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Antibiotics play a critical role in neonatal sepsis but excessive use is associated with adverse outcomes and the current prescribing rates of antibiotics are unacceptably high.To reduce antibiotic over-use in preterm neonates by implementing an antibiotic stewardship programme using a quality improvement (QI) initiative.This study was conducted at a neonatal intensive care unit in a resource-limited setting. The reasons for antibiotic over-use were analysed and an antibiotic stewardship programme was implemented by using a QI initiative. The duration of the QI was a 1-month baseline phase followed by 3 months of implementation which was undertaken in the form of Plan-Do-Study-Act (P-D-S-A) cycles. The sustainment phase was observed for 2 months. All neonates admitted to the preterm unit were included. The outcome measure was the antibiotic usage expressed as days of therapy (DOT)/1000 patient days.In the baseline phase, DOT/1000 patient days was 1464 which fell to 706, 511, and 442 DOT/1000 patient days, respectively, over 3 months, resulting in a 65% reduction in antibiotic usage. This was achieved by a combination of efforts directed towards defining the conditions for no antibiotics, revising existing antibiotic policy, stopping orders at 48 hours, de-escalation to the narrowest spectrum antibiotic, stopping prophylactic antifungal agents and limited use of broad-spectrum antibiotics. The results were achieved without an increase in culture-positive sepsis or mortality.Implementation of a tailored antibiotic stewardship programme through a QI initiative was effective and safe in reducing antibiotic use in preterm neonates in a resource-limited setting.AIIMS, All India Institute of Medical Sciences; DOT, days of therapy; HIC, high-income countries; ICMR, Indian Council of Medical Research; LMIC, low- to middle-income countries; LOS, late-onset sepsis; NICU, neonatal intensive care unit; NNF, National Neonatology Forum; P-D-S-A, plan-do-study-act; QI, quality improvement; SNCU, Special newborn care unit.

AIM Kangaroo mother care (KMC) can be challenging in multiple births and more so in resource-limited settings. This study aims at increasing the mean duration of KMC with early initiation in twin preterm neonates born at a tertiary care hospital using a quality improvement (QI) initiative. METHODS Barriers for poor KMC practice in twin preterm neonates born at the tertiary care hospital were analysed and baseline data were collected over a period of 4 months using a predesigned proforma. A QI team was formed and suggested solutions were prioritised through focus group discussions in the form of Plan-Do-Study-Act (P-D-S-A) cycles. Each cycle was of one-month duration and three cycles were implemented, followed by the sustenance phase studied at 1-month post-implementation. RESULTS There were a total of 238 twin deliveries in the study period, of which 169 twin pairs were included in the study. At the end of implementation, the average day of initiation of KMC improved from 8th to 3rd day of life and the duration of KMC increased significantly from an average of 2.70 h/infant/day to 7.88 h/infant/day. CONCLUSION This QI project focused on the improvement of KMC practice in twin preterm neonates in a tertiary care hospital where results were achieved with maximal utilisation of available hospital resources and low-cost interventions. This study design is generalizable to other hospitals in resource-limited settings where family participatory care can be strengthened to overcome the challenges of KMC in multiple births.

Retinal ganglion cell (RGC) neurodegeneration in glaucoma has potential links with amyloid-β (Aβ) deposition. Targeting the Aβ pathway was shown to reduce RGC apoptosis and protect RGCs from degeneration. We report exploratory studies on the amyloid Aβ

Understanding the neural basis of human movement in naturalistic scenarios is critical for expanding neuroscience research beyond constrained laboratory paradigms. Here, we describe our Annotated Joints in Long-term Electrocorticography for 12 human participants (AJILE12) dataset, the largest human neurobehavioral dataset that is publicly available; the dataset was recorded opportunistically during passive clinical epilepsy monitoring. AJILE12 includes synchronized intracranial neural recordings and upper body pose trajectories across 55 semi-continuous days of naturalistic movements, along with relevant metadata, including thousands of wrist movement events and annotated behavioral states. Neural recordings are available at 500 Hz from at least 64 electrodes per participant, for a total of 1280 hours. Pose trajectories at 9 upper-body keypoints were estimated from 118 million video frames. To facilitate data exploration and reuse, we have shared AJILE12 on The DANDI Archive in the Neurodata Without Borders (NWB) data standard and developed a browser-based dashboard.

Limited data exist regarding outcomes of acute variceal bleeding (AVB) in patients with acute-on-chronic liver failure (ACLF), especially in those with hepatic failure. We evaluated the outcomes of AVB in patients with ACLF in a multinational cohort of APASL ACLF Research Consortium (AARC).Prospectively maintained data from AARC database on patients with ACLF who developed AVB (ACLF-AVB) was analysed. This data included demographic profile, severity of liver disease, and rebleeding and mortality in 6 weeks. These outcomes were compared with a propensity score matched (PSM) cohort of ACLF matched for severity of liver disease (MELD, AARC score) without AVB (ACLF without AVB).Of the 4434 ACLF patients, the outcomes in ACLF-AVB (n = 72) [mean age-46 ± 10.4 years, 93% males, 66% with alcoholic liver disease, 65% with alcoholic hepatitis, AARC score: 10.1 ± 2.2, MELD score: 34 (IQR: 27-40)] were compared with a PSM cohort selected in a ratio of 1:2 (n = 143) [mean age-44.9 ± 12.5 years, 82.5% males, 48% alcoholic liver disease, 55.7% alcoholic hepatitis, AARC score: 9.4 ± 1.5, MELD score: 32 (IQR: 24-40)] of ACLF-without AVB. Despite PSM, ACLF patients with AVB had a higher baseline HVPG than without AVB (25.00 [IQR: 23.00-28.00] vs. 17.00 [15.00-21.75] mmHg; p = 0.045). The 6-week mortality in ACLF patients with or without AVB was 70.8% and 53.8%, respectively (p = 0.025). The 6-week rebleeding rate was 23% in ACLF-AVB. Presence of ascites [hazard ratio (HR) 2.2 (95% CI 1.03-9.8), p = 0.026], AVB [HR 1.9 (95% CI 1.2-2.5, p = 0.03)], and MELD score [HR 1.7 (95% CI 1.1-2.1), p = 0.001] independently predicted mortality in the overall ACLF cohort.Development of AVB confers poor outcomes in patients with ACLF with a high 6-week mortality. Elevated HVPG at baseline represents a potential risk factor for future AVB in ACLF.

