FiPhoPHA

FiPhoPHA provides post hoc statistical analysis tools for fiber photometry data in Python.

Key Features:

• Downsampling: Reduces data density for manageable analysis of continuous fiber photometry recordings.
• Bootstrapped Confidence Intervals: Generates CIs for analyzing peri-event signals between groups and compared with baseline.
• Permutation Tests: Enables comparison of peri-event signals across comparison periods.
• Mean Time Binning: Sorts data into mean time bins as an optional analysis approach.

Scientific Applications:

• Neural Activity Monitoring: Analyzes population neural activity data collected via fiber photometry in freely behaving animals.
• Neuropeptide and Transmitter Release Analysis: Assesses in vivo fluorescence signals reflecting neuropeptide or transmitter release events.
• Peri-Event Signal Comparison: Compares peri-event signals between experimental groups and against baseline periods.

Methodology:

The package implements downsampling, bootstrapping for confidence interval estimation, permutation testing, and mean time binning as computational methods for statistical analysis of fiber photometry data.