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

Dementia01:30

Dementia

102
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....
102
Alzheimer's Disease: Overview01:26

Alzheimer's Disease: Overview

451
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...
451
Cognitive Enhancers: Cholinesterase Inhibitors and NMDA Receptor Antagonists01:30

Cognitive Enhancers: Cholinesterase Inhibitors and NMDA Receptor Antagonists

107
Cognitive enhancers, also known as "smart drugs," are substances used to enhance memory, mental alertness, and concentration. These can be natural or synthetic and improve cognition in conditions like Alzheimer's disease (AD) and other neurodegenerative diseases. Some common examples include caffeine, amphetamines, methylphenidate, modafinil, arecoline, donepezil, vortioxetine, and piracetam. These enhancers work on the principle of synaptic plasticity and altered circuit function.
107

You might also read

Related Articles

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

Sort by
Same author

Joint trajectories of brain atrophy, white matter hyperintensities and cognition quantify brain maintenance.

Nature communications·2026
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

Miro1 mutations disrupt cellular calcium homeostasis via dysregulation of mitochondria-ER-contact-sites, rendering iPSC-derived neurons more susceptible to lipid peroxidation.

Neurobiology of disease·2026
Same author

Cognitive and Neuroimaging Divergence Between Juvenile and Adult FUS Amyotrophic Lateral Sclerosis.

Annals of clinical and translational neurology·2026
Same author

Innate immune signaling as a potential pathomechanistic biomarker for distinct subtypes in amyotrophic lateral sclerosis.

Amyotrophic lateral sclerosis & frontotemporal degeneration·2026
Same author

VAPB confers selective neuroprotection by driving autophagic degradation of pathogenic aggregates in ALS.

Acta neuropathologica communications·2026

Related Experiment Video

Updated: Jun 13, 2025

Author Spotlight: Advancing Alzheimer's Research – 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

981

An Unsupervised XAI Framework for Dementia Detection with Context Enrichment.

Devesh Singh, Yusuf Brima, Fedor Levin

    Medrxiv : the Preprint Server for Health Sciences
    |June 12, 2025
    PubMed
    Summary

    Explainable AI (XAI) methods improve dementia diagnosis by integrating brain imaging features with AI predictions. This study validates XAI's potential for enhancing clinical decision support systems in neurological research.

    More Related Videos

    A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
    12:18

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

    Published on: January 11, 2020

    7.5K
    Application of Granger Causality Analysis of the Directed Functional Connection in Alzheimer's Disease and Mild Cognitive Impairment
    08:43

    Application of Granger Causality Analysis of the Directed Functional Connection in Alzheimer's Disease and Mild Cognitive Impairment

    Published on: August 7, 2017

    7.9K

    Related Experiment Videos

    Last Updated: Jun 13, 2025

    Author Spotlight: Advancing Alzheimer's Research – 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

    981
    A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
    12:18

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

    Published on: January 11, 2020

    7.5K
    Application of Granger Causality Analysis of the Directed Functional Connection in Alzheimer's Disease and Mild Cognitive Impairment
    08:43

    Application of Granger Causality Analysis of the Directed Functional Connection in Alzheimer's Disease and Mild Cognitive Impairment

    Published on: August 7, 2017

    7.9K

    Area of Science:

    • Neuroimaging and Artificial Intelligence
    • Clinical Decision Support Systems
    • Explainable Artificial Intelligence (XAI)

    Background:

    • Explainable Artificial Intelligence (XAI) enhances the transparency and trustworthiness of AI predictions in clinical decision support systems, particularly for brain imaging analysis.
    • Limited validation of XAI explanation quality hinders its clinical adoption, necessitating robust evaluation frameworks.
    • Convolutional Neural Networks (CNNs) are powerful tools for analyzing brain MRI scans but require interpretable outputs for clinical trust.

    Purpose of the Study:

    • To introduce and evaluate a framework for assessing XAI methods in dementia research by combining neuroanatomical features with CNN relevance maps.
    • To refine XAI explanation spaces and explore different approaches for generating clinically relevant explanations for AI-driven diagnostic tools.
    • To determine the potential of validated XAI methods in improving the diagnostic efficiency of AI-based decision support systems for dementia.

    Main Methods:

    • A CNN was trained on brain MRI scans from 3253 participants across six cohorts (ADNI, AIBL, DELCODE, DESCRIBE, EDSD, NIFD).
    • Clustering analysis used morphological features as proxy ground truth to benchmark explanation space configurations.
    • Three post-hoc XAI methods were implemented: model simplification, explanation-by-example, and textual explanations, followed by qualitative clinical evaluation.

    Main Results:

    • Morphology-enriched explanation spaces demonstrated improved clustering performance, enhancing both homogeneity and completeness.
    • Model simplification explanations effectively distinguished between participants who would convert to dementia and those who remained stable.
    • Explanation-by-example visualized potential cognitive trajectories, while textual explanations provided rule-based summaries of pathological findings.

    Conclusions:

    • The study successfully refined XAI explanation spaces and demonstrated the utility of various explanation generation approaches.
    • The evaluated XAI methods show promise for enhancing diagnostic efficiency within AI-based decision support systems for dementia research.
    • Clinical assessments confirmed the potential of these XAI techniques, highlighting both challenges and opportunities for future applications.