Motor behaviors are central to many functions and dysfunctions of the brain, and understanding their neural basis has consequently been a major focus in neuroscience. However, most studies of motor behaviors have been restricted to artificial, repetitive paradigms, far removed from natural movements performed “in the wild.” Here, we leveraged recent advances in machine learning and computer vision to analyze intracranial recordings from 12 human subjects during thousands of spontaneous, unstructured arm reach movements, observed over several days for each subject. These naturalistic movements elicited cortical spectral power patterns consistent with findings from controlled paradigms, but with considerable neural variability across subjects and events. We modeled interevent variability using 10 behavioral and environmental features; the most important features explaining this variability were reach angle and day of recording. Our work is among the first studies connecting behavioral and neural variability across cortex in humans during unstructured movements and contributes to our understanding of long-term naturalistic behavior. Significance Statement Understanding the neural basis of human movement has long been a key focus in neuroscience. However, researchers often study constrained, monotonous tasks that differ greatly from the rich and diverse natural movements we actually make. Here, we use data-intensive computational approaches to reveal patterns in neural activity underlying naturalistic human arm movements. While such movements match previous experimental findings on average, there is substantial neural variability from one movement to the next. We partially explain this variability by aspects of the movement observed, though much variability remains unaccounted for. Our study sheds light on how the brain generates natural arm movements and emphasizes the critical need to study brain activity in more unstructured settings that mimic daily life.

BACKGROUND "Kangaroo mother care," a type of newborn care involving skin-to-skin contact with the mother or other caregiver, reduces mortality in infants with low birth weight (

Background: alcohol may have additional neurotoxic ill-effects in patients with alcohol related cirrhosis apart from hepatic encephalopathy. We aimed to evaluate minimal hepatic encephalopathy (MHE) with Psychometric Hepatic Encephalopathy (PHES) score and Critical Flicker Frequency (CFF) in alcohol (ALD) and non-alcoholic steatohepatitis related (NASH) related cirrhosis. Methods: 398 patients were screened between March 2016 and December 2018; of which 71 patients were included in ALD group and 69 in NASH group. All included patients underwent psychometric tests which included number connection test A and B (NCT-A and NCT-B), serial dot test (SDT), digit symbol test (DST), line tracing test (LTT) and CFF. MHE was diagnosed when their PHES was

Background Recent technological advances in brain recording and machine learning algorithms are enabling the study of neural activity underlying spontaneous human behaviors, beyond the confines of cued, repeated trials. However, analyzing such unstructured data lacking a priori experimental design remains a significant challenge, especially when the data is multi-modal and long-term. New method Here we describe an automated, behavior-first approach for analyzing simultaneously recorded long-term, naturalistic electrocorticography (ECoG) and behavior video data. We identify and characterize spontaneous human upper-limb movements by combining computer vision, discrete latent-variable modeling, and string pattern-matching on the video. Results Our pipeline discovers and annotates over 40,000 instances of naturalistic arm movements in long term (7–9 day) behavioral videos, across 12 subjects. Analysis of the simultaneously recorded brain data reveals neural signatures of movement that corroborate previous findings. Our pipeline produces large training datasets for brain–computer interfacing applications, and we show decoding results from a movement initiation detection task. Comparison with existing methods Spontaneous movements capture real-world neural and behavior variability that is missing from traditional cued tasks. Building beyond window-based movement detection metrics, our unsupervised discretization scheme produces a queryable pose representation, allowing localization of movements with finer temporal resolution. Conclusions Our work addresses the unique analytic challenges of studying naturalistic human behaviors and contributes methods that may generalize to other neural recording modalities beyond ECoG. We publish our curated dataset and believe that it will be a valuable resource for future studies of naturalistic movements.

ObjectiveAdvances in neural decoding have enabled brain-computer interfaces to perform increasingly complex and clinically-relevant tasks. However, such decoders are often tailored to specific participants, days, and recording sites, limiting their practical long-term usage. Therefore, a fundamental challenge is to develop neural decoders that can robustly train on pooled, multi-participant data and generalize to new participants.ApproachWe introduce a new decoder, HTNet, which uses a convolutional neural network with two innovations: (1) a Hilbert transform that computes spectral power at data-driven frequencies and (2) a layer that projects electrode-level data onto predefined brain regions. The projection layer critically enables applications with intracranial electrocorticography (ECoG), where electrode locations are not standardized and vary widely across participants. We trained HTNet to decode arm movements using pooled ECoG data from 11 of 12 participants and tested performance on unseen ECoG or electroencephalography (EEG) participants; these pretrained models were also subsequently fine-tuned to each test participant.Main resultsHTNet outperformed state-of-the-art decoders when tested on unseen participants, even when a different recording modality was used. By fine-tuning these generalized HTNet decoders, we achieved performance approaching the best tailored decoders with as few as 50 ECoG or 20 EEG events. We were also able to interpret HTNet’s trained weights and demonstrate its ability to extract physiologically-relevant features.SignificanceBy generalizing to new participants and recording modalities, robustly handling variations in electrode placement, and allowing participant-specific fine-tuning with minimal data, HTNet is applicable across a broader range of neural decoding applications compared to current state-of-the-art decoders.

Studying the neural correlates of sleep can lead to revelations in our understanding of sleep and its interplay with different neurological disorders. Sleep research relies on manual annotation of sleep stages based on rules developed for healthy adults. Automating sleep stage annotation can expedite sleep research and enable us to better understand atypical sleep patterns. Our goal was to create a fully unsupervised approach to label sleep and wake states in human electro-corticography (ECoG) data from epilepsy patients. Here, we demonstrate that with continuous data from a single ECoG electrode, hidden semi-Markov models (HSMM) perform best in classifying sleep/wake states without excessive transitions, with a mean accuracy (n=4) of 85.2% compared to using K-means clustering (72.2%) and hidden Markov models (81.5%). Our results confirm that HSMMs produce meaningful labels for ECoG data and establish the groundwork to apply this model to cluster sleep stages and potentially other behavioral states.

