This article will discuss very different ways of using machine learning that may be less familiar,
and we will demonstrate through examples the role of these concepts in medical imaging. …

We explored the effects of aging on 2 large-scale brain networks, the default mode network (DMN)
and the task-positive network (TPN). During functional magnetic resonance imaging …

We introduce a data-analysis framework and performance metrics for evaluating and optimizing
the interaction between activation tasks, experimental designs, and the methodological …

The rate of progress in human neurosciences is limited by the inability to easily apply a wide
range of analysis methods to the plethora of different datasets acquired in labs around the …

This article reviews the evaluation and optimization of the preprocessing steps for blood-oxygenation-level-dependent
(BOLD) functional magnetic resonance imaging (fMRI). This …

Positron emission tomographic studies of regional cerebral metabolic rate for glucose (rCMRGlc)
and cerebral blood flow were performed in 7 vegetative and 3 locked‐in patients to …

Generalization can be defined quantitatively and can be used to assess the performance of
principal component analysis (PCA). The generalizability of PCA depends on the number of …

Using covariance-based multivariate analysis, we examined patterns of functional connectivity
in rCBF on a practice-extended version of the Stroop color-word paradigm. Color-word …

Background Major Depressive Disorder (MDD) is among the most prevalent and disabling
medical conditions worldwide. Identification of clinical and biological markers (“biomarkers”) …

Interest is increasing in applying discriminative multivariate analysis techniques to the
analysis of functional neuroimaging data. Model interpretation is of great importance in the …