Invited Sessions Details

Novel Methods and Applications in Spatial and Spatiotemporal Statistics

Presenter: Cici Bauer

Session Synopsis:

Novel Methods and Applications in Spatial and Spatiotemporal Statistics

Presenter: Alix Gittleman

Session Synopsis:

Novel Methods and Applications in Spatial and Spatiotemporal Statistics

Presenter: Farouk Nathoo

Session Synopsis:

Persistent Homology and Functional Principal Components for Brain Decoding

Brain decoding involves the determination of a subject's cognitive state or an associated stimulus from functional neuroimaging data measuring brain activity. In this setting the cognitive state is typically characterized by an element of a finite set, and the neuroimaging data comprise voluminous amounts of spatiotemporal data measuring some aspect of the neural signal. The associated statistical problem is one of classification from high-dimensional data. We explore the use of functional principal component analysis, mutual information networks, and persistent homology for examining the data through exploratory analysis and for constructing features characterizing the neural signal for brain decoding. We review each approach from this perspective, and we incorporate the features into a classifier based on symmetric multinomial logistic regression with elastic net regularization. Two complimentary approaches for applying persistent homology to extract features from spatiotemporal data are discussed. The approaches are illustrated in an application where the task is to infer, from brain activity measured with magnetoencephalography (MEG), the type of video stimulus shown to a subject.