Biology subjects

van Wassenhove, V.

Publications and source records attributed to van Wassenhove, V..

7 recordsLinked to original sources

bioRxiv2018

Self-similarity and multifractality in human brain activity: a wavelet-based analysis of scale-free brain dynamics

BackgroundThe temporal structure of macroscopic brain activity displays both oscillatory and scale-free dynamics. While the functional relevance of neural oscillations has been largely investigated, both the nature and the role of scale-free dynamics in brain processing have been disputed.\n\nNew MethodHere, we offer a novel method to rigorously enrich the characterization of scale-free brain activity using a robust wavelet-based assessment of self-similarity and multifractality. For this, we analyzed human brain activity recorded with magnetoencephalography (MEG) while participants were at rest or performing a task.\n\nResultsFirst, we report consistent infraslow (from 0.1 to 1.5 Hz) scalefree dynamics (i.e., self-similarity and multifractality) in resting-state and task data. Second, we observed a fronto-occipital gradient of self-similarity reminiscent of the known hierarchy of temporal scales from sensory to higherorder cortices; the anatomical gradient was more pronounced in task than in rest. Third, we observed a significant increase of multifractality during task as compared to rest. Additionally, the decrease in self-similarity and the increase in multifractality from rest to task were negatively correlated in regions involved in the task, suggesting a shift from structured global temporal dynamics in resting-state to locally bursty and non Gaussian scalefree structures during task.\n\nComparison with Existing Method(s)We showed that the wavelet leader based multifractal approach extends power spectrum estimation methods in the way of characterizing finely scale-free brain dynamics.\n\nConclusionsAltogether, our approach provides novel fine-grained characterizations of scale-free dynamics in human brain activity.\n\nHighlightsO_LIWe estimated scale-free human brain dynamics using wavelet-leader formalism.\nC_LIO_LIHigh-to-low self-similarity defined a fronto-occipital gradient.\nC_LIO_LIThe gradient was enhanced in task compared to resting-state.\nC_LIO_LIScale-free brain dynamics showed multifractal properties.\nC_LIO_LISelf-similarity decreased whereas multifractality increased from rest to task.\nC_LI

neuroscience↗

bioRxiv2017

Temporal metacognition as the decoding of self-generated brain dynamics

Metacognition, the ability to know about ones thought process, is self-referential. Here, we combined psychophysics and time-resolved neuroimaging to explore metacognitive inference on the accuracy of a self-generated behavior. Human participants generated a time interval and evaluated the signed magnitude of their temporal production. We show that both self-generation and self-evaluation relied on the power of beta oscillations ({beta}; 15-40 Hz) with increases in early {beta} power predictive of increases in duration. We characterized the dynamics of {beta} power in a low dimensional space ({beta} state-space trajectories) as a function of timing and found that the more distinct trajectories, the more accurate metacognitive inferences were. These results suggest that {beta} states instantiates an internal variable determining the fate of the timing networks trajectory, possibly as release from inhibition. Altogether, our study describes oscillatory mechanisms for timing, suggesting that temporal metacognition relies on inferential processes of self-generated dynamics.

neuroscience↗

bioRxiv2017

Oscillatory multiplexing indexes precision

Oscillatory coupling has been implicated in the representation and in the processing of information in the brain. Specific hypotheses suggest that oscillatory coupling may be relevant for the temporal coding of information but to which extent this may translate to conscious timing is unknown. Here, we tested the hypothesis that the temporal precision of self-generated timed actions may be controlled by phase-amplitude coupling (PAC). Using a timing task, we show the existence of significant alpha-beta (-{beta}) PAC, robust at the individual level, and specific to temporal production. Second, an increase in the strength of -{beta} PAC was associated with a smaller variance in time production, i.e. an increased precision in timing, but there was no correlation with the duration of the produced interval. Our results suggest an active role for -{beta} coupling in maintaining the precision of the endogenous temporal goal during time production: specifically, oscillations may maintain the content of current cognitive states, thus securing the endogenous temporal code for duration estimation instantiated in {beta} band. Oscillatory multiplexing may thus index the variance of neuronal computations, which translates into the precision of behavioral performance.

neuroscience↗

bioRxiv2017

The psychological arrow of time and the human brain dynamics of event ordering

When navigating the real-world, the spatiotemporal sequencing of events is intrinsically bound to ones physical trajectory; when recollecting the past or imagining the future, the temporal and spatial dimension of events can be independently manipulated. Yet, the rules enabling the flexible use of spatial and temporal cognitive maps likely differ in one major way as time is directional (oriented from past-to-future) whereas space is not. Using combined magneto- and electroencephalography, we sought to capture such differences by characterizing time-resolved brain activity while participants mentally ordered memories from different mental perspectives in time (past/future) or space (west/east). We report two major neural dissociations underlying the mental ordering of events in time and in space: first, brain responses evoked by the temporal order and the temporal distance of events-to-self dissociated at early and late latencies, respectively whereas spatial order and distance of events-to-self elicited late brain responses simultaneously. Second, brain responses distinguishing self-position in time and the temporal order of events involved sources in the hippocampal formation; spatial perspective, order and distance did not. These results suggest that the neural dynamics evoked by the temporal ordering of a series of events retrieved from long-term memory, i.e. the psychological time arrow, entails dedicated cognitive processes in the hippocampal formation that are fundamentally distinct from the mapping of spatial location.

