MATLAB scripts to plot data from a fiber photometry recording.

• MATLAB (last tested with R2022a)

• Install MATLAB with the following toolboxes:
  • Curve Fitting Toolbox
  • Signal Processing Toolbox
  • Statistics and Machine Learning Toolbox
• Download and extract these scripts to Documents/MATLAB folder.

• Run setup.m to add FPA dependencies to the MATLAB search path.
• See and edit examples according to your experimental setup.

fpa = FPA(time, signal, reference, configuration);
fpa.plot();

The GUI is under development and has limitted functionality compared to the script.

GUI();

Remove baseline from data, correct from motion artifacts; normalize, filter, and detect peaks of spontaneous activity in user defined epochs.

Processing steps:

• Resample data to a given frequency.
• Replace artifacts with linear interpolation in flagged regions.
• Baseline correction modeled as an exponential decay of the low-pass filtered data.
• Correct for motion artifacts by subtracting reference to signal, after a polynomial fit.
• Remove fast oscillations with a low-pass filter.
• Normalize data as df/f or z-score according to settings.
• Find peaks of spontaneous activity in low-pass filtered data.
• Figure 1:
  • Raw signal and reference, and baseline model.
  • Baseline corrected signal and reference.
  • Motion correction.
  • Normalization.
  • Peaks.
• Figure 2:
  • Power spectrum for each epoch.
• Figure 3:
  • Boxplot.
• Remaining figures:
  • Peak triggers, event-triggers, epoch start-triggers, and epoch stop-triggers.
  • Triggered average of peaks (if any), event-triggers, epoch start, and epoch stop.

configuration is a struct with the following fields (defaults are used for missing fields):

• conditionEpochs - Epochs for different conditions: {'epoch1', [start1, end1, start2, end2, ...], 'epoch2', ...}
• baselineEpochs - Time epochs (s) to include for baseline correction.
• thresholdEpochs
• baselineLowpassFrequency - Frequency representative of baseline.
• airPLS - Baseline correction for all data using airPLS (true, false, or airPLS inputs).
• artifactEpochs - Time epochs (s) to remove.
• resamplingFrequency - Resampling frequency (Hz).
• lowpassFrequency - Lowest frequency permitted in normalized signal.
• peaksLowpassFrequency - Lowest frequency to detect peaks.
• triggeredWindow - Length of time to capture around each peak of spontaneous activity.
• fitReference - Shift and scale reference to fit signal.

Also see normalization and thresholding configurations below.

See source code for default values:

edit('FPA');

Units for time and frequency are seconds and hertz respectively.

Normalization is calculated as (f - f0) / f1 where f0 and f1 can be data provided by the user or calculated using given functions:

configuration.f0 = @mean;
configuration.f1 = @mean;

configuration.f0 = @mean;
configuration.f1 = @std;

configuration.f0 = @median;
configuration.f1 = @mad;

configuration.f0 = @median;
configuration.f1 = @std;

epochs = [0, 100, 500, 550, 1000, Inf]
configuration.f0 = {@mean, epochs};
configuration.f1 = {@mean, epochs};

window = 60;
configuration.f0 = {@movmean, window};
configuration.f1 = {@movmean, window};

[further combinations possible with @movmean, @movmedian, @movstd, @movmad, @mov...].

f0 = ones(size(time));
f1 = ones(size(time)) * 10;
configuration.f0 = f0;
configuration.f1 = f1;

Fluorescence deflections are considered peaks when they exceed a threshold calculated as k * f2 + f3 and they are provided by the user as configuration.threshold = {k, f2, f3}

  configuration.threshold = {2.91, @mad, @median}

  configuration.threshold = {2.91, @mad, 0}

  configuration.threshold = {2.00, @std, @mean}

[data, names] = loadData(filename)

Returns a data matrix where columns correspond to channels listed in names, that is, time followed by other data columns.

Expected format #1:

Expected format #2:

[data, names, sheetName] = loadData(filename, <sheetNumber|sheetName>)

Returns a data matrix where columns correspond to channels listed in names. A sheet number or a sheet name can be passed as the second parameter (default is the first sheet in the document). The sheet name is also returned.

Expected format for each sheed in a document:

[data, units, names] = loadABF(filename)

[data, names] = loadTDT(folder)

Parses a project folder stored by a TDT DAQ and returns a data matrix where columns correspond to channels listed in names.

[time, data, units, names, comments] = loadLabChart(filename)

Loads a mat file exported by LabChart in an intuitive manner.

[time, data, units, names] = loadAdicht(filename)

Loads an aditch file.

Data are organized in 2D cells where row indices select channels and column indices select blocks:

    time{channel, block}
    data{channel, block}

Units are organized in a cell array where row indices select channels and column indices select blocks:

• Data units:

    units{channel, block}

Channel names are organized in a cell array:

    names{channel}

ttl = loadInscopixTTL(filename)

Returns timestamps where pin IO1 changes from low to high state.

[labels, start, finish, distance, speed] = loadCleverSys(filename, <sheet>)
epochs = loadCleverSys(filename, <sheet>)

Returns a list of event epochs generated by CleverSys.

[labels, start, stop] = loadBoris(filename)
epochs = loadBoris(filename)

Returns a list of event epochs scored with BORIS.

See Changelog

© 2018 Leonardo Molina

This project is licensed under the GNU GPLv3 License.