Age-related differences in neural activities during risk taking as revealed by functional MRI

Previous research has clearly documented that risky decision making is different in young and older adults. Yet, there has been a relative dearth of research that seeks to understand such age-related changes in the neural activities associated with risk taking. To address this research issue, 21 men (12 young men, mean age 29.9±6.2 years and 9 older men, mean age 65.2±4.2 years) performed a risky-gains task while their brain activities were monitored by an fMRI scanner. The older adults, relative to their younger peers, presented with contralateral prefrontal activity, particularly at the orbitofrontal cortex. Furthermore, stronger activation of the right insula was observed for the older-aged participants compared to the younger-aged adults. The findings of this study are consistent with the a priori speculations established in accordance with the HAROLD model as well as previous findings. Findings of this study suggest that when making risky decisions, there may be possible neuropsychological mechanisms underlying the change in impulsive and risk-taking behaviors during the course of natural ageing. © 2007 The Author(s).published_or_final_versio

Argumentation in Decision Support for Medical Care Planning for Patients and Clinicians.

Developing a care plan for a patient requires an understanding of interactions and dependencies between procedures, and of their possible outcomes for an individual patient, and it requires the planner to keep track of this information as the proposed plan evolves. This is difficult even for experienced clinicians, but increasingly patients are expected (and expect) to participate. We describe an argumentation-based planning support system designed to ameliorate the cognitive load imposed by the planning and communication elements of such tasks. An initial evaluation study in the field of genetic counseling produced promising results. The approach may provide a general aid for clinicians and patients in visualizing, customizing, evaluating and communicating about care plans

Lie Detection using functional MRI

published_or_final_versio

Conceptualizing cultures of violence and cultural change

The historiography of violence has undergone a distinct cultural turn as attention has shifted from examining violence as a clearly defined (and countable) social problem to analysing its historically defined 'social meaning'. Nevertheless, the precise nature of the relationship between 'violence' and 'culture' is still being established. How are 'cultures of violence' formed? What impact do they have on violent behaviour? How do they change? This essay examines some of the conceptual aspects of the relationship between culture and violence. It brings together empirical research into nineteenth-century England with recent research results from other European contexts to examine three aspects of the relationship between culture and violence. These are organised under the labels 'seeing violence', 'identifying the violent' and 'changing violence'. Within a particular society, narratives regarding particular kinds of behaviour shape cultural attitudes. The notion 'violence' is thus defined in relation to physically aggressive acts as well as by being connected to other kinds of attitudes and contexts. As a result, the boundaries between physical aggression which is legitimate and that which is illegitimate (and thus 'violence') are set. Once 'violence' is defined, particular cultures form ideas about who is responsible for it: reactions to violence become associated with social arrangements such as class and gender as well as to attitudes toward the self. Finally, cultures of violence make efforts to tame or eradicate illegitimate forms of physical aggression. This process is not only connected to the development of new forms of power (e.g., new policing or punishment strategies) but also to less tangible cultural influences which aim at changing the behaviour defined as violence (in particular among the social groups identified as violent). Even if successful, this three-tiered process of seeing violence, identifying the violent and changing violence continues anew, emphasising the ways that cultures of violence develop through a continuous process of reevaluation and reinvention

Using multi-modal neuroimaging to characterise social brain specialisation in infants

The specialised regional functionality of the mature human cortex partly emerges through experience-dependent specialisation during early development. Our existing understanding of functional specialisation in the infant brain is based on evidence from unitary imaging modalities and has thus focused on isolated estimates of spatial or temporal selectivity of neural or haemodynamic activation, giving an incomplete picture. We speculate that functional specialisation will be underpinned by better coordinated haemodynamic and metabolic changes in a broadly orchestrated physiological response. To enable researchers to track this process through development, we develop new tools that allow the simultaneous measurement of coordinated neural activity (EEG), metabolic rate, and oxygenated blood supply (broadband near-infrared spectroscopy) in the awake infant. In 4- to 7-month-old infants, we use these new tools to show that social processing is accompanied by spatially and temporally specific increases in coupled activation in the temporal-parietal junction, a core hub region of the adult social brain. During non-social processing, coupled activation decreased in the same region, indicating specificity to social processing. Coupling was strongest with high-frequency brain activity (beta and gamma), consistent with the greater energetic requirements and more localised action of high-frequency brain activity. The development of simultaneous multimodal neural measures will enable future researchers to open new vistas in understanding functional specialisation of the brain

Simple, Fast and Accurate Implementation of the Diffusion Approximation Algorithm for Stochastic Ion Channels with Multiple States