Purpose: To assess the effect of a quality improvement (QI) bundle on improving breast milk output among very low birth weight (VLBW) mothers. Design: Before and after nonrandomized QI project. Sample: Mothers who delivered a VLBW infant in October and November 2015 were the prospective cohort. Those who delivered a VLBW infant in August and September 2015 were the retrospective cohort. The QI bundle consisted of early expression of milk, use of breast pumps, frequent expressions, videos, and regular counseling. This bundle was done for the prospective cohort. Outcomes measures: Quantity of expressed breast milk on day 7. Results: There were 13 mothers in the retrospective cohort and 18 mothers in the prospective one. The mean birth weight (1297.80 and 1207.70 g, p = .19) and gestation (32.5 and 31.5 wk, p = .27) were similar. There was a significant increase in the milk output on day 7 in the prospective group 113.6 ± 45 vs 182 ± 63 mL (p = .001).

A Bayesian model suggests that when interacting with a group, humans simulate the “mind of the group” to choose an action., To make decisions in a social context, humans have to predict the behavior of others, an ability that is thought to rely on having a model of other minds known as “theory of mind.” Such a model becomes especially complex when the number of people one simultaneously interacts with is large and actions are anonymous. Here, we present results from a group decision-making task known as the volunteer’s dilemma and demonstrate that a Bayesian model based on partially observable Markov decision processes outperforms existing models in quantitatively predicting human behavior and outcomes of group interactions. Our results suggest that in decision-making tasks involving large groups with anonymous members, humans use Bayesian inference to model the “mind of the group,” making predictions of others’ decisions while also simulating the effects of their own actions on the group’s dynamics in the future.

We present BrainNet which, to our knowledge, is the first multi-person non-invasive direct brain-to-brain interface for collaborative problem solving. The interface combines electroencephalography (EEG) to record brain signals and transcranial magnetic stimulation (TMS) to deliver information noninvasively to the brain. The interface allows three human subjects to collaborate and solve a task using direct brain-to-brain communication. Two of the three subjects are designated as “Senders” whose brain signals are decoded using real-time EEG data analysis. The decoding process extracts each Sender’s decision about whether to rotate a block in a Tetris-like game before it is dropped to fill a line. The Senders’ decisions are transmitted via the Internet to the brain of a third subject, the “Receiver,” who cannot see the game screen. The Senders’ decisions are delivered to the Receiver’s brain via magnetic stimulation of the occipital cortex. The Receiver integrates the information received from the two Senders and uses an EEG interface to make a decision about either turning the block or keeping it in the same orientation. A second round of the game provides an additional chance for the Senders to evaluate the Receiver’s decision and send feedback to the Receiver’s brain, and for the Receiver to rectify a possible incorrect decision made in the first round. We evaluated the performance of BrainNet in terms of (1) Group-level performance during the game, (2) True/False positive rates of subjects’ decisions, and (3) Mutual information between subjects. Five groups, each with three human subjects, successfully used BrainNet to perform the collaborative task, with an average accuracy of 81.25%. Furthermore, by varying the information reliability of the Senders by artificially injecting noise into one Sender’s signal, we investigated how the Receiver learns to integrate noisy signals in order to make a correct decision. We found that like conventional social networks, BrainNet allows Receivers to learn to trust the Sender who is more reliable, in this case, based solely on the information transmitted directly to their brains. Our results point the way to future brain-to-brain interfaces that enable cooperative problem solving by humans using a “social network” of connected brains.

IntroductionThe COVID-19 pandemic is disrupting health systems globally. Maternity care disruptions have been surveyed, but not those related to vulnerable small newborns. We aimed to survey reported disruptions to small and sick newborn care worldwide and undertake thematic analysis of healthcare providers’ experiences and proposed mitigation strategies.MethodsUsing a widely disseminated online survey in three languages, we reached out to neonatal healthcare providers. We collected data on COVID-19 preparedness, effects on health personnel and on newborn care services, including kangaroo mother care (KMC), as well as disruptors and solutions.ResultsWe analysed 1120 responses from 62 countries, mainly low and middle-income countries (LMICs). Preparedness for COVID-19 was suboptimal in terms of guidelines and availability of personal protective equipment. One-third reported routine testing of all pregnant women, but 13% had no testing capacity at all. More than 85% of health personnel feared for their own health and 89% had increased stress. Newborn care practices were disrupted both due to reduced care-seeking and a compromised workforce. More than half reported that evidence-based interventions such as KMC were discontinued or discouraged. Separation of the mother–baby dyad was reported for both COVID-positive mothers (50%) and those with unknown status (16%). Follow-up care was disrupted primarily due to families’ fear of visiting hospitals (~73%).ConclusionNewborn care providers are stressed and there is lack clarity and guidelines regarding care of small newborns during the pandemic. There is an urgent need to protect life-saving interventions, such as KMC, threatened by the pandemic, and to be ready to recover and build back better.

Cortical stimulation through electrocorticographic (ECoG) electrodes is a potential method for providing sensory feedback in future prosthetic and rehabilitative applications. Here, we evaluate human subjects’ ability to continuously modulate their motor behavior based on feedback from direct surface stimulation of the somatosensory cortex. Subjects wore a dataglove that measured their hand aperture position and received one of three stimuli over the hand sensory cortex based on their current hand position as compared to a target aperture position. Using cortical stimulation feedback, subjects adjusted their hand aperture to move towards the target aperture region. One subject was able to achieve accuracies and R2 values well above chance (best performance: R2 = 0.93; accuracy = 0.76/1). Performance dropped during the catch trial (same stimulus independent of the position) to below chance levels, suggesting that the subject had been using the varied sensory feedback to modulate their motor behavior. To our knowledge, this study represents one of the first demonstrations of using direct cortical surface stimulation of the human sensory cortex to perform a motor task, and is a first step towards developing closed-loop human sensorimotor brain-computer interfaces.

Goal: To identify and overcome barriers to creating new neurotechnologies capable of restoring both motor and sensory function in individuals with neurological conditions. Methods: This report builds upon the outcomes of a joint workshop between the US National Science Foundation and the German Research Foundation on New Perspectives in Neuroengineering and Neurotechnology convened in Arlington, VA, USA, November 13–14, 2014. Results: The participants identified key technological challenges for recording and manipulating neural activity, decoding, and interpreting brain data in the presence of plasticity, and early considerations of ethical and social issues pertinent to the adoption of neurotechnologies. Conclusions: The envisaged progress in neuroengineering requires tightly integrated hardware and signal processing efforts, advances in understanding of physiological adaptations to closed-loop interactions with neural devices, and an open dialog with stakeholders and potential end-users of neurotechnology. Significance: The development of new neurotechnologies (e.g., bidirectional brain–computer interfaces) could significantly improve the quality of life of people living with the effects of brain or spinal cord injury, or other neurodegenerative diseases. Focused efforts aimed at overcoming the remaining barriers at the electrode tissue interface, developing implantable hardware with on-board computation, and refining stimulation methods to precisely activate neural tissue will advance both our understanding of brain function and our ability to treat currently intractable disorders of the nervous system.

