Future developments in brain-machine interface research

Neuroprosthetic devices based on brain-machine interface technology hold promise for the restoration of body mobility in patients suffering from devastating motor deficits caused by brain injury, neurologic diseases and limb loss. During the last decade, considerable progress has been achieved in this multidisciplinary research, mainly in the brain-machine interface that enacts upper-limb functionality. However, a considerable number of problems need to be resolved before fully functional limb neuroprostheses can be built. To move towards developing neuroprosthetic devices for humans, brain-machine interface research has to address a number of issues related to improving the quality of neuronal recordings, achieving stable, long-term performance, and extending the brain-machine interface approach to a broad range of motor and sensory functions. Here, we review the future steps that are part of the strategic plan of the Duke University Center for Neuroengineering, and its partners, the Brazilian National Institute of Brain-Machine Interfaces and the École Polytechnique Fédérale de Lausanne (EPFL) Center for Neuroprosthetics, to bring this new technology to clinical fruition

Paratuberculosis sero-status and milk production, SCC and calving interval in Irish dairy herds

The objective of this study was to investigate the impact of paratuberculosis sero-status on milk yield, fat, protein, somatic cell count and calving interval in Irish dairy herds. Serum from all animals over 12 months of age (n = 2,602) in 34 dairy herds was tested for antibodies to Mycobacterium avium subsp. paratuberculosis using an ELISA. Herds were categorised by sero-status into positive, non-negative and negative, where a positive herd contained two or more positive cows, a non-negative herd contained only one positive cow and a negative herd contained no positive cows. Data at animal, parity and herd-level were analysed by multiple regression using general linear models. Positive herds (mean herd size = 129 cows) and non-negative herds (81 cows) were larger than negative herds (72 cows) (P < 0.01). Negative herds had the highest economic breeding index (EBI), while positive herds had the highest estimated breeding value (EBV) for milk yield. There was no significant effect of paratuberculosis sero-status at animal, parity or herd-level on milk yield, milk fat or protein production, somatic cell count score (SCCS) or calving interval. Negative herds tended to have a lower SCCS than positive and nonnegative herds (P = 0.087). This study only examined the effects of paratuberculosis sero-status but did not examine the clinical effects of Johne's disease at the farm or dairy industry levels

Lung adenocarcinoma originates from retrovirus infection of proliferating type 2 pneumocytes during pulmonary post-natal development or tissue repair

Jaagsiekte sheep retrovirus (JSRV) is a unique oncogenic virus with distinctive biological properties. JSRV is the only virus causing a naturally occurring lung cancer (ovine pulmonary adenocarcinoma, OPA) and possessing a major structural protein that functions as a dominant oncoprotein. Lung cancer is the major cause of death among cancer patients. OPA can be an extremely useful animal model in order to identify the cells originating lung adenocarcinoma and to study the early events of pulmonary carcinogenesis. In this study, we demonstrated that lung adenocarcinoma in sheep originates from infection and transformation of proliferating type 2 pneumocytes (termed here lung alveolar proliferating cells, LAPCs). We excluded that OPA originates from a bronchioalveolar stem cell, or from mature post-mitotic type 2 pneumocytes or from either proliferating or non-proliferating Clara cells. We show that young animals possess abundant LAPCs and are highly susceptible to JSRV infection and transformation. On the contrary, healthy adult sheep, which are normally resistant to experimental OPA induction, exhibit a relatively low number of LAPCs and are resistant to JSRV infection of the respiratory epithelium. Importantly, induction of lung injury increased dramatically the number of LAPCs in adult sheep and rendered these animals fully susceptible to JSRV infection and transformation. Furthermore, we show that JSRV preferentially infects actively dividing cell in vitro. Overall, our study provides unique insights into pulmonary biology and carcinogenesis and suggests that JSRV and its host have reached an evolutionary equilibrium in which productive infection (and transformation) can occur only in cells that are scarce for most of the lifespan of the sheep. Our data also indicate that, at least in this model, inflammation can predispose to retroviral infection and cancer

Molecular complexity determines the number of olfactory notes and the pleasantness of smells

