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Expand Up @@ -29,243 +29,10 @@ and model evaluation/feature visualization.
.. image:: img/scope.png
:align: center

In what follows, we quickly summarize the main functionality of skbold.
For more information (e.g. API documentation) and examples, check out
the code documentation at `ReadTheDocs <skbold.readthedocs.io>`_!

Mvp-objects
-----------
One of skbold's main features is the data-structure ``Mvp`` (an abbreviation
of MultiVoxel Pattern). This custom object allows for an efficient way
to store and access data and metadata necessary for multivoxel analyses of fMRI data.
A nice feature of this ``Mvp`` objects is that they can easily load data
(i.e., sets of nifti-files) from disk and automatically organize it in
a format that is used in ML-analyses (i.e., a sample-by-feature matrix).

So, at the core, an ``Mvp``-object is simply a collection of data - a 2D array
of samples by features - and fMRI-specific metadata necessary to perform
customized preprocessing and feature engineering. However, machine learning
analyses, or more generally any type of multivoxel-type analysis (i.e. MVPA),
can be done in two basic ways, which provide the basis of the two 'flavors'
of ``Mvp``-objects: ``MvpWithin`` and ``MvpBetween``, as explained in more
detail below.

MvpWithin
~~~~~~~~~
One way is to perform analyses *within subjects*. This means that a model is
fit on each subjects' data separately. Data, in this context, often refers to
single-trial data, in which each trial comprises a sample in our data-matrix and
the values per voxel constitute our features. This type of analysis is
alternatively called *single-trial decoding*, and is often performed as an
alternative to (whole-brain) univariate analysis.

.. image:: img/MvpWithin.png
:align: center

Ultimately, this type of analysis aims to predict some kind of attribute of the
trials (for example condition/class membership in classification analyses or some
continuous feature in regression analyses), which skbold calls ``y``, based
on a model trained on the samples-by-features matrix, which skbold calls ``X``.
After obtaining model performance scores (such as accuracy, F1-score, or R-squared)
for each subject, a group-level random effects (RFX) analysis can be done on
these scores. Skbold does not offer any functionality in terms of group-level
analyses; we advise researchers to look into the `prevalance inference <http://www.sciencedirect.com/science/article/pii/S1053811916303470>`_ method of Allefeld and colleagues.

MvpBetween
~~~~~~~~~~
With the increase in large-sample neuroimaging datasets, another
type of MVPA starts to become feasible, which we'll call *between subject*
analyses. In this type of analysis, single subjects constitute the data's
samples and a corresponding single multivoxel pattern constitutes the data's
features. The type of multivoxel pattern, or 'feature-set', can be any set
of voxel values. For example, features from a single first-level contrast
(note: this should be a condition average contrast, as opposed to single-trial
contrasts in MvpWithin!) can be used. But voxel patterns from VBM, TBSS (DTI),
and dual-regression maps can equally well be used. Crucially, this package
allows for the possibility to stack feature-sets such that models can be fit
on features from multiple data-types simultaneously.

.. image:: img/MvpBetween.png

MvpResults: model evaluation and feature visualization
------------------------------------------------------
Given that an appropriate ``Mvp``-object exists, it is really easy to
implement a machine learning analysis using standard *scikit-learn*
modules. However, as fMRI datasets are often relatively small, K-fold
cross-validation is often performed to keep the training-set as large as
possible. Additionally, it might be informative to visualize which features
are used and are most important in your model. (But, note that feature mapping
should not be the main objective of decoding analyses!) Doing this - model
evaluation and feature visualization across multiple folds - complicates the
process of implementing machine learning pipelines on fMRI data.

The ``MvpResults`` object offers a solution to the above complications. Simply
pass your scikit-learn pipeline to MvpResults after every fold and it
automatically calculates a set of model evaluation metrics (accuracy,
precision, recall, etc.) and keeps track of which features are used and how
'important' these features are (in terms of the value of their weights).

Feature selection/extraction
----------------------------
The ``feature_selection`` and ``feature_extraction`` modules in skbold contain
a set of scikit-learn type transformers that can perform various types of
feature selection and extraction specific to multivoxel fMRI-data.
For example, the RoiIndexer-transformer takes a (partially masked) whole-brain
pattern and indexes it with a specific region-of-interest defined in a
nifti-file. The transformer API conforms to scikit-learn transformers, and as
such, (almost all of them) can be used in scikit-learn pipelines.

To get a better idea of the package's functionality - including the use of
Mvp-objects, transformers, and MvpResults - a typical analysis workflow using
``skbold`` is described below.

Examples
--------
For some example usages of the ``Mvp``-objects and how to incorporate them
in a ``scikit-learn``-based ML-pipeline, check the examples below:

An example workflow: MvpWithin
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Suppose you have data from an fMRI-experiment for a set of subjects who were
presented with images which were either emotional or neutral in terms of their
content. You've modelled them using a single-trial GLM (i.e. each trial is
modelled as a separate event/regressor) and calculated their corresponding
contrasts against baseline. The resulting FEAT-directory then contains
a directory ('stats') with contrast-estimates (COPEs) for each trial. Now,
using MvpWithin, it is easy to extract a sample by features matrix and some
meta-data associated with it, as shown below.

