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We present Visbrain, a Python open-source package that offers a comprehensive visualization suite for neuroimaging and electrophysiological brain data. Visbrain consists of two levels of abstraction: (1) objects which represent highly configurable neuro-oriented visual primitives (3D brain, sources connectivity, etc.) and (2) graphical user interfaces for higher level interactions. The object level offers flexible and modular tools to produce and automate the production of figures using an approach similar to that of Matplotlib with subplots. The second level visually connects these objects by controlling properties and interactions through graphical interfaces. The current release of Visbrain (version 0.4.2) contains 14 different objects and three responsive graphical user interfaces, built with PyQt: Signal , for the inspection of time-series and spectral properties, Brain for any type of visualization involving a 3D brain and Sleep for polysomnographic data visualization and sleep analysis. Each module has been developed in tight collaboration with end-users, i.e., primarily neuroscientists and domain experts, who bring their experience to make Visbrain as transparent as possible to the recording modalities (e.g., intracranial EEG, scalp-EEG, MEG, anatomical and functional MRI). Visbrain is developed on top of VisPy, a Python package providing high-performance 2D and 3D visualization by leveraging the computational power of the graphics card. Visbrain is available on Github and comes with a documentation, examples, and datasets (http://visbrain.org). [ABSTRACT FROM AUTHOR]

Background: Fragile X syndrome (FXS) is a neurodevelopmental genetic disorder causing cognitive and behavioural deficits. Repetition suppression (RS), a learning phenomenon in which stimulus repetitions result in diminished brain activity, has been found to be impaired in FXS. Alterations in RS have been associated with behavioural problems in FXS; however, relations between RS and intellectual functioning have not yet been elucidated. Methods: EEG was recorded in 14 FXS participants and 25 neurotypical controls during an auditory habituation paradigm using repeatedly presented pseudowords. Non-phased locked signal energy was compared across presentations and between groups using linear mixed models (LMMs) in order to investigate RS effects across repetitions and brain areas and a possible relation to non-verbal IQ (NVIQ) in FXS. In addition, we explored group differences according to NVIQ and we probed the feasibility of training a support vector machine to predict cognitive functioning levels across FXS participants based on single-trial RS features. Results: LMM analyses showed that repetition effects differ between groups (FXS vs. controls) as well as with respect to NVIQ in FXS. When exploring group differences in RS patterns, we found that neurotypical controls revealed the expected pattern of RS between the first and second presentations of a pseudoword. More importantly, while FXS participants in the ≤ 42 NVIQ group showed no RS, the > 42 NVIQ group showed a delayed RS response after several presentations. Concordantly, single-trial estimates of repetition effects over the first four repetitions provided the highest decoding accuracies in the classification between the FXS participant groups. Conclusion: Electrophysiological measures of repetition effects provide a non-invasive and unbiased measure of brain responses sensitive to cognitive functioning levels, which may be useful for clinical trials in FXS. [ABSTRACT FROM AUTHOR]

Visual object recognition seems to occur almost instantaneously. However, not only does it require hundreds of milliseconds of processing, but our eyes also typically fixate the object for hundreds of milliseconds. Consequently, information reaching our eyes at different moments is processed in the brain together. Moreover, information received at different moments during fixation is likely to be processed differently, notably because different features might be selectively attended at different moments. Here, we introduce a novel reverse correlation paradigm that allows us to uncover with millisecond precision the processing time course of specific information received on the retina at specific moments. Using faces as stimuli, we observed that processing at several electrodes and latencies was different depending on the moment at which information was received. Some of these variations were caused by a disruption occurring 160–200 ​ms after the face onset, suggesting a role of the N170 ERP component in gating information processing; others hinted at temporal compression and integration mechanisms. Importantly, the observed differences were not explained by simple adaptation or repetition priming, they were modulated by the task, and they were correlated with differences in behavior. These results suggest that top-down routines of information sampling are applied to the continuous visual input, even within a single eye fixation. • Light reaching our eyes at different moments is processed in the brain simultaneously. • We introduce a method to uncover the processing specific to each of these moments. • Processing is different depending on when information is received on the retina. • These variations are multiple and occur within a single short fixation. • These variations are at least partly top-down in origin and translate to behavior. [ABSTRACT FROM AUTHOR]

Rhythmic neuronal synchronization across large-scale networks is thought to play a key role in the regulation of conscious states. Changes in neuronal oscillation amplitude across states of consciousness have been widely reported, but little is known about possible changes in the temporal dynamics of these oscillations. The temporal structure of brain oscillations may provide novel insights into the neural mechanisms underlying consciousness. To address this question, we examined long-range temporal correlations (LRTC) of EEG oscillation amplitudes recorded during both wakefulness and anesthetic-induced unconsciousness. Importantly, the time-varying EEG oscillation envelopes were assessed over the course of a sevoflurane sedation protocol during which the participants alternated between states of consciousness and unconsciousness. Both spectral power and LRTC in oscillation amplitude were computed across multiple frequency bands. State-dependent differences in these features were assessed using non-parametric tests and supervised machine learning. We found that periods of unconsciousness were associated with increases in LRTC in beta (15–30Hz) amplitude over frontocentral channels and with a suppression of alpha (8–13Hz) amplitude over occipitoparietal electrodes. Moreover, classifiers trained to predict states of consciousness on single epochs demonstrated that the combination of beta LRTC with alpha amplitude provided the highest classification accuracy (above 80%). These results suggest that loss of consciousness is accompanied by an augmentation of temporal persistence in neuronal oscillation amplitude, which may reflect an increase in regularity and a decrease in network repertoire compared to the brain's activity during resting-state consciousness. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Fragile X Syndrome (FXS) is a neurodevelopmental genetic disorder associated with cognitive and behavioural deficits. In particular, neuronal habituation processes have been shown to be altered in FXS patients. Yet, while such deficits have been primarily explored using auditory stimuli, less is known in the visual modality. Here, we investigated the putative alteration of repetition suppression using faces in FXS patients compared to controls that had the same age distribution. Electroencephalographic (EEG) signals were acquired while participants were presented with 18 different faces, each repeated ten times successively. The repetition suppression effect was probed by comparing the brain responses to the first and second presentation, based on task-evoked event-related potentials (ERP) as well as on task-induced oscillatory activity. We found different patterns of habituation for controls and patients both in ERP and oscillatory power. While the N170 was not affected by face repetition in controls, it was altered in FXS patients. Conversely, while a repetition suppression effect was observed in the theta band (4–8 Hz) over frontal and parieto-occipital areas in controls, it was not seen in FXS patients. These results provide the first evidence for diminished ERP and oscillatory habituation effects in response to face repetitions in FXS. These findings extend previous observations of impairments in learning mechanisms and may be linked to deficits in the maturation processes of synapses caused by the mutation. The present study contributes to bridging the gap between animal models of synaptic plasticity dysfunctions and human research in FXS. [ABSTRACT FROM AUTHOR]

Abstract: Epilepsy is, of course, not one disease but rather a huge number of disorders that can present with seizures. In common, they all reflect brain dysfunction. Moreover, they can affect the mind and, of course, behavior. While animals too may suffer from epilepsy, as far as we know, the electrical discharges are less likely to affect the mind and behavior, which is not surprising. While the epileptic seizures themselves are episodic, the mental and behavioral changes continue, in many cases, interictally. The episodic mental and behavioral manifestations are more dramatic, while the interictal ones are easier to study with anatomical and functional studies. The following extended summaries complement those presented in Part 1. [Copyright &y& Elsevier]