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

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

Cognition-mortality associations are more pronounced when estimated jointly in longitudinal and time-to-event models

With aging populations worldwide, there is growing interest in links between cognitive decline and elevated mortality risk-and, by extension, analytic approaches to further clarify these associations. Toward this end, some researchers have compared cognitive trajectories of survivors vs. decedents while others have examined longitudinal changes in cognition as predictive of mortality risk. A two-stage modeling framework is typically used in this latter approach; however, several recent studies have used joint longitudinal-survival modeling (i.e., estimating longitudinal change in cognition conditionally on mortality risk, and vice versa). Methodological differences inherent to these approaches may influence estimates of cognitive decline and cognition-mortality associations. These effects may vary across cognitive domains insofar as changes in broad fluid and crystallized abilities are differentially sensitive to aging and mortality risk. We compared these analytic approaches as applied to data from a large-sample, repeated-measures study of older adults ( N = 5,954; ages 50-87 years at assessment; 4,453 deceased at last census). Cognitive trajectories indicated worse performance in decedents and when estimated jointly with mortality risk, but this was attenuated after adjustment for health-related covariates. Better cognitive performance predicted lower mortality risk, and, importantly, cognition-mortality associations were more pronounced when estimated in joint models. Associations between mortality risk and crystallized abilities only emerged under joint estimation. This may have important implications for cognitive reserve, which posits that knowledge and skills considered well-preserved in later life (i.e., crystallized abilities) may compensate for declines in abilities more prone to neurodegeneration, such as recall memory and problem solving. Joint longitudinal-survival models thus appear to be important (and currently underutilized) for research in cognitive epidemiology. </p

A randomized controlled trial to evaluate the acceptability and effectiveness of two eating disorders prevention interventions: the HEIDI BP-HW project

Abstract Background Eating disorders (ED) are common in Switzerland, as in other Western countries, with a prevalence of any ED of 3.5%. However, no specific prevention intervention has been evaluated in the French-speaking part of the country. In this study, we assessed the acceptability and effectiveness of two well-validated eating disorders prevention interventions: the Body Project intervention (BP), based on cognitive dissonance techniques, and the Healthy Weight intervention (HW), based on the implementation of a healthy lifestyle. Methods Forty female students, aged 18–28, with body dissatisfaction, were randomized into three arms: a BP group, an HW group, and a waiting-list control group (WLCG). The primary outcome measure was body dissatisfaction. Secondary outcomes were thin-ideal internalization, dietary restraint, negative affect, and ED psychopathology. Thirty-three participants completed the assessments before and after the one-month interventions or waiting period. A follow-up measurement was conducted one month after the interventions to assess the stability of the results. Results Both interventions, delivered via a virtual web platform, were considered acceptable. The reduction in body dissatisfaction was greater in the BP group (r = 0.7; p < 0.01) or the HW group (r = 0.6; p < 0.01) than in the WLCG, with large effect sizes. Dietary restraint and shape concern were also significantly reduced in the BP group (r = 0.6 and r = 0.7, respectively; p < 0.01) and HW group (r = 0.5 and r = 0.5, respectively; p < 0.05) compared to the WLCG, with moderate to large effect sizes. The results obtained in each intervention group were stable at the one-month follow-up. Conclusions This study showed encouraging results in young women with body dissatisfaction, arguing in favor of the French adaptations of the BP and HW interventions. However, the feasibility of recruitment was difficult, partly due to the pandemic situation at the time of the study, and should be further considered to improve dissemination. Trial registration ClinicalTrials.gov Identifier: NCT04558073, 22/09/2020 and Swiss National Clinical Trial Portal (SNCTP000003978)