Time-varying functional connectivity predicts fluctuations in sustained attention in a serial tapping task.

The mechanisms for how large-scale brain networks contribute to sustained attention are unknown. Attention fluctuates from moment to moment, and this continuous change is consistent with dynamic changes in functional connectivity between brain networks involved in the internal and external allocation of attention. In this study, we investigated how brain network activity varied across different levels of attentional focus (i.e., "zones"). Participants performed a finger-tapping task, and guided by previous research, in-the-zone performance or state was identified by low reaction time variability and out-of-the-zone as the inverse. In-the-zone sessions tended to occur earlier in the session than out-of-the-zone blocks. This is unsurprising given the way attention fluctuates over time. Employing a novel method of time-varying functional connectivity, called the quasi-periodic pattern analysis (i.e., reliable, network-level low-frequency fluctuations), we found that the activity between the default mode network (DMN) and task positive network (TPN) is significantly more anti-correlated during in-the-zone states versus out-of-the-zone states. Furthermore, it is the frontoparietal control network (FPCN) switch that differentiates the two zone states. Activity in the dorsal attention network (DAN) and DMN were desynchronized across both zone states. During out-of-the-zone periods, FPCN synchronized with DMN, while during in-the-zone periods, FPCN switched to synchronized with DAN. In contrast, the ventral attention network (VAN) synchronized more closely with DMN during in-the-zone periods compared with out-of-the-zone periods. These findings demonstrate that time-varying functional connectivity of low frequency fluctuations across different brain networks varies with fluctuations in sustained attention or other processes that change over time.

Periodic subcellular structures undergo long-range synchronized reorganization during C. elegans epidermal development.

Periodic pattern formation on the cellular and tissue scale is an important process and has been extensively studied. However, periodic pattern formation at the subcellular level still remains poorly understood. The C. elegans epidermis displays a highly ordered parallel stripe pattern as part of its subcellular structure, making it an ideal model to study the formation and reorganization of periodic patterns within cells. Here, we show that the initial formation of periodic striped patterns in the C. elegans epidermis is dependent on actin and spectrin, and requires the apical membrane attachment structures for maintenance. The periodic subcellular structures do not accommodate cell growth by continuously making new stripes. Instead, they increase the number of stripes by going through one round of uniform duplication, which is independent of the increasing epidermal length or the developmental cycles. This long-range synchronized reorganization of subcellular structures is achieved by physical links established by extracellular collagens together with extension forces generated from epidermal cell growth. Our studies uncover a novel strategy employed by evenly spaced and interlinked subcellular structures to maintain their integrity and equidistribution during cell growth and tissue development.

Mammalian Cell Interaction with Periodic Surface Nanostructures.

Here, we report on the nanopatterning of different aromatic polymer substrates achieved by KrF excimer laser treatment. The conditions for the construction of the laser-induced periodic surface structures, the so-called LIPSS pattern, were established by optimized laser fluence and a number of pulses. The polymer substrates were polyethylene naphthalate (PEN), polyethersulfone (PES), and polystyrene (PS), which were chosen since they are thermally, chemically, and mechanically resistant polymers with high absorption coefficients at the excimer laser wavelength. The surface morphology of the treated substrates was investigated by atomic force microscopy and scanning electron microscopy, and the roughness and effective surface area on the modified samples were determined. Elemental concentration was characterized by energy-dispersive (EDX) analysis, surface chemistry was determined with X-ray photoelectron spectroscopy (XPS). The samples with the formation of LIPSS induced by 10 mJ·cm-2 with 1000, 3000, and 6000 pulses were used for subsequent in vitro cytocompatibility tests using human cells from osteosarcoma (U-2 OS). The LIPSS pattern and its ability of significant cell guidance were confirmed for some of the studied samples. Cell morphology, adhesion, and proliferation were evaluated. The results strongly contribute to the development of novel applications using nanopatterned polymers, e.g., in tissue engineering, cell analysis or in combination with metallization for sensor construction.

Characteristics of the nocturnal desaturation waveform pattern of SpO2 in COPD patients: an observational study.

