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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...

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Updated: Jan 16, 2026

Network Analysis of the Default Mode Network Using Functional Connectivity MRI in Temporal Lobe Epilepsy

Network Analysis of the Default Mode Network Using Functional Connectivity MRI in Temporal Lobe Epilepsy

Published on: August 5, 2014

A deep learning-enriched framework for analyzing brain functional connectivity.

Davide Borra¹, Elisa Magosso^2,3

¹Department of Electrical, Electronic and Information Engineering "Guglielmo Marconi" (DEI), University of Bologna, Cesena Campus, Cesena, 47522, Italy. davide.borra2@unibo.it.

Scientific Reports

|October 3, 2025

Summary

This summary is machine-generated.

This study introduces a novel deep learning framework, FCNet, to analyze directed functional connectivity in the brain. It reveals informative frequency patterns and information flow for understanding brain states during motor imagery tasks.

Keywords:

Connectivity inflow and outflow EEG Explainable artificial intelligence Functional connectivity Interpretable neural networks Spectral granger causality

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Last Updated: Jan 16, 2026

Network Analysis of the Default Mode Network Using Functional Connectivity MRI in Temporal Lobe Epilepsy

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Published on: August 5, 2014

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

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Published on: November 8, 2012

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

Area of Science:

Neuroscience
Computational Neuroscience
Machine Learning

Background:

Brain function relies on directed communication between regions, utilizing asymmetric connections.
Deep learning models excel at decoding brain states but struggle with characterizing information flow in functional networks.
Analyzing spectral directed functional connectivity is crucial for understanding complex brain processes.

Purpose of the Study:

To develop a deep learning-enriched framework for analyzing spectral directed functional connectivity.
To create novel, non-linear inflow and outflow measures for brain networks.
To identify informative frequency content and connectivity patterns for brain states.

Main Methods:

Designed 'Functional-Connectivity-Net' (FCNet), an interpretable convolutional neural network, trained to discriminate brain states from functional connectivity.
Utilized DeepLIFT for explaining network decisions and identifying key frequency and connectivity features.
Applied the framework to EEG functional connectivity data from scalp and cortex during motor imagery tasks.

Main Results:

FCNet's explanations align with known spectral connectivity changes during motor imagery.
Novel network-based measures effectively capture connectivity changes, comparable to graph theory metrics.
The framework successfully identified informative frequencies and connectivity inflow/outflows.

Conclusions:

The proposed deep learning framework enhances the analysis of spectral directed functional connectivity.
It provides valuable insights into the predictability and informative components of brain functional networks.
This approach aids in understanding brain states and information flow during cognitive and motor tasks.