Importance Great interest exists in identifying methods to predict neuropsychiatric disease
states and treatment outcomes from high-dimensional data, including neuroimaging and …

Since the second half of the twentieth century, intracranial electroencephalography (iEEG),
including both electrocorticography (ECoG) and stereo-electroencephalography (sEEG) …

To understand the architecture of human language, it is critical to examine diverse
languages; however, most cognitive neuroscience research has focused on only a handful …

Identifying brain biomarkers of disease risk is a growing priority in neuroscience. The ability
to identify meaningful biomarkers is limited by measurement reliability; unreliable measures …

Numerous machine learning (ML) approaches have been proposed for automatic
classification of Alzheimer's disease (AD) from brain imaging data. In particular, over 30 …

Abstract The use of Linear Mixed-effects Models (LMMs) is set to dominate statistical
analyses in psychological science and may become the default approach to analyzing …

Background Brain activation in response to spoken motor commands can be detected by
electroencephalography (EEG) in clinically unresponsive patients. The prevalence and …

Fallacies about the nature of biases have shadowed a proper cognitive understanding of
biases and their sources, which in turn lead to ways that minimize their impact. Six such …

Cognitive pupillometry is the measurement of pupil size to investigate cognitive processes
such as attention, mental effort, working memory, and many others. Currently, there is no …

Neuroimaging is a fast-developing research area in which anatomical and functional images
of human brains are collected using techniques such as functional magnetic resonance …