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

Understanding the modularity of fMRI-derived brain networks or connectomescan inform the study of brain function organization. However, fMRI connectomesadditionally involve negative edges, which are not rigorously accounted for byexisting approaches to modularity that either ignores or arbitrarily weightthese connections. Furthermore, most Q maximization-based modularity algorithmsyield variable results with suboptimal reproducibility. Here we present analternative, reproducible approach that exploits how frequent the BOLD-signalcorrelation between two nodes is negative. We validated this novelprobability-based modularity approach on two independent publicly-availableresting-state connectome dataset (the Human Connectome Project and the 1000Functional Connectomes) and demonstrated that negative correlations alone aresufficient in understanding resting-state modularity. In fact, this approach a)permits a dual formulation, leading to equivalent solutions regardless ofwhether one considers positive or negative edges; b) is theoretically linked tothe Ising model defined on the connectome, thus yielding modularity result thatmaximizes data likelihood. We additionally were able to detect sex differencesin modularity that the most widely utilized methods did not. Results confirmedthe superiority of our approach in that: a) correlations with the highestprobability of being negative are consistently placed between modules, b) dueto the equivalent dual forms, no arbitrary weighting factor is required tobalance the influence between negative and positive correlations, unlikeexisting Q maximization-based modularity approaches. As datasets like HCPbecome widely available for analysis by the neuroscience community at large,appropriate computational tools to understand the neurobiological informationof negative edges in fMRI connectomes are increasingly important.

The Importance of Being Negative: A serious treatment of non-trivial edges in brain functional connectome