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

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Published on: August 5, 2014

Functional connectome fingerprinting using shallow feedforward neural networks.

Gokce Sarar^1,2, Bhaskar Rao², Thomas Liu^3,4,5

¹Center for Functional MRI, University of California San Diego, La Jolla, CA 92093.

Proceedings of the National Academy of Sciences of the United States of America

|April 8, 2021

Summary

This summary is machine-generated.

Shallow feedforward neural networks accurately identify individuals using resting-state functional MRI (rsfMRI) correlation matrices, even with short 20-second scans. This approach surpasses previous methods relying on temporal features.

Keywords:

connectome fMRI fingerprinting neural network

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Last Updated: Nov 9, 2025

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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:

Neuroimaging
Machine Learning
Brain Connectivity

Background:

Resting-state functional MRI (rsfMRI) enables subject identification via correlation matrices.
Accuracy decreases with shorter scan durations.
Recurrent neural networks (RNNs) show promise for short-duration data but utilize temporal features absent in correlation matrices.

Purpose of the Study:

To develop a machine learning model for accurate subject identification using rsfMRI correlation matrices with reduced scan times.
To evaluate the performance of shallow feedforward neural networks (FFNNs) on short-duration rsfMRI data.

Main Methods:

Utilized shallow feedforward neural networks (FFNNs).
Input data consisted solely of rsfMRI correlation matrices.
Evaluated performance across various data segment lengths, including segments as short as 20 seconds.

Main Results:

Achieved state-of-the-art identification accuracies using FFNNs on rsfMRI correlation matrices.
Demonstrated high accuracy with data segments as short as 20 seconds.
Performance remained robust across different input data size combinations.

Conclusions:

Shallow FFNNs effectively leverage information within rsfMRI correlation matrices for subject identification.
This method offers a viable alternative for accurate identification with significantly reduced rsfMRI scan durations.
The findings suggest FFNNs are well-suited for analyzing static connectivity patterns in rsfMRI data.