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Computing hemodynamic response functions from concurrent spectral fiber-photometry and fMRI data.

Tzu-Hao H Chao1,2,3, Wei-Ting Zhang1,2,3, Li-Ming Hsu1,2,3

  • 1University of North Carolina at Chapel Hill, Center for Animal MRI, Chapel Hill. North Carolina, United States.

Neurophotonics
|January 10, 2022
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Summary

Researchers developed a fiber-photometry platform to calculate brain-region specific hemodynamic response functions (HRFs) in rats. This method improves functional magnetic resonance imaging (fMRI) analysis by using empirical HRFs over the standard canonical HRF.

Keywords:
Fiber-photometryHemodynamic response functionMRI compatiblefMRImulti-modalrat

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Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Neuroimaging

Background:

  • The canonical hemodynamic response function (HRF) is widely used in animal fMRI studies, despite evidence of regional and species-specific variations.
  • Developing brain-region specific HRF calculation methods is crucial for advancing hemodynamic animal studies.

Purpose of the Study:

  • To establish a functional magnetic resonance imaging (fMRI)-compatible, spectral, fiber-photometry platform for calculating and validating hemodynamic response functions (HRFs) in any rat brain region.
  • To compare the performance of empirically derived HRFs against the canonical HRF in animal fMRI data analysis.

Main Methods:

  • Simultaneous measurement of neuronal activity (GCaMP6f), local cerebral blood volume (CBV) using Rhodamine B dye, and whole-brain CBV via fMRI with Feraheme contrast agent.
  • Calculation of empirical HRFs using fiber photometry recordings from various rat cortical regions during resting-state and task-based paradigms.

Main Results:

  • Empirical HRFs were calculated for multiple rat cortical areas, revealing they are faster and narrower than the canonical HRF.
  • No significant differences were found between the empirical HRFs of different cortical regions.
  • Empirical HRFs demonstrated superior detection performance compared to the canonical HRF in general linear model analyses of fMRI data.

Conclusions:

  • Fiber-photometry-based HRF calculations are viable and effective for improving fMRI analysis in animal models.
  • The developed platform is scalable for multi-site recordings and adaptable for studying complex neurovascular coupling dynamics.