Limited usefulness of neurocognitive functioning indices as predictive markers for treatment response to methylphenidate or neurofeedback@home in children and adolescents with ADHD.

Introduction: Earlier studies exploring the value of executive functioning (EF) indices for assessing treatment effectiveness and predicting treatment response in attention-deficit/hyperactivity disorder (ADHD) mainly focused on pharmacological treatment options and revealed rather heterogeneous results. Envisioning the long-term goal of personalized treatment selection and intervention planning, this study comparing methylphenidate treatment (MPH) and a home-based neurofeedback intervention (NF@Home) aimed to expand previous findings by assessing objective as well as subjectively reported EF indices and by analyzing their value as treatment and predictive markers. Methods: Children and adolescents (n = 146 in the per protocol sample) aged 7-13 years with a formal diagnosis of an inattentive or combined presentation of ADHD were examined. We explored the EF performance profile using the Conners Continuous Performance Task (CPT) and the BRIEF self-report questionnaire within our prospective, multicenter, randomized, reference drug-controlled NEWROFEED study with sites in five European countries (France, Spain, Switzerland, Germany, and Belgium). As primary outcome for treatment response, the clinician-rated ADHD Rating Scale-IV was used. Patients participating in this non-inferiority trial were randomized to either NF@home (34-40 sessions of TBR or SMR NF depending on the pre-assessed individual alpha peak frequency) or MPH treatment (ratio: 3:2). Within a mixed-effects model framework, analyses of change were calculated to explore the predictive value of neurocognitive indices for ADHD symptom-related treatment response. Results: For a variety of neurocognitive indices, we found a significant pre-post change during treatment, mainly in the MPH group. However, the results of the current study reveal a rather limited prognostic value of neurocognitive indices for treatment response to either NF@Home or MPH treatment. Some significant effects emerged for parent-ratings only. Discussion: Current findings indicate a potential value of self-report (BRIEF global score) and some objectively measured neurocognitive indices (CPT commission errors and hit reaction time variability) as treatment markers (of change) for MPH. However, we found a rather limited prognostic value with regard to predicting treatment response not (yet) allowing recommendation for clinical use. Baseline symptom severity was revealed as the most relevant predictor, replicating robust findings from previous studies.

Personalized at-home neurofeedback compared to long-acting methylphenidate in children with ADHD: NEWROFEED, a European randomized noninferiority trial.

BACKGROUND: Neurofeedback is considered a promising intervention for the treatment of attention-deficit hyperactivity disorder (ADHD). NEWROFEED is a prospective, multicentre, randomized (3:2), reference drug-controlled trial in children with ADHD aged between 7 and 13 years. The main objective of NEWROFEED was to demonstrate the noninferiority of personalized at-home neurofeedback (NF) training versus methylphenidate in the treatment of children with ADHD. METHODS: The NF group (n = 111) underwent eight visits and two treatment phases of 16 to 20 at-home sessions with down-training of the theta/beta ratio (TBR) for children with high TBR and enhancing the sensorimotor rhythm (SMR) for the others. The control group (n = 67) received optimally titrated long-acting methylphenidate. The primary endpoint was the change between baseline and endpoint in the Clinician ADHD-RS-IV total score in the per-protocol population (90 NF/59 controls). TRIAL REGISTRATION: US National Institute of Health, ClinicalTrials.gov #NCT02778360. RESULTS: Our study failed to demonstrate noninferiority of NF versus methylphenidate (mean between-group difference 8.09 90% CI [8.09; 10.56]). However, both treatment groups showed significant pre-post improvements in core ADHD symptoms and in a broader range of problems. Reduction in the Clinician ADHD-RS-IV total score between baseline and final visit (D90) was 26.7% (SMD = 0.89) in the NF and 46.9% (SMD = 2.03) in the control group. NF effects increased whereas those of methylphenidate were stable between intermediate and final visit. CONCLUSIONS: Based on clinicians' reports, the effects of at-home NF were inferior to those of methylphenidate as a stand-alone treatment.

Personalized at-home neurofeedback compared with long-acting methylphenidate in an european non-inferiority randomized trial in children with ADHD.

