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

Population imaging markedly increased the size of functional-imagingdatasets, shedding new light on the neural basis of inter-individualdifferences. Analyzing these large data entails new scalability challenges,computational and statistical. For this reason, brain images are typicallysummarized in a few signals, for instance reducing voxel-level measures withbrain atlases or functional modes. A good choice of the corresponding brainnetworks is important, as most data analyses start from these reduced signals.We contribute finely-resolved atlases of functional modes, comprising from 64to 1024 networks. These dictionaries of functional modes (DiFuMo) are trainedon millions of fMRI functional brain volumes of total size 2.4TB, spanned over27 studies and many research groups. We demonstrate the benefits of extractingreduced signals on our fine-grain atlases for many classic functional dataanalysis pipelines: stimuli decoding from 12,334 brain responses, standard GLManalysis of fMRI across sessions and individuals, extraction of resting-statefunctional-connectomes biomarkers for 2,500 individuals, data compression andmeta-analysis over more than 15,000 statistical maps. In each of these analysisscenarii, we compare the performance of our functional atlases with that ofother popular references, and to a simple voxel-level analysis. Resultshighlight the importance of using high-dimensional "soft" functional atlases,to represent and analyse brain activity while capturing its functionalgradients. Analyses on high-dimensional modes achieve similar statisticalperformance as at the voxel level, but with much reduced computational cost andhigher interpretability. In addition to making them available, we providemeaningful names for these modes, based on their anatomical location. It willfacilitate reporting of results.

Fine-grain atlases of functional modes for fMRI analysis