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Lars Kasper

Bio: Lars Kasper is an academic researcher from University of Zurich. The author has contributed to research in topics: Iterative reconstruction & Image quality. The author has an hindex of 20, co-authored 56 publications receiving 1799 citations. Previous affiliations of Lars Kasper include University Health Network & Max Planck Society.


Papers
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Journal ArticleDOI
TL;DR: A nonlinear extension of DCM that models such processes (to second order) at the neuronal population level is presented and it is found that attention-induced increases in V5 responses could be best explained as a gating of the V1-->V5 connection by activity in posterior parietal cortex.

412 citations

Journal ArticleDOI
16 Oct 2013-Neuron
TL;DR: While fMRI measures do not reveal the exact neuron types activated in midbrain and basal forebrain, they suggest a dichotomy between neuromodulatory systems, linking dopamine to low-level PEs about stimulus outcome and acetylcholine to more abstract PEsabout stimulus probabilities.

265 citations

Journal ArticleDOI
TL;DR: Through its platform-independent Matlab implementation, open-source distribution, and modular structure, the PhysIO Toolbox renders physiological noise correction an accessible preprocessing step for fMRI data.

252 citations

Journal ArticleDOI
TL;DR: In experiments on a whole‐body MR system, it is shown that the proposed method yields impulse response functions of high temporal and spectral resolution, which holds promise for a range of applications, including pre‐emphasis optimization, quality assurance, and image reconstruction.
Abstract: This work demonstrates a fast, sensitive method of characterizing the dynamic performance of MR gradient systems. The accuracy of gradient time-courses is often compromised by field imperfections of various causes, including eddy currents and mechanical oscillations. Characterizing these perturbations is instrumental for corrections by pre-emphasis or post hoc signal processing. Herein, a gradient chain is treated as a linear time-invariant system, whose impulse response function is determined by measuring field responses to known gradient inputs. Triangular inputs are used to probe the system and response measurements are performed with a dynamic field camera consisting of NMR probes. In experiments on a whole-body MR system, it is shown that the proposed method yields impulse response functions of high temporal and spectral resolution. Besides basic properties such as bandwidth and delay, it also captures subtle features such as mechanically induced field oscillations. For validation, measured response functions were used to predict gradient field evolutions, which was achieved with an error below 0.2%. The field camera used records responses of various spatial orders simultaneously, rendering the method suitable also for studying cross-responses and dynamic shim systems. It thus holds promise for a range of applications, including pre-emphasis optimization, quality assurance, and image reconstruction.

165 citations

Journal ArticleDOI
TL;DR: The results suggest that humans employ hierarchical generative models to infer on the changing intentions of others, use volatility estimates to inform decision-making in social interactions, and integrate estimates of advice accuracy with non-social sources of information.
Abstract: Inferring on others' (potentially time-varying) intentions is a fundamental problem during many social transactions. To investigate the underlying mechanisms, we applied computational modeling to behavioral data from an economic game in which 16 pairs of volunteers (randomly assigned to “player” or “adviser” roles) interacted. The player performed a probabilistic reinforcement learning task, receiving information about a binary lottery from a visual pie chart. The adviser, who received more predictive information, issued an additional recommendation. Critically, the game was structured such that the adviser's incentives to provide helpful or misleading information varied in time. Using a meta-Bayesian modeling framework, we found that the players' behavior was best explained by the deployment of hierarchical learning: they inferred upon the volatility of the advisers' intentions in order to optimize their predictions about the validity of their advice. Beyond learning, volatility estimates also affected the trial-by-trial variability of decisions: participants were more likely to rely on their estimates of advice accuracy for making choices when they believed that the adviser's intentions were presently stable. Finally, our model of the players' inference predicted the players' interpersonal reactivity index (IRI) scores, explicit ratings of the advisers' helpfulness and the advisers' self-reports on their chosen strategy. Overall, our results suggest that humans (i) employ hierarchical generative models to infer on the changing intentions of others, (ii) use volatility estimates to inform decision-making in social interactions, and (iii) integrate estimates of advice accuracy with non-social sources of information. The Bayesian framework presented here can quantify individual differences in these mechanisms from simple behavioral readouts and may prove useful in future clinical studies of maladaptive social cognition.

155 citations


Cited by
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Christopher M. Bishop1
01 Jan 2006
TL;DR: Probability distributions of linear models for regression and classification are given in this article, along with a discussion of combining models and combining models in the context of machine learning and classification.
Abstract: Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

10,141 citations

Journal ArticleDOI
TL;DR: The inception of this journal has been foreshadowed by an ever-increasing number of publications on functional connectivity, causal modeling, connectomics, and multivariate analyses of distributed patterns of brain responses.
Abstract: Over the past 20 years, neuroimaging has become a predominant technique in systems neuroscience. One might envisage that over the next 20 years the neuroimaging of distributed processing and connectivity will play a major role in disclosing the brain's functional architecture and operational principles. The inception of this journal has been foreshadowed by an ever-increasing number of publications on functional connectivity, causal modeling, connectomics, and multivariate analyses of distributed patterns of brain responses. I accepted the invitation to write this review with great pleasure and hope to celebrate and critique the achievements to date, while addressing the challenges ahead.

2,822 citations

Journal ArticleDOI
TL;DR: Analysis of signals in tactile afferent neurons and central processes in humans reveals how contact events are encoded and used to monitor and update task performance.
Abstract: During object manipulation tasks, the brain selects and implements action-phase controllers that use sensory predictions and afferent signals to tailor motor output to the physical properties of the objects involved. Analysis of signals in tactile afferent neurons and central processes in humans reveals how contact events are encoded and used to monitor and update task performance.

1,569 citations

Journal ArticleDOI
TL;DR: The hierarchical Bayesian approach is considerably more robust than either of the other approaches in the presence of outliers and is expected to prove useful for a wide range of group studies, not only in the context of DCM, but also for other modelling endeavours, e.g. comparing different source reconstruction methods for EEG/MEG.

1,353 citations

Journal ArticleDOI
TL;DR: It is argued that this neurobiological mechanism can explain failures of self-monitoring, leading to a mechanistic explanation for first-rank symptoms as pathognomonic features of schizophrenia, and may provide a basis for future diagnostic classifications with physiologically defined patient subgroups.
Abstract: Over the last 2 decades, a large number of neurophysiological and neuroimaging studies of patients with schizophrenia have furnished in vivo evidence for dysconnectivity, ie, abnormal functional integration of brain processes. While the evidence for dysconnectivity in schizophrenia is strong, its etiology, pathophysiological mechanisms, and significance for clinical symptoms are unclear. First, dysconnectivity could result from aberrant wiring of connections during development, from aberrant synaptic plasticity, or from both. Second, it is not clear how schizophrenic symptoms can be understood mechanistically as a consequence of dysconnectivity. Third, if dysconnectivity is the primary pathophysiology, and not just an epiphenomenon, then it should provide a mechanistic explanation for known empirical facts about schizophrenia. This article addresses these 3 issues in the framework of the dysconnection hypothesis. This theory postulates that the core pathology in schizophrenia resides in aberrant N-methyl-D-aspartate receptor (NMDAR)-mediated synaptic plasticity due to abnormal regulation of NMDARs by neuromodulatory transmitters like dopamine, serotonin, or acetylcholine. We argue that this neurobiological mechanism can explain failures of self-monitoring, leading to a mechanistic explanation for first-rank symptoms as pathognomonic features of schizophrenia, and may provide a basis for future diagnostic classifications with physiologically defined patient subgroups. Finally, we test the explanatory power of our theory against a list of empirical facts about schizophrenia.

1,073 citations