The rostro-caudal gradient in the prefrontal cortex and its modulation by subthalamic deep brain stimulation in Parkinson’s disease

Acknowledgements The authors thank Benjamin Rahm (University of Freiburg) and Michael Fox (Harvard Medical School) for valuable comments on a previous version of this manuscript. This work was supported by a grant of the BrainLinks-BrainTools Cluster of Excellence funded by the German Research Foundation (DFG, grant number EXC 1086) to C.P.K., F.A., T.P., B.O.S., C.W, and V.A.C.; A.H. was supported by the German Research Foundation (Deutsche Forschungsgemeinschaft, Emmy Noether Stipend 410169619 and 424778381 – TRR 295) as well as Deutsches Zentrum für Luft- und Raumfahrt (DynaSti grant within the EU Joint Programme Neurodegenerative Disease Research, JPND). Funding Open Access funding enabled and organized by Projekt DEAL.Peer reviewedPublisher PD

Comparing families of dynamic causal models

Mathematical models of scientific data can be formally compared using Bayesian model evidence. Previous applications in the biological sciences have mainly focussed on model selection in which one first selects the model with the highest evidence and then makes inferences based on the parameters of that model. This “best model” approach is very useful but can become brittle if there are a large number of models to compare, and if different subjects use different models. To overcome this shortcoming we propose the combination of two further approaches: (i) family level inference and (ii) Bayesian model averaging within families. Family level inference removes uncertainty about aspects of model structure other than the characteristic of interest. For example: What are the inputs to the system? Is processing serial or parallel? Is it linear or nonlinear? Is it mediated by a single, crucial connection? We apply Bayesian model averaging within families to provide inferences about parameters that are independent of further assumptions about model structure. We illustrate the methods using Dynamic Causal Models of brain imaging data

The CERN Neutrino beam to Gran Sasso (NGS)

The conceptual technical design of the NGS (CERN neutrino beam to Gran Sasso) facility has been presented in the report CERN 98-02 / INFN-AE/98-05. Additional information, in particular an update on various neutrino beam options for the NGS facility, has been provided in a memorandum to the CERN-SPSC Committee (CERN-SPSC/98-35). In the present report, further improvements on the NGS design and performance, in particular new scenarios for SPS proton cycles for NGS operation and a new version of the NGS "high energy" neutrino beam for nt appearance experiments, are described. This new NGS reference beam is estimated to provide three times more nt events per year than the beam presented in the 1998 report. The radiological aspects of the NGS facility have been re-examined with the new beam design. An updated version of the construction schedule is also presented

2dFLenS and KiDS: determining source redshift distributions with cross-correlations

We develop a statistical estimator to infer the redshift probability distribution of a photometric sample of galaxies from its angular cross-correlation in redshift bins with an overlapping spectroscopic sample. This estimator is a minimum-variance weighted quadratic function of the data: a quadratic estimator. This extends and modifies the methodology presented by McQuinn & White. The derived source redshift distribution is degenerate with the source galaxy bias, which must be constrained via additional assumptions. We apply this estimator to constrain source galaxy redshift distributions in the Kilo-Degree imaging survey through cross-correlation with the spectroscopic 2-degree Field Lensing Survey, presenting results first as a binned step-wise distribution in the range z < 0.8, and then building a continuous distribution using a Gaussian process model. We demonstrate the robustness of our methodology using mock catalogues constructed from N-body simulations, and comparisons with other techniques for inferring the redshift distribution

Introduction: building the history of language learning and teaching (HoLLT)

The papers presented in this issue are the result of a workshop held at the University of Nottingham in December 2012 as part of an Arts and Humanities Research Council research network Towards a History of Modern Foreign Language Teaching and Learning (2012–14) intended to stimulate historical research into language teaching and learning. This, the first workshop in the programme, focused on exchanging information on the history of language learning and teaching (HoLLT) across the different language traditions, for it had become clear to us that scholars working within their own language disciplines were often relatively unaware of work outside these. We hope that this special issue — with overview articles on the history of English, French, German, and Spanish as second/foreign languages — will help overcome that lack of awareness and facilitate further research collaboration. Charting the history of language teaching and learning will, in turn, make us all better informed in facing challenges and changes to policy and practice now and in the future. It is instructive in the current climate, for example, to realize that grave doubts were held about whether second foreign languages could survive alongside French in British schools in the early twentieth century (McLelland, forthcoming), or to look back at earlier attempts to establish foreign languages in primary schools (Bayley, 1989; Burstall et al., 1974; Hoy, 1977). As we write, language learning in England is undergoing yet more radical change. Language teaching for all children from the age of seven is being made compulsory in primary schools from 2014, while at Key Stage 3 (up to age 16), where a foreign language has not been compulsory since 2002, the most recent programme of study for England has virtually abandoned the recent focus on intercultural competence and now requires learners to ‘read great literature in the original language’,1 a radical change in emphasis compared to the previous half-century, which seems to reflect a very different view of what language learning is for. We seem to be little closer in 2014 than we were at the dawn of the twentieth century to answering with any certainty the questions that lie at the very foundations of language teaching: who should learn a foreign language, why learners learn, what they need to learn, and what we want to teach them — answers that we need before we can consider how we want to teach. The research programme begun under our research network is intended to help us to take ‘the long view’ on such questions

Of Toasters and Molecular Ticker Tapes

Experiments in systems neuroscience can be seen as consisting of three steps: (1) selecting the signals we are interested in, (2) probing the system with carefully chosen stimuli, and (3) getting data out of the brain. Here I discuss how emerging techniques in molecular biology are starting to improve these three steps. To estimate its future impact on experimental neuroscience, I will stress the analogy of ongoing progress with that of microprocessor production techniques. These techniques have allowed computers to simplify countless problems; because they are easier to use than mechanical timers, they are even built into toasters. Molecular biology may advance even faster than computer speeds and has made immense progress in understanding and designing molecules. These advancements may in turn produce impressive improvements to each of the three steps, ultimately shifting the bottleneck from obtaining data to interpreting it