The impact of phase entrainment on auditory detection is highly variable: Revisiting a key finding

Ample evidence shows that the human brain carefully tracks acoustic temporal regularities in the input, perhaps by entraining cortical neural oscillations to the rate of the stimulation. To what extent the entrained oscillatory activity influences processing of upcoming auditory events remains debated. Here, we revisit a critical finding from Hickok et al. (2015) that demonstrated a clear impact of auditory entrainment on subsequent auditory detection. Participants were asked to detect tones embedded in stationary noise, following a noise that was amplitude modulated at 3 Hz. Tonal targets occurred at various phases relative to the preceding noise modulation. The original study (N = 5) showed that the detectability of the tones (presented at near-threshold intensity) fluctuated cyclically at the same rate as the preceding noise modulation. We conducted an exact replication of the original paradigm (N = 23) and a conceptual replication using a shorter experimental procedure (N = 24). Neither experiment revealed significant entrainment effects at the group level. A restricted analysis on the subset of participants (36%) who did show the entrainment effect revealed no consistent phase alignment between detection facilitation and the preceding rhythmic modulation. Interestingly, both experiments showed group-wide presence of a non-cyclic behavioural pattern, wherein participants' detection of the tonal targets was lower at early and late time points of the target period. The two experiments highlight both the sensitivity of the task to elicit oscillatory entrainment and the striking individual variability in performance

The anticipation of events in time

Humans anticipate events signaled by sensory cues. It is commonly assumed that two uncertainty parameters modulate the brain's capacity to predict: the hazard rate (HR) of event probability and the uncertainty in time estimation which increases with elapsed time. We investigate both assumptions by presenting event probability density functions (PDFs) in each of three sensory modalities. We show that perceptual systems use the reciprocal PDF and not the HR to model event probability density. We also demonstrate that temporal uncertainty does not necessarily grow with elapsed time but can also diminish, depending on the event PDF. Previous research identified neuronal activity related to event probability in multiple levels of the cortical hierarchy (sensory (V4), association (LIP), motor and other areas) proposing the HR as an elementary neuronal computation. Our results—consistent across vision, audition, and somatosensation—suggest that the neurobiological implementation of event anticipation is based on a different, simpler and more stable computation than HR: the reciprocal PDF of events in time

Low-Frequency Oscillations Code Speech during Verbal Working Memory

Item does not contain fulltextThe way the human brain represents speech in memory is still unknown. An obvious characteristic of speech is its evolvement over time. During speech processing, neural oscillations are modulated by the temporal properties of the acoustic speech signal, but also acquired knowledge on the temporal structure of language influences speech perception-related brain activity. This suggests that speech could be represented in the temporal domain, a form of representation that the brain also uses to encode autobiographic memories. Empirical evidence for such a memory code is lacking. We investigated the nature of speech memory representations using direct cortical recordings in the left perisylvian cortex during delayed sentence reproduction in female and male patients undergoing awake tumor surgery. Our results reveal that the brain endogenously represents speech in the temporal domain. Temporal pattern similarity analyses revealed that the phase of frontotemporal low-frequency oscillations, primarily in the beta range, represents sentence identity in working memory. The positive relationship between beta power during working memory and task performance suggests that working memory representations benefit from increased phase separation.SIGNIFICANCE STATEMENT Memory is an endogenous source of information based on experience. While neural oscillations encode autobiographic memories in the temporal domain, little is known on their contribution to memory representations of human speech. Our electrocortical recordings in participants who maintain sentences in memory identify the phase of left frontotemporal beta oscillations as the most prominent information carrier of sentence identity. These observations provide evidence for a theoretical model on speech memory representations and explain why interfering with beta oscillations in the left inferior frontal cortex diminishes verbal working memory capacity. The lack of sentence identity coding at the syllabic rate suggests that sentences are represented in memory in a more abstract form compared with speech coding during speech perception and production

A glossary for research on human crowd dynamics

This article presents a glossary of terms that are frequently used in research on human crowds. This topic is inherently multidisciplinary as it includes work in and across computer science, engineering, mathematics, physics, psychology and social science, for example. We do not view the glossary presented here as a collection of finalised and formal definitions. Instead, we suggest it is a snapshot of current views and the starting point of an ongoing process that we hope will be useful in providing some guidance on the use of terminology to develop a mutual understanding across disciplines. The glossary was developed collaboratively during a multidisciplinary meeting. We deliberately allow several definitions of terms, to reflect the confluence of disciplines in the field. This also reflects the fact not all contributors necessarily agree with all definitions in this glossary

Brain oscillations and connectivity in autism spectrum disorders (ASD):new approaches to methodology, measurement and modelling

Although atypical social behaviour remains a key characterisation of ASD, the presence ofsensory and perceptual abnormalities has been given a more central role in recentclassification changes. An understanding of the origins of such aberrations could thus prove afruitful focus for ASD research. Early neurocognitive models of ASD suggested that thestudy of high frequency activity in the brain as a measure of cortical connectivity mightprovide the key to understanding the neural correlates of sensory and perceptual deviations inASD. As our review shows, the findings from subsequent research have been inconsistent,with a lack of agreement about the nature of any high frequency disturbances in ASD brains.Based on the application of new techniques using more sophisticated measures of brainsynchronisation, direction of information flow, and invoking the coupling between high andlow frequency bands, we propose a framework which could reconcile apparently conflictingfindings in this area and would be consistent both with emerging neurocognitive models ofautism and with the heterogeneity of the condition

Causality analysis between brain areas based on multivariate autoregressive models of MEG sensor data

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Investigating causality between interacting brain areas with multivariate autoregressive models of MEG sensor data

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Investigating causality between interacting brain areas with multivariate autoregressive models of MEG sensor data

Item does not contain fulltextIn this work we investigate the feasibility of building a MAR model directly on MEG sensor measurements and projecting the model in brain locations where causality is calculated through Partial Directed Coherence (PDC). This method overcomes the problems of model non-robustness and large computation times encountered during causality analysis by existing methods, which first project entire MEG sensor time-series into a large number of brain locations and then the MAR model is built on this large number of time-series.Second International Conference on Cognitive Neurodynamics - 200

New approaches for studying amplitude correlations and directionality with MEG

Contains fulltext : 218404.pdf (publisher's version ) (Open Access)The human brain is a complex system highly optimized for goal-oriented processing of sensory information. This efficient processing relies on the highly coordinated (in space and time) interplay of neural populations. It has been hypothesized that the temporally precise interaction of neural oscillations is a likely mechanism for neural communication. Recent years have seen rapid methodological developments for the analysis of these interactions. Two analysis approaches will be presented that tap into different aspects of neural interactions: 1) The study of task-related power modulations and amplitude correlations at the source level based on a general linear model. The analysis was performed on MEG data from a continuous visuomotor tracking task and allowed identification of frequency-specific networks related to visual stimulation, motor planning and execution and tracking performance. 2) Frequency-specific analysis of Granger causality is an interesting tool to detect directed information transfer between distant brain areas. Still, the of relevant regions of interest for analysis is difficult. Estimation of autoregressive models on sensor data followed by projections of model coefficients into source space leads to a computationally efficient approach that can be used for exhaustive mapping of directionality in the brain. Implications for data analysis and ideas for further developments will be discussed. Event abstract at: Biomag 2010 - 17th International Conference on Biomagnetism Conference, 28 March - 01 April 2010, Dubrovnik, Croatia.1 p