Publications

OBJECTIVES: Sleepwalkers have consistently shown N3 sleep discontinuity, especially after sleep deprivation. In healthy subjects, sleep spindles activity has been positively correlated to sleep stability. We aimed to compare spindles density during N3 sleep between sleepwalkers and healthy controls.

METHODS: Two cohorts of 10 and 21 adult sleepwalkers respectively controlled with 10 and 18 healthy volunteers underwent one baseline and one recovery sleep recording after 38h (cohort 1) and 25h (cohort 2) of sleep deprivation. For the two recordings, we performed an automatic detection of spindles (11-16Hz) from EEG signal during N3 sleep, restricted to the first sleep cycle and repeated for all cycles. For better interpretation of results, we extended the analysis to N2 sleep and we also measured the density of slow waves oscillation (SWO) (0.5-4Hz) during the same periods.

RESULTS: Compared to controls, sleepwalkers showed significantly lower spindle densities during N3 sleep considering the first sleep cycle (both cohorts) or all cycles (cohort 1). SWO densities did not differ (cohort 1) or were lower (cohort 2) for sleepwalkers. The effect of sleep deprivation did not interact with the effect of group on spindles and SWO densities.

CONCLUSION: This work suggests that the instability of N3 sleep inherent to sleepwalkers may be underpinned by a specific alteration of spindles activity.

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).

The curation of neuroscience entities is crucial to ongoing efforts in neuroinformatics and computational neuroscience, such as those being deployed in the context of continuing large-scale brain modelling projects. However, manually sifting through thousands of articles for new information about modelled entities is a painstaking and low-reward task. Text mining can be used to help a curator extract relevant information from this literature in a systematic way. We propose the application of text mining methods for the neuroscience literature. Specifically, two computational neuroscientists annotated a corpus of entities pertinent to neuroscience using active learning techniques to enable swift, targeted annotation. We then trained machine learning models to recognise the entities that have been identified. The entities covered are Neuron Types, Brain Regions, Experimental Values, Units, Ion Currents, Channels, and Conductances and Model organisms. We tested a traditional rule-based approach, a conditional random field and a model using deep learning named entity recognition, finding that the deep learning model was superior. Our final results show that we can detect a range of named entities of interest to the neuroscientist with a macro average precision, recall and F1 score of 0.866, 0.817 and 0.837 respectively. The contributions of this work are as follows: 1) We provide a set of Named Entity Recognition (NER) tools that are capable of detecting neuroscience entities with performance above or similar to prior work. 2) We propose a methodology for training NER tools for neuroscience that requires very little training data to get strong performance. This can be adapted for any sub-domain within neuroscience. 3) We provide a small corpus with annotations for multiple entity types, as well as annotation guidelines to help others reproduce our experiments.

Somatosensory thalamocortical (TC) neurons from the ventrobasal (VB) thalamus are central components in the flow of sensory information between the periphery and the cerebral cortex, and participate in the dynamic regulation of thalamocortical states including wakefulness and sleep. This property is reflected at the cellular level by the ability to generate action potentials in two distinct firing modes, called tonic firing and low-threshold bursting. Although the general properties of TC neurons are known, we still lack a detailed characterization of their morphological and electrical properties in the VB thalamus. The aim of this study was to build biophysically-detailed models of VB TC neurons explicitly constrained with experimental data from rats. We recorded the electrical activity of VB neurons (N = 49) and reconstructed morphologies in 3D (N = 50) by applying standardized protocols. After identifying distinct electrical types, we used a multi-objective optimization to fit single neuron electrical models (e-models), which yielded multiple solutions consistent with the experimental data. The models were tested for generalization using electrical stimuli and neuron morphologies not used during fitting. A local sensitivity analysis revealed that the e-models are robust to small parameter changes and that all the parameters were constrained by one or more features. The e-models, when tested in combination with different morphologies, showed that the electrical behavior is substantially preserved when changing dendritic structure and that the e-models were not overfit to a specific morphology. The models and their analysis show that automatic parameter search can be applied to capture complex firing behavior, such as co-existence of tonic firing and low-threshold bursting over a wide range of parameter sets and in combination with different neuron morphologies.

