Biology subjects

Giraud, A.-L.

Publications and source records attributed to Giraud, A.-L..

28 records · Page 2Linked to original sources

bioRxiv2020

EEG and fMRI connectomes are reliably related: a simultaneous EEG-fMRI study from 1.5T to 7T

Both electroencephalography (EEG) and functional Magnetic Resonance Imaging (fMRI) are non-invasive methods that show complementary aspects of human brain activity. Despite measuring different proxies of brain activity, both the measured blood-oxygenation (fMRI) and neurophysiological recordings (EEG) are indirectly coupled. The electrophysiological and BOLD signal can map the underlying functional connectivity structure at the whole brain scale at different timescales. Previous work demonstrated a moderate but significant correlation between resting-state functional connectivity of both modalities, however there is a wide range of technical setups to measure simultaneous EEG-fMRI and the reliability of those measures between different setups remains unknown. This is true notably with respect to different magnetic field strengths (low and high field) and different spatial sampling of EEG (medium to high-density electrode coverage). Here, we investigated the reproducibility of the bimodal EEG-fMRI functional connectome in the most comprehensive resting-state simultaneous EEG-fMRI dataset compiled to date including a total of 72 subjects from four different imaging centers. Data was acquired from 1.5T, 3T and 7T scanners with simultaneously recorded EEG using 64 or 256 electrodes. We demonstrate that the whole-brain monomodal connectivity reproducibly correlates across different datasets and that a moderate crossmodal correlation between EEG and fMRI connectivity of r{approx}0.3 can be reproducibly extracted in low- and high-field scanners. The crossmodal correlation was strongest in the EEG-{beta} frequency band but exists across all frequency bands. Both homotopic and within intrinsic connectivity network (ICN) connections contributed the most to the crossmodal relationship. This study confirms, using a considerably diverse range of recording setups, that simultaneous EEG-fMRI offers a consistent estimate of multimodal functional connectomes in healthy subjects that are dominantly linked through a functional core of ICNs across spanning across the different timescales measured by EEG and fMRI. This opens new avenues for estimating the dynamics of brain function and provides a better understanding of interactions between EEG and fMRI measures. This observed level of reproducibility also defines a baseline for the study of alterations of this coupling in pathological conditions and their role as potential clinical markers.

neuroscience↗

bioRxiv2020

Selective enhancement of low-gamma activity by tACS improves phonemic processing and reading accuracy in dyslexia

The phonological deficit in dyslexia is associated with altered low-gamma oscillatory function in left auditory cortex, but a causal relationship between oscillatory function and phonemic processing has never been established. After confirming a deficit at 30 Hz with electroencephalography (EEG), we applied 20 minutes of transcranial alternating current stimulation (tACS) to transiently restore this activity in adults with dyslexia. The intervention significantly improved phonological processing and reading accuracy as measured immediately after tACS. The effect was selective to 30 Hz stimulation, and proportional to dyslexia severity. Importantly, we observed that the focal intervention on the left auditory cortex also decreased 30 Hz activity in the right superior temporal cortex, resulting in reinstating a left dominance for the oscillatory response, as present in controls. These findings formally establish a causal role of neural oscillations in phonological processing, and offer solid neurophysiological grounds for a potential correction of low-gamma anomalies, and for alleviating of the phonological deficit in dyslexia.

neuroscience↗

bioRxiv2019

Multi-timescale hybrid components of the functional brain connectome: A bimodal EEG-fMRI decomposition

Concurrent electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) bridge brain connectivity across timescales. During concurrent EEG-fMRI resting-state recordings, whole-brain functional connectivity (FC) strength is spatially correlated across modalities. However, cross-modal investigations have commonly remained correlational, and joint analysis of EEG-fMRI connectivity is largely unexplored. Here we investigated if there exist (spatially) independent FC networks linked between modalities. We applied the recently proposed hybrid connectivity independent component analysis (connICA) framework to two concurrent EEG-fMRI resting state datasets (total 40 subjects). Two robust components were found across both datasets. The first component has a uniformly distributed EEG-frequency fingerprint linked mainly to intrinsic connectivity networks (ICNs) in both modalities. Conversely, the second component is sensitive to different EEG-frequencies and is primarily linked to intra-ICN connectivity in fMRI but to inter-ICN connectivity in EEG. The first hybrid component suggests that connectivity dynamics within well-known ICNs span timescales, from millisecond-range in all canonical frequencies of FCEEG to second-range of FCfMRI. Conversely, the second component additionally exposes linked but spatially divergent neuronal processing at the two timescales. This work reveals the existence of joint spatially independent components, suggesting that parts of resting-state connectivity are co-expressed in a linked manner across EEG and fMRI over individuals.

