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Determining laminar neuronal activity from BOLD fMRI using a generative model.

Kamil Uludag1, Martin Havlicek2

  • 1Techna Institute & Koerner Scientist in MR Imaging, University Health Network, Toronto, Canada; Center for Neuroscience Imaging Research, Institute for Basic Science & Department of Biomedical Engineering, Sungkyunkwan University, Suwon, Republic of Korea.

Progress in Neurobiology
|April 30, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces a generative functional MRI (fMRI) model to accurately map brain activity across cortical depths. The model reveals that condition differences in laminar BOLD signals better reflect neuronal activity than single profiles.

Keywords:
Cognitive neuroscienceFunctional MRIHigh spatial resolutionLaminar fMRINeuronal activityPhysiological modeling

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Area of Science:

  • Neuroimaging
  • Human Brain Activity Mapping

Background:

  • Laminar functional MRI (fMRI) using Blood-Oxygen-Level-Dependent (BOLD) contrast allows non-invasive study of human brain circuits.
  • Cortical depth profiles of BOLD signals exhibit spatiotemporal biases, limiting accurate laminar neuronal activity assessment.

Purpose of the Study:

  • To develop a generative fMRI signal model that links cortical depth-dependent neuronal activity (excitatory and inhibitory) with BOLD signal sampling in finite voxels.
  • To investigate the accuracy of laminar BOLD signals in representing neuronal activity.

Main Methods:

  • Proposed a generative fMRI signal model incorporating neuronal activity and BOLD signal sampling.
  • Analyzed the relationship between neuronal activity and BOLD signal depth profiles.
  • Investigated the impact of angular dependence and BOLD signal sampling on laminar activity assessment.
  • Applied Bayesian model inversion to the generative model.

Main Results:

  • Condition differences in laminar BOLD signals may more accurately reflect neuronal activity than single-condition depth profiles.
  • Angular dependence of the BOLD signal can introduce variability, potentially masking true activity profiles.
  • Analysis requires consideration of multiple BOLD signal depths, even when neuronal interest is limited to a few depths.
  • Bayesian model inversion enhanced sensitivity and specificity for assessing depth-dependent neuronal changes.

Conclusions:

  • The generative model provides a framework for understanding laminar BOLD signal biases.
  • Recommendations include using the centroid method for displaying laminar BOLD data and employing Bayesian inversion for improved analysis.
  • Accurate laminar neuroimaging requires careful consideration of signal biases and advanced modeling techniques.