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 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...

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's Disease: Overview

Alzheimer's Disease: Overview

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...

Protein Networks

Protein Networks

An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...

Alzheimer Disease ll: Pathophysiology

Alzheimer Disease ll: Pathophysiology

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...

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...

You might also read

Related Articles

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

Sort by

Same author

Ribonucleic acid binding protein-mediated regulation of luteinizing hormone receptor expression in granulosa cells: relationship to sterol metabolism.

Molecular endocrinology (Baltimore, Md.)·2007

Same author

Psychological stress-induced oxidative stress as a model of sub-healthy condition and the effect of TCM.

Evidence-based complementary and alternative medicine : eCAM·2007

Same author

Overexpression of OsCOIN, a putative cold inducible zinc finger protein, increased tolerance to chilling, salt and drought, and enhanced proline level in rice.

Planta·2007

Same author

Edge-based scoring and searching method for identifying condition-responsive protein-protein interaction sub-network.

Bioinformatics (Oxford, England)·2007

Same author

[The value of long-term postoperative follow-up after curative resection of lung cancer and common problems associated with it].

Nihon Geka Gakkai zasshi·2007

Same author

Identification of a type III thioesterase reveals the function of an operon crucial for Mtb virulence.

Chemistry & biology·2007

Search research articles

Related Experiment Video

Updated: Apr 20, 2026

Modeling the Functional Network for Spatial Navigation in the Human Brain

Modeling the Functional Network for Spatial Navigation in the Human Brain

Published on: October 13, 2023

Thickness network features for prognostic applications in dementia.

Pradeep Reddy Raamana¹, Michael W Weiner², Lei Wang³

¹Department of Engineering Science, School of Engineering Science, Simon Fraser University, Burnaby, BC, Canada.

Neurobiology of Aging

|December 3, 2014

Summary

Novel thickness network features effectively distinguish individuals with mild cognitive impairment (MCI) who will convert to Alzheimer's disease from controls. These imaging biomarkers show significant potential for early Alzheimer's disease detection and prognosis.

Keywords:

Alzheimer Cortical thickness Early detection Fusion Mild cognitive impairment Multiple kernel learning Network properties

More Related Videos

Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease

Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease

Published on: July 24, 2019

Using Retinal Imaging to Study Dementia

Using Retinal Imaging to Study Dementia

Published on: November 6, 2017

Related Experiment Videos

Last Updated: Apr 20, 2026

Modeling the Functional Network for Spatial Navigation in the Human Brain

Modeling the Functional Network for Spatial Navigation in the Human Brain

Published on: October 13, 2023

Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease

Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease

Published on: July 24, 2019

Using Retinal Imaging to Study Dementia

Using Retinal Imaging to Study Dementia

Published on: November 6, 2017

Area of Science:

Neuroimaging
Biomarker Discovery
Computational Neuroscience

Background:

Cortical thickness analysis is established for Alzheimer's disease (AD) biomarkers.
Interregional covariation of cortical thickness remains underexplored for AD.
Novel imaging features are needed for early AD detection and prognosis.

Purpose of the Study:

To introduce and evaluate novel features based on interregional covariation of cortical thickness.
To assess the diagnostic and prognostic utility of these features in Alzheimer's Disease Neuroimaging Initiative (ADNI) data.
To explore the potential of these features for computer-aided prognostic applications.

Main Methods:

Cortical labels partitioned into graph nodes using spatial k-means clustering.
Graph constructed based on inter-nodal thickness differences, forming a thickness network.
Network features (nodal degree, betweenness, clustering coefficient) computed and fused using multiple kernel learning.

Main Results:

Thickness network features achieved an AUC of 0.83 in discriminating mild cognitive impairment (MCI) converters from controls (CN).
Features demonstrated strong predictive utility in classifying AD/CN (AUC 0.92) and discriminating CN from MCI (AUC 0.75).
Distinguishing MCI converters from nonconverters yielded an AUC of 0.68.

Conclusions:

Thickness network features are novel biomarkers derived from single MRI scans.
These features show significant potential for computer-aided prognostic applications in neurodegenerative diseases.
The findings highlight the importance of interregional cortical thickness covariation in AD progression.