14. Statistical methods for functional neuroimaging
The video recording of this talk can be viewed here.
Organizer: Ying Guo
Emory University
Email: yguo2@emory.edu
Chair: Ying Guo
Emory University
Email: yguo2@emory.edu
Speakers:
1. Hernando Ombao
King Abdullah University of Science and Technology (KAUST)
Email: hernando.ombao@kaust.edu.sa
Title: Exploring Non-Linear Spectral Interactions in Multivariate Time Series
Time: 9:00am-9:20am
Abstract:
Advances in imaging technology have given neuroscientists unprecedented access to examine various facets of how the brain “works”. Brain activity is complex. A full understanding of brain activity requires careful study of its multi-scale spatial-temporal organization (from neurons to regions of interest; and from transient events to long-term temporal dynamics). Motivated by these challenges, we will explore some characterizations of dependence between components of a multivariate time series and then apply these to the study of brain functional connectivity. This is potentially interesting for brain scientists because functional brain networks are associated with cognitive function and mental and neurological diseases. There is no single measure of dependence that can capture all facets of brain connectivity. In this talk, we shall present some new models for exploring potential non-linear cross-frequency interactions. These interactions include the impact of phase of one oscillatory activity in one component on the amplitude of another oscillation. The proposed approach captures lead-lag relationships and hence can be used as a general framework for spectral causality. This is joint work with Marco Pinto (KAUST and Oslo Metropolitan University).
2. Brian Caffo
Johns Hopkins University
Email: bcaffo@gmail.com
Title: Covariance regression for connectome outcomes
Time: 9:20am-9:40am
Abstract:
In this talk, we cover methodology for jointly analyzing a collection of covariance or correlation matrices that depend on other variables. This covariance-as-an-outcome regression problem arises commonly in the study of brain imaging, where the covariance matrix in question is an estimate of functional or structural connectivity. Two main approaches to covariance regression exists: outer product models and joint diagonalization approaches. We investigate joint diagonalization approaches and discuss the benefits and costs of this solution. We distinguish between diagonalization approaches where the eigenvectors are selected in the absence of covariate information and those that chose the eigenvectors so that the result regression model holds best. The methods are applied to resting state functional magnetic resonance imaging data in a study of aphasia and potential interventions.
3. Robert T. Krafty
Emory University
Email: robert.t.krafty@emory.edu
Title: Adaptive Spectral Analysis of High-Dimensional EEG with Application to Monitoring Transcranial Magnetic Stimulation during Psychosis
Time: 9:40am-10:00am
Abstract:
Motivated by the analysis of high-density EEG (hdEEG), in this talk we discuss a nonparametric approach to spectral analysis of a high-dimensional multivariate nonstationary time. The procedure is based on a novel frequency-domain factor model that provides a flexible yet parsimonious representation of spectral matrices from a large number of simultaneously observed time series. Real and imaginary parts of the factor loading matrices are modeled independently using a prior that is formulated from the tensor product of penalized splines and multiplicative gamma process shrinkage priors, allowing for infinitely many factors with loadings increasingly shrunk towards zero as the column index increases. Formulated in a fully Bayesian framework, the time series is adaptively partitioned into approximately stationary segments, where both the number and location of partition points are assumed unknown. Stochastic approximation Monte Carlo (SAMC) techniques are used to accommodate the unknown number of segments, and a conditional Whittle likelihood-based Gibbs sampler is developed for efficient sampling within segments. By averaging over the distribution of partitions, the proposed method can approximate both abrupt and slowly varying changes in spectral matrices. The method is used to analyze hdEEG from a patient receiving transcranial magnetic stimulation (TMS) while hospitalized for a first-break psychotic episode.