BACKGROUND: Nocturnal desaturation is common in patients with chronic obstructive pulmonary disease (COPD) and impacts disease exacerbation and prognosis. In our previous study, we developed a diagnostic algorithm to classify nocturnal desaturation from SpO2 waveform patterns based on data from patients receiving home oxygen therapy. In this study, we aimed to investigate nocturnal desaturation in patients with COPD based on SpO2 waveform patterns and the associations between the waveforms and clinical data. METHODS: We investigated patients diagnosed with COPD and measured SpO2 and nasal airflow with a type 4 portable long-term recordable pulse oximeter. Then, we classified the SpO2 waveforms with the algorithm and compared the clinical data. RESULTS: One hundred fifty-three patients (136 male and 17 female) were analysed. One hundred twenty-eight of the 153 (83.7%) patients had nocturnal desaturation, with an intermittent pattern (70.6%), sustained pattern (13.1%) and periodic pattern (68.0%). Intriguingly, desaturation with an intermittent pattern was associated with the apnoea-hypopnea index obtained with the portable monitor, and desaturation with a sustained pattern was associated with the cumulative percentage of time at a SpO2 below 90%. CONCLUSIONS: We found that nocturnal desaturation was frequently observed in patients with COPD and could be classified into 3 types of waveform patterns.

Using a Deep Learning Method and Data from Two-Dimensional (2D) Marker-Less Video-Based Images for Walking Speed Classification.

Human body measurement data related to walking can characterize functional movement and thereby become an important tool for health assessment. Single-camera-captured two-dimensional (2D) image sequences of marker-less walking individuals might be a simple approach for estimating human body measurement data which could be used in walking speed-related health assessment. Conventional body measurement data of 2D images are dependent on body-worn garments (used as segmental markers) and are susceptible to changes in the distance between the participant and camera in indoor and outdoor settings. In this study, we propose five ratio-based body measurement data that can be extracted from 2D images and can be used to classify three walking speeds (i.e., slow, normal, and fast) using a deep learning-based bidirectional long short-term memory classification model. The results showed that average classification accuracies of 88.08% and 79.18% could be achieved in indoor and outdoor environments, respectively. Additionally, the proposed ratio-based body measurement data are independent of body-worn garments and not susceptible to changes in the distance between the walking individual and camera. As a simple but efficient technique, the proposed walking speed classification has great potential to be employed in clinics and aged care homes.

A sense of place, many times over - pattern formation and evolution of repetitive morphological structures.

Fifty years ago, Lewis Wolpert introduced the concept of "positional information" to explain how patterns form in a multicellular embryonic field. Using morphogen gradients, whose continuous distributions of positional values are discretized via thresholds into distinct cellular states, he provided, at the theoretical level, an elegant solution to the "French Flag problem." In the intervening years, many experimental studies have lent support to Wolpert's ideas. However, the embryonic patterning of highly repetitive morphological structures, as often occurring in nature, can reveal limitations in the strict implementation of his initial theory, given the number of distinct threshold values that would have to be specified. Here, we review how positional information is complemented to circumvent these inadequacies, to accommodate tissue growth and pattern periodicity. In particular, we focus on functional anatomical assemblies composed of such structures, like the vertebrate spine or tetrapod digits, where the resulting segmented architecture is intrinsically linked to periodic pattern formation and unidirectional growth. These systems integrate positional information and growth with additional patterning cues that, we suggest, increase robustness and evolvability. We discuss different experimental and theoretical models to study such patterning systems, and how the underlying processes are modulated over evolutionary timescales to enable morphological diversification.

A New Method Combining Pattern Prediction and Preference Prediction for Next Basket Recommendation.

Market basket prediction, which is the basis of product recommendation systems, is the concept of predicting what customers will buy in the next shopping basket based on analysis of their historical shopping records. Although product recommendation systems develop rapidly and have good performance in practice, state-of-the-art algorithms still have plenty of room for improvement. In this paper, we propose a new algorithm combining pattern prediction and preference prediction. In pattern prediction, sequential rules, periodic patterns and association rules are mined and probability models are established based on their statistical characteristics, e.g., the distribution of periods of a periodic pattern, to make a more precise prediction. Products that have a higher probability will have priority to be recommended. If the quantity of recommended products is insufficient, then we make a preference prediction to select more products. Preference prediction is based on the frequency and tendency of products that appear in customers' individual shopping records, where tendency is a new concept to reflect the evolution of customers' shopping preferences. Experiments show that our algorithm outperforms those of the baseline methods and state-of-the-art methods on three of four real-world transaction sequence datasets.

