Direct brain recordings reveal continuous encoding of structure in random stimuli

The brain excels at processing sensory input, even in rich or chaotic environments. Mounting evidence attributes this to the creation of sophisticated internal models of the environment that draw on statistical structures in the unfolding sensory input. Understanding how and where this modeling takes place is a core question in statistical learning and predictive processing. In this context, we address the role of transitional probabilities as an implicit structure supporting the encoding of a random auditory stream. Leveraging information-theoretical principles and the high spatiotemporal resolution of intracranial electroencephalography, we analyzed the trial-by-trial high-frequency activity representation of transitional probabilities. This unique approach enabled us to demonstrate how the brain continuously encodes structure in random stimuli and revealed the involvement of a network outside of the auditory system, including hippocampal, frontal, and temporal regions. Linking the frame-works of statistical learning and predictive processing, our work illuminates an implicit process that can be crucial for the swift detection of patterns and unexpected events in the environment.Fil: Fuhrer, Julian. University of Oslo; NoruegaFil: Kyrre, Glette. University of Oslo; NoruegaFil: Ivanovic, Jugoslav. University of Oslo; NoruegaFil: Gunnar Larsson, Pål. University of Oslo; NoruegaFil: Bekinschtein, Tristán Andrés. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. University of Cambridge; Reino UnidoFil: Kochen, Sara Silvia. Universidad Nacional Arturo Jauretche. Unidad Ejecutora de Estudios en Neurociencias y Sistemas Complejos. Provincia de Buenos Aires. Ministerio de Salud. Hospital Alta Complejidad en Red El Cruce Dr. Néstor Carlos Kirchner Samic. Unidad Ejecutora de Estudios en Neurociencias y Sistemas Complejos. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Unidad Ejecutora de Estudios en Neurociencias y Sistemas Complejos; ArgentinaFil: Knight, Robert T.. University of California at Berkeley; Estados UnidosFil: Tørresen, Jim. University of Oslo; NoruegaFil: Solbakk, Anne Kristin. University of Oslo; Noruega. Helgeland Hospital; NoruegaFil: Endestad, Tor. University of Oslo; Noruega. Helgeland Hospital; NoruegaFil: Blenkmann, Alejandro Omar. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. University of Oslo; Norueg

Validation of a new approach for distinguishing anesthetized from awake state in patients using directed transfer function applied to raw EEG

Abstract We test whether a measure based on the directed transfer function (DTF) calculated from short segments of electroencephalography (EEG) time-series can be used to monitor the state of the patients also during sevoflurane anesthesia as it can for patients undergoing propofol anesthesia. We collected and analyzed 25-channel EEG from 7 patients (3 females, ages 41–56 years) undergoing surgical anesthesia with sevoflurane, and quantified the sensor space directed connectivity for every 1-s epoch using DTF. The resulting connectivity parameters were compared to corresponding parameters from our previous study (n = 8, patients anesthetized with propofol and remifentanil, but otherwise using a similar protocol). Statistical comparisons between and within studies were done using permutation statistics, a data driven algorithm based on the DTF-parameters was employed to classify the epochs as coming from awake or anesthetized state. According to results of the permutation tests, DTF-parameter topographies were significantly different between the awake and anesthesia state at the group level. However, the topographies were not significantly different when comparing results computed from sevoflurane and propofol data, neither in the awake nor in anesthetized state. Optimizing the algorithm for simultaneously having high sensitivity and specificity in classification yielded an accuracy of 95.1% (SE = 0.96%), with sensitivity of 98.4% (SE = 0.80%) and specificity of 94.8% (SE = 0.10%). These findings indicate that the DTF changes in a similar manner when humans undergo general anesthesia caused by two distinct anesthetic agents with different molecular mechanisms of action

Distinguishing Anesthetized from Awake State in Patients: A New Approach Using One Second Segments of Raw EEG

Objective: The objective of this study was to test whether properties of 1-s segments of spontaneous scalp EEG activity can be used to automatically distinguish the awake state from the anesthetized state in patients undergoing general propofol anesthesia. Methods: Twenty five channel EEG was recorded from 10 patients undergoing general intravenous propofol anesthesia with remifentanil during anterior cervical discectomy and fusion. From this, we extracted properties of the EEG by applying the Directed Transfer Function (DTF) directly to every 1-s segment of the raw EEG signal. The extracted properties were used to develop a data-driven classification algorithm to categorize patients as “anesthetized” or “awake” for every 1-s segment of raw EEG. Results: The properties of the EEG signal were significantly different in the awake and anesthetized states for at least 8 of the 25 channels (p < 0.05, Bonferroni corrected Wilcoxon rank-sum tests). Using these differences, our algorithms achieved classification accuracies of 95.9%. Conclusion: Properties of the DTF calculated from 1-s segments of raw EEG can be used to reliably classify whether the patients undergoing general anesthesia with propofol and remifentanil were awake or anesthetized. Significance: This method may be useful for developing automatic real-time monitors of anesthesia

Norwegian population-based study of long-term effects, safety, and predictors of response of vagus nerve stimulation treatment in drug-resistant epilepsy: The NORPulse study