Studies have shown that both nicotine and sleep spindles are associated with enhanced memorisation. Further, a few recent studies have shown how cholinergic input through nicotinic and muscarinic receptors can trigger or modulate sleep processes in general, and sleep spindles in particular. To better understand the interaction between nicotine and sleep spindles, we compared in a single blind randomised study the characteristics of sleep spindles in 10 healthy participants recorded for 2 nights, one with a nicotine patch and one with a sham patch. We investigated differences in sleep spindle duration, amplitude, intra-spindle oscillation frequency and density (i.e. spindles per min). We found that under nicotine, spindles are more numerous (average increase: 0.057 spindles per min; 95% confidence interval: [0.025-0.089]; p = .0004), have higher amplitude (average amplification: 0.260 μV; confidence interval: [0.119-0.402]; p = .0032) and last longer (average lengthening: 0.025 s; confidence interval: [0.017-0.032]; p = 2.7e-11). These results suggest that nicotine can increase spindle activity by acting on nicotinic acetylcholine receptors, and offer an attractive hypothesis for common mechanisms that may support memorisation improvements previously reported to be associated with nicotine and sleep spindles.

Sleep spindles and K-complexes are among the most prominent micro-events observed in electroencephalographic (EEG) recordings during sleep. These EEG microstructures are thought to be hallmarks of sleep-related cognitive processes. Although tedious and time-consuming, their identification and quantification is important for sleep studies in both healthy subjects and patients with sleep disorders. Therefore, procedures for automatic detection of spindles and K-complexes could provide valuable assistance to researchers and clinicians in the field. Recently, we proposed a framework for joint spindle and K-complex detection (Lajnef et al., 2015a) based on a Tunable Q-factor Wavelet Transform (TQWT; Selesnick, 2011a) and morphological component analysis (MCA). Using a wide range of performance metrics, the present article provides critical validation and benchmarking of the proposed approach by applying it to open-access EEG data from the Montreal Archive of Sleep Studies (MASS; O'Reilly et al., 2014). Importantly, the obtained scores were compared to alternative methods that were previously tested on the same database. With respect to spindle detection, our method achieved higher performance than most of the alternative methods. This was corroborated with statistic tests that took into account both sensitivity and precision (i.e., Matthew's coefficient of correlation (MCC), F1, Cohen κ). Our proposed method has been made available to the community via an open-source tool named Spinky (for spindle and K-complex detection). Thanks to a GUI implementation and access to Matlab and Python resources, Spinky is expected to contribute to an open-science approach that will enhance replicability and reliable comparisons of classifier performances for the detection of sleep EEG microstructure in both healthy and patient populations.

BACKGROUND: Although it is well recognized that autism is associated with altered patterns of over- and under-connectivity, specifics are still a matter of debate. Little has been done so far to synthesize available literature using whole-brain electroencephalography (EEG) and magnetoencephalography (MEG) recordings.

OBJECTIVES: 1) To systematically review the literature on EEG/MEG functional and effective connectivity in autism spectrum disorder (ASD), 2) to synthesize and critically appraise findings related with the hypothesis that ASD is characterized by long-range underconnectivity and local overconnectivity, and 3) to provide, based on the literature, an analysis of tentative factors that are likely to mediate association between ASD and atypical connectivity (e.g., development, topography, lateralization).

METHODS: Literature reviews were done using PubMed and PsychInfo databases. Abstracts were screened, and only relevant articles were analyzed based on the objectives of this paper. Special attention was paid to the methodological characteristics that could have created variability in outcomes reported between studies.

RESULTS: Our synthesis provides relatively strong support for long-range underconnectivity in ASD, whereas the status of local connectivity remains unclear. This observation was also mirrored by a similar relationship with lower frequencies being often associated with underconnectivity and higher frequencies being associated with both under- and over-connectivity. Putting together these observations, we propose that ASD is characterized by a general trend toward an under-expression of lower-band wide-spread integrative processes compensated by more focal, higher-frequency, locally specialized, and segregated processes. Further investigation is, however, needed to corroborate the conclusion and its generalizability across different tasks. Of note, abnormal lateralization in ASD, specifically an elevated left-over-right EEG and MEG functional connectivity ratio, has been also reported consistently across studies.

