The Hayling Sentence Completion Task (HSCT) is known to
activate left hemisphere frontal and temporal language regions. However, the effective connectivity between frontal and temporal language regions associated with the task has yet to be examined. The aims of the study were to examine activation and effective connectivity during the HSCT using a functional magnetic resonance imaging (fMRI) paradigm in which participants made overt verbal responses. We predicted that producing an incongruent response (response suppression), compared to a congruent one (response initiation), would be associated with greater activation in the left prefrontal cortex and an increase
in the effective connectivity between temporal and frontal
regions. Fifteen participants were scanned while completing
80 sentence stems. The congruency and constraint of sentences varied across trials. Dynamic Causal Modeling (DCM) and Bayesian Model Selection (BMS) were used to compare a set of alternative DCMs of fronto-temporal connectivity. The HSCT activated regions in the left temporal and prefrontal cortices, and the cuneus. Response suppression was associated with greater activation in the left middle and orbital frontal gyri and the bilateral precuneus than response initiation. Left middle temporal and frontal regions identified by the conventional fMRI
analyses were entered into the DCM analysis. Using a systematic BMS procedure, the optimal DCM showed that the connection from the left middle temporal gyrus, which was driven by verbal stimuli per se, was significantly increased in strength during response suppression compared to initiation. Greater effective connectivity between left temporal and prefrontal regions during response suppression may reflect the transfer of information from posterior temporal regions where semantic and lexical information
is stored to prefrontal regions where it is manipulated
in preparation for an appropriate response.
In a recent fMRI study with identical word stimuli we demonstrated task-dependent lateralization of brain activity during visual processing, with left-hemispheric activations for letter decisions and right-hemispheric activations for visuospatial decisions (Stephan, K.E.,
Marshall, J.C., Friston, K.J., Rowe, J.B., Ritzl, A., Zilles, K., Fink, G.R., 2003. Lateralized Cognitive
Processes and Lateralized Task Control in the Human Brain. Science 301, 384–386). In order to explore the temporal dynamics of these lateralized processes we here recorded
multichannel event-related potentials (ERPs) using the same stimuli. ERP data were analysed with current source density reconstruction (CDR). Contrasting the ERP results
elicited by the two tasks, source deconvolution showed enhanced activity during letter decisions in Broca's area from 200–250 ms during letter decisions and during visuospatial decisions in the right posterior parietal cortex (PPC) from 175–200 msand 250–275 ms. Prior to
these activations ERP data revealed an initiation of activity within the anterior cingulate cortex (ACC) from 125–150 ms followed by a late activation of this region from 400–425 ms. Consistent with our previous fMRI study the current electrophysiological data support the
notion that lateralized cognitive processes may depend on task requirements rather than stimulus properties. The current results extend our previous findings as they allow insights into the temporal dynamics of these lateralized processes and their relations to task control processes. The temporal deconvolution of ERPs suggests an early differential involvement of Broca's area in letter-processing and of PPC during visuospatial processing. In addition, activation of ACC prior and after this differential activation is consistent with previous
findings suggesting that this area may be involved in cognitive control.
Mit dem Ausbruch des Ersten Weltkrieges am 1. August 1914 brach die liberale Wirtschaftsordnung nach einer langen Phase der Expansion buchstäblich über Nacht zusammen. Der Krieg entwickelte sich rasch zu einem hochtechnisierten Produktions- und Abnutzungskampf, der Millionen von Menschen das Leben kostete. Um die «Materialschlachten» durchstehen zu können, wurde nicht nur die Kontrolle und optimale Nutzung wirtschaftlicher Ressourcen, sondern auch die Schwächung der Schlagkraft des Gegners zu einer zentralen Aufgabe der Kriegsführung. Obwohl die Schweiz als neutrales Land nicht direkt in den Krieg verstrickt war, wirkten sich der immer härter geführte Wirtschaftskrieg und die Blockadepolitik der Entente auch auf die Schweizer Wirtschaft aus. Neuen Absatzmärkten und teilweise hohen Gewinnen standen eine zunehmende Regulierungsdichte und eine immer stärker eingeschränkte Handlungsfreiheit gegenüber. Anhand von 16 Fallstudien ermöglicht der vorliegende Band erstmals einen Einblick in die wechselvolle und spannende Geschichte von Schweizer Unternehmen im Ersten Weltkrieg. Entstanden ist eine Publikation, die alle wichtigen Branchen abdeckt und damit eine solide Grundlage für dieses noch kaum erforschte Thema legt.
The neurophysiology of eye movements has been studied extensively, and several computational models have been proposed for decision-making processes that underlie the generation of eye movements towards a visual stimulus in a situation of uncertainty. One class of models, known as linear rise-to-threshold models, provides an economical, yet broadly applicable, explanation for the observed
variability in the latency between the onset of a peripheral visual target and the saccade towards it. So
far, however, these models do not account for the dynamics of learning across a sequence of stimuli, and they do not apply to situations in which subjects are exposed to events with conditional probabilities. In this methodological paper, we extend the class of linear rise-to-threshold models to address these limitations. Specifically, we reformulate previous models in terms of a generative, hierarchical model, by combining two separate sub-models that account for the interplay between learning of target locations across trials and the decision-making process within trials. We derive a maximum-likelihood scheme for
parameter estimation as well as model comparison on the basis of log likelihood ratios. The utility of the
integrated model is demonstrated by applying it to empirical saccade data acquired from three healthy
subjects. Model comparison is used (i) to show that eye movements do not only reflect marginal but also
conditional probabilities of target locations, and (ii) to reveal subject-specific learning profiles over trials.
