closest correlate of cognitive decline in Alzheimer’s disease (AD). Understanding heterogeneity and longitudinal progression of atrophy during the disease course will play a key role in understanding AD pathogenesis. We propose a framework for longitudinal clu...

Descripción completa

Detalles Bibliográficos
Autores principales:	Poulakis, Konstantinos, Ferreira, Daniel, Pereira, Joana B., Smedby, Örjan, Vemuri, Prashanthi, Westman, Eric
Formato:	Online Artículo Texto
Lenguaje:	English
Publicado:	Impact Journals 2020
Materias:	Research Paper
Acceso en línea:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7377879/ https://www.ncbi.nlm.nih.gov/pubmed/32644944 http://dx.doi.org/10.18632/aging.103623

Ejemplares similares

Multi-cohort and longitudinal Bayesian clustering study of stage and subtype in Alzheimer’s disease
por: Poulakis, Konstantinos, et al.
Publicado: (2022)

Longitudinal deterioration of white-matter integrity: heterogeneity in the ageing population
por: Poulakis, Konstantinos, et al.
Publicado: (2021)

Clustering and disease subtyping in Neuroscience, toward better methodological adaptations
por: Poulakis, Konstantinos, et al.
Publicado: (2023)

Predicting Short-term MCI-to-AD Progression Using Imaging, CSF, Genetic Factors, Cognitive Resilience, and Demographics
por: Varatharajah, Yogatheesan, et al.
Publicado: (2019)

Brain myoinositol as a potential marker of amyloid-related pathology: A longitudinal study
por: Voevodskaya, Olga, et al.
Publicado: (2019)

Bayesian inference and unsupervised ML @ Four-tops
por: Alvarez de los Alvarez de San Luis, Ezequiel
Publicado: (2021)

FASTER: an unsupervised fully automated sleep staging method for mice
por: Sunagawa, Genshiro A, et al.
Publicado: (2013)

Effect of intellectual enrichment on AD biomarker trajectories: Longitudinal imaging study
por: Vemuri, Prashanthi, et al.
Publicado: (2016)

Unsupervised Bayesian linear unmixing of gene expression microarrays
por: Bazot, Cécile, et al.
Publicado: (2013)

“A net for everyone”: fully personalized and unsupervised neural networks trained with longitudinal data from a single patient
por: Strack, Christian, et al.
Publicado: (2023)

Regression models for analyzing radiological visual grading studies – an empirical comparison
por: Saffari, S. Ehsan, et al.
Publicado: (2015)

Unsupervised Human Detection with an Embedded Vision System on a Fully Autonomous UAV for Search and Rescue Operations
por: Lygouras, Eleftherios, et al.
Publicado: (2019)

Shape Information Improves the Cross-Cohort Performance of Deep Learning-Based Segmentation of the Hippocampus
por: Brusini, Irene, et al.
Publicado: (2020)

Cointegration and Unit Root Tests: A Fully Bayesian Approach
por: Diniz, Marcio A., et al.
Publicado: (2020)

Learning effects in visual grading assessment of model-based reconstruction algorithms in abdominal Computed Tomography
por: Kataria, Bharti, et al.
Publicado: (2023)

Unsupervised Bayesian learning for rice panicle segmentation with UAV images
por: Hayat, Md Abul, et al.
Publicado: (2020)

A Bayesian Approach to Unsupervised, Non-Intrusive Load Disaggregation
por: Massidda, Luca, et al.
Publicado: (2022)

Self-organizing maps on “what-where” codes towards fully unsupervised classification
por: Sa-Couto, Luis, et al.
Publicado: (2023)

“Exceptional brain aging” without Alzheimer’s disease: triggers, accelerators, and the net sum game
por: Vemuri, Prashanthi
Publicado: (2018)

Unsupervised data-driven stratification of mentalizing heterogeneity in autism
por: Lombardo, Michael V., et al.
Publicado: (2016)

Unsupervised neurobiology-driven stratification of clinical heterogeneity in depression
por: Colombo, F., et al.
Publicado: (2023)

Fully learnable deep wavelet transform for unsupervised monitoring of high-frequency time series
por: Michau, Gabriel, et al.
Publicado: (2022)

A Fully Unsupervised Deep Learning Framework for Non-Rigid Fundus Image Registration
por: Benvenuto, Giovana A., et al.
Publicado: (2022)

Associations between different tau-PET patterns and longitudinal atrophy in the Alzheimer’s disease continuum: biological and methodological perspectives from disease heterogeneity
por: Mohanty, Rosaleena, et al.
Publicado: (2023)

Correction: Associations between different tau‑PET patterns and longitudinal atrophy in the Alzheimer’s disease continuum: biological and methodological perspectives from disease heterogeneity
por: Mohanty, Rosaleena, et al.
Publicado: (2023)

Comparison of subtyping methods for neuroimaging studies in Alzheimer’s disease: a call for harmonization
por: Mohanty, Rosaleena, et al.
Publicado: (2020)

GPU facilitated unsupervised visual feature acquisition
por: Lemoine, Blake, et al.
Publicado: (2012)

A semi-parametric Bayesian model for unsupervised differential co-expression analysis
por: Freudenberg, Johannes M, et al.
Publicado: (2010)

Unsupervised Bayesian Ising Approximation for decoding neural activity and other biological dictionaries
por: Hernández, Damián G, et al.
Publicado: (2022)

A Bayesian model for unsupervised detection of RNA splicing based subtypes in cancers
por: Wang, David, et al.
Publicado: (2023)

Tempo: an unsupervised Bayesian algorithm for circadian phase inference in single-cell transcriptomics
por: Auerbach, Benjamin J., et al.
Publicado: (2022)

Bayesian Fully Convolutional Networks for Brain Image Registration
por: Cui, Kunpeng, et al.
Publicado: (2021)

Characterization and wearability evaluation of a fully portable wrist exoskeleton for unsupervised training after stroke
por: Lambelet, Charles, et al.
Publicado: (2020)

Artifact removal from sEMG signals recorded during fully unsupervised daily activities
por: Costa-García, Álvaro, et al.
Publicado: (2023)

The heterogeneity within Alzheimer's disease
por: Ferreira, Daniel, et al.
Publicado: (2018)

Unsupervised learning of aging principles from longitudinal data
por: Avchaciov, Konstantin, et al.
Publicado: (2022)

Clustering 16S rRNA for OTU prediction: a method of unsupervised Bayesian clustering
por: Hao, Xiaolin, et al.
Publicado: (2011)

An Adaptive Homeostatic Algorithm for the Unsupervised Learning of Visual Features
por: Perrinet, Laurent U.
Publicado: (2019)

Unsupervised neural network models of the ventral visual stream
por: Zhuang, Chengxu, et al.
Publicado: (2021)

Unsupervised classification of plethysmography signals with advanced visual representations
por: Germain, Thibaut, et al.
Publicado: (2023)

Cannot write session to /tmp/vufind_sessions/sess_pp2c7kl7fu55e2u7b0snvdjfqe