Journal of Physiology-Paris最新文献

A family can be viewed as a system respecting the principle of homeostasis and therefore considered as a system in equilibrium or out of equilibrium, or even both simultaneously or consecutively. Within a family system, there are oscillatory phenomena and synchronization of the emotional, behavioral and relational rhythms of each member of the family system as well as synchronization of this system with others. A disruption of family synchronies, consisting of successive desynchronizations and resynchronizations, can take place in order for a change to occur. We created a mobile team for preadolescents and adolescents with psychological difficulties and their families; this mobile team enables to trigger a change by acting on the family synchronies like a ‘social zeitgeber’, i.e. an external factor synchronizing social and biological rhythms. The mobile team acts by disrupting the synchronies of the family system and this disruption is facilitated by a state of crisis experienced by the family. More specifically, the mobile team intervention provokes changes in the family representations associated with changes in the family emotional climate, measured by expressed emotion, due to disruptions in the synchronization of certain rhythms (desynchronization and then resynchronization of these rhythms), and could therefore be considered as a ‘social zeitgeber’. It creates an experience that becomes part of the individual’s and family’s history and could be reactivated in the future if necessary. Finally, it allows the family and the adolescent with difficulties to reach new perspectives and representations which participates to the process of change, but also to benefit at the same time from a secure basis and frame created by the (re)synchronization of family emotions through the intervention of the mobile team following a ritualized procedure.

Restoring communication in case of aphasia is a key challenge for neurotechnologies. To this end, brain-computer strategies can be envisioned to allow artificial speech synthesis from the continuous decoding of neural signals underlying speech imagination. Such speech brain-computer interfaces do not exist yet and their design should consider three key choices that need to be made: the choice of appropriate brain regions to record neural activity from, the choice of an appropriate recording technique, and the choice of a neural decoding scheme in association with an appropriate speech synthesis method. These key considerations are discussed here in light of (1) the current understanding of the functional neuroanatomy of cortical areas underlying overt and covert speech production, (2) the available literature making use of a variety of brain recording techniques to better characterize and address the challenge of decoding cortical speech signals, and (3) the different speech synthesis approaches that can be considered depending on the level of speech representation (phonetic, acoustic or articulatory) envisioned to be decoded at the core of a speech BCI paradigm.

Brain-Computer Interfaces (BCIs) are systems which translate brain neural activity into commands for external devices. BCI users generally alternate between No-Control (NC) and Intentional Control (IC) periods. NC/IC discrimination is crucial for clinical BCIs, particularly when they provide neural control over complex effectors such as exoskeletons. Numerous BCI decoders focus on the estimation of continuously-valued limb trajectories from neural signals. The integration of NC support into continuous decoders is investigated in the present article. Most discrete/continuous BCI hybrid decoders rely on static state models which don’t exploit the dynamic of NC/IC state succession. A hybrid decoder, referred to as Markov Switching Linear Model (MSLM), is proposed in the present article. The MSLM assumes that the NC/IC state sequence is generated by a first-order Markov chain, and performs dynamic NC/IC state detection. Linear continuous movement models are probabilistically combined using the NC and IC state posterior probabilities yielded by the state decoder. The proposed decoder is evaluated for the task of asynchronous wrist position decoding from high dimensional space-time-frequency ElectroCorticoGraphic (ECoG) features in monkeys. The MSLM is compared with another dynamic hybrid decoder proposed in the literature, namely a Switching Kalman Filter (SKF). A comparison is additionally drawn with a Wiener filter decoder which infers NC states by thresholding trajectory estimates. The MSLM decoder is found to outperform both the SKF and the thresholded Wiener filter decoder in terms of False Positive Ratio and NC/IC state detection error. It additionally surpasses the SKF with respect to the Pearson Correlation Coefficient and Root Mean Squared Error between true and estimated continuous trajectories.

In recent years, new recording technologies have advanced such that oscillations of neuronal networks can be identified from simultaneous, multisite recordings at high temporal and spatial resolutions. However, because of the deluge of multichannel data generated by these experiments, achieving the full potential of parallel neuronal recordings also depends on the development of new mathematical methods capable of extracting meaningful information related to time, frequency and space. In this review, we aim to bridge this gap by focusing on the new analysis tools developed for the automated detection of high-frequency oscillations (HFOs, >40 Hz) in local field potentials. For this, we provide a revision of different aspects associated with physiological and pathological HFOs as well as the several stages involved in their automatic detection including preprocessing, selection, rejection and analysis through time-frequency processes. Beyond basic research, the automatic detection of HFOs would greatly assist diagnosis of epilepsy disorders based on the recognition of these typical pathological patterns in the electroencephalogram (EEG). Also, we emphasize how these HFO detection methods can be applied and the properties that might be inferred from neuronal signals, indicating potential future directions.

In recent years, a consensus has emerged that somatosensory feedback needs to be provided for upper limb neuroprostheses to be useful. An increasingly promising approach to sensory restoration is to electrically stimulate neurons along the somatosensory neuraxis to convey information about the state of the prosthetic limb and about contact with objects. To date, efforts toward artificial sensory feedback have consisted mainly of demonstrating that some sensory information could be conveyed using a small number of stimulation patterns, generally delivered through single electrodes. However impressive these achievements are, results from different studies are hard to compare, as each research team implements different stimulation patterns and tests the elicited sensations differently. A critical question is whether different stimulation strategies will generalize from contrived laboratory settings to activities of daily living. Here, we lay out some key specifications that an artificial somatosensory channel should meet, discuss how different approaches should be evaluated, and caution about looming challenges that the field of sensory restoration will face.

