Correction: Expectancy-based rhythmic entrainment as continuous Bayesian inference.
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ABSTRACT:
SUBMITTER: Cannon J
PROVIDER: S-EPMC8668110 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:When presented with complex rhythmic auditory stimuli, humans are able to track underlying temporal structure (e.g., a "beat"), both covertly and with their movements. This capacity goes far beyond that of a simple entrained oscillator, drawing on contextual and enculturated timing expectations and adjusting rapidly to perturbations in event timing, phase, and tempo. Previous modeling work has described how entrainment to rhythms may be shaped by event timing expectations, but sheds little light on any underlying computational principles that could unify the phenomenon of expectation-based entrainment with other brain processes. Inspired by the predictive processing framework, we propose that the problem of rhythm tracking is naturally characterized as a problem of continuously estimating an underlying phase and tempo based on precise event times and their correspondence to timing expectations. We present two inference problems formalizing this insight: PIPPET (Phase Inference from Point Process Event Timing) and PATIPPET (Phase and Tempo Inference). Variational solutions to these inference problems resemble previous "Dynamic Attending" models of perceptual entrainment, but introduce new terms representing the dynamics of uncertainty and the influence of expectations in the absence of sensory events. These terms allow us to model multiple characteristics of covert and motor human rhythm tracking not addressed by other models, including sensitivity of error corrections to inter-event interval and perceived tempo changes induced by event omissions. We show that positing these novel influences in human entrainment yields a range of testable behavioral predictions. Guided by recent neurophysiological observations, we attempt to align the phase inference framework with a specific brain implementation. We also explore the potential of this normative framework to guide the interpretation of experimental data and serve as building blocks for even richer predictive processing and active inference models of timing.
| S-EPMC8216548 | biostudies-literature
Project description:MotivationRecent work has demonstrated the feasibility of using non-numerical, qualitative data to parameterize mathematical models. However, uncertainty quantification (UQ) of such parameterized models has remained challenging because of a lack of a statistical interpretation of the objective functions used in optimization.ResultsWe formulated likelihood functions suitable for performing Bayesian UQ using qualitative observations of underlying continuous variables or a combination of qualitative and quantitative data. To demonstrate the resulting UQ capabilities, we analyzed a published model for immunoglobulin E (IgE) receptor signaling using synthetic qualitative and quantitative datasets. Remarkably, estimates of parameter values derived from the qualitative data were nearly as consistent with the assumed ground-truth parameter values as estimates derived from the lower throughput quantitative data. These results provide further motivation for leveraging qualitative data in biological modeling.Availability and implementationThe likelihood functions presented here are implemented in a new release of PyBioNetFit, an open-source application for analyzing Systems Biology Markup Language- and BioNetGen Language-formatted models, available online at www.github.com/lanl/PyBNF.Supplementary informationSupplementary data are available at Bioinformatics online.
| S-EPMC7214020 | biostudies-literature
Project description:Interval-censored longitudinal data taken from a Norwegian study of individuals with Parkinson's disease are investigated with respect to the onset of dementia. Of interest are risk factors for dementia and the subdivision of total life expectancy (LE) into LE with and without dementia. To estimate LEs using extrapolation, a parametric continuous-time 3-state illness-death Markov model is presented in a Bayesian framework. The framework is well suited to allow for heterogeneity via random effects and to investigate additional computation using model parameters. In the estimation of LEs, microsimulation is used to take into account random effects. Intensities of moving between the states are allowed to change in a piecewise-constant fashion by linking them to age as a time-dependent covariate. Possible right censoring at the end of the follow-up can be incorporated. The model is applicable in many situations where individuals are followed over a long time period. In describing how a disease develops over time, the model can help to predict future need for health care.
| S-EPMC2742499 | biostudies-literature
Project description:Single-molecule-localization-based superresolution microscopy requires accurate sample drift correction to achieve good results. Common approaches for drift compensation include using fiducial markers and direct drift estimation by image correlation. The former increases the experimental complexity and the latter estimates drift at a reduced temporal resolution. Here, we present, to our knowledge, a new approach for drift correction based on the Bayesian statistical framework. The technique has the advantage of being able to calculate the drifts for every image frame of the data set directly from the single-molecule coordinates. We present the theoretical foundation of the algorithm and an implementation that achieves significantly higher accuracy than image-correlation-based estimations.
| S-EPMC4643251 | biostudies-literature
Project description:[This corrects the article DOI: 10.1371/journal.pcbi.1006717.].
