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Related Experiment Video

Updated: Dec 28, 2025

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

Optimising network modelling methods for fMRI.

Usama Pervaiz¹, Diego Vidaurre², Mark W Woolrich³

¹Oxford Centre for Functional MRI of the Brain (FMRIB), Wellcome Centre for Integrative Neuroimaging, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, OX3 9DU, United Kingdom.

|February 17, 2020

Summary

This summary is machine-generated.

This study optimizes neuroimaging pipelines for predicting behavior from brain connectivity. Researchers tested over 9000 variants across large datasets to find the best methods for functional connectivity analysis and prediction.

Keywords:

Connectome Convolutional neural networks Deep learning Functional Connectivity Netmat Riemannian geometry

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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

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Last Updated: Dec 28, 2025

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Published on: October 13, 2023

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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

Area of Science:

Neuroimaging
Computational Neuroscience
Machine Learning

Background:

Predictive modeling of brain functional connectivity and behavior is a key goal in neuroimaging.
A standardized pipeline for functional connectivity analysis is lacking, hindering reproducible research.

Purpose of the Study:

To systematically investigate and optimize pipelines for predicting behavioral traits from whole-brain functional connectivity.
To evaluate the independent and joint performance of various analytical choices within these pipelines.

Main Methods:

Compared four parcellation techniques, five functional connectivity measures, ambient vs. tangent space, shrinkage estimators, six confound handling methods, and four classifiers.
Evaluated over 9000 pipeline variants on UK Biobank and Human Connectome Project resting-state fMRI data (∼14,000 individuals).
Validated top-performing pipelines on ABIDE and ACPI datasets (∼1,000 subjects) for generalizability.

Main Results:

Identified optimal combinations of methods for robust functional connectivity analysis and prediction.
Demonstrated the impact of different pipeline choices on predictive model performance.
Established generalizable network modeling methods for brain-behavior relationships.

Conclusions:

The study provides a framework for optimizing functional connectivity pipelines in neuroimaging.
Findings contribute to more reliable prediction of behavioral traits from brain data.
Highlights the importance of joint optimization of analytical steps for improved predictive accuracy.