Integrated human-machine interface for closed-loop stimulation using implanted and wearable devices

Recent development in implantable devices for electrical brain stimulation includes sensing and embedded computing capabilities that enable adaptive stimulation strategies. Applications include stimulation triggered by pathologic brain activity and endogenous rhythms, such as circadian rhythms. We developed and tested a system that integrates an electrical brain stimulation & sensing implantable device with embedded computing and uses a distributed system with commercial electronics, smartphone and smartwatch for patient annotations, extensive behavioral testing, and adaptive stimulation in subjects in their natural environments. The system enables precise time synchronization of the external components with the brain stimulating device and is coupled with automated analysis of continuous streaming electrophysiology synchronized with patient reports. The system leverages a real-time bi-directional interface between devices and patients with epilepsy living in their natural environment

Distributed brain co-processor for tracking spikes, seizures and behaviour during electrical brain stimulation

Early implantable epilepsy therapy devices provided open-loop electrical stimulation without brain sensing, computing, or an interface for synchronized behavioural inputs from patients. Recent epilepsy stimulation devices provide brain sensing but have not yet developed analytics for accurately tracking and quantifying behaviour and seizures. Here we describe a distributed brain co-processor providing an intuitive bi-directional interface between patient, implanted neural stimulation and sensing device, and local and distributed computing resources. Automated analysis of continuous streaming electrophysiology is synchronized with patient reports using a handheld device and integrated with distributed cloud computing resources for quantifying seizures, interictal epileptiform spikes and patient symptoms during therapeutic electrical brain stimulation. The classification algorithms for interictal epileptiform spikes and seizures were developed and parameterized using long-term ambulatory data from nine humans and eight canines with epilepsy, and then implemented prospectively in out-of-sample testing in two pet canines and four humans with drug-resistant epilepsy living in their natural environments. Accurate seizure diaries are needed as the primary clinical outcome measure of epilepsy therapy and to guide brain-stimulation optimization. The brain co-processor system described here enables tracking interictal epileptiform spikes, seizures and correlation with patient behavioural reports. In the future, correlation of spikes and seizures with behaviour will allow more detailed investigation of the clinical impact of spikes and seizures on patients

Anesthesia of Epinephelus marginatus with essential oil of Aloysia polystachya: an approach on blood parameters

This study investigated the anesthetic potential of the essential oil (EO) of Aloysia polystachya in juveniles of dusky grouper (Epinephelus marginatus). Fish were exposed to different concentrations of EO of A. polystachya to evaluate time of induction and recovery from anesthesia. In the second experiment, fish were divided into four groups: control, ethanol and 50 or 300 mu L L-1 EO of A. polystachya, and each group was submitted to induction for 3.5 min and recovery for 5 or 10 min. The blood gases and glucose levels showed alterations as a function of the recovery times, but Na+ and K+ levels did not show any alteration. In conclusion, the EO from leaves of A. polystachya is an effective anesthetic for dusky grouper, because anesthesia was reached within the recommended time at EO concentrations of 300 and 400 mu L L-1. However, most evaluated blood parameters showed compensatory responses due to EO exposure.Fundacao de Amparo a Pesquisa do Estado do Rio Grande do Sul/Programa de Apoio a Nucleos de Excelencia (FAPERGS/PRONEX) [10/0016-8]; Conselho Nacional de Desenvolvimento Cientifico e Tecnologico (CNPq) [470964/2009-0]; Coordenacao de Aperfeicoamento de Pessoal de Nivel Superior, Brazil (CAPES)info:eu-repo/semantics/publishedVersio

Visual imagery and false memory for pictures:a functional magnetic resonance imaging study in healthy participants

BACKGROUND: Visual mental imagery might be critical in the ability to discriminate imagined from perceived pictures. Our aim was to investigate the neural bases of this specific type of reality-monitoring process in individuals with high visual imagery abilities. METHODS: A reality-monitoring task was administered to twenty-six healthy participants using functional magnetic resonance imaging. During the encoding phase, 45 words designating common items, and 45 pictures of other common items, were presented in random order. During the recall phase, participants were required to remember whether a picture of the item had been presented, or only a word. Two subgroups of participants with a propensity for high vs. low visual imagery were contrasted. RESULTS: Activation of the amygdala, left inferior occipital gyrus, insula, and precuneus were observed when high visual imagers encoded words later remembered as pictures. At the recall phase, these same participants activated the middle frontal gyrus and inferior and superior parietal lobes when erroneously remembering pictures. CONCLUSIONS: The formation of visual mental images might activate visual brain areas as well as structures involved in emotional processing. High visual imagers demonstrate increased activation of a fronto-parietal source-monitoring network that enables distinction between imagined and perceived pictures

Characterizing individual differences in functional connectivity using dual-regression and seed-based approaches

A central challenge for neuroscience lies in relating inter-individual variability to the functional properties of specific brain regions. Yet, considerable variability exists in the connectivity patterns between different brain areas, potentially producing reliable group differences. Using sex differences as a motivating example, we examined two separate resting-state datasets comprising a total of 188 human participants. Both datasets were decomposed into resting-state networks (RSNs) using a probabilistic spatial independent component analysis (ICA). We estimated voxel-wise functional connectivity with these networks using a dual-regression analysis, which characterizes the participant-level spatiotemporal dynamics of each network while controlling for (via multiple regression) the influence of other networks and sources of variability. We found that males and females exhibit distinct patterns of connectivity with multiple RSNs, including both visual and auditory networks and the right frontal–parietal network. These results replicated across both datasets and were not explained by differences in head motion, data quality, brain volume, cortisol levels, or testosterone levels. Importantly, we also demonstrate that dual-regression functional connectivity is better at detecting inter-individual variability than traditional seed-based functional connectivity approaches. Our findings characterize robust—yet frequently ignored—neural differences between males and females, pointing to the necessity of controlling for sex in neuroscience studies of individual differences. Moreover, our results highlight the importance of employing network-based models to study variability in functional connectivity

