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Calcium Signal Extraction with CaliAli

After confirming alignment quality, you can proceed to CNMF-E extraction.

Step 1: Set initialization thresholds

Use the initialization GUI to set min_corr and min_pnr:

CaliAli_set_initialization_parameters(CaliAli_options)
Check_initialization_parameters(CaliAli_options)

Adjust thresholds if the seed preview is too sparse or too dense.

Step 2: Run CNMF-E

Run:

CaliAli_cnmfe()

This opens a file selector and processes one file at a time. For scripted usage without a picker, see FAQ.

How does CaliAli deconvolve calcium signals?

CaliAli employs the original FOOPSI method with an AR(1) autoregressive model for initialization and matrix factorization (which is faster). During the final post-processing of traces, a thresholded FOOPSI approach with an AR(2) model is utilized (which is slower but more accurate). Learn more in the OASIS documentation.

During extraction, CaliAli writes checkpoint files you can reload to continue analysis.

The checkpoint files will be created as follows: 

.
└─ <"file_name">_aligned_source_extraction/
   └─ frames_<"xxx">/
      └─ LOGS_<"DATE">/
         ├─ <"DATE-TIME">.mat "Checkpoint #1"
         ├─ <"DATE-TIME">.mat "Checkpoint #2"
         ├─ <"DATE-TIME">.mat "Checkpoint #3"

You can easily monitor the extracted calcium transients by running view_traces(neuron)

Post-processing detected components

CaliAli includes a GUI to label false positives. After loading neuron from a checkpoint:

ix = postprocessing_app();
neuron.delete(ix);

You can inspect flagged components with neuron.viewNeurons(find(ix), neuron.C_raw);.

Merging components

After deleting false positives, you can merge overlapping components:

neuron.merge_high_corr(1, [0.1, 0.3, -inf]);

Note that you can create a new checkpoint at any point running save_workspace(neuron);

Picking Neurons from Residual

Some neurons may remain un-extracted after the first pass. To recover candidates, run manually_update_residuals().

Preparing extracted signals for analysis

CaliAli performs final detrending and noise scaling automatically at the end of the pipeline. Related functions:

You have successfully extracted neuronal signals using CaliAli. Don't forget to save the results with save_workspace(neuron)