GraphVar: A user-friendly toolbox for comprehensive graph analyses of functional brain connectivity Releases

graphvar GraphVar_2.03a

johndkk@www.nitrc.org (Johann Kruschwitz) — Fri, 28 Aug 2020 6:39:00 GMT

Release info GraphVar 2.03a:

- removed a bug for the selection of the within subj ID buttom (callback went missing in the previous release - sorry!)

graphvar GraphVar_2.03

johndkk@www.nitrc.org (Johann Kruschwitz) — Thu, 09 Apr 2020 14:06:00 GMT

Release info GraphVar 2.03:

- included network construction methods as provided in BrainNetClass (i.e., high-order FC, FC based on sparse representations, FC based on group sparse representations)
- when using these embedded methods please cite the corresponding paper from Zhou et al., 2020

graphvar GraphVar 2.02b

johndkk@www.nitrc.org (Johann Kruschwitz) — Wed, 20 Nov 2019 15:08:00 GMT

Release info GraphVar 2.02b:

- removed a bug for the case of calculation on raw matrices with parametric p-values and FDR correction that entered the last release. For some scenarios this resulted in too liberal control of false positives.
Other settings were not affected
- removed a bug for local path lenght (global was claulated instead)

graphvar GraphVar 2.02

johndkk@www.nitrc.org (Johann Kruschwitz) — Mon, 15 Apr 2019 12:51:00 GMT

Release info GraphVar 2.02:

- added community functionalities as in Fornito et al., 2012: https://www.pnas.org/content/109/31/12788
- added dynamic network measures as in Seizemore et al., 2017: https://www.sciencedirect.com/science/article/pii/S1053811917305645
- fixed a bug when displaying R2 values (issues with the X axis)
- added 'Result', '-v7.3' to execute stats (i.e., now any size of result can be saved)
- fixed bug when running permutation analyses with 3 between factors (worked previously and got lost in some updates)
- fixed bug when running GraphVar on Retina displays

graphvar GraphVar 2.01c: Machine Learning

johndkk@www.nitrc.org (Johann Kruschwitz) — Mon, 19 Nov 2018 10:28:00 GMT

Release info GraphVar 2.01c:

- fixed an issue for LinSVM (classification, regression, probabilisitc): tuned hyperparameters derived from nested-cross validation
were not applied to the models (i.e., prediction was similar to no hyperparameter optimization). ElasticNet was unaffected.

graphvar GraphVar 2.01b: Machine Learning

johndkk@www.nitrc.org (Johann Kruschwitz) — Fri, 06 Jul 2018 14:57:00 GMT

Release info GraphVar 2.01b:

- fixed an issue with manual hyperparameter entry for elastic net (... wrong GUI handle, did not affect results)
- fixed an issue when diplaying p-values of negative weights in the machine learning results viewer
(this was just a visaul issue that did not affect classification/regression results).

graphvar GraphVar 2.01: Machine Learning

johndkk@www.nitrc.org (Johann Kruschwitz) — Wed, 21 Mar 2018 9:42:00 GMT

Release info GraphVar 2.01:

- added a new compiled MEX file for use on MAC with newer Matlab versions
- Matlab 2018 colorbar compatibility issue resolved
- fixed bug "missing brainsheet" in SampleWorkspace

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
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.

---> there is a preprint version of a corresponding new GraphVar ML articel on arxiv.org <---

graphvar GraphVar 2.0: Machine Learning

johndkk@www.nitrc.org (Johann Kruschwitz) — Wed, 28 Feb 2018 17:50:00 GMT

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
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.
---> there is a preprint version of a corresponding new GraphVar ML articel on arxiv.org <---

graphvar GraphVar 1.03 'Turbo GLM'

johndkk@www.nitrc.org (Johann Kruschwitz) — Thu, 11 May 2017 11:51:00 GMT

We found that in the GraphVar 1.0 update, the function to calculate the clustering coefficient
for binary undirected graphs was accidentally replaced with a function to calculate the number of
triangles around each node. The clustering coefficient, however, is defined as the number of triangles
around each node divided by the number of possible triangles. In other words, the values that were
returned are simply the unscaled form of the binary undirected clustering coefficient.

The functions for calculating the clustering coefficient on weighted network (clustering_coef_wu; clustering_coef_wd)
and the binary clustering coefficient fpr directed networks (clustering_coef_bd) were not affected.

However, even though the values are thus conceptually related, the number of triangles and the
actual clustering coefficient share only roughly 20% of their variance. Therefore, please
re-run your analyses if you have used the clustering coefficient for binary undirected graphs
with a GraphVar version 1.00, 1.01, or 1.02.
GraphVar 1.03 now entails the original BCT clustering coefficient
function for binary undirected networks (clustering_coef_bu.m)
We are very sorry for the inconvenience!

graphvar GraphVar 1.02 'Turbo GLM'

johndkk@www.nitrc.org (Johann Kruschwitz) — Fri, 02 Dec 2016 11:39:00 GMT

Release info GraphVar 1.02:

1. Fixed a bug where the direction of the effect of continuous by continous
interactions was reversed in some models. This issue did not affect the
p-values of the regressors.