Sc-Seq of polyploid hepatocytes reveal increasing CNV with the basal ploidy state

Single-cell whole genome sequencing of wt and caspase-2 deficient murine hepatocytes before and after regeneration reveals increasing levels of copy number variation with basal ploidy. Polyploid hepatocytes from wt and caspase-2 deficient mice were analyzed using single-cell whole genome sequencing. We addressed the question whether caspase-2, the polyploid state or the liver regeneration process impact on the degree of aneuploidy in the liver. We sequenced the ploidy states of 4c, 8c and 16c nuclei isolated form livers of these mice before and 7 days after 2/3 partial hepatectomy (PH). The data was analyzed using the R package AneuFinder (developer version 1.10.1) to call copy number variations (Bakker et al., 2016, van den Bos et al., 2019). The following settings for AneuFinder were used: Fixed ground ploidy for the respective ploidy state of the sample, variable bins (average size of 2Mb), 500kb step size, significance level 250kb, spikiness >= 0.11. The data deposited here includes all single profile plots of the sequenced and analyzed nuclei before and after curation based on spikiness and read count

Sc-Seq of polyploid hepatocytes reveal increasing CNV with the basal ploidy state

Single-cell whole genome sequencing of wt and caspase-2 deficient murine hepatocytes before and after regeneration reveals increasing levels of copy number variation with basal ploidy. Polyploid hepatocytes from wt and caspase-2 deficient mice were analyzed using single-cell whole genome sequencing. We addressed the question whether caspase-2, the polyploid state or the liver regeneration process impact on the degree of aneuploidy in the liver. We sequenced the ploidy states of 4c, 8c and 16c nuclei isolated form livers of these mice before and 7 days after 2/3 partial hepatectomy (PH). The data was analyzed using the R package AneuFinder (developer version 1.10.1) to call copy number variations (Bakker et al., 2016, van den Bos et al., 2019). The following settings for AneuFinder were used: Fixed ground ploidy for the respective ploidy state of the sample, variable bins (average size of 2Mb), 500kb step size, significance level 250kb, spikiness >= 0.11. The data deposited here includes all single profile plots of the sequenced and analyzed nuclei before and after curation based on spikiness and read count

Deep generative networks for algorithm development in implantable neural technology

Electrical stimulation of deep brain structures is an established therapy for drug-resistant focal epilepsy. The emerging implantable neural sensing and stimulating (INSS) technology enables simultaneous delivery of chronic deep brain stimulation (DBS) and recording of electrical brain activity from deep brain structures while patients live in their home environment. Long-term intracranial electroencephalography (iEEG) iEEG signals recorded by INSS devices represent an opportunity to investigate brain neurophysiology and how DBS affects neural circuits. However, novel algorithms and data processing pipelines need to be developed to facilitate research of these long-term iEEG signals. Early-stage analytical infrastructure development for INSS applications can be limited by lacking iEEG data that might not always be available. Here, we investigate the feasibility of utilizing the Deep Generative Adversarial Network (DCGAN) for synthetic iEEG data generation. We trained DCGAN using 3-second iEEG segments and validated synthetic iEEG usability by training a classification model, using synthetic iEEG only and providing a good classification performance on unseen real iEEG with an F1 score 0.849. Subsequently, we demonstrated the feasibility of utilizing the synthetic iEEG in the INSS application development by training a deep learning network for DBS artifact removal using synthetic data only and demonstrated the performance on real iEEG signals. The presented strategy of on-demand generating synthetic iEEG will benefit early-stage algorithm development for INSS applications

Valence-Dependent Coupling of Prefrontal-Amygdala Effective Connectivity during Facial Affect Processing

Despite the importance of the prefrontal-amygdala (AMY) network for emotion processing, valence-dependent coupling within this network remains elusive. In this study, we assessed the effect of emotional valence on brain activity and effective connectivity. We tested which functional pathways within the prefrontal-AMY network are specifically engaged during the processing of emotional valence. Thirty-three healthy adults were examined with functional magnetic resonance imaging while performing a dynamic faces and dynamic shapes matching task. The valence of the facial expressions varied systematically between positive, negative, and neutral across the task. Functional contrasts determined core areas of the emotion processing circuitry, comprising the medial prefrontal cortex (MPFC), the right lateral prefrontal cortex (LPFC), the AMY, and the right fusiform face area (FFA). Dynamic causal modelling demonstrated that the bidirectional coupling within the prefrontal-AMY circuitry is modulated by emotional valence. Additionally, Bayesian model averaging showed significant bottom-up connectivity from the AMY to the MPFC during negative and neutral, but not positive, valence. Thus, our study provides strong evidence for alterations of bottom-up coupling within the prefrontal-AMY network as a function of emotional valence. Thereby our results not only advance the understanding of the human prefrontal-AMY circuitry in varying valence context, but, moreover, provide a model to examine mechanisms of valence-sensitive emotional dysregulation in neuropsychiatric disorders