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

Methods of Obtaining Topography01:25

Methods of Obtaining Topography

323
Topography involves measuring and mapping land elevations, natural features, and artificial structures to create accurate representations of the terrain. Topographic surveying relies on traditional and modern methods, each with distinct advantages and limitations.Traditional Surveying Methods:Transit stadia surveys and plane table surveys were widely used traditional surveying methods. These techniques relied on instruments like theodolites and stadia rods for measuring distances and angles,...
323
Pharmacokinetics in Geriatric Patients: Effect of Age on Drug Metabolism01:18

Pharmacokinetics in Geriatric Patients: Effect of Age on Drug Metabolism

222
Geriatric patients show significant variation in how their bodies process medications, which can change how effective and safe treatments are. The liver is the primary organ where drug metabolism occurs, involving two main types of chemical reactions: phase I and II. Phase I metabolism is driven by the cytochrome P450 enzyme system, which includes key types such as CYP3A, CYP2D6, and CYP2C9. Research indicates that while aging doesn't notably alter the levels or activity of these enzymes, it...
222
What is Variation?01:14

What is Variation?

18.6K
Apart from the measures of central tendency, distribution, outliers, and the changing characteristics of data with time, an important characteristic of any data set is its variation or spread. In some data sets, the data values are concentrated closely near the mean; in others, the data values are more widely spread out from the mean.
The range, standard deviation, standard error, and variance are the different measures of variation.
Range: The range is the difference between its maximum and...
18.6K
What is Metabolism?00:52

What is Metabolism?

132.0K
Overview
132.0K
Variation01:19

Variation

8.0K
An important characteristic of any set of data is the variation in the data. In some data sets, the data values are concentrated closely near the mean; in other data sets, the data values are more widely spread out from the mean. The most common measure of variation, or spread, is the standard deviation, which is the square root of variance.
When independent and dependent variables are plotted on a scatter plot, the slope of a line is a value that describes the rate of change between the two...
8.0K
Predicting Molecular Geometry02:27

Predicting Molecular Geometry

46.0K
VSEPR Theory for Determination of Electron Pair Geometries
46.0K

You might also read

Related Articles

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

Sort by
Same author

Maturational delay and asymmetric information flow of brain connectivity in SHR model of ADHD revealed by topological analysis of metabolic networks.

Scientific reports·2020
Same author

Clinical outcome and predictive factors for docetaxel and epirubicin neoadjuvant chemotherapy of locally advanced breast cancer.

The Korean journal of internal medicine·2020
Same author

Hypofractionated Radiation Therapy for Progressive Heterotopic Ossification: The Relationship between Dose and Efficacy.

International journal of radiation oncology, biology, physics·2020
Same author

Deep learning-based interpretation of basal/acetazolamide brain perfusion SPECT leveraging unstructured reading reports.

European journal of nuclear medicine and molecular imaging·2020
Same author

Cognitive signature of brain FDG PET based on deep learning: domain transfer from Alzheimer's disease to Parkinson's disease.

European journal of nuclear medicine and molecular imaging·2019
Same author

Clinical implication of 18F-NaF PET/computed tomography indexes of aortic calcification in coronary artery disease patients: correlations with cardiovascular risk factors.

Nuclear medicine communications·2019

Related Experiment Video

Updated: Feb 7, 2026

Wholemount Immunohistochemistry for Revealing Complex Brain Topography
09:23

Wholemount Immunohistochemistry for Revealing Complex Brain Topography

Published on: April 5, 2012

16.1K

Predicting Aging of Brain Metabolic Topography Using Variational Autoencoder.

Hongyoon Choi1, Hyejin Kang1, Dong Soo Lee1,2,3

  • 1Department of Nuclear Medicine, Seoul National University College of Medicine, Seoul, South Korea.

Frontiers in Aging Neuroscience
|July 28, 2018
PubMed
Summary

This study developed a generative model to predict brain metabolism changes with aging using 18F-Fluorodeoxyglucose PET scans. The model also identified APOE4

Keywords:
APOE4FDG PETbrain metabolismdeep generative modelvariational autoencoder

More Related Videos

Abbiategrasso Brain Bank Protocol for Collecting, Processing and Characterizing Aging Brains
12:28

Abbiategrasso Brain Bank Protocol for Collecting, Processing and Characterizing Aging Brains

Published on: June 3, 2020

18.3K
Metabolic Analysis of Drosophila melanogaster Larval and Adult Brains
07:06

Metabolic Analysis of Drosophila melanogaster Larval and Adult Brains

Published on: August 7, 2018

10.0K

Related Experiment Videos

Last Updated: Feb 7, 2026

Wholemount Immunohistochemistry for Revealing Complex Brain Topography
09:23

Wholemount Immunohistochemistry for Revealing Complex Brain Topography

Published on: April 5, 2012

16.1K
Abbiategrasso Brain Bank Protocol for Collecting, Processing and Characterizing Aging Brains
12:28

Abbiategrasso Brain Bank Protocol for Collecting, Processing and Characterizing Aging Brains

Published on: June 3, 2020

18.3K
Metabolic Analysis of Drosophila melanogaster Larval and Adult Brains
07:06

Metabolic Analysis of Drosophila melanogaster Larval and Adult Brains

Published on: August 7, 2018

10.0K

Area of Science:

  • Neuroscience
  • Medical Imaging
  • Computational Biology

Background:

  • Aging leads to variable brain changes, making precise prediction of brain topography challenging.
  • Understanding neural correlates of aging and neurodegeneration is crucial for early detection and intervention.

Purpose of the Study:

  • To predict age-related brain metabolic changes using generative modeling of 18F-Fluorodeoxyglucose Positron Emission Tomography (PET) scans.
  • To investigate the influence of APOE4 status on age-related metabolic degeneration in the brain.

Main Methods:

  • Developed a generative model using a cross-sectional PET dataset from cognitively normal subjects of varying ages.
  • The model generated future brain PET images based on age and individual features.
  • Correlated predicted metabolic changes with actual changes from follow-up data and analyzed APOE4 effects.

Main Results:

  • Successfully predicted age-related metabolic changes in the brain, creating an "aging movie" of brain metabolism.
  • Estimated normal population distribution of brain metabolic topography across different ages.
  • Revealed that APOE4 status significantly impacts age-related metabolic changes in specific brain regions like the hippocampus and amygdala.

Conclusions:

  • The predictive model offers insights into cognitive aging and individual variability in metabolic degeneration.
  • APOE4 is identified as a potential factor influencing age-related metabolic changes in the elderly.
  • This predictive approach holds promise for developing preclinical biomarkers for brain disorders.