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Purpose: Neonatal near miss (NNM) allows for the detection of risk factors associated with serious newborn complications and death, the prevention of which could reduce neonatal mortality. This study was conducted with the objective of identifying predictors for NNM in a tertiary hospital in Bangalore city.Methods: This was an unmatched case-control study involving 120 NNM cases and 120 controls. NNM was determined using Pileggi-Castro’s pragmatic and management criteria. Data was collected from in-patient hospital records and interviews of postpartum mothers. Multiple logistic regression of exposure variables was performed to calculate adjusted odds ratio (AOR) with 95% confidence interval (CI).Results: Significant predictors were maternal age ≥30 years (AOR, 5.32; 95% CI, 1.12 to 9.29; P=0.041), inadequate antenatal care (ANC) (AOR, 8.35; 95% CI, 1.98 to 51.12; P=0.032)

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.

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 (

The original predictive coding model of Rao & Ballard [1] focused on spatial prediction to explain spatial receptive fields and contextual effects in the visual cortex. Here, we introduce a new dynamic predictive coding model that achieves spatiotemporal prediction of complex natural image sequences using time-varying transition matrices. We overcome the limitations of static linear transition models (as in, e.g., Kalman filters) using a hypernetwork to adjust the transition matrix dynamically for every time step, allowing the model to predict using a time-varying mixture of possible transition dynamics. We developed a single level model with recurrent modulation of transition weights by a hypernetwork and a two-level hierarchical model with top-down modulation based on a hypernetwork. At each time step, the model predicts the next input and estimates a sparse neural code by minimizing prediction error. When exposed to natural movies, the model learned localized, oriented spatial filters as well as both separable and inseparable (direction-selective) space-time receptive fields at the first level, similar to those found in the primary visual cortex (V1). Longer timescale responses and stability at the second level also emerged naturally from minimizing prediction errors for the first level dynamics. Our results suggest that the multiscale temporal response properties of cortical neurons could be the result of the cortex learning a hierarchical generative model of the visual world with higher order areas predicting the transition dynamics of lower order areas.

Direct cortical stimulation (DCS) has been used extensively as a tool in human neuroscience research and clinically in neurosurgery. Implanted electrocorticography electrodes can deliver electrical stimulation to the cortical surface, eliciting or inhibiting neural activity as an experimental manipulation, or to probe neural tissue function during epilepsy and tumor resection surgeries. Current applications of DCS pave the way for new clinical translation to restore sensation to those who have lost it through spinal-cord injury or stroke. DCS of specific sensory cortical regions in the human brain elicits sensory percepts in specific regions of the body and recent research has shown that DCS can elicit meaningful somatosensory percepts, which may be used for neuroprosthetic feedback and integrated into tasks. In the future, further research using more finely spaced electrodes, tailored patterns of stimulation, and longer term studies will shed light on the intricacies of sensory processing in humans as well as bring these neuroprosthetic applications closer to widespread clinical reality.

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).

To study the impact of a quality improvement (QI) initiative using care bundle approach on Central-line associated bloodstream infections (CLABSI) rates. A QI team for infection control in NICU was formed in a tertiary-care neonatal intensive care unit (NICU) from June 2015 to August 2016. Baseline data were collected over first 3 months followed by the intervention period of 1 year. Measures with respect to strengthening hand hygiene and central line bundle care were implemented during the intervention period. Audits assessing the compliance to hand hygiene and CLABSI bundle protocols were used as process indicators. Multiple PDSA cycles were used to strengthen the practices of proposed interventions, documentation of data and audits of the processes during the study period. The QI initiative achieved a 89% reduction in CLABSI from the baseline rate of 31.7 to 3.5 per 1000 line-days. The blood stream Infections reduced from 7.3 to 2.3 per 1000 patient-days. The overall mortality showed a reduction from 2.9% to 1.7 % during the intervention period. There was a significant improvement in compliance with hand hygiene protocol and compliance with CLABSI protocols. This study demonstrated that simple measures involving hand hygiene and strengthening of the care bundle approach through quality improvement could significantly reduce the blood stream Infections and CLABSI rates.

