Drug-eluting balloon versus standard balloon angioplasty for infrapopliteal arterial revascularization in critical limb ischemia: 12-month results from the IN.PACT DEEP randomized trial.

BACKGROUND Drug-eluting balloons (DEB) may reduce infrapopliteal restenosis and reintervention rates versus percutaneous transluminal angioplasty (PTA) and improve wound healing/limb preservation. OBJECTIVES The goal of this clinical trial was to assess the efficacy and safety of IN.PACT Amphirion drug-eluting balloons (IA-DEB) compared to PTA for infrapopliteal arterial revascularization in patients with critical limb ischemia (CLI). METHODS Within a prospective, multicenter, randomized, controlled trial with independent clinical event adjudication and angiographic and wound core laboratories 358 CLI patients were randomized 2:1 to IA-DEB or PTA. The 2 coprimary efficacy endpoints through 12 months were clinically driven target lesion revascularization (CD-TLR) and late lumen loss (LLL). The primary safety endpoint through 6 months was a composite of all-cause mortality, major amputation, and CD-TLR. RESULTS Clinical characteristics were similar between the 2 groups. Significant baseline differences between the IA-DEB and PTA arms included mean lesion length (10.2 cm vs. 12.9 cm; p = 0.002), impaired inflow (40.7% vs. 28.8%; p = 0.035), and previous target limb revascularization (32.2% vs. 21.8%; p = 0.047). Primary efficacy results of IA-DEB versus PTA were CD-TLR of 9.2% versus 13.1% (p = 0.291) and LLL of 0.61 ± 0.78 mm versus 0.62 ± 0.78 mm (p = 0.950). Primary safety endpoints were 17.7% versus 15.8% (p = 0.021) and met the noninferiority hypothesis. A safety signal driven by major amputations through 12 months was observed in the IA-DEB arm versus the PTA arm (8.8% vs. 3.6%; p = 0.080). CONCLUSIONS In patients with CLI, IA-DEB had comparable efficacy to PTA. While primary safety was met, there was a trend towards an increased major amputation rate through 12 months compared to PTA. (Study of IN.PACT Amphirion™ Drug Eluting Balloon vs. Standard PTA for the Treatment of Below the Knee Critical Limb Ischemia [INPACT-DEEP]; NCT00941733).

Learning Spike-Based Population Codes by Reward and Population Feedback

We investigate a recently proposed model for decision learning in a population of spiking neurons where synaptic plasticity is modulated by a population signal in addition to reward feedback. For the basic model, binary population decision making based on spike/no-spike coding, a detailed computational analysis is given about how learning performance depends on population size and task complexity. Next, we extend the basic model to n-ary decision making and show that it can also be used in conjunction with other population codes such as rate or even latency coding.

Spatio-temporal credit assignment in population learning

Learning by reinforcement is important in shaping animal behavior, and in particular in behavioral decision making. Such decision making is likely to involve the integration of many synaptic events in space and time. However, using a single reinforcement signal to modulate synaptic plasticity, as suggested in classical reinforcement learning algorithms, a twofold problem arises. Different synapses will have contributed differently to the behavioral decision, and even for one and the same synapse, releases at different times may have had different effects. Here we present a plasticity rule which solves this spatio-temporal credit assignment problem in a population of spiking neurons. The learning rule is spike-time dependent and maximizes the expected reward by following its stochastic gradient. Synaptic plasticity is modulated not only by the reward, but also by a population feedback signal. While this additional signal solves the spatial component of the problem, the temporal one is solved by means of synaptic eligibility traces. In contrast to temporal difference (TD) based approaches to reinforcement learning, our rule is explicit with regard to the assumed biophysical mechanisms. Neurotransmitter concentrations determine plasticity and learning occurs fully online. Further, it works even if the task to be learned is non-Markovian, i.e. when reinforcement is not determined by the current state of the system but may also depend on past events. The performance of the model is assessed by studying three non-Markovian tasks. In the first task, the reward is delayed beyond the last action with non-related stimuli and actions appearing in between. The second task involves an action sequence which is itself extended in time and reward is only delivered at the last action, as it is the case in any type of board-game. The third task is the inspection game that has been studied in neuroeconomics, where an inspector tries to prevent a worker from shirking. Applying our algorithm to this game yields a learning behavior which is consistent with behavioral data from humans and monkeys, revealing themselves properties of a mixed Nash equilibrium. The examples show that our neuronal implementation of reward based learning copes with delayed and stochastic reward delivery, and also with the learning of mixed strategies in two-opponent games.

