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

Structural Classification of Joints01:20

Structural Classification of Joints

Joints, also known as articulations, are classified based on their structural characteristics, i.e., based on whether the articulating surfaces of the adjacent bones are directly connected by fibrous connective tissue or cartilage, or whether the articulating surfaces contact each other within a fluid-filled joint cavity. These differences serve to divide the joints of the body into three structural classifications.
A fibrous joint is where the adjacent bones are united by fibrous connective...
Multicompartment Models: Overview01:14

Multicompartment Models: Overview

Multicompartment models are mathematical constructs that depict how drugs are distributed and eliminated within the body. They segment the body into several compartments, symbolizing various physiological or anatomical areas connected through drug transfer processes such as absorption, metabolism, distribution, and elimination.
These models offer a more comprehensive representation of drug behavior in the body than one-compartment models. They accommodate the complexity of drug distribution,...
State Space Representation01:27

State Space Representation

The frequency-domain technique, commonly used in analyzing and designing feedback control systems, is effective for linear, time-invariant systems. However, it falls short when dealing with nonlinear, time-varying, and multiple-input multiple-output systems. The time-domain or state-space approach addresses these limitations by utilizing state variables to construct simultaneous, first-order differential equations, known as state equations, for an nth-order system.
Consider an RLC circuit, a...
Resonance and Hybrid Structures02:16

Resonance and Hybrid Structures

According to the theory of resonance, if two or more Lewis structures with the same arrangement of atoms can be written for a molecule, ion, or radical, the actual distribution of electrons is an average of that shown by the various Lewis structures.
Resonance Structures and Resonance Hybrids
The Lewis structure of a nitrite anion (NO2−) may actually be drawn in two different ways, distinguished by the locations of the N–O and N=O bonds.
Neural Circuits01:25

Neural Circuits

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...
Hybridization of Atomic Orbitals I03:24

Hybridization of Atomic Orbitals I

The mathematical expression known as the wave function, ψ, contains information about each orbital and the wavelike properties of electrons in an isolated atom. When atoms are bound together in a molecule, the wave functions combine to produce new mathematical descriptions that have different shapes. This process of combining the wave functions for atomic orbitals is called hybridization and is mathematically accomplished by the linear combination of atomic orbitals. The new orbitals that...

You might also read

Related Articles

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

Sort by
Same author

Phenotype discovery of traumatic brain injury segmentations from heterogeneous multi-site data.

Proceedings of SPIE--the International Society for Optical Engineering·2026
Same author

Impact of reconstruction kernel variability on segmentation consistency in low-dose thoracic CT.

Proceedings of SPIE--the International Society for Optical Engineering·2026
Same author

Personalized White Matter Bundle Segmentation for Early Childhood.

Proceedings of SPIE--the International Society for Optical Engineering·2026
Same author

Contrastive Patient-level Pretraining Enables Longitudinal and Multimodal Fusion for Lung Cancer Risk Prediction.

Proceedings of machine learning research·2026
Same author

DeepFixel: Crossing white matter fiber identification through spherical convolutional neural networks.

Proceedings of SPIE--the International Society for Optical Engineering·2026
Same author

HARMONIZATION MITIGATES DIFFUSION MRI SCANNER EFFECTS IN INFANCY: INSIGHTS FROM THE HEALTHY BRAIN AND CHILD DEVELOPMENT (HBCD) STUDY.

Proceedings. IEEE International Symposium on Biomedical Imaging·2026
Same journal

From Geometry to Intensity: A Coarse-to-Fine Pipeline for Unsupervised 3D Ultrasound Stitching.

Proceedings of SPIE--the International Society for Optical Engineering·2026
Same journal

AVA: Automated Viewability Analysis for Ureteroscopic Intrarenal Surgery.

Proceedings of SPIE--the International Society for Optical Engineering·2026
Same journal

Kidney Endoscopy Video to Preoperative CT Alignment for Depth Estimation.

Proceedings of SPIE--the International Society for Optical Engineering·2026
Same journal

Deep learning‑based cell type prediction in lung tissue from brightfield histology using CODEX-derived labels.

Proceedings of SPIE--the International Society for Optical Engineering·2026
Same journal

Reconstructing physiological signals from fMRI across the adult lifespan.

