Testing conditional quantile independence with functional covariate.

We propose a new non-parametric conditional independence test for a scalar response and a functional covariate over a continuum of quantile levels. We build a Cramer-von Mises type test statistic based on an empirical process indexed by random projections of the functional covariate, effectively avoiding the "curse of dimensionality" under the projected hypothesis, which is almost surely equivalent to the null hypothesis. The asymptotic null distribution of the proposed test statistic is obtained under some mild assumptions. The asymptotic global and local power properties of our test statistic are then investigated. We specifically demonstrate that the statistic is able to detect a broad class of local alternatives converging to the null at the parametric rate. Additionally, we recommend a simple multiplier bootstrap approach for estimating the critical values. The finite-sample performance of our statistic is examined through several Monte Carlo simulation experiments. Finally, an analysis of an EEG data set is used to show the utility and versatility of our proposed test statistic.

Prognostic value of early EEG abnormalities in severe stroke patients requiring mechanical ventilation: a pre-planned analysis of the SPICE prospective multicenter study.

INTRODUCTION: Prognostication of outcome in severe stroke patients necessitating invasive mechanical ventilation poses significant challenges. The objective of this study was to assess the prognostic significance and prevalence of early electroencephalogram (EEG) abnormalities in adult stroke patients receiving mechanical ventilation. METHODS: This study is a pre-planned ancillary investigation within the prospective multicenter SPICE cohort study (2017-2019), conducted in 33 intensive care units (ICUs) in the Paris area, France. We included adult stroke patients requiring invasive mechanical ventilation, who underwent at least one intermittent EEG examination during their ICU stay. The primary endpoint was the functional neurological outcome at one year, determined using the modified Rankin scale (mRS), and dichotomized as unfavorable (mRS 4-6, indicating severe disability or death) or favorable (mRS 0-3). Multivariable regression analyses were employed to identify EEG abnormalities associated with functional outcomes. RESULTS: Of the 364 patients enrolled in the SPICE study, 153 patients (49 ischemic strokes, 52 intracranial hemorrhages, and 52 subarachnoid hemorrhages) underwent at least one EEG at a median time of 4 (interquartile range 2-7) days post-stroke. Rates of diffuse slowing (70% vs. 63%, p = 0.37), focal slowing (38% vs. 32%, p = 0.15), periodic discharges (2.3% vs. 3.7%, p = 0.9), and electrographic seizures (4.5% vs. 3.7%, p = 0.4) were comparable between patients with unfavorable and favorable outcomes. Following adjustment for potential confounders, an unreactive EEG background to auditory and pain stimulations (OR 6.02, 95% CI 2.27-15.99) was independently associated with unfavorable outcomes. An unreactive EEG predicted unfavorable outcome with a specificity of 48% (95% CI 40-56), sensitivity of 79% (95% CI 72-85), and positive predictive value (PPV) of 74% (95% CI 67-81). Conversely, a benign EEG (defined as continuous and reactive background activity without seizure, periodic discharges, triphasic waves, or burst suppression) predicted favorable outcome with a specificity of 89% (95% CI 84-94), and a sensitivity of 37% (95% CI 30-45). CONCLUSION: The absence of EEG reactivity independently predicts unfavorable outcomes at one year in severe stroke patients requiring mechanical ventilation in the ICU, although its prognostic value remains limited. Conversely, a benign EEG pattern was associated with a favorable outcome.

Stable maintenance of multiple representational formats in human visual short-term memory.

Visual short-term memory (VSTM) enables humans to form a stable and coherent representation of the external world. However, the nature and temporal dynamics of the neural representations in VSTM that support this stability are barely understood. Here we combined human intracranial electroencephalography (iEEG) recordings with analyses using deep neural networks and semantic models to probe the representational format and temporal dynamics of information in VSTM. We found clear evidence that VSTM maintenance occurred in two distinct representational formats which originated from different encoding periods. The first format derived from an early encoding period (250 to 770 ms) corresponded to higher-order visual representations. The second format originated from a late encoding period (1,000 to 1,980 ms) and contained abstract semantic representations. These representational formats were overall stable during maintenance, with no consistent transformation across time. Nevertheless, maintenance of both representational formats showed substantial arrhythmic fluctuations, i.e., waxing and waning in irregular intervals. The increases of the maintained representational formats were specific to the phases of hippocampal low-frequency activity. Our results demonstrate that human VSTM simultaneously maintains representations at different levels of processing, from higher-order visual information to abstract semantic representations, which are stably maintained via coupling to hippocampal low-frequency activity.

