neuroHarmonize 2.4.5
Harmonization tools for multi-center neuroimaging studies.
Requires Python: >=3.6

# neuroHarmonize

Harmonization tools for multi-site neuroimaging analysis. Part of the work
reported in our paper with data from the ISTAGING consoritum [^1].

**If you have identified a bug or are requesting a new feature, please submit an [issue](https://github.com/rpomponio/neuroHarmonize/issues) on GitHub so that progress can be tracked.**

contact (see note above): raymond (dot) pomponio (at) outlook (dot) com

Overview
---------

This package extends the functionality of the package developed by Jean-Philippe Fortin and Nick Cullen [^2], `neuroCombat`. **New in version 2.4.x:** `neuroCombat` is now a formal dependency.

The reference implementation, `neuroCombat`, allows the user to perform a
harmonization procedure using the ComBat [^3] algorithm for correcting
multi-site data.

This package, `neuroHarmonize`, provides similar functionality, with
additional features:

1. Support for working with NIFTI images, with the `nibabel` package.
2. Separate train/test datasets.
3. Specify covariates with generic nonlinear effects. Implemented using
   Generalized Additive Models (GAMs) from the `statsmodels` package.
4. Skip the empirical Bayes (EB) step of ComBat, if desired.

Installation
------------

Latest development version: `2.4.5` (in dev mode)

Requirements:

* `git >= 2.17.2`
* `python >= 3.6.8`

**Option 1: Install from PyPI (Stable Version)**

   ```
   pip install neuroHarmonize
   ```

**Option 2: Install from GitHub (Development Version)**

   ```
   pip install git+https://github.com/rpomponio/neuroHarmonize
   ```

Quick Start
-----------

*Please note:* the ComBat [^3] algorithm corrects for site effects but
intentionally preserves covariate effects. If you wish to remove covariate
effects as well you can use the argument `return_s_data`.

You must provide a **data matrix** which is a `numpy.array` containing the
features to be harmonized. For example, an `array` of brain volumes:

```

  array([[3138.0, 3164.2,  ..., 206.4],
         [1708.4, 2351.2,  ..., 364.0],
         ...,
         [1119.6, 1071.6,  ..., 326.6]])
```

The dimensionality of this matrix must be: **N_samples x N_features**

You must also provide a **covariate matrix** which is a `pandas.DataFrame`
containing all covariates to control for during harmonization. All covariates
must be encoded numerically (you must handle categorical covariates in a
pre-processing step, see `pandas.get_dummies`). The `DataFrame` must
also contain a single column called "SITE" with labels that identify sites
(the labels in "SITE" need not be numeric).

```

       SITE   AGE  SEX_M
  0  SITE_A  76.5      1
  1  SITE_B  80.1      1
  2  SITE_A  82.9      0
  ...   ...   ...    ...
  9  SITE_B  82.1      0
```

The dimensionality of this dataframe must be: **N_samples x N_Covariates**

The order of samples must be identical in the **covariate_matrix** and the
**data_matrix**.

After preparing both inputs, you can call `harmonizationLearn` to harmonize
the provided dataset.

Example usage:

```python
from neuroHarmonize import harmonizationLearn
import pandas as pd
import numpy as np
# load your data and all numeric covariates
my_data = pd.read_csv('brain_volumes.csv')
my_data = np.array(my_data)
covars = pd.read_csv('subject_info.csv')
# run harmonization and store the adjusted data
my_model, my_data_adj = harmonizationLearn(my_data, covars)
```

The dimensionality of the matrix `my_data_adj` will be identical to
`my_data`: **N_samples x N_features**

Working with NIFTI Images
-------------------------

To work with NIFTI images, you compute a mask using a `pandas.DataFrame` which
contains file paths for all of the images in the **training set**.

```python
import pandas as pd
import numpy as np
from neuroHarmonize.harmonizationNIFTI import createMaskNIFTI
nifti_list = pd.read_csv('brain_images_paths.csv')
nifti_avg, nifti_mask, affine, hdr0 = createMaskNIFTI(nifti_list, threshold=0)
```

After the mask is created, you can flatten the images to a 2D `numpy.array`
very similar to what is done with the tabular data example above.

```python
from neuroHarmonize.harmonizationNIFTI import flattenNIFTIs
nifti_array = flattenNIFTIs(nifti_list, 'thresholded_mask.nii.gz')
```

The next step is identical to working with tabular data. You simply pass the 2D
array to `neuroHarmonize.harmonizationLearn`.

```python
import neuroHarmonize as nh
covars = pd.read_csv('subject_info.csv')
my_model, nifti_array_adj = nh.harmonizationLearn(nifti_array, covars)
nh.saveHarmonizationModel(my_model, 'MY_MODEL')
```

Lastly, you can apply the model sequentially to images in a larger dataset with
`applyModelNIFTIs`. When performing NIFTI harmonization, loading the entire set
of images may exceed memory capacity. This function will reduce the burden on
memory by applying the model to images one-by-one and saving the results as NIFTIs.

```python
from neuroHarmonize.harmonizationNIFTI import applyModelNIFTIs
# load pre-trained model
my_model = nh.loadHarmonizationModel('MY_MODEL')
applyModelNIFTIs(covars, my_model, nifti_list, 'thresholded_mask.nii.gz')
```

Applying Pre-Trained Models to New Data
---------------------------------------

This feature allows you to train a harmonization model on a subset of data, then
apply the model to the entire set. For example, in longitudinal analyses, one may
wish to train a harmonization model on baseline cases and apply the model to
follow-up cases, to avoid double-counting subjects.

