A cortical network processes auditory error signals during human speech production to maintain fluency

Hearing one’s own voice is critical for fluent speech production as it allows for the detection and correction of vocalization errors in real time. This behavior known as the auditory feedback control of speech is impaired in various neurological disorders ranging from stuttering to aphasia; however, the underlying neural mechanisms are still poorly understood. Computational models of speech motor control suggest that, during speech production, the brain uses an efference copy of the motor command to generate an internal estimate of the speech output. When actual feedback differs from this internal estimate, an error signal is generated to correct the internal estimate and update necessary motor commands to produce intended speech. We were able to localize the auditory error signal using electrocorticographic recordings from neurosurgical participants during a delayed auditory feedback (DAF) paradigm. In this task, participants hear their voice with a time delay as they produced words and sentences (similar to an echo on a conference call), which is well known to disrupt fluency by causing slow and stutter-like speech in humans. We observed a significant response enhancement in auditory cortex that scaled with the duration of feedback delay, indicating an auditory speech error signal. Immediately following auditory cortex, dorsal precentral gyrus (dPreCG), a region that has not been implicated in auditory feedback processing before, exhibited a markedly similar response enhancement, suggesting a tight coupling between the 2 regions. Critically, response enhancement in dPreCG occurred only during articulation of long utterances due to a continuous mismatch between produced speech and reafferent feedback. These results suggest that dPreCG plays an essential role in processing auditory error signals during speech production to maintain fluency

Spectrotemporal modulation provides a unifying framework for auditory cortical asymmetries

The principles underlying functional asymmetries in cortex remain debated. For example, it is accepted that speech is processed bilaterally in auditory cortex, but a left hemisphere dominance emerges when the input is interpreted linguistically. The mechanisms, however, are contested, such as what sound features or processing principles underlie laterality. Recent findings across species (humans, canines and bats) provide converging evidence that spectrotemporal sound features drive asymmetrical responses. Typically, accounts invoke models wherein the hemispheres differ in time-frequency resolution or integration window size. We develop a framework that builds on and unifies prevailing models, using spectrotemporal modulation space. Using signal processing techniques motivated by neural responses, we test this approach, employing behavioural and neurophysiological measures. We show how psychophysical judgements align with spectrotemporal modulations and then characterize the neural sensitivities to temporal and spectral modulations. We demonstrate differential contributions from both hemispheres, with a left lateralization for temporal modulations and a weaker right lateralization for spectral modulations. We argue that representations in the modulation domain provide a more mechanistic basis to account for lateralization in auditory cortex

Characterisation of medullary astrocytic populations in respiratory nuclei and alterations in sudden unexpected death in epilepsy

Central failure of respiration during a seizure is one possible mechanism for sudden unexpected death in epilepsy (SUDEP). Neuroimaging studies indicate volume loss in the medulla in SUDEP and a post mortem study has shown reduction in neuromodulatory neuropeptidergic and monoaminergic neurones in medullary respiratory nuclear groups. Specialised glial cells identified in the medulla are considered essential for normal respiratory regulation including astrocytes with pacemaker properties in the pre-Botzinger complex and populations of subpial and perivascular astrocytes, sensitive to increased pCO2, that excite respiratory neurones. Our aim was to explore niches of medullary astrocytes in SUDEP cases compared to controls. In 48 brainstems from three groups, SUDEP (20), epilepsy controls (10) and non-epilepsy controls (18), sections through the medulla were labelled for GFAP, vimentin and functional markers, astrocytic gap junction protein connexin43 (Cx43) and adenosine A1 receptor (A1R). Regions including the ventro-lateral medulla (VLM; for the pre-Bötzinger complex), Median Raphe (MR) and lateral medullary subpial layer (MSPL) were quantified using image analysis for glial cell populations and compared between groups. Findings included morphologically and regionally distinct vimentin/Cx34-positive glial cells in the VLM and MR in close proximity to neurones. We noted a reduction of vimentin-positive glia in the VLM and MSPL and Cx43 glia in the MR in SUDEP cases compared to control groups (p < 0.05-0.005). In addition, we identified vimentin, Cx43 and A1R positive glial cells in the MSPL region which likely correspond to chemosensory glia identified experimentally. In conclusion, altered medullary glial cell populations could contribute to impaired respiratory regulatory capacity and vulnerability to SUDEP and warrant further investigation

Long-term surveillance of SUDEP in drug-resistant epilepsy patients treated with VNS therapy.

