  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 applications.
  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 investigators.
