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

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
Alzheimer's Disease: Treatment01:22

Alzheimer's Disease: Treatment

1.3K
Alzheimer's Disease (AD), a neurodegenerative disorder, is pathologically identified by amyloid plaques and neurofibrillary tangles composed of tau protein. AD pharmacotherapy aims to manage cognitive symptoms, delay disease progression, and treat behavioral symptoms. The treatment is primarily symptomatic and palliative, with no definitive disease-modifying therapy available. Cholinesterase inhibitors, including donepezil (Aricept), rivastigmine (Exelon), and galantamine (Razadyne), are...
1.3K
Alzheimer Disease l: Introduction01:29

Alzheimer Disease l: Introduction

21
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...
21
Alzheimer Disease ll: Pathophysiology01:23

Alzheimer Disease ll: Pathophysiology

35
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...
35
Classification of Illness01:17

Classification of Illness

9.4K
The meaning of illness is individualized to each person who experiences an alteration in health. In contrast, disease is a medical term indicating a pathological change in the structure and function of the body or mind. It is a condition that has specific symptoms and boundaries.
An illness is a response to a disease in which the person's level of functioning is changed compared with a previous level. The general classification of illness includes acute and chronic.
Acute illness is severe...
9.4K
Seizures: Classification01:13

Seizures: Classification

2.5K
Epilepsy is primarily characterized by unpredictable seizures, either provoked by an identifiable factor, such as injury or illness, or unprovoked, occurring spontaneously without apparent cause.
Seizures are typically classified into two main categories: focal and generalized seizures.
Focal Seizures
Focal seizures originate from specific regions of the brain. These seizures are further sub-classified into two types:
2.5K

You might also read

Related Articles

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

Sort by
Same author

Denoising Diffusion-Weighted Images Using Grouped Iterative Hard Thresholding of Multi-Channel Framelets.

Computational diffusion MRI : MICCAI Workshop·2017
Same author

Robust Construction of Diffusion MRI Atlases with Correction for Inter-Subject Fiber Dispersion.

Computational diffusion MRI : MICCAI Workshop·2017
Same author

Robust Fusion of Diffusion MRI Data for Template Construction.

Scientific reports·2017
Same author

Learning-Based Multimodal Image Registration for Prostate Cancer Radiation Therapy.

Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention·2017
Same author

Segmenting hippocampal subfields from 3T MRI with multi-modality images.

Medical image analysis·2017
Same author

Joint Discriminative and Representative Feature Selection for Alzheimer's Disease Diagnosis.

Machine learning in medical imaging. MLMI (Workshop)·2017
Same journal

LEARNABLE HIERARCHICAL VISUAL CONTEXTS FOR TUMOR SEGMENTATION IN COMPUTED TOMOGRAPHY IMAGES.

Proceedings. IEEE International Symposium on Biomedical Imaging·2026
Same journal

DUAL CROSS-ATTENTION SIAMESE TRANSFORMER FOR RECTAL TUMOR REGROWTH ASSESSMENT IN WATCH-AND-WAIT ENDOSCOPY.

Proceedings. IEEE International Symposium on Biomedical Imaging·2026
Same journal

LUMEN: LONGITUDINAL MULTI-MODAL RADIOLOGY MODEL FOR PROGNOSIS AND DIAGNOSIS.

Proceedings. IEEE International Symposium on Biomedical Imaging·2026
Same journal

OVERVIEW OF THE CXR-LT 2026 CHALLENGE: MULTI-CENTER LONG-TAILED AND ZERO SHOT CHEST X-RAY CLASSIFICATION.

Proceedings. IEEE International Symposium on Biomedical Imaging·2026
Same journal

CROSS-MODAL FINE-TUNING OF 3D CONVOLUTIONAL FOUNDATION MODELS FOR ADHD CLASSIFICATION WITH LOW-RANK ADAPTATION.

Proceedings. IEEE International Symposium on Biomedical Imaging·2026
Same journal

AN IN SILICO STUDY OF LOW-INTENSITY FOCUSED ULTRASOUND DISPLACEMENT MAPPING WITH A 220 KHZ CLINICAL PHASED-ARRAY TRANSDUCER.

Proceedings. IEEE International Symposium on Biomedical Imaging·2026
See all related articles

Related Experiment Video

Updated: May 3, 2026

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

2.0K

KERNEL-BASED MULTI-TASK JOINT SPARSE CLASSIFICATION FOR ALZHEIMER'S DISEASE.

Yaping Wang1, Manhua Liu2, Lei Guo3

  • 1School of Automation, Northwestern Polytechnical University, Xi'an, Shaanxi Province, China ; Department of Radiology and BRIC, University of North Carolina at Chapel Hill, USA.

Proceedings. IEEE International Symposium on Biomedical Imaging
|January 21, 2014
PubMed
Summary
This summary is machine-generated.

This study introduces a new kernel-based model combining MRI and PET scans for diagnosing Alzheimer's disease (AD) and mild cognitive impairment (MCI). The method significantly improves classification accuracy, aiding in early detection of neurodegenerative disorders.

Keywords:
Alzheimer’s disease (AD)Kernel-based classificationMulti-task joint sparse representationSparse representation based classifier

More Related Videos

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
A Fine Motor Task to Study Joint Kinematics in a Preclinical Model of Neurodegenerative Disease
05:39

A Fine Motor Task to Study Joint Kinematics in a Preclinical Model of Neurodegenerative Disease

Published on: June 13, 2025

1.0K

Related Experiment Videos

Last Updated: May 3, 2026

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

2.0K
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
A Fine Motor Task to Study Joint Kinematics in a Preclinical Model of Neurodegenerative Disease
05:39

A Fine Motor Task to Study Joint Kinematics in a Preclinical Model of Neurodegenerative Disease

Published on: June 13, 2025

1.0K

Area of Science:

  • Neuroimaging
  • Machine Learning
  • Medical Diagnostics

Background:

  • Neurodegenerative disorders like Alzheimer's disease (AD) and mild cognitive impairment (MCI) require accurate diagnostic tools.
  • Multi-modality imaging, such as MRI and PET, offers complementary data for enhanced diagnostic capabilities.

Purpose of the Study:

  • To develop and evaluate a novel kernel-based multi-task sparse representation model for improved classification of AD and MCI.
  • To leverage the combined strengths of MRI and PET imaging features for more precise diagnosis.

Main Methods:

  • Proposed a kernel-based multi-task sparse representation model integrating MRI and PET data.
  • Employed multi-task learning to enforce class-level joint sparsity across imaging modalities.
  • Extended the framework to the reproducing kernel Hilbert space (RKHS) to capture nonlinear feature relationships.

Main Results:

  • Achieved 93.3% accuracy in classifying Alzheimer's disease (AD) from healthy controls.
  • Attained 78.9% accuracy in classifying mild cognitive impairment (MCI) from healthy controls.
  • Demonstrated the model's effectiveness using the Alzheimer's Disease Neuroimaging Initiative (ADNI) database.

Conclusions:

  • The proposed kernel-based multi-task sparse representation model shows significant promise for the accurate classification of AD and MCI.
  • Combining multi-modality imaging data through advanced sparse representation techniques enhances diagnostic performance.
  • This approach offers a valuable tool for early detection and study of neurodegenerative disorders.