Gaussian processes and random fields have a long history, covering multiple approaches to
representing spatial and spatio-temporal dependence structures, such as covariance …

Inference in neuroimaging typically occurs at the level of focal brain areas or circuits. Yet,
increasingly, well-powered studies paint a much richer picture of broad-scale effects …

Abstract Integrated Nested Laplace Approximations (INLA) has been a successful
approximate Bayesian inference framework since its proposal by Rue et al.(2009). The …

There is a growing demand for performing larger-scale Bayesian inference tasks, arising
from greater data availability and higher-dimensional model parameter spaces. In this work …

Simultaneous electroencephalography–functional MRI (EEG‐fMRI) is a unique and
noninvasive method for epilepsy presurgical evaluation. When selecting voxels by null …

Eucommia ulmoides, a single extant species of Eucommiaceae, is a perennial deciduous
tree distributed across central China. The bark of E. ulmoides is rich in chlorogenic acid and …

Introduction Analysis of task fMRI studies is typically based on using ordinary least squares
within a voxel-or vertex-wise linear regression framework known as the general linear …

Functional networks often guide our interpretation of spatial maps of brain–phenotype
associations. However, methods for assessing enrichment of associations within networks of …

Neuropsychiatric disorder (ND) is often accompanied by abnormal functional connectivity
(FC) patterns in specific task contexts. The distinctive task-specific FC patterns can provide …

Clusterwise inference is a popular approach in neuroimaging to increase sensitivity, but
most existing methods are currently restricted to the General Linear Model (GLM) for testing …