Brain-computer interfaces (BCIs) aim to restore independence to people with severe motor disabilities by allowing control of a cursor on a computer screen or other effectors with neural activity. However, physiological and/or recording-related nonstationarities in neural signals can limit long-term decoding stability, and it would be tedious for users to pause use of the BCI whenever neural control degrades to perform decoder recalibration routines. We recently demonstrated that a kinematic decoder (i.e. a decoder that controls cursor movement) can be recalibrated using data acquired during practical point-and-click control of the BCI by retrospectively inferring users’ intended movement directions based on their subsequent selections. Here, we extend these methods to allow the click decoder to also be recalibrated using data acquired during practical BCI use. We retrospectively labeled neural data patterns as corresponding to “click” during all time bins in which the click log-likelihood (decoded using linear discriminant analysis, or LDA) had been above the click threshold that was used during real-time neural control. We labeled as “non-click” those periods that the kinematic decoder’s retrospective target inference (RTI) heuristics determined to be consistent with intended cursor movement. Once these neural activity patterns were labeled, the click decoder was calibrated using standard supervised classifier training methods. Combined with real-time bias correction and baseline firing rate tracking, this set of “retrospectively labeled” decoder calibration methods enabled a BrainGate participant with amyotrophic lateral sclerosis (T9) to type freely across 11 research sessions spanning 29 days, maintaining high-performance neural control over cursor movement and click without needing to interrupt virtual keyboard use for explicit calibration tasks. By eliminating the need for tedious calibration tasks with prescribed targets and pre-specified click times, this approach advances the potential clinical utility of intracortical BCIs for individuals with severe motor disability.

Imitation plays a critical role in the development of intersubjectivity and serves as a prerequisite for understanding the emotions and intentions of others. In our review, we consider spontaneous motor imitation between children and their peers as a developmental process involving repetition and perspective-taking as well as flexibility and reciprocity. During childhood, this playful dynamic challenges developing visuospatial abilities and requires temporal coordination between partners. As such, we address synchrony as form of communication and social signal per se, that leads, from an experience of similarity, to the interconnection of minds. In this way, we argue that, from a developmental perspective, rhythmic interpersonal coordination through childhood imitative interactions serves as a precursor to higher- level social and cognitive abilities, such as theory of mind (TOM) and empathy. Finally, to clinically illustrate our idea, we focus on developmental coordination disorder (DCD), a condition characterized not only by learning difficulties, but also childhood deficits in motor imitation. We address the challenges faced by these children on an emotional and socio-interactional level through the perspective of their impairments in intra- and interpersonal synchrony.

This article focuses on stress vulnerability in schizophrenia through an integrated clinical and biological approach. The objective of this article is to better understand the relationships between vulnerability, stress and schizophrenia. First, the concept of vulnerability is defined and several models of vulnerability in schizophrenia are reviewed. Second, a section is developed on the biology of stress, and more specifically on the stress responses of the hypothalamo-pitutary adrenal (HPA) axis. Then, studies of cortisol circadian rhythms are summarized, suggesting hyper-reactivity of the HPA axis in patients with schizophrenia and high risk individuals for schizophrenia. The results support the models of stress vulnerability in schizophrenia and the hypothesis of high cortisol levels as an endophenotype in this disorder. In conclusion, this article highlights the interest of studying the cortisol circadian rhythms in schizophrenia and opens the perspective to identify high risk individuals for schizophrenia by measuring circadian patterns of salivary cortisol.

Autism Spectrum Disorder (ASD) is a frequent neurodevelopmental disorder. ASD is probably the result of intricate interactions between genes and environment altering progressively the development of brain structures and functions. Circadian rhythms are a complex intrinsic timing system composed of almost as many clocks as there are body cells. They regulate a variety of physiological and behavioral processes such as the sleep-wake rhythm. ASD is often associated with sleep disorders and low levels of melatonin. This first point raises the hypothesis that circadian rhythms could have an implication in ASD etiology. Moreover, circadian rhythms are generated by auto-regulatory genetic feedback loops, driven by transcription factors CLOCK and BMAL1, who drive transcription daily patterns of a wide number of clock-controlled genes (CCGs) in different cellular contexts across tissues. Among these, are some CCGs coding for synapses molecules associated to ASD susceptibility. Furthermore, evidence emerges about circadian rhythms control of time brain development processes.

In recent years, arrays of extracellular electrodes have been developed and manufactured to record simultaneously from hundreds of electrodes packed with a high density. These recordings should allow neuroscientists to reconstruct the individual activity of the neurons spiking in the vicinity of these electrodes, with the help of signal processing algorithms. Algorithms need to solve a source separation problem, also known as spike sorting. However, these new devices challenge the classical way to do spike sorting. Here we review different methods that have been developed to sort spikes from these large-scale recordings. We describe the common properties of these algorithms, as well as their main differences. Finally, we outline the issues that remain to be solved by future spike sorting algorithms.