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

Neuroimage. 2024 Oct 1:299:120816. doi: 10.1016/j.neuroimage.2024.120816. Epub 2024 Aug 30.

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.

Keywords: Beer-lambert law; Functional hemodynamic changes; Linear regression; Noise subtraction method; Wide-field fluorescent imaging.

MeSH terms

  • Animals
  • Brain / blood supply
  • Brain / diagnostic imaging
  • Cerebral Blood Volume / physiology
  • Cerebrovascular Circulation / physiology
  • Hemodynamics* / physiology
  • Humans
  • Image Processing, Computer-Assisted / methods
  • Linear Models
  • Male
  • Optical Imaging / methods