To date, fMRI research has been concerned primarily with evincing generic principles of
brain function through averaging data from multiple subjects. Given rapid developments in …

The ability to discriminate signal from noise plays a key role in the analysis and
interpretation of functional magnetic resonance imaging (fMRI) measures of brain activity …

Background Physiological noise is one of the major confounds for fMRI. A common class of
correction methods model noise from peripheral measures, such as ECGs or pneumatic …

Advances in functional magnetic resonance imaging (fMRI) acquisition have improved
signal to noise to the point where the physiology of the subject is the dominant noise source …

With the rapid increase in new fNIRS users employing commercial software, there is a
concern that many studies are biased by suboptimal processing methods. The purpose of …

Rapid imaging techniques are increasingly used in functional MRI studies because they
allow a greater number of samples to be acquired per unit time, thereby increasing statistical …

Accurate identification of brain function is necessary to understand the neurobiology of
cognitive ageing, and thereby promote well-being across the lifespan. A common tool used …

Human brain connectivity yields significant potential as a noninvasive biomarker. Several
studies have used fMRI-based connectivity fingerprinting to characterize individual patterns …

Functional magnetic resonance imaging (fMRI) and particularly resting state fMRI (rs-fMRI) is
widely used to investigate resting state brain networks (RSNs) on the systems level. Echo …

Resting‐state functional magnetic resonance imaging (rsfMRI) has been developed as a
method of investigating spontaneous neural activity. Based on its low‐frequency signal …