Speaking is one of the most complex actions we perform, yet nearly all of us learn to do it effortlessly. Production of fluent speech requires the precise, coordinated movement of multiple articulators (e.g., lips, jaw, tongue, larynx) over rapid time scales. Here, we used high-resolution, multi-electrode cortical recordings during the production of consonant-vowel syllables to determine the organization of speech sensorimotor cortex in humans. We found speech articulator representations that were somatotopically arranged on ventral pre- and post-central gyri and partially overlapping at individual electrodes. These representations were temporally coordinated as sequences during syllable production. Spatial patterns of cortical activity revealed an emergent, population-level representation, which was organized by phonetic features. Over tens of milliseconds, the spatial patterns transitioned between distinct representations for different consonants and vowels. These results reveal the dynamic organization of speech sensorimotor cortex during the generation of multi-articulator movements underlying our ability to speak.

The Brain Imaging Data Structure (BIDS) is a community-driven specification for organizing neuroscience data and metadata with the aim to make datasets more transparent, reusable, and reproducible. Intracranial electroencephalography (iEEG) data offer a unique combination of high spatial and temporal resolution measurements of the living human brain. To improve internal (re)use and external sharing of these unique data, we present a specification for storing and sharing iEEG data: iEEG-BIDS.

Sensory processing involves identification of stimulus features, but also integration with the surrounding sensory and cognitive context. Previous work in animals and humans has shown fine-scale sensitivity to context in the form of learned knowledge about the statistics of the sensory environment, including relative probabilities of discrete units in a stream of sequential auditory input. These statistics are a defining characteristic of one of the most important sequential signals humans encounter: speech. For speech, extensive exposure to a language tunes listeners to the statistics of sound sequences. To address how speech sequence statistics are neurally encoded, we used high-resolution direct cortical recordings from human lateral superior temporal cortex as subjects listened to words and nonwords with varying transition probabilities between sound segments. In addition to their sensitivity to acoustic features (including contextual features, such as coarticulation), we found that neural responses dynamically encoded the language-level probability of both preceding and upcoming speech sounds. Transition probability first negatively modulated neural responses, followed by positive modulation of neural responses, consistent with coordinated predictive and retrospective recognition processes, respectively. Furthermore, transition probability encoding was different for real English words compared with nonwords, providing evidence for online interactions with high-order linguistic knowledge. These results demonstrate that sensory processing of deeply learned stimuli involves integrating physical stimulus features with their contextual sequential structure. Despite not being consciously aware of phoneme sequence statistics, listeners use this information to process spoken input and to link low-level acoustic representations with linguistic information about word identity and meaning.

Peripheral immune cells and brain microglia exhibit an activated phenotype in premanifest Huntington’s disease (HD) patients that persists chronically and correlates with clinical measures of neurodegeneration. However, whether activation of the immune system contributes to neurodegeneration in HD, or is a consequence thereof, remains unclear. Signaling through cannabinoid receptor 2 (CB2) dampens immune activation. Here, we show that the genetic deletion of CB2 receptors in a slowly progressing HD mouse model accelerates the onset of motor deficits and increases their severity. Treatment of mice with a CB2 receptor agonist extends life span and suppresses motor deficits, synapse loss, and CNS inflammation, while a peripherally restricted CB2 receptor antagonist blocks these effects. CB2 receptors regulate blood interleukin-6 (IL-6) levels, and IL-6 neutralizing antibodies partially rescue motor deficits and weight loss in HD mice. These findings support a causal link between CB2 receptor signaling in peripheral immune cells and the onset and severity of neurodegeneration in HD, and they provide a novel therapeutic approach to treat HD.

Objective Electrocorticography (ECoG) has become an important tool in human neuroscience and has tremendous potential for emerging applications in neural interface technology. Electrode array design parameters are outstanding issues for both research and clinical applications, and these parameters depend critically on the nature of the neural signals to be recorded. Here, we investigate the functional spatial resolution of neural signals recorded at the human cortical surface. We empirically derive spatial spread functions to quantify the shared neural activity for each frequency band of the electrocorticogram. Approach Five subjects with high-density (4mm center-to-center spacing) ECoG grid implants participated in speech perception and production tasks while neural activity was recorded from the speech cortex, including superior temporal gyrus, precentral gyrus, and postcentral gyrus. The cortical surface field potential was decomposed into traditional EEG frequency bands. Signal similarity between electrode pairs for each frequency band was quantified using a Pearson correlation coefficient. Main results The correlation of neural activity between electrode pairs was inversely related to the distance between the electrodes; this relationship was used to quantify spatial falloff functions for cortical subdomains. As expected, lower frequencies remained correlated over larger distances than higher frequencies. However, both the envelope and phase of gamma and high gamma frequencies (30-150Hz) are largely uncorrelated (<90%) at 4mm, the smallest spacing of the high-density arrays. Thus, ECoG arrays smaller than 4mm have significant promise for increasing signal resolution at high frequencies, whereas less additional gain is achieved for lower frequencies. Significance Our findings quantitatively demonstrate the dependence of ECoG spatial resolution on the neural frequency of interest. We demonstrate that this relationship is consistent across patients and across cortical areas during activity.

