Information theoretic interpretation of frequency domain connectivity measures

To provide adequate multivariate measures of information flow between neural structures, modified expressions of Partial Directed Coherence (PDC) and Directed Transfer Function (DTF), two popular multivariate connectivity measures employed in neuroscience, are introduced and their formal relationship to mutual information rates are proved.Comment: 17 pages, 1 figur

Kernel Methods for Nonlinear Connectivity Detection

In this paper, we show that the presence of nonlinear coupling between time series may be detected employing kernel feature space representations alone dispensing with the need to go back to solve the pre-image problem to gauge model adequacy. As a consequence, the canonical methodology for model construction, diagnostics, and Granger connectivity inference applies with no change other than computation using kernels in lieu of second-order moments.Comment: 14 pages, 14 figures, preliminary version being prepared for submission to a refereed journa

Linguistic-discursive variations in written narratives by elementary school children in Northern Patagonia

En este trabajo estudiamos narrativas libres escritas individualmente en clase y sobre papel por 54 alumnos de tercer y séptimo grado de escuelas primarias con diferencias socioeducativas en Norpatagonia, Argentina. Buscamos conocer los variados modos en que estos alumnos resuelven la producción narrativa escrita atendiendo tanto al ajuste a las prescripciones normativas de la escritura, y a las pautas que delinean el esquema narrativo, como a la elección de alternativas habilitadas por la gramática y las convenciones del género. Categorizamos los textos según tres niveles lingüísticos: textual (unidad de análisis: el texto), lexicogramatical y morfo-ortográfico (unidad de análisis: la palabra). Para estudiar las asociaciones entre las categorías definidas en cada nivel y el grado/escuela de los niños, aplicamos procedimientos de la estadística descriptiva multivariada. Los resultados mostraron una progresión evolutivo-educativa en los niveles textual y morfo-ortográfico, y patrones estilísticos en el nivel lexicogramatical, asociados a aspectos socioculturales. En conjunto, estos resultados dieron cuenta de una variedad de grados y formas de apropiación de la escritura narrativa que emergen de los modos en que los niños abordan el doble desafío de ajustarse a prescripciones y de explorar y explotar horizontes de elección.In this paper we study the written narratives used by 54 third- and seventh graders in elementary schools with different socio-educational characteristics in Northern Patagonia, Argentina, when writing individually, on paper, and during class, a text of their choice. We aim to capture the various ways in which these students solve the narrative production, considering the adjustment to specific prescriptions for written language and to conventional features of narrative structure, as of the lexicogrammatical choices according to grammar and genre. We categorized the 54 texts according to three linguistic levels: textual (unit of analysis: text), lexicogrammatical, and morpho-orthographic (unit of analysis: word). We applied diverse techniques of Multivariate Descriptive Statistics, in order to capture associations between categories in each linguistic level and children's grade/school. Results showed socio-educational trends at the textual and morpho-orthographic levels, and stilistic patterns at the lexicogrammatical level, related to socio-cultural traits. In sum, these results account for a variety of degrees and ways of appropriating narrative writing, which emerge from the double perspective of adjustment to prescriptions, as well as exploration and exploitation of a range of options.Fil: Iparraguirre, María Sol. Universidad Nacional del Comahue. Instituto Patagónico de Estudios de Humanidades y Ciencias Sociales. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Patagonia Norte. Instituto Patagónico de Estudios de Humanidades y Ciencias Sociales; Argentina. Universidad Nacional del Comahue. Centro Regional Universitario Bariloche; ArgentinaFil: Baccalá, Nora Belkis. Universidad Nacional del Comahue. Centro Regional Universitario Bariloche; ArgentinaFil: Scheuer, Nora. Universidad Nacional del Comahue. Instituto Patagónico de Estudios de Humanidades y Ciencias Sociales. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Patagonia Norte. Instituto Patagónico de Estudios de Humanidades y Ciencias Sociales; Argentina. Universidad Nacional del Comahue. Centro Regional Universitario Bariloche; Argentin

Causally Linking Neural Dominance to Perceptual Dominance in a Multisensory Conflict

When different senses are in conflict, one sense may dominate the perception of other sense, but it is not known whether the sensory cortex associated with the dominant modality exerts directional influence, at the functional brain level, over the sensory cortex associated with the dominated modality; in short, the link between sensory dominance and neuronal dominance is not established. In a task involving audio-visual conflict, using magnetoencephalography recordings in humans, we first demonstrated that the neuronal dominance – auditory cortex functionally influencing visual cortex – was associated with the sensory dominance – sound qualitatively altering visual perception. Further, we found that pre-stimulus auditory-to-visual connectivity could predict the perceptual outcome on a trial-by-trial basis. Subsequently, we performed an effective connectivity-guided neurofeedback electroencephalography experiment and showed that participants who were briefly trained to increase the neuronal dominance from auditory to visual cortex showed higher sensory, i.e. auditory, dominance during the conflict task immediately after the training. These results shed new light into the interactive neuronal nature of multisensory integration and open up exciting opportunities by enhancing or suppressing targeted mental functions subserved by effective connectivity

