fMRI lag structure during waking up from early sleep stages.

The brain mechanisms by which we transition from sleep to a conscious state remain largely unknown in humans, partly because of methodological challenges. Here we study a pre-existing dataset of waking up participants originally designed for a study of dreaming (Horikawa, Tamaki, Miyawaki, & Kamitani, 2013) and suggest that suddenly awakening from early sleep stages results from a two-stage process that involves a sequence of cortical and subcortical brain activity. First, subcortical and sensorimotor structures seem to be recruited before most cortical regions, followed by fast, ignition-like whole-brain activation-with frontal regions engaging a little after the rest of the brain. Second, a comparably slower and possibly mirror-reversed stage might take place, with cortical regions activating before subcortical structures and the cerebellum. This pattern of activation points to a key role of subcortical structures for the initiation and maintenance of conscious states.

Perturbations in dynamical models of whole-brain activity dissociate between the level and stability of consciousness.

Consciousness transiently fades away during deep sleep, more stably under anesthesia, and sometimes permanently due to brain injury. The development of an index to quantify the level of consciousness across these different states is regarded as a key problem both in basic and clinical neuroscience. We argue that this problem is ill-defined since such an index would not exhaust all the relevant information about a given state of consciousness. While the level of consciousness can be taken to describe the actual brain state, a complete characterization should also include its potential behavior against external perturbations. We developed and analyzed whole-brain computational models to show that the stability of conscious states provides information complementary to their similarity to conscious wakefulness. Our work leads to a novel methodological framework to sort out different brain states by their stability and reversibility, and illustrates its usefulness to dissociate between physiological (sleep), pathological (brain-injured patients), and pharmacologically-induced (anesthesia) loss of consciousness.

The audiovisual structure of onomatopoeias: An intrusion of real-world physics in lexical creation.

Sound-symbolic word classes are found in different cultures and languages worldwide. These words are continuously produced to code complex information about events. Here we explore the capacity of creative language to transport complex multisensory information in a controlled experiment, where our participants improvised onomatopoeias from noisy moving objects in audio, visual and audiovisual formats. We found that consonants communicate movement types (slide, hit or ring) mainly through the manner of articulation in the vocal tract. Vowels communicate shapes in visual stimuli (spiky or rounded) and sound frequencies in auditory stimuli through the configuration of the lips and tongue. A machine learning model was trained to classify movement types and used to validate generalizations of our results across formats. We implemented the classifier with a list of cross-linguistic onomatopoeias simple actions were correctly classified, while different aspects were selected to build onomatopoeias of complex actions. These results show how the different aspects of complex sensory information are coded and how they interact in the creation of novel onomatopoeias.

Brain-heart interactions reveal consciousness in noncommunicating patients.

OBJECTIVE: We here aimed at characterizing heart-brain interactions in patients with disorders of consciousness. We tested how this information impacts data-driven classification between unresponsive and minimally conscious patients. METHODS: A cohort of 127 patients in vegetative state/unresponsive wakefulness syndrome (VS/UWS; n = 70) and minimally conscious state (MCS; n = 57) were presented with the local-global auditory oddball paradigm, which distinguishes 2 levels of processing: short-term deviation of local auditory regularities and global long-term rule violations. In addition to previously validated markers of consciousness extracted from electroencephalograms (EEG), we computed autonomic cardiac markers, such as heart rate (HR) and HR variability (HRV), and cardiac cycle phase shifts triggered by the processing of the auditory stimuli. RESULTS: HR and HRV were similar in patients across groups. The cardiac cycle was not sensitive to the processing of local regularities in either the VS/UWS or MCS patients. In contrast, global regularities induced a phase shift of the cardiac cycle exclusively in the MCS group. The interval between the auditory stimulation and the following R peak was significantly shortened in MCS when the auditory rule was violated. When the information for the cardiac cycle modulations and other consciousness-related EEG markers were combined, single patient classification performance was enhanced compared to classification with solely EEG markers. INTERPRETATION: Our work shows a link between residual cognitive processing and the modulation of autonomic somatic markers. These results open a new window to evaluate patients with disorders of consciousness via the embodied paradigm, according to which body-brain functions contribute to a holistic approach to conscious processing. Ann Neurol 2017;82:578-591.

Physiologically driven avian vocal synthesizer.

In this work, we build an electronic syrinx, i.e., a programmable electronic device capable of integrating biomechanical model equations for the avian vocal organ in order to synthesize song. This vocal prosthesis is controlled by the bird's neural instructions to respiratory and the syringeal motor systems, thus opening great potential for studying motor control and its modification by sensory feedback mechanisms. Furthermore, a well-functioning subject-controlled vocal prosthesis can lay the foundation for similar devices in humans and thus provide directly health-related data and procedures.

Versatile biologically inspired electronic neuron.

We present a biologically inspired electronic neuron based on a conductance model. The channels are constructed using linearly voltage controlled field effect transistors. A two channel and a three channel circuit is developed. The dynamical behavior of this system is studied, showing for the two channel circuit either class-I or class-II excitability and for the three channel circuit bursting and spike frequency adaptation. Voltage-clamp-type measurements, similar to the ones frequently used in neuroscience, are employed in order to determine the conductance characteristics of the electronic channels. We develop an empirical model based on these measurements that reproduces the different dynamical behaviors of the electronic neuron. We found that post-inhibitory rebound is present in the two channel circuit. Reliability and precision of spike timing is induced in the three channel circuit by injecting noise in the control variable of the slow channel that provides a negative feedback. The circuit is appropriate for the design of large scale electronic neural devices that can be used in mixed electronic-biological systems.