NEAR: An artifact removal pipeline for human newborn EEG data

Electroencephalography (EEG) is arising as a valuable method to investigate neurocognitive functions shortly after birth. However, obtaining high-quality EEG data from human newborn recordings is challenging. Compared to adults and older infants, datasets are typically much shorter due to newborns’ limited attentional span and much noisier due to non-stereotyped artifacts mainly caused by uncontrollable movements. We propose Newborn EEG Artifact Removal (NEAR), a pipeline for EEG artifact removal designed explicitly for human newborns. NEAR is based on two key steps: 1) A novel bad channel detection tool based on the Local Outlier Factor (LOF), a robust outlier detection algorithm; 2) A parameter calibration procedure for adapting to newborn EEG data the algorithm Artifacts Subspace Reconstruction (ASR), developed for artifact removal in mobile adult EEG. Tests on simulated data showed that NEAR outperforms existing methods in removing representative newborn non-stereotypical artifacts. NEAR was validated on two developmental populations (newborns and 9-month-old infants) recorded with two different experimental designs (frequency-tagging and ERP). Results show that NEAR artifact removal successfully reproduces established EEG responses from noisy datasets, with a higher statistical significance than the one obtained by existing artifact removal methods. The EEGLAB-based NEAR pipeline is freely available at https://github.com/vpKumaravel/NEAR.     

A practical introduction to EEG Time-Frequency Principal Components Analysis (TF-PCA)

This EEG methods tutorial provides both a conceptual and practical introduction to a promising data reduction approach for time-frequency representations of EEG data: Time-Frequency Principal Components Analysis (TF-PCA). Briefly, the unique value of TF-PCA is that it provides a data-reduction approach that does not rely on strong a priori constraints regarding the specific timing or frequency boundaries for an effect of interest. Given that the time-frequency characteristics of various neurocognitive process are known to change across development, the TF-PCA approach is thus particularly well suited for the analysis of developmental TF data. This tutorial provides the background knowledge, theory, and practical information needed to allow individuals with basic EEG experience to begin applying the TF-PCA approach to their own data. Crucially, this tutorial article is accompanied by a companion GitHub repository that contains example code, data, and a step-by-step guide of how to perform TF-PCA: https://github.com/NDCLab/tfpca-tutorial. Although this tutorial is framed in terms of the utility of TF-PCA for developmental data, the theory, protocols and code covered in this tutorial article and companion GitHub repository can be applied more broadly across populations of interest.     

A very brief self-report scale for measuring insomnia severity using two items from the Insomnia Severity Index - development and validation in a clinical population

ObjectiveTo develop a very brief scale with selected items from the Insomnia Severity Index (ISI), and to investigate the psychometric properties of the proposed scale in a psychiatric sample.MethodsPatient data from seven Cognitive Behavioral Therapy (CBT) for insomnia trials and from regular care were used in psychometric analyses (N = 280-15 653). The samples included patients screening (N = 6936) or receiving treatment (N = 1725) for insomnia and other psychiatric conditions. Six criteria relating to component structure, sensitivity to change and clinical representativeness were used to select items. Psychometric analyses for the proposed very brief scale were performed.ResultsOne item representing satisfaction/dissatisfaction with current sleep pattern and one item representing interferences with daily functioning, were selected to create the 2-item ISI version. Correlations with the full scale were high at screening, pre and post, and for change (0.82-0.94). Categorical omega was ⍵_C = 0.86. With a cut-off of 6 points, the scale could detect Insomnia Disorder with a sensitivity of 84% and a specificity of 76%, which was close to the full ISI showing 86% and 80% respectively.ConclusionsThe systematic psychometric evaluation based on a large sample from different contexts makes the proposed 2-item ISI version (ISI-2) a strong candidate for a very brief scale measuring insomnia, both for detecting cases and for measuring change during CBT with an overall high discriminative validity. ISI-2 is especially useful in clinical settings or population studies where there is a need to measure more than one condition at a time without overburdening patients.Clinical trialsTrials used in this analysis: ClinicalTrials.gov identifier: NCT01105052 (https://www.clinicaltrials.gov/ct2/show/NCT01105052) (sample b), ClinicalTrials.gov identifier: NCT01256099 (https://clinicaltrials.gov/ct2/show/NCT01256099) (sample c and d), German clinical trial (DRKS), registration ID: DRKS00008745 (https://www.drks.de/drks_web/navigate.do?navigationId=trial.HTML&TRIAL_ID=DRKS00008745) (sample e), ClinicalTrials.gov identifier: NCT01663844 (https://clinicaltrials.gov/ct2/show/NCT01663844) (sample f and g), ClinicalTrials.gov Identifier: NCT02743338 (https://clinicaltrials.gov/ct2/show/NCT02743338) (sample h).     

