Tipping elements of the Indian monsoon : Prediction of onset and withdrawal

Funded by LINC project. Grant Number: 289447 EC's Marie Curie ITN program. Grant Number: FP7-PEOPLE-2011-ITN RFBR. Grant Number: 16-07-01186 Government of Russian Federation. Grant Number: 14.Z50.31.0033Peer reviewedPublisher PD

Dynamic models in fMRI

Most statistical methods for assessing activated voxels in fMRI experiments are based on correlation or regression analysis. In this context the main assumptions are that the baseline can be described by a few known basis-functions or variables and that the effect of the stimulus, i.e. the activation, stays constant over time. As these assumptions are in many cases neither necessary nor correct, a new dynamic approach that does not depend on those suppositions will be presented. This allows for simultaneous nonparametric estimation of the baseline as well as the time-varying effect of stimulation. This method of estimating the stimulus related areas of the brain furthermore provides the possibility of an analysis of the temporal and spatial development of the activation within an fMRI-experiment

Thermal response of Sanabria lake to global change (NW Spain)

Are large water bodies able to act as sensors of global change? As accumulators of water and heat, some of their thermal characteristics might be altered by long term (decadal) hydrometeorological changes and thus may be used as indicators of the effects of global change on fluvial ecosystems. This work focuses on the effect of global change (climate change plus water quantity and land use changes) in the internal organization of Sanabria Lake, specifically in its thermal annual cycle. The existence of temporal trends in the thermal behaviour of the lake was investigated based on the water temperature profiles are available since 1986. Data analyses include the non-parametric Mann-Kendall trend test and the Sen slope estimate to evaluate long term and seasonal patterns of hydrometeorological and in-lake thermal variables. The main results surprisingly point to a net lake cooling that could be explained by a stronger thermocline and a weaker vertical transfer of heat to the hypolimnion during the stratification period. These results contribute to understand and quantify the effects of global change on Iberian freshwater bodies.Peer ReviewedPostprint (published version

Bayesian P-Splines to investigate the impact of covariates on Multiple Sclerosis clinical course

This paper aims at proposing suitable statistical tools to address heterogeneity in repeated measures, within a Multiple Sclerosis (MS) longitudinal study. Indeed, due to unobservable sources of heterogeneity, modelling the effect of covariates on MS severity evolves as a very difficult feature. Bayesian P-Splines are suggested for modelling non linear smooth effects of covariates within generalized additive models. Thus, based on a pooled MS data set, we show how extending Bayesian P-splines to mixed effects models (Lang and Brezger, 2001), represents an attractive statistical approach to investigate the role of prognostic factors in affecting individual change in disability

Measures of Analysis of Time Series (MATS): A MATLAB Toolkit for Computation of Multiple Measures on Time Series Data Bases

In many applications, such as physiology and finance, large time series data bases are to be analyzed requiring the computation of linear, nonlinear and other measures. Such measures have been developed and implemented in commercial and freeware softwares rather selectively and independently. The Measures of Analysis of Time Series ({\tt MATS}) {\tt MATLAB} toolkit is designed to handle an arbitrary large set of scalar time series and compute a large variety of measures on them, allowing for the specification of varying measure parameters as well. The variety of options with added facilities for visualization of the results support different settings of time series analysis, such as the detection of dynamics changes in long data records, resampling (surrogate or bootstrap) tests for independence and linearity with various test statistics, and discrimination power of different measures and for different combinations of their parameters. The basic features of {\tt MATS} are presented and the implemented measures are briefly described. The usefulness of {\tt MATS} is illustrated on some empirical examples along with screenshots.Comment: 25 pages, 9 figures, two tables, the software can be downloaded at http://eeganalysis.web.auth.gr/indexen.ht

A Semi-parametric Technique for the Quantitative Analysis of Dynamic Contrast-enhanced MR Images Based on Bayesian P-splines

Dynamic Contrast-enhanced Magnetic Resonance Imaging (DCE-MRI) is an important tool for detecting subtle kinetic changes in cancerous tissue. Quantitative analysis of DCE-MRI typically involves the convolution of an arterial input function (AIF) with a nonlinear pharmacokinetic model of the contrast agent concentration. Parameters of the kinetic model are biologically meaningful, but the optimization of the non-linear model has significant computational issues. In practice, convergence of the optimization algorithm is not guaranteed and the accuracy of the model fitting may be compromised. To overcome this problems, this paper proposes a semi-parametric penalized spline smoothing approach, with which the AIF is convolved with a set of B-splines to produce a design matrix using locally adaptive smoothing parameters based on Bayesian penalized spline models (P-splines). It has been shown that kinetic parameter estimation can be obtained from the resulting deconvolved response function, which also includes the onset of contrast enhancement. Detailed validation of the method, both with simulated and in vivo data, is provided

Correlated microtiming deviations in jazz and rock music

Musical rhythms performed by humans typically show temporal fluctuations. While they have been characterized in simple rhythmic tasks, it is an open question what is the nature of temporal fluctuations, when several musicians perform music jointly in all its natural complexity. To study such fluctuations in over 100 original jazz and rock/pop recordings played with and without metronome we developed a semi-automated workflow allowing the extraction of cymbal beat onsets with millisecond precision. Analyzing the inter-beat interval (IBI) time series revealed evidence for two long-range correlated processes characterized by power laws in the IBI power spectral densities. One process dominates on short timescales ($t < 8$ beats) and reflects microtiming variability in the generation of single beats. The other dominates on longer timescales and reflects slow tempo variations. Whereas the latter did not show differences between musical genres (jazz vs. rock/pop), the process on short timescales showed higher variability for jazz recordings, indicating that jazz makes stronger use of microtiming fluctuations within a measure than rock/pop. Our results elucidate principles of rhythmic performance and can inspire algorithms for artificial music generation. By studying microtiming fluctuations in original music recordings, we bridge the gap between minimalistic tapping paradigms and expressive rhythmic performances