Biology subjects

Schoffelen, J.-M.

Publications and source records attributed to Schoffelen, J.-M..

7 recordsLinked to original sources

bioRxiv2018

Dysregulated Oscillatory Connectivity in the Visual System in Autism Spectrum Disorder

Autism Spectrum Disorder is increasingly associated with atypical perceptual and sensory symptoms. Here we explore the hypothesis that aberrant sensory processing in Autism Spectrum Disorder could be linked to atypical intra- (local) and inter-regional (global) brain connectivity. To elucidate oscillatory dynamics and connectivity in the visual domain we used magnetoencephalography and a simple visual grating paradigm with a group of 18 adolescent autistic participants and 18 typically developing controls. Both groups showed similar increases in gamma (40-80Hz) and decreases in alpha (8-13Hz) frequency power in occipital cortex. However, systematic group differences emerged when analysing intra- and inter-regional connectivity in detail. Firstly, directed connectivity was estimated using non-parametric Granger causality between visual areas V1 and V4. Feedforward V1-to-V4 connectivity, mediated by gamma oscillations, was equivalent between Autism Spectrum Disorder and control groups, but importantly, feedback V4-to-V1 connectivity, mediated by alpha (8-13Hz) oscillations, was significantly reduced in the Autism Spectrum Disorder group. This reduction was positively correlated with autistic quotient scores, consistent with an atypical visual hierarchy in autism, characterised by reduced top-down modulation of visual input via alpha-band oscillations. Secondly, at the local level in V1, coupling of alpha-phase to gamma amplitude (alpha-gamma phase amplitude coupling, PAC) was reduced in the Autism Spectrum Disorder group. This implies dysregulated local visual processing, with gamma oscillations decoupled from patterns of wider alpha-band phase synchrony (i.e. reduced PAC), possibly due to an excitation-inhibition imbalance. More generally, these results are in agreement with predictive coding accounts of neurotypical perception and indicate that visual processes in autism are less modulated by contextual feedback information.

neuroscience↗

bioRxiv2018

Frequency-specific brain dynamics related to prediction during language comprehension

The brains remarkable capacity to process spoken language virtually in real time requires fast and efficient information processing machinery. In this study, we investigated how frequency-specific brain dynamics relate to models of probabilistic language prediction during auditory narrative comprehension. We recorded MEG activity while participants were listening to auditory stories in Dutch. Using trigram statistical language models, we estimated for every word in a story its conditional probability of occurrence. On the basis of word probabilities, we computed how unexpected the current word is given its context (word perplexity) and how (un)predictable the current linguistic context is (word entropy). We then evaluated whether source-reconstructed MEG oscillations at different frequency bands are modulated as a function of these language processing metrics. We show that theta-band source dynamics are increased in high relative to low entropy states, likely reflecting lexical computations. Beta-band dynamics are increased in situations of low word entropy and perplexity possibly reflecting maintenance of ongoing cognitive context. These findings lend support to the idea that the brain engages in the active generation and evaluation of predicted language based on the statistical properties of the input signal.

neuroscience↗

bioRxiv2018

Stimulus-induced Gamma Power Predicts the Amplitude of the Subsequent Visual Evoked Response

The efficiency of neuronal information transfer in activated brain networks may affect behavioral performance. Gamma-band synchronization has been proposed to be a mechanism that facilitates neuronal processing of behaviorally relevant stimuli. In line with this, it has been shown that strong gamma-band activity in visual cortical areas leads to faster responses to a visual go cue. We investigated whether there are directly observable consequences of trial-by-trial fluctuations in non-invasively observed gamma-band activity on the neuronal response. Specifically, we hypothesizedthat the amplitude of the visual evoked response to a go cue can be predicted by gamma power in the visual system, in the window preceding the evoked response. Thirty-three human subjects (22 female) performed a visual speeded response task while their magnetoencephalogram (MEG) was recorded. The participants had to respond to a pattern reversal of a concentric moving grating. We estimated single trial stimulus-induced visual cortical gamma power, and correlated this with the estimated single trial amplitude of the most prominent event-related field (ERF) peak within the first 100 ms after the pattern reversal. In parieto-occipital cortical areas, the amplitude of the ERF correlated positively with gamma power, and correlated negatively with reaction times. No effects were observed for the alpha and beta frequency bands, despite clear stimulus onset induced modulation at those frequencies. These results support a mechanistic model, in which gamma-band synchronization enhances the neuronal gain to relevant visual input, thus leading to more efficient downstream processing and to faster responses.\n\nSignificance statementGamma-band activity has been associated with many cognitive functions and improved behavioral performance. For example, high amplitude gamma-band activity in visual cortical areas before a go cue leads to faster reaction times. However, it remains unclear through which neural mechanism(s) gamma-band activity eventually affects behavior. We tested whether the strength of induced gamma-band activity affects evoked activity elicited by a subsequent visual stimulus. We found enhanced amplitudes of early visual evoked activity, and faster responses with higher gamma power. This suggests that gamma-band activity affects the neuronal gain to new sensory input, and thus these results bridge the gap between gamma power and behavior, and support the putative role of gamma-band activity in the efficiency of cortical processing.

neuroscience↗

bioRxiv2017

Integrated analysis of anatomical and electrophysiological human intracranial data