Limited data are available regarding the evolution over time of the rate of sudden unexpected death in epilepsy patients (SUDEP) in drug-resistant epilepsy. The objective is to analyze a database of 40 443 patients with epilepsy implanted with vagus nerve stimulation (VNS) therapy in the United States (from 1988 to 2012) and assess whether SUDEP rates decrease during the postimplantation follow-up period. Patient vital status was ascertained using the Centers for Disease Control and Prevention's National Death Index (NDI). An expert panel adjudicated classification of cause of deaths as SUDEP based on NDI data and available narrative descriptions of deaths. We tested the hypothesis that SUDEP rates decrease with time using the Mann-Kendall nonparametric trend test and by comparing SUDEP rates of the first 2 years of follow-up (years 1-2) to longer follow-up (years 3-10). Our cohort included 277 661 person-years of follow-up and 3689 deaths, including 632 SUDEP. Primary analysis demonstrated a significant decrease in age-adjusted SUDEP rate during follow-up (S = -27 P = .008), with rates of 2.47/1000 for years 1-2 and 1.68/1000 for years 3-10 (rate ratio 0.68; 95% confidence interval [CI] 0.53-0.87; P = .002). Sensitivity analyses confirm these findings. Our data suggest that SUDEP risk significantly decreases during long-term follow-up of patients with refractory epilepsy receiving VNS Therapy. This finding might reflect several factors, including the natural long-term dynamic of SUDEP rate, attrition, and the impact of VNS Therapy. The role of each of these factors cannot be confirmed due to the limitations of the study

Learning hierarchical sequence representations across human cortex and hippocampus

Sensory input arrives in continuous sequences that humans experience as segmented units, e.g., words and events. The brain’s ability to discover regularities is called statistical learning. Structure can be represented at multiple levels, including transitional probabilities, ordinal position, and identity of units. To investigate sequence encoding in cortex and hippocampus, we recorded from intracranial electrodes in human subjects as they were exposed to auditory and visual sequences containing temporal regularities. We find neural tracking of regularities within minutes, with characteristic profiles across brain areas. Early processing tracked lower-level features (e.g., syllables) and learned units (e.g., words), while later processing tracked only learned units. Learning rapidly shaped neural representations, with a gradient of complexity from early brain areas encoding transitional probability, to associative regions and hippocampus encoding ordinal position and identity of units. These findings indicate the existence of multiple, parallel computational systems for sequence learning across hierarchically organized cortico-hippocampal circuits

Sudden Unexpected Death in Epilepsy: A PersonaliZed Prediction Tool

OBJECTIVE: To develop and validate a tool for individualised prediction of Sudden Unexpected Death in Epilepsy (SUDEP) risk, we re-analysed data from one cohort and three case-control studies undertaken 1980-2005. METHODS: We entered 1273 epilepsy cases (287 SUDEP, 986 controls) and 22 clinical predictor variables into a Bayesian logistic regression model. RESULTS: Cross-validated individualized model predictions were superior to baseline models developed from only average population risk or from generalised tonic-clonic seizure frequency (pairwise difference in leave-one-subject-out expected log posterior density = 35.9, SEM +/-12.5, and 22.9, SEM +/-11.0 respectively). The mean cross-validated (95% Credibility Interval) Area Under the Receiver Operating Curve was 0.71 (0.68 to 0.74) for our model versus 0.38 (0.33 to 0.42) and 0.63 (0.59 to 0.67) for the baseline average and generalised tonic-clonic seizure frequency models respectively. Model performance was weaker when applied to non-represented populations. Prognostic factors included generalized tonic-clonic and focal-onset seizure frequency, alcohol excess, younger age of epilepsy onset and family history of epilepsy. Anti-seizure medication adherence was associated with lower risk. CONCLUSIONS: Even when generalised to unseen data, model predictions are more accurate than population-based estimates of SUDEP. Our tool can enable risk-based stratification for biomarker discovery and interventional trials. With further validation in unrepresented populations it may be suitable for routine individualized clinical decision-making. Clinicians should consider assessment of multiple risk factors, and not only focus on the frequency of convulsions