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

Neural Circuits01:25

Neural Circuits

2.4K
Neural circuits and neuronal pools are two of the main structures found in the nervous system. Neural circuits are networks of neurons that work together to carry out a specific task or process. They consist of interconnected neurons and glial cells, which provide structural and metabolic support.
Neuronal pools are collections of nerve cells with similar functions and interact through chemical and electrical signals. These pools include both interneurons (the central neural circuit nodes that...
2.4K
Sequence Networks of Rotating Machines01:24

Sequence Networks of Rotating Machines

402
A Y-connected synchronous generator, grounded through a neutral impedance, is designed to produce balanced internal phase voltages with only positive-sequence components. The generator's sequence networks include a source voltage that is exclusively in the positive-sequence network. The sequence components of line-to-ground voltages at the generator terminals illustrate this configuration.
Zero-sequence current induces a voltage drop across the generator's neutral impedance and other...
402
Classification of Neurotransmitters01:30

Classification of Neurotransmitters

4.7K
Neurotransmitters play a crucial role in the communication between neurons in the autonomic nervous system. Neurons in the autonomic nervous system can be cholinergic or adrenergic depending on the neurotransmitters synthesized. Cholinergic neurons use acetylcholine as their primary neurotransmitter. This includes all the preganglionic fibers of the sympathetic and pre- and postganglionic fibers of the parasympathetic nervous systems. In addition, neurons of the somatic nervous system also use...
4.7K
Classification of Signals01:30

Classification of Signals

1.2K
In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
1.2K
Classification of Systems-I01:26

Classification of Systems-I

468
Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
468
Classification of Systems-II01:31

Classification of Systems-II

400
Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
400

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: Dec 5, 2025

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms
08:51

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Published on: November 1, 2019

5.9K

DS-GCNs: Connectome Classification using Dynamic Spectral Graph Convolution Networks with Assistant Task Training.

Xiaodan Xing1,2, Qingfeng Li3, Mengya Yuan3

  • 1United Imaging Intelligence Co., Ltd., Shanghai 201210, China.

Cerebral Cortex (New York, N.Y. : 1991)
|October 20, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces novel graph convolutional networks (GCNs) for Alzheimer's disease classification using dynamic functional connectivity (FC) brain networks. The approach achieved high accuracy by integrating demographic data, improving neurological disease diagnosis.

Keywords:
ConnectomeGCNfMRI

More Related Videos

Author Spotlight: Advancing Large-Scale Neural Dynamics Through HD-MEA Technology
09:44

Author Spotlight: Advancing Large-Scale Neural Dynamics Through HD-MEA Technology

Published on: March 8, 2024

5.5K
Dynamic Inter-subject Functional Connectivity Reveals Moment-to-Moment Brain Network Configurations Driven by Continuous or Communication Paradigms
08:36

Dynamic Inter-subject Functional Connectivity Reveals Moment-to-Moment Brain Network Configurations Driven by Continuous or Communication Paradigms

Published on: March 21, 2019

7.5K

Related Experiment Videos

Last Updated: Dec 5, 2025

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms
08:51

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Published on: November 1, 2019

5.9K
Author Spotlight: Advancing Large-Scale Neural Dynamics Through HD-MEA Technology
09:44

Author Spotlight: Advancing Large-Scale Neural Dynamics Through HD-MEA Technology

Published on: March 8, 2024

5.5K
Dynamic Inter-subject Functional Connectivity Reveals Moment-to-Moment Brain Network Configurations Driven by Continuous or Communication Paradigms
08:36

Dynamic Inter-subject Functional Connectivity Reveals Moment-to-Moment Brain Network Configurations Driven by Continuous or Communication Paradigms

Published on: March 21, 2019

7.5K

Area of Science:

  • Neuroscience
  • Artificial Intelligence
  • Medical Imaging Analysis

Background:

  • Functional connectivity (FC) matrices are crucial for understanding brain interactions and classifying neurological diseases.
  • Brain networks, or connectomes, can be represented as graph structures, enabling advanced analytical techniques.
  • Existing methods may not fully capture the dynamic nature of brain activity crucial for accurate disease classification.

Purpose of the Study:

  • To develop and evaluate novel graph convolutional networks (GCNs) for extracting disease-related features from FC matrices.
  • To incorporate the time-dependent nature of brain activity by analyzing dynamic FC matrices.
  • To enhance classification performance by utilizing demographic information as auxiliary prediction tasks.

Main Methods:

  • Computed dynamic FC matrices using a sliding window approach to capture time-varying brain activity.
  • Implemented a graph convolution-based Long Short-Term Memory (LSTM) layer to process dynamic graph data.
  • Utilized demographic data (age, gender) as additional outputs in a multi-task learning framework with shared parameters.

Main Results:

  • The proposed GCN architecture demonstrated high performance in classifying Alzheimer's disease patients from normal controls on the ADNI II dataset.
  • Achieved a classification accuracy of 90.0%, sensitivity of 91.7%, and specificity of 88.6%.
  • The integration of demographic information as assistant tasks contributed to improved classification outcomes.

Conclusions:

  • Novel GCNs effectively extract features from dynamic FC matrices for neurological disease classification.
  • The multi-task learning approach, incorporating demographic data, enhances diagnostic accuracy for conditions like Alzheimer's disease.
  • This method offers a promising avenue for improving the early detection and diagnosis of brain disorders.