Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Alzheimer's Disease: Overview01:26

Alzheimer's Disease: Overview

666
Alzheimer's Disease (AD) is a continually advancing neurodegenerative disorder, distinguished by escalating memory loss, cognitive dysfunction, and dementia. The disease unfolds in three stages: preclinical, mild cognitive impairment (MCI), and dementia. Its onset is insidious, and the progression gradual, with the cause not well explained by other disorders.
The clinical diagnosis of AD hinges on the presence of memory and other cognitive impairments. Biomarkers, such as changes in Aβ...
666
Alzheimer's Disease: Treatment01:22

Alzheimer's Disease: Treatment

260
Alzheimer's Disease (AD), a neurodegenerative disorder, is pathologically identified by amyloid plaques and neurofibrillary tangles composed of tau protein. AD pharmacotherapy aims to manage cognitive symptoms, delay disease progression, and treat behavioral symptoms. The treatment is primarily symptomatic and palliative, with no definitive disease-modifying therapy available. Cholinesterase inhibitors, including donepezil (Aricept), rivastigmine (Exelon), and galantamine (Razadyne), are...
260
Neural Regulation01:37

Neural Regulation

39.8K
Digestion begins with a cephalic phase that prepares the digestive system to receive food. When our brain processes visual or olfactory information about food, it triggers impulses in the cranial nerves innervating the salivary glands and stomach to prepare for food.
39.8K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Trajectories of brain structure and function in young adult carriers of genetic frontotemporal dementia variants.

medRxiv : the preprint server for health sciences·2026
Same author

Cerebrospinal Fluid Biomarkers in Cerebral Amyloid Angiopathy With and Without Spontaneous Lobar Hemorrhage.

Journal of the American Heart Association·2025
Same author

Unified Discontinuous Galerkin Analysis of a Thermo/Poro-viscoelasticity Model.

Journal of scientific computing·2025
Same author

Autophagy Markers Are Altered in Alzheimer's Disease, Dementia with Lewy Bodies and Frontotemporal Dementia.

International journal of molecular sciences·2024
Same author

Aβ1-6<sub>A2V</sub>(D) peptide, effective on Aβ aggregation, inhibits tau misfolding and protects the brain after traumatic brain injury.

Molecular psychiatry·2023
Same author

A novel bio-inspired strategy to prevent amyloidogenesis and synaptic damage in Alzheimer's disease.

Molecular psychiatry·2022

Related Experiment Video

Updated: Sep 9, 2025

Author Spotlight: Advancing Alzheimer's Research &#8211; Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

1.2K

Predicting Alzheimer's Disease Progression from Sparse Multimodal Data by NeuralODE Models.

Andrea Zanin1,2, Stefano Pagani2, Mattia Corti2

  • 1IoTique, V.le Trento 37D, Rovereto, 38062, TN, Italy.

Biorxiv : the Preprint Server for Biology
|September 5, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a new AI model to predict Alzheimer's disease progression using limited patient data. The model improves early diagnosis and tracks biomarker changes for better neurodegenerative disease monitoring.

Keywords:
Alzheimer’s diseasedisease progression modelingmachine learningneurodegenerative disorderspatient-specific trajectoriessparse and multimodal clinical data

More Related Videos

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
14:27

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

15.8K
Basics of Multivariate Analysis in Neuroimaging Data
06:35

Basics of Multivariate Analysis in Neuroimaging Data

Published on: July 24, 2010

17.0K

Related Experiment Videos

Last Updated: Sep 9, 2025

Author Spotlight: Advancing Alzheimer's Research &#8211; Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

1.2K
Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
14:27

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

15.8K
Basics of Multivariate Analysis in Neuroimaging Data
06:35

Basics of Multivariate Analysis in Neuroimaging Data

Published on: July 24, 2010

17.0K

Area of Science:

  • Neuroscience
  • Artificial Intelligence
  • Biomedical Data Science

Background:

  • Alzheimer's disease (AD) progression varies significantly among patients, complicating diagnosis and care.
  • Current data-driven models often require extensive, specific datasets not readily available in clinical settings.
  • Accurate prediction of individual disease trajectories is crucial for effective management of neurodegenerative disorders.

Purpose of the Study:

  • To develop a novel modeling framework for predicting individual Alzheimer's disease trajectories.
  • To utilize sparse, irregularly sampled, multi-modal clinical data for disease progression modeling.
  • To enhance early diagnosis and monitoring of neurodegenerative diseases.

Main Methods:

  • Implementation of (recurrent) Neural Ordinary Differential Equations (NODEs).
  • Forecasting patient disease progression and biomarker evolution over time.
  • Utilizing sparse, multi-modal clinical data for model training and validation.

Main Results:

  • The developed model accurately detected early signs of Alzheimer's disease.
  • It effectively tracked changes in biomarker trajectories, aligning with clinical knowledge.
  • Demonstrated superior performance compared to common data-driven alternatives on the ADNI cohort.

Conclusions:

  • The proposed modeling framework offers a versatile tool for personalized Alzheimer's disease diagnosis and monitoring.
  • This approach addresses the limitations of existing models in handling sparse, real-world clinical data.
  • The findings support the potential of AI in advancing the management of neurodegenerative diseases.