Applications of machine learning (ML) methods have been used extensively to solve various
complex challenges in recent years in various application areas, such as medical, financial …

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

Decoding specific cognitive states from brain activity constitutes a major goal of
neuroscience. Previous studies of brain-state classification have focused largely on …

Standard univariate analysis of neuroimaging data has revealed a host of neuroanatomical
and functional differences between healthy individuals and patients suffering a wide range …

Conclusion The application of machine learning methods to neuroimaging has risen more
rapidly than could have been predicted 15 years ago. It has been a very exciting new …

Lie detection techniques are frequently used, but most of them have been criticized for the
lack of empirical support for their predictive validity and presumed underlying mechanisms …

In this paper, we give a short introduction to machine learning and survey its applications in
radiology. We focused on six categories of applications in radiology: medical image …

A common goal of neuroimaging research is to use imaging data to identify the mental
processes that are engaged when a subject performs a mental task. The use of reasoning …

A fundamental challenge for systems neuroscience is to quantitatively relate its three major
branches of research: brain-activity measurement, behavioral measurement, and …

Over the past decade fMRI researchers have developed increasingly sensitive techniques
for analyzing the information represented in BOLD activity. The most popular of these …