Continuous Versus Routine Standardized Electroencephalogram for Outcome Prediction in Critically Ill Adults: Analysis From a Randomized Trial.

OBJECTIVES: To investigate electroencephalogram (EEG) features' relation with mortality or functional outcome after disorder of consciousness, stratifying patients between continuous EEG and routine EEG. DESIGN: Retrospective analysis of data from a randomized controlled trial. SETTING: Multiple adult ICUs. PATIENTS: Data from 364 adults with acute disorder of consciousness, randomized to continuous EEG (30-48 hr; n = 182) or repeated 20-minute routine electroencephalogram (n = 182). INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Correlations between electrographic features and mortality and modified Rankin scale at 6 months (good 0-2) were assessed. Background continuity, higher frequency, and reactivity correlated with survival and good modified Rankin scale. Rhythmic and periodic patterns carried dual prognostic information: lateralized periodic discharges were associated with mortality and bad modified Rankin scale. Generalized rhythmic delta activity correlated with survival, good modified Rankin scale, and lower occurrence of status epilepticus. Presence of sleep-spindles and continuous EEG background was associated with good outcome in the continuous EEG subgroup. In the routine EEG group, a model combining background frequency, continuity, reactivity, sleep-spindles, and lateralized periodic discharges was associated with mortality at 70.91% (95% CI, 59.62-80.10%) positive predictive value and 63.93% (95% CI, 58.67-68.89%) negative predictive value. In the continuous EEG group, a model combining background continuity, reactivity, generalized rhythmic delta activity, and lateralized periodic discharges was associated with mortality at 84.62% (95%CI, 75.02-90.97) positive predictive value and 74.77% (95% CI, 68.50-80.16) negative predictive value. CONCLUSIONS: Standardized EEG interpretation provides reliable prognostic information. Continuous EEG provides more information than routine EEG.

Novelty is not surprise: Human exploratory and adaptive behavior in sequential decision-making.

Classic reinforcement learning (RL) theories cannot explain human behavior in the absence of external reward or when the environment changes. Here, we employ a deep sequential decision-making paradigm with sparse reward and abrupt environmental changes. To explain the behavior of human participants in these environments, we show that RL theories need to include surprise and novelty, each with a distinct role. While novelty drives exploration before the first encounter of a reward, surprise increases the rate of learning of a world-model as well as of model-free action-values. Even though the world-model is available for model-based RL, we find that human decisions are dominated by model-free action choices. The world-model is only marginally used for planning, but it is important to detect surprising events. Our theory predicts human action choices with high probability and allows us to dissociate surprise, novelty, and reward in EEG signals.

Envelope reconstruction of speech and music highlights stronger tracking of speech at low frequencies.

The human brain tracks amplitude fluctuations of both speech and music, which reflects acoustic processing in addition to the encoding of higher-order features and one's cognitive state. Comparing neural tracking of speech and music envelopes can elucidate stimulus-general mechanisms, but direct comparisons are confounded by differences in their envelope spectra. Here, we use a novel method of frequency-constrained reconstruction of stimulus envelopes using EEG recorded during passive listening. We expected to see music reconstruction match speech in a narrow range of frequencies, but instead we found that speech was reconstructed better than music for all frequencies we examined. Additionally, models trained on all stimulus types performed as well or better than the stimulus-specific models at higher modulation frequencies, suggesting a common neural mechanism for tracking speech and music. However, speech envelope tracking at low frequencies, below 1 Hz, was associated with increased weighting over parietal channels, which was not present for the other stimuli. Our results highlight the importance of low-frequency speech tracking and suggest an origin from speech-specific processing in the brain.

Neural surprise in somatosensory Bayesian learning.

