Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Neural Circuits

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...

Brain Imaging

Brain Imaging

Brain imaging technologies provide critical insights into both the structure and function of the human brain, enabling medical professionals and researchers to diagnose, study, and treat neurological disorders or psychiatric disorders more effectively.
These technologies include computerized axial tomography (CAT or CT scans), positron-emission tomography (PET scans), magnetic resonance imaging (MRI), functional magnetic resonance imaging (fMRI), and Transcranial Magnetic Stimulation (TMS).

You might also read

Related Articles

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

Sort by

Same author

Unified Brain Surface and Volume Registration.

... International Conference on Learning Representations·2026

Same author

Deep-learning-based Optimization of the Under-sampling Pattern in MRI.

IEEE transactions on computational imaging·2026

Same author

Learning-based non-linear registration robust to MRI-sequence contrast.

Proceedings of the International Society for Magnetic Resonance in Medicine ... Scientific Meeting and Exhibition. International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition·2026

Same author

Longitudinal FreeSurfer with non-linear subject-specific template improves sensitivity to cortical thinning.

Proceedings of the International Society for Magnetic Resonance in Medicine ... Scientific Meeting and Exhibition. International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition·2026

Same author

DEEP-LEARNING CORTICAL REGISTRATION GUIDED BY STRUCTURAL AND DIFFUSION MRI AND CONNECTIVITY.

Proceedings. IEEE International Symposium on Biomedical Imaging·2026

Same author

Weak supervision of H&E slides reveals systems-level biology and functional states that govern therapeutic resistance.

bioRxiv : the preprint server for biology·2026

Search research articles

Related Experiment Video

Updated: Jun 6, 2026

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Published on: November 1, 2019

Structural connectome analysis using a graph-based deep model for prediction of non-imaging variables.

Anees Kazi^1,2, Jocelyn Mora¹, Bruce Fischl^1,2

¹Athinoula A. Martinos Center for Biomedical Imaging, Radiology Department, Massachusetts General Hospital, Boston, MA, United States.

Frontiers in Neuroscience

|June 5, 2026

Summary

This study introduces a novel graph-based machine learning model for predicting non-imaging variables like age and cognitive scores from brain connectivity data. The model shows improved accuracy, particularly for age prediction, advancing brain health research.

Keywords:

age brain connectivity deep learning dementia diffusion MRI graph convolutional network (GCN)graph neural network (GNN)prediction

More Related Videos

Modeling the Functional Network for Spatial Navigation in the Human Brain

Modeling the Functional Network for Spatial Navigation in the Human Brain

Published on: October 13, 2023

Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging

Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging

Published on: November 8, 2012

Related Experiment Videos

Last Updated: Jun 6, 2026

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Published on: November 1, 2019

Modeling the Functional Network for Spatial Navigation in the Human Brain

Modeling the Functional Network for Spatial Navigation in the Human Brain

Published on: October 13, 2023

Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging

Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging

Published on: November 8, 2012

Area of Science:

Neuroscience
Machine Learning
Medical Imaging

Background:

Structural brain connectivity from diffusion MRI is crucial for understanding brain function.
Predicting demographic and clinical variables from brain data can offer insights into health and disease.
Existing graph-based machine learning methods have limitations in capturing complex brain network patterns.

Purpose of the Study:

To develop and validate a novel graph-based machine learning model for predicting non-imaging variables using structural brain connectivity.
To assess the model's performance in predicting age and Mini-Mental State Examination (MMSE) scores.
To introduce a new model architecture with a Connectivity Attention Block for enhanced graph representation.

Main Methods:

Utilized diffusion magnetic resonance imaging (dMRI) data to construct brain connectivity graphs.
Proposed a machine learning model inspired by graph convolutional networks (GCNs) with a parallel multi-branch architecture.
Introduced a novel Connectivity Attention Block for learning graph embeddings and graph-level attention.

Main Results:

The proposed model achieved higher performance in predicting age compared to existing machine learning algorithms.
The model demonstrated competitive performance for MMSE score prediction.
Ablation studies and comparisons with existing methods validated the model's effectiveness.

Conclusions:

The novel GCN-inspired model effectively predicts demographic and clinical variables from brain connectivity graphs.
The Connectivity Attention Block enhances the model's ability to represent brain graphs.
This approach advances the understanding of how the connectome relates to health and disease across populations.