Cell type-specific processing of non-motor signals in the external pallidum

The external globus pallidus (GPe) regulates motor control. However, whether the GPe encodes non-motor information remains unclear. Two recent studies, by Johansson and Ketzef, and Katabi et al., provide in vivo evidence for GPe neuron processing of sensory stimulation and reward cues via a division of labor among its cell types.     

Three players in the 'toxic affair' between botulinum neurotoxin type A and neurons

Joensuu and colleagues have recently shown that botulinum neurotoxin (BoNT) type A exploits a heterotrimeric complex in the presynaptic membrane to bind to and enter neurons using a Trojan horse-like mechanism. Similar processes may be relevant to the neuronal entry of different botulinum toxin serotypes and other neuropathogens.     

How well can a large number of polysomnography sleep measures predict subjective sleep quality in insomnia patients?

ObjectiveThe determinants of sleep quality (sQUAL) are poorly understood. We evaluated how well a large number of objective polysomnography (PSG) parameters can predict sQUAL in insomnia patients participating in trials of sleep medications or placebo.MethodsPSG recordings over multiple nights from two clinical drug development programs involving 1158 insomnia patients treated with suvorexant or placebo and 903 insomnia patients treated with gaboxadol or placebo were used post-hoc to analyze univariate and multivariate associations between sQUAL and 98 PSG sleep parameters plus patient's age and gender. Analyses were performed separately for each of the two clinical trial databases. For univariate associations, within-subject correlations were estimated using mixed effect modeling of bi-variate longitudinal data with one variable being a given PSG variable and the other being sQUAL. To evaluate how accurately sQUAL could be predicted by all PSG variables jointly plus patient's age and gender, the Random Forest multivariate technique was used. Random Forest was also used to evaluate the accuracy of sQUAL prediction by subjective sleep measures plus age and gender, and to quantitatively describe the relative importance of each variable for predicting sQUAL.ResultsIn the univariate analyses, total sleep time (TST) had the largest correlation with sQUAL compared with all other PSG sleep parameters, and the magnitude of the correlation between each PSG sleep architecture parameter and sQUAL generally increased with the strength of their associations with TST. In the multivariate analyses, the overall accuracy of sQUAL prediction, even with the large number of PSG parameters plus patient's age and gender, was moderate (area under the Receiver Operating Characteristic curve (AROC): 71.2–71.8%). Ranking of PSG parameters by their contribution to sQUAL indicated that TST was the most important predictor of sQUAL among all PSG variables. Subjective TST and subjective number of awakenings jointly with patient's age classified sQUAL with higher accuracy (AROC: 78.7–81.7%) than PSG variables plus age and gender. The pattern of findings was consistent across the two clinical trial databases.ConclusionIn insomnia patients participating in trials of sleep medications or placebo, PSG variables had a moderate but consistent pattern of association with sQUAL across two separate clinical trial databases. Of the PSG variables evaluated, TST was the best predictor of sQUAL.Clinical trialstrial registration at www.clinicaltrials.gov: NCT01097616; NCT01097629; NCT00094627; NCT00094666.     

Modeling human mutations to understand TRIO GEF function during development

In a recent study, Bonnet and colleagues leveraged in silico structure prediction and human genetic data to understand the molecular regulation of the Rac1-activating guanie nucleotide exchange factor (Rac1-GEF) domain of Trio. Their work sheds new light on the role of Trio during axon guidance and explores the mechanism by which Trio GEF function is regulated in health and dysregulated in disease.     

Optimizing preprocessing and analysis pipelines for single-subject fMRI. I. Standard temporal motion and physiological noise correction methods

Subject‐specific artifacts caused by head motion and physiological noise are major confounds in BOLD fMRI analyses. However, there is little consensus on the optimal choice of data preprocessing steps to minimize these effects. To evaluate the effects of various preprocessing strategies, we present a framework which comprises a combination of (1) nonparametric testing including reproducibility and prediction metrics of the data‐driven NPAIRS framework (Strother et al. [2002]: NeuroImage 15:747–771), and (2) intersubject comparison of SPM effects, using DISTATIS (a three‐way version of metric multidimensional scaling (Abdi et al. [2009]: NeuroImage 45:89–95). It is shown that the quality of brain activation maps may be significantly limited by sub‐optimal choices of data preprocessing steps (or “pipeline”) in a clinical task‐design, an fMRI adaptation of the widely used Trail‐Making Test. The relative importance of motion correction, physiological noise correction, motion parameter regression, and temporal detrending were examined for fMRI data acquired in young, healthy adults. Analysis performance and the quality of activation maps were evaluated based on Penalized Discriminant Analysis (PDA). The relative importance of different preprocessing steps was assessed by (1) a nonparametric Friedman rank test for fixed sets of preprocessing steps, applied to all subjects; and (2) evaluating pipelines chosen specifically for each subject. Results demonstrate that preprocessing choices have significant, but subject‐dependant effects, and that individually‐optimized pipelines may significantly improve the reproducibility of fMRI results over fixed pipelines. This was demonstrated by the detection of a significant interaction with motion parameter regression and physiological noise correction, even though the range of subject head motion was small across the group (? 1 voxel). Optimizing pipelines on an individual‐subject basis also revealed brain activation patterns either weak or absent under fixed pipelines, which has implications for the overall interpretation of fMRI data, and the relative importance of preprocessing methods. Hum Brain Mapp, 2012. © 2011 Wiley Periodicals, Inc.     

