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

Gene Conversion02:08

Gene Conversion

10.6K
Other than maintaining genome stability via DNA repair, homologous recombination plays an important role in diversifying the genome. In fact, the recombination of sequences forms the molecular basis of genomic evolution. Random and non-random permutations of genomic sequences create a library of new amalgamated sequences. These newly formed genomes can determine the fitness and survival of cells. In bacteria, homologous and non-homologous types of recombination lead to the evolution of new...
10.6K
Gene Conversion02:08

Gene Conversion

3.0K
3.0K
Correlations02:20

Correlations

35.8K
Correlation means that there is a relationship between two or more variables (such as ice cream consumption and crime), but this relationship does not necessarily imply cause and effect. When two variables are correlated, it simply means that as one variable changes, so does the other. We can measure correlation by calculating a statistic known as a correlation coefficient. A correlation coefficient is a number from -1 to +1 that indicates the strength and direction of the relationship between...
35.8K
Predicting Molecular Geometry02:27

Predicting Molecular Geometry

45.6K
VSEPR Theory for Determination of Electron Pair Geometries
45.6K
Correlation and Causation01:27

Correlation and Causation

42.4K
Statistical tests can calculate whether there is a relationship, or correlation, between independent and dependent variables. An indirect relationship of the variables signifies a correlation, while a direct relationship shows causation. If it is determined that no connection exists between the variables, then the correlation is a coincidence.
Correlation versus Causation
If the dependent variable increases or decreases when the independent variable increases, there is a positive or negative...
42.4K
Conversion of Units01:36

Conversion of Units

35.4K
Sometimes, there is a need to convert from one unit to another one. For instance, reading a cookbook in which quantities are expressed in units of liters or ounces may require conversion of quantities to cups. Or, when looking up directions on how to get to a location, we may be interested to know how many miles we are going to walk. In this case, we would have to convert units of feet or meters to miles.
The first step in the unit conversion is to list the given units and the units required...
35.4K

You might also read

Related Articles

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

Sort by
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 author

Single- and Multiple-Shell Uniform Sampling Schemes for Diffusion MRI Using Spherical Codes.

IEEE transactions on medical imaging·2017

Related Experiment Video

Updated: Jan 24, 2026

A Protocol for Computer-Based Protein Structure and Function Prediction
16:41

A Protocol for Computer-Based Protein Structure and Function Prediction

Published on: November 3, 2011

69.7K

Temporal Correlation Structure Learning for MCI Conversion Prediction.

Xiaoqian Wang1, Weidong Cai2, Dinggang Shen3

  • 1Department of Electrical and Computer Engineering, University of Pittsburgh, Pittsburgh, USA.

Medical Image Computing and Computer-Assisted Intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention
|May 21, 2019
PubMed
Summary
This summary is machine-generated.

This study introduces a new deep learning method to better predict Alzheimer's disease progression in individuals with Mild Cognitive Impairment (MCI). The model utilizes temporal data to improve diagnostic accuracy for early Alzheimer's detection.

Keywords:
Alzheimer’s diseaseDeep learningMCI conversion predictionTemporal correlation structure

More Related Videos

Cortical Actin Flow in T Cells Quantified by Spatio-temporal Image Correlation Spectroscopy of Structured Illumination Microscopy Data
09:09

Cortical Actin Flow in T Cells Quantified by Spatio-temporal Image Correlation Spectroscopy of Structured Illumination Microscopy Data

Published on: December 17, 2015

10.2K
RNA Secondary Structure Prediction Using High-throughput SHAPE
13:42

RNA Secondary Structure Prediction Using High-throughput SHAPE

Published on: May 31, 2013

32.2K

Related Experiment Videos

Last Updated: Jan 24, 2026

A Protocol for Computer-Based Protein Structure and Function Prediction
16:41

A Protocol for Computer-Based Protein Structure and Function Prediction

Published on: November 3, 2011

69.7K
Cortical Actin Flow in T Cells Quantified by Spatio-temporal Image Correlation Spectroscopy of Structured Illumination Microscopy Data
09:09

Cortical Actin Flow in T Cells Quantified by Spatio-temporal Image Correlation Spectroscopy of Structured Illumination Microscopy Data

Published on: December 17, 2015

10.2K
RNA Secondary Structure Prediction Using High-throughput SHAPE
13:42

RNA Secondary Structure Prediction Using High-throughput SHAPE

Published on: May 31, 2013

32.2K

Area of Science:

  • Neuroscience
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Mild Cognitive Impairment (MCI) is a critical transitional stage preceding Alzheimer's disease (AD).
  • Accurately differentiating MCI patients who will progress to AD from those who will not is vital for early intervention and diagnosis.
  • Current diagnostic methods often struggle with limited data and fail to leverage longitudinal disease progression information.

Purpose of the Study:

  • To develop an advanced deep learning framework for improved prediction of Alzheimer's disease conversion from MCI.
  • To address the limitations of traditional methods by incorporating temporal correlations and generative data augmentation.

Main Methods:

  • A novel deep learning framework was designed to analyze temporal correlations between adjacent time points in disease progression.
  • A generative framework was employed to learn data distributions and enhance training data reliability.
  • The model was trained and validated using data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort.

Main Results:

  • The proposed deep learning framework demonstrated superior performance in distinguishing MCI converters from non-converters compared to traditional methods.
  • The integration of temporal information and generative data augmentation significantly improved predictive accuracy.
  • Experimental results on the ADNI cohort confirmed the model's effectiveness.

Conclusions:

  • The novel deep learning approach effectively utilizes longitudinal data to enhance the prediction of Alzheimer's disease progression in MCI patients.
  • This framework offers a promising tool for more accurate early diagnosis and intervention strategies in Alzheimer's research.
  • The findings highlight the importance of temporal dynamics and data augmentation in improving diagnostic models for neurodegenerative diseases.