The Bayesian Power Imaging (BPI) method for magnetic source imaging

In the biomagnetic inverse problem the main interest is the activation of a region of interest, i.e. the power dissipated in that region. The Bayesian power imaging method (BPI) provides a quantified probability that the activation of a region of interest is above a given threshold. This paper introduces the method and derives the equations used. The method is illustrated in this paper using both experimental and simulated data

The Bayesian Power Imaging (BPI) test for task/control experiments

The Bayesian power imaging (BPI) method is a new approach to the biomagnetic inverse problem that is introduced in [1]. In this paper the method is extended to analyze not one but two sets of experimental data in order to highlight the differences between them

A Bayesian test for the appropriateness of a model in the biomagnetic inverse problem

This paper extends the work of Clarke [1] on the Bayesian foundations of the biomagnetic inverse problem. It derives expressions for the expectation and variance of the a posteriori source current probability distribution given a prior source current probability distribution, a source space weight function and a data set. The calculation of the variance enables the construction of a Bayesian test for the appropriateness of any source model that is chosen as the a priori infomation. The test is illustrated using both simulated (multi-dipole) data and the results of a study of early latency processing of images of human faces. [1] C.J.S. Clarke. Error estimates in the biomagnetic inverse problem. Inverse Problems, 10:77--86, 1994.Comment: 13 pages, 16 figures. Submitted to Inverse Problem

The FuturICT education accelerator

Education is a major force for economic and social wellbeing. Despite high aspirations, education at all levels can be expensive and ineffective. Three Grand Challenges are identified: (1) enable people to learn orders of magnitude more effectively, (2) enable people to learn at orders of magnitude less cost, and (3) demonstrate success by exemplary interdisciplinary education in complex systems science. A ten year ‘man-on-the-moon’ project is proposed in which FuturICT’s unique combination of Complexity, Social and Computing Sciences could provide an urgently needed transdisciplinary language for making sense of educational systems. In close dialogue with educational theory and practice, and grounded in the emerging data science and learning analytics paradigms, this will translate into practical tools (both analytical and computational) for researchers, practitioners and leaders; generative principles for resilient educational ecosystems; and innovation for radically scalable, yet personalised, learner engagement and assessment. The proposed Education Accelerator will serve as a ‘wind tunnel’ for testing these ideas in the context of real educational programmes, with an international virtual campus delivering complex systems education exploiting the new understanding of complex, social, computationally enhanced organisational structure developed within FuturICT

The FuturICT education accelerator

Education is a major force for economic and social wellbeing. Despite high aspirations, education at all levels can be expensive and ineffective. Three Grand Challenges are identified: (1) enable people to learn orders of magnitude more effectively, (2) enable people to learn at orders of magnitude less cost, and (3) demonstrate success by exemplary interdisciplinary education in complex systems science. A ten year ‘man-on-the-moon’ project is proposed in which FuturICT’s unique combination of Complexity, Social and Computing Sciences could provide an urgently needed transdisciplinary language for making sense of educational systems. In close dialogue with educational theory and practice, and grounded in the emerging data science and learning analytics paradigms, this will translate into practical tools (both analytical and computational) for researchers, practitioners and leaders; generative principles for resilient educational ecosystems; and innovation for radically scalable, yet personalised, learner engagement and assessment. The proposed Education Accelerator will serve as a ‘wind tunnel’ for testing these ideas in the context of real educational programmes, with an international virtual campus delivering complex systems education exploiting the new understanding of complex, social, computationally enhanced organisational structure developed within FuturICT

eAssessment and the independent learner

This study informs the development of eAssessment aimed at independent learners, particularly those studying through open and distance learning. It was motivated by the perception that recent developments in computing and communications technologies could lead to eAssessments fulfilling a much wider role than had hitherto been possible, combining motivating interactions with rich, and instant, feedback to create engaging eAssessments across a broad range of learning outcomes. By using a flexible eAssessment framework and providing pedagogic and technical support we have enabled colleagues in different disciplines to develop novel question types targeted at their particular learning outcomes. The activities have succeeded in engaging students by focusing on an interactive exchange around defined learning outcomes. Multiple attempts and instant iterative feedback are standard. The generation of the assessments was academically demanding and at times technically complex but the resource demands are mitigated by large scale re-use. Colleagues who engaged in this initiative have stimulated wider use of eAssessment within the university. The iCMA initiative has demonstrated that the scope of eAssessment can be extended considerably and is pedagogically valuable. Wider implementation requires investment in two areas; the underlying computational systems and staff development

Semantic and phonological task-set priming and stimulus processing investigated using magnetoencephalography (MEG)

In this study the neural substrates of semantic and phonological task priming and task performance were investigated using single word task-primes. Magnetoencephalography (MEG) data were analysed using Synthetic Aperture Magnetometry (SAM) to determine the spatiotemporal and spectral characteristics of cortical responses. Comparisons were made between the task-prime conditions for evidence of differential effects as a function of the nature of the task being primed, and between the task-prime and the task performance responses for evidence of parallels in activation associated with preparation for and completion of a specific task. Differential priming effects were found. Left middle temporal and inferior frontal voxels showed a statistically significant power decrease associated with the semantic task-prime, and a power increase associated with the phonological task-prime, within beta and gamma frequency bands respectively. Similarities between the task-related differential effects associated with task-prime presentation and those associated with target stimulus presentation were also found. For example, within the semantic task condition, left superior frontal and middle temporal regions showed a significant power decrease within both task-prime and target epochs; within the phonological task condition there were significant parietal and cerebellar power decreases within both types of epoch. In addition there was evidence within the priming epochs of dissociable patterns of activity which could be interpreted as indices of de-activation of task-irrelevant networks. Following a phonological task-prime, significant power increases were observed in those inferior frontal and middle temporal regions in which significant power decreases were associated with semantic task priming and performance. (c) 2006 Elsevier Ltd. All rights reserved

The Bayesian Power Imaging (BPI) method

This paper introduces the method and derives the equations used. The method is illustrated in this paper using both experimental and simulated data. Another paper [1] in this volume extends the method to compare task and control experiment