Self-organizing hair peg-like structures from dissociated skin progenitor cells: New insights for human hair follicle organoid engineering and Turing patterning in an asymmetric morphogenetic field.

Human skin progenitor cells will form new hair follicles, although at a low efficiency, when injected into nude mouse skin. To better study and improve upon this regenerative process, we developed an in vitro system to analyse the morphogenetic cell behaviour in detail and modulate physical-chemical parameters to more effectively generate hair primordia. In this three-dimensional culture, dissociated human neonatal foreskin keratinocytes self-assembled into a planar epidermal layer while fetal scalp dermal cells coalesced into stripes, then large clusters, and finally small clusters resembling dermal condensations. At sites of dermal clustering, subjacent epidermal cells protruded to form hair peg-like structures, molecularly resembling hair pegs within the sequence of follicular development. The hair peg-like structures emerged in a coordinated, formative wave, moving from periphery to centre, suggesting that the droplet culture constitutes a microcosm with an asymmetric morphogenetic field. In vivo, hair follicle populations also form in a progressive wave, implying the summation of local periodic patterning events with an asymmetric global influence. To further understand this global patterning process, we developed a mathematical simulation using Turing activator-inhibitor principles in an asymmetric morphogenetic field. Together, our culture system provides a suitable platform to (a) analyse the self-assembly behaviour of hair progenitor cells into periodically arranged hair primordia and (b) identify parameters that impact the formation of hair primordia in an asymmetric morphogenetic field. This understanding will enhance our future ability to successfully engineer human hair follicle organoids.

Dynamic resting state fMRI analysis in mice reveals a set of Quasi-Periodic Patterns and illustrates their relationship with the global signal.

Time-resolved 'dynamic' over whole-period 'static' analysis of low frequency (LF) blood-oxygen level dependent (BOLD) fluctuations provides many additional insights into the macroscale organization and dynamics of neural activity. Although there has been considerable advancement in the development of mouse resting state fMRI (rsfMRI), very little remains known about its dynamic repertoire. Here, we report for the first time the detection of a set of recurring spatiotemporal Quasi-Periodic Patterns (QPPs) in mice, which show spatial similarity with known resting state networks. Furthermore, we establish a close relationship between several of these patterns and the global signal. We acquired high temporal rsfMRI scans under conditions of low (LA) and high (HA) medetomidine-isoflurane anesthesia. We then employed the algorithm developed by Majeed et al. (2011), previously applied in rats and humans, which detects and averages recurring spatiotemporal patterns in the LF BOLD signal. One type of observed patterns in mice was highly similar to those originally observed in rats, displaying propagation from lateral to medial cortical regions, which suggestively pertain to a mouse Task-Positive like network (TPN) and Default Mode like network (DMN). Other QPPs showed more widespread or striatal involvement and were no longer detected after global signal regression (GSR). This was further supported by diminished detection of subcortical dynamics after GSR, with cortical dynamics predominating. Observed QPPs were both qualitatively and quantitatively determined to be consistent across both anesthesia conditions, with GSR producing the same outcome. Under LA, QPPs were consistently detected at both group and single subject level. Under HA, consistency and pattern occurrence rate decreased, whilst cortical contribution to the patterns diminished. These findings confirm the robustness of QPPs across species and demonstrate a new approach to study mouse LF BOLD spatiotemporal dynamics and mechanisms underlying functional connectivity. The observed impact of GSR on QPPs might help better comprehend its controversial role in conventional resting state studies. Finally, consistent detection of QPPs at single subject level under LA promises a step forward towards more reliable mouse rsfMRI and further confirms the importance of selecting an optimal anesthesia regime.

Detecting silent seizures by their sound.