Tracking statistical regularities of the environment is important for shaping human behavior and perception. Evidence suggests that the brain learns environmental dependencies using Bayesian principles. However, much remains unknown about the employed algorithms, for somesthesis in particular. Here, we describe the cortical dynamics of the somatosensory learning system to investigate both the form of the generative model as well as its neural surprise signatures. Specifically, we recorded EEG data from 40 participants subjected to a somatosensory roving-stimulus paradigm and performed single-trial modeling across peri-stimulus time in both sensor and source space. Our Bayesian model selection procedure indicates that evoked potentials are best described by a non-hierarchical learning model that tracks transitions between observations using leaky integration. From around 70ms post-stimulus onset, secondary somatosensory cortices are found to represent confidence-corrected surprise as a measure of model inadequacy. Indications of Bayesian surprise encoding, reflecting model updating, are found in primary somatosensory cortex from around 140ms. This dissociation is compatible with the idea that early surprise signals may control subsequent model update rates. In sum, our findings support the hypothesis that early somatosensory processing reflects Bayesian perceptual learning and contribute to an understanding of its underlying mechanisms.

Dynamic Electrode-to-Image (DETI) mapping reveals the human brain's spatiotemporal code of visual information.

A number of neuroimaging techniques have been employed to understand how visual information is transformed along the visual pathway. Although each technique has spatial and temporal limitations, they can each provide important insights into the visual code. While the BOLD signal of fMRI can be quite informative, the visual code is not static and this can be obscured by fMRI's poor temporal resolution. In this study, we leveraged the high temporal resolution of EEG to develop an encoding technique based on the distribution of responses generated by a population of real-world scenes. This approach maps neural signals to each pixel within a given image and reveals location-specific transformations of the visual code, providing a spatiotemporal signature for the image at each electrode. Our analyses of the mapping results revealed that scenes undergo a series of nonuniform transformations that prioritize different spatial frequencies at different regions of scenes over time. This mapping technique offers a potential avenue for future studies to explore how dynamic feedforward and recurrent processes inform and refine high-level representations of our visual world.

A large-scale brain network mechanism for increased seizure propensity in Alzheimer's disease.

People with Alzheimer's disease (AD) are 6-10 times more likely to develop seizures than the healthy aging population. Leading hypotheses largely consider hyperexcitability of local cortical tissue as primarily responsible for increased seizure prevalence in AD. However, in the general population of people with epilepsy, large-scale brain network organization additionally plays a role in determining seizure likelihood and phenotype. Here, we propose that alterations to large-scale brain network organization seen in AD may contribute to increased seizure likelihood. To test this hypothesis, we combine computational modelling with electrophysiological data using an approach that has proved informative in clinical epilepsy cohorts without AD. EEG was recorded from 21 people with probable AD and 26 healthy controls. At the time of EEG acquisition, all participants were free from seizures. Whole brain functional connectivity derived from source-reconstructed EEG recordings was used to build subject-specific brain network models of seizure transitions. As cortical tissue excitability was increased in the simulations, AD simulations were more likely to transition into seizures than simulations from healthy controls, suggesting an increased group-level probability of developing seizures at a future time for AD participants. We subsequently used the model to assess seizure propensity of different regions across the cortex. We found the most important regions for seizure generation were those typically burdened by amyloid-beta at the early stages of AD, as previously reported by in-vivo and post-mortem staging of amyloid plaques. Analysis of these spatial distributions also give potential insight into mechanisms of increased susceptibility to generalized (as opposed to focal) seizures in AD vs controls. This research suggests avenues for future studies testing patients with seizures, e.g. co-morbid AD/epilepsy patients, and comparisons with PET and MRI scans to relate regional seizure propensity with AD pathologies.

Alpha and high gamma phase amplitude coupling during motor imagery and weighted cross-frequency coupling to extract discriminative cross-frequency patterns.

Motor imagery modulates specific neural oscillations like actual movement does. Representatively, suppression of the alpha power (e.g., event-related desynchronization [ERD]) is the typical pattern of motor imagery in the motor cortex. However, in addition to this amplitude-based feature, the coupling across frequencies includes important information about the brain functions and the existence of such complex information has been reported in various invasive studies. Yet, the interaction across multiple frequencies during motor imagery processing is still unclear and has not been widely studied, particularly concerning the non-invasive signals. In this study, we provide empirical evidence of the comodulation between the phase of alpha rhythm and the amplitude of high gamma rhythm during the motor imagery process. We used electroencephalography (EEG) in our investigation during the imagination of left- or right-hand movement recorded from 52 healthy subjects, and quantified the ERD of alpha and phase-amplitude coupling (PAC) which is a relative change of modulation index to the base line period (before the cue). As a result, we found that the coupling between the phase of alpha (8-12 Hz) and the amplitude of high gamma (70-120 Hz) and this PAC decreases during motor imagery and then rebounds to the baseline like alpha ERD (r = 0.29 to 0.42). This correlation between PAC and ERD was particularly stronger in the ipsilateral area. In addition, trials that demonstrated higher alpha power during the ready period (before the cue) showed a larger ERD during motor imagery and similarly, trials with higher modulation index during the ready period yielded a greater decrease in PAC during imagery. In the classification analysis, we found that the effective phase frequency that showed better decoding accuracy in left and right-hand imagery, varied across subjects. Motivated by result, we proposed a weighted cross-frequency coupling (WCFC) method that extracts the maximal discriminative feature by combining band power and CFC. In the evaluation, WCFC with only two electrodes yielded a performance comparable to the conventional algorithm with 64 electrodes in classifying left and right-hand motor imagery. These results indicate that the phase-amplitude frequency plays an important role in motor imagery, and that optimizing this frequency ranges is crucial for extracting information features to decode the motor imagery types.

