Multiscale Bayesian State Space Model for Granger Causality Analysis of Brain Signal

Modelling time-varying and frequency-specific relationships between two brain signals is becoming an essential methodological tool to answer heoretical questions in experimental neuroscience. In this article, we propose to estimate a frequency Granger causality statistic that may vary in time in order to evaluate the functional connections between two brain regions during a task. We use for that purpose an adaptive Kalman filter type of estimator of a linear Gaussian vector autoregressive model with coefficients evolving over time. The estimation procedure is achieved through variational Bayesian approximation and is extended for multiple trials. This Bayesian State Space (BSS) model provides a dynamical Granger-causality statistic that is quite natural. We propose to extend the BSS model to include the \`{a} trous Haar decomposition. This wavelet-based forecasting method is based on a multiscale resolution decomposition of the signal using the redundant \`{a} trous wavelet transform and allows us to capture short- and long-range dependencies between signals. Equally importantly it allows us to derive the desired dynamical and frequency-specific Granger-causality statistic. The application of these models to intracranial local field potential data recorded during a psychological experimental task shows the complex frequency based cross-talk between amygdala and medial orbito-frontal cortex. Keywords: \`{a} trous Haar wavelets; Multiple trials; Neuroscience data; Nonstationarity; Time-frequency; Variational methods The published version of this article is Cekic, S., Grandjean, D., Renaud, O. (2018). Multiscale Bayesian state-space model for Granger causality analysis of brain signal. Journal of Applied Statistics. https://doi.org/10.1080/02664763.2018.145581

Variability of Affective Responses to Odors: Culture, Gender, and Olfactory Knowledge

Emotion and odor scales (EOS) measuring odor-related affective feelings were recently developed for three different countries (Switzerland, United Kingdom, and Singapore). The first aim of this study was to investigate gender and cultural differences in verbal affective response to odors, measured with EOS and the usual pleasantness scale. To better understand this variability, the second aim was to investigate the link between affective reports and olfactory knowledge (familiarity and identification). Responses of 772 participants smelling 56-59 odors were collected in the three countries. Women rated odors as more intense and identified them better in all countries, but no reliable sex differences were found for verbal affective responses to odors. Disgust-related feelings revealed odor-dependent sex differences, due to sex differences in identification and categorization. Further, increased odor knowledge was related to more positive affects as reported with pleasantness and odor-related feeling evaluations, which can be related to top-down influences on odor representation. These top-down influences were thought, for example, to relate to beliefs about odor properties or to categorization (edible vs. nonedible). Finally, the link between odor knowledge and olfactory affect was generally asymmetrical and significant only for pleasant odors, not for unpleasant ones that seemed to be more resistant to cognitive influences. This study, for the first time using emotional scales that are appropriate to the olfactory domain, brings new insights into the variability of affective responses to odors and its relationship to odor knowledg

Time-frequency Granger causality with application to nonstationary brain signals

This PhD thesis concerns the modelling of time-varying causal relationships between two signals, with a focus on signals measuring neural activities. The ability to compute a dynamic and frequency-specific causality statistic in this context is essential and Granger causality provides a natural statistical tool. In Chapter 1 we propose a review of the existing methods allowing one to measure time-varying frequency-specific Granger causality and discuss their advantages and drawbacks. Based on this review, we propose in Chapter 2 an estimator of a linear Gaussian vector autoregressive model with coefficients evolving over time. Estimation procedure is achieved through variational Bayesian approximation and the model provides a dynamical Granger-causality statistic that is quite natural. We propose an extension to the `a trous Haar decomposition that allows us to derive the desired dynamical and frequency-specific Granger-causality statistic. In Chapter 3 we propose an application of the model to real experimental data

Not screens but their context of use impact cognitive development: a commentary on Yang et al. (2023)

There have been extensive debates about the impact of the digital transformation on human development. A recent study by Yang and colleagues highlights the importance of considering context of use, beyond amount of use. In their study, children from parents who reported having TV‐on during family meals when they were 2 years old showed poorer cognitive development at age 3.5 as compared to those with TV‐off during family meals. This highlights the importance of considering the context of use when studying effect of screen use. While Yang et al. discuss the distracting effects of TV‐on sensory processing, we propose an alternative – and not mutually exclusive – interpretation based on TV induced deprivation of family interactions. On a more practical note, this should encourage to preserve screen‐free time, especially during structured time such as family meals, in order to maintain family interactions known to be critical to development.</p

Time, frequency, and time-varying Granger-causality measures in neuroscience

This article proposes a systematic methodological review and an objective criticism of existing methods enabling the derivation of time, frequency, and time-varying Granger-causality statistics in neuroscience. The capacity to describe the causal links between signals recorded at different brain locations during a neuroscience experiment is indeed of primary interest for neuroscientists, who often have very precise prior hypotheses about the relationships between recorded brain signals. The increasing interest and the huge number of publications related to this topic calls for this systematic review, which describes the very complex methodological aspects underlying the derivation of these statistics. In this article, we first present a general framework that allows us to review and compare Granger-causality statistics in the time domain, and the link with transfer entropy. Then, the spectral and the time-varying extensions are exposed and discussed together with their estimation and distributional properties. Although not the focus of this article, partial and conditional Granger causality, dynamical causal modelling, directed transfer function, directed coherence, partial directed coherence, and their variant are also mentioned

Multiscale Bayesian State Space Model for Granger Causality Analysis of Brain Signal

Modelling time-varying and frequency-specific relationships between two brain signals is becoming an essential methodological tool to answer heoretical questions in experimental neuroscience. In this article, we propose to estimate a frequency Granger causality statistic that may vary in time in order to evaluate the functional connections between two brain regions during a task. We use for that purpose an adaptive Kalman filter type of estimator of a linear Gaussian vector autoregressive model with coefficients evolving over time. The estimation procedure is achieved through variational Bayesian approximation and is extended for multiple trials. This Bayesian State Space (BSS) model provides a dynamical Granger-causality statistic that is quite natural. We propose to extend the BSS model to include the `{a} trous Haar decomposition. This wavelet-based forecasting method is based on a multiscale resolution decomposition of the signal using the redundant `{a} trous wavelet transform and allows us to capture short- and long-range dependencies between signals. Equally importantly it allows us to derive the desired dynamical and frequency-specific Granger-causality statistic. The application of these models to intracranial local field potential data recorded during a psychological experimental task shows the complex frequency based cross-talk between amygdala and medial orbito-frontal cortex