Objective. Electrical stimulation of the human brain is commonly used for eliciting and inhibiting neural activity for clinical diagnostics, modifying abnormal neural circuit function for therapeutics, and interrogating cortical connectivity. However, recording electrical signals with concurrent stimulation results in dominant electrical artifacts that mask the neural signals of interest. Here we develop a method to reproducibly and robustly recover neural activity during concurrent stimulation. We concentrate on signal recovery across an array of electrodes without channel-wise fine-tuning of the algorithm. Our goal includes signal recovery with trains of stimulation pulses, since repeated, high-frequency pulses are often required to induce desired effects in both therapeutic and research domains. We have made all of our code and data publicly available. Approach. We developed an algorithm that automatically detects templates of artifacts across many channels of recording, creating a dictionary of learned templates using unsupervised clustering. The artifact template that best matches each individual artifact pulse is subtracted to recover the underlying activity. To assess the success of our method, we focus on whether it extracts physiologically interpretable signals from real recordings. Main results. We demonstrate our signal recovery approach on invasive electrophysiologic recordings from human subjects during stimulation. We show the recovery of meaningful neural signatures in both electrocorticographic (ECoG) arrays and deep brain stimulation (DBS) recordings. In addition, we compared cortical responses induced by the stimulation of primary somatosensory (S1) by natural peripheral touch, as well as motor cortex activity with and without concurrent S1 stimulation. Significance. Our work will enable future advances in neural engineering with simultaneous stimulation and recording.

Direct cortical stimulation (DCS) of primary somatosensory cortex (S1) could help restore sensation and provide task-relevant feedback in a neuroprosthesis. However, the psychophysics of S1 DCS is poorly studied, including any comparison to cutaneous haptic stimulation. We compare the response times to DCS of human hand somatosensory cortex through electrocorticographic grids with response times to haptic stimuli delivered to the hand in four subjects. We found that subjects respond significantly slower to S1 DCS than to natural, haptic stimuli for a range of DCS train durations. Median response times for haptic stimulation varied from 198 ms to 313 ms, while median responses to reliably perceived DCS ranged from 254 ms for one subject, all the way to 528 ms for another. We discern no significant impact of learning or habituation through the analysis of blocked trials, and find no significant impact of cortical stimulation train duration on response times. Our results provide a realistic set of expectations for latencies with somatosensory DCS feedback for future neuroprosthetic applications and motivate the study of neural mechanisms underlying human perception of somatosensation via DCS.

The human ventral temporal cortex has regions that are known to selectively process certain categories of visual inputs; they are specialized for the content (“faces,” “places,” “tools”) and not the form (“line,” “patch”) of the image being seen. In our study, human patients with implanted electrocorticography (ECoG) electrode arrays were shown sequences of simple face and house pictures. We quantified neuronal population activity, finding robust face-selective sites on the fusiform gyrus and house-selective sites on the lingual/parahippocampal gyri. The magnitude and timing of single trials were compared between novel (“house-face”) and repeated (“face-face”) stimulus-type responses. More than half of the category-selective sites showed significantly greater total activity for novel stimulus class. Approximately half of the face-selective sites (and none of the house-selective sites) showed significantly faster latency to peak (∼50 ms) for novel stimulus class. This establishes subregions within category-selective areas that are differentially tuned to novelty in sequential context, where novel stimuli are processed faster in some regions, and with increased activity in others.

OBJECTIVE To assess the effectiveness of expressed breast milk (EBM) on neonatal pain during screening for retinopathy of prematurity (ROP). METHODS Neonates who were on oral feeds undergoing ROP screening were included. Babies were randomized into intervention group (EBM + Standard practice) and control group. The standard practice is proparacaine, nesting and swaddling. Pain was assessed by PIPP scale, during and at 1 and 5 min after the procedure by the principal investigator who was blinded. RESULTS The groups were similar in baseline characteristics. The group receiving EBM had significantly lower PIPP scores during the procedure 12.7 ± 1.69 compared to the control group 15.5 ± 1.78 (p < 0.05). The beneficial effect persisted at 1 min and 5 min after the procedure 6.20 ± 1.9 vs. 12.4 ± 2.54 (p ≤ 0.05) at 1 min; 3.2 ± 1.5 and 6.85 ± 2.4 (p < 0.05) at 5 min. CONCLUSION Oral EBM significantly reduces pain during and after ROP screening.

Electrocorticography (ECoG) signals can provide high spatio-temporal resolution and high signal to noise ratio recordings of local neural activity from the surface of the brain. Previous studies have shown that broad-band, spatially focal, high-frequency increases in ECoG signals are highly correlated with movement and other cognitive tasks and can be volitionally modulated. However, significant additional information may be present in inter-electrode interactions, but adding additional higher order inter-electrode interactions can be impractical from a computational aspect, if not impossible.In this paper we present a new method of calculating high frequency interactions between electrodes called Short-Time Windowed Covariance (STWC) that builds on mathematical techniques currently used in neural signal analysis, along with an implementation that accelerates the algorithm by orders of magnitude by leveraging commodity, off-the-shelf graphics processing unit (GPU) hardware.Using the hardware-accelerated implementation of STWC, we identify many types of event-related inter-electrode interactions from human ECoG recordings on global and local scales that have not been identified by previous methods. Unique temporal patterns are observed for digit flexion in both low- (10mm spacing) and high-resolution (3mm spacing) electrode arrays.Covariance is a commonly used metric for identifying correlated signals, but the standard covariance calculations do not allow for temporally varying covariance. In contrast STWC allows and identifies event-driven changes in covariance without identifying spurious noise correlations.STWC can be used to identify event-related neural interactions whose high computational load is well suited to GPU capabilities.