The first consensus report of the working party of the Asian Pacific Association for the Study of the Liver (APASL) set up in 2004 on acute-on-chronic liver failure (ACLF) was published in 2009. With international groups volunteering to join, the “APASL ACLF Research Consortium (AARC)” was formed in 2012, which continued to collect prospective ACLF patient data. Based on the prospective data analysis of nearly 1400 patients, the AARC consensus was published in 2014. In the past nearly four-and-a-half years, the AARC database has been enriched to about 5200 cases by major hepatology centers across Asia. The data published during the interim period were carefully analyzed and areas of contention and new developments in the field of ACLF were prioritized in a systematic manner. The AARC database was also approached for answering some of the issues where published data were limited, such as liver failure grading, its impact on the ‘Golden Therapeutic Window’, extrahepatic organ dysfunction and failure, development of sepsis, distinctive features of acute decompensation from ACLF and pediatric ACLF and the issues were analyzed. These initiatives concluded in a two-day meeting in October 2018 at New Delhi with finalization of the new AARC consensus. Only those statements, which were based on evidence using the Grade System and were unanimously recommended, were accepted. Finalized statements were again circulated to all the experts and subsequently presented at the AARC investigators meeting at the AASLD in November 2018. The suggestions from the experts were used to revise and finalize the consensus. After detailed deliberations and data analysis, the original definition of ACLF was found to withstand the test of time and be able to identify a homogenous group of patients presenting with liver failure. New management options including the algorithms for the management of coagulation disorders, renal replacement therapy, sepsis, variceal bleed, antivirals and criteria for liver transplantation for ACLF patients were proposed. The final consensus statements along with the relevant background information and areas requiring future studies are presented here., Article Highlights Updated on the basis of AARC data of >3300 cases enrolled into AARC registry prospectivelyACLF is distinct form Acute Decompensation of cirrhosisNewer sections on DILI-ACLF, AIH-ACLF, PVT/HVOTO–ACLFReversibility of Chronic Liver Disease in ACLFPortal and systemic hemodynamics and their relevance in ACLFAcute Portal Hypertension and Variceal progression in ACLFAARC score as a guide for treatment strategies in ACLFACLF in Children-first consensus on pediatric ACLF

Continuous positive airway pressure (CPAP) requires nasopharyngeal suctioning for airway patency, which is painful. Other procedures have used breast milk and 25% dextrose as analgesics. We aimed to compare their analgesic efficacy during nasopharyngeal suctioning in preterm neonates on CPAP. In this blinded randomized controlled trial, babies received 25% dextrose or breast milk orally. Pain before, during and after was assessed using the Premature Infant Pain Profile (PIPP) score. Analysis was done for 40 babies. The mean PIPP score in the 25% dextrose group during the procedure was 11.25 ± 2.73 and 13.2 ± 2.55 (p = 0.02) with the intervention and without. In the breast milk group the PIPP score during the procedure was 11.35 ± 3.05 and 13.45 ± 3.27 (p = 0.04); this difference persisted even after the procedure. There was no significant difference between the interventions. Both interventions significantly reduce pain. The analgesic effect of breast milk was sustained.

Electrocorticographic brain computer interfaces (ECoG-BCIs) offer tremendous opportunities for restoring function in individuals suffering from neurological damage and for advancing basic neuroscience knowledge. ECoG electrodes are already commonly used clinically for monitoring epilepsy and have greater spatial specificity in recording neuronal activity than techniques such as electroencephalography (EEG). Much work to date in the field has focused on using ECoG signals recorded from cortex as control outputs for driving end effectors. An equally important but less explored application of an ECoG-BCI is directing input into cortex using ECoG electrodes for direct electrical stimulation (DES). Combining DES with ECoG recording enables a truly bidirectional BCI, where information is both read from and written to the brain. We discuss the advantages and opportunities, as well as the barriers and challenges presented by using DES in an ECoG-BCI. In this article, we review ECoG electrodes, the physics and physiology of DES, and the use of electrical stimulation of the brain for the clinical treatment of disorders such as epilepsy and Parkinson’s disease. We briefly discuss some of the translational, regulatory, financial, and ethical concerns regarding ECoG-BCIs. Next, we describe the use of ECoG-based DES for providing sensory feedback and for probing and modifying cortical connectivity. We explore future directions, which may draw on invasive animal studies with penetrating and surface electrodes as well as non-invasive stimulation methods such as transcranial magnetic stimulation (TMS). We conclude by describing enabling technologies, such as smaller ECoG electrodes for more precise targeting of cortical areas, signal processing strategies for simultaneous stimulation and recording, and computational modeling and algorithms for tailoring stimulation to each individual brain.