Statistical power: Implications for planning MEG studies.

Statistical power is key for robust, replicable science. Here, we systematically explored how numbers of trials and subjects affect statistical power in MEG sensor-level data. More specifically, we simulated "experiments" using the MEG resting-state dataset of the Human Connectome Project (HCP). We divided the data in two conditions, injected a dipolar source at a known anatomical location in the "signal condition", but not in the "noise condition", and detected significant differences at sensor level with classical paired t-tests across subjects, using amplitude, squared amplitude, and global field power (GFP) measures. Group-level detectability of these simulated effects varied drastically with anatomical origin. We thus examined in detail which spatial properties of the sources affected detectability, looking specifically at the distance from closest sensor and orientation of the source, and at the variability of these parameters across subjects. In line with previous single-subject studies, we found that the most detectable effects originate from source locations that are closest to the sensors and oriented tangentially with respect to the head surface. In addition, cross-subject variability in orientation also affected group-level detectability, boosting detection in regions where this variability was small and hindering detection in regions where it was large. Incidentally, we observed a considerable covariation of source position, orientation, and their cross-subject variability in individual brain anatomical space, making it difficult to assess the impact of each of these variables independently of one another. We thus also performed simulations where we controlled spatial properties independently of individual anatomy. These additional simulations confirmed the strong impact of distance and orientation and further showed that orientation variability across subjects affects detectability, whereas position variability does not. Importantly, our study indicates that strict unequivocal recommendations as to the ideal number of trials and subjects for any experiment cannot be realistically provided for neurophysiological studies and should be adapted according to the brain regions under study.

Body site distribution of pediatric-onset morphea and association with extracutaneous manifestations.

BACKGROUND: The distribution of pediatric-onset morphea and site-based likelihood for extracutaneous complications has not been well characterized. OBJECTIVE: To characterize the lesional distribution of pediatric-onset morphea and to determine the sites with the highest association of extracutaneous manifestations. METHODS: A retrospective cross-sectional study was performed. Using clinical photographs, morphea lesions were mapped onto body diagrams using customized software. RESULTS: A total of 823 patients with 2522 lesions were included. Lesions were more frequent on the superior (vs inferior) anterior aspect of the head and extensor (vs flexor) extremities. Linear morphea lesions were more likely on the head and neck, whereas plaque and generalized morphea lesions were more likely on the trunk. Musculoskeletal complications were more likely with lesions on the extensor (vs flexor) extremity (odds ratio [OR], 2.0; 95% confidence interval [CI], 1.2-3.4), whereas neurologic manifestations were more likely with lesions on the anterior (vs posterior) (OR, 2.8; 95% CI, 1.7-4.6) and superior (vs inferior) aspect of the head (OR, 2.3; 95% CI, 1.6-3.4). LIMITATIONS: Retrospective nature and the inclusion of only patients with clinical photographs. CONCLUSION: The distribution of pediatric-onset morphea is not random and varies with body site and within individual body sites. The risk stratification of extracutaneous manifestations by body site may inform decisions about screening for extracutaneous manifestations, although prospective studies are needed.

Negative drift of sedation depth in critically ill patients receiving constant minimum alveolar concentration of isoflurane, sevoflurane, or desflurane: a randomized controlled trial.

