A multi-dataset evaluation of frame censoring for task-based fMRI

By Jones et al.

Subject motion during fMRI can affect our ability to accurately measure signals of interest. In recent years, frame censoring—that is, statistically excluding motion-contaminated data within the general linear model using nuisance regressors—has appeared in several task-based fMRI studies as a mitigation strategy. However, there have been few systematic investigations quantifying its efficacy. In the present study, we compared the performance of frame censoring to several other common motion correction approaches for task-based fMRI using open data and reproducible workflows.....

Age-related degradation of optic radiation white matter connectivity differentially predicts visual and non-visual executive functions

By Webb et al.

Healthy aging is accompanied by degraded white matter connectivity, which has been suggested to contribute to cognitive dysfunction observed in aging, especially in relation to fluid measures of cognition. Prior research linking white matter microstructure and cognition, however, has largely been limited to major association and heteromodal white matter tracts. The optic radiations (OR), which transfer visual sensory-perceptual information from thalamic lateral geniculate nucleus to primary visual cortex, are generally considered lower-level input-relay white matter tracts...

Quantitative streamlines tractography: methods and inter-subject normalisation

By Smith et al.

A stage for neuroscience and art: the OHBM BrainArt SIG perspective

By Borghesani et al.

Science and art have been intertwined for centuries, as both embody means for humans to represent, communicate and interpret our external and internal words. The collective effort to gather and organize knowledge about the brain blends well with a wide array of human creative activities, from visual and performing arts to interactive media...

An Empirically Driven Guide on Using Bayes Factors for M/EEG Decoding

By Teichmann et al.

Bayes Factors can be used to provide quantifiable evidence for contrasting hypotheses and have thus become increasingly popular in cognitive science. However, Bayes Factors are rarely used to statistically assess the results of neuroimaging experiments. Here we provide an empirically driven guide on implementing...

Electroencephalography Robust Statistical Linear Modelling Using a Single Weight Per Trial

By Pernet et al.

Being able to remove or weigh down the influence of outlier data is desirable for any statistical model. While magnetic and electrocephalographic (MEEG) data are often averaged across trials per condition, it is becoming common practice to use information from all trials to build statistical linear models...

Prenatal and Childhood Adverse Events and Child Brain Morphology: A Population-Based Study

By Cortes Hidalgo et al.

Prenatal and childhood adverse events have been shown to be related to children's cognitive and psychological development. However, the influence of early-life adversities on child brain morphology is not well understood, and most studies are based on small samples and often examine only one...

Sources of Information Waste in Neuroimaging: Mishandling Structures, Thinking Dichotomously, and Over-Reducing Data

By Chen et al.

Functional magnetic resonance imaging (fMRI) is a mainstay technique of human neuroscience, which allows the study of the neural correlates of many functions , including perception, emotion, and cognition. The basic spatial unit of fMRI data is a voxel ranging from 1 to 3 mm on each side. As data are collected across time...