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

Blood Studies for Cardiovascular System I: Cardiac Biomarkers01:20

Blood Studies for Cardiovascular System I: Cardiac Biomarkers

749
Cardiac biomarkers are enzymes, proteins, and hormones released into the blood when cardiac cells are injured. They are powerful tools for triaging.
The essential diagnostic tools for detecting myocardial necrosis and monitoring individuals suspected of having acute coronary syndrome (ACS) include:
Troponins
Troponins, particularly cardiac troponins I and T, are the most precise and sensitive markers of myocardial injury. They are detectable within 4-6 hours of myocardial injury and remain...
749
Blood Studies for Cardiovascular System II: CRP, Hcy, and Cardiac Natriuretic Peptide Markers01:19

Blood Studies for Cardiovascular System II: CRP, Hcy, and Cardiac Natriuretic Peptide Markers

516
Cardiac biomarkers are critical in diagnosing, prognosing, and managing cardiovascular diseases. Routine measurement of specific biomarkers such as B-type natriuretic peptide (BNP), C-reactive protein (CRP), and homocysteine (Hcy) is common practice in clinical settings to evaluate heart function and predict cardiovascular events.
These markers indicate stress or strain on the heart muscle:
Natriuretic Peptides (BNP)
Cardiac myocytes produce these hormones in response to ventricular stretching...
516

You might also read

Related Articles

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

Sort by
Same author

Distance-weighted Sinkhorn loss for Alzheimer's disease classification.

iScience·2024
Same author

Integrating imaging and genomic data for the discovery of distinct glioblastoma subtypes: a joint learning approach.

Scientific reports·2024
Same author

Plasma Biomarkers as Predictors of Progression to Dementia in Individuals with Mild Cognitive Impairment.

Journal of Alzheimer's disease : JAD·2024
Same author

Genetic and Clinical Correlates of AI-Based Brain Aging Patterns in Cognitively Unimpaired Individuals.

JAMA psychiatry·2024
Same author

Dimensional Neuroimaging Endophenotypes: Neurobiological Representations of Disease Heterogeneity Through Machine Learning.

ArXiv·2024
Same author

Five dominant dimensions of brain aging are identified via deep learning: associations with clinical, lifestyle, and genetic measures.

medRxiv : the preprint server for health sciences·2024
Same journal

Unveiling the procoagulant state in Alzheimer's disease: A novel PET imaging strategy.

Alzheimer's & dementia : the journal of the Alzheimer's Association·2026
Same journal

Estimated labor market outcomes of people progressing from preclinical to early-stage Alzheimer's disease in the United States.

Alzheimer's & dementia : the journal of the Alzheimer's Association·2026
Same journal

Amyloid exacerbates tau and alpha-synuclein pathologies, behavioral impairments, and neuroinflammation in a mixed dementia model.

Alzheimer's & dementia : the journal of the Alzheimer's Association·2026
Same journal

Multimorbidity burden and patterns associated with DeepBrainNet-derived brain-age gap in dementia-free older adults: A community-based study.

Alzheimer's & dementia : the journal of the Alzheimer's Association·2026
Same journal

Reply to "Shifting the emphasis of brain health literacy from individuals to systems to reduce inequalities".

Alzheimer's & dementia : the journal of the Alzheimer's Association·2026
Same journal

Shifting the emphasis of brain health literacy from individuals to systems to reduce inequalities.

Alzheimer's & dementia : the journal of the Alzheimer's Association·2026
See all related articles

Related Experiment Video

Updated: Jan 7, 2026

Dried Blood Spot Collection of Health Biomarkers to Maximize Participation in Population Studies
07:20

Dried Blood Spot Collection of Health Biomarkers to Maximize Participation in Population Studies

Published on: January 28, 2014

37.1K

Biomarkers.

Ioanna Skampardoni1, Guray Erus1, Ilya M Nasrallah1

  • 1Centre for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA, USA.

Alzheimer'S & Dementia : the Journal of the Alzheimer'S Association
|December 24, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces Coupled Cross-sectional and Longitudinal Non-negative Matrix Factorization (CCL-NMF) to analyze brain aging heterogeneity. CCL-NMF integrates static and dynamic brain changes, outperforming cross-sectional methods for predicting neurodegeneration and disease progression.

More Related Videos

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

7.9K
Ecotoxicological Methodologies to Evaluate Biomarkers at Different Scales in Neotropical Anurans
08:14

Ecotoxicological Methodologies to Evaluate Biomarkers at Different Scales in Neotropical Anurans

Published on: April 28, 2023

702

Related Experiment Videos

Last Updated: Jan 7, 2026

Dried Blood Spot Collection of Health Biomarkers to Maximize Participation in Population Studies
07:20

Dried Blood Spot Collection of Health Biomarkers to Maximize Participation in Population Studies

Published on: January 28, 2014

37.1K
Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

7.9K
Ecotoxicological Methodologies to Evaluate Biomarkers at Different Scales in Neotropical Anurans
08:14

Ecotoxicological Methodologies to Evaluate Biomarkers at Different Scales in Neotropical Anurans

Published on: April 28, 2023

702

Area of Science:

  • Neuroimaging
  • Machine Learning
  • Computational Neuroscience

Background:

  • Understanding brain aging heterogeneity is crucial for early detection of neurodegeneration and clinical trial recruitment.
  • Machine learning shows promise but often neglects dynamic pathological changes using only cross-sectional data.
  • This study utilizes a novel framework, Coupled Cross-sectional and Longitudinal Non-negative Matrix Factorization (CCL-NMF), to analyze brain atrophy heterogeneity.

Purpose of the Study:

  • To develop and apply a novel machine learning framework (CCL-NMF) that integrates both static and dynamic brain changes for analyzing aging-related brain atrophy.
  • To compare the performance of CCL-NMF against a purely cross-sectional model (Surreal-GAN) in understanding brain aging heterogeneity.
  • To assess the predictive performance of CCL-NMF for various clinical outcomes and its applicability to external datasets.

Main Methods:

  • CCL-NMF framework combines cross-sectional and longitudinal data using a mutually constrained NMF approach.
  • Structural MRI data from 48,949 individuals (≥50 years) across 12 neuroimaging studies were analyzed.
  • Comparative analysis with Surreal-GAN, regression models, and Cox proportional hazards models were used for performance assessment, with out-of-sample loading estimation via NiChart.

Main Results:

  • CCL-NMF identified seven distinct brain atrophy components linked to Alzheimer's disease, cognitive decline, and cardiovascular risk factors.
  • CCL-NMF provided a richer representation and superior predictive performance compared to the cross-sectional Surreal-GAN model.
  • High reliability of CCL-NMF loadings was demonstrated, enabling seamless out-of-sample application.

Conclusions:

  • CCL-NMF provides a robust and interpretable framework for understanding brain aging and neurodegeneration by integrating diverse data types.
  • The framework outperforms purely cross-sectional approaches, offering enhanced predictive accuracy for clinical outcomes.
  • CCL-NMF facilitates easy application to external datasets via a web-accessible server, promoting broader research use.