Pyrcca: Regularized Kernel Canonical Correlation Analysis in Python and Its Applications to Neuroimaging

Canonical correlation analysis (CCA) is a valuable method for interpreting cross-covariance across related datasets of different dimensionality. There are many potential applications of CCA to neuroimaging data analysis. For instance, CCA can be used for finding functional similarities across fMRI datasets collected from multiple subjects without resampling individual datasets to a template anatomy. In this paper, we introduce Pyrcca, an open-source Python module for executing CCA between two or more datasets. Pyrcca can be used to implement CCA with or without regularization, and with or without linear or a Gaussian kernelization of the datasets. We demonstrate an application of CCA implemented with Pyrcca to neuroimaging data analysis. We use CCA to find a data-driven set of functional response patterns that are similar across individual subjects in a natural movie experiment. We then demonstrate how this set of response patterns discovered by CCA can be used to accurately predict subject responses to novel natural movie stimuli

Exploring the latent space between brain and behaviour using eigen-decomposition methods

Machine learning methods have been successfully used to analyse neuroimaging data for a variety of applications, including the classification of subjects with different brain disorders. However, most studies still rely on the labelling of the subjects, constraining the study of several brain diseases within a paradigm of pre-defined clinical labels, which have shown to be unreliable in some cases. The lack of understanding regarding the association between brain and behaviour presents itself as an interesting challenge for more exploratory machine learning approaches, which could potentially help in the study of diseases whose clinical labels have shown limitations. The aim of this project is to explore the possibility of using eigen-decomposition approaches to find multivariate associative effects between brain structure and behaviour in an exploratory way. This thesis addresses a number of issues associated with eigen-decomposition methods, in order to enable their application to investigate brain/behaviour relationships in a reliable way. The first contribution was showing the advantages of an alternative matrix deflation approach to be used with Sparse Partial Least Squares (SPLS). The modified SPLS method was later used to model the associations between clinical/demographic data and brain structure, without relying on a priori assumptions on the sparsity of each data source. A novel multiple hold-out SPLS framework was then proposed, which allowed for the detection of robust multivariate associative effects between brain structure and individual questionnaire items. The linearity assumption of most machine learning methods used in neuroimaging might be a limitation, since these methods will not have enough flexibility to detect non-linear associations. In order to address this issue, a novel Sparse Canonical Correlation Analysis (SCCA) method was proposed, which allows one to use sparsity constraints in one data source (e.g. neuroimaging data), with non-linear transformations of the data in the other source (e.g. clinical data)

Brain Computer Interfaces and Emotional Involvement: Theory, Research, and Applications

This reprint is dedicated to the study of brain activity related to emotional and attentional involvement as measured by Brain–computer interface (BCI) systems designed for different purposes. A BCI system can translate brain signals (e.g., electric or hemodynamic brain activity indicators) into a command to execute an action in the BCI application (e.g., a wheelchair, the cursor on the screen, a spelling device or a game). These tools have the advantage of having real-time access to the ongoing brain activity of the individual, which can provide insight into the user’s emotional and attentional states by training a classification algorithm to recognize mental states. The success of BCI systems in contemporary neuroscientific research relies on the fact that they allow one to “think outside the lab”. The integration of technological solutions, artificial intelligence and cognitive science allowed and will allow researchers to envision more and more applications for the future. The clinical and everyday uses are described with the aim to invite readers to open their minds to imagine potential further developments