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[ENH]: Support "xi" correlation metric for functional connectivity #333

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synchon merged 17 commits from feat/xi-correlation-fc into main

2025-04-01 12:02:43 +00:00

synchon commented

2025-03-18 16:22:40 +00:00

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Are you requiring a new dataset or marker?

I understand this is not a marker or dataset request

Which feature do you want to include?

Adapt https://github.com/czbiohub-sf/xicor to be used as a correlation metric for functional connectivity markers.

How do you imagine this integrated in junifer?

As an option for nilearn's ConnectivityMeasure.

Do you have a sample code that implements this outside of junifer?

https://github.com/czbiohub-sf/xicor

Anything else to say?

No response

### Are you requiring a new dataset or marker? - [X] I understand this is not a marker or dataset request ### Which feature do you want to include? Adapt https://github.com/czbiohub-sf/xicor to be used as a correlation metric for functional connectivity markers. ### How do you imagine this integrated in junifer? As an option for nilearn's ConnectivityMeasure. ### Do you have a sample code that implements this outside of junifer? ```shell https://github.com/czbiohub-sf/xicor ``` ### Anything else to say? _No response_

synchon commented

2025-03-18 16:40:00 +00:00

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Went with https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.chatterjeexi.html#chatterjeexi as it doesn't require adding a new dependency to the project and eases the burden of maintenance.

fraimondo commented

2025-03-18 16:40:57 +00:00

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If the implementation is the same, then off we go!

I think we needed to allow for custom metrics for this to happen.

If the implementation is the same, then off we go! I think we needed to allow for custom metrics for this to happen.

synchon commented

2025-03-18 16:43:25 +00:00

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If the implementation is the same, then off we go!

Implementation is exact and tuned for scipy which is a win.

I think we needed to allow for custom metrics for this to happen.

The custom object is JuniferConnectivityMeasure which is in main for quite some time now. It also enabled us to add Spearman's correlation.

> If the implementation is the same, then off we go! Implementation is exact and tuned for scipy which is a win. > I think we needed to allow for custom metrics for this to happen. The custom object is `JuniferConnectivityMeasure` which is in main for quite some time now. It also enabled us to add Spearman's correlation.

codecov[bot] commented

2025-03-18 17:02:09 +00:00

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All modified and coverable lines are covered by tests ✅

Project coverage is 85.77%. Comparing base (621e056) to head (ad32c59).

Additional details and impacted files

@@            Coverage Diff             @@
##             main     #333      +/-   ##
==========================================
+ Coverage   85.74%   85.77%   +0.03%     
==========================================
  Files         133      133              
  Lines        5663     5675      +12     
  Branches      959      962       +3     
==========================================
+ Hits         4856     4868      +12     
  Misses        618      618              
  Partials      189      189

Flag	Coverage Δ
junifer	`85.77% <100.00%> (+0.03%)`	⬆️

Flags with carried forward coverage won't be shown. Click here to find out more.

Files with missing lines	Coverage Δ
...r/external/nilearn/junifer_connectivity_measure.py	`93.49% <100.00%> (+0.63%)`	⬆️
...ional_connectivity/functional_connectivity_base.py	`96.66% <100.00%> (+0.11%)`	⬆️

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2025-03-18 17:05:03 +00:00

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fraimondo (Migrated from github.com) approved these changes

2025-03-28 08:35:19 +00:00