BACKGROUND: Neurofeedback (NF) has gained increasing interest among non-pharmacological treatments for Attention Deficit Hyperactivity Disorder (ADHD). NF training aims to enhance self-regulation of brain activities. The goal of the NEWROFEED study is to assess the efficacy of a new personalized NF training device, using two different protocols according to each child's electroencephalographic pattern, and designed for use at home. This study is a non-inferiority trial comparing NF to methylphenidate. METHODS: The study is a prospective, multicentre, randomized, reference drug-controlled trial. One hundred seventy-nine children with ADHD, aged 7 to 13 years will be recruited in 13 clinical centres from 5 European countries. Subjects will be randomized to two groups: NF group (Neurofeedback Training Group) and MPH group (Methylphenidate group). Outcome measures include clinicians, parents and teachers' assessments, attention measures and quantitative EEG (qEEG). Patients undergo eight visits over a three-month period: pre-inclusion visit, inclusion visit, 4 "discovery" (NF group) or titration visits (MPH group), an intermediate and a final visit. Patients will be randomized to either the MPH or NF group. Children in the NF group will undergo either an SMR or a Theta/Beta training protocol according to their baselineTheta/Beta Ratio obtained from the qEEG. DISCUSSION: This is the first non-inferiority study between a personalized NF device and pharmacological treatment. Innovative aspects of Mensia Koala™ include the personalization of the training protocol according to initial qEEG characteristics (SMR or Theta/Beta training protocols) and an improved accessibility of NF due to the opportunity to train at home with monitoring by the clinician through a dedicated web portal. TRIAL REGISTRATION: NCT02778360 . Date registration (retrospectively registered): 5-12-2016. Registered May 19, 2016.

Alpha-phase synchrony EEG training for multi-resistant chronic low back pain patients: an open-label pilot study.

PURPOSE: Chronic low back pain (cLBP) affects a quarter of a population during its lifetime. The most severe cases include patients not responding to interventions such as 5-week-long in-hospital multi-disciplinary protocols. This document reports on a pilot study offering an alpha-phase synchronization (APS) brain rehabilitation intervention to a population of n = 16 multi-resistant cLBP patients. METHODS: The intervention consists of 20 sessions of highly controlled electroencephalography (EEG) APS operant conditioning (neurofeedback) paradigm delivered in the form of visual feedback. Visual analogue scale for pain, Dallas, Hamilton, and HAD were measured before, after, at 6-month and 12-month follow-up. Full-scalp EEG data were analyzed to study significant changes in the brain's electrical activity. RESULTS: The intervention showed a great and lasting response of most measured clinical scales. The clinical improvement was lasting beyond the 6-month follow-up endpoints. The EEG data confirm that patients did control (intra-session trends) and learned to better control (intersession trends) their APS neuromarker resulting in (nonsignificant) baseline changes in their resting state activity. Last and most significantly, the alpha-phase concentration (APC) neuromarker, specific to phase rather than amplitude, was found to correlate significantly with the reduction in clinical symptoms in a typical dose-response effect. CONCLUSION: This first experiment highlights the role of the APC neuromarker in relation to the nucleus accumbens activity and its role on nociception and the chronicity of pain. This study suggests APC rehabilitation could be used clinically for the most severe cases of cLBP. Its excellent safety profile and availability as a home-use intervention makes it a potentially disruptive tool in the context of nonsteroidal anti-inflammatory drugs and opioid abuses. These slides can be retrieved under Electronic Supplementary Material.

Is there a cluster of high theta-beta ratio patients in attention deficit hyperactivity disorder?

OBJECTIVE: It has been suggested that there exists a subgroup of ADHD patients that have a high theta-beta ratio (TBR). The aim of this study was to analyze the distribution of TBR values in ADHD patients and validate the presence of a high-TBR cluster using objective metrics. METHODS: The TBR was extracted from eyes-open resting state EEG recordings of 363 ADHD patients, aged 5-21 years. The TBR distribution was estimated with three Bayesian Gaussian Mixture Models (BGMMs) with one, two, and three components, respectively. The pairwise comparison of BGMMs was carried out with deviance tests to identify the number of components that best represented the data. RESULTS: The two-component BGMM modeled the TBR values significantly better than the one-component BGMM (p-value = 0.005). No significant difference was observed between the two-component and three-component BGMM (p-value = 0.850). CONCLUSION: These results suggest that there exist indeed two TBR clusters within the ADHD population. SIGNIFICANCE: This work offers a global framework to understanding values found in the literature and suggest guidelines on how to compute theta-beta ratio values. Moreover, using objective data-driven method we confirm the existence of a high theta-beta ratio cluster.

