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[Preprint]. 2023 Jul 28:2023.07.21.550098.
doi: 10.1101/2023.07.21.550098.

FiPhA: An Open-Source Platform for Fiber Photometry Analysis

Matthew F Bridge  1 , Leslie R Wilson  2 , Sambit Panda  2 , Korey D Stevanovic  2 , Ayland C Letsinger  2 , Sandra McBride  1 , Jesse D Cushman  2
Affiliations

FiPhA: An Open-Source Platform for Fiber Photometry Analysis

Matthew F Bridge et al. bioRxiv. .

Update in

Abstract

Significance: Fiber photometry is a widely used technique in modern behavioral neuroscience, employing genetically encoded fluorescent sensors to monitor neural activity and neurotransmitter release in awake-behaving animals, However, analyzing photometry data can be both laborious and time-consuming.

Aim: We propose the FiPhA (Fiber Photometry Analysis) app, which is a general-purpose fiber photometry analysis application. The goal is to develop a pipeline suitable for a wide range of photometry approaches, including spectrally resolved, camera-based, and lock-in demodulation.

Approach: FiPhA was developed using the R Shiny framework and offers interactive visualization, quality control, and batch processing functionalities in a user-friendly interface.

Results: This application simplifies and streamlines the analysis process, thereby reducing labor and time requirements. It offers interactive visualizations, event-triggered average processing, powerful tools for filtering behavioral events and quality control features.

Conclusions: FiPhA is a valuable tool for behavioral neuroscientists working with discrete, event-based fiber photometry data. It addresses the challenges associated with analyzing and investigating such data, offering a robust and user-friendly solution without the complexity of having to hand-design custom analysis pipelines. This application thus helps standardize an approach to fiber photometry analysis.

Keywords: R; Shiny; calcium imaging; event processing; fiber photometry.

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Figures

Fig. 1
Fig. 1
Typical workflow for analysis with the FiPhA app.
Fig. 2
Fig. 2
Import options for data collected using spectrally resolved photometry systems. (A) The linear unmixing algorithm showing a GCaMP and tdTomato fluorescence peak as well as a reference spectrum. (B) Importing data using the summary statistic option with calculating the area under the curve. (C) Dataset Preview of a 900 second photometry recording session aligned with TTL pulses of 5 tone presentations.
Fig. 3
Fig. 3
Functions located within the Signal Analysis tab (A), power density spectrum (B), lag autocorrelation (C), and spectrogram.
Fig. 4
Fig. 4
Event-triggered averages for a mouse hearing 5 separate tone presentations. (A) Screenshot of events window depictingshowing events being created with a 3 second baseline and 5 seconds after the end of the tone. (B) Visualizations of the signal can be made using different normalization methods such as Δ delta F/F, z-score, and the robust z-score. Equations for computing these quantities are in Sec. 2.2.2.
Fig. 5
Fig. 5
Data analysis options for viewing event-triggered averages. (A) normalized z-score of the data traces of all event-triggered averages calculated during 5 tone presentations. (B) event heatmap of same 5 tone presentation. (C) Interval summaries depictingshowing box and whisker plots of the mean values of all events for each interval and normalization scheme.
Fig. 6
Fig. 6
Various export options within the FiPhA app. The user can export as an excel workbook or an R Data Format file to be used for further analysis. The export options allow theenable users to easily copy and paste the data into other graphing software.
Fig. 7
Fig. 7
A possible workflow for computing event-triggered averages in the FiPhA app using data collected from a mouse running on a wheel before and after having a drug manipulation. (A) Behavioral tracking (blue) and photometry signal (black) were collected from two separate recordings (before and after injection) and later joined within the FiPhA app (B). (C) Events were then computed with a 20 second baseline and various filtering options, including aggregating smaller running events. Two separate graphs were then created in FiPhA by plotting (D) a normalized and averaged trace graph of all wheel-running events and (E) a heatmap of each normalized running wheel event.
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