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Computing hemodynamic response functions from concurrent spectral fiber-photometry and fMRI data.
Chao, Tzu-Hao H; Zhang, Wei-Ting; Hsu, Li-Ming; Cerri, Domenic H; Wang, Tzu-Wen; Shih, Yen-Yu I.
Afiliação
  • Chao TH; University of North Carolina at Chapel Hill, Center for Animal MRI, Chapel Hill. North Carolina, United States.
  • Zhang WT; University of North Carolina at Chapel Hill, Biomedical Research Imaging Center, Chapel Hill. North Carolina, United States.
  • Hsu LM; University of North Carolina at Chapel Hill, Department of Neurology, Chapel Hill. North Carolina, United States.
  • Cerri DH; University of North Carolina at Chapel Hill, Center for Animal MRI, Chapel Hill. North Carolina, United States.
  • Wang TW; University of North Carolina at Chapel Hill, Biomedical Research Imaging Center, Chapel Hill. North Carolina, United States.
  • Shih YI; University of North Carolina at Chapel Hill, Department of Neurology, Chapel Hill. North Carolina, United States.
Neurophotonics ; 9(3): 032205, 2022 Jul.
Article em En | MEDLINE | ID: mdl-35005057
Significance: Although emerging evidence suggests that the hemodynamic response function (HRF) can vary by brain region and species, a single, canonical, human-based HRF is widely used in animal studies. Therefore, the development of flexible, accessible, brain-region specific HRF calculation approaches is paramount as hemodynamic animal studies become increasingly popular. Aim: To establish an fMRI-compatible, spectral, fiber-photometry platform for HRF calculation and validation in any rat brain region. Approach: We used our platform to simultaneously measure (a) neuronal activity via genetically encoded calcium indicators (GCaMP6f), (b) local cerebral blood volume (CBV) from intravenous Rhodamine B dye, and (c) whole brain CBV via fMRI with the Feraheme contrast agent. Empirical HRFs were calculated with GCaMP6f and Rhodamine B recordings from rat brain regions during resting-state and task-based paradigms. Results: We calculated empirical HRFs for the rat primary somatosensory, anterior cingulate, prelimbic, retrosplenial, and anterior insular cortical areas. Each HRF was faster and narrower than the canonical HRF and no significant difference was observed between these cortical regions. When used in general linear model analyses of corresponding fMRI data, the empirical HRFs showed better detection performance than the canonical HRF. Conclusions: Our findings demonstrate the viability and utility of fiber-photometry-based HRF calculations. This platform is readily scalable to multiple simultaneous recording sites, and adaptable to study transfer functions between stimulation events, neuronal activity, neurotransmitter release, and hemodynamic responses.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies Idioma: En Revista: Neurophotonics Ano de publicação: 2022 Tipo de documento: Article País de afiliação: Estados Unidos

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies Idioma: En Revista: Neurophotonics Ano de publicação: 2022 Tipo de documento: Article País de afiliação: Estados Unidos
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