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

432
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...
432
Cognitive Development During Adulthood01:30

Cognitive Development During Adulthood

46
Cognitive development continues throughout adulthood, undergoing significant shifts across early, middle, and late stages. Individual transition occurs from adolescent idealism to pragmatic and adaptable thinking in early adulthood. During this period, individuals learn to integrate personal beliefs with the recognition that other perspectives are equally valid. Exposure to the complexities of modern society, diverse experiences, and higher education contribute to this adaptive thought process,...
46
Dementia01:30

Dementia

92
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....
92
Language and Cognition01:27

Language and Cognition

317
Language serves as a bridge between ideas and communication, influencing how individuals perceive and interact with the world. Psychologists have long debated whether language shapes thought or vice versa. This discussion gained grip with Edward Sapir and Benjamin Lee Whorf in the 1940s, who proposed that language determines thought, a concept known as linguistic determinism. They suggested that the vocabulary and structure of a language influence how its speakers think and perceive reality.
317

You might also read

Related Articles

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

Sort by
Same author

Tumor Mutation Signature Reveals the Risk Factors of Lung Adenocarcinoma with <i>EGFR</i> or <i>KRAS</i> Mutation.

Cancer control : journal of the Moffitt Cancer Center·2025
Same author

A comprehensive analysis of vasculogenic mimicry related genes to predict the survival rate of HCC and its influence on the tumor microenvironment.

Frontiers in genetics·2025
Same author

Identification and knockout of rhamnose synthase CiRHM1 enhances accumulation of flavone aglycones in chrysanthemum flower.

Plant biotechnology journal·2024
Same author

Constructing Quasi-Localized High-Concentration Solvation Structures to Stabilize Battery Interfaces in Nonflammable Phosphate-Based Electrolyte.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)·2024
Same author

Low- and Intermediate-Grade Lateral Sinus Dural Arteriovenous Fistulas: Factors Affecting the Outcome of Endovascular Treatment over 18-Year Experience in a High-Volume Neurovascular Center.

AJNR. American journal of neuroradiology·2024
Same author

The Manipulation of Ring-Open Polymerization Process to Boost the Electrochemical Performance for Solid-State Lithium Metal Batteries.

ChemSusChem·2024

Related Experiment Video

Updated: May 29, 2025

Whole-brain Segmentation and Change-point Analysis of Anatomical Brain MRI&#8212;Application in Premanifest Huntington's Disease
09:06

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

Published on: June 9, 2018

12.1K

Predicting cognitive decline: Deep-learning reveals subtle brain changes in pre-MCI stage.

Ling Yue1, Yongsheng Pan2, Wei Li1

  • 1Department of Geriatric Psychiatry, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, 600 South Wanping Road, 200032, Shanghai, China; Alzheimer's Disease and Related Disorders Center, Shanghai Jiao Tong University, 600 South Wanping Road, 200032, Shanghai, China.

The Journal of Prevention of Alzheimer'S Disease
|February 7, 2025
PubMed
Summary

This study uses deep learning and MRI scans to predict mild cognitive impairment (MCI) progression in individuals with normal cognition. The developed Progressive Index (PI) shows potential for early detection and intervention in Alzheimer's Disease risk states.

Keywords:
Deep-learningMRIMild cognitive impairmentPredictionRegion-of-interest

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

7.4K
The 4 Mountains Test: A Short Test of Spatial Memory with High Sensitivity for the Diagnosis of Pre-dementia Alzheimer's Disease
06:23

The 4 Mountains Test: A Short Test of Spatial Memory with High Sensitivity for the Diagnosis of Pre-dementia Alzheimer's Disease

Published on: October 13, 2016

32.1K

Related Experiment Videos

Last Updated: May 29, 2025

Whole-brain Segmentation and Change-point Analysis of Anatomical Brain MRI&#8212;Application in Premanifest Huntington's Disease
09:06

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

Published on: June 9, 2018

12.1K
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

7.4K
The 4 Mountains Test: A Short Test of Spatial Memory with High Sensitivity for the Diagnosis of Pre-dementia Alzheimer's Disease
06:23

The 4 Mountains Test: A Short Test of Spatial Memory with High Sensitivity for the Diagnosis of Pre-dementia Alzheimer's Disease

Published on: October 13, 2016

32.1K

Area of Science:

  • Neuroimaging and Artificial Intelligence
  • Computational Neuroscience
  • Geriatric Medicine

Background:

  • Mild cognitive impairment (MCI) and subjective cognitive decline (SCD) are recognized risk states for dementia, including Alzheimer's Disease (AD).
  • Predicting conversion from normal cognition (NC) to MCI is crucial for early intervention but remains challenging.
  • Subtle, early neuropathological brain changes preceding clinical symptoms necessitate advanced detection methods.

Purpose of the Study:

  • To identify early structural neuroimaging biomarkers differentiating stable vs. progressive cognitive decline.
  • To develop and validate a predictive model for MCI conversion using deep learning on MRI data.
  • To establish a 'Progressive Index' (PI) for assessing AD conversion risk.

Main Methods:

  • A deep-learning framework combining a single-ROI network (SRNet) and a multi-ROI network (MRNet) was developed.
  • The framework was trained on the Alzheimer's Disease Neuroimaging Initiative-1 (ADNI-1) database (n=845).
  • Validation was performed on ADNI-2 (n=321) and the China Longitudinal Aging Study (CLAS) (n=109) datasets.

Main Results:

  • The deep learning model identified key brain regions (hippocampus, amygdala, temporal lobe, insula, cerebellum) associated with progression.
  • The derived Progressive Index (PI) effectively categorized subjects by cognitive status (stable vs. progressive).
  • The PI demonstrated strong predictive capability in the CLAS dataset (p<0.001), with enhanced performance when combined with clinical measures (AUC=0.812).

Conclusions:

  • Deep learning analysis of MRI scans can effectively predict MCI progression in individuals with normal cognition.
  • The study highlights the potential of deep learning for identifying early brain alterations in the transition from NC to MCI.
  • This approach offers a promising avenue for understanding disease mechanisms and facilitating early detection of AD risk.