A Statistical Framework for Analysis of Trial-Level Temporal Dynamics in Fiber Photometry Experiments.

ABSTRACT: Fiber photometry has become a popular technique to measure neural activity in vivo, but common analysis strategies can reduce detection of effects because they condense within-trial signals into summary measures, and discard trial-level information by averaging across-trials. We propose a novel photometry statistical framework based on functional linear mixed modeling, which enables hypothesis testing of variable effects at every trial time-point, and uses trial-level signals without averaging. This makes it possible to compare the timing and magnitude of signals across conditions while accounting for between-animal differences. Our framework produces a series of plots that illustrate covariate effect estimates and statistical significance at each trial time-point. By

SUBMITTER: Loewinger G 

PROVIDER: S-EPMC10659337 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature