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 Disease ll: Pathophysiology01:23

Alzheimer Disease ll: Pathophysiology

7
Alzheimer disease involves structural changes in the brain that begin long before symptoms appear. The most distinctive features are extracellular neuritic plaques and intracellular neurofibrillary tangles.Neuritic plaques form in the cerebral cortex and around blood vessels. These plaques contain a dense core of beta-amyloid (Aβ)—a toxic protein fragment that clumps outside neurons. The core is surrounded by damaged neuronal extensions, as well as reactive astrocytes and...
7
Alzheimer Disease l: Introduction01:29

Alzheimer Disease l: Introduction

7
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...
7
Dementia l: Introduction01:22

Dementia l: Introduction

7
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...
7
Organization of the Brain01:30

Organization of the Brain

3.6K
The brain is an integral component of the nervous system and serves as the center for processing sensory inputs, making decisions, and directing bodily actions. This complex organ is organized into three primary sections: the hindbrain, midbrain, and forebrain, each responsible for a range of vital functions.
Hindbrain
The hindbrain, located at the base of the brain, plays a vital role in regulating automatic processes that sustain life. It includes the medulla oblongata, which is essential for...
3.6K
Alzheimer's Disease: Overview01:26

Alzheimer's Disease: Overview

1.7K
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...
1.7K

You might also read

Related Articles

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

Sort by
Same author

Zero-Calibration MI Decoding via Self-Supervised Representation and Ensemble Learning.

IEEE transactions on bio-medical engineering·2026
Same author

TFANet: A Time-Frequency Aware Network With Joint Entropy Coding for High-Ratio EEG Compression.

IEEE transactions on bio-medical engineering·2025
Same author

From Frequency to Temporal: Three Simple Steps Achieve Lightweight High-Performance Motor Imagery Decoding.

IEEE transactions on bio-medical engineering·2025
Same author

Motor Imagery Recognition Based on GMM-JCSFE Model.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society·2024
Same author

s-TBN: A New Neural Decoding Model to Identify Stimulus Categories From Brain Activity Patterns.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society·2024
Same author

A feature enhanced EEG compression model using asymmetric encoding-decoding network<sup></sup>.

Journal of neural engineering·2024

Related Experiment Video

Updated: Apr 22, 2026

A Method for Investigating Age-related Differences in the Functional Connectivity of Cognitive Control Networks Associated with Dimensional Change Card Sort Performance
09:01

A Method for Investigating Age-related Differences in the Functional Connectivity of Cognitive Control Networks Associated with Dimensional Change Card Sort Performance

Published on: May 7, 2014

9.5K

Characterizing structural association alterations within brain networks in normal aging using Gaussian Bayesian

Xiaojuan Guo1, Yan Wang2, Kewei Chen3

  • 1Information Processing Lab, College of Information Science and Technology, Beijing Normal University Beijing, China ; State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University Beijing, China.

Frontiers in Computational Neuroscience
|October 18, 2014
PubMed
Summary
This summary is machine-generated.

Aging significantly alters brain structural networks, particularly sensory/motor regions. Gaussian Bayesian networks reveal age-related connection reductions in auditory, visual, and motor networks, aiding in distinguishing young and old individuals.

Keywords:
Bayesian networksaginggray matter volumestructural associationstructural networks

More Related Videos

Modeling the Functional Network for Spatial Navigation in the Human Brain
05:55

Modeling the Functional Network for Spatial Navigation in the Human Brain

Published on: October 13, 2023

1.7K
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

7.5K

Related Experiment Videos

Last Updated: Apr 22, 2026

A Method for Investigating Age-related Differences in the Functional Connectivity of Cognitive Control Networks Associated with Dimensional Change Card Sort Performance
09:01

A Method for Investigating Age-related Differences in the Functional Connectivity of Cognitive Control Networks Associated with Dimensional Change Card Sort Performance

Published on: May 7, 2014

9.5K
Modeling the Functional Network for Spatial Navigation in the Human Brain
05:55

Modeling the Functional Network for Spatial Navigation in the Human Brain

Published on: October 13, 2023

1.7K
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

7.5K

Area of Science:

  • Neuroimaging
  • Neuroscience
  • Gerontology

Background:

  • Multivariate neuroimaging studies show aging affects brain structural networks.
  • Sensory/motor networks (auditory, visual, motor) are understudied in normal aging.

Purpose of the Study:

  • To investigate aging effects on structural associations within sensory/motor networks using Gaussian Bayesian networks (BN).
  • To assess the ability of BN models to differentiate between young and old individuals based on brain network structures.

Main Methods:

  • Utilized Gaussian Bayesian networks (BN) to analyze structural associations between brain regions within auditory, visual, and motor networks.
  • Employed volumetric MRI data from young (N=109) and old (N=82) adult groups.
  • Examined inter-regional directed relationships and connection strengths.

Main Results:

  • Identified structural associations between homotopic brain regions in all three networks.
  • Observed significant reductions in connection strength in the old group across all three networks.
  • Found fewer connections in the visual network of the old group compared to the young group.
  • Achieved high accuracy (90.05%, 73.82%, 88.48%) in distinguishing age groups for auditory, visual, and motor networks, respectively.

Conclusions:

  • Gaussian Bayesian networks are effective tools for studying normal aging processes with statistical power.
  • Age-related differences in structural inter-regional interactions provide insights into anatomical changes during normal aging.