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

Vision transformer autoencoders captures local and non-local features in brain imaging to reveal novel genetic associations.

Communications biology·2026
Same author

Replicability of unsupervised deep learning derived image phenotypes.

bioRxiv : the preprint server for biology·2026
Same author

Genetic architecture of white matter microstructure captured by unsupervised deep representation learning of fractional anisotropy maps.

Nature communications·2026
Same author

Improving Vancomycin Therapeutic Drug Monitoring With a Deep Learning-Based Two-Compartment Predictive Model: Development and Validation Study.

JMIR AI·2026
Same author

HiFiMAP: High-resolution fast identity-by-descent mapping test.

medRxiv : the preprint server for health sciences·2026
Same author

Haplotype-based Parallel PBWT for Biobank Scale Data.

IEEE ... International Conference on Computational Advances in Bio and Medical Sciences : [proceedings]. IEEE International Conference on Computational Advances in Bio and Medical Sciences·2026
Same journal

Evidence for progressive neurodegeneration in iatrogenic cerebral amyloid angiopathy.

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

Human brain connectome profiles mediate the relationship between pathology burden and clinical phenotypes in Alzheimer's disease.

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

Kat5 cKO mouse replicates biological domain signatures associated with Alzheimer's disease.

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

Evaluation of CSF and plasma tau species as fluid surrogate candidates for tau PET in prodromal to moderate Alzheimer's disease.

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

Associations of self-reported obstructive sleep apnea with cognition and dementia risk in cognitively unimpaired middle-aged adults.

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

Inflammation profiles in Alzheimer's disease relate to cognition and neurodegeneration.

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.

Xingzhong Zhao1, Wei He1, Ziqian Xie1

  • 1The University of Texas Health Science Center at Houston, Houston, TX, USA.

Alzheimer'S & Dementia : the Journal of the Alzheimer'S Association
|December 25, 2025
PubMed
Summary
This summary is machine-generated.

Unsupervised deep learning of fractional anisotropy (FA) images created novel phenotypes (UDIP-FAs) that better capture white matter (WM) heritability and genetic links to brain disorders. This approach offers a more robust way to study WM microstructural integrity and its relation to neurological conditions.

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
  • Genetics
  • Machine Learning

Background:

  • Fractional anisotropy (FA) is a key MRI biomarker for white matter (WM) microstructural integrity.
  • Current atlas-based methods for FA analysis have limitations due to extraction variability and ignoring complex WM tract interactions.
  • Understanding FA's genetic architecture is vital for neurodevelopment, aging, and diseases like Alzheimer's.

Purpose of the Study:

  • To develop a novel, unbiased method for deriving WM imaging phenotypes using deep learning.
  • To investigate the genetic architecture of these new phenotypes and their association with brain disorders.
  • To explore the biological mechanisms linking WM structure to brain health and disease.

Main Methods:

  • Trained an unsupervised deep neural network on FA images from 6,000 UK Biobank participants to create 128-dimensional Unsupervised Deep Learning-Derived Imaging Phenotypes of FA (UDIP-FAs).
  • Utilized perturbation-based Decoder Interpretation and brain disorder classification tasks for evaluation.
  • Conducted Genome-wide association study (GWAS) on UDIP-FAs with 25,875 participants, followed by validation, functional annotation, and gene mapping.

Main Results:

  • UDIP-FAs could classify six brain disorders with AUC 0.64±0.08.
  • UDIP-FAs showed significantly higher SNP heritability (mean 50.81%) than traditional FA phenotypes.
  • GWAS identified 3782 significant SNPs mapped to 156 UDIP-FA related genes (UFAGs), enriched in oligodendrocyte precursor cells (OPCs) and glial cells.
  • UFAGs ZIC1 and ZIC4 were found to regulate Alzheimer's disease risk genes.
  • UDIP-FAs showed significant genetic correlations with intelligence.

Conclusions:

  • UDIP-FAs offer a more unbiased and heritable description of WM microstructural integrity.
  • This deep learning approach aids in uncovering the genetic structure of WM.
  • Provides a promising method for exploring biological links between WM and brain disorders.