Decoding sequence learning from single-trial intracranial EEG in humans.

We propose and validate a multivariate classification algorithm for characterizing changes in human intracranial electroencephalographic data (iEEG) after learning motor sequences. The algorithm is based on a Hidden Markov Model (HMM) that captures spatio-temporal properties of the iEEG at the level of single trials. Continuous intracranial iEEG was acquired during two sessions (one before and one after a night of sleep) in two patients with depth electrodes implanted in several brain areas. They performed a visuomotor sequence (serial reaction time task, SRTT) using the fingers of their non-dominant hand. Our results show that the decoding algorithm correctly classified single iEEG trials from the trained sequence as belonging to either the initial training phase (day 1, before sleep) or a later consolidated phase (day 2, after sleep), whereas it failed to do so for trials belonging to a control condition (pseudo-random sequence). Accurate single-trial classification was achieved by taking advantage of the distributed pattern of neural activity. However, across all the contacts the hippocampus contributed most significantly to the classification accuracy for both patients, and one fronto-striatal contact for one patient. Together, these human intracranial findings demonstrate that a multivariate decoding approach can detect learning-related changes at the level of single-trial iEEG. Because it allows an unbiased identification of brain sites contributing to a behavioral effect (or experimental condition) at the level of single subject, this approach could be usefully applied to assess the neural correlates of other complex cognitive functions in patients implanted with multiple electrodes.

Penser la médecine communautaire s'apprend avec les pairs. [The spirit of community medicine: students learning from students].

At the University of Lausanne third-year medical students are given the task of spending a month investigating a question of community medicine. In 2009, four students evaluated the legitimacy of health insurers intervening in the management of depression. They found that health insurers put pressure on public authorities during the development of legislation governing the health system and reimbursement for treatment. This fact emerged during the scientific investigation led jointly by the team in the course of the "module of immersion in community medicine." This paper presents each step of their study. The example chosen illustrates the learning objectives covered by the module.

Learning from failure - rationale and design for a study about discontinuation of randomized trials (DISCO study).

BACKGROUND: Randomized controlled trials (RCTs) may be discontinued because of apparent harm, benefit, or futility. Other RCTs are discontinued early because of insufficient recruitment. Trial discontinuation has ethical implications, because participants consent on the premise of contributing to new medical knowledge, Research Ethics Committees (RECs) spend considerable effort reviewing study protocols, and limited resources for conducting research are wasted. Currently, little is known regarding the frequency and characteristics of discontinued RCTs. METHODS/DESIGN: Our aims are, first, to determine the prevalence of RCT discontinuation for specific reasons; second, to determine whether the risk of RCT discontinuation for specific reasons differs between investigator- and industry-initiated RCTs; third, to identify risk factors for RCT discontinuation due to insufficient recruitment; fourth, to determine at what stage RCTs are discontinued; and fifth, to examine the publication history of discontinued RCTs.We are currently assembling a multicenter cohort of RCTs based on protocols approved between 2000 and 2002/3 by 6 RECs in Switzerland, Germany, and Canada. We are extracting data on RCT characteristics and planned recruitment for all included protocols. Completion and publication status is determined using information from correspondence between investigators and RECs, publications identified through literature searches, or by contacting the investigators. We will use multivariable regression models to identify risk factors for trial discontinuation due to insufficient recruitment. We aim to include over 1000 RCTs of which an anticipated 150 will have been discontinued due to insufficient recruitment. DISCUSSION: Our study will provide insights into the prevalence and characteristics of RCTs that were discontinued. Effective recruitment strategies and the anticipation of problems are key issues in the planning and evaluation of trials by investigators, Clinical Trial Units, RECs and funding agencies. Identification and modification of barriers to successful study completion at an early stage could help to reduce the risk of trial discontinuation, save limited resources, and enable RCTs to better meet their ethical requirements.