Clinical and Experimental Factors Influencing the Efficacy of Neurofeedback in ADHD: A Meta-Analysis.

Meta-analyses have been extensively used to evaluate the efficacy of neurofeedback (NFB) treatment for Attention Deficit/Hyperactivity Disorder (ADHD) in children and adolescents. However, each meta-analysis published in the past decade has contradicted the methods and results from the previous one, thus making it difficult to determine a consensus of opinion on the effectiveness of NFB. This works brings continuity to the field by extending and discussing the last and much controversial meta-analysis by Cortese et al. (1). The extension comprises an update of that work including the latest control trials, which have since been published and, most importantly, offers a novel methodology. Specifically, NFB literature is characterized by a high technical and methodological heterogeneity, which partly explains the current lack of consensus on the efficacy of NFB. This work takes advantage of this by performing a Systematic Analysis of Biases (SAOB) in studies included in the previous meta-analysis. Our extended meta-analysis (k = 16 studies) confirmed the previously obtained results of effect sizes in favor of NFB efficacy as being significant when clinical scales of ADHD are rated by parents (non-blind, p-value = 0.0014), but not when they are rated by teachers (probably blind, p-value = 0.27). The effect size is significant according to both raters for the subset of studies meeting the definition of "standard NFB protocols" (parents' p-value = 0.0054; teachers' p-value = 0.043, k = 4). Following this, the SAOB performed on k = 33 trials identified three main factors that have an impact on NFB efficacy: first, a more intensive treatment, but not treatment duration, is associated with higher efficacy; second, teachers report a lower improvement compared to parents; third, using high-quality EEG equipment improves the effectiveness of the NFB treatment. The identification of biases relating to an appropriate technical implementation of NFB certainly supports the efficacy of NFB as an intervention. The data presented also suggest that the probably blind assessment of teachers may not be considered a good proxy for blind assessments, therefore stressing the need for studies with placebo-controlled intervention as well as carefully reported neuromarker changes in relation to clinical response.

The Riemannian Potato Field: A Tool for Online Signal Quality Index of EEG.

Electroencephalographic (EEG) recordings are contaminated by instrumental, environmental, and biological artifacts, resulting in low signal-to-noise ratio. Artifact detection is a critical task for real-time applications where the signal is used to give a continuous feedback to the user. In these applications, it is therefore necessary to estimate online a signal quality index (SQI) in order to stop the feedback when the signal quality is unacceptable. In this paper, we introduce the Riemannian potato field (RPF) algorithm as such SQI. It is a generalization and extensionof theRiemannian potato, a previouslypublished real-time artifact detection algorithm, whose performance is degraded as the number of channels increases. The RPF overcomes this limitation by combining the outputs of several smaller potatoes into a unique SQI resulting in a higher sensitivity and specificity, regardless of the number of electrodes. We demonstrate these results on a clinical dataset totalizing more than 2200 h of EEG recorded at home, that is, in a non-controlled environment.

Correction to: Esmolol indirectly stimulates vagal nerve activity in endotoxemic pigs.

Following publication of the original article [1], the author reported these required corrections to Fig. 5 and Fig. 6.

Esmolol indirectly stimulates vagal nerve activity in endotoxemic pigs.

