Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Dementia

Dementia

Dementia is a collective term for cognitive disorders primarily affecting memory, thinking, and reasoning. It is not a specific disease but a syndrome, with Alzheimer's disease being the most common cause, accounting for approximately 60-80% of cases. Other types include vascular dementia, Lewy body dementia, and frontotemporal dementia. Dementia affects millions worldwide, particularly older adults, though it is not a normal part of aging.
The progression of dementia is generally gradual.

Alzheimer Disease l: Introduction

Alzheimer Disease l: Introduction

Alzheimer disease is a chronic, progressive, and irreversible neurodegenerative disorder and the most common cause of dementia in older adults. It leads to gradual neuronal loss, causing cognitive decline, behavioral changes, and loss of functional independence.Risk Factors and EtiologyThe disease is multifactorial. Age is the strongest risk factor, with prevalence doubling every 5 years after age 65. Genetic factors include mutations in genes such as APP, PSEN1, and PSEN2, which are associated...

Dementia l: Introduction

Dementia l: Introduction

Dementia is an acquired, progressive syndrome characterized by a decline in multiple cognitive domains severe enough to impair daily functioning and reduce independence. Although memory loss is a central feature, the diagnosis requires additional deficits involving language, executive function, visuospatial skills, judgment, calculation, or abstract reasoning. These cognitive impairments reflect underlying neurodegenerative or vascular processes that gradually disrupt neuronal networks...

You might also read

Related Articles

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

Sort by

Same author

Transformer-DAPT: AI-based dynamic assessment of ischemic and bleeding risks in patients on DAPT following PCI.

NPJ digital medicine·2026

Same author

RLBWT-based LCP computation in compressed space for terabase-scale pangenome analysis.

Bioinformatics (Oxford, England)·2026

Same author

'A Moment for Me': The Role of Respite in Sustaining Mental Health Carers.

International journal of mental health nursing·2026

Same author

Vision transformer autoencoders captures local and non-local features in brain imaging to reveal novel genetic associations.

Communications biology·2026

Same author

Predicting Autopsy-Confirmed Neuropathology across Clinical, Neuroimaging, and CSF Biomarkers using Machine Learning.

bioRxiv : the preprint server for biology·2026

Same author

Replicability of unsupervised deep learning derived image phenotypes.

bioRxiv : the preprint server for biology·2026

Search research articles

Related Experiment Video

Updated: Jun 27, 2026

Whole-brain Segmentation and Change-point Analysis of Anatomical Brain MRI—Application in Premanifest Huntington's Disease

Whole-brain Segmentation and Change-point Analysis of Anatomical Brain MRI—Application in Premanifest Huntington's Disease

Published on: June 9, 2018

Deep learning to predict future cognitive decline: a multimodal approach using brain MRI and clinical data.

Tamoghna Chattopadhyay¹, Pavithra Senthilkumar¹, Rahul H Ankarath¹

¹Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, United States.

Frontiers in Neuroimaging

|February 20, 2026

Summary

Predicting dementia progression is challenging. Combining brain MRI scans with clinical data using deep learning shows potential, but clinical factors alone can be strong predictors.

Keywords:

Alzheimer’s disease prognostics AutoGluon artificial intelligence in hospitals clinical decision support systems clinical decline clinical dementia rating deep learning multimodal analysis

More Related Videos

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

DeepOmicsAE: Representing Signaling Modules in Alzheimer's Disease with Deep Learning Analysis of Proteomics, Metabolomics, and Clinical Data

DeepOmicsAE: Representing Signaling Modules in Alzheimer's Disease with Deep Learning Analysis of Proteomics, Metabolomics, and Clinical Data

Published on: December 15, 2023

Related Experiment Videos

Last Updated: Jun 27, 2026

Whole-brain Segmentation and Change-point Analysis of Anatomical Brain MRI—Application in Premanifest Huntington's Disease

Whole-brain Segmentation and Change-point Analysis of Anatomical Brain MRI—Application in Premanifest Huntington's Disease

Published on: June 9, 2018

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

DeepOmicsAE: Representing Signaling Modules in Alzheimer's Disease with Deep Learning Analysis of Proteomics, Metabolomics, and Clinical Data

DeepOmicsAE: Representing Signaling Modules in Alzheimer's Disease with Deep Learning Analysis of Proteomics, Metabolomics, and Clinical Data

Published on: December 15, 2023

Area of Science:

Neuroscience
Artificial Intelligence
Medical Informatics

Background:

Predicting clinical decline in aging individuals with cognitive impairment is crucial for personalized treatment and clinical trials.
Key metrics like the Clinical Dementia Rating scale 'sum of boxes' (sobCDR) are vital for tracking disease progression.

Purpose of the Study:

To compare deep learning approaches for predicting 2-year changes in sobCDR scores.
To evaluate a hybrid convolutional neural network (CNN) integrating 3D brain MRI with clinical/demographic data against an automated machine learning (AutoML) framework.

Main Methods:

Trained a hybrid CNN using 3D T1-weighted brain MRI and tabular data (age, sex, BMI, baseline sobCDR).
Benchmarked CNN against AutoGluon, an AutoML multimodal framework.
Evaluated models on 2,319 participants from ADNI, OASIS-3, and NACC cohorts.

Main Results:

Multimodal fusion of image and tabular data shows promise for dementia prognostics.
Deep learning on MRI data may not always add significant predictive value when clinical covariates are highly predictive.
AutoML-based multimodal fusion offers a robust baseline when tabular data are strongly predictive.

Conclusions:

Deep learning can fuse brain imaging and clinical data for personalized dementia prognostics.
The utility of multimodal fusion depends on the data types and the predictive power of existing clinical variables.
Understanding the relative value of different data modalities is key for selecting appropriate prognostic strategies.