OBJECTIVE: The traditional approach to interpreting electroencephalograms (EEGs) requires physicians with formal training to visually assess the waveforms. This approach can be less practical in critical settings where a trained EEG specialist is not readily available to review the EEG and diagnose ongoing subclinical seizures, such as nonconvulsive status epilepticus. METHODS: We have developed a novel method by which EEG data are converted to sound in real time by letting the underlying electrophysiological signal modulate a voice tone that is in the audible range. Here, we explored whether individuals without any prior EEG training could listen to 15-second sonified EEG and determine whether the EEG represents seizures or nonseizure conditions. We selected 84 EEG samples to represent seizures (n = 7), seizure-like activity (n = 25), or nonperiodic, nonrhythmic activity (normal or focal/generalized slowing, n = 52). EEGs from single channels in the left and right hemispheres were then converted to sound files. After a 4-minute training video, medical students (n = 34) and nurses (n = 30) were asked to designate each audio sample as "seizure" or "nonseizure." We then compared their performance with that of EEG-trained neurologists (n = 12) and medical students (n = 29) who also diagnosed the same EEGs on visual display. RESULTS: Nonexperts listening to single-channel sonified EEGs detected seizures with remarkable sensitivity (students, 98% ± 5%; nurses, 95% ± 14%) compared to experts or nonexperts reviewing the same EEGs on visual display (neurologists, 88% ± 11%; students, 76% ± 19%). If the EEGs contained seizures or seizure-like activity, nonexperts listening to sonified EEGs rated them as seizures with high specificity (students, 85% ± 9%; nurses, 82% ± 12%) compared to experts or nonexperts viewing the EEGs visually (neurologists, 90% ± 7%; students, 65% ± 20%). SIGNIFICANCE: Our study confirms that individuals without EEG training can detect ongoing seizures or seizure-like rhythmic periodic patterns by listening to sonified EEG. Although sonification of EEG cannot replace the traditional approaches to EEG interpretation, it provides a meaningful triage tool for fast assessment of patients with suspected subclinical seizures.

New onset epilepsy among patients with periodic discharges on continuous electroencephalographic monitoring.

OBJECTIVE: To evaluate the incidence of new onset epilepsy and associated risk factors in patients with periodic patterns on continuous electroencephalography (cEEG) during critical illness. METHODS: The local cEEG database and then medical records were reviewed from January 1, 2013 to June 30, 2013 to find adult patients with no history of epilepsy who had periodic discharges-either lateralized (LPDs) or generalized (GPDs)-or nonperiodic/nonepileptogenic (NP/NE) findings on cEEG and ≥3 months of clinical follow-up. Clinical seizure after discharge was the primary outcome. Chi-square test, Kruskal-Wallis test, and Cox proportional hazards models were used for statistical analysis. RESULTS: A total of 195 patients (median age = 67.8 years) were included. There were 53 (27%), 73 (37%), and 69 (35%) patients with LPDs, GPDs, and NP/NE findings on cEEG, respectively. These three groups did not differ by demographic or clinical variables. A total of 29 (15%) patients (LPDs = 20 [38%], GPDs = 4 [6%], and NP/NE = 5 [7%]) developed epilepsy during a median follow-up of 32.1 (95% confidence interval [CI] = 13.2-42.8) months. The hazard ratio for epilepsy development among LPD patients was 7.7 (95% CI = 2.9-20.7) times compared to the NP/NE group, and the risk further increased to 11.4 (95% CI = 4-31.4) times if they also had electrographic seizures. This association remained significant despite adjusting for each covariate at a time. SIGNIFICANCE: Patients with LPDs on cEEG during critical illness are at least seven times more likely to develop epilepsy compared to patients with NP/NE findings. This risk is further increased if patients with LPDs have electrographic seizures. In comparison, the presence of GPDs does not seem to impact the risk for developing epilepsy. cEEG findings at the time of acute insult have potential to serve as prognostic biomarkers for epilepsy development.

Quasi-periodic patterns (QPP): large-scale dynamics in resting state fMRI that correlate with local infraslow electrical activity.

Functional connectivity measurements from resting state blood-oxygen level dependent (BOLD) functional magnetic resonance imaging (fMRI) are proving a powerful tool to probe both normal brain function and neuropsychiatric disorders. However, the neural mechanisms that coordinate these large networks are poorly understood, particularly in the context of the growing interest in network dynamics. Recent work in anesthetized rats has shown that the spontaneous BOLD fluctuations are tightly linked to infraslow local field potentials (LFPs) that are seldom recorded but comparable in frequency to the slow BOLD fluctuations. These findings support the hypothesis that long-range coordination involves low frequency neural oscillations and establishes infraslow LFPs as an excellent candidate for probing the neural underpinnings of the BOLD spatiotemporal patterns observed in both rats and humans. To further examine the link between large-scale network dynamics and infraslow LFPs, simultaneous fMRI and microelectrode recording were performed in anesthetized rats. Using an optimized filter to isolate shared components of the signals, we found that time-lagged correlation between infraslow LFPs and BOLD is comparable in spatial extent and timing to a quasi-periodic pattern (QPP) found from BOLD alone, suggesting that fMRI-measured QPPs and the infraslow LFPs share a common mechanism. As fMRI allows spatial resolution and whole brain coverage not available with electroencephalography, QPPs can be used to better understand the role of infraslow oscillations in normal brain function and neurological or psychiatric disorders.

