Biomedical subjects

K E Stephan

Publications and source records attributed to K E Stephan.

11 recordsLinked to original sources

PubMed2005

CoCoDat: a database system for organizing and selecting quantitative data on single neurons and neuronal microcircuitry.

We present a novel database system for organizing and selecting quantitative experimental data on single neurons and neuronal microcircuitry that has proven useful for reference-keeping, experimental planning and computational modelling. Building on our previous experience with large neuroscientific databases, the system takes into account the diversity and method-dependence of single cell and microcircuitry data and provides tools for entering and retrieving published data without a priori interpretation or summarizing. Data representation is based on the framework suggested by biophysical theory and enables flexible combinations of data on membrane conductances, ionic and synaptic currents, morphology, connectivity and firing patterns. Innovative tools have been implemented for data retrieval with optional relaxation of search criteria along the conceptual dimensions of brain region, cortical layer, cell type and subcellular compartment. The relaxation procedures help to overcome the traditional trade-off between exact, non-interpreted data representation in the original nomenclature and convenient data retrieval. We demonstrate the use of these tools for the construction, tuning and validation of a multicompartmental model of a layer V pyramidal cell from the rat barrel cortex. CoCoDat is freely available at . Its application is scalable from offline use by individual researchers via local laboratory networks to a federation of distributed web sites in platform-independent XML format using Axiope tools.

Action Potentials↗

PubMed2005

Mixed-effects and fMRI studies.

This note concerns mixed-effect (MFX) analyses in multisession functional magnetic resonance imaging (fMRI) studies. It clarifies the relationship between mixed-effect analyses and the two-stage "summary statistics" procedure (Holmes, A.P., Friston, K.J., 1998. Generalisability, random effects and population inference. NeuroImage 7, S754) that has been adopted widely for analyses of fMRI data at the group level. We