Brain-Computer Interfaces (BCIs) benefit greatly from performance feedback, but current systems lack automatic, task-independent feedback. Cortical responses elicited from user error have the potential to serve as state-based feedback to BCI decoders. To gain a better understanding of local error potentials, we investigate responsive cortical power underlying error-related potentials (ErrPs) from the human cortex during a one-dimensional center-out BCI task, tracking the topography of high-gamma (70-100 Hz) band power (HBP) specific to BCI error. We measured electrocorticography (ECoG) in three human subjects during dynamic, continuous control over BCI cursor velocity. Subjects used motor imagery and rest to move the cursor towards and subsequently dwell within a target region. We then identified and labeled epochs where the BCI decoder incorrectly moved the cursor in the direction opposite of the subject’s expectations (i.e., BCI error). We found increased HBP in various cortical areas 100-500 ms following BCI error with respect to epochs of correct, intended control. Significant responses were noted in primary somatosensory, motor, premotor, and parietal areas and generally regardless of whether the subject was using motor imagery or rest to move the cursor towards the target. Parts of somatosensory, temporal, and parietal areas exclusively had increased HBP when subjects were using motor imagery. In contrast, only part of the parietal cortex near the angular gyrus exclusively had an increase in HBP during rest. This investigation is, to our knowledge, the first to explore cortical fields changes in the context of continuous control in ECoG BCI. We present topographical changes in HBP characteristic specific to the generation of error. By focusing on continuous control, instead of on discrete control for simple selection, we investigate a more naturalistic setting and provide high ecological validity for characterizing error potentials. Such potentials could be considered as design elements for co-adaptive BCIs in the future as task-independent feedback to the decoder, allowing for more robust and individualized BCIs.

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.

Background COVID-19 is disrupting health services for mothers and newborns, particularly in low- and middle-income countries (LMIC). Preterm newborns are particularly vulnerable. We undertook analyses of the benefits of kangaroo mother care (KMC) on survival among neonates weighing ≤2000 g compared with the risk of SARS-CoV-2 acquired from infected mothers/caregivers. Methods We modelled two scenarios over 12 months. Scenario 1 compared the survival benefits of KMC with universal coverage (99%) and mortality risk due to COVID-19. Scenario 2 estimated incremental deaths from reduced coverage and complete disruption of KMC. Projections were based on the most recent data for 127 LMICs (~90% of global births), with results aggregated into five regions. Findings Our worst-case scenario (100% transmission) could result in 1,950 neonatal deaths from COVID-19. Conversely, 125,680 neonatal lives could be saved with universal KMC coverage. Hence, the benefit of KMC is 65-fold higher than the mortality risk of COVID-19. If recent evidence of 10% transmission was applied, the ratio would be 630-fold. We estimated a 50% reduction in KMC coverage could result in 12,570 incremental deaths and full disruption could result in 25,140 incremental deaths, representing a 2·3–4·6% increase in neonatal mortality across the 127 countries. Interpretation The survival benefit of KMC far outweighs the small risk of death due to COVID-19. Preterm newborns are at risk, especially in LMICs where the consequences of disruptions are substantial. Policymakers and healthcare professionals need to protect services and ensure clearer messaging to keep mothers and newborns together, even if the mother is SARS-CoV-2-positive. Funding Eunice Kennedy Shriver National Institute of Child Health & Human Development; Bill & Melinda Gates Foundation; Elma Philanthropies; Wellcome Trust; and Joint Global Health Trials scheme of Department of Health and Social Care, Department for International Development, Medical Research Council, and Wellcome Trust.