Spatio-temporal credit assignment in population learning

Learning by reinforcement is important in shaping animal behavior. But behavioral decision making is likely to involve the integration of many synaptic events in space and time. So in using a single reinforcement signal to modulate synaptic plasticity a twofold problem arises. Different synapses will have contributed differently to the behavioral decision and, even for one and the same synapse, releases at different times may have had different effects. Here we present a plasticity rule which solves this spatio-temporal credit assignment problem in a population of spiking neurons. The learning rule is spike time dependent and maximizes the expected reward by following its stochastic gradient. Synaptic plasticity is modulated not only by the reward but by a population feedback signal as well. While this additional signal solves the spatial component of the problem, the temporal one is solved by means of synaptic eligibility traces. In contrast to temporal difference based approaches to reinforcement learning, our rule is explicit with regard to the assumed biophysical mechanisms. Neurotransmitter concentrations determine plasticity and learning occurs fully online. Further, it works even if the task to be learned is non-Markovian, i.e. when reinforcement is not determined by the current state of the system but may also depend on past events. The performance of the model is assessed by studying three non-Markovian tasks. In the first task the reward is delayed beyond the last action with non-related stimuli and actions appearing in between. The second one involves an action sequence which is itself extended in time and reward is only delivered at the last action, as is the case in any type of board-game. The third is the inspection game that has been studied in neuroeconomics. It only has a mixed Nash equilibrium and exemplifies that the model also copes with stochastic reward delivery and the learning of mixed strategies.

Reinforcement learning in dendritic structures

The discovery of binary dendritic events such as local NMDA spikes in dendritic subbranches led to the suggestion that dendritic trees could be computationally equivalent to a 2-layer network of point neurons, with a single output unit represented by the soma, and input units represented by the dendritic branches. Although this interpretation endows a neuron with a high computational power, it is functionally not clear why nature would have preferred the dendritic solution with a single but complex neuron, as opposed to the network solution with many but simple units. We show that the dendritic solution has a distinguished advantage over the network solution when considering different learning tasks. Its key property is that the dendritic branches receive an immediate feedback from the somatic output spike, while in the corresponding network architecture the feedback would require additional backpropagating connections to the input units. Assuming a reinforcement learning scenario we formally derive a learning rule for the synaptic contacts on the individual dendritic trees which depends on the presynaptic activity, the local NMDA spikes, the somatic action potential, and a delayed reinforcement signal. We test the model for two scenarios: the learning of binary classifications and of precise spike timings. We show that the immediate feedback represented by the backpropagating action potential supplies the individual dendritic branches with enough information to efficiently adapt their synapses and to speed up the learning process.

Reinforcement learning in dendritic structures

The discovery of binary dendritic events such as local NMDA spikes in dendritic subbranches led to the suggestion that dendritic trees could be computationally equivalent to a 2-layer network of point neurons, with a single output unit represented by the soma, and input units represented by the dendritic branches. Although this interpretation endows a neuron with a high computational power, it is functionally not clear why nature would have preferred the dendritic solution with a single but complex neuron, as opposed to the network solution with many but simple units. We show that the dendritic solution has a distinguished advantage over the network solution when considering different learning tasks. Its key property is that the dendritic branches receive an immediate feedback from the somatic output spike, while in the corresponding network architecture the feedback would require additional backpropagating connections to the input units. Assuming a reinforcement learning scenario we formally derive a learning rule for the synaptic contacts on the individual dendritic trees which depends on the presynaptic activity, the local NMDA spikes, the somatic action potential, and a delayed reinforcement signal. We test the model for two scenarios: the learning of binary classifications and of precise spike timings. We show that the immediate feedback represented by the backpropagating action potential supplies the individual dendritic branches with enough information to efficiently adapt their synapses and to speed up the learning process.

