GUI guide

The meanap-gui desktop app (PyQt6) mirrors the MATLAB App Designer interface: one tab per section of the pipeline. Parameters round-trip to and from a Params dataclass (meanap.params.Params, see the API reference) via each panel’s load()/save() methods, and can be saved/reloaded as JSON from the toolbar (New, Open params…, Save params…).

In a hurry?

Quickstart skips all of this via the 🧪 Test pipeline button, which fills in sensible defaults and the bundled example dataset automatically.

Paths

Where MEA-NAP reads your data from and writes results to.

Field	Description
MEA-NAP folder	Location of your MEA-NAP clone.
Raw data folder	Folder containing your recordings in .mat format. All recordings for one batch analysis should live in the same folder.
Spreadsheet file	.csv or .xlsx listing each recording’s filename, group, and age/DIV — see Setting up MEA-NAP for the required columns (that guide applies equally to the Python port).
Spreadsheet range	Which rows of the spreadsheet to read, e.g. A2:A100000 (1-indexed file lines, header = line 1).
Custom group order	Optional comma-separated group names (e.g. WT,KO) to control display/plot order instead of alphabetical.
Spike data folder	Only needed if you’re starting from step 2+ using previously-detected spike times instead of raw data.
Output data folder / Output folder name	Where results are written, and the name of the run’s output subfolder.
Previous analysis folder	Only needed when re-using a prior run (Use prior analysis on the Pipeline tab).

Recording

Sampling and hardware settings, used during spike detection and for mapping channels to spatial electrode coordinates.

Field	Description
Sampling frequency	The recording’s native sampling rate in Hz (e.g. 25000).
Downsample frequency	Rate used for some plots/metrics that don’t need full resolution (e.g. 1000).
Potential difference unit	uV, mV, or V — must match your raw data’s units.
Channel layout	Electrode grid layout: MCS60, Axion64, Mea256, or Custom. See MATLAB vs. Python for which layouts have confirmed coordinate parity.

Spike detection

Field	Description
Detect spikes	Whether to run spike detection at all (uncheck if step 1 was already run and you only want steps 2+).
Re-check previous spike data	Re-run detection checks against existing spike-time output without redetecting.
Relative thresholds	MAD-multiplier thresholds below the median, comma-separated (e.g. 3, 4, 5).
Absolute thresholds (µV)	Fixed voltage thresholds instead of relative ones — leave blank to use relative thresholds.
Wavelet methods	One or more of bior1.5, bior1.3, db2, mea (multi-select list).
Wavelet cost	Cost parameter for the continuous wavelet transform (default -0.12).
Spike method for analysis	Which detection method’s output feeds steps 2–4: bior1p5, bior1p3, mergedAll, mergedWavelet, thr4p5, thr5p0, thr3p5.
Low-pass / high-pass cutoff	Bandpass filter applied before detection (default 600–8000 Hz).
Refractory period	Minimum inter-spike interval (ms) enforced during detection.
Max spikes for template	Cap on spikes used to build the spike-shape template.
Multiple templates / Template method	Whether to cluster spikes into multiple templates, and by which method (PCA, spikeWidthAndAmplitude, amplitudeAndWidthAndSymmetry).

Which spike detection method should I use?

bior1.5 (a biorthogonal wavelet CWT) is MEA-NAP’s flagship method and the default spikes_method. The Python port’s wavelet detector currently reaches ~82–84% F1 agreement with MATLAB’s native CWT implementation (PyWavelets approximates the wavelet via a cascade algorithm rather than MATLAB’s exact one) — see MATLAB vs. Python. The simple threshold methods (thr4, thr5) match MATLAB exactly.

Connectivity

Functional connectivity via the spike time tiling coefficient (STTC) and its significance thresholding.

Field	Description
Lag values (ms)	One or more STTC synchronicity windows to compute, comma-separated (e.g. 10, 15, 25). Each lag produces its own adjacency matrix and downstream network metrics.
Truncate recording / Truncation length	Optionally analyze only the first N seconds of each recording (useful for very long recordings).
Weighted / Binary	Whether the adjacency matrix keeps STTC values as edge weights or collapses to a 0/1 connection.
Iterations	Number of circular-shift surrogates used for significance thresholding (default 200).
Tail percentile	Upper-tail cutoff for significance (default 0.05).
Plot random checks / Number of checks to plot	Optionally save diagnostic plots for a few random thresholding surrogates.

Why does step 3 take so long?

Probabilistic thresholding runs Iterations circular-shift surrogates per lag, per recording to build a null distribution for each edge — this is the dominant cost of a full pipeline run. It’s also inherently non-deterministic: even two MATLAB runs of the same recording won’t produce bit-identical thresholded matrices. See MATLAB vs. Python.

CAT-NAP (2P)

Calcium-imaging analysis, triggered by pointing the pipeline at a folder of suite2p output rather than raw MEA .mat files. Full walkthrough: CAT-NAP.

Network Viewer

Interactive exploration of a completed run’s functional connectivity network, with optional cell-type overlays. Full walkthrough: Network Viewer.

Pipeline

Run controls and step selection.

Field	Description
Start at step / Stop at step	Which of the 4 steps to run, inclusive (1–4). Moving one past the other drags the other along, so the range always stays valid.
Use prior analysis	Load results from Previous analysis folder (Paths tab) instead of recomputing.
Optional steps	Extra steps to run alongside the core 4, e.g. generateCSV.
Verbose level	Normal, Verbose, or Debug logging detail in the status log.
Time each step	Records per-step wall-clock time to step_durations.json in the output folder.

The four buttons under Run:

• 🧪 Test pipeline — downloads the bundled example dataset and runs the full pipeline against it (see Quickstart).

• ▶ Run pipeline — runs against whatever’s configured in Paths/Recording/etc.

• ■ Stop — cancels a running pipeline at the next step boundary.

• 🌐 View report — (re)generates report.html for the current output folder and opens it — see Output report.