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https://github.com/spinicist/nanslice

A Python implementation of 'dual-coding'
https://github.com/spinicist/nanslice

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A Python implementation of 'dual-coding'

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README 

        
# Not Another Neuroimaging Slicer - nanslice #



Credit / Blame / Contact - Tobias Wood - [email protected]



This Source Code Form is subject to the terms of the Mozilla Public

License, v. 2.0. If a copy of the MPL was not distributed with this

file, You can obtain one at http://mozilla.org/MPL/2.0/.

 

If you find the tools useful the author would love to hear from you.



# Brief Description #



![Screenshot](doc/dualcode.png)



This is a pure Python module for creating slices through neuro-imaging datasets.

The main motivation for building this was to implement the 'Dual-Coding'

visualisation method that can be found in this paper: 

http://dx.doi.org/10.1016/j.neuron.2012.05.001. However, it then expanded to

include standard visualisation methods, and an interactive viewer for Jupyter

notebooks.



Documentation can be found at https://nanslice.readthedocs.io/en/latest/.



A Jupyter Notebook demonstrating the module can be found at https://mybinder.org/v2/gh/spinicist/nanslice/master?filepath=doc%2Fexample.ipynb.

When using a Jupyter Notebook (as a standalone) or in VS Code, you need to choose the `widget` backend:



```python

import matplotlib

%matplotlib widget

```



Unfortunately, the `widget` backend is not supported yet in [PyCharm](https://youtrack.jetbrains.com/issue/PY-38292) or [Colab](https://github.com/googlecolab/colabtools/issues/86).



In dual-coding instead of plotting thresholded blobs of T-statistics or p-values

on top of structural images, transparency (or alpha) is used to convey the 

p-value of T-statistic, while color can be used to convey the effect size or

difference in group means etc. Finally, contours can be added at a specific

p-value, e.g. p < 0.05. In this way, 'dual-coded' overlays contain all the

information that standard overlays do, but also show much of the data that is

'hidden' beneath the p-value threshold.



Whether you think this is useful or not will depend on your attitude towards

p-values and thresholds. Personally, I think that sub-threshold but

anatomically plausible blobs are at least worth *showing* to readers, who can

then make their own mind up about significance.



This is a sister project to https://github.com/spinicist/QUIT. I mainly work

with quantitative T1 & T2 maps, where group mean difference or "percent change"

is a meaningful, well-defined quantity. If you use these tools to plot "percent

BOLD signal change", I hope you know what you what you are doing and wish you

luck with your reviewers.



# Installation #



NaNSlice is available on `PyPI`. Run `pip install nanslice` to install the

stable version. Alternatively, clone the repository from Github and then run

`pip install -e .` to use the development version.



# Performance #



These are Python scripts. The core sampling/blending code was written over 3

evenings while on the Bruker programming course. Most of nanviewer was written

in literally 4 hours across a Monday and Tuesday. After a refactoring, it is

surprisingly responsive on my MacBook. The Jupyter viewer, on the other hand,

is not wildly performant. Patches are welcome!