The normative dimension of social life from a second-person perspective

This thesis argues that insofar as we want to account for the normative dimension of social life, we must be careful to avoid construing that normative dimension in such a way as to exclude that which the second-person perspective reveals is important to social life and our ability to participate in it.¦The second-person perspective reveals that social life ought to be understood as a mix or balance of the regular and the irregular, where, in addition, those one interacts with are always to some extent experienced as other in a way that is neither immediately, nor perhaps ultimately, understandable. For persons to be able to participate in social life, conceived of in this way, they must have abilities that allow them to be, to some extent, hesitant and tentative in their relations with others, and thus tolerant of ambiguity, uncertainty and unpredictability, and responsive to and capable of learning from the otherness of others in the course of interacting with them.¦Incorporating the second-person perspective means we have to make some changes to the way we think about the normative in general, and the normative dimension of social life in particular. It does not mean giving up on the distinction between the normative and the regular - that continues to be fundamentally important but it does mean not excluding, as part of social life and as worthy of explanation, all that which is irregular. A radical way of putting it would be to say that there must be a sense in which the irregular is part of the normative. A less radical way, and the way adopted by this thesis, is to say that any account of the normative dimension of social life must not be such as to exclude the importance of irregularity from social life. This will mean 1) not characterising conventions, norms and rules as determinants of appropriateness and inappropriateness; 2) not thinking of them as necessary; 3) not thinking of them as necessarily governing minds; and 4) not thinking of them as necessarily shared.¦-¦L'argument principal de la thèse est que, pour rendre compte de la dimension normative de la vie sociale, il faut veiller à ne pas exclure la perspective de la deuxième personne - une perspective importante pour comprendre la vie sociale et la capacité requise pour y participer.¦Cette perspective nous permet d'imaginer la vie sociale comme un mélange ou un équilibre entre le régulier et l'irrégulier, l'interaction entre des individus pouvant être appréhendée comme l'expérience de chaque personne avec «l'autre» d'une manière qui n'est pas immédiatement compréhensible, et qui ne peut pas, peut-être, être ultimement comprise. Pour participer à la vie sociale, l'on doit avoir la capacité de rester hésitant et «réactif» dans ses relations avec les autres, de rester ouvert à leur altérité et de tolérer l'ambiguïté, l'incertitude et l'imprévisibilité des interactions sociales.¦Adopter une perspective «à la deuxième personne» conduit à une autre manière de penser la normativité en général, et la dimension normative de la vie sociale en particulier. Cela ne veut pas dire qu'il faut abandonner la distinction entre le normatif et le régulier - une distinction qui garde une importance fondamentale - mais qu'il faut reconnaître l'irrégulier comme faisant partie de la vie sociale et comme étant digne, en tant que tel, d'être expliqué. Une conception radicale pourrait même concevoir l'irrégulier comme faisant partie intégrante de la normativité. Une approche moins radicale, qui est celle adoptée dans cette thèse, est de dire que tout compte-rendu de la dimension normative de la vie sociale doit prendre en considération l'importance de l'irrégularité dans la vie sociale. Une telle approche implique que les conventions, normes et règles (1) ne déterminent pas ce qui est approprié ou inapproprié; (2) ne sont pas toujours nécessaires ; (3) ne gouvernent pas le fonctionnement de l'esprit ; et (4) ne sont pas nécessairement partagées.

Machine learning algorithms for spatial data. Case studies: Environmental pollution, natural hazards, renewable resources

This paper presents general problems and approaches for the spatial data analysis using machine learning algorithms. Machine learning is a very powerful approach to adaptive data analysis, modelling and visualisation. The key feature of the machine learning algorithms is that they learn from empirical data and can be used in cases when the modelled environmental phenomena are hidden, nonlinear, noisy and highly variable in space and in time. Most of the machines learning algorithms are universal and adaptive modelling tools developed to solve basic problems of learning from data: classification/pattern recognition, regression/mapping and probability density modelling. In the present report some of the widely used machine learning algorithms, namely artificial neural networks (ANN) of different architectures and Support Vector Machines (SVM), are adapted to the problems of the analysis and modelling of geo-spatial data. Machine learning algorithms have an important advantage over traditional models of spatial statistics when problems are considered in a high dimensional geo-feature spaces, when the dimension of space exceeds 5. Such features are usually generated, for example, from digital elevation models, remote sensing images, etc. An important extension of models concerns considering of real space constrains like geomorphology, networks, and other natural structures. Recent developments in semi-supervised learning can improve modelling of environmental phenomena taking into account on geo-manifolds. An important part of the study deals with the analysis of relevant variables and models' inputs. This problem is approached by using different feature selection/feature extraction nonlinear tools. To demonstrate the application of machine learning algorithms several interesting case studies are considered: digital soil mapping using SVM, automatic mapping of soil and water system pollution using ANN; natural hazards risk analysis (avalanches, landslides), assessments of renewable resources (wind fields) with SVM and ANN models, etc. The dimensionality of spaces considered varies from 2 to more than 30. Figures 1, 2, 3 demonstrate some results of the studies and their outputs. Finally, the results of environmental mapping are discussed and compared with traditional models of geostatistics.

Multitask learning of environmental spatial data

The present research deals with an application of artificial neural networks for multitask learning from spatial environmental data. The real case study (sediments contamination of Geneva Lake) consists of 8 pollutants. There are different relationships between these variables, from linear correlations to strong nonlinear dependencies. The main idea is to construct a subsets of pollutants which can be efficiently modeled together within the multitask framework. The proposed two-step approach is based on: 1) the criterion of nonlinear predictability of each variable ?k? by analyzing all possible models composed from the rest of the variables by using a General Regression Neural Network (GRNN) as a model; 2) a multitask learning of the best model using multilayer perceptron and spatial predictions. The results of the study are analyzed using both machine learning and geostatistical tools.

The development and pilot-testing of a training curriculum in adolescent medicine and health.

PURPOSE: To select and propose a set of knowledge, attitudes, and skills essential for the care of adolescents; to encourage the development of adolescent health multidisciplinary networks; and to set up training programs in as many European countries as possible. METHODS: The curriculum was developed by 16 physicians from 11 European countries with various professional specializations. In line with modern guidelines in medical education, it is a modular, flexible instrument which covers the main teaching areas in the field, such as basic skills (i.e. setting, rights and confidentiality, gender and cultural issues) as well as specific themes (i.e. sexual and reproductive health, eating disorders, chronic conditions). It consists of 17 thematic modules, each containing detailed objectives, learning approaches, examples, and evaluation methods. RESULT: Two international one-week summer schools were used to assess the feasibility and appropriateness of the curriculum. The overall evaluation was good, with most of the items surpassing three on a four-point Likert scale. However, it pointed to several aspects (process and content) which will need to be refined in the future, such as an increase in interactive sessions (role playing), and a better mix of clinical and public health issues.