BACKGROUND: There is an increasing interest in beta-blockade as a therapeutic approach to sepsis following consistent experimental findings of attenuation of inflammation and improved survival with beta1 selective antagonist. However, the mechanism of these beneficial effects remains very uncertain. Thus, this study is aimed at investigating the effects of a beta-1 selective blockade on sympathetic/parasympathetic activity in endotoxin-challenged pigs using heart rate variability. The hypothesis is that an adrenergic blockade could promote parasympathetic activity. Indeed, the increase of parasympathetic activity is a mechanism recently described as beneficial in septic states. METHODS: Fifty-one endotoxin-challenged pigs were studied. After 30 min of endotoxin infusion and 30 min of evolution without intervention, the pigs were randomly assigned the placebo or esmolol treatment and were observed for 200 min. Overall heart rate variability was assessed continuously, in the temporal domain by standard deviation of RR intervals (SDNN, ms),and in the frequency domain by spectral powers of low frequency (LF, ms2 × 103/Hz) and high frequency (HF, ms2 × 103/Hz) bands. RESULTS: Variations of power in these frequency bands were interpreted as putative markers of sympathetic (LF) and parasympathetic (HF) activity. In LPS treated animals, Esmolol did not increase SDNN, but instead decreased LF and increased HF power. CONCLUSION: These spectral modifications associated to a beta-blocker treatment after an endotoxemic challenge are interpreted as a significant decrease of sympathetic activity and an indirect increase of vagal autonomic tone.

Online denoising of eye-blinks in electroencephalography.

OBJECTIVE: Due to its high temporal resolution, electroencephalography (EEG) has become a broadly-used technology for real-time brain monitoring applications such as neurofeedback (NFB) and brain-computer interfaces (BCI). However, since EEG signals are prone to artifacts, denoising is a crucial step that enables adequate subsequent data processing and interpretation. The aim of this study is to compare manual denoising to unsupervised online denoising, which is essential to real-time applications. METHODS: Denoising EEG for real-time applications requires the implementation of unsupervised and online methods. In order to permit genericity, these methods should not rely on electrooculography (EOG) traces nor on temporal/spatial templates of the artifacts. Two blind source separation (BSS) methods are analyzed in this paper with the aim of automatically correcting online eye-blink artifacts: the algorithm for multiple unknown signals extraction (AMUSE) and the approximate joint diagonalization of Fourier cospectra (AJDC). The chosen gold standard is a manual review of the EEG database carried out retrospectively by a human operator. Comparison is carried out using the spectral properties of the continuous EEG and event-related potentials (ERP). RESULTS AND CONCLUSION: The AJDC algorithm addresses limitations observed in AMUSE and outperforms it. No statistical difference is found between the manual and automatic approaches on a database composed of 15 healthy individuals, paving the way for an automated, operator-independent, and real-time eye-blink correction technique.

A "datathon" model to support cross-disciplinary collaboration.

In recent years, there has been a growing focus on the unreliability of published biomedical and clinical research. To introduce effective new scientific contributors to the culture of health care, we propose a "datathon" or "hackathon" model in which participants with disparate, but potentially synergistic and complementary, knowledge and skills effectively combine to address questions faced by clinicians. The continuous peer review intrinsically provided by follow-up datathons, which take up prior uncompleted projects, might produce more reliable research, either by providing a different perspective on the study design and methodology or by replication of prior analyses.

A physiological time series dynamics-based approach to patient monitoring and outcome prediction.

Cardiovascular variables such as heart rate (HR) and blood pressure (BP) are regulated by an underlying control system, and therefore, the time series of these vital signs exhibit rich dynamical patterns of interaction in response to external perturbations (e.g., drug administration), as well as pathological states (e.g., onset of sepsis and hypotension). A question of interest is whether "similar" dynamical patterns can be identified across a heterogeneous patient cohort, and be used for prognosis of patients' health and progress. In this paper, we used a switching vector autoregressive framework to systematically learn and identify a collection of vital sign time series dynamics, which are possibly recurrent within the same patient and may be shared across the entire cohort. We show that these dynamical behaviors can be used to characterize the physiological "state" of a patient. We validate our technique using simulated time series of the cardiovascular system, and human recordings of HR and BP time series from an orthostatic stress study with known postural states. Using the HR and BP dynamics of an intensive care unit (ICU) cohort of over 450 patients from the MIMIC II database, we demonstrate that the discovered cardiovascular dynamics are significantly associated with hospital mortality (dynamic modes 3 and 9, p=0.001, p=0.006 from logistic regression after adjusting for the APACHE scores). Combining the dynamics of BP time series and SAPS-I or APACHE-III provided a more accurate assessment of patient survival/mortality in the hospital than using SAPS-I and APACHE-III alone (p=0.005 and p=0.045). Our results suggest that the discovered dynamics of vital sign time series may contain additional prognostic value beyond that of the baseline acuity measures, and can potentially be used as an independent predictor of outcomes in the ICU.

