Cannabinoid signaling and risk in Huntington's disease.

Dysregulated endocannabinoid (eCB) signaling and the loss of cannabinoid receptors (CB1Rs) are important phenotypes of Huntington's disease (HD) but the precise contribution that eCB signaling has at the circuit level is unknown. Using a computational model of spiking neurons, synapses, and eCB signaling, we demonstrate that eCB signaling functions as a homeostatic control mechanism, minimizing excess glutamate. Furthermore, our model demonstrates that metabolic risk, quantified by excess glutamate, increases with cortico-striatal long-term depression (LTD) and/or increased cortico-striatal activity, and replicates a progressive loss of cannabinoid receptors on inhibitory terminals as a function of the excitatory/inhibitory ratio.

Commotio Cordis Resulting from Sibling Rivalry.

A 17-year-old girl with no significant medical, surgical, or family history presented to the emergency department following an episode of sudden cardiac arrest after being punched in the chest by her brother. Bystander CPR was immediately initiated, and she was found to be in ventricular fibrillation by emergency services. The patient had return of spontaneous circulation after one defibrillation event. No other significant injuries were found, and she eventually experienced a complete neurologic recovery and was discharged with no other identified cause for her arrest. The objective of this clinical case report is to highlight this unusual and rare injury to increase awareness and avoid incorrect diagnosis.

Feasibility of bystander-administered naloxone delivered by drone to opioid overdose victims.

BACKGROUND: Currently, ≤5% of bystanders witnessing an opioid overdose (OD) in the US administer antidote to the victim. A possible model to mitigate this crisis would be a system that enables 9-1-1 dispatchers to both rapidly deliver naloxone by drone to bystanders at a suspected opioid OD and direct them to administer it while awaiting EMS arrival. METHODS: A simulated 9-1-1 dispatcher directed thirty subjects via 2-way radio to retrieve naloxone nasal spray from atop a drone located outside the simulation building and then administer it using scripted instructions. The primary outcome measure was time from first contact with the dispatcher to administration of the medication. RESULTS: All subjects administered the medication successfully. The mean time interval from 9 -1-1 contact until antidote administration was 122 [95%CI 109-134] sec. There was a significant reduction in time interval if subjects had prior medical training (p = 0.045) or had prior experience with use of a nasal spray device (p = 0.030). Five subjects had difficulty using the nasal spray and four subjects had minor physical impairments, but these barriers did not result in a significant difference in time to administration (p = 0.467, p = 0.30). A significant number of subjects (29/30 [97%], p = 0.044) indicated that they felt confident they could administer intranasal naloxone to an opioid OD victim after participating in the simulation. CONCLUSIONS: Our results suggest that bystanders can carry out 9-1-1 dispatcher instructions to fetch drone-delivered naloxone and potentially decrease the time interval to intranasal administration which supports further development and testing of a such a system.

Intrinsic Spine Dynamics Are Critical for Recurrent Network Learning in Models With and Without Autism Spectrum Disorder.

It is often assumed that Hebbian synaptic plasticity forms a cell assembly, a mutually interacting group of neurons that encodes memory. However, in recurrently connected networks with pure Hebbian plasticity, cell assemblies typically diverge or fade under ongoing changes of synaptic strength. Previously assumed mechanisms that stabilize cell assemblies do not robustly reproduce the experimentally reported unimodal and long-tailed distribution of synaptic strengths. Here, we show that augmenting Hebbian plasticity with experimentally observed intrinsic spine dynamics can stabilize cell assemblies and reproduce the distribution of synaptic strengths. Moreover, we posit that strong intrinsic spine dynamics impair learning performance. Our theory explains how excessively strong spine dynamics, experimentally observed in several animal models of autism spectrum disorder, impair learning associations in the brain.

Cortico-Striatal Cross-Frequency Coupling and Gamma Genesis Disruptions in Huntington's Disease Mouse and Computational Models.

Abnormal gamma band power across cortex and striatum is an important phenotype of Huntington's disease (HD) in both patients and animal models, but neither the origin nor the functional relevance of this phenotype is well understood. Here, we analyzed local field potential (LFP) activity in freely behaving, symptomatic R6/2 and Q175 mouse models and corresponding wild-type (WT) controls. We focused on periods of quiet rest, which show strong γ activity in HD mice. Simultaneous recording from motor cortex and its target area in dorsal striatum in the R6/2 model revealed exaggerated functional coupling over that observed in WT between the phase of delta frequencies (1-4 Hz) in cortex and striatum and striatal amplitude modulation of low γ frequencies (25-55 Hz; i.e., phase-amplitude coupling, PAC), but no evidence that abnormal cortical activity alone can account for the increase in striatal γ power. Both HD mouse models had stronger coupling of γ amplitude to δ phase and more unimodal phase distributions than their WT counterparts. To assess the possible role of striatal fast-spiking interneurons (FSIs) in these phenomena, we developed a computational model based on additional striatal recordings from Q175 mice. Changes in peak γ frequency and power ratio were readily reproduced by our computational model, accounting for several experimental findings reported in the literature. Our results suggest that HD is characterized by both a reorganization of cortico-striatal drive and specific population changes related to intrastriatal synaptic coupling.

