Contributions in Merlin

We describe work that addresses the cortical basis for the analysis of auditory objects using 'generic' sounds that do not correspond to any particular events or sources (like vowels or voices) that have semantic association. The experiments involve the manipulation of synthetic sounds to produce systematic changes of stimulus features, such as spectral envelope. Conventional analyses of normal functional imaging data demonstrate that the analysis of spectral envelope and perceived timbral change involves a network consisting of planum temporale (PT) bilaterally and the right superior temporal sulcus (STS). Further analysis of imaging data using dynamic causal modelling (DCM) and Bayesian model selection was carried out in the right hemisphere areas to determine the effective connectivity between these auditory areas. Specifically, the objective was to determine if the analysis of spectral envelope in the network is done in a serial fashion (that is from HG to PT to STS) or parallel fashion (that is PT and STS receives input from HG simultaneously). Two families of models, serial and parallel (16 in total) that represent different hypotheses about the connectivity between HG, PT and STS were selected. The models within a family differ with respect to the pathway that is modulated by the analysis of spectral envelope. After the models are identified, Bayesian model selection procedure is then used to select the 'optimal' model from the specified models. The data strongly support a particular serial model containing modulation of the HG to PT effective connectivity during spectral envelope variation. Parallel work in neurological subjects addresses the effect of lesions to different parts of this network. We have recently studied in detail subjects with 'dystimbria': an alteration in the perceived quality of auditory objects distinct from pitch or loudness change. The subjects have lesions of the normal network described above with normal perception of pitch strength but abnormal perception of the analysis of spectral envelope change.

Dynamic causal modelling (DCM) has been applied recently to event-related responses (ERPs) measured with EEG/MEG. DCM attempts to explain ERPs using a network of interacting cortical sources and waveform differences in terms of coupling changes among sources. The aim of this work was to establish the validity of DCM by assessing its reproducibility across subjects. We used an oddball paradigm to elicit mismatch responses. Sources of cortical activity were modelled as equivalent current dipoles, using a biophysical informed spatiotemporal forward model that included connections among neuronal subpopulations in each source. Bayesian inversion provided estimates of changes in coupling among sources and the marginal likelihood of each model. By specifying different connectivity models we were able to evaluate three different hypotheses: differences in the ERPs to rare and frequent events are mediated by changes in forward connections (F-model), backward connections (B-model) or both (FB-model). The results were remarkably consistent over subjects. In all but one subject, the forward model was better than the backward model. This is an important result because these models have the same number of parameters (i.e., the complexity). Furthermore, the FB-model was significantly better than both, in 7 out of 11 subjects. This is another important result because it shows that a more complex model (that can fit the data more accurately) is not necessarily the most likely model. At the group level the FB-model supervened. We discuss these findings in terms of the validity and usefulness of DCM in characterising EEG/MEG data and its ability to model ERPs in a mechanistic fashion.

Complex processes resulting from interaction of multiple elements can rarely be understood by analytical scientific approaches alone; additional, mathematical models of system dynamics are required. This insight, which disciplines like physics have embraced for a long time already, is gradually gaining importance in the study of cognitive processes by functional neuroimaging. In this field, causal mechanisms in neural systems are described in terms of effective connectivity. Recently, dynamic causal modelling (DCM) was introduced as a generic method to estimate effective connectivity from neuroimaging data in a Bayesian fashion. One of the key advantages of DCM over previous methods is that it distinguishes between neural state equations and modality-specific forward models that translate neural activity into a measured signal. Another strength is its natural relation to Bayesian model selection (BMS) procedures. In this article, we review the conceptual and mathematical basis of DCM and its implementation for functional magnetic resonance imaging data and event-related potentials. After introducing the application of BMS in the context of DCM, we conclude with an outlook to future extensions of DCM. These extensions are guided by the long-term goal of using dynamic system models for pharmacological and clinical applications, particularly with regard to synaptic plasticity.

The aim of this study was to measure the contextual influence of globally coherent motion on visual cortical responses using functional magnetic resonance imaging. Our motivation was to test a prediction from representational theories of perception (i.e. predictive coding) that primary visual responses should be suppressed by top-down influences during coherent motion. We used a sparse stimulus array such that each element could not fall within the same classical receptive field of primary visual cortex neurons (i.e. precluding lateral interactions within V1). This enabled us to attribute differences, in striate cortex responses, to extra-classical receptive field effects mediated by backward connections. In accord with theoretical predictions we were able to demonstrate suppression of striate cortex activations to coherent relative to incoherent motion. These results suggest that suppression of primary visual cortex responses to coherent motion reflect extra-classical effects mediated by backward connections.