An open-source software for automatic calculation of respiratory parameters based on esophageal pressure.

PURPOSE: We have developed a software that automatically calculates respiratory effort indices, including intrinsic end expiratory pressure (PEEPi) and esophageal pressure-time product (PTPeso). MATERIALS AND METHODS: The software first identifies respiratory periods. Clean signals are averaged to provide a reference mean cycle from which respiratory parameters are extracted. The onset of the inspiratory effort is detected automatically by looking backward from the onset of inspiratory flow to the first point where the esophageal pressure derivative is equal to zero (inflection point). PEEPi is derived from this point. Twenty-three recordings from 16 patients were analyzed with the algorithm and compared with experts' manual analysis of signals: 15 recordings were performed during spontaneous breathing, 1 during non-invasive mechanical ventilation, and 7 under both conditions. RESULTS: For all values, the coefficients of determinations (r(2)) exceeded 0.94 (p<0.001). The bias (mean difference) between PEEPi calculated by hand and automatically was -0.26±0.52cmH2O during spontaneous breathing and the precisions (standard deviations of the differences) was 0.52cmH2O with limits of agreement of 0.78 and -1.30cmH2O. The mean difference between PTPeso calculated by hand and automatically was -0.38±1.42cmH2Os/cycle with limits of agreement of 2.46 and -3.22cmH2Os/cycle. CONCLUSIONS: Our program provides a reliable method for the automatic calculation of PEEPi and respiratory effort indices, which may facilitate the use of these variables in clinical practice. The software is open source and can be improved with the development and validation of new respiratory parameters.

Making big data useful for health care: a summary of the inaugural mit critical data conference.

With growing concerns that big data will only augment the problem of unreliable research, the Laboratory of Computational Physiology at the Massachusetts Institute of Technology organized the Critical Data Conference in January 2014. Thought leaders from academia, government, and industry across disciplines-including clinical medicine, computer science, public health, informatics, biomedical research, health technology, statistics, and epidemiology-gathered and discussed the pitfalls and challenges of big data in health care. The key message from the conference is that the value of large amounts of data hinges on the ability of researchers to share data, methodologies, and findings in an open setting. If empirical value is to be from the analysis of retrospective data, groups must continuously work together on similar problems to create more effective peer review. This will lead to improvement in methodology and quality, with each iteration of analysis resulting in more reliability.

Dynamic data during hypotensive episode improves mortality predictions among patients with sepsis and hypotension.

OBJECTIVES: To determine if a prediction rule for hospital mortality using dynamic variables in response to treatment of hypotension in patients with sepsis performs better than current models. DESIGN: Retrospective cohort study. SETTING: All ICUs at a tertiary care hospital. PATIENTS: Adult patients admitted to ICUs between 2001 and 2007 of whom 2,113 met inclusion criteria and had sufficient data. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: We developed a prediction algorithm for hospital mortality in patients with sepsis and hypotension requiring medical intervention using data from the Multiparameter Intelligent Monitoring in Intensive Care II. We extracted 189 candidate variables, including treatments, physiologic variables and laboratory values collected before, during, and after a hypotensive episode. Thirty predictors were identified using a genetic algorithm on a training set (n=1500) and validated with a logistic regression model on an independent validation set (n=613). The final prediction algorithm used included dynamic information and had good discrimination (area under the receiver operating curve=82.0%) and calibration (Hosmer-Lemeshow C statistic=10.43, p=0.06). This model was compared with Acute Physiology and Chronic Health Evaluation IV using reclassification indices and was found to be superior with an Net Reclassification Improvement of 0.19 (p<0.001) and an Integrated Discrimination Improvement of 0.09 (p<0.001). CONCLUSIONS: Hospital mortality predictions based on dynamic variables surrounding a hypotensive event is a new approach to predicting prognosis. A model using these variables has good discrimination and calibration and offers additional predictive prognostic information beyond established ones.