1. Refining hemodynamic correction in in vivo wide-field fluorescent imaging through linear regression analysis
- Author
-
Jing Li, Fan Yang, Kathleen Zhang, Shiqiang Wu, James Niemeyer, Mingrui Zhao, Peijuan Luo, Nan Li, Rongxin Li, Dan Li, Weihong Lin, Jyun-you Liou, Theodore H. Schwartz, and Hongtao Ma
- Subjects
Wide-field fluorescent imaging ,Functional hemodynamic changes ,Noise subtraction method ,Linear regression ,Beer-lambert law ,Neurosciences. Biological psychiatry. Neuropsychiatry ,RC321-571 - 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.
- Published
- 2024
- Full Text
- View/download PDF