The majority of subjects who attempt to learn control of a brain–computer interface (BCI) can do so with adequate training. Much like when one learns to type or ride a bicycle, BCI users report transitioning from a deliberate, cognitively focused mindset to near automatic control as training progresses. What are the neural correlates of this process of BCI skill acquisition? Seven subjects were implanted with electrocorticography (ECoG) electrodes and had multiple opportunities to practice a 1D BCI task. As subjects became proficient, strong initial task-related activation was followed by lessening of activation in prefrontal cortex, premotor cortex, and posterior parietal cortex, areas that have previously been implicated in the cognitive phase of motor sequence learning and abstract task learning. These results demonstrate that, although the use of a BCI only requires modulation of a local population of neurons, a distributed network of cortical areas is involved in the acquisition of BCI proficiency.

Motivated by the growing evidence for Bayesian computation in the brain, we show how a two-layer recurrent network of Poisson neurons can perform both approximate Bayesian inference and learning for any hidden Markov model. The lower-layer sensory neurons receive noisy measurements of hidden world states. The higher-layer neurons infer a posterior distribution over world states via Bayesian inference from inputs generated by sensory neurons. We demonstrate how such a neuronal network with synaptic plasticity can implement a form of Bayesian inference similar to Monte Carlo methods such as particle filtering. Each spike in a higher-layer neuron represents a sample of a particular hidden world state. The spiking activity across the neural population approximates the posterior distribution over hidden states. In this model, variability in spiking is regarded not as a nuisance but as an integral feature that provides the variability necessary for sampling during inference. We demonstrate how the network can learn the likelihood model, as well as the transition probabilities underlying the dynamics, using a Hebbian learning rule. We present results illustrating the ability of the network to perform inference and learning for arbitrary hidden Markov models.

Neural correlates of movement planning onset and direction may be present in human electrocorticography in the signal dynamics of both motor and non-motor cortical regions. We use a three-stage model of jPCA reduced-rank hidden Markov model (jPCA-RR-HMM), regularized shrunken-centroid discriminant analysis (RDA), and LASSO regression to extract direction-sensitive planning information and movement onset in an upper-limb 3D isometric force task in a human subject. This mode achieves a relatively high true positive force-onset prediction rate of 60% within 250ms, and an above-chance 36% accuracy (17% chance) in predicting one of six planned 3D directions of isometric force using pre-movement signals. We also find direction-distinguishing information up to 400ms before force onset in the pre-movement signals, captured by electrodes placed over the limb-ipsilateral dorsal premotor regions. This approach can contribute to more accurate decoding of higher-level movement goals, at earlier timescales, and inform sensor placement. Our results also contribute to further understanding of the spatiotemporal features of human motor planning., 4 pages, 3 figures, accepted to EMBC 2016 (38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society)

Non-alcoholic fatty liver disease (NAFLD) is considered an integral part of metabolic syndrome (MS). We aimed to explore the inter-relations of MS and dietary composition in subjects with and without hepatic steatosis and to identify the nutritional risk factors contributing to NAFLD. In all 98 subjects with steatosis and 102 controls were selected for the study after screening 260 consecutive healthy individuals. Anthropometric and nutritional information, biochemical data and clinical profile were analyzed. Prevalence of MS was determined based on the consensus statement for diagnosis of MS for Asian Indians. Multiple logistic regression analysis was done to predict the dietary risk factors in NAFLD. Prevalence of MS was 44.9% among NAFLD cases and 25.5% among controls (P=0.003). Subjects with NAFLD had significantly higher values of body mass index (BMI), waist circumference (WC), percent body fat, total cholesterol, triglycerides and blood pressure than controls. The total calorie intake, percent of carbohydrate and fat intake of NAFLD cases was significantly higher than controls. Multiple logistic regression analysis showed BMI (odds ratio 6.03 (95% confidence interval 3.26–11.14)), WC (5.49 (2.59–11.57)) and percent dietary fat intake (2.51 (1.99–3.31)) as independent nutritional risk factors in NAFLD. In this study, there is a high prevalence of MS among subjects with steatosis and metabolic disorders were closely related to NAFLD. BMI, WC and percent fat intake are independent dietary risk factors in NAFLD. Decreased nutritional intake with restricted fat may constitute an important therapy in subjects with NAFLD.

Objective A primary control signal in brain-computer interfaces (BCIs) have been cortical signals related to movement. However, in cases where natural motor function remains, BCI control signals may interfere with other possibly simultaneous activity for useful ongoing movement. We sought to determine if the brain could learn to control both a BCI and concurrent overt movement execution in such cases. Approach We designed experiments where BCI and overt movements must be used concurrently and in coordination to achieve a 2D centre out control. Power in the 70-90 Hz band of human electrocorticography (ECoG) signals, was used to generate BCI control commands for vertical movement of the cursor. These signals were deliberately recorded from the same human cortical site that produced the strongest movement related activity associated with the concurrent overt finger movements required for the horizontal movement of the cursor. Main results We demonstrate that three subjects were able to perform the concurrent BCI task, controlling BCI and natural movements simultaneously and to a large extent independently. We conclude that the brain is capable of dissociating the original control signal dependency on movement, producing specific BCI control signals in the presence of motor related responses from the ongoing overt behaviour with which the BCI signal was initially correlated. Significance We demonstrate a novel human brain-computer interface (BCI) which can be used to control movement concurrently and in coordination with movements of the natural limbs. This demonstrates the dissociation of cortical activity from the behaviour with which it was originally associated despite the ongoing behaviour and shows the feasibility of achieving simultaneous BCI control of devices with natural movements.

Gaze following is a key component of human social cognition. Gaze following directs attention to areas of high information value and accelerates social, causal, and cultural learning. An issue for both robotic and infant learning is whose gaze to follow. The hypothesis tested in this study is that infants use information derived from an entity's interactions with other agents as evidence about whether that entity is a perceiver. A robot was programmed so that it could engage in communicative, imitative exchanges with an adult experimenter. Infants who saw the robot act in this social-communicative fashion were more likely to follow its line of regard than those without such experience. Infants use prior experience with the robot's interactions as evidence that the robot is a psychological agent that can see. Infants want to look at what the robot is seeing, and thus shift their visual attention to the external target.

