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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 Stimulation (TMS).

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Related Experiment Video

Updated: May 11, 2026

Analyzing Neural Activity and Connectivity Using Intracranial EEG Data with SPM Software

Analyzing Neural Activity and Connectivity Using Intracranial EEG Data with SPM Software

Published on: October 30, 2018

Predicting intrinsic brain activity.

R Cameron Craddock¹, Michael P Milham, Stephen M LaConte

¹Virginia Tech Carilion Research Institute, Roanoke, VA 24016, USA.

|May 28, 2013

Summary

This summary is machine-generated.

Multivariate supervised learning models brain connectivity more accurately than standard methods. This approach enhances understanding of brain networks and improves resting-state fMRI data analysis.

Keywords:

Effective connectivity Functional connectivity Functional magnetic resonance imaging MVPA Multi-voxel pattern analysis Multivariate Regression Resting state fMRI

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Last Updated: May 11, 2026

Analyzing Neural Activity and Connectivity Using Intracranial EEG Data with SPM Software

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Published on: October 30, 2018

Concurrent EEG and Functional MRI Recording and Integration Analysis for Dynamic Cortical Activity Imaging

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Published on: June 30, 2018

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Area of Science:

Neuroimaging
Machine Learning
Computational Neuroscience

Background:

Multivariate supervised learning is effective for decoding brain states from neuroimaging data.
These methods are typically used for task-based analyses but are applicable to intrinsic connectivity.
Standard bivariate methods offer less accurate representations of the connectome compared to multivariate approaches.

Purpose of the Study:

To describe a method for learning multivariate models of brain connectivity.
To apply this method within the non-parametric prediction accuracy, influence, and reproducibility-resampling (NPAIRS) framework.
To assess the utility of multivariate regression connectivity modeling for optimizing experimental parameters and data quality.

Main Methods:

Application of multivariate supervised learning to intrinsic effective and functional connectivity.
Utilizing the non-parametric prediction accuracy, influence, and reproducibility-resampling (NPAIRS) framework.
Analyzing the multivariate interactions between all brain regions simultaneously.

Main Results:

Models of connectivity incorporate multivariate interactions for a more accurate connectome representation.
Models can decode or predict intrinsic brain activity time series.
Prediction accuracy measures regional integration and aids in evaluating fMRI data acquisition and preprocessing pipelines.
Spatial distribution of prediction accuracy and reproducibility aligns with established functional hierarchies.

Conclusions:

Multivariate supervised learning provides a more accurate and comprehensive model of brain connectivity.
This method offers a robust approach for decoding brain states and assessing network integration.
The technique is valuable for optimizing experimental parameters and ensuring the quality of functional neuroimaging data.