The phenomena that emerge from the interaction of the stochastic opening and closing of ion channels (channel noise) with the non-linear neural dynamics are essential to our understanding of the operation of the nervous system. The effects that channel noise can have on neural dynamics are generally studied using numerical simulations of stochastic models. Algorithms based on discrete Markov Chains (MC) seem to be the most reliable and trustworthy, but even optimized algorithms come with a non-negligible computational cost. Diffusion Approximation (DA) methods use Stochastic Differential Equations (SDE) to approximate the behavior of a number of MCs, considerably speeding up simulation times. However, model comparisons have suggested that DA methods did not lead to the same results as in MC modeling in terms of channel noise statistics and effects on excitability. Recently, it was shown that the difference arose because MCs were modeled with coupled activation subunits, while the DA was modeled using uncoupled activation subunits. Implementations of DA with coupled subunits, in the context of a specific kinetic scheme, yielded similar results to MC. However, it remained unclear how to generalize these implementations to different kinetic schemes, or whether they were faster than MC algorithms. Additionally, a steady state approximation was used for the stochastic terms, which, as we show here, can introduce significant inaccuracies. We derived the SDE explicitly for any given ion channel kinetic scheme. The resulting generic equations were surprisingly simple and interpretable - allowing an easy and efficient DA implementation. The algorithm was tested in a voltage clamp simulation and in two different current clamp simulations, yielding the same results as MC modeling. Also, the simulation efficiency of this DA method demonstrated considerable superiority over MC methods.Comment: 32 text pages, 10 figures, 1 supplementary text + figur

Probing Evolutionary Repeatability: Neutral and Double Changes and the Predictability of Evolutionary Adaptation

The question of how organisms adapt is among the most fundamental in evolutionary biology. Two recent studies investigated the evolution of Escherichia coli in response to challenge with the antibiotic cefotaxime. Studying five mutations in the beta-lactamase gene that together confer significant antibiotic resistance, the authors showed a complex fitness landscape that greatly constrained the identity and order of intermediates leading from the initial wildtype genotype to the final resistant genotype. Out of 18 billion possible orders of single mutations leading from non-resistant to fully-resistant form, they found that only 27 (1.5x10(-7)%) pathways were characterized by consistently increasing resistance, thus only a tiny fraction of possible paths are accessible by positive selection. I further explore these data in several ways.Allowing neutral changes (those that do not affect resistance) increases the number of accessible pathways considerably, from 27 to 629. Allowing multiple simultaneous mutations also greatly increases the number of accessible pathways. Allowing a single case of double mutation to occur along a pathway increases the number of pathways from 27 to 259, and allowing arbitrarily many pairs of simultaneous changes increases the number of possible pathways by more than 100 fold, to 4800. I introduce the metric 'repeatability,' the probability that two random trials will proceed via the exact same pathway. In general, I find that while the total number of accessible pathways is dramatically affected by allowing neutral or double mutations, the overall evolutionary repeatability is generally much less affected.These results probe the conceivable pathways available to evolution. Even when many of the assumptions of the analysis of Weinreich et al. (2006) are relaxed, I find that evolution to more highly cefotaxime resistant beta-lactamase proteins is still highly repeatable

Climate change, climatic variation and extreme biological responses

Extreme climatic events could be major drivers of biodiversity change, but it is unclear whether extreme biological changes are (i) individualistic (species- or group-specific), (ii) commonly associated with unusual climatic events and/or (iii) important determinants of long-term population trends. Using population time series for 238 widespread species (207 Lepidoptera and 31 birds) in England since 1968, we found that population 'crashes' (outliers in terms of species' year-to-year population changes) were 46% more frequent than population 'explosions'. (i) Every year, at least three species experienced extreme changes in population size, and in 41 of the 44 years considered, some species experienced population crashes while others simultaneously experienced population explosions. This suggests that, even within the same broad taxonomic groups, species are exhibiting individualistic dynamics, most probably driven by their responses to different, short-term events associated with climatic variability. (ii) Six out of 44 years showed a significant excess of species experiencing extreme population changes (5 years for Lepidoptera, 1 for birds). These 'consensus years' were associated with climatically extreme years, consistent with a link between extreme population responses and climatic variability, although not all climatically extreme years generated excess numbers of extreme population responses. (iii) Links between extreme population changes and long-term population trends were absent in Lepidoptera and modest (but significant) in birds. We conclude that extreme biological responses are individualistic, in the sense that the extreme population changes of most species are taking place in different years, and that long-term trends of widespread species have not, to date, been dominated by these extreme changes.This article is part of the themed issue 'Behavioural, ecological and evolutionary responses to extreme climatic events'

Veterinary Conduct and Animal Welfare

This paper is a lecture presented to the same Association but fifteen years later: the 131st Annual Congress in 1984. This second presentation contemplates two points: First, it tries to indicate how this criticism has gradually emerged and a historical outline is put forth of the development of veterinary medicine, a differentiation being made between a mythical, a technical, and a critical approach. Second, a discussion of how veterinarians have to associate themselves with this criticism in their professional conduct is presented. This discussion is necessary for two reasons. Veterinarians have increasingly become aware that they bear a professional responsibility not only for animal health but also for animal welfare; and, veterinarians are expected to give their views in concrete situations

Forecasting Player Behavioral Data and Simulating in-Game Events

Understanding player behavior is fundamental in game data science. Video games evolve as players interact with the game, so being able to foresee player experience would help to ensure a successful game development. In particular, game developers need to evaluate beforehand the impact of in-game events. Simulation optimization of these events is crucial to increase player engagement and maximize monetization. We present an experimental analysis of several methods to forecast game-related variables, with two main aims: to obtain accurate predictions of in-app purchases and playtime in an operational production environment, and to perform simulations of in-game events in order to maximize sales and playtime. Our ultimate purpose is to take a step towards the data-driven development of games. The results suggest that, even though the performance of traditional approaches such as ARIMA is still better, the outcomes of state-of-the-art techniques like deep learning are promising. Deep learning comes up as a well-suited general model that could be used to forecast a variety of time series with different dynamic behaviors