High gamma (HG) power changes during motor activity, especially at frequencies above 70 Hz, play an important role in functional cortical mapping and as control signals for BCI (brain computer interface) applications. Most studies of HG activity have used ECoG (electrocorticography) which provides high-quality spatially localized signals, but is an invasive method. Recent studies have shown that non-invasive modalities such as EEG and MEG can also detect task related HG power changes. We show here that a 27 channel EEG (electroencephalography) montage provides high-quality spatially localized signals non-invasively for HG frequencies ranging from 83 to 101 Hz. We used a generic head model, a weighted minimum norm least squares (MNLS) inverse method, and a self-paced finger movement paradigm. The use of an inverse method enables us to map the EEG onto a generic cortex model. We find the HG activity during the task to be well localized in the contralateral motor area. We find HG power increases prior to finger movement, with average latencies of 462 ms and 82 ms before EMG (electromyogram) onset. We also find significant phase-locking between contra- and ipsilateral motor areas over a similar HG frequency range; here the synchronization onset precedes the EMG by 400 ms. We also compare our results to ECoG data from a similar paradigm and find EEG mapping and ECoG in good agreement. Our findings demonstrate that mapped EEG provides information on two important parameters for functional mapping and BCI which are usually only found in HG of ECoG signals: spatially localized power increases and bihemispheric phase-locking.

Although no historical information exists about the Indus civilization (flourished ca . 2600–1900 B.C.), archaeologists have uncovered about 3,800 short samples of a script that was used throughout the civilization. The script remains undeciphered, despite a large number of attempts and claimed decipherments over the past 80 years. Here, we propose the use of probabilistic models to analyze the structure of the Indus script. The goal is to reveal, through probabilistic analysis, syntactic patterns that could point the way to eventual decipherment. We illustrate the approach using a simple Markov chain model to capture sequential dependencies between signs in the Indus script. The trained model allows new sample texts to be generated, revealing recurring patterns of signs that could potentially form functional subunits of a possible underlying language. The model also provides a quantitative way of testing whether a particular string belongs to the putative language as captured by the Markov model. Application of this test to Indus seals found in Mesopotamia and other sites in West Asia reveals that the script may have been used to express different content in these regions. Finally, we show how missing, ambiguous, or unreadable signs on damaged objects can be filled in with most likely predictions from the model. Taken together, our results indicate that the Indus script exhibits rich synactic structure and the ability to represent diverse content. both of which are suggestive of a linguistic writing system rather than a nonlinguistic symbol system.

Posttransplant malignancy developing in an allograft is an uncommon complication of organ transplantation. The tumor may represent malignant transformation of donor or recipient cells that were previously normal, metastatic malignancy of recipient origin or malignancy transmitted from organ donor to recipient. Establishing the origin of the malignancy is critical to treatment algorithms. It is generally believed allograft removal and immunosuppression withdrawal will lead to resolution of transmitted malignancies in cases where the renal allograft is the origin. We report a male patient who developed metastatic ovarian malignancy secondary to donor transmission.

Human cognition is thought to be mediated by large-scale interactions between distant sites in the neocortex. Synchronization between different cortical areas has been suggested as one possible mechanism for corticocortical interaction. Here, we report robust, directional cross-frequency synchronization between distant sensorimotor sites in human neocortex during a movement task. In four subjects, electrocorticographic recordings from the cortical surface revealed a low-frequency rhythm (10–13 Hz) that combined with a higher frequency (77–82 Hz) in a ventral region of the premotor cortex to produce a third rhythm at the sum of these two frequencies in a distant motor site. Such cross-frequency coupling implies a nonlinear interaction between these cortical sites. These findings demonstrate that task-specific, phase–phase coupling can support communication between distant areas of the human neocortex.

We describe a brain-computer interface for controlling a humanoid robot directly using brain signals obtained non-invasively from the scalp through electroencephalography (EEG). EEG has previously been used for tasks such as controlling a cursor and spelling a word, but it has been regarded as an unlikely candidate for more complex forms of control owing to its low signal-to-noise ratio. Here we show that by leveraging advances in robotics, an interface based on EEG can be used to command a partially autonomous humanoid robot to perform complex tasks such as walking to specific locations and picking up desired objects. Visual feedback from the robot's cameras allows the user to select arbitrary objects in the environment for pick-up and transport to chosen locations. Results from a study involving nine users indicate that a command for the robot can be selected from four possible choices in 5 s with 95% accuracy. Our results demonstrate that an EEG-based brain-computer interface can be used for sophisticated robotic interaction with the environment, involving not only navigation as in previous applications but also manipulation and transport of objects.

Electrocorticographic spectral changes during movement show a behavioral inflection in the classic gamma band (30-70 Hz). We quantify this inflection and demonstrate that it limits classification accuracy. We call for the designation of a functionally defined band above it, which we denote the chi-band.

Seasonal variations in groundwater reveal lesser concentrations of major ions except NO3(-) during post-monsoon seasons in shallow aquifers as compared to deeper aquifers. The F(-) concentration from deeper aquifers is high in both seasons and shows a moderate positive relationship with weathering depth and is >5 mg/L in compound lava flow. Groundwater is mainly a Ca-HCO3 type in shallow aquifers and mixed type in deeper aquifers. Fluoride shows a positive correlation with pH, Na(+), HCO3(-) in shallow aquifers and an inverse correlation with Ca(2+) and HCO3(-) from deeper aquifers in both seasons. Approximately 45% of the samples are not suitable for drinking from both aquifers but suitable for irrigation purposes. Rock-water interaction, moderate alkalinity, sluggish movement, and higher residence time are the main causes for high F(-) in deeper aquifers as compared to shallow aquifers. As recommendations, drinking water requirement may be met from shallow aquifers/surface water and fluoride rich groundwater for other purposes. Most effective defluoridation techniques like ion exchange and reverse osmosis may be adopted along with integrated fluorosis mitigation measures and rooftop rainwater harvesting. Supplementary calcium and phosphorous rich food should be provided to children and creating awareness about safe drinking water habits, side effects of high F(-), and NO3(-) rich groundwater, improving oral hygiene conditions are other measures.

We demonstrate the feasibility of real-time cortical mapping from arrays of subdural electrodes using the electrocorticographic signal power in the higher spectral frequencies (76-200 Hz, or "chi-index"). Hand area was mapped offline in eight individuals using brief baseline and hand-movement measurements. In one patient, hand sensorimotor cortex was identified online during a handshake. We propose that this high-frequency component of the electrocorticogram provides a generic, reliable, clinically useful correlate of local cortical function.

