Probabilistic PARAFAC2.

The Parallel Factor Analysis 2 (PARAFAC2) is a multimodal factor analysis model suitable for analyzing multi-way data when one of the modes has incomparable observation units, for example, because of differences in signal sampling or batch sizes. A fully probabilistic treatment of the PARAFAC2 is desirable to improve robustness to noise and provide a principled approach for determining the number of factors, but challenging because direct model fitting requires that factor loadings be decomposed into a shared matrix specifying how the components are consistently co-expressed across samples and sample-specific orthogonality-constrained component profiles. We develop two probabilistic formulations of the PARAFAC2 model along with variational Bayesian procedures for inference: In the first approach, the mean values of the factor loadings are orthogonal leading to closed form variational updates, and in the second, the factor loadings themselves are orthogonal using a matrix Von Mises-Fisher distribution.

Multiway Decomposition Followed by Reconvolution of Fluorescence Time Decay Data.

The use of time-resolved emission spectroscopy (TRES) is increasing as these instruments become more available, and the prices are decreasing, especially with the development of cheaper LED light sources. In this article, we propose a new methodology for analyzing TRES data. It combines two existing methods: PARAFAC and reconvolution.

PARAFAC2×N: Coupled decomposition of multi-modal data with drift in N modes.

Analysis of GC×GC-TOFMS data for large numbers of poorly-resolved peaks, and for large numbers of samples remains an enduring problem that hinders the widespread application of the technique. For multiple samples, GC×GC-TOFMS data for specific chromatographic regions manifests as a 4 order tensor of I mass spectral acquisitions, J mass channels, K modulations, and L samples. Chromatographic drift is common along both the first-dimension (modulations), and along the second-dimension (mass spectral acquisitions), while drift along the mass channel is for all practical purposes nonexistent.

Combining electro- and magnetoencephalography data using directional archetypal analysis.

Metastable microstates in electro- and magnetoencephalographic (EEG and MEG) measurements are usually determined using modified -means accounting for polarity invariant states. However, hard state assignment approaches assume that the brain traverses microstates in a discrete rather than continuous fashion. We present multimodal, multisubject directional archetypal analysis as a scale and polarity invariant extension to archetypal analysis using a loss function based on the Watson distribution.

Social ties between team members affect patient satisfaction: a data-driven approach to handling complex network analyses.

Research from outside the medical field suggests that social ties between team-members influence knowledge sharing, improve coordination, and facilitate task completion. However, the relative importance of social ties among team-members for patient satisfaction remains unknown. In this study, we explored the association between social ties within emergency teams performing simulated caesarean sections (CS) and patient-actor satisfaction.

Classification of social anhedonia using temporal and spatial network features from a social cognition fMRI task.

Previous studies have suggested that the degree of social anhedonia reflects the vulnerability for developing schizophrenia. However, only few studies have investigated how functional network changes are related to social anhedonia. The aim of this fMRI study was to classify subjects according to their degree of social anhedonia using supervised machine learning.