| S-EPMC8357164 | biostudies-literature
Project description:Dance is an icon of human expression. Despite astounding diversity around the world's cultures and dazzling abundance of reminiscent animal systems, the evolution of dance in the human clade remains obscure. Dance requires individuals to interactively synchronize their whole-body tempo to their partner's, with near-perfect precision. This capacity is motorically-heavy, engaging multiple neural circuitries, but also dependent on an acute socio-emotional bond between partners. Hitherto, these factors helped explain why no dance forms were present amongst nonhuman primates. Critically, evidence for conjoined full-body rhythmic entrainment in great apes that could help reconstruct possible proto-stages of human dance is still lacking. Here, we report an endogenously-effected case of ritualized dance-like behaviour between two captive chimpanzees - synchronized bipedalism. We submitted video recordings to rigorous time-series analysis and circular statistics. We found that individual step tempo was within the genus' range of "solo" bipedalism. Between-individual analyses, however, revealed that synchronisation between individuals was non-random, predictable, phase concordant, maintained with instantaneous centi-second precision and jointly regulated, with individuals also taking turns as "pace-makers". No function was apparent besides the behaviour's putative positive social affiliation. Our analyses show a first case of spontaneous whole-body entrainment between two ape peers, thus providing tentative empirical evidence for phylogenies of human dance. Human proto-dance, we argue, may have been rooted in mechanisms of social cohesion among small groups that might have granted stress-releasing benefits via gait-synchrony and mutual-touch. An external sound/musical beat may have been initially uninvolved. We discuss dance evolution as driven by ecologically-, socially- and/or culturally-imposed "captivity".
| S-EPMC6908706 | biostudies-literature
Project description:The stochastic behavior of single ion channels is most often described as an aggregated continuous-time Markov process with discrete states. For ligand-gated channels each state can represent a different conformation of the channel protein or a different number of bound ligands. Single-channel recordings show only whether the channel is open or shut: states of equal conductance are aggregated, so transitions between them have to be inferred indirectly. The requirement to filter noise from the raw signal further complicates the modeling process, as it limits the time resolution of the data. The consequence of the reduced bandwidth is that openings or shuttings that are shorter than the resolution cannot be observed; these are known as missed events. Postulated models fitted using filtered data must therefore explicitly account for missed events to avoid bias in the estimation of rate parameters and therefore assess parameter identifiability accurately. In this article, we present the first, to our knowledge, Bayesian modeling of ion-channels with exact missed events correction. Bayesian analysis represents uncertain knowledge of the true value of model parameters by considering these parameters as random variables. This allows us to gain a full appreciation of parameter identifiability and uncertainty when estimating values for model parameters. However, Bayesian inference is particularly challenging in this context as the correction for missed events increases the computational complexity of the model likelihood. Nonetheless, we successfully implemented a two-step Markov chain Monte Carlo method that we called "BICME", which performs Bayesian inference in models of realistic complexity. The method is demonstrated on synthetic and real single-channel data from muscle nicotinic acetylcholine channels. We show that parameter uncertainty can be characterized more accurately than with maximum-likelihood methods. Our code for performing inference in these ion channel models is publicly available.
| S-EPMC4968427 | biostudies-literature
Project description:BackgroundNeuronal elements underlying perception, cognition, and action exhibit distinct oscillatory phenomena, measured in humans by electro- or magnetoencephalography (EEG/MEG). So far, the correlative or causal nature of the link between brain oscillations and functions has remained elusive. A compelling demonstration of causality would primarily generate oscillatory signatures that are known to correlate with particular cognitive functions and then assess the behavioral consequences. Here, we provide the first direct evidence for causal entrainment of brain oscillations by transcranial magnetic stimulation (TMS) using concurrent EEG.ResultsWe used rhythmic TMS bursts to directly interact with an MEG-identified parietal α-oscillator, activated by attention and linked to perception. With TMS bursts tuned to its preferred α-frequency (α-TMS), we confirmed the three main predictions of entrainment of a natural oscillator: (1) that α-oscillations are induced during α-TMS (reproducing an oscillatory signature of the stimulated parietal cortex), (2) that there is progressive enhancement of this α-activity (synchronizing the targeted, α-generator to the α-TMS train), and (3) that this depends on the pre-TMS phase of the background α-rhythm (entrainment of natural, ongoing α-oscillations). Control conditions testing different TMS burst profiles and TMS-EEG in a phantom head confirmed specificity of α-boosting to the case of synchronization between TMS train and neural oscillator.ConclusionsThe periodic electromagnetic force that is generated during rhythmic TMS can cause local entrainment of natural brain oscillations, emulating oscillatory signatures activated by cognitive tasks. This reveals a new mechanism of online TMS action on brain activity and can account for frequency-specific behavioral TMS effects at the level of biologically relevant rhythms.
| S-EPMC3176892 | biostudies-literature
Project description:[This corrects the article DOI: 10.1371/journal.pcbi.1003150.].
| S-EPMC6019100 | biostudies-literature
Project description:We consider the modeling of data generated by a latent continuous-time Markov jump process with a state space of finite but unknown dimensions. Typically in such models, the number of states has to be pre-specified, and Bayesian inference for a fixed number of states has not been studied until recently. In addition, although approaches to address the problem for discrete-time models have been developed, no method has been successfully implemented for the continuous-time case. We focus on reversible jump Markov chain Monte Carlo which allows the trans-dimensional move among different numbers of states in order to perform Bayesian inference for the unknown number of states. Specifically, we propose an efficient split-combine move which can facilitate the exploration of the parameter space, and demonstrate that it can be implemented effectively at scale. Subsequently, we extend this algorithm to the context of model-based clustering, allowing numbers of states and clusters both determined during the analysis. The model formulation, inference methodology, and associated algorithm are illustrated by simulation studies. Finally, we apply this method to real data from a Canadian healthcare system in Quebec.Supplementary informationThe online version supplementary material available at 10.1007/s11222-021-10032-8.
| S-EPMC8550639 | biostudies-literature