Dopamine-related deficit in reward learning after catecholamine depletion in unmedicated, remitted subjects with bulimia nervosa

Disturbances in reward processing have been implicated in bulimia nervosa (BN). Abnormalities in processing reward-related stimuli might be linked to dysfunctions of the catecholaminergic neurotransmitter system, but findings have been inconclusive. A powerful way to investigate the relationship between catecholaminergic function and behavior is to examine behavioral changes in response to experimental catecholamine depletion (CD). The purpose of this study was to uncover putative catecholaminergic dysfunction in remitted subjects with BN who performed a reinforcement-learning task after CD. CD was achieved by oral alpha-methyl-para-tyrosine (AMPT) in 19 unmedicated female subjects with remitted BN (rBN) and 28 demographically matched healthy female controls (HC). Sham depletion administered identical capsules containing diphenhydramine. The study design consisted of a randomized, double-blind, placebo-controlled crossover, single-site experimental trial. The main outcome measures were reward learning in a probabilistic reward task analyzed using signal-detection theory. Secondary outcome measures included self-report assessments, including the Eating Disorder Examination-Questionnaire. Relative to healthy controls, rBN subjects were characterized by blunted reward learning in the AMPT-but not in placebo-condition. Highlighting the specificity of these findings, groups did not differ in their ability to perceptually distinguish between stimuli. Increased CD-induced anhedonic (but not eating disorder) symptoms were associated with a reduced response bias toward a more frequently rewarded stimulus. In conclusion, under CD, rBN subjects showed reduced reward learning compared with healthy control subjects. These deficits uncover disturbance of the central reward processing systems in rBN related to altered brain catecholamine levels, which might reflect a trait-like deficit increasing vulnerability to BN.

Gradient estimation in dendritic reinforcement learning

We study synaptic plasticity in a complex neuronal cell model where NMDA-spikes can arise in certain dendritic zones. In the context of reinforcement learning, two kinds of plasticity rules are derived, zone reinforcement (ZR) and cell reinforcement (CR), which both optimize the expected reward by stochastic gradient ascent. For ZR, the synaptic plasticity response to the external reward signal is modulated exclusively by quantities which are local to the NMDA-spike initiation zone in which the synapse is situated. CR, in addition, uses nonlocal feedback from the soma of the cell, provided by mechanisms such as the backpropagating action potential. Simulation results show that, compared to ZR, the use of nonlocal feedback in CR can drastically enhance learning performance. We suggest that the availability of nonlocal feedback for learning is a key advantage of complex neurons over networks of simple point neurons, which have previously been found to be largely equivalent with regard to computational capability.

Learning real-world stimuli in a neural network with spike-driven synaptic dynamics

We present a model of spike-driven synaptic plasticity inspired by experimental observations and motivated by the desire to build an electronic hardware device that can learn to classify complex stimuli in a semisupervised fashion. During training, patterns of activity are sequentially imposed on the input neurons, and an additional instructor signal drives the output neurons toward the desired activity. The network is made of integrate-and-fire neurons with constant leak and a floor. The synapses are bistable, and they are modified by the arrival of presynaptic spikes. The sign of the change is determined by both the depolarization and the state of a variable that integrates the postsynaptic action potentials. Following the training phase, the instructor signal is removed, and the output neurons are driven purely by the activity of the input neurons weighted by the plastic synapses. In the absence of stimulation, the synapses preserve their internal state indefinitely. Memories are also very robust to the disruptive action of spontaneous activity. A network of 2000 input neurons is shown to be able to classify correctly a large number (thousands) of highly overlapping patterns (300 classes of preprocessed Latex characters, 30 patterns per class, and a subset of the NIST characters data set) and to generalize with performances that are better than or comparable to those of artificial neural networks. Finally we show that the synaptic dynamics is compatible with many of the experimental observations on the induction of long-term modifications (spike-timing-dependent plasticity and its dependence on both the postsynaptic depolarization and the frequency of pre- and postsynaptic neurons).