The exquisite spatiotemporal precision of human intracranial EEG recordings (iEEG) permits characterizing neural processing with a level of detail that is inaccessible to scalp-EEG, MEG, or fMRI. However, the same qualities that make iEEG an exceptionally powerful tool also present unique challenges. Until now, the fusion of anatomical data (MRI and CT images) with the electrophysiological data and its subsequent analysis has relied on technologically and conceptually challenging combinations of software. Here, we describe a comprehensive protocol that addresses the complexities associated with human iEEG, providing complete transparency and flexibility in the evolution of raw data into illustrative representations. The protocol is directly integrated with an open source toolbox for electrophysiological data analysis (FieldTrip). This allows iEEG researchers to build on a continuously growing body of scriptable and reproducible analysis methods that, over the past decade, have been developed and employed by a large research community. We demonstrate the protocol for an example complex iEEG data set to provide an intuitive and rapid approach to dealing with both neuroanatomical information and large electrophysiological data sets. We explain how the protocol can be largely automated, taking under an hour to complete, and readily adjusted to iEEG data sets with other characteristics.

neuroscience↗

bioRxiv2017

Ghost interactions in MEG/EEG source space: A note of caution on inter-areal coupling measures

When combined with source modeling, magneto- (MEG) and electroencephalography (EEG) can be used to study long-range interactions among cortical processes non-invasively. Estimation of such inter-areal connectivity is nevertheless hindered by instantaneous field spread and volume conduction, which artificially introduce linear correlations and impair source separability in cortical current estimates. To overcome the inflating effects of linear source mixing inherent to standard interaction measures, alternative phase- and amplitude-correlation based connectivity measures, such as imaginary coherence and orthogonalized amplitude correlation have been proposed. Being by definition insensitive to zero-lag correlations, these techniques have become increasingly popular in the identification of correlations that cannot be attributed to field spread or volume conduction. We show here, however, that while these measures are immune to the direct effects of linear mixing, they may still reveal large numbers of spurious false positive connections through field spread in the vicinity of true interactions. This fundamental problem affects both region-of-interest-based analyses and all-to-all connectome mappings. Most importantly, beyond defining and illustrating the problem of spurious, or \"ghost\" interactions, we provide a rigorous quantification of this effect through extensive simulations. Additionally, we further show that signal mixing also significantly limits the separability of neuronal phase and amplitude correlations. We conclude that spurious correlations must be carefully considered in connectivity analyses in MEG/EEG source space even when using measures that are immune to zero-lag correlations.\n\nHighlights{checkmark} Reliable estimation of neuronal coupling with MEG and EEG is challenged by signal mixing\n{checkmark}A number of coupling techniques attempt to overcome this limitation by excluding zero-lag interactions\n{checkmark}Contrary to what is commonly admitted, our simulations illustrate that such interaction metrics will still yield false positives\n{checkmark}Spurious, or \"ghost\", interactions are generally detected between sources in the vicinity of true phase-lagged interacting sources\n{checkmark}Signal mixing also severely affects the mutual separability of phase and amplitude correlations

neuroscience↗

bioRxiv2017

Stimulus familiarity and expectation jointly modulate neural activity in the visual ventral stream

Prior knowledge about the visual world can change how a visual stimulus is processed. Two forms of prior knowledge are often distinguished: stimulus familiarity (i.e., whether a stimulus has been seen before) and stimulus expectation (i.e., whether a stimulus is expected to occur, based on the context). Neurophysiological studies in monkeys have shown suppression of spiking activity both for expected and for familiar items in object-selective inferotemporal cortex (IT). It is an open question, however, if and how these types of knowledge interact in their modulatory effects on the sensory response. In order to address this issue and to examine whether previous findings generalize to non-invasively measured neural activity in humans of both sexes, we separately manipulated stimulus familiarity and expectation, while non-invasively recording human brain activity using magnetoencephalography (MEG). We observed independent suppression of neural activity by familiarity and expectation, specifically in the lateral occipital complex (LOC), the putative human homologue of monkey IT. Familiarity also led to sharpened response dynamics, which was predominantly observed in early visual cortex. Together, these results show that distinct types of sensory knowledge jointly determine the amount of neural resources dedicated to object processing in the visual ventral stream.

neuroscience↗

bioRxiv2017

MEG-BIDS: an extension to the Brain Imaging Data Structure for magnetoencephalography

We present a significant extension of the Brain Imaging Data Structure (BIDS) to support the specific aspects of magnetoencephalography (MEG) data. MEG provides direct measurement of brain activity with millisecond temporal resolution and unique source imaging capabilities. So far, BIDS has provided a solution to structure the organization of magnetic resonance imaging (MRI) data, which nature and acquisition parameters are different. Despite the lack of standard data format for MEG, MEG-BIDS is a principled solution to store, organize and share the typically-large data volumes produced. It builds on BIDS for MRI, and therefore readily yields a multimodal data organization by construction. This is particularly valuable for the anatomical and functional registration of MEG source imaging with MRI. With MEG-BIDS and a growing range of software adopting the standard, the MEG community has a solution to minimize curation overheads, reduce data handling errors and optimize usage of computational resources for analytics. The standard also includes well-defined metadata, to facilitate future data harmonization and sharing efforts.

neuroscience↗