Medically refractory epilepsy accounts for more than 30% of the epilepsy population. Scalp EEG electrodes have limited ability to localize seizure onset from deep structures and implantation of subdural electrodes with long term monitoring provides additional information. Apart from clinical application, this patient population provides a unique opportunity for acquiring electrocorticography data in research paradigms. We present a method for rapid localization of electrodes using lateral and anterior-posterior X-rays. Skull landmarks and proportions are used for co-registration with the standardized Talairach coordinate system. This MATLAB-based "Location on Cortex" (LOC) package facilitates rapid visualization of clinical and experimental data in a user-friendly manner.

In the first large study of its kind, we quantified changes in electrocorticographic signals associated with motor movement across 22 subjects with subdural electrode arrays placed for identification of seizure foci. Patients underwent a 5–7 d monitoring period with array placement, before seizure focus resection, and during this time they participated in the study. An interval-based motor-repetition task produced consistent and quantifiable spectral shifts that were mapped on a Talairach-standardized template cortex. Maps were created independently for a high-frequency band (HFB) (76–100 Hz) and a low-frequency band (LFB) (8–32 Hz) for several different movement modalities in each subject. The power in relevant electrodes consistently decreased in the LFB with movement, whereas the power in the HFB consistently increased. In addition, the HFB changes were more focal than the LFB changes. Sites of power changes corresponded to stereotactic locations in sensorimotor cortex and to the results of individual clinical electrical cortical mapping. Sensorimotor representation was found to be somatotopic, localized in stereotactic space to rolandic cortex, and typically followed the classic homunculus with limited extrarolandic representation.

We present, to our knowledge, the first demonstration that a non-invasive brain-to-brain interface (BBI) can be used to allow one human to guess what is on the mind of another human through an interactive question-and-answering paradigm similar to the “20 Questions” game. As in previous non-invasive BBI studies in humans, our interface uses electroencephalography (EEG) to detect specific patterns of brain activity from one participant (the “respondent”), and transcranial magnetic stimulation (TMS) to deliver functionally-relevant information to the brain of a second participant (the “inquirer”). Our results extend previous BBI research by (1) using stimulation of the visual cortex to convey visual stimuli that are privately experienced and consciously perceived by the inquirer; (2) exploiting real-time rather than off-line communication of information from one brain to another; and (3) employing an interactive task, in which the inquirer and respondent must exchange information bi-directionally to collaboratively solve the task. The results demonstrate that using the BBI, ten participants (five inquirer-respondent pairs) can successfully identify a “mystery item” using a true/false question-answering protocol similar to the “20 Questions” game, with high levels of accuracy that are significantly greater than a control condition in which participants were connected through a sham BBI.

Electrocorticography (ECoG) has been demonstrated to be an effective modality as a platform for brain-computer interfaces (BCIs). Through our experience with ten subjects, we further demonstrate evidence to support the power and flexibility of this signal for BCI usage. In a subset of four patients, closed-loop BCI experiments were attempted with the patient receiving online feedback that consisted of one-dimensional cursor movement controlled by ECoG features that had shown correlation with various real and imagined motor and speech tasks. All four achieved control, with final target accuracies between 73%-100%. We assess the methods for achieving control and the manner in which enhancing online control can be accomplished by rescreening during online tasks. Additionally, we assess the relevant issues of the current experimental paradigm in light of their clinical constraints.

Objective: To report a case of a child with bipolar disorder found to have an unbalanced translocation involving the long arm of chromosome 8, a region that has been previously implicated in genome-wide linkage scans. Case report: A 7-year-old boy with a complex psychiatric symptom presentation including attention deficits, distractibility, impulsivity, pressured speech, sleep disturbance, aggressive behavior, and hypersexuality diagnosed with bipolar disorder. He also showed evidence of borderline intellectual and adaptive functioning and had mild dysmorphic features with a duplication of distal 8q that arose as an unbalanced chromosomal translocation due to a maternal 15p;8q insertion. Conclusion: This finding of an unbalanced translocation provides further evidence to support previous linkage studies of a potential causative gene on 8q for bipolar disorder.

We have previously shown how individual calcium oxalate stones of about 1 cm can be grown in vitro. While this proved a design concept, it was severely limited as an experimental tool because of the time required to undertake comparative studies. Here we describe a development of this system in which six parallel pairs of stone generators are supplied with feed solutions generating a medium that is supersaturated with calcium oxalate. Twelve stones were grown simultaneously in aseptically prepared artificial urine over a period of 32 days from 100 mg to about 250 mg. Flow rates, pH and [Ca(2+)] were stable and reproducible over the course of the experiment. Sodium azide (0.02%) was included in the growth medium of six stones and caused a modest decrease in growth rate from 5.5 to 3.4 mg/day. The experimental design is such that this was readily detectable both visually and statistically ( p0.001). This multiple stone growing system ("a stone farm") shows improved consistency and illustrates the statistical power of the technique. Azide has only a minor effect on the growth kinetics and can be used as an antibacterial agent in studies involving urinary macromolecules. The technique is suitable for practical and meaningful investigation of calcium oxalate stone formation in vitro.

A fundamental challenge in robotics today is building robots that can learn new skills by observing humans and imitating human actions. We propose a new Bayesian approach to robotic learning by imitation inspired by the developmental hypothesis that children use self-experience to bootstrap the process of intention recognition and goal-based imitation. Our approach allows an autonomous agent to: (i) learn probabilistic models of actions through self-discovery and experience, (ii) utilize these learned models for inferring the goals of human actions, and (iii) perform goal-based imitation for robotic learning and human-robot collaboration. Such an approach allows a robot to leverage its increasing repertoire of learned behaviors to interpret increasingly complex human actions and use the inferred goals for imitation, even when the robot has very different actuators from humans. We demonstrate our approach using two different scenarios: (i) a simulated robot that learns human-like gaze following behavior, and (ii) a robot that learns to imitate human actions in a tabletop organization task. In both cases, the agent learns a probabilistic model of its own actions, and uses this model for goal inference and goal-based imitation. We also show that the robotic agent can use its probabilistic model to seek human assistance when it recognizes that its inferred actions are too uncertain, risky, or impossible to perform, thereby opening the door to human-robot collaboration.