Exogenous melatonin as a treatment for secondary sleep disorders: A systematic review and meta-analysis

Melatonin is a physiological indoleamine involved in circadian rhythm regulation and it is currently used for secondary sleep disorders supported by empirical evidence. A small amount of evidence and some controversial results have been obtained in some randomized controlled trials (RCT). The objective of this meta-analysis is to determine the efficacy of exogenous melatonin versus placebo in managing secondary sleep disorders. Literature retrieval of eligible RCT was performed in 5 databases (PubMed, Embase, Cochrane Library, ClinicalTrials.gov, and Web of Science). In total, 7 studies of 205 patients were included. Pooled data demonstrate that exogenous melatonin lowers sleep onset latency and increases total sleep time, whereas it has little if any effect on sleep efficiency. Although, the efficacy of melatonin still requires further confirmation, this meta-analysis clearly supports the use of melatonin as a management for patients with secondary sleep disorders.     

Removing muscle and eye artifacts using blind source separation techniques in ictal EEG source imaging

ObjectiveThe contamination of muscle and eye artifacts during an ictal period of the EEG significantly distorts source estimation algorithms. Recent blind source separation (BSS) techniques based on canonical correlation (BSS-CCA) and independent component analysis with spatial constraints (SCICA) have shown much promise in the removal of these artifacts. In this study we want to use BSS-CCA and SCICA as a preprocessing step before the source estimation during the ictal period.MethodsBoth the contaminated and cleaned ictal EEG were subjected to the RAP-MUSIC algorithm. This is a multiple dipole source estimation technique based on the separation of the EEG in signal and noise subspace. The source estimates were compared with the subtracted ictal SPECT (iSPECT) coregistered to magnetic resonance imaging (SISCOM) by means of the euclidean distance between the iSPECT activations and the dipole location estimates. SISCOM results in an image denoting the ictal onset zone with a propagation.ResultsWe applied the artifact removal and the source estimation on 8 patients. Qualitatively, we can see that 5 out of 8 patients show an improvement of the dipoles. The dipoles are nearer to or have tighter clusters near the iSPECT activation. From the median of the distance measure, we could appreciate that 5 out of 8 patients show improvement.ConclusionsThe results show that BSS-CCA and SCICA can be applied to remove artifacts, but the results should be interpreted with care. The results of the source estimation can be misleading due to excessive noise or modeling errors. Therefore, the accuracy of the source estimation can be increased by preprocessing the ictal EEG segment by BSS-CCA and SCICA.SignificanceThis is a pilot study where EEG source localization in the presurgical evaluation can be made more reliable, if preprocessing techniques such as BSS-CCA and SCICA are used prior to EEG source analysis on ictal episodes.     

Endogenous retroviruses can propagate TDP-43 proteinopathy

How does neurodegeneration spread in the brain? Leveraging TDP-43 fly models of amyotrophic lateral sclerosis (ALS), Chang and Dubnau recently reported that the endogenous retrovirus (ERV) mdg4 can trigger and transmit TDP-43 proteinopathy in vivo. Their results suggest that human ERVs could be targeted to develop future ALS therapies.     

How iron can drive neurodegeneration

Iron overload in neurodegenerative diseases is well established but of uncertain significance. In a recent article, Ryan et al. reveal that microglia are especially vulnerable to iron overload-induced ferroptosis. Their evidence for microglial ferroptosis in clinical specimens indicates that ferroptosis inhibitors may hold therapeutic promise for these diseases.     

A dual-system,machine-learning approach reveals how daily pubertal hormones relate to psychological well-being in everyday life

The two studies presented in this paper seek to resolve mixed findings in research linking activity of pubertal hormones to daily adolescent outcomes. In study 1 we used a series of Confirmatory Factor Analyses to compare the fit of one and two-factor models of seven steroid hormones (n = 994 participants, 8084 samples) of the HPA and HPG axes, using data from a field study (https://www.icpsr.umich.edu/web/ICPSR/studies/38180) collected over ten consecutive weekdays in a representative sample of teens starting high school. In study 2, we fit a Bayesian model to our large dataset to explore how hormone activity was related to outcomes that have been demonstrated to be linked to mental health and wellbeing (self-reports of daily affect and stress coping). Results reveal, first that a two-factor solution of adolescent hormones showed good fit to our data, and second, that HPG activity, rather than the more often examined HPA activity, was associated with improved daily affect ratios and stress coping. These findings suggest that field research, when it is combined with powerful statistical techniques, may help to improve our understanding of the relationship between adolescent hormones and daily measures of well-being.     

