Release Name: GraphVar 2.0: Machine Learning

Release info GraphVar 2.0:

Background: We previously presented GraphVar as a user-friendly
MATLAB toolbox for comprehensive graph analyses of functional
brain connectivity. Here we introduce a comprehensive extension of
the toolbox allowing users to seamlessly explore easily customizable
decoding models across functional connectivity measures as
well as additional features.
New Method: GraphVar 2.0 provides machine learning (ML)
model construction, validation and exploration. Machine learning
can be performed across any combination of network measures
and additional variables, allowing for a flexibility in neuroimaging
Results: In addition to previously integrated functionalities, such
as network construction and graph-theoretical analyses of brain
connectivity with a high-speed general linear model (GLM), users
can now perform customizable ML across connectivity matrices,
network metrics and additionally imported variables. The new
extension also provides parametric and nonparametric testing of
classifier and regressor performance, data export, figure generation
and high quality export.
Comparison with existing methods: Compared to other existing
toolboxes, GraphVar 2.0 offers (1) comprehensive customization,
(2) an all-in-one user friendly interface, (3) customizable model
design and manual hyperparameter entry, (4) interactive results
exploration and data export, (5) automated cueing for modelling
multiple outcome variables within the same session, (6) an easy to
follow introductory review.
Conclusions: GraphVar 2.0 allows comprehensive, user-friendly
exploration of encoding (GLM) and decoding (ML) modelling
approaches on functional connectivity measures making big data
neuroscience readily accessible to a broader audience of neuroimaging
---> there is a preprint version of a corresponding new GraphVar ML articel on <---

---- Known Bugs on Windows and MAC with Matlab 2017 and ElasticNet machine learning -----------

This GraphVar version contains a compiled version of Glmnet for Matlab
provided here:
As this mex file was compiled under Matlab 2012a we experienced compatibility issues when
running ElasticNet models on MAC and Windows with newer Matlab versions (2017). On MAC the file
appears as invalid and on Windows ElasticNet may crash in some cases and cause Matlab to freeze.
If you experience these issues please downgrade to an older Matlab version or use Ubuntu/Linux systems
(here we did not experience any issues).
We highly appologize for this inconvenience and will try uploading a more compatible mex file with
the next major fix (within the next weeks).