Refining hemodynamic correction in in vivo wide-field fluorescent imaging through linear regression analysis

Accurate interpretation of in vivo wide-field fluorescent imaging (WFFI) data requires precise separation of raw fluorescence signals into neural and hemodynamic components. The classical Beer-Lambert law-based approach, which uses concurrent 530-nm illumination to estimate relative changes in cerebral blood volume (CBV), fails to account for the scattering and reflection of 530-nm photons from non-neuronal components leading to biased estimates of CBV changes and subsequent misrepresentation of neural activity. This study introduces a novel linear regression approach designed to overcome this limitation. This correction provides a more reliable representation of CBV changes and neural activity in fluorescence data. Our method is validated across multiple datasets, demonstrating its superiority over the classical approach.

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Publication Details

Journal
NeuroImage
Published
2024-08-30
DOI
https://doi.org/10.1016/j.neuroimage.2024.120816
Primary Topic
Optical Imaging and Spectroscopy Techniques
Type
article
Field-Weighted Citation Impact
0.00

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article

Refining hemodynamic correction in in vivo wide-field fluorescent imaging through linear regression analysis

Jyun-you Liou, Fan Yang, Peijuan Luo, Theodore H. Schwartz et al.
NeuroImage
Optical Imaging and Spectroscopy Techniques
article

Refining hemodynamic correction in in vivo wide-field fluorescent imaging through linear regression analysis

Jyun-you Liou, Fan Yang, Peijuan Luo, Theodore H. Schwartz, Rongxin Li, Weihong Lin, James E. Niemeyer, Mingrui Zhao, Hongtao Ma, Shiqiang Wu, Nan Li, Jing Li, Kathleen Zhang, Dan Li
article en

Abstract

Accurate interpretation of in vivo wide-field fluorescent imaging (WFFI) data requires precise separation of raw fluorescence signals into neural and hemodynamic components. The classical Beer-Lambert law-based approach, which uses concurrent 530-nm illumination to estimate relative changes in cerebral blood volume (CBV), fails to account for the scattering and reflection of 530-nm photons from non-neuronal components leading to biased estimates of CBV changes and subsequent misrepresentation of neural activity. This study introduces a novel linear regression approach designed to overcome this limitation. This correction provides a more reliable representation of CBV changes and neural activity in fluorescence data. Our method is validated across multiple datasets, demonstrating its superiority over the classical approach.

NeuroImageVol. 299
NewYork–Presbyterian Hospital (US), Jilin University (CN), Cornell University (US), Presbyterian Hospital (US), First Hospital of Jilin University (CN)
National Natural Science Foundation of China, Jilin Scientific and Technological Development Program
Openalex Percentile: Top 77%
Optical Imaging and Spectroscopy Techniques
DOIOpen Access PDFOpenAlex
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