Decoding, ie prediction from brain images or signals, calls for empirical evaluation of its
predictive power. Such evaluation is achieved via cross-validation, a method also used to …

Human neuroimaging research has transitioned from map** local effects to develo**
predictive models of mental events that integrate information distributed across multiple …

Mild cognitive impairment (MCI) is a transitional stage between age-related cognitive
decline and Alzheimer's disease (AD). For the effective treatment of AD, it would be …

Electrical Impedance Tomography (EIT) has emerged as a valuable medical imaging
modality, which visualizes the conductivity distribution of a subject by performing multi …

Brain-imaging research has predominantly generated insight by means of classical
statistics, including regression-type analyses and null-hypothesis testing using t-test and …

Multivariate machine learning methods are increasingly used to analyze neuroimaging data,
often replacing more traditional “mass univariate” techniques that fit data one voxel at a time …

With the advent of sophisticated acquisition and analysis techniques, decoding the contents
of someone's experience has become a reality. We propose a straightforward linear …

The application of deep learning (DL) models to neuroimaging data poses several
challenges, due to the high dimensionality, low sample size, and complex temporo-spatial …

The use of imaging markers to predict clinical outcomes can have a great impact in public
health. The aim of this article is to develop a class of generalized scalar-on-image …

Functional brain images are rich and noisy data that can capture indirect signatures of
neural activity underlying cognition in a given experimental setting. Can data mining …