Learning by the Dendritic Prediction of Somatic Spiking

Recent modeling of spike-timing-dependent plasticity indicates that plasticity involves as a third factor a local dendritic potential, besides pre- and postsynaptic firing times. We present a simple compartmental neuron model together with a non-Hebbian, biologically plausible learning rule for dendritic synapses where plasticity is modulated by these three factors. In functional terms, the rule seeks to minimize discrepancies between somatic firings and a local dendritic potential. Such prediction errors can arise in our model from stochastic fluctuations as well as from synaptic input, which directly targets the soma. Depending on the nature of this direct input, our plasticity rule subserves supervised or unsupervised learning. When a reward signal modulates the learning rate, reinforcement learning results. Hence a single plasticity rule supports diverse learning paradigms.

Learning to drive: learners' self-reported cognitive failure level predicts driving instructor's observation rating of driving performance

OBJECTIVES Evidence increases that cognitive failure may be used to screen for drivers at risk. Until now, most studies have relied on driving learners. This exploratory pilot study examines self-report of cognitive failure in driving beginners and error during real driving as observed by driving instructors. METHODS Forty-two driving learners of 14 driving instructors filled out a work-related cognitive failure questionnaire. Driving instructors observed driving errors during the next driving lesson. In multiple linear regression analysis, driving errors were regressed on cognitive failure with the number of driving lessons as an estimator of driving experience controlled. RESULTS Higher cognitive failure predicted more driving errors (p < .01) when age, gender and driving experience were controlled in analysis. CONCLUSIONS Cognitive failure was significantly associated with observed driving errors. Systematic research on cognitive failure in driving beginners is recommended.

'Saying it without words': a qualitative study of oncology staff's experiences with speaking up about safety concerns.

OBJECTIVES To explore the experiences of oncology staff with communicating safety concerns and to examine situational factors and motivations surrounding the decision whether and how to speak up using semistructured interviews. SETTING 7 oncology departments of six hospitals in Switzerland. PARTICIPANTS Diverse sample of 32 experienced oncology healthcare professionals. RESULTS Nurses and doctors commonly experience situations which raise their concerns and require questioning, clarifying and correcting. Participants often used non-verbal communication to signal safety concerns. Speaking-up behaviour was strongly related to a clinical safety issue. Most episodes of 'silence' were connected to hygiene, isolation and invasive procedures. In contrast, there seemed to exist a strong culture to communicate questions, doubts and concerns relating to medication. Nearly all interviewees were concerned with 'how' to say it and in particular those of lower hierarchical status reflected on deliberate 'voicing tactics'. CONCLUSIONS Our results indicate a widely accepted culture to discuss any concerns relating to medication safety while other issues are more difficult to voice. Clinicians devote considerable efforts to evaluate the situation and sensitively decide whether and how to speak up. Our results can serve as a starting point to develop a shared understanding of risks and appropriate communication of safety concerns among staff in oncology.

Interprofessional Education in Anatomy: Learning Together in Medical and Nursing Training

Teamwork and the interprofessional collaboration of all health professions are a guarantee of patient safety and highly qualified treatment in patient care. In the daily clinical routine, physicians and nurses must work together, but the education of the different health professions occurs separately in various places, mostly without interrelated contact. Such training abets mutual misunderstanding and cements professional protectionism, which is why interprofessional education can play an important role in dismantling such barriers to future cooperation. In this article, a pilot project in interprofessional education involving both medical and nursing students is presented, and the concept and the course of training are described in detail. The report illustrates how nursing topics and anatomy lectures can be combined for interprofessional learning in an early phase of training. Evaluation of the course showed that the students were highly satisfied with the collaborative training and believed interprofessional education (IPE) to be an important experience for their future profession and understanding of other health professionals. The results show that the IPE teaching concept, which combines anatomy and nursing topics, provides an optimal setting for learning together and helps nurses and doctors in training to gain knowledge about other health professionals’ roles, thus evolving mutual understanding.

Team training to establish a safety culture in dialysis access surgery.

Operating room (OR) team safety training and learning in the field of dialysis access is well suited for the use of simulators, simulated case learning and root cause analysis of adverse outcomes. The objectives of OR team training are to improve communication and leadership skills, to use checklists and to prevent errors. Other objectives are to promote a change in the attitudes towards vascular access from learning through mistakes in a nonpunitive environment, to positively impact the employee performance and to increase staff retention by making the workplace safer, more efficient and user friendly.