One major unresolved problem in olfaction research is to relate the percept to the molecular structure of stimuli. The present study examined this issue and showed for the first time a quantitative structure-odor relationship in which the more structurally complex a monomolecular odorant, the more numerous the olfactory notes it evokes. Low-complexity odorants were also rated as more aversive, reflecting the fact that low molecular complexity may serve as a warning cue for the olfactory system. Taken together, these findings suggest that molecular complexity provides a framework to explain the subjective experience of smells

Interactivity and Reward-Related Neural Activation during a Serious Videogame

This study sought to determine whether playing a “serious” interactive digital game (IDG) – the Re-Mission videogame for cancer patients – activates mesolimbic neural circuits associated with incentive motivation, and if so, whether such effects stem from the participatory aspects of interactive gameplay, or from the complex sensory/perceptual engagement generated by its dynamic event-stream. Healthy undergraduates were randomized to groups in which they were scanned with functional magnetic resonance imaging (FMRI) as they either actively played Re-Mission or as they passively observed a gameplay audio-visual stream generated by a yoked active group subject. Onset of interactive game play robustly activated mesolimbic projection regions including the caudate nucleus and nucleus accumbens, as well as a subregion of the parahippocampal gyrus. During interactive gameplay, subjects showed extended activation of the thalamus, anterior insula, putamen, and motor-related regions, accompanied by decreased activation in parietal and medial prefrontal cortex. Offset of interactive gameplay activated the anterior insula and anterior cingulate. Between-group comparisons of within-subject contrasts confirmed that mesolimbic activation was significantly more pronounced in the active playgroup than in the passive exposure control group. Individual difference analyses also found the magnitude of parahippocampal activation following gameplay onset to correlate with positive attitudes toward chemotherapy assessed both at the end of the scanning session and at an unannounced one-month follow-up. These findings suggest that IDG-induced activation of reward-related mesolimbic neural circuits stems primarily from participatory engagement in gameplay (interactivity), rather than from the effects of vivid and dynamic sensory stimulation

Surprised at All the Entropy: Hippocampal, Caudate and Midbrain Contributions to Learning from Prediction Errors

Influential concepts in neuroscientific research cast the brain a predictive machine that revises its predictions when they are violated by sensory input. This relates to the predictive coding account of perception, but also to learning. Learning from prediction errors has been suggested for take place in the hippocampal memory system as well as in the basal ganglia. The present fMRI study used an action-observation paradigm to investigate the contributions of the hippocampus, caudate nucleus and midbrain dopaminergic system to different types of learning: learning in the absence of prediction errors, learning from prediction errors, and responding to the accumulation of prediction errors in unpredictable stimulus configurations. We conducted analyses of the regions of interests' BOLD response towards these different types of learning, implementing a bootstrapping procedure to correct for false positives. We found both, caudate nucleus and the hippocampus to be activated by perceptual prediction errors. The hippocampal responses seemed to relate to the associative mismatch between a stored representation and current sensory input. Moreover, its response was significantly influenced by the average information, or Shannon entropy of the stimulus material. In accordance with earlier results, the habenula was activated by perceptual prediction errors. Lastly, we found that the substantia nigra was activated by the novelty of sensory input. In sum, we established that the midbrain dopaminergic system, the hippocampus, and the caudate nucleus were to different degrees significantly involved in the three different types of learning: acquisition of new information, learning from prediction errors and responding to unpredictable stimulus developments. We relate learning from perceptual prediction errors to the concept of predictive coding and related information theoretic accounts

Expert Financial Advice Neurobiologically “Offloads” Financial Decision-Making under Risk

BACKGROUND: Financial advice from experts is commonly sought during times of uncertainty. While the field of neuroeconomics has made considerable progress in understanding the neurobiological basis of risky decision-making, the neural mechanisms through which external information, such as advice, is integrated during decision-making are poorly understood. In the current experiment, we investigated the neurobiological basis of the influence of expert advice on financial decisions under risk. METHODOLOGY/PRINCIPAL FINDINGS: While undergoing fMRI scanning, participants made a series of financial choices between a certain payment and a lottery. Choices were made in two conditions: 1) advice from a financial expert about which choice to make was displayed (MES condition); and 2) no advice was displayed (NOM condition). Behavioral results showed a significant effect of expert advice. Specifically, probability weighting functions changed in the direction of the expert's advice. This was paralleled by neural activation patterns. Brain activations showing significant correlations with valuation (parametric modulation by value of lottery/sure win) were obtained in the absence of the expert's advice (NOM) in intraparietal sulcus, posterior cingulate cortex, cuneus, precuneus, inferior frontal gyrus and middle temporal gyrus. Notably, no significant correlations with value were obtained in the presence of advice (MES). These findings were corroborated by region of interest analyses. Neural equivalents of probability weighting functions showed significant flattening in the MES compared to the NOM condition in regions associated with probability weighting, including anterior cingulate cortex, dorsolateral PFC, thalamus, medial occipital gyrus and anterior insula. Finally, during the MES condition, significant activations in temporoparietal junction and medial PFC were obtained. CONCLUSIONS/SIGNIFICANCE: These results support the hypothesis that one effect of expert advice is to "offload" the calculation of value of decision options from the individual's brain

