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

被引:14
|
作者
Chao, Tzu-Hao H. [1 ,2 ,3 ]
Zhang, Wei-Ting [1 ,2 ,3 ]
Hsu, Li-Ming [1 ,2 ,3 ]
Cerri, Domenic H. [1 ,2 ,3 ]
Wang, Tzu-Wen [1 ,2 ]
Shih, Yen-Yu, I [1 ,2 ,3 ,4 ]
机构
[1] Univ N Carolina, Ctr Anim MRI, Chapel Hill, NC 27515 USA
[2] Univ N Carolina, Biomed Res Imaging Ctr, Chapel Hill, NC 27515 USA
[3] Univ N Carolina, Dept Neurol, Chapel Hill, NC 27515 USA
[4] Univ N Carolina, Dept Biomed Engn, Chapel Hill, NC 27515 USA
关键词
Hemodynamic response function; fMRI; Fiber-photometry; rat; multi-modal; MRI compatible; RESTING-STATE; BOLD-FMRI; NEURAL ACTIVITY; IMPULSE-RESPONSE; BLOOD-FLOW; NEGATIVE BOLD; MRI; CONNECTIVITY; VARIABILITY; DYNAMICS;
D O I
10.1117/1.NPh.9.3.032205
中图分类号
Q189 [神经科学];
学科分类号
071006 ;
摘要
Significance: EDITED FOR TESTING 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. (C) The Authors. Published by SPIE under a Creative Commons Attribution 4.0 International License.
引用
收藏
页数:25
相关论文
共 50 条
  • [21] Spatiotemporal hemodynamic response functions derived from physiology
    Aquino, K. M.
    Robinson, P. A.
    Drysdale, P. M.
    JOURNAL OF THEORETICAL BIOLOGY, 2014, 347 : 118 - 136
  • [22] A semi-parametric model of the hemodynamic response for multi-subject fMRI data
    Zhang, Tingting
    Li, Fan
    Beckes, Lane
    Coan, James A.
    NEUROIMAGE, 2013, 75 : 136 - 145
  • [23] Using patient-specific hemodynamic response functions in combined EEG-fMRI studies in epilepsy
    Kang, JK
    Bénar, CG
    Al-Asmi, A
    Khani, YA
    Pike, GB
    Dubeau, F
    Gotman, J
    NEUROIMAGE, 2003, 20 (02) : 1162 - 1170
  • [24] A State Space Based Approach in Non-Linear Hemodynamic Response Modeling with fMRI Data
    Hettiarachchi, Imali T.
    Pathirana, Pubudu N.
    Brotchie, Peter
    2010 ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC), 2010, : 2391 - 2394
  • [25] Hemodynamic response function in patients with stroke-induced aphasia: Implications for fMRI data analysis
    Bonakdarpour, B.
    Parrish, T. B.
    Thompson, C. K.
    NEUROIMAGE, 2007, 36 (02) : 322 - 331
  • [26] Using voxel-specific hemodynamic response function in EEG-fMRI data analysis
    Lu, Yingli
    Bagshaw, Andrew P.
    Grova, Christophe
    Kobayashi, Ellane
    Dubeau, Francois
    Gotman, Jean
    NEUROIMAGE, 2006, 32 (01) : 238 - 247
  • [27] Reproducible paired sources from concurrent EEG-fMRI data using BICAR
    Brown, Kevin S.
    Kasper, Ryan
    Giesbrecht, Barry
    Carlson, Jean M.
    Grafton, Scott T.
    JOURNAL OF NEUROSCIENCE METHODS, 2013, 219 (02) : 205 - 219
  • [28] Measuring fiber position errors from spectral data
    Chen, Jian-Jun
    Bai, Zhong-Rui
    Luo, A-Li
    Zhao, Yong-Heng
    RESEARCH IN ASTRONOMY AND ASTROPHYSICS, 2015, 15 (04) : 608 - 616
  • [29] Measuring fiber position errors from spectral data
    Jian-Jun Chen
    Zhong-Rui Bai
    A-Li Luo
    Yong-Heng Zhao
    ResearchinAstronomyandAstrophysics, 2015, 15 (04) : 608 - 616
  • [30] The hemodynamic response to somatosensory stimulation in mice depends on the anesthetic used: Implications on analysis of mouse fMRI data
    Schlegel, Felix
    Schroeter, Aileen
    Rudin, Markus
    NEUROIMAGE, 2015, 116 : 40 - 49