Objective This study was undertaken to evaluate the efficacy of vagus nerve stimulation (VNS) over time, and to determine which patient groups derive the most benefit. Methods Long-term outcomes are reported in 436 epilepsy patients from a VNS quality registry (52.8% adults, 47.2% children), with a median follow-up of 75 months. Patients were stratified according to evolution of response into constant responders, fluctuating responders, and nonresponders. The effect was evaluated at 6, 12, 24, 36, and 60 months. Multivariate regression analysis was used to identify predictors of response. Results The cumulative probability of ≥50% seizure reduction was 60%; however, 15% of patients showed a fluctuating course. Of those becoming responders, 89.5% (230/257) did so within 2 years. A steady increase in effect was observed among constant responders, with 48.7% (19/39) of those becoming seizure-free and 29.3% (39/133) with ≥75% seizure reduction achieving these effects within 2–5 years. Some effect (25%–<50%) at 6 months was a positive predictor of becoming a responder (odds ratio [OR] = 10.18, p < .0001) and having ≥75% reduction at 2 years (OR = 3.34, p = .03). Patients without intellectual disability had ORs of 3.34 and 3.11 of having ≥75% reduction at 2 and 5 years, respectively, and an OR of 6.22 of being seizure-free at last observation. Patients with unchanged antiseizure medication over the observation period showed better responder rates at 2 (63.0% vs. 43.1%, p = .002) and 5 years (63.4% vs. 46.3%, p = .031) than patients whose antiseizure medication was modified. Responder rates were higher for posttraumatic (70.6%, p = .048) and poststroke epilepsies (75.0%, p = .05) than other etiologies (46.5%). Significance Our data indicate that the effect of VNS increases over time and that there are important clinical decision points at 6 and 24 months for evaluating and adjusting the treatment. There should be better selection of candidates, as certain patient groups and epilepsy etiologies respond more favorably

Data_Sheet_1_Modeling intracranial electrodes. A simulation platform for the evaluation of localization algorithms.pdf

IntroductionIntracranial electrodes are implanted in patients with drug-resistant epilepsy as part of their pre-surgical evaluation. This allows the investigation of normal and pathological brain functions with excellent spatial and temporal resolution. The spatial resolution relies on methods that precisely localize the implanted electrodes in the cerebral cortex, which is critical for drawing valid inferences about the anatomical localization of brain function. Multiple methods have been developed to localize the electrodes, mainly relying on pre-implantation MRI and post-implantation computer tomography (CT) images. However, they are hard to validate because there is no ground truth data to test them and there is no standard approach to systematically quantify their performance. In other words, their validation lacks standardization. Our work aimed to model intracranial electrode arrays and simulate realistic implantation scenarios, thereby providing localization algorithms with new ways to evaluate and optimize their performance.ResultsWe implemented novel methods to model the coordinates of implanted grids, strips, and depth electrodes, as well as the CT artifacts produced by these. We successfully modeled realistic implantation scenarios, including different sizes, inter-electrode distances, and brain areas. In total, ∼3,300 grids and strips were fitted over the brain surface, and ∼850 depth electrode arrays penetrating the cortical tissue were modeled. Realistic CT artifacts were simulated at the electrode locations under 12 different noise levels. Altogether, ∼50,000 thresholded CT artifact arrays were simulated in these scenarios, and validated with real data from 17 patients regarding the coordinates’ spatial deformation, and the CT artifacts’ shape, intensity distribution, and noise level. Finally, we provide an example of how the simulation platform is used to characterize the performance of two cluster-based localization methods.ConclusionWe successfully developed the first platform to model implanted intracranial grids, strips, and depth electrodes and realistically simulate thresholded CT artifacts and their noise. These methods provide a basis for developing more complex models, while simulations allow systematic evaluation of the performance of electrode localization techniques. The methods described in this article, and the results obtained from the simulations, are freely available via open repositories. A graphical user interface implementation is also accessible via the open-source iElectrodes toolbox.</p

An electrophysiological marker of arousal level in humans

Deep non-rapid eye movement sleep (NREM) and general anesthesia with propofol are prominent states of reduced arousal linked to the occurrence of synchronized oscillations in the electroencephalogram (EEG). Although rapid eye movement (REM) sleep is also associated with diminished arousal levels, it is characterized by a desynchronized, ‘wake-like’ EEG. This observation implies that reduced arousal states are not necessarily only defined by synchronous oscillatory activity. Using intracranial and surface EEG recordings in four independent data sets, we demonstrate that the 1/f spectral slope of the electrophysiological power spectrum, which reflects the non-oscillatory, scale-free component of neural activity, delineates wakefulness from propofol anesthesia, NREM and REM sleep. Critically, the spectral slope discriminates wakefulness from REM sleep solely based on the neurophysiological brain state. Taken together, our findings describe a common electrophysiological marker that tracks states of reduced arousal, including different sleep stages as well as anesthesia in humans

Cognition in adult patients with newly diagnosed non-lesional temporal lobe epilepsy

Objective To evaluate whether cognitive performance is affected in newly diagnosed temporal lobe epilepsy (TLE) and to determine the most vulnerable cognitive domains. Methods In this baseline longitudinal study, differences in memory and non-memory cognitive functions were assessed using comprehensive neuropsychological test batteries in 21 adult patients with newly diagnosed non-lesional TLE and individually matched controls. In addition, the analyses included ratings of self-perceived emotional status. Results The patients performed more poorly than the control group regarding delayed visual memory (p = 0.013) and executive function tasks related to switching (Trail Making Test and verbal fluency shifting; p = 0.025 and p = 0.03, respectively). We found no differences in verbal learning and memory, attention/working memory/processing speed, and other executive functions. Significance Our results show that patients with TLE often have specific cognitive deficits at time of diagnosis, even in the absence of structural brain abnormalities. This supports the hypothesis that memory dysfunction is linked to an underlying pathology rather than to the effect of recurrent seizures, long-term use of anti-seizure medication, or other epilepsy-related factors. As certain executive functions are affected at an early stage, the pathology may involve brain regions beyond the temporal lobe and may comprise larger brain networks. These results indicate the need for greater awareness of cognition at the time of diagnosis of TLE and before initiation of treatment, and integration of neuropsychological assessment into early routine clinical care