Neurodata Without Borders: Neurophysiology (NWB:N) is a data standard for neurophysiology, providing neuroscientists with a common standard to share, archive, use, and build common analysis tools for neurophysiology data. With NWB:N version 2.0 (NWB:N 2.0) we made significant advances towards creating a usable standard, software ecosystem, and vibrant community for standardizing neurophysiology data. In this manuscript we focus in particular on the NWB:N data standard schema and present advances towards creating an accessible data standard for neurophysiology. IntroductionMotivation: Brain function is produced by the coordinated activity of multiple neuronal types that are widely distributed across many brain regions. Neuronal signals are acquired using extra-and intracellular recordings, and increasingly optical imaging, during sensory, motor, and cognitive tasks. Neurophysiology research generates large, complex and heterogeneous datasets at terabyte scale. The data size and complexity is expected to continue to grow with the increasing sophistication of experimental apparatuses. Lack of standards for neurophysiology data and related metadata is the single greatest impediment to fully extracting return-on-investment from neurophysiology experiments, impeding interchange and reuse of data and reproduction of derived conclusions. This gap motivated the launch of the Neurodata Without Borders : Neurophysiology (NWB:N) data standards project. The goal of NWB:N is to develop a standardized format and methods for neurophysiology data and metadata.Background: The first NWB:N 1.0.x standard was the result of a 1 year pilot project in 2015 12 . As part of this pilot, neurophysiologists and software developers met during two hackathons to create a common data format for recordings and metadata of cellular electro-and optical physiology experiments (Fig. 1, top). Despite the important advances that NWB:N 1.0 made towards creating a neurophysiology data standard, the standard was not easily accessible to users. To enhance broad adoption, a sustainable software and community strategy and easy-to-use, high-level application programming interfaces (APIs) were desperately needed. Here we describe NWB:N 2.0, a modern ecosystem for data standardization and accessible data standard for neurophysiology.A Brief History of NWB:N 2.0: The development of the second version of NWB:N began in Janurary 2017 with the start of the Kavli funded NWB4HPC project. The goal was to develop infrastructure and algorithms to enable data-driven discovery and dissemination on high-performance computing systems for the BRAIN Initiative (Fig. 1, bottom). One main goal of the project was to develop the next version of NWB:N to enhance its adoption, with an initial focus on high-level APIs for read, write, and extension of the original NWB:N 1.0.x standard. This standard represented a critical first step toward a unified framework for neural data, but it became clear that in order to achieve these goals we needed an advanced software architecture, a well...

Background To dissect the intricate workings of neural circuits, it is essential to gain precise control over subsets of neurons while retaining the ability to monitor larger-scale circuit dynamics. This requires the ability to both evoke and record neural activity simultaneously with high spatial and temporal resolution. New Method In this paper we present approaches that address this need by combining micro-electrocorticography (μECoG) with optogenetics in ways that avoid photovoltaic artifacts. Results We demonstrate that variations of this approach are broadly applicable across three commonly studied mammalian species—mouse, rat, and macaque monkey—and that the recorded μECoG signal shows complex spectral and spatio-temporal patterns in response to optical stimulation. Comparison with existing methods While optogenetics provides the ability to excite or inhibit neural subpopulations in a targeted fashion, large-scale recording of resulting neural activity remains challenging. Recent advances in optical physiology, such as genetically encoded Ca2+ indicators, are promising but currently do not allow simultaneous recordings from extended cortical areas due to limitations in optical imaging hardware. Conclusions We demonstrate techniques for the large-scale simultaneous interrogation of cortical circuits in three commonly used mammalian species.

A fundamental challenge in neuroscience is to understand what structure in the world is represented in spatially distributed patterns of neural activity from multiple single-trial measurements. This is often accomplished by learning a simple, linear transformations between neural features and features of the sensory stimuli or motor task. While successful in some early sensory processing areas, linear mappings are unlikely to be ideal tools for elucidating nonlinear, hierarchical representations of higher-order brain areas during complex tasks, such as the production of speech by humans. Here, we apply deep networks to predict produced speech syllables from a dataset of high gamma cortical surface electric potentials recorded from human sensorimotor cortex. We find that deep networks had higher decoding prediction accuracy compared to baseline models. Having established that deep networks extract more task relevant information from neural data sets relative to linear models (i.e., higher predictive accuracy), we next sought to demonstrate their utility as a data analysis tool for neuroscience. We first show that deep network’s confusions revealed hierarchical latent structure in the neural data, which recapitulated the underlying articulatory nature of speech motor control. We next broadened the frequency features beyond high-gamma and identified a novel high-gamma-to-beta coupling during speech production. Finally, we used deep networks to compare task-relevant information in different neural frequency bands, and found that the high-gamma band contains the vast majority of information relevant for the speech prediction task, with little-to-no additional contribution from lower-frequency amplitudes. Together, these results demonstrate the utility of deep networks as a data analysis tool for basic and applied neuroscience.