CONCLUSIONS: The large variability in study samples and methodology makes a systematic quantitative analysis (i.e. meta-analysis) of this body of research impossible. Nevertheless, a general trend supporting the hypothesis of long-range functional underconnectivity can be observed. Further research is necessary to more confidently determine the status of the hypothesis of short-range overconnectivity. Frequency-band specific patterns and their relationships with known symptoms of autism also need to be further clarified.

Large models of complex neuronal circuits require specifying numerous parameters, with values that often need to be extracted from the literature, a tedious and error-prone process. To help establishing shareable curated corpora of annotations, we have developed a literature curation framework comprising an annotation format, a Python API (NeuroAnnotation Toolbox; NAT), and a user-friendly graphical interface (NeuroCurator). This framework allows the systematic annotation of relevant statements and model parameters. The context of the annotated content is made explicit in a standard way by associating it with ontological terms (e.g., species, cell types, brain regions). The exact position of the annotated content within a document is specified by the starting character of the annotated text, or the number of the figure, the equation, or the table, depending on the context. Alternatively, the provenance of parameters can also be specified by bounding boxes. Parameter types are linked to curated experimental values so that they can be systematically integrated into models. We demonstrate the use of this approach by releasing a corpus describing different modeling parameters associated with thalamo-cortical circuitry. The proposed framework supports a rigorous management of large sets of parameters, solving common difficulties in their traceability. Further, it allows easier classification of literature information and more efficient and systematic integration of such information into models and analyses.

We introduce Sleep, a new Python open-source graphical user interface (GUI) dedicated to visualization, scoring and analyses of sleep data. Among its most prominent features are: (1) Dynamic display of polysomnographic data, spectrogram, hypnogram and topographic maps with several customizable parameters, (2) Implementation of several automatic detection of sleep features such as spindles, K-complexes, slow waves, and rapid eye movements (REM), (3) Implementation of practical signal processing tools such as re-referencing or filtering, and (4) Display of main descriptive statistics including publication-ready tables and figures. The software package supports loading and reading raw EEG data from standard file formats such as European Data Format, in addition to a range of commercial data formats. Most importantly, Sleep is built on top of the VisPy library, which provides GPU-based fast and high-level visualization. As a result, it is capable of efficiently handling and displaying large sleep datasets. Sleep is freely available (http://visbrain.org/sleep) and comes with sample datasets and an extensive documentation. Novel functionalities will continue to be added and open-science community efforts are expected to enhance the capacities of this module.

Face recognition is a highly specialized capability that has implicit and explicit memory components. Studies show that learning tasks with facial components are dependent on rapid eye movement and non-rapid eye movement sleep features, including rapid eye movement sleep density and fast sleep spindles. This study aimed to investigate the relationship between sleep-dependent consolidation of memory for faces and partial rapid eye movement sleep deprivation, rapid eye movement density, and fast and slow non-rapid eye movement sleep spindles. Fourteen healthy participants spent 1 night each in the laboratory. Prior to bed they completed a virtual reality task in which they interacted with computer-generated characters. Half of the participants (REMD group) underwent a partial rapid eye movement sleep deprivation protocol and half (CTL group) had a normal amount of rapid eye movement sleep. Upon awakening, they completed a face recognition task that contained a mixture of previously encountered faces from the task and new faces. Rapid eye movement density and fast and slow sleep spindles were detected using in-house software. The REMD group performed worse than the CTL group on the face recognition task; however, rapid eye movement duration and rapid eye movement density were not related to task performance. Fast and slow sleep spindles showed differential relationships to task performance, with fast spindles being positively and slow spindles negatively correlated with face recognition. The results support the notion that rapid eye movement and non-rapid eye movement sleep characteristics play complementary roles in face memory consolidation. This study also raises the possibility that fast and slow spindles contribute in opposite ways to sleep-dependent memory consolidation.