neuroscience↗

bioRxiv2019

Audio-visual combination of syllables involves time-sensitive dynamics following from fusion failure

In face-to-face communication, audio-visual (AV) stimuli can be fused, combined or perceived as mismatching. While the left superior temporal sulcus (STS) is admittedly the locus of AV integration, the process leading to combination is unknown. Analysing behaviour and time-/source-resolved human MEG data, we show that while fusion and combination both involve early detection of AV physical features discrepancy in the STS, combination is associated in with activity of AV asynchrony-sensitive regions (auditory and inferior frontal cortices). Based on dynamic causal modelling, and neural signal decoding, we further show that AV speech integration outcome primarily depends on whether the STS can quickly converge onto an existing multimodal syllable representation, and that combination results from subsequent temporal processing, presumably re-ordering, of discrepant AV stimuli.

neuroscience↗

bioRxiv2019

Converging intracortical signatures of two separated processing timescales in human early auditory cortex.

Neural oscillations in auditory cortex are argued to support parsing and representing speech constituents at their corresponding temporal scales. Yet, how incoming sensory information interacts with ongoing spontaneous brain activity, what features of the neuronal microcircuitry underlie spontaneous and stimulus-evoked spectral fingerprints, and what these fingerprints entail for stimulus encoding, remain largely open questions. We used a combination of human invasive electrophysiology, computational modeling and decoding techniques to assess the information encoding properties of brain activity and to relate them to a plausible underlying neuronal microarchitecture. We analyzed intracortical auditory EEG activity from 10 patients while they were listening to short sentences. Pre-stimulus neural activity in early auditory cortical regions often exhibited power spectra with a shoulder in the delta range and a small bump in the beta range. Speech decreased power in the beta range, and increased power in the delta-theta and gamma ranges. Using multivariate machine learning techniques, we assessed the spectral profile of information content for two aspects of speech processing: detection and discrimination. We obtained better phase than power information decoding, and a bimodal spectral profile of information content with better decoding at low (delta-theta) and high (gamma) frequencies than at intermediate (beta) frequencies. These experimental data were reproduced by a simple rate model made of two subnetworks with different timescales, each composed of coupled excitatory and inhibitory units, and connected via a negative feedback loop. Modeling and experimental results were similar in terms of pre-stimulus spectral profile (except for the iEEG beta bump), spectral modulations with speech, and spectral profile of information content. Altogether, we provide converging evidence from both univariate spectral analysis and decoding approaches for a dual timescale processing infrastructure in human auditory cortex, and show that it is consistent with the dynamics of a simple rate model.\n\nAuthor summaryLike most animal vocalizations, speech results from a pseudo-rhythmic process that reflects the convergence of motor and auditory neural substrates and the natural resonance properties of the vocal apparatus towards efficient communication. Here, we leverage the excellent temporal and spatial resolution of intracranial EEG to demonstrate that neural activity in human early auditory cortical areas during speech perception exhibits a dual-scale spectral profile of power changes, with speech increasing power in low (delta-theta) and high (gamma - high-gamma) frequency ranges, while decreasing power in intermediate (alpha-beta) frequencies. Single-trial multivariate decoding also resulted in a bimodal spectral profile of information content, with better decoding at low and high frequencies than at intermediate ones. From both spectral and informational perspectives, these patterns are consistent with the activity of a relatively simple computational model comprising two reciprocally connected excitatory/inhibitory sub-networks operating at different (low and high) timescales. By combining experimental, decoding and modeling approaches, we provide consistent evidence for the existence, information coding value and underlying neuronal architecture of dual timescale processing in human auditory cortex.

neuroscience↗

bioRxiv2018

Integrating prediction errors at two time scales permits rapid recalibration of speech sound categories

Speech perception is assumed to arise from internal models of specific sensory features associated speech sounds. When these features change, the listener should recalibrate its internal model by appropriately weighing new versus old evidence in a volatility dependent manner. Models of speech recalibration have classically ignored volatility. Those that explicitly consider volatility have been designed to describe human behavior in tasks where sensory cues are associated with arbitrary experimenter-defined categories or rewards. In such settings, a model that maintains a single representation of the category but continuously adapts the learning rate works well. Using neurocomputational modelling we show that recalibration of existing "natural" categories is better described when sound categories are represented at different time scales. We illustrate our proposal by modeling the rapid recalibration of speech categories (Luttke et al. 2016).

neuroscience↗

bioRxiv2018

Coherence-based spectro-spatial fillters for stimulus features prediction from electrocorticographic recordings.