Invariant inhibition to calculate prediction errors?

The ventral tegmental area (VTA) has a pivotal role in motivated behavior. Much of the research on the VTA has focused on the mesocorticolimbic dopamine projections and their role in the computation of a ‘reward prediction error’ (RPE) for reward-guided learning. In a recent study, Zhou et al. report that VTA GABA neurons, the axons of which innervate the ventral pallidum (VP), have a unique role in signaling reward value to the basal ganglia and guiding reward seeking.     

Validation of breath biomarkers for obstructive sleep apnea

Background and objectivesObstructive sleep apnea (OSA) is an underdiagnosed respiratory disease with negative metabolic and cardiovascular effects. The current gold standard for diagnosing OSA is in-hospital polysomnography, a time-consuming and costly procedure, often inconvenient for the patient. Recent studies revealed evidence for the potential of breath analysis for the diagnosis of OSA based on a disease-specific metabolic pattern. However, none of these findings were validated in a larger and broader cohort, an essential step for its application in clinics.MethodsIn the present study, we validated a panel of breath biomarkers in a cohort of patients with possible OSA (N = 149). These markers were previously identified in our group by secondary electrospray ionization high-resolution mass spectrometry (SESI-HRMS).ResultsHere, we could confirm significant differences between metabolic patterns in exhaled breath from OSA patients compared to control subjects without OSA as well as the association of breath biomarker levels with disease severity. Our prediction of the diagnosis for the patients from this completely independent validation study using a classification model trained on the data from the previous study resulted in an area under the receiver operating characteristic curve of 0.66, which is comparable to questionnaire-based OSA screenings.ConclusionsThus, our results suggest that breath analysis by SESI-HRMS might be useful to screen for OSA as an objective measure. However, its true predictive power should be tested in combination with OSA screening questionnaires.Clinical trial“Mass Spectral Fingerprinting in Obstructive Sleep Apnoea”, NCT02810158, www.ClinicalTrials.gov.     

Application of Independent Component Analysis for the Data Mining of Simultaneous Eeg–fMRI: Preliminary Experience on Sleep Onset

The simultaneous acquisition of electroencephalogram (EEG) and functional MRI (fMRI) signals is potentially advantageous because of the superior resolution that is achieved in both the temporal and spatial domains, respectively. However, ballistocardiographic artifacts along with ocular artifacts are a major obstacle for the detection of the EEG signatures of interest. Since the sources corresponding to these artifacts are independent from those producing the EEG signatures, we applied the Infomax-based independent component analysis (ICA) technique to separate the EEG signatures from the artifacts. The isolated EEG signatures were further utilized to model the canonical hemodynamic response functions (HRFs). Subsequently, the brain areas from which these EEG signatures originated were identified as locales of activation patterns from the analysis of fMRI data. Upon the identification and subsequent evaluation of brain areas generating interictal epileptic discharge (IED) spikes from an epileptic subject, the presented method was successfully applied to detect the theta and alpha rhythms that are sleep onset-related EEG signatures along with the subsequent neural circuitries from a sleep-deprived volunteer. These results suggest that the ICA technique may be useful for the preprocessing of simultaneous EEG–fMRI acquisitions, especially when a reference paradigm is unavailable.     

Artifact processing in topographic mapping of electroencephalographic activity in neuropsychopharmacology.

Mapping of the electroencephalogram (EEG) has been found to be a valuable method in clinical neuropsychopharmacology. It is evident that careful treatment of artifacts is of utmost importance for EEG data processing, as artifacts that contaminate the EEG data can lead to spurious results. The artifact-processing method described in this article splits signal analysis into a preprocessing step, yielding individual electro-oculographic (EOG) regression factors for EOG minimization, and into a processing step, yielding target variables. The combination of avoiding, minimizing, and identifying artifacts, as well as visual checking of face validity, will help remove artifactual effects from the EEG.     

Awakening from anesthesia switches cortical wave modes

Spontaneous cortical activity exhibits diverse spatiotemporal dynamics across brain states, but the organizational principles during state transitions remain unclear. In a recent study combining cortex-wide voltage imaging and neural modeling, Liang and colleagues revealed that global–local competition and long-range connections underlie the emergence of complex cortical wave patterns during awakening from anesthesia.