Experimental datasets are growing rapidly in size, scope, and detail, but the value of these
datasets is limited by unwanted measurement noise. It is therefore tempting to apply …

Progress in many scientific disciplines is hindered by the presence of independent noise.
Technologies for measuring neural activity (calcium imaging, extracellular electrophysiology …

Standard neuroimaging data analysis based on traditional principles of experimental
design, modelling, and statistical inference is increasingly complemented by novel analysis …

Most neuroimaging studies display results that represent only a tiny fraction of the collected
data. While it is conventional to present" only the significant results" to the reader, here we …

Pooling magnetic resonance imaging (MRI) data across research studies, or utilizing shared
data from imaging repositories, presents exceptional opportunities to advance and enhance …

Abstract Functional Magnetic Resonance Imaging (fMRI) based on Blood Oxygenation Level
Dependent (BOLD) contrast has become one of the most powerful tools in neuroscience …

Purpose: MR image quality and subsequent brain morphometric analysis are inevitably
affected by noise. The purpose of this study was to evaluate the effectiveness of an artificial …

Multivariate decoding methods were developed originally as tools to enable accurate
predictions in real-world applications. The realization that these methods can also be …

The Journal of Neuroscience is committed to editorial transparency and scientific
excellence. Consistent with these goals, this editorial is the first of a series aimed at …

The accuracy of the magnetic resonance (MR) image diagnosis depends on the quality of
the image, which degrades mainly due to noise and artifacts. The noise is introduced …