These individual learning profiles are sufficiently distinct that test samples can be successfully mapped
onto the correct subject by a naïve Bayes classifier. Altogether, our approach extends the class of linear
rise-to-threshold models of saccadic decision making, overcomes some of their previous limitations, and
enables statistical inference both about learning of target locations across trials and the decision-making process within trials.
Cultural boundaries have often been the basis for discrimination, nationalism, religious wars, and genocide. Little is known, however, about how cultural groups form or the evolutionary forces behind group affiliation and in-group favoritism. Hence, we examine these forces and show that arbitrary symbolic markers, though initially meaningless, evolve to play a key role in cultural group formation and in-group favoritism because they enable a population of heterogeneous individuals to solve important coordination problems. This process requires that individuals differ in some critical but unobservable way and that their markers are freely and flexibly chosen. If these conditions are met, markers become accurate predictors of behavior. The resulting social environment
includes strong incentives to bias interactions toward others with the same marker, and subjects accordingly show strong in-group favoritism. When markers do not acquire meaning as accurate predictors of behavior, players show a dramatically reduced taste for in-group favoritism. Our results support the prominent evolutionary hypothesis that cultural processes can reshape the selective pressures facing individuals and so favor the evolution of behavioral traits not previously advantaged.
Bayesian model selection (BMS) is a powerful method for determining the most likely among a set of competing hypotheses about the mechanisms that generated observed data. BMS has recently found widespread application in neuroimaging, particularly in the context of dynamic causal modelling (DCM). However, so far, combining BMS results from several subjects has relied on simple (fixed effects) metrics, e.g. the group Bayes factor (GBF), that do not account for group heterogeneity or outliers. In this paper, we compare the GBF with two random effects methods for BMS at the between-subject or group level. These methods provide inference on model-space using a classical and Bayesian perspective respectively. First, a classical (frequentist) approach uses the log model evidence as a subject-specific summary statistic. This enables one to use analysis of variance to test for differences in log-evidences over models, relative to inter-subject differences. We then consider the same problem in Bayesian terms and describe a novel hierarchical model, which is optimised to furnish a probability density on the models themselves. This new variational Bayes method rests on treating the model as a random variable and estimating the parameters of a Dirichlet distribution which describes the probabilities for all models considered. These probabilities then define a multinomial distribution over model space, allowing one to compute how likely it is that a specific model generated the data of a randomly chosen subject as well as the exceedance probability of one model being more likely than any other model. Using empirical and synthetic data, we show that optimising a conditional density of the model probabilities, given the log-evidences for each model over subjects, is more informative and appropriate than both the GBF and frequentist tests of the log-evidences. In particular, we found that the hierarchical Bayesian approach is considerably more robust than either of the other approaches in the presence of outliers. We expect that this new random effects method will 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 or selecting among competing computational models of learning and decision-making.
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.
In this paper, we describe a dynamic causal model (DCM) of steady-state responses in electrophysiological data that are summarised in terms of their cross-spectral density. These spectral data-features are generated by a biologically plausible, neural-mass model of coupled electromagnetic sources; where each source comprises three sub-populations. Under linearity and stationarity assumptions, the model's biophysical parameters (e.g., post-synaptic receptor density and time constants) prescribe the cross-spectral density of responses measured directly (e.g., local field potentials) or indirectly through some lead-field (e.g., electroencephalographic and magnetoencephalographic data). Inversion of the ensuing DCM provides conditional probabilities on the synaptic parameters of intrinsic and extrinsic connections in the underlying neuronal network. This means we can make inferences about synaptic physiology, as well as changes induced by pharmacological or behavioural manipulations, using the cross-spectral density of invasive or non-invasive electrophysiological recordings. In this paper, we focus on the form of the model, its inversion and validation using synthetic and real data. We conclude with an illustrative application to multi-channel local field potential data acquired during a learning experiment in mice.
Distinct regions in the left inferior frontal gyrus (IFG) preferentially support the processing of different word-types (e.g., real words, pseudowords) and tasks (e.g., lexical decisions, phonological decisions) in visual word recognition. However, the functional connectivity underlying the task-related specialisation of regions in the left IFG is not yet well understood. In this study we investigated the neural mechanisms driving the interaction of WORD-TYPE (real word vs. pseudoword) and TASK (lexical vs. phonological decision) in Brodmann's area (BA) 45 in the left IFG using dynamic causal modelling (DCM). Four different models were compared, all of which included left BA44, left BA45, and left inferior temporal gyrus (ITG). In each model, the visual presentation of words and pseudowords is assumed to directly evoke activity in the ITG and is then thought to be subsequently propagated to BA45 and to BA44 via direct intrinsic connections. The models differed with regard to which connections were modulated by the different tasks. Both tasks were assumed to either modulate the ITG_BA45 connection (Model #1), or the BA44_BA45 connection (Model #2), or both connections in parallel (Model #3). In Model #4 lexical decisions modulated the ITG_BA45 connection, whereas phonological decisions modulated the BA44_BA45 connection. Bayesian model selection revealed a superiority of Model #1. In this model, the strength of the ITG_BA45 connection was enhanced during lexical decisions. This model is in line with the hypothesis that left BA 45 supports explicit lexical decisions during visual word recognition based on lexical access in the ITG.
