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Blood Flow Imaging with Ultrafast Doppler
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Extremely fast pRF mapping for real-time applications.

Salil Bhat1, Michael Lührs2, Rainer Goebel3

  • 1Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, the Netherlands; Maastricht Brain Imaging Centre, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, the Netherlands.

Neuroimage
|October 28, 2021
PubMed
Summary
This summary is machine-generated.

We developed a fast population receptive field (pRF) mapping method using hashed-Gaussian encoding. This technique significantly reduces computation time, enabling real-time brain-computer interface applications for decoding visual information from brain activity.

Keywords:
Population receptive field mappingReal-time fMRIStimulus encodingVision

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Area of Science:

  • Computational neuroimaging
  • Neuroscience
  • Brain-computer interfaces

Background:

  • Population receptive field (pRF) mapping analyzes receptive field properties and topography.
  • Inverting pRFs enables stimulus reconstruction from brain activity, crucial for brain-computer interfaces (BCIs).
  • Current pRF mapping is computationally intensive and time-consuming, hindering real-time applications.

Purpose of the Study:

  • To develop a novel, fast pRF mapping procedure suitable for real-time applications.
  • To reduce the computational burden of pRF mapping.
  • To enable direct decoding of visualized information for advanced BCIs.

Main Methods:

  • Introduced a novel pRF mapping procedure utilizing hashed-Gaussian encoding.
  • Employed ridge regression for offline analysis and gradient descent for real-time applications.
  • Validated the model-agnostic approach using in silico simulations and empirical fMRI data (3T and 7T).

Main Results:

  • The proposed method significantly reduces computational resources.
  • Achieved estimation of receptive fields and parameters for millions of voxels in seconds.
  • Demonstrated feasibility for real-time processing.

Conclusions:

  • The novel hashed-Gaussian encoding significantly accelerates pRF mapping.
  • This fast mapping facilitates real-time applications, including pRF-based BCIs.
  • The method is validated and suitable for high-throughput neuroimaging analysis.