Scanning for oscillations

Objective. Oscillations are an important aspect of brain activity, but they often have a low signal- to-noise ratio (SNR) due to source-to-electrode mixing with competing brain activity and noise. Filtering can improve the SNR of narrowband signals, but it introduces ringing effects that may masquerade as genuine oscillations, leading to uncertainty as to the true oscillatory nature of the phenomena. Likewise, time–frequency analysis kernels have a temporal extent that blurs the time course of narrowband activity, introducing uncertainty as to timing and causal relations between events and/or frequency bands. Approach. Here, we propose a methodology that reveals narrowband activity within multichannel data such as electroencephalography, magnetoencephalography, electrocorticography or local field potential. The method exploits the between-channel correlation structure of the data to suppress competing sources by joint diagonalization of the covariance matrices of narrowband filtered and unfiltered data. Main results. Applied to synthetic and real data, the method effectively extracts narrowband components at unfavorable SNR. Significance. Oscillatory components of brain activity, including weak sources that are hard or impossible to observe using standard methods, can be detected and their time course plotted accurately. The method avoids the temporal artifacts of standard filtering and time–frequency analysis methods with which it remains complementary

Variabilité sensorielle et état cérébral : modèles, psychophysique, électrophysiologie

The same sensory input does not always trigger the same reaction. In laboratory experiments, a given stimulus may elicit a different response on each trial, particularly near the sensory threshold. This is usually attributed to an unspecific source of noise that affects the sensory representation of the stimulus or the decision process. In this thesis we explore the hypothesis that response variability can in part be attributed to measurable, spontaneous fluctuations of ongoing brain state. For this purpose, we develop and test two sets of tools. One is a set of models and psychophysical methods to follow variations of perceptual performance with good temporal resolution and accuracy on different time scales. These methods rely on the adaptive procedures that were developed for the efficient measurements of static sensory thresholds and are extended here for the purpose of tracking time-varying thresholds. The second set of tools we develop encompass data analysis methods to extract from electroencephalography (EEG) signals a quantity that is predictive of behavioral performance on various time scales. We applied these tools to joint recordings of EEG and behavioral data acquired while normal listeners performed a frequency-discrimination task on near-threshold auditory stimuli. Unlike what was reported in the literature for visual stimuli, we did not find evidence for any effects of ongoing low-frequency EEG oscillations on auditory performance. However, we found that a substantial part of judgment variability can be accounted for by effects of recent stimulus-response history on an ongoing decision.La même entrée sensorielle ne provoque pas toujours la même réaction. Dans les expériences en laboratoire, un stimulus donné peut engendrer une réponse différente à chaque nouvel essai, en particulier à proximité du seuil sensoriel. Ce phénomène est généralement attribué à une source de bruit non spécifique qui affecte la représentation sensorielle du stimulus ou le processus décisionnel. Dans cette thèse, nous examinons l'hypothèse selon laquelle cette variabilité des réponses peut être attribuée en partie à des fluctuations mesurables et spontanées de l'état cérébral. Dans ce but, nous développons et évaluons deux ensembles d'outils. L’un est un ensemble de modèles et de méthodes psychophysiques permettant de suivre les variations de la performance perceptive avec une bonne résolution temporelle et avec précision, sur différentes échelles de temps. Ces méthodes s’appuient sur des procédures adaptatives initialement développées pour mesurer efficacement les seuils de perception statiques et sont étendues ici dans le but de suivre des seuils qui varient au cours du temps. Le deuxième ensemble d'outils que nous développons comprend des méthodes d'analyse de données pour extraire de signaux d’électroencéphalographie (EEG) une quantité prédictive de la performance comportementale à diverses échelles de temps. Nous avons appliqué ces outils à des enregistrements conjoints d’EEG et de données comportementales collectées pendant que des auditeurs normo-entendants réalisaient une tâche de discrimination de fréquence sur des stimuli auditifs proche du seuil de discrimination. Contrairement à ce qui a été rapporté dans la littérature concernant des stimuli visuels, nous n'avons pas trouvé de preuve d’un quelconque effet des oscillations EEG spontanées de basse fréquence sur la performance auditive. En revanche, nous avons trouvé qu'une part importante de la variabilité des jugements peut s’expliquer par des effets de l'historique récent des stimuli et des réponses sur la décision prise à un moment donné

Continuous monitoring of neonatal cortical activity: A major step forward.

Montazeri Moghadam et al.1 report an automated algorithm to visually convert EEG recordings to real-time quantified interpretations of EEG in neonates. The resulting measure of the brain state of the newborn (BSN) bridges several gaps in neurocritical care monitoring

Using Interleaved Stimulation and EEG to Measure Temporal Smoothing and Growth of the Sustained Neural Response to Cochlear-Implant Stimulation.

Two EEG experiments measured the sustained neural response to amplitude-modulated (AM) high-rate pulse trains presented to a single cochlear-implant (CI) electrode. Stimuli consisted of two interleaved pulse trains with AM rates F1 and F2 close to 80 and 120 Hz respectively, and where F2 = 1.5F1. Following Carlyon et al. (J Assoc Res Otolaryngol, 2021), we assume that such stimuli can produce a neural distortion response (NDR) at F0 = F2-F1 Hz if temporal dependencies ("smoothing") in the auditory system are followed by one or more neural nonlinearities. In experiment 1, the rate of each pulse train was 480 pps and the gap between pulses in the F1 and F2 pulse trains ranged from 0 to 984 µs. The NDR had a roughly constant amplitude for gaps between 0 and about 200-400 µs, and decreased for longer gaps. We argue that this result is consistent with a temporal dependency, such as facilitation, operating at the level of the auditory nerve and/or with co-incidence detection by cochlear-nucleus neurons. Experiment 2 first measured the NDR for stimuli at each listener's most comfortable level ("MCL") and for F0 = 37, 40, and 43 Hz. This revealed a group delay of about 42 ms, consistent with a thalamic/cortical source. We then showed that the NDR grew steeply with stimulus amplitude and, for most listeners, decreased by more than 12 dB between MCL and 75% of the listener's dynamic range. We argue that the NDR is a potentially useful objective estimate of MCL

Multiway canonical correlation analysis of brain data

International audienceBrain data recorded with electroencephalography (EEG), magnetoencephalography (MEG) and related techniques often have poor signal-to-noise ratios due to the presence of multiple competing sources and artifacts. A common remedy is to average responses over repeats of the same stimulus, but this is not applicable for temporally extended stimuli that are presented only once (speech, music, movies, natural sound). An alternative is to average responses over multiple subjects that were presented with identical stimuli, but differences in geometry of brain sources and sensors reduce the effectiveness of this solution. Multiway canonical correlation analysis (MCCA) brings a solution to this problem by allowing data from multiple subjects to be fused in such a way as to extract components common to all. This paper reviews the method, offers application examples that illustrate its effectiveness, and outlines the caveats and risks entailed by the method