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.

Determining hand grasping forces and kinetic direction and magnitude are important for the development of dexterous neural prosthetics. While many earlier decoding methods have successfully predicted upper-limb kinematic output from cortical signals in the sensorimotor parietal and premotor regions, the full extent of the regions that characterize kinetic behavior is unknown. In this study, we found that neural dynamics based on electrocorticography (ECoG) recorded from the human brain surface can successfully encode structured and unstructured grasping and arm kinetic output. We found a time-averaged linear relationship between gamma band spectral ECoG power and sustained grasping force output with visual feedback. In the kinetic grasping task, we obtained classification accuracy of 47% (25% = chance) using quadratic discriminant analysis. Additionally, we also found a similar linear relationship between spectral power and cued isometric force generation, without concurrent visual feedback, in arm force application; this feature could also classify arm force output categories with an accuracy of 41% (33% = chance). In addition, we applied quadratic discriminant analysis with top 12 principal components to attain approximately 26% accuracy (chance is 16%) in determining arm kinetic force direction. We found that the gamma band spectral power from both experiments in posterior parietal cortex, as well as projections of high gamma variability along the top principal components, can be successfully used to predict sustained kinetic outputs and to explain both structured and unstructured force output variability. Our findings contribute to a deeper understanding of neural dynamics for fine kinetic behavior.

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.

Cutting fluids are popularly used in machining to reduce cutting forces, temperatures and thus prolong the tool life. However, cutting fluids are complex compounds containing different ingredients some of which are toxic. Constant exposure to these fluids causes severe health hazards to the workers. Further, the disposal of the cutting fluids adds to the cost due to the required chemical treatment. Hence, the concept of using minimum quantity lubrication (MQL) has come into picture. Further, to make the fluids more benign, vegetable-based compounds are being used to replace the toxic ingredients. This is done in different levels, i.e., either only the mineral oil in the fluid is replaced by vegetable oil or even the emulsifier is replaced by a vegetable alternative. The present chapter discusses MQL and vegetable-based cutting fluids for sustainable machining. Results obtained during the application of vegetable oils are presented.

Can the human brain learn to interpret inputs from a virtual world delivered directly through brain stimulation? We answer this question by describing the first demonstration of humans playing a computer game utilizing only direct brain stimulation and no other sensory inputs. The demonstration also provides the first instance of artificial sensory information, in this case depth, being delivered directly to the human brain through noninvasive methods. Our approach utilizes transcranial magnetic stimulation (TMS) of the human visual cortex to convey binary information about obstacles in a virtual maze. At certain intensities, TMS elicits visual percepts known as phosphenes, which transmits information to the subject about their current location within the maze. Using this computer-brain interface (CBI), five subjects successfully navigated an average of 92% of all the steps in a variety of virtual maze worlds. They also became more accurate in solving the task over time. These results suggest that humans can learn to utilize information delivered directly and noninvasively to their brains to solve tasks that cannot be solved using their natural senses, opening the door to human sensory augmentation and novel modes of human-computer interaction.

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)

Fully automated decoding of human activities and intentions from direct neural recordings is a tantalizing challenge in brain-computer interfacing. Most ongoing efforts have focused on training decoders on specific, stereotyped tasks in laboratory settings. Implementing brain-computer interfaces (BCIs) in natural settings requires adaptive strategies and scalable algorithms that require minimal supervision. Here we propose an unsupervised approach to decoding neural states from human brain recordings acquired in a naturalistic context. We demonstrate our approach on continuous long-term electrocorticographic (ECoG) data recorded over many days from the brain surface of subjects in a hospital room, with simultaneous audio and video recordings. We first discovered clusters in high-dimensional ECoG recordings and then annotated coherent clusters using speech and movement labels extracted automatically from audio and video recordings. To our knowledge, this represents the first time techniques from computer vision and speech processing have been used for natural ECoG decoding. Our results show that our unsupervised approach can discover distinct behaviors from ECoG data, including moving, speaking and resting. We verify the accuracy of our approach by comparing to manual annotations. Projecting the discovered cluster centers back onto the brain, this technique opens the door to automated functional brain mapping in natural settings.