Dopamine Increases a Value-Independent Gambling Propensity

Although the impact of dopamine on reward learning is well documented, its influence on other aspects of behavior remains the subject of much ongoing work. Dopaminergic drugs are known to increase risk-taking behavior, but the underlying mechanisms for this effect are not clear. We probed dopamine’s role by examining the effect of its precursor L-DOPA on the choices of healthy human participants in an experimental paradigm that allowed particular components of risk to be distinguished. We show that choice behavior depended on a baseline (ie, value-independent) gambling propensity, a gambling preference scaling with the amount/variance, and a value normalization factor. Boosting dopamine levels specifically increased just the value-independent baseline gambling propensity, leaving the other components unaffected. Our results indicate that the influence of dopamine on choice behavior involves a specific modulation of the attractiveness of risky options—a finding with implications for understanding a range of reward-related psychopathologies including addiction

A Specific and Rapid Neural Signature for Parental Instinct

Darwin originally pointed out that there is something about infants which prompts adults to respond to and care for them, in order to increase individual fitness, i.e. reproductive success, via increased survivorship of one's own offspring. Lorenz proposed that it is the specific structure of the infant face that serves to elicit these parental responses, but the biological basis for this remains elusive. Here, we investigated whether adults show specific brain responses to unfamiliar infant faces compared to adult faces, where the infant and adult faces had been carefully matched across the two groups for emotional valence and arousal, as well as size and luminosity. The faces also matched closely in terms of attractiveness. Using magnetoencephalography (MEG) in adults, we found that highly specific brain activity occurred within a seventh of a second in response to unfamiliar infant faces but not to adult faces. This activity occurred in the medial orbitofrontal cortex (mOFC), an area implicated in reward behaviour, suggesting for the first time a neural basis for this vital evolutionary process. We found a peak in activity first in mOFC and then in the right fusiform face area (FFA). In mOFC the first significant peak (p<0.001) in differences in power between infant and adult faces was found at around 130 ms in the 10–15 Hz band. These early differences were not found in the FFA. In contrast, differences in power were found later, at around 165 ms, in a different band (20–25 Hz) in the right FFA, suggesting a feedback effect from mOFC. These findings provide evidence in humans of a potential brain basis for the “innate releasing mechanisms” described by Lorenz for affection and nurturing of young infants. This has potentially important clinical applications in relation to postnatal depression, and could provide opportunities for early identification of families at risk

Temporal-Difference Reinforcement Learning with Distributed Representations

Temporal-difference (TD) algorithms have been proposed as models of reinforcement learning (RL). We examine two issues of distributed representation in these TD algorithms: distributed representations of belief and distributed discounting factors. Distributed representation of belief allows the believed state of the world to distribute across sets of equivalent states. Distributed exponential discounting factors produce hyperbolic discounting in the behavior of the agent itself. We examine these issues in the context of a TD RL model in which state-belief is distributed over a set of exponentially-discounting “micro-Agents”, each of which has a separate discounting factor (γ). Each µAgent maintains an independent hypothesis about the state of the world, and a separate value-estimate of taking actions within that hypothesized state. The overall agent thus instantiates a flexible representation of an evolving world-state. As with other TD models, the value-error (δ) signal within the model matches dopamine signals recorded from animals in standard conditioning reward-paradigms. The distributed representation of belief provides an explanation for the decrease in dopamine at the conditioned stimulus seen in overtrained animals, for the differences between trace and delay conditioning, and for transient bursts of dopamine seen at movement initiation. Because each µAgent also includes its own exponential discounting factor, the overall agent shows hyperbolic discounting, consistent with behavioral experiments