Independent component analysis (ICA) is a matrix factorization approach where the signals
captured by each individual matrix factors are optimized to become as mutually independent …

The emergence of functional magnetic resonance imaging (fMRI) marked a significant
technological breakthrough in the real-time measurement of the functioning human brain in …

This book explores various state-of-the-art aspects behind the statistical analysis of
neuroimaging data. It examines the development of novel statistical approaches to model …

Background ADHD is one of the most common psychiatric disorders in children and
adolescents. Altered functional connectivity has been associated with ADHD symptoms. This …

Independent component analysis (ICA) has been shown to be a powerful blind source
separation technique for analyzing functional magnetic resonance imaging (fMRI) data sets …

Background Brain networks in fMRI are typically identified using spatial independent
component analysis (ICA), yet other mathematical constraints provide alternate biologically …

The finger print recognition, face recognition, hand geometry, iris recognition, voice scan,
signature, retina scan and several other biometric patterns are being used for recognition of …

Background Analyzing the human transcriptome is crucial in advancing precision medicine,
and the plethora of over half a million human microarray samples in the Gene Expression …

Functional principal component analysis is one of the most commonly employed
approaches in functional and longitudinal data analysis and we extend it to analyze …

Brain networks extracted by independent component analysis (ICA) from magnitude‐only
fMRI data are usually denoised using various amplitude‐based thresholds. By contrast …