Computational Inference of Neural Information Flow Networks

Determining how information flows along anatomical brain pathways is a fundamental requirement for understanding how animals perceive their environments, learn, and behave. Attempts to reveal such neural information flow have been made using linear computational methods, but neural interactions are known to be nonlinear. Here, we demonstrate that a dynamic Bayesian network (DBN) inference algorithm we originally developed to infer nonlinear transcriptional regulatory networks from gene expression data collected with microarrays is also successful at inferring nonlinear neural information flow networks from electrophysiology data collected with microelectrode arrays. The inferred networks we recover from the songbird auditory pathway are correctly restricted to a subset of known anatomical paths, are consistent with timing of the system, and reveal both the importance of reciprocal feedback in auditory processing and greater information flow to higher-order auditory areas when birds hear natural as opposed to synthetic sounds. A linear method applied to the same data incorrectly produces networks with information flow to non-neural tissue and over paths known not to exist. To our knowledge, this study represents the first biologically validated demonstration of an algorithm to successfully infer neural information flow networks

Measuring Directed Functional Connectivity Using Non-Parametric Directionality Analysis : Validation and Comparison with Non-Parametric Granger Causality

BACKGROUND: 'Non-parametric directionality' (NPD) is a novel method for estimation of directed functional connectivity (dFC) in neural data. The method has previously been verified in its ability to recover causal interactions in simulated spiking networks in Halliday et al. (2015). METHODS: This work presents a validation of NPD in continuous neural recordings (e.g. local field potentials). Specifically, we use autoregressive models to simulate time delayed correlations between neural signals. We then test for the accurate recovery of networks in the face of several confounds typically encountered in empirical data. We examine the effects of NPD under varying: a) signal-to-noise ratios, b) asymmetries in signal strength, c) instantaneous mixing, d) common drive, e) data length, and f) parallel/convergent signal routing. We also apply NPD to data from a patient who underwent simultaneous magnetoencephalography and deep brain recording. RESULTS: We demonstrate that NPD can accurately recover directed functional connectivity from simulations with known patterns of connectivity. The performance of the NPD measure is compared with non-parametric estimators of Granger causality (NPG), a well-established methodology for model-free estimation of dFC. A series of simulations investigating synthetically imposed confounds demonstrate that NPD provides estimates of connectivity that are equivalent to NPG, albeit with an increased sensitivity to data length. However, we provide evidence that: i) NPD is less sensitive than NPG to degradation by noise; ii) NPD is more robust to the generation of false positive identification of connectivity resulting from SNR asymmetries; iii) NPD is more robust to corruption via moderate amounts of instantaneous signal mixing. CONCLUSIONS: The results in this paper highlight that to be practically applied to neural data, connectivity metrics should not only be accurate in their recovery of causal networks but also resistant to the confounding effects often encountered in experimental recordings of multimodal data. Taken together, these findings position NPD at the state-of-the-art with respect to the estimation of directed functional connectivity in neuroimaging

Attention-dependent modulation of cortical taste circuits revealed by granger causality with signal-dependent noise

We show, for the first time, that in cortical areas, for example the insular, orbitofrontal, and lateral prefrontal cortex, there is signal-dependent noise in the fMRI blood-oxygen level dependent (BOLD) time series, with the variance of the noise increasing approximately linearly with the square of the signal. Classical Granger causal models are based on autoregressive models with time invariant covariance structure, and thus do not take this signal-dependent noise into account. To address this limitation, here we describe a Granger causal model with signal-dependent noise, and a novel, likelihood ratio test for causal inferences. We apply this approach to the data from an fMRI study to investigate the source of the top-down attentional control of taste intensity and taste pleasantness processing. The Granger causality with signal-dependent noise analysis reveals effects not identified by classical Granger causal analysis. In particular, there is a top-down effect from the posterior lateral prefrontal cortex to the insular taste cortex during attention to intensity but not to pleasantness, and there is a top-down effect from the anterior and posterior lateral prefrontal cortex to the orbitofrontal cortex during attention to pleasantness but not to intensity. In addition, there is stronger forward effective connectivity from the insular taste cortex to the orbitofrontal cortex during attention to pleasantness than during attention to intensity. These findings indicate the importance of explicitly modeling signal-dependent noise in functional neuroimaging, and reveal some of the processes involved in a biased activation theory of selective attention