neuroscience↗

bioRxiv2017

Non-linear Auto-Regressive Models for Cross-Frequency Coupling in Neural Time Series

We address the issue of reliably detecting and quantifying cross-frequency coupling (CFC) in neural time series. Based on non-linear auto-regressive models, the proposed method provides a generative and parametric model of the time-varying spectral content of the signals. As this method models the entire spectrum simultaneously, it avoids the pitfalls related to incorrect filtering or the use of the Hilbert transform on wide-band signals. As the model is probabilistic, it also provides a score of the model \"goodness of fit\" via the likelihood, enabling easy and legitimate model selection and parameter comparison; this data-driven feature is unique to our model-based approach. Using three datasets obtained with invasive neurophysiological recordings in humans and rodents, we demonstrate that these models are able to replicate previous results obtained with other metrics, but also reveal new insights such as the influence of the amplitude of the slow oscillation. Using simulations, we demonstrate that our parametric method can reveal neural couplings with shorter signals than non-parametric methods. We also show how the likelihood can be used to find optimal filtering parameters, suggesting new properties on the spectrum of the driving signal, but also to estimate the optimal delay between the coupled signals, enabling a directionality estimation in the coupling.\n\nAuthor SummaryNeural oscillations synchronize information across brain areas at various anatomical and temporal scales. Of particular relevance, slow fluctuations of brain activity have been shown to affect high frequency neural activity, by regulating the excitability level of neural populations. Such cross-frequency-coupling can take several forms. In the most frequently observed type, the power of high frequency activity is time-locked to a specific phase of slow frequency oscillations, yielding phase-amplitude-coupling (PAC). Even when readily observed in neural recordings, such non-linear coupling is particularly challenging to formally characterize. Typically, neuroscientists use band-pass filtering and Hilbert transforms with ad-hoc correlations. Here, we explicitly address current limitations and propose an alternative probabilistic signal modeling approach, for which statistical inference is fast and well-posed. To statistically model PAC, we propose to use non-linear auto-regressive models which estimate the spectral modulation of a signal conditionally to a driving signal. This conditional spectral analysis enables easy model selection and clear hypothesis-testing by using the likelihood of a given model. We demonstrate the advantage of the model-based approach on three datasets acquired in rats and in humans. We further provide novel neuroscientific insights on previously reported PAC phenomena, capturing two mechanisms in PAC: influence of amplitude and directionality estimation.

neuroscience↗

bioRxiv2017

Decoding the categorization of visual motion with magnetoencephalography

Brain decoding techniques are particularly efficient at deciphering weak and distributed neural patterns. Brain decoding has primarily been used in cognitive neurosciences to predict differences between pairs of stimuli (e.g. faces vs. houses), but how distinct brain/perceptual states can be decoded following the presentation of continuous sensory stimuli is unclear. Here, we developed a novel approach to decode brain activity recorded with magnetoencephalography while participants discriminated the coherence of two intermingled clouds of dots. Seven levels of visual motion coherence were tested and participants reported the colour of the most coherent cloud. The decoding approach was formulated as a ranked-classification problem, in which the model was evaluated by its capacity to predict the order of a pair of trials, each tested with two distinct visual motion coherence levels. Two brain states were decoded as a function of the degree of visual motion coherence. Importantly, perceptual motion coherence thresholds were found to match the decoder boundaries in a fully data-driven way. The algorithm revealed the earliest categorization in hMT+, followed by V1/V2, IPS, and vlPFC.

neuroscience↗

bioRxiv2017

A Bayesian Perspective on Accumulation in the Magnitude System

Theoretical and empirical work posits the existence of a common magnitude system in the brain. Such a proposal implies that manipulating stimuli in one magnitude dimension (e.g. time) should interfere with the subjective estimation of another magnitude dimension (e.g. space). Here, we asked whether a generalized Bayesian magnitude estimation system would sample sensory evidence using a common, amodal prior. Two psychophysical experiments separately tested participants on their perception of duration, surface, and numerosity when non-target magnitude dimensions and the rate of sensory evidence accumulation were manipulated. First, we found that duration estimation was resilient to changes in surface or numerosity, whereas lengthening (shortening) duration yielded under- (over-) estimations of surface and numerosity. Second, the perception of numerosity and surface were affected by changes in the rate of sensory evidence accumulation, whereas duration was not. Our results suggest that a generalized magnitude system based on Bayesian computations would minimally necessitate multiple priors.

neuroscience↗