Proceedings of SPIE--the International Society for Optical Engineering·2026
Same journal

Axially Swept Light-Sheet Microscopy using scattering and fluorescence contrast mechanisms.

Proceedings of SPIE--the International Society for Optical Engineering·2026
See all related articles

Related Experiment Video

Updated: May 26, 2026

A Multimodal Imaging- and Stimulation-based Method of Evaluating Connectivity-related Brain Excitability in Patients with Epilepsy
08:23

A Multimodal Imaging- and Stimulation-based Method of Evaluating Connectivity-related Brain Excitability in Patients with Epilepsy

Published on: November 13, 2016

Characterizing Continuous and Discrete Hybrid Latent Spaces for Structural Connectomes.

Gaurav Rudravaram1, Lianrui Zuo1, Adam M Saunders1

  • 1Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, USA.

Proceedings of Spie--The International Society for Optical Engineering
|May 25, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces a novel variational autoencoder (VAE) with a hybrid latent space to analyze complex brain connectomes. The method effectively separates site-specific variations from other data factors in large-scale neuroimaging datasets.

Keywords:
Structural connectivitylatent spacesrepresentation learning

More Related Videos

Recording and Analyzing Multimodal Large-Scale Neuronal Ensemble Dynamics on CMOS-Integrated High-Density Microelectrode Array
09:44

Recording and Analyzing Multimodal Large-Scale Neuronal Ensemble Dynamics on CMOS-Integrated High-Density Microelectrode Array

Published on: March 8, 2024

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

Related Experiment Videos

Last Updated: May 26, 2026

A Multimodal Imaging- and Stimulation-based Method of Evaluating Connectivity-related Brain Excitability in Patients with Epilepsy
08:23

A Multimodal Imaging- and Stimulation-based Method of Evaluating Connectivity-related Brain Excitability in Patients with Epilepsy

Published on: November 13, 2016

Recording and Analyzing Multimodal Large-Scale Neuronal Ensemble Dynamics on CMOS-Integrated High-Density Microelectrode Array
09:44

Recording and Analyzing Multimodal Large-Scale Neuronal Ensemble Dynamics on CMOS-Integrated High-Density Microelectrode Array

Published on: March 8, 2024

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

Area of Science:

  • Neuroscience
  • Machine Learning
  • Biostatistics

Background:

  • Structural connectomes map brain connectivity but are high-dimensional and difficult to analyze.
  • Existing low-dimensional methods like PCA and standard autoencoders struggle to model mixed continuous and discrete variability in connectomes.
  • Understanding variability sources is crucial for aging, cognition, and neurodegenerative disease research.

Purpose of the Study:

  • To develop and evaluate a variational autoencoder (VAE) with a hybrid latent space capable of jointly modeling discrete and continuous variability in structural connectomes.
  • To assess the VAE's ability to disentangle different sources of variation, such as imaging site effects, in large-scale connectome datasets.
  • To demonstrate the potential of this hybrid approach for improved interpretability and analysis of high-dimensional brain network data.

Main Methods:

  • Proposed a variational autoencoder (VAE) with a hybrid latent space designed to simultaneously model discrete and continuous factors within connectome data.
  • Analyzed a large dataset comprising 5,761 structural connectomes from 6 Alzheimer's disease studies, encompassing diverse demographics and cognitive statuses.
  • Trained the hybrid VAE in an unsupervised manner and evaluated its performance in capturing and separating sources of variability, particularly site-specific differences.

Main Results:

  • The hybrid VAE successfully modeled both discrete and continuous components of variability in the connectome data.
  • The discrete component of the latent space effectively captured site-related differences, achieving an Adjusted Rand Index (ARI) of 0.65.
  • This performance significantly surpassed traditional methods like PCA and standard VAE followed by clustering (p << 0.05), highlighting the hybrid model's superiority in disentangling variability.

Conclusions:

  • The proposed hybrid latent space VAE offers a powerful unsupervised method for disentangling distinct sources of variability in structural connectomes.
  • This approach demonstrates significant advantages over conventional techniques in identifying subtle factors like imaging site effects.
  • The findings suggest promising potential for large-scale connectome analysis, aiding research in aging, cognition, and neurodegenerative diseases.