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Reorganize docs
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j1c committed Apr 8, 2024
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5 changes: 4 additions & 1 deletion .gitignore
Expand Up @@ -16,4 +16,7 @@ dist/
.pytest_cache/
_.py
_.ipynb
.idea
.idea

# sphinx documentation
docs/_build/*
8 changes: 4 additions & 4 deletions docs/diffusion.rst
Expand Up @@ -30,7 +30,7 @@ Output Summary Table
* - Tractography
- tensor field
- fiber tracts
-
-
* - Graph Generation
- fiber tracts, parcellations
- connectome
Expand All @@ -40,7 +40,7 @@ Output Summary Table
Output subject-level pipeline summary
=====================================

.. figure:: ../_static/ndmg-d-detailed-pipeline.jpg
.. figure:: ./_static/ndmg-d-detailed-pipeline.jpg
:align: left
:figwidth: 700px

Expand All @@ -58,7 +58,7 @@ The transform mapping the T1w volume to the template is then applied to the dMRI

Finally, `NDMG` produces a QA plot showing three slices of the first BO volume of the aligned dMRI image overlaid on the MNI152 template in the three principle coordinate planes (Figure S2).

.. figure:: ../_static/registration-qa.png
.. figure:: ./_static/registration-qa.png
:align: left
:figwidth: 700px

Expand All @@ -74,7 +74,7 @@ Once the dMRI volumes have been aligned to the template, `NDMG` begins diffusion

While high-dimensional diffusion models, such as orientation distribution functions (ODFs) or q-ball, enable reconstruction of crossing fibers and complex fiber trajectories, these methods are designed for images with a large number of diffusion volumes/directions for a given image. Because `NDMG` is designed to be robust across a wide range of dMRI studies, including diffusion tensor imaging, `NDMG` uses a lower-dimensional tensor model. The model, described in detail on DiPy_'s website, computes a 6-component tensor for each voxel in the image. This reduces the dMRI image stack to a single 6-dimensional image that can be used for tractography. `NDMG` generates a QA plot showing slices of the FA map derived from the tensors in nine panels, as below (Figure S3)

.. figure:: ../_static/tensor-qa.png
.. figure:: ./_static/tensor-qa.png
:align: left
:figwidth: 700px

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2 changes: 1 addition & 1 deletion docs/index.rst
Expand Up @@ -9,7 +9,7 @@ Overview of m2g

NeuroData’s MR Graphs package, `m2g`, is the successor of the MRCAP, MIGRAINE, and m2g pipelines. m2g combines dMRI, fMRI, and sMRI data from a single subject to estimate a high-level connectome reliably and scalably.

The m2g pipeline has been developed as a one-click solution for human connectome estimation by providing robust and reliable estimates of connectivity across a wide range of datasets. The pipelines are explained and derivatives analyzed in our pre-print, available on BiorXiv_.
The m2g pipeline has been developed as a one-click solution for human connectome estimation by providing robust and reliable estimates of connectivity across a wide range of datasets. The pipelines are explained and derivatives analyzed in our pre-print, available on BioRxiv_.

Free software
-------------
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137 changes: 137 additions & 0 deletions docs/license.rst
@@ -0,0 +1,137 @@
License
=======
m2g is distributed with Polyform license.

::

# PolyForm Noncommercial License 1.0.0

<https://polyformproject.org/licenses/noncommercial/1.0.0>

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