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Simultaneous Data Collection of fMRI and fNIRS Measurements Using a Whole-Head Optode Array and Short-Distance Channels
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Neuronal event detection in fMRI time series using iterative deconvolution techniques.

Luis Hernandez-Garcia1, Magnus O Ulfarsson

  • 1Functional MRI laboratory, University of Michigan, MI 48109-2108, USA. hernan@umich.edu

Magnetic Resonance Imaging
|January 15, 2011
PubMed
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This study introduces a new algorithm to accurately detect neuronal activity bursts from Blood Oxygen Level Dependent (BOLD) signals. The method effectively identifies neural events in fMRI data, even with noisy conditions.

Area of Science:

  • Neuroimaging
  • Signal Processing
  • Computational Neuroscience

Background:

  • Blood Oxygen Level Dependent (BOLD) signals are widely used to infer neuronal activity in functional magnetic resonance imaging (fMRI).
  • Deconvolving BOLD time series to precisely identify neuronal events remains a significant challenge in neuroimaging analysis.

Purpose of the Study:

  • To develop and validate an iterative estimation algorithm for accurate deconvolution of neuronal activity from BOLD time series.
  • To assess the algorithm's performance in identifying neuronal activity bursts under various fMRI conditions.

Main Methods:

  • An iterative estimation algorithm employing majorization-minimization was developed.
  • The cost function incorporated penalties for l(1) norm, total variation, and negativity to solve the inverse problem.

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Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time
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Published on: July 1, 2014

Related Experiment Videos

Last Updated: Jun 5, 2026

Simultaneous Data Collection of fMRI and fNIRS Measurements Using a Whole-Head Optode Array and Short-Distance Channels
08:19

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

Published on: October 20, 2023

Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time
07:12

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

Published on: July 1, 2014

  • Algorithm accuracy was evaluated using simulated data and experimental fMRI data (blocked and event-related designs).

    • Main Results:

    • The algorithm accurately identifies the occurrence of neuronal activity bursts from BOLD time series.
    • Simulations indicated sensitivity to contrast-to-noise ratio and hemodynamic model errors, but low sensitivity to noise autocorrelation.
    • The method proved effective for event detection under typical fMRI conditions.

    Conclusions:

    • The presented iterative algorithm offers an effective tool for detecting neuronal activity from BOLD signals.
    • This method enhances the precision of event detection in fMRI studies, particularly for event-related designs.
    • The algorithm's robustness to noise and its accuracy in simulations and real data support its utility in neuroscience research.