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Related Experiment Video

Updated: Jun 26, 2026

Functional Mapping with Simultaneous MEG and EEG
06:04

Functional Mapping with Simultaneous MEG and EEG

Published on: June 14, 2010

Identifying neural drivers with functional MRI: an electrophysiological validation.

Olivier David1, Isabelle Guillemain, Sandrine Saillet

  • 1INSERM, U836, Grenoble Institut des Neurosciences, Grenoble, France. odavid@ujf-grenoble.fr

Plos Biology
|December 26, 2008
PubMed
Summary

Functional magnetic resonance imaging (fMRI) can identify neural drivers by analyzing hidden brain states, not just raw signals. This study demonstrates improved brain connectivity estimation from fMRI in an epilepsy model.

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Area of Science:

  • Neuroscience
  • Neuroimaging
  • Epilepsy Research

Background:

  • Identifying neural drivers is crucial for understanding brain function and dysfunction.
  • Functional magnetic resonance imaging (fMRI) is a key tool, but its ability to pinpoint neural drivers is debated.
  • Absence epilepsy provides a model to study brain dynamics and connectivity.

Purpose of the Study:

  • To investigate whether fMRI can identify neural drivers of brain activity.
  • To compare fMRI-derived connectivity with electroencephalography (EEG) measures in an epilepsy model.
  • To explore methods for improving interregional coupling estimation from fMRI data.

Main Methods:

  • Simultaneous electroencephalography (EEG) and fMRI were performed in a rat model of absence epilepsy.

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Functional Near Infrared Spectroscopy of the Sensory and Motor Brain Regions with Simultaneous Kinematic and EMG Monitoring During Motor Tasks
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Last Updated: Jun 26, 2026

Functional Mapping with Simultaneous MEG and EEG
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Published on: June 14, 2010

A Multimodal Imaging- and Stimulation-based Method of Evaluating Connectivity-related Brain Excitability in Patients with Epilepsy
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A Multimodal Imaging- and Stimulation-based Method of Evaluating Connectivity-related Brain Excitability in Patients with Epilepsy

Published on: November 13, 2016

Functional Near Infrared Spectroscopy of the Sensory and Motor Brain Regions with Simultaneous Kinematic and EMG Monitoring During Motor Tasks
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  • Intracerebral EEG (iEEG) recordings were obtained from key brain regions (S1BF, thalamus, striatum).
  • fMRI connectivity was analyzed using raw time series and hidden state variables with Granger causality and Dynamic Causal Modelling, compared against iEEG-derived coupling.

    • Main Results:

    • Direct functional connectivity analysis of fMRI signals failed due to regional hemodynamic variations.
    • Estimating neural drivers from fMRI was successful only after explicitly removing hemodynamic effects.
    • Connectivity estimation improved when analyzing hidden neural states within fMRI data.

    Conclusions:

    • Standard fMRI connectivity analysis is insufficient for identifying neural drivers when hemodynamics vary.
    • Analyzing hidden neural states in fMRI data offers a promising approach to improve connectivity estimation.
    • This study provides experimental evidence for enhancing brain connectivity studies using functional neuroimaging.