BACKGROUND: Intensive care unit (ICU) physicians have extended the minimum alveolar concentration (MAC) to deliver and monitor long-term volatile sedation in critically ill patients. There is limited evidence of MAC's reliability in controlling sedation depth in this setting. We hypothesized that sedation depth, measured by the electroencephalography (EEG)-derived Narcotrend-Index (burst-suppression N_Index 0-awake N_Index 100), might drift downward over time despite constant MAC values. METHODS: This prospective single-centre randomized clinical study was conducted at a University Hospital Surgical Intensive Care Unit and included consecutive, postoperative ICU patients fulfilling the inclusion criteria. Patients were randomly assigned to receive uninterrupted inhalational sedation with isoflurane, sevoflurane, or desflurane. The end-expiratory concentration of the anaesthetics and the EEG-derived index were measured continuously in time-stamped pairs. Sedation depth was also monitored using Richmond-Agitation-Sedation-Scale (RASS). The paired t-test and linear models (bootstrapped or multilevel) have been employed to analyze MAC, N_Index and RASS across the three groups. RESULTS: Thirty patients were recruited (female/male: 10/20, age 64 ± 11, Simplified Acute Physiology Score II 30 ± 10). In the first 24 h, 21.208 pairs of data points (N_Index and MAC) were recorded. The median MAC of 0.58 ± 0.06 remained stable over the sedation time in all three groups. The t-test indicated in the isoflurane and sevoflurane groups a significant drop in RASS and EEG-derived N_Index in the first versus last two sedation hours. We applied a multilevel linear model on the entire longitudinal data, nested per patient, which produced the formula N_Index = 43 - 0.7·h (R2 = 0.76), showing a strong negative correlation between sedation's duration and the N_Index. Bootstrapped linear models applied for each sedation group produced: N_Index of 43-0.9, 45-0.8, and 43-0.4·h for isoflurane, sevoflurane, and desflurane, respectively. The regression coefficient for desflurane was almost half of those for isoflurane and sevoflurane, indicating a less pronounced time-effect in this group. CONCLUSIONS: Maintaining constant MAC does not guarantee stable sedation depth. Thus, the patients necessitate frequent clinical assessments or, when unfeasible, continuous EEG monitoring. The differences across different volatile anaesthetics regarding their time-dependent negative drift requires further exploration. TRIAL REGISTRATION: NCT03860129.

Preoperative Cognitive Abnormality, Intraoperative Electroencephalogram Suppression, and Postoperative Delirium: A Mediation Analysis.

BACKGROUND: Postoperative delirium is a common complication that hinders recovery after surgery. Intraoperative electroencephalogram suppression has been linked to postoperative delirium, but it is unknown if this relationship is causal or if electroencephalogram suppression is merely a marker of underlying cognitive abnormalities. The hypothesis of this study was that intraoperative electroencephalogram suppression mediates a nonzero portion of the effect between preoperative abnormal cognition and postoperative delirium. METHODS: This is a prespecified secondary analysis of the Electroencephalography Guidance of Anesthesia to Alleviate Geriatric Syndromes (ENGAGES) randomized trial, which enrolled patients age 60 yr or older undergoing surgery with general anesthesia at a single academic medical center between January 2015 and May 2018. Patients were randomized to electroencephalogram-guided anesthesia or usual care. Preoperative abnormal cognition was defined as a composite of previous delirium, Short Blessed Test cognitive score greater than 4 points, or Eight Item Interview to Differentiate Aging and Dementia score greater than 1 point. Duration of intraoperative electroencephalogram suppression was defined as number of minutes with suppression ratio greater than 1%. Postoperative delirium was detected via Confusion Assessment Method or chart review on postoperative days 1 to 5. RESULTS: Among 1,113 patients, 430 patients showed evidence of preoperative abnormal cognition. These patients had an increased incidence of postoperative delirium (151 of 430 [35%] vs.123 of 683 [18%], P < 0.001). Of this 17.2% total effect size (99.5% CI, 9.3 to 25.1%), an absolute 2.4% (99.5% CI, 0.6 to 4.8%) was an indirect effect mediated by electroencephalogram suppression, while an absolute 14.8% (99.5% CI, 7.2 to 22.5%) was a direct effect of preoperative abnormal cognition. Randomization to electroencephalogram-guided anesthesia did not change the mediated effect size (P = 0.078 for moderation). CONCLUSIONS: A small portion of the total effect of preoperative abnormal cognition on postoperative delirium was mediated by electroencephalogram suppression. Study precision was too low to determine if the intervention changed the mediated effect.

