Nonparametric directionality measures for time series and point process data

The need to determine the directionality of interactions between neural signals is a key requirement for analysis of multichannel recordings. Approaches most commonly used are parametric, typically relying on autoregressive models. A number of concerns have been expressed regarding parametric approaches, thus there is a need to consider alternatives. We present an alternative nonparametric approach for construction of directionality measures for bivariate random processes. The method combines time and frequency domain representations of bivariate data to decompose the correlation by direction. Our framework generates two sets of complementary measures, a set of scalar measures, which decompose the total product moment correlation coefficient summatively into three terms by direction and a set of functions which decompose the coherence summatively at each frequency into three terms by direction: forward direction, reverse direction and instantaneous interaction. It can be undertaken as an addition to a standard bivariate spectral and coherence analysis, and applied to either time series or point-process (spike train) data or mixtures of the two (hybrid data). In this paper, we demonstrate application to spike train data using simulated cortical neurone networks and application to experimental data from isolated muscle spindle sensory endings subject to random efferent stimulation

Causation Among Socioeconomic Time-Series

Using annual U. S. time series data from 1950-1974, formal tests of causation are performed among three socioeconomic phenomena: women's labor force participation rates, fertility rates, and divorce rates. Box-Jenkins and other techniques are employed with Granger-Sims type definition of causation based on leads and lags. Women's labor force participation appears to be causally prior to both fertility and divorce; the direction of effect on fertility is negative and on divorce, positive. Additional tests with alternative definitions of variables and a longer (1924-1974) time span also exhibit causal influence from fertility to divorce (with no feedback). When per capita income is also tested for causal influence, it, too, appears causally prior to fertility and divorce.

Time Series Analysis as a Method to Examine Acoustical Influences on Real-time Perception of Music

Multivariate analyses of dynamic correlations between continuous acoustic properties (intensity and spectral flatness) and real-time listener perceptions of change and expressed affect (arousal and valence) in music are developed, by an extensive application of autoregressive Time Series Analysis (TSA). TSA offers a large suite of techniques for modeling autocorrelated time series, such as constitute both music’s acoustic properties and its perceptual impacts. A logical analysis sequence from autoregressive integrated moving average regression with exogenous variables (ARIMAX), to vector autoregression (VAR) is established. Information criteria discriminate amongst models, and Granger Causality indicates whether a correlation might be a causal one. A 3 min electroacoustic extract from Wishart’s Red Bird is studied. It contains digitally generated and transformed sounds, and animate sounds, and our approach also permits an analysis of their impulse action on the temporal evolution and the variance in the perceptual time series. Intensity influences perceptions of change and expressed arousal substantially. Spectral flatness influences valence, while animate sounds influence the valence response and its variance. This TSA approach is applicable to a wide range of questions concerning acoustic- perceptual relationships in music

Passive seismic recording of cryoseisms in Adventdalen, Svalbard

A series of transient seismic events were discovered in passive seismic recordings from 2-D geophone arrays deployed at a frost polygon site in Adventdalen, Svalbard. These events contain a high proportion of surface wave energy and produce high-quality dispersion images using an apparent offset re-sorting and inter-trace delay minimisation technique to locate the seismic source, followed by cross-correlation beamforming dispersion imaging. The dispersion images are highly analogous to surface wave studies of pavements and display a complex multimodal dispersion pattern. Supported by theoretical modelling based on a highly simplified arrangement of horizontal layers, we infer that a ∼3.5–4.5 m thick, stiff, high-velocity layer overlies a ∼30 m thick layer that is significantly softer and slower at our study site. Based on previous studies we link the upper layer with syngenetic ground ice formed in aeolian sediments, while the underlying layer is linked to epigenetic permafrost in marine-deltaic sediments containing unfrozen saline pore water. Comparing events from spring and autumn indicates that temporal variation can be resolved via passive seismic monitoring. The transient seismic events that we record occur during periods of rapidly changing air temperature. This correlation, along with the spatial clustering along the elevated river terrace in a known frost polygon, ice-wedge area and the high proportion of surface wave energy, constitutes the primary evidence for us to interpret these events as frost quakes, a class of cryoseism. In this study we have proved the concept of passive seismic monitoring of permafrost in Adventdalen, Svalbard

Neuromagnetic Decomposition of Social Interaction

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