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.

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.

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.

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.

The responses of neurons in cortical areas V2 and V4 can be significantly modulated by attention to particular locations within an input image. We show that such effects emerge naturally when perception is viewed as a probabilistic inference process governed by Bayesian principles and implemented in hierarchical cortical networks. The proposed model can explain a rich variety of attention-related responses in cortical area V4 including multiplicative modulation of tuning curves, restoration of neural responses in the presence of distracting stimuli, and influence of attention on neighboring unattended locations. Our results suggest a new interpretation of attention as a cortical mechanism for reducing perceptual uncertainty by combining top-down task-relevant information with bottom-up sensory inputs in a probabilistic manner.

Complex cells in primary visual cortex exhibit highly nonlinear receptive field properties such as phase-invariant direction selectivity and antagonistic interactions between individually excitatory stimuli. Traditional models assume that these properties are governed by the outputs of antecedent simple cells, but these models are at odds with studies showing that complex cells may receive direct inputs from the lateral geniculate nucleus (LGN) or can be driven by stimuli that fail to activate simple cells. Using a biophysically detailed model of recurrently connected cortical neurons, we show that complex cell-like direction selectivity may emerge without antecedent simple cell inputs, as a consequence of spike-timing dependent synaptic plasticity during visual development. The directionally-selective receptive fields of model neurons, as determined by reverse correlation and 2-bar interaction maps, were similar to those obtained from complex cells in awake monkey primary visual cortex. These results suggest a new interpretation of complex cells as integral components of an adaptive cortical circuit for motion detection and prediction.

Human visual processing is of such complexity that, despite decades of focused research, many basic questions remain unanswered. Although we know that the inferotemporal cortex is a key region in object recognition, we don’t fully understand its physiologic role in brain function, nor do we have the full set of tools to explore this question. Here we show that electrical potentials from the surface of the human brain contain enough information to decode a subject’s perceptual state accurately, and with fine temporal precision. Electrocorticographic (ECoG) arrays were placed over the inferotemporal cortical areas of seven subjects. Pictures of faces and houses were quickly presented while each subject performed a simple visual task. Results showed that two well-known types of brain signals—event-averaged broadband power and event-averaged raw potential—can independently or together be used to classify the presented image. When applied to continuously recorded brain activity, our decoding technique could accurately predict whether each stimulus was a face, house, or neither, with ~20 ms timing error. These results provide a roadmap for improved brain-computer interfacing tools to help neurosurgeons, research scientists, engineers, and, ultimately, patients.

High gamma oscillations (70–150 Hz; HG) are rapidly evolving, spatially localized neurophysiological signals that are believed to be the best representative signature of engaged neural populations. The HG band has been best characterized from invasive electrophysiological approaches such as electrocorticography because of the increased signal-to-noise ratio that results when by-passing the scalp and skull. Despite the recent observation that HG activity can be detected non-invasively by electroencephalography (EEG), it is unclear to what extent EEG can accurately resolve the spatial distribution of HG signals during active task engagement. We have overcome some of the limitations inherent to acquiring HG signals across the scalp by utilizing individual head anatomy in combination with an inverse modeling method. We applied a linearly constrained minimum variance (LCMV) beamformer method on EEG data during a motor imagery paradigm to extract a time-frequency spectrogram at every voxel location on the cortex. To confirm spatially distributed patterns of HG responses, we contrasted overlapping maps of the EEG HG signal with blood oxygen level dependence (BOLD) functional magnetic resonance imaging (fMRI) data acquired from the same set of neurologically normal subjects during a separate session. We show that scalp-based HG band activity detected by EEG during motor imagery spatially co-localizes with BOLD fMRI data. Taken together, these results suggest that EEG can accurately resolve spatially specific estimates of local cortical high frequency signals, potentially opening an avenue for non-invasive measurement of HG potentials from diverse sets of neurologically impaired populations for diagnostic and therapeutic purposes.