.. code:: python
from skbold.core import MvpWithin
feat_dir = '~/project/sub001.feat'
mask_file = '~/GrayMatterMask.nii.gz' # mask all non-gray matter!
read_labels = True # parse labels (targets) from design.con file!
remove_contrast = ['nuisance_regressor_x'] # do not load nuisance regressor!
ref_space = 'epi' # extract patterns in functional space (alternatively: 'mni')
statistic = 'tstat' # use the tstat*.nii.gz files (in *.feat/stats) as patterns
remove_zeros = True # remove voxels which are zero in each trial
mvp = MvpWithin(source=feat_dir, read_labels=read_labels,
remove_contrast=remove_contrast, ref_space=ref_space,
statistic=statistic, remove_zeros=remove_zeros,
mask=mask_file)
mvp.create() # extracts and stores (meta)data from FEAT-directory!
mvp.write(path='~/', name='mvp_sub001') # saves to disk!
Now, we have an Mvp-object on which machine learning pipeline can be applied:

.. code:: python
import joblib
from sklearn.preprocessing import StandardScaler
from sklearn.svm import SVC
from sklearn.pipeline import Pipeline
from sklearn.model_selection import StratifiedKFold
from sklearn.metrics import accuracy_score, f1_score
from skbold.feature_selection import fisher_criterion_score, SelectAboveCutoff
from skbold.feature_extraction import RoiIndexer
from skbold.postproc import MvpResults
mvp = joblib.load('~/mvp_sub001.jl')
roiindex = RoiIndexer(mvp=mvp,
mask='Amygdala',
atlas_name='HarvardOxford-Subcortical',
lateralized=False) # loads in bilateral mask
# Extract amygdala patterns from whole-brain
mvp.X = roiindex.fit().transform(mvp.X)
# Define pipeline
pipe = Pipeline([
('scaler', StandardScaler()),
('anova', SelectAboveCutoff(fisher_criterion_score, cutoff=5)),
('svm', SVC(kernel='linear'))
])
cv = StratifiedKFold(y=mvp.y, n_splits=5)
# Initialization of MvpResults; 'forward' indicates that it keeps track of
# the forward model corresponding to the weights of the backward model
# (see Haufe et al., 2014, Neuroimage)
mvp_results = MvpResults(mvp=mvp, n_iter=len(cv), feature_scoring='forward',
f1=f1_score, accuracy=accuracy_score)
for train_idx, test_idx in cv.split(mvp.X, mvp.y):
train, test = mvp.X[train_idx, :], mvp.X[test_idx, :]
train_y, test_y = mvp.y[train_idx], mvp.y[train_idx]
pipe.fit(train, train_y)
pred = pipe.predict(test)
mvp_results.update(test_idx, pred, pipeline=pipe) # update after each fold!
mvp_results.compute_scores() # compute!
mvp_results.write(out_path) # write file with metrics and niftis with feature-scores!
An example workflow: MvpBetween
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Suppose you have MRI data from a large set of subjects (let's say >50),
including (task-based) functional MRI, structural MRI (T1-weighted images,
DTI), and behavioral data (e.g. questionnaires, behavioral tasks). Such a
dataset would qualify for a *between subject* decoding analysis using the
MvpBetween object. To use the MvpBetween functionality effectively, it is
important that the data is organized sensibly. An example is given below.

.. image:: img/MvpBetween_dirstructure.png

In this example, each subject has three different data-sources: two FEAT-
directories (with functional contrasts) and one VBM-file. Let's say that we'd
like to use all of these sources of information together to predict some
behavioral variable, neuroticism for example (as measured with e.g. the
NEO-FFI). The most important argument passed to MvpBetween is ``source``.
This variable, a dictionary, should contain the data-types you want to extract
and their corresponding paths (with wildcards at the place of subject-specific
parts):

.. code:: python
import os
from skbold import roidata_path
gm_mask = os.path.join(roidata_path, 'GrayMatter.nii.gz')
source = dict(
Contrast_t1cope1={'path': '~/Project_dir/sub*/Task1.feat/cope1.nii.gz'},
Contrast_t2cope2={'path': '~/Project_dir/sub*/Task2.feat/cope2.nii.gz'},
VBM={'path': '~/Project_dir/sub*/vbm.nii.gz', 'mask': gm_mask}
)
Now, to initialize the MvpBetween object, we need some more info:

.. code:: python
from skbold.core import MvpBetween
subject_idf='sub-0??' # this is needed to extract the subject names to
# cross-reference across data-sources
subject_list=None # can be a list of subject-names to include
mvp = MvpBetween(source=source, subject_idf=subject_idf, mask=None,
subject_list=None)
# like with MvpWithin, you can simply call create() to start the extraction!
mvp.create()
# and write to disk using write()
mvp.write(path='~/', name='mvp_between') # saves to disk!
This is basically all you need to create a MvpBetween object! It is very
similar to MvpWithin in terms of attributes (including ``X``, ``y``, and
various meta-data attributes). In fact, MvpResults works exactly in the same
way for MvpWithin and MvpBetween! The major difference is that MvpResults
keeps track of the feature-information for each feature-set separately and
writes out a summarizing nifti file for each feature-set. Transformers also
work the same for MvpBetween objects/data, with the exception of the
cluster-threshold transformer.
Documentation
-------------
Please see skbold's `ReadTheDocs <skbold.readthedocs.io>`_ page for more
info on how to use skbold!

Installation & dependencies
---------------------------
Expand Down Expand Up @@ -294,16 +61,11 @@ for reading/writing nifti-files. Also, skbold uses `FSL <https://fsl.fmrib.ox.ac
``convert2epi`` and ``convert2mni`` functions, so avoid this functionality if
you don't have a working FSL installation.

Documentation
-------------
For those reading this on Github, documentation can be found on
`ReadTheDocs <skbold.readthedocs.io>`_!

Authors & credits
-----------------
This package is being develop by `Lukas Snoek <lukas-snoek.com>`_
from the University of Amsterdam with contributions from
`Steven Miletic<https://github.com/StevenM1>`_ and help from
`Steven Miletic <https://github.com/StevenM1>`_ and help from
`Joost van Amersfoort <https://github.com/y0ast>`_.

License and contact
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