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

In contrast to conventional, univariate analysis, various types ofmultivariate analysis have been applied to functional magnetic resonanceimaging (fMRI) data. In this paper, we compare two contemporary approaches formultivariate regression on task-based fMRI data: linear regression with ridgeregularization and non-linear symbolic regression using genetic programming.The data for this project is representative of a contemporary fMRI experimentaldesign for visual stimuli. Linear and non-linear models were generated for 10subjects, with another 4 withheld for validation. Model quality is evaluated bycomparing $R$ scores (Pearson product-moment correlation) in various contexts,including single run self-fit, within-subject generalization, andbetween-subject generalization. Propensity for modelling strategies to overfitis estimated using a separate resting state scan. Results suggest that neithermethod is objectively or inherently better than the other.

On the Generalizability of Linear and Non-Linear Region of Interest-Based Multivariate Regression Models for fMRI Data