Unifying Amplitude and Phase Analysis: A Compositional Data Approach to Functional Multivariate Mixed-Effects Modeling of Mandarin Chinese.

Mandarin Chinese is characterized by being a tonal language; the pitch (or F0) of its utterances carries considerable linguistic information. However, speech samples from different individuals are subject to changes in amplitude and phase, which must be accounted for in any analysis that attempts to provide a linguistically meaningful description of the language. A joint model for amplitude, phase, and duration is presented, which combines elements from functional data analysis, compositional data analysis, and linear mixed effects models. By decomposing functions via a functional principal component analysis, and connecting registration functions to compositional data analysis, a joint multivariate mixed effect model can be formulated, which gives insights into the relationship between the different modes of variation as well as their dependence on linguistic and nonlinguistic covariates. The model is applied to the COSPRO-1 dataset, a comprehensive database of spoken Taiwanese Mandarin, containing approximately 50,000 phonetically diverse sample F0 contours (syllables), and reveals that phonetic information is jointly carried by both amplitude and phase variation. Supplementary materials for this article are available online.

Multi-resolution Bayesian regression in PET dynamic studies using wavelets.

In the kinetic analysis of dynamic PET data, one usually posits that the variation of the data through one dimension, time, can be described by a mathematical model encapsulating the relevant physiological features of the radioactive tracer. In this work, we posit that the remaining dimension, space, can also be modeled as a physiological feature, and we introduce this concept into a new computational procedure for the production of parametric maps. An organ and, in the instance considered here, the brain presents similarities in the physiological properties of its elements across scales: computationally, this similarity can be implemented in two stages. Firstly, a multi-scale decomposition of the dynamic frames is created through the wavelet transform. Secondly, kinetic analysis is performed in wavelet space and the kinetic parameters estimated at low resolution are used as priors to inform estimates at higher resolutions. Kinetic analysis in the above scheme is achieved by extension of the Patlak analysis through Bayesian linear regression that retains the simplicity and speed of the original procedure. Application to artificial and real data (FDG and FDOPA) demonstrates the ability of the procedure to reduce remarkably the variance of parametric maps (up to 4-fold reduction) without introducing sizeable bias. Significance of the methodology and extension of the procedure to other data (fMRI) and models are discussed.

Dynamic functional changes associated with cognitive skill learning of an adapted version of the Tower of London task.

In this study, we used a modified version of the Tower of London (TOL) planning task, in conjunction with positron emission tomography, to examine the neural substrates mediating cognitive skill learning. Twelve healthy, right-handed participants solved a total of 90 different TOL problems. They were scanned on four occasions during the fast learning stage as well as in a condition designed to control for internally guided movements. Practice of the TOL task resulted in a significant decrease in planning, execution, and total time taken to solve the problems. Consistent with the results of previous studies, early performance of the TOL task was associated with increased blood flow activity in the dorsolateral prefrontal, orbitofrontal, and parietal cortices on the left, as well as in the caudate nucleus, cerebellum, and premotor cortex, bilaterally. Interestingly, however, activity in the left caudate nucleus was maintained from the beginning to the end of the learning process, suggesting that this structure plays a role in this type of cognitive skill learning. In addition, correlational analyses revealed that improved performance on the TOL task was associated with a significant decrease of activity in the medial orbitofrontal and frontopolar cortices over the course of learning, areas thought to be involved in decision making, guessing, and monitoring of feedback information. In sum, the results lend further support to the idea that the learning of cognitive skills requiring planning and working memory capacities is mediated through a fronto-striatal network.

Estimating the delay of the fMRI response.

We propose a fast, efficient, general, simple, valid, and robust method of estimating and making inference about the delay of the fMRI response modeled as a temporal shift of the hemodynamic response function (HRF). We estimate the shift unbiasedly using two optimally chosen basis functions for a spectrum of time shifted HRFs. This is done at every voxel, to create an image of estimated delays and their standard deviations. This can be used to compare delays for the same stimulus at different voxels, or for different stimuli at the same voxel. Our method is compared to other alternatives and validated on an fMRI data set from an experiment in pain perception.