Modeling neuronal avalanches and long-range temporal correlations at the emergence of collective oscillations: Continuously varying exponents mimic M/EEG results.

We revisit the CROS ("CRitical OScillations") model which was recently proposed as an attempt to reproduce both scale-invariant neuronal avalanches and long-range temporal correlations. With excitatory and inhibitory stochastic neurons locally connected in a two-dimensional disordered network, the model exhibits a transition where alpha-band oscillations emerge. Precisely at the transition, the fluctuations of the network activity have nontrivial detrended fluctuation analysis (DFA) exponents, and avalanches (defined as supra-threshold activity) have power law distributions of size and duration. We show that, differently from previous results, the exponents governing the distributions of avalanche size and duration are not necessarily those of the mean-field directed percolation universality class (3/2 and 2, respectively). Instead, in a narrow region of parameter space, avalanche exponents obtained via a maximum-likelihood estimator vary continuously and follow a linear relation, in good agreement with results obtained from M/EEG data. In that region, moreover, the values of avalanche and DFA exponents display a spread with positive correlations, reproducing human MEG results.

Spatial neuronal synchronization and the waveform of oscillations: Implications for EEG and MEG.

Neuronal oscillations are ubiquitous in the human brain and are implicated in virtually all brain functions. Although they can be described by a prominent peak in the power spectrum, their waveform is not necessarily sinusoidal and shows rather complex morphology. Both frequency and temporal descriptions of such non-sinusoidal neuronal oscillations can be utilized. However, in non-invasive EEG/MEG recordings the waveform of oscillations often takes a sinusoidal shape which in turn leads to a rather oversimplified view on oscillatory processes. In this study, we show in simulations how spatial synchronization can mask non-sinusoidal features of the underlying rhythmic neuronal processes. Consequently, the degree of non-sinusoidality can serve as a measure of spatial synchronization. To confirm this empirically, we show that a mixture of EEG components is indeed associated with more sinusoidal oscillations compared to the waveform of oscillations in each constituent component. Using simulations, we also show that the spatial mixing of the non-sinusoidal neuronal signals strongly affects the amplitude ratio of the spectral harmonics constituting the waveform. Finally, our simulations show how spatial mixing can affect the strength and even the direction of the amplitude coupling between constituent neuronal harmonics at different frequencies. Validating these simulations, we also demonstrate these effects in real EEG recordings. Our findings have far reaching implications for the neurophysiological interpretation of spectral profiles, cross-frequency interactions, as well as for the unequivocal determination of oscillatory phase.

A functional generalized F-test for signal detection with applications to event-related potentials significance analysis.

Motivated by the analysis of complex dependent functional data such as event-related brain potentials (ERP), this paper considers a time-varying coefficient multivariate regression model with fixed-time covariates for testing global hypotheses about population mean curves. Based on a reduced-rank modeling of the time correlation of the stochastic process of pointwise test statistics, a functional generalized F-test is proposed and its asymptotic null distribution is derived. Our analytical results show that the proposed test is more powerful than functional analysis of variance testing methods and competing signal detection procedures for dependent data. Simulation studies confirm such power gain for data with patterns of dependence similar to those observed in ERPs. The new testing procedure is illustrated with an analysis of the ERP data from a study of neural correlates of impulse control.

A Bayesian approach to joint modeling of matrix-valued imaging data and treatment outcome with applications to depression studies.

In this paper, we propose a unified Bayesian joint modeling framework for studying association between a binary treatment outcome and a baseline matrix-valued predictor. Specifically, a joint modeling approach relating an outcome to a matrix-valued predictor through a probabilistic formulation of multilinear principal component analysis is developed. This framework establishes a theoretical relationship between the outcome and the matrix-valued predictor, although the predictor is not explicitly expressed in the model. Simulation studies are provided showing that the proposed method is superior or competitive to other methods, such as a two-stage approach and a classical principal component regression in terms of both prediction accuracy and estimation of association; its advantage is most notable when the sample size is small and the dimensionality in the imaging covariate is large. Finally, our proposed joint modeling approach is shown to be a very promising tool in an application exploring the association between baseline electroencephalography data and a favorable response to treatment in a depression treatment study by achieving a substantial improvement in prediction accuracy in comparison to competing methods.

Electroencephalographic features in patients undergoing extracorporeal membrane oxygenation.