Gap junctions in Turing-type periodic feather pattern formation.

Periodic patterning requires coordinated cell-cell interactions at the tissue level. Turing showed, using mathematical modeling, how spatial patterns could arise from the reactions of a diffusive activator-inhibitor pair in an initially homogenous two-dimensional field. Most activators and inhibitors studied in biological systems are proteins, and the roles of cell-cell interaction, ions, bioelectricity, etc. are only now being identified. Gap junctions (GJs) mediate direct exchanges of ions or small molecules between cells, enabling rapid long-distance communications in a cell collective. They are therefore good candidates for propagating non-protein-based patterning signals that may act according to the Turing principles. Here, we explore the possible roles of GJs in Turing-type patterning using feather pattern formation as a model. We found seven of the twelve investigated GJ isoforms are highly dynamically expressed in the developing chicken skin. In ovo functional perturbations of the GJ isoform, connexin 30, by siRNA and the dominant-negative mutant applied before placode development led to disrupted primary feather bud formation, including patches of smooth skin and buds of irregular sizes. Later, after the primary feather arrays were laid out, inhibition of gap junctional intercellular communication in the ex vivo skin explant culture allowed the emergence of new feather buds in temporal waves at specific spatial locations relative to the existing primary buds. The results suggest that gap junctional communication may facilitate the propagation of long-distance inhibitory signals. Thus, the removal of GJ activity would enable the emergence of new feather buds if the local environment is competent and the threshold to form buds is reached. We propose Turing-based computational simulations that can predict the appearance of these ectopic bud waves. Our models demonstrate how a Turing activator-inhibitor system can continue to generate patterns in the competent morphogenetic field when the level of intercellular communication at the tissue scale is modulated.

Comparison of Resting-State Functional MRI Methods for Characterizing Brain Dynamics.

Resting-state functional MRI (fMRI) exhibits time-varying patterns of functional connectivity. Several different analysis approaches have been developed for examining these resting-state dynamics including sliding window connectivity (SWC), phase synchrony (PS), co-activation pattern (CAP), and quasi-periodic patterns (QPP). Each of these approaches can be used to generate patterns of activity or inter-areal coordination which vary across time. The individual frames can then be clustered to produce temporal groupings commonly referred to as "brain states." Several recent publications have investigated brain state alterations in clinical populations, typically using a single method for quantifying frame-wise functional connectivity. This study directly compares the results of k-means clustering in conjunction with three of these resting-state dynamics methods (SWC, CAP, and PS) and quantifies the brain state dynamics across several metrics using high resolution data from the human connectome project. Additionally, these three dynamics methods are compared by examining how the brain state characterizations vary during the repeated sequences of brain states identified by a fourth dynamic analysis method, QPP. The results indicate that the SWC, PS, and CAP methods differ in the clusters and trajectories they produce. A clear illustration of these differences is given by how each one results in a very different clustering profile for the 24s sequences explicitly identified by the QPP algorithm. PS clustering is sensitive to QPPs with the mid-point of most QPP sequences grouped into the same single cluster. CAPs are also highly sensitive to QPPs, separating each phase of the QPP sequences into different sets of clusters. SWC (60s window) is less sensitive to QPPs. While the QPPs are slightly more likely to occur during specific SWC clusters, the SWC clustering does not vary during the 24s QPP sequences, the goal of this work is to improve both the practical and theoretical understanding of different resting-state dynamics methods, thereby enabling investigators to better conceptualize and implement these tools for characterizing functional brain networks.

Enhanced Patterned Cocatalyst TiO2/Fe2O3 Photoanodes for Water-Splitting.

In this study, we used a hot-pressing process to enhance the photocatalytic properties of TiO2/Fe2O3 bimetallic oxide with a periodic patterned structure on the surface to increase photon absorption for photocatalysis in the oxygen evolution reaction for water splitting. The hot-pressed samples show that combining the two metal oxides improves the absorption band edge of the electrode at different wavelengths. The patterned structure obtained using the hot-pressing process successfully improves photon absorption, resulting in a two-fold enhancement compared with a flat surface electrode.

Index Modulation Embedded in Type I Waveguide Written by Femtosecond Laser in Fused Silica.

Slit-shaped laser beams focused in bulk optical materials can realize embedded waveguides with circular cross sections consisting of positive index change type I traces. In these kinds of waveguide traces, a peculiar periodical refractive index modulation was observed in type I waveguides with two different femtosecond lasers. The direction of refractive index modulation can be controlled with the slit configuration, and its period can be controlled by mechanical perturbation of the stages and the scanning speed. We argue that platform perturbation and dynamical thermal transport processes during the scan are generating factors in the appearance of this modulation. The embedded microstructures in waveguides can provide spectrum modulation, which may have potential applications in optical sensing, filtering, and phase control.

Multi-level medical periodic patterns from human movement behaviors.

Human movement behaviors could reveal many interesting medical patterns. Due to the advances in location-aware devices, a large volume of human movement behaviors has been captured in the form of spatio-temporal trajectories. These spatio-temporal trajectories are useful resources for medical data mining, and they could be used to classify which trajectory passes through medical centres and which one does not. Traditional approaches utilise time-series datasets while ignoring spatio-temporal semantics in order to detect periodic patterns in medical domains. They also fail to consider the inherent hierarchical nature of patterns. We investigate a medical data mining framework that generates multi-level medical periodic patterns. A Geolife dataset is used to test the feasibility and applicability of our framework. Experiments demonstrate that the proposed framework successfully distinguishes those who periodically visit medical centres from those who do not, and also to find multi-level medical periodic patterns revealing interesting hierarchical medical behaviours. One potential application includes an automated personalised medical service. For instance, medical institutions can send personalised relative medicine information to people who regularly visit certain medical centres. It will be useful for the discovery and diagnosis of diseases for patients.

Semiology, clustering, periodicity and natural history of seizures in an experimental occipital cortical epilepsy model.

Focal neocortical epilepsy is a common form of epilepsy and there is a need to develop animal models that allow the evaluation of novel therapeutic strategies to treat this type of epilepsy. Tetanus toxin (TeNT) injection into the rat visual cortex induces focal neocortical epilepsy without preceding status epilepticus. The latency to first seizure ranged from 3 to 7 days. Seizure duration was bimodal, with both short (approximately 30 s) and long-lasting (>100 s) seizures occurring in the same animals. Seizures were accompanied by non-motor features such as behavioural arrest, or motor seizures with or without evolution to generalized tonic-clonic seizures. Seizures were more common during the sleep phase of a light-dark cycle. Seizure occurrence was not random, and tended to cluster with significantly higher probability of recurrence within 24 h of a previous seizure. Across animals, the number of seizures in the first week could be used to predict the number of seizures in the following 3 weeks. The TeNT model of occipital cortical epilepsy is a model of acquired focal neocortical epilepsy that is well-suited for preclinical evaluation of novel anti-epileptic strategies. We provide here a detailed analysis of the epilepsy phenotypes, seizure activity, electrographic features and the semiology. In addition, we provide a predictive framework that can be used to reduce variation and consequently animal use in preclinical studies of potential treatments.

Monitoring burst suppression in critically ill patients: Multi-centric evaluation of a novel method.

OBJECTIVE: To develop a computational method to detect and quantify burst suppression patterns (BSP) in the EEGs of critical care patients. A multi-center validation study was performed to assess the detection performance of the method. METHODS: The fully automatic method scans the EEG for discontinuous patterns and shows detected BSP and quantitative information on a trending display in real-time. The method is designed to work without setting any patient specific parameters and to be insensitive to EEG artifacts and periodic patterns. For validation a total of 3982 h of EEG from 88 patients were analyzed from three centers. Each EEG was annotated by two reviewers to assess the detection performance and the inter-rater agreement. RESULTS: Average inter-rater agreement between pairs of reviewers was κ=0.69. On average 22% of the review segments included BSP. An average sensitivity of 90% and a specificity of 84% were measured on the consensus annotations of two reviewers. More than 95% of the periodic patterns in the EEGs were correctly suppressed. CONCLUSION: A fully automatic method to detect burst suppression patterns was assessed in a multi-center study. The method showed high sensitivity and specificity. SIGNIFICANCE: Clinically applicable burst suppression detection method validated in a large multi-center study.