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Related Experiment Video

Updated: Dec 16, 2025

Topographical Estimation of Visual Population Receptive Fields by fMRI

Topographical Estimation of Visual Population Receptive Fields by fMRI

Published on: February 3, 2015

R-fMRI reconstruction from k-t undersampled data using a subject-invariant dictionary model and VB-EM with nested

Prachi H Kulkarni¹, S N Merchant¹, Suyash P Awate²

¹Electrical Engineering (EE) Department, Indian Institute of Technology (IIT) Bombay, Mumbai, India.

Medical Image Analysis

|July 6, 2020

Summary

This summary is machine-generated.

This study introduces a new method for faster resting-state functional magnetic resonance imaging (R-fMRI) scans. The advanced reconstruction technique improves spatial resolution, offering better insights into brain functional networks.

Keywords:

Dictionary prior Expectation maximization Nested minorization R-fMRI Reconstruction Robust Sparse Spatial regularization Subject-invariant Undersample Variational Bayesian factorization

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Last Updated: Dec 16, 2025

Topographical Estimation of Visual Population Receptive Fields by fMRI

Topographical Estimation of Visual Population Receptive Fields by fMRI

Published on: February 3, 2015

Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time

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Published on: July 1, 2014

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Simultaneous Data Collection of fMRI and fNIRS Measurements Using a Whole-Head Optode Array and Short-Distance Channels

Published on: October 20, 2023

Area of Science:

Neuroimaging
Biophysics
Computational Neuroscience

Background:

Resting-state functional magnetic resonance imaging (R-fMRI) provides crucial data on cerebral cortex functional networks.
Achieving higher spatial resolution in R-fMRI typically involves trade-offs in scan speed using complex pulse sequences or k-space undersampling with signal priors.

Purpose of the Study:

To develop a novel R-fMRI reconstruction framework to improve spatial resolution by undersampling in k-space and time.
To introduce an advanced inference framework for R-fMRI reconstruction that provides uncertainty estimates.

Main Methods:

Proposed a model-based R-fMRI reconstruction using a subject-invariant, spatially regularized dictionary prior.
Developed a variational Bayesian expectation maximization with nested minorization (VB-EM-NM) inference framework.
Evaluated the framework using simulated R-fMRI data and functional network estimation from brain R-fMRI reconstructions.

Main Results:

The proposed framework significantly improves over the state-of-the-art in R-fMRI reconstruction.
The method enables substantially higher spatial resolution compared to existing techniques.
The VB-EM-NM inference framework provides uncertainty estimates for the reconstructions.

Conclusions:

The novel R-fMRI reconstruction framework enhances spatial resolution and improves functional network estimation.
The developed inference method offers uncertainty quantification, a key advantage over conventional approaches.
This work paves the way for more detailed and reliable analysis of brain functional connectivity.