The mechanisms underlying interhemispheric integration (IHI) remain poorly understood, particularly for lateralized cognitive processes. To test competing theories of IHI, we constructed and fitted dynamic causal models to functional magnetic resonance data from two visual tasks that operated on identical stimuli but showed opposite hemispheric dominance. Using a systematic Bayesian model selection procedure, we found that, in the ventral visual stream, which was activated by letter judgments, interhemispheric connections mediated asymmetric information transfer from the nonspecialized right to the specialized left hemisphere when the latter did not have direct access to stimulus information. Notably, this form of IHI did not engage all areas activated by the task but was specific for areas in the lingual and fusiform gyri. In the dorsal stream, activated by spatial judgments, it did not matter which hemisphere received the stimulus: interhemispheric coupling increased bidirectionally, reflecting recruitment of the nonspecialized left hemisphere. Again, not all areas activated by the task were involved in this form of IHI; instead, it was restricted to interactions between areas in the superior parietal gyrus. Overall, our results provide direct neurophysiological evidence, in terms of effective connectivity, for the existence of context-dependent mechanisms of IHI that are implemented by specific visual areas during task-driven lateralization.

Traditionally, anatomical and physiological descriptions of hemispheric specialization have focused on hemispheric asymmetries of local brain structure or local functional properties, respectively. This article reviews the current state of an alternative approach that aims at unraveling the causes and functional principles of hemispheric specialization in terms of asymmetries in connectivity. Starting with an overview of the historical origins of the concept of lateralization, we briefly review recent evidence from anatomical and developmental studies that asymmetries in structural connectivity may be a critical factor shaping hemispheric specialization. These differences in anatomical connectivity, which are found both at the intra- and inter-regional level, are likely to form the structural substrate of different functional principles of information processing in the two hemispheres. The main goal of this article is to describe how these functional principles can be characterized using functional neuroimaging in combination with models of functional and effective connectivity. We discuss the methodology of established models of connectivity which are applicable to data from positron emission tomography and functional magnetic resonance imaging and review published studies that have applied these approaches to characterize asymmetries of connectivity during lateralized tasks. Adopting a model-based approach enables functional imaging to proceed from mere descriptions of asymmetric activation patterns to mechanistic accounts of how these asymmetries are caused.

It is a longstanding scientific insight that understanding processes that result from the interaction of multiple elements require mathematical models of system dynamics (von Bertalanffy 1969). This notion is an increasingly important theme in neuroscience, particularly in neuroimaging, where causal mechanisms in neural systems are described in terms of effective connectivity. Here, we review established models of effective connectivity that are applied to data acquired with positron emission tomography (PET), functional magnetic resonance imaging (fMRI), electroencephalography (EEG) or magnetoencephalography (MEG). We start with an outline of general systems theory, a very general framework for formalizing the description of systems. This framework will guide the subsequent description of various established models of effective connectivity, including structural equation modeling (SEM), multivariate autoregressive modeling (MAR) and dynamic causal modeling (DCM). We focus particularly on DCM which distinguishes between neural state equations and a biophysical forward model that translates neural activity into a measured signal. After presenting some examples of applications of DCM to fMRI and EEG data, we conclude with some thoughts on pharmacological and clinical applications of models of effective connectivity.

When moving figures are occluded and revealed piecemeal as they move across a narrow slit, observers may perceive them as integrated but distorted. They may also perceive much more of the figure as simultaneously visible than is actually presented at any moment. We obtained quantitative measures of both the perceived distortion and perceived simultaneity under free viewing conditions and related these phenomena to spontaneous pursuit eye movements, the retinal painting produced by this pursuit, and the occurrence of saccades. We found both shape compressions and expansions, depending on figure velocity. We also obtained quantitative evidence that observers perceived slices of the moving figures far wider than the slit through which they were presented. Eye-motion records and retinal stabilization revealed that spontaneous pursuit and the spatially extended images that could have been painted out by this pursuit played no role in the perceived global shape distortions and made only a small contribution to the perceived simultaneity. Therefore, under free viewing conditions, both the distortions and simultaneity of these "anorthoscopic" figure percepts must be the consequence of a postretinal process that integrates the figures over space and time independent of eye motions.