If you have previously trained a harmonization model with `harmonizationLearn`,
you may apply the model parameters to new data with `harmonizationApply`.

First load the model:

```python
from neuroHarmonize import harmonizationApply, loadHarmonizationModel
import pandas as pd
import numpy as np
# load a pre-trained model
my_model = loadHarmonizationModel('../models/my_model')
```

Next, prepare the holdout data on which you will apply the model. This data
must look exactly like the training data for `harmonizationLearn`, including
the same number and order of covariates. If the holdout data contains a
different number of sites, an error will be thrown.

After preparing the holdout data simply apply the model:

```python
df_holdout = pd.read_csv('../data/brain_volumes_holdout.csv')
my_holdout_data = np.array(df_holdout)
covars = pd.read_csv('subject_info_holdout.csv')
my_holdout_data_adj = harmonizationApply(my_holdout_data, covars, my_model)
```

Specifying Nonlinear Covariate Effects
--------------------------------------

You may specify nonlinear covariate effects with the optional argument:
`smooth_terms`. For example, you may want to specify age as a nonlinear
term in the harmonization model, if age exhibits nonlinear relationships with
brain volumes. This can be done easily with `harmonizationLearn`:

```python
from neuroHarmonize import harmonizationLearn
import pandas as pd
import numpy as np
# load your data and all numeric covariates
my_data = pd.read_csv('brain_volumes.csv')
my_data = np.array(my_data)
covars = pd.read_csv('subject_info.csv')
# run harmonization with NONLINEAR effects of age
my_model, my_data_adj = harmonizationLearn(data, covars, smooth_terms=['AGE'])
```

When applying nonlinear models to holdout data, you may get an error: "some data
points fall outside the outermost knots, and I'm not sure how to handle them".
This is documented: https://github.com/statsmodels/statsmodels/issues/2361.
The current workaround is to use the optional argument: `smooth_term_bounds`,
which controls the boundary knots for nonlinear estimation. You should specify
boundaries that contain the limits of the entire dataset, including holdout data.

Empirical Bayes
---------------

Note the default behavior is to run the empirical Bayes (EB) step of ComBat, which
is useful for harmonizing multiple features that are similar such as genes or
brain regional volumes.

To run without EB, simply pass the optional argument `eb=False` to
`harmonizationLearn`. This is convenient when harmonizing a small number of
features, e.g. fewer than 10.

When `eb=True`, ComBat uses Empirical Bayes to fit a prior distribution for
the site effects for each site. You may wish to visualize fit of the prior
distribution, along with the observed distribution of site effects. The following
code example plots both distributions for the location effect of site 1.

```python
import matplotlib.pyplot as plt
import seaborn as sns
from neuroHarmonize import loadHarmonizationModel
model = loadHarmonizationModel('../models/my_model')
site_01 = stats.norm.rvs(size=10000, loc=model['gamma_bar'][0], scale=np.sqrt(model['t2'][0]))
sns.kdeplot(site_01, color='blue', label='Site-1-prior')
sns.kdeplot(model['gamma_hat'][0, :], color='blue', label='Site-1-observed', linestyle='--')
plt.show()
```

![Fig 1](figure_1.png)

Reference Sites
---------------

Recently other implementations have been including the ability to specify a
reference site or scanner, such that all data will be harmonized to that site.

**This feature was added in version 2.4.x.** It can be used as in the following
example:

```python
from neuroHarmonize import harmonizationLearn
import pandas as pd
import numpy as np
# load your data and all numeric covariates
my_data = pd.read_csv('brain_volumes.csv')
my_data = np.array(my_data)
covars = pd.read_csv('subject_info.csv')
# run harmonization and store the adjusted data
my_model, my_data_adj = harmonizationLearn(my_data, covars, ref_batch='SITE_A')
```

Please submit any issues to [GitHub](https://github.com/rpomponio/neuroHarmonize/issues)
if you run into trouble with this feature, as I am still testing its utility.

Citations
---------

[^1]: Pomponio, R., Shou, H., Davatzikos, C., et al., (2019).
   "Harmonization of large MRI datasets for the analysis of brain imaging
   patterns throughout the lifespan." Neuroimage 208.
   https://doi.org/10.1016/j.neuroimage.2019.116450.
[^2]: Fortin, J. P., N. Cullen, Y. I. Sheline, W. D. Taylor, I. Aselcioglu,
   P. A. Cook, P. Adams, C. Cooper, M. Fava, P. J. McGrath, M. McInnis,
   M. L. Phillips, M. H. Trivedi, M. M. Weissman and R. T. Shinohara (2017).
   "Harmonization of cortical thickness measurements across scanners and sites."
   Neuroimage 167: 104-120. https://doi.org/10.1016/j.neuroimage.2017.11.024.
[^3]: W. Evan Johnson and Cheng Li, Adjusting batch effects in microarray expression data using empirical Bayes methods. Biostatistics, 8(1):118-127, 2007. https://doi.org/10.1093/biostatistics/kxj037.