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

1.9K
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...
1.9K
Alzheimer's Disease: Treatment01:22

Alzheimer's Disease: Treatment

1.1K
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...
1.1K

You might also read

Related Articles

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

Sort by
Same journal

Neuroinformed spectro-temporal-spatial attention with bi-hemispheric learning for EEG emotion recognition.

Biomedical physics & engineering express·2026
Same journal

Prediction of conduction velocity distribution and motor unit action potential from evoked compound muscle action potential: a novel metaheuristic-based framework.

Biomedical physics & engineering express·2026
Same journal

CRGFFNet: An adaptive inter-channel relation guided feature fusion network for enhanced EEG personality recognition.

Biomedical physics & engineering express·2026
Same journal

Exploration on early diagnosis of cerebral hemorrhage: a new method based on a portable sensor using vortex damping technology.

Biomedical physics & engineering express·2026
Same journal

A retinal vessel segmentation network with multiscale feature extraction and cross-layer attention fusion (MAF-Net).

Biomedical physics & engineering express·2026
Same journal

Customized 3D-printed breast prostheses using patient imaging at the point of care: feasibility and proof-of-concept study.

Biomedical physics & engineering express·2026

Related Experiment Video

Updated: Mar 14, 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

MTC-MSFFNet: a multi-task classification model based on multi-source feature fusion for Alzheimer's disease.

Junxi Gao1

  • 1School of Biomedical Engineering, Northeastern University, Shenyang, People's Republic of China.

Biomedical Physics & Engineering Express
|March 12, 2026
PubMed
Summary

This study introduces MTC-MSFFNet, a novel model for accurately classifying Alzheimer's disease (AD) stages, including subtypes of mild cognitive impairment (MCI). The model achieves high accuracy in distinguishing between cognitively normal, MCI, and AD, and between stable and progressive MCI.

Keywords:
Alzheimer’s diseasemulti-source featuremulti-task classification

More Related Videos

Basics of Multivariate Analysis in Neuroimaging Data
06:35

Basics of Multivariate Analysis in Neuroimaging Data

Published on: July 24, 2010

17.4K
Hybrid PET/MRI Imaging of Alzheimer's Disease Based on 18F-AV-1451
05:17

Hybrid PET/MRI Imaging of Alzheimer's Disease Based on 18F-AV-1451

Published on: April 18, 2025

1.0K

Related Experiment Videos

Last Updated: Mar 14, 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
Basics of Multivariate Analysis in Neuroimaging Data
06:35

Basics of Multivariate Analysis in Neuroimaging Data

Published on: July 24, 2010

17.4K
Hybrid PET/MRI Imaging of Alzheimer's Disease Based on 18F-AV-1451
05:17

Hybrid PET/MRI Imaging of Alzheimer's Disease Based on 18F-AV-1451

Published on: April 18, 2025

1.0K

Area of Science:

  • Neuroimaging
  • Medical Diagnostics
  • Artificial Intelligence

Background:

  • Accurate prediction of Alzheimer's disease (AD) progression is vital for timely intervention.
  • Distinguishing between stable mild cognitive impairment (sMCI) and progressive mild cognitive impairment (pMCI) is critical for personalized treatment.
  • Current classification models often overlook the nuances within mild cognitive impairment (MCI).

Purpose of the Study:

  • To develop a multi-task classification model for accurate diagnosis of Alzheimer's disease (AD) stages.
  • To differentiate between cognitively normal (CN), mild cognitive impairment (MCI), and AD.
  • To further classify MCI into stable (sMCI) and progressive (pMCI) subtypes.

Main Methods:

  • Proposed MTC-MSFFNet, a multi-task classification model utilizing multi-source feature fusion.
  • Integrated brain structure maps (hippocampus, entorhinal cortex, gray matter) with structural magnetic resonance imaging (sMRI) data.
  • Employed task-specific weight learning and dedicated classification heads for each diagnostic objective.

Main Results:

  • Achieved 98.09% average accuracy for classifying cognitively normal (CN), mild cognitive impairment (MCI), and Alzheimer's disease (AD).
  • Attained 95.16% average accuracy for differentiating between stable mild cognitive impairment (sMCI) and progressive mild cognitive impairment (pMCI).
  • Validated on a combined dataset from the Alzheimer's Disease Neuroimaging Initiative (ADNI) and Open Access Series of Imaging Studies (OASIS).

Conclusions:

  • The MTC-MSFFNet demonstrates high efficacy in diagnosing AD and its subtypes.
  • The model shows significant potential for assisting clinicians in creating personalized treatment strategies.
  • Advanced neuroimaging analysis can improve the accuracy of Alzheimer's disease staging.