BACKGROUND: Neurologic injury is one of the most frequent causes of death in patients undergoing extracorporeal membrane oxygenation (ECMO). As neurological examination is often unreliable in sedated patients, additional neuromonitoring is needed. However, the value of electroencephalogram (EEG) in adult ECMO patients has not been well assessed. Therefore, the aim of this study was to assess the occurrence of electroencephalographic abnormalities in patients treated with extracorporeal membrane oxygenation (ECMO) and their association with 3-month neurologic outcome. METHODS: Retrospective analysis of all patients undergoing venous-venous (V-V) or venous-arterial (V-A) ECMO with a concomitant EEG recording (April 2009-December 2018), either recorded intermittently or continuously. EEG background was classified into four categories: mild/moderate encephalopathy (i.e., mostly defined by the presence of reactivity), severe encephalopathy (mostly defined by the absence of reactivity), burst-suppression (BS) and suppressed background. Epileptiform activity (i.e., ictal EEG pattern, sporadic epileptiform discharges or periodic discharges) and asymmetry were also reported. EEG findings were analyzed according to unfavorable neurological outcome (UO, defined as Glasgow Outcome Scale < 4) at 3 months after discharge. RESULTS: A total of 139 patients (54 [41-62] years; 60 (43%) male gender) out of 596 met the inclusion criteria and were analyzed. Veno-arterial (V-A) ECMO was used in 98 (71%); UO occurred in 99 (71%) patients. Continuous EEG was performed in 113 (81%) patients. The analysis of EEG background showed that 29 (21%) patients had severe encephalopathy, 4 (3%) had BS and 19 (14%) a suppressed background. In addition, 11 (8%) of patients had seizures or status epilepticus, 10 (7%) had generalized periodic discharges or lateralized periodic discharges, and 27 (19%) had asymmetry on EEG. In the multivariate analysis, the occurrence of ischemic stroke or intracranial hemorrhage (OR 4.57 [1.25-16.74]; p = 0.02) and a suppressed background (OR 10.08 [1.24-82.20]; p = 0.03) were independently associated with UO. After an adjustment for covariates, an increasing probability for UO was observed with more severe EEG background categories. CONCLUSIONS: In adult patients treated with ECMO, EEG can identify patients with a high likelihood of poor outcome. In particular, suppressed background was independently associated with unfavorable neurological outcome.

Electroencephalography at the time of Covid-19 pandemic in Italy.

OBJECTIVE: During the Covid-19 pandemic, government restrictions limited health care to urgent needs. Neurophysiology centers had to suddenly reschedule their activities, with a lack of specific recommendations about electroencephalography (EEG) execution. During the pandemic phase 1, we launched an online survey to understand the flaws and strengths of the EEG management in Italy at the time of Covid-19 pandemic. METHODS: A 45-item online survey (published from April 16 to 30, 2020), endorsed by the Italian Society of Clinical Neurophysiology (SINC), the Italian League Against epilepsy (LICE), and the Italian Association of Neurophysiology technologists (AITN), collected EEG management data (EEG's number and type, indications, personnel and patients safety, devices' sanification) during the Covid-19 pandemic. RESULTS: We received responses from 206 centers. The number of EEGs performed was reduced by 76 ± 20%, and several types of specific EEG (video-EEG, ambulatory-EEG, LTM, polysomnography) were reduced at a minimum. Half of the centers performed inpatient EEGs only for urgencies. Repetitive seizures, encephalitis, and non-convulsive status epilepticus were the most common indications. Covid-19-positive patients received less EEG than negative ones (p < 0.0001). EEG requests came mainly not only from neurologists (n = 176) but also from general practitioners (n = 40), emergentists (n = 79), intensivists (n = 72), and other specialists (n = 53). Those centers which continued performing outpatient EEG examinations were instructed to perform the EEG after a Covid-19-related symptom screening for patients and using personal protective equipment (PPE) through all the procedure. Inpatient EEGs were performed using FFP2/FFP3 masks by neurophysiology technologists in only 50% of cases. Patients executed hyperventilation only for real clinical needs, but often (56%) with a mask. CONCLUSIONS: Italian neurophysiology centers strongly adhered to government restrictions of lockdown. Some issues emerged, ranging from the evaluation of a proper indication for EEG, technical procedures of EEG recording, and protection of neurophysiology technicians.