Use of and interest in mobile health for diabetes self-care in vulnerable populations.

OBJECTIVE: We aimed to assess use of and interest in mobile health (mHealth) technology and in-person services for diabetes self-care in vulnerable populations. METHODS: We delivered a self-administered cross-sectional survey. Participants were recruited at two primary care practices (P1 and P2) with P1 located in a medically underserved area and P2 in an affluent suburb. Two-sample t-tests and chi-square tests were used with p < 0.05 significant. In addition, a secondary analysis was performed to analyse differences in use and interest in mHealth by age. RESULTS: Of 75 eligible patients, 60 completed the survey (80% response rate). P1 patients had significantly higher interest in three of five categories of in-person diabetes support services, one of four categories of health-related text messages (TM), and three of eight categories of mHealth applications (p < 0.05). Smartphone users reported higher interest in TM (p = 0.004) and mHealth applications for diabetes self-care (p = 0.004). Younger patients were more likely to have a smartphone (p < 0.006), use the Internet (p < 0.0012), use smartphone applications (p < 0.0004), and to be interested in using applications to manage their diabetes (p < 0.004). DISCUSSION: This study shows substantial patient interest in TM and mHealth applications for diabetes self-care and suggests that patients in underserved areas may have particularly high interest in using mHealth solutions in primary care. Younger patients and smartphone users were more likely to be interested in using applications to manage their diabetes. As more patients use smartphones, interest in using mHealth to support patient self-care and strengthen primary care infrastructure will continue to grow.

Spatio-temporal pattern recognizers using spiking neurons and spike-timing-dependent plasticity.

It has previously been shown that by using spike-timing-dependent plasticity (STDP), neurons can adapt to the beginning of a repeating spatio-temporal firing pattern in their input. In the present work, we demonstrate that this mechanism can be extended to train recognizers for longer spatio-temporal input signals. Using a number of neurons that are mutually connected by plastic synapses and subject to a global winner-takes-all mechanism, chains of neurons can form where each neuron is selective to a different segment of a repeating input pattern, and the neurons are feed-forwardly connected in such a way that both the correct input segment and the firing of the previous neurons are required in order to activate the next neuron in the chain. This is akin to a simple class of finite state automata. We show that nearest-neighbor STDP (where only the pre-synaptic spike most recent to a post-synaptic one is considered) leads to "nearest-neighbor" chains where connections only form between subsequent states in a chain (similar to classic "synfire chains"). In contrast, "all-to-all spike-timing-dependent plasticity" (where all pre- and post-synaptic spike pairs matter) leads to multiple connections that can span several temporal stages in the chain; these connections respect the temporal order of the neurons. It is also demonstrated that previously learnt individual chains can be "stitched together" by repeatedly presenting them in a fixed order. This way longer sequence recognizers can be formed, and potentially also nested structures. Robustness of recognition with respect to speed variations in the input patterns is shown to depend on rise-times of post-synaptic potentials and the membrane noise. It is argued that the memory capacity of the model is high, but could theoretically be increased using sparse codes.

Modeling peripheral olfactory coding in Drosophila larvae.

The Drosophila larva possesses just 21 unique and identifiable pairs of olfactory sensory neurons (OSNs), enabling investigation of the contribution of individual OSN classes to the peripheral olfactory code. We combined electrophysiological and computational modeling to explore the nature of the peripheral olfactory code in situ. We recorded firing responses of 19/21 OSNs to a panel of 19 odors. This was achieved by creating larvae expressing just one functioning class of odorant receptor, and hence OSN. Odor response profiles of each OSN class were highly specific and unique. However many OSN-odor pairs yielded variable responses, some of which were statistically indistinguishable from background activity. We used these electrophysiological data, incorporating both responses and spontaneous firing activity, to develop a bayesian decoding model of olfactory processing. The model was able to accurately predict odor identity from raw OSN responses; prediction accuracy ranged from 12%-77% (mean for all odors 45.2%) but was always significantly above chance (5.6%). However, there was no correlation between prediction accuracy for a given odor and the strength of responses of wild-type larvae to the same odor in a behavioral assay. We also used the model to predict the ability of the code to discriminate between pairs of odors. Some of these predictions were supported in a behavioral discrimination (masking) assay but others were not. We conclude that our model of the peripheral code represents basic features of odor detection and discrimination, yielding insights into the information available to higher processing structures in the brain.

STDP pattern onset learning depends on background activity.

Spike-timing dependent plasticity is a learning mechanism used extensively within neural modelling. The learning rule has previously been shown to allow a neuron to learn a repeated spatio-temporal pattern among its afferents and respond at its onset. In this study we reconfirm these previous results and additionally adduce that such learning is dependent on background activity. Furthermore, we found that the onset learning is unstable when in a noisy framework. Specifically, if the level of background activity changes during learning the response latency of a neuron may increase and with the adding of additional noise the distribution of response latencies degrades. Consequently, we present preliminary insights into the neuron's encoding: viz. that a neuron may encode the coincidence of spikes from a subsection of a stimulus' afferents, but the temporal precision of the onset response depends on some background activity, which must be similar to that present during learning.