The traditional approach in neuroscience relies on encoding models where brain responses to different stimuli are related to the latter to establish reproducible dependencies. To reduce neuronal and experimental noise, brain signals are usually averaged across trials to detect reliable and coherent brain activity. However, neural representations of stimulus features can be spread over time, frequency, and space, motivating the use of alternative methods that relate stimulus features to brain responses. We propose a Coherence-based spectro-spatial filter method that reconstructs stimulus features from intracortical brain signals. The proposed method models trials of an experiment as realizations of a random process and extracts patterns that are common across brain signals and the presented stimuli. These patterns, originating from different recording sites, are then combined (spatial filtering) to form a final prediction. Our results from three different cognitive tasks (motor movements, speech perception and speech production), concur to show that the proposed method significantly improves the ability to predict stimulus features over traditional methods such as multilinear regression with distributed lags and artificial neural networks. Furthermore, analyses of the model parameters show anatomical discriminability for execution of different motor movements. This anatomical discriminability is also observed in the perception and production of different words. These features could be exploited in the design of neuroprosthesis, as well as for exploring normal brain functioning.

neuroscience↗

bioRxiv2018

Combining predictive coding with neural oscillations optimizes on-line speech processing

Speech comprehension requires segmenting continuous speech to connect it on-line with discrete linguistic neural representations. This process relies on theta-gamma oscillation coupling, which tracks syllables and encodes them in decipherable neural activity. Speech comprehension also strongly depends on contextual cues predicting speech structure and content. To explore the effects of theta-gamma coupling on bottom-up/top-down dynamics during on-line speech perception, we designed a generative model that can recognize syllable sequences in continuous speech. The model uses theta oscillations to detect syllable onsets and align both gamma-rate encoding activity with syllable boundaries and predictions with speech input. We observed that the model performed best when theta oscillations were used to align gamma units with input syllables, i.e. when bidirectional information flows were coordinated, and internal timing knowledge was exploited. This work demonstrates that notions of predictive coding and neural oscillations can usefully be brought together to account for dynamic on-line sensory processing.

neuroscience↗

bioRxiv2018

Concurrent EEG- and fMRI-derived functional connectomes exhibit linked dynamics

Connectivity across distributed brain regions commonly measured with functional Magnetic Resonance Imaging (fMRI) exhibits infraslow (<0.1Hz) spatial reconfigurations of potentially critical importance to cognition. Cognitively relevant neural communication, however, employs synchrony at fast speeds. It is unclear how fast oscillation-coupling across the whole-brain connectome relates to connectivity changes in fMRI, an indirect measure of neural activity. In two datasets, electroencephalography (EEG) revealed that synchronization in all canonical oscillation-bands reconfigures at infraslow speeds, coinciding with connectivity changes in concurrently recorded fMRI in corresponding region-pairs. The cross-modal tie of connectivity dynamics was widely distributed across the connectome irrespective of EEG frequency-band. However, the cross-modal tie was strongest in visual to somatomotor connections for slower EEG-bands, and in connections involving the Default Mode Network for faster EEG-bands. The findings provide evidence that functionally relevant neural synchrony in all oscillation-bands slowly reconfigures across the whole-brain connectome, and that fMRI can reliably measure such dynamics.

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bioRxiv2017

Focal Versus Distributed Temporal Cortex Activity For Speech Sound Category Assignment

Percepts and words can be decoded from largely distributed neural activity measures. The existence of widespread representations might, however, conflict with the fundamental notions of hierarchical processing and efficient coding. Using fMRI and MEG during syllable identification, we first show that sensory and decisional activity co-localize to a restricted part of the posterior superior temporal cortex. Next, using intracortical recordings we demonstrate that early and focal neural activity in this region distinguishes correct from incorrect decisions and can be machine-decoded to classify syllables. Crucially, significant machine-decoding was possible from neuronal activity sampled across widespread regions, despite weak or absent sensory or decision-related responses. These findings show that a complex behavior like speech sound categorization relies on an efficient readout of focal neural activity, while distributed activity, although decodable by machine-learning, reflects collateral processes of sensory perception and decision.

neuroscience↗