English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Background: We previously presented GraphVar as a user-friendly MATLABtoolbox for comprehensive graph analyses of functional brain connectivity. Herewe introduce a comprehensive extension of the toolbox allowing users toseamlessly explore easily customizable decoding models across functionalconnectivity 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 anycombination of network measures and additional variables, allowing for aflexibility in neuroimaging applications.  Results: In addition to previously integrated functionalities, such asnetwork construction and graph-theoretical analyses of brain connectivity witha high-speed general linear model (GLM), users can now perform customizable MLacross connectivity matrices, network metrics and additionally importedvariables. The new extension also provides parametric and nonparametric testingof classifier and regressor performance, data export, figure generation andhigh quality export.  Comparison with existing methods: Compared to other existing toolboxes,GraphVar 2.0 offers (1) comprehensive customization, (2) an all-in-one userfriendly interface, (3) customizable model design and manual hyperparameterentry, (4) interactive results exploration and data export, (5) automatedcueing for modelling multiple outcome variables within the same session, (6) aneasy to follow introductory review.  Conclusions: GraphVar 2.0 allows comprehensive, user-friendly exploration ofencoding (GLM) and decoding (ML) modelling approaches on functionalconnectivity measures making big data neuroscience readily accessible to abroader audience of neuroimaging investigators.

GraphVar 2.0: A user-friendly toolbox for machine learning on functional connectivity measures