Visual cognition depends critically on the moment-to-moment orientation of gaze. To change the gaze to a new location in space, that location must be computed and used by the oculomotor system. One of the most common sources of information for this computation is the visual appearance of an object. A crucial question is: How is the appearance information contained in the photometric array is converted into a target position? This paper proposes a such a model that accomplishes this calculation. The model uses iconic scene representations derived from oriented spatiochromatic filters at multiple scales. Visual search for a target object proceeds in a coarse-to-fine fashion with the target's largest scale filter responses being compared first. Task-relevant target locations are represented as saliency maps which are used to program eye movements. A central feature of the model is that it separates the targeting process, which changes gaze, from the decision process, which extracts information at or near the new gaze point to guide behavior. The model provides a detailed explanation for center-of-gravity saccades that have been observed in many previous experiments. In addition, the model's targeting performance has been compared with the eye movements of human subjects under identical conditions in natural visual search tasks. The results show good agreement both quantitatively (the search paths are strikingly similar) and qualitatively (the fixations of false targets are comparable).

The effect of in vitro dilution of artificial urine or human urine on the crystallization of calcium oxalate was examined in a mixed suspension, mixed product removal crystallization system. Direct growth inhibition by components of artificial urine was not significant and supersaturation was the dominant factor in determining crystal nucleation and growth rates. Dilution of human urine caused a decrease in crystal growth rate that was independent of the input calcium and oxalate concentrations, suggesting that dilution of growth inhibitors could be physiologically more important than any reduction in supersaturation. This loss of growth inhibition was counteracted by a reduction in nucleation promotion, with the net effect that the mass of crystals declined. Correlation of crystallization measurements with urinary concentration (osmotic pressure) confirmed these observations, with a negative relationship for growth rate and a positive relationship for nucleation rate and suspension density. Increasing the concentration of urine shifts the crystallization balance from low nucleation/high growth to high nucleation/low growth. Calcium oxalate crystalluria in healthy urine is therefore less likely at early stages of urine development in the nephron and the likelihood can be further reduced by increased fluid output. Our results suggest that lowering the heterogeneous nucleation activity by dilution is more than sufficient to override the loss of growth inhibition.

Brain–computer interfaces (BCIs) are devices that record from the nervous system, provide input directly to the nervous system, or do both. Sensory BCIs such as cochlear implants have already had notable clinical success and motor BCIs have shown great promise for helping patients with severe motor deficits. Clinical and engineering outcomes aside, BCIs can also be tremendously powerful tools for scientific inquiry into the workings of the nervous system. They allow researchers to inject and record information at various stages of the system, permitting investigation of the brain in vivo and facilitating the reverse engineering of brain function. Most notably, BCIs are emerging as a novel experimental tool for investigating the tremendous adaptive capacity of the nervous system.

Objective. Brain–computer interfaces (BCIs) are confronted with two fundamental challenges: (a) the uncertainty associated with decoding noisy brain signals, and (b) the need for co-adaptation between the brain and the interface so as to cooperatively achieve a common goal in a task. We seek to mitigate these challenges. Approach. We introduce a new approach to brain–computer interfacing based on partially observable Markov decision processes (POMDPs). POMDPs provide a principled approach to handling uncertainty and achieving co-adaptation in the following manner: (1) Bayesian inference is used to compute posterior probability distributions (‘beliefs’) over brain and environment state, and (2) actions are selected based on entire belief distributions in order to maximize total expected reward; by employing methods from reinforcement learning, the POMDP’s reward function can be updated over time to allow for co-adaptive behaviour. Main results. We illustrate our approach using a simple non-invasive BCI which optimizes the speed–accuracy trade-off for individual subjects based on the signal-to-noise characteristics of their brain signals. We additionally demonstrate that the POMDP BCI can automatically detect changes in the user’s control strategy and can co-adaptively switch control strategies on-the-fly to maximize expected reward. Significance. Our results suggest that the framework of POMDPs offers a promising approach for designing BCIs that can handle uncertainty in neural signals and co-adapt with the user on an ongoing basis. The fact that the POMDP BCI maintains a probability distribution over the user’s brain state allows a much more powerful form of decision making than traditional BCI approaches, which have typically been based on the output of classifiers or regression techniques. Furthermore, the co-adaptation of the system allows the BCI to make online improvements to its behaviour, adjusting itself automatically to the user’s changing circumstances.

We illustrate a general principal of electrical potential measurements from the surface of the cerebral cortex, by revisiting and reanalyzing experimental work from the visual, language and motor systems. A naive decomposition technique of electrocorticographic power spectral measurements reveals that broadband spectral changes reliably track task engagement. These broadband changes are shown to be a generic correlate of local cortical function across a variety of brain areas and behavioral tasks. Furthermore, they fit a power-law form that is consistent with simple models of the dendritic integration of asynchronous local population firing. Because broadband spectral changes covary with diverse perceptual and behavioral states on the timescale of 20-50 ms, they provide a powerful and widely applicable experimental tool.

Messenger RNAs encoding many oncoproteins and cytokines are relatively unstable. Their instability, which ensures appropriate levels and timing of expression, is controlled in part by proteins that bind to A+U-rich instability elements (AREs) present in the 3'-untranslated regions of the mRNAs. cDNAs encoding the AUF1 family of ARE-binding proteins were cloned from human and murine cDNA libraries. In the present study monochromosomal somatic cell hybrids were used to localize two AUF1 loci to human chromosomes 4 and X. In situ hybridization analyses using P1 clones as probes identified the 4q21.1-q21.2 and Xq12 regions as the locations of the AUF1 genes.

Background Heel prick is a frequent painful procedure in newborns. A lancet or a 26-gauge needle is used for a heel prick in India. Objective To compare the pain caused by heel prick with a lancet or a 26-gauge needle in newborns admitted in the neonatal intensive care unit (NICU) using the preterm infant pain profile (PIPP). Methods This randomized controlled trial was conducted over 2 months in a Level III NICU with a sample size of 40 subjects (20 in each group), which was required for the study to have a power of 80% with an alpha error of 0.05. Hemodynamically stable newborns on at least those on partial oral feeds undergoing heel prick for routine glucose monitoring were randomized into two groups within 48 h of NICU admission after informed parental consent: heel prick with a lancet or with a 26-gauge needle using computer-generated random numbers. Two milliliters of expressed breast milk was given 2 min before the heel prick. Pain before, during and after (1 and 5 min) was assessed using the PIPP score. The primary outcome measure was the PIPP score. The secondary outcome measures were the duration of audible cry and the number of pricks needed for an adequate sample. Statistical analysis was done using the Mann Whitney U test and Friedman's test on SPSS v.21. A p value of