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 …

Purpose The required training sample size for a particular machine learning (ML) model
applied to medical imaging data is often unknown. The purpose of this study was to provide …

In this article, the authors aim to maximally utilize multimodality neuroimaging and genetic
data for identifying Alzheimer's disease (AD) and its prodromal status, Mild Cognitive …

In the past years, mass univariate statistical analyses of neuroimaging data have been
complemented by the use of multivariate pattern analyses, especially based on machine …

Lesion analysis is a classic approach to study brain functions. Because brain function is a
result of coherent activations of a collection of functionally related voxels, lesion‐symptom …

Abstract Machine learning is a powerful tool for creating computational models relating brain
function to behavior, and its use is becoming widespread in neuroscience. However, these …

Recently mutual information based feature selection criteria have gained popularity for their
superior performances in different applications of pattern recognition and machine learning …

Associations between datasets can be discovered through multivariate methods like
Canonical Correlation Analysis (CCA) or Partial Least Squares (PLS). A requisite property …

Pattern recognition applied to whole-brain neuroimaging data, such as functional Magnetic
Resonance Imaging (fMRI), has proved successful at discriminating psychiatric patients from …

The traditional goal of quantitative analytics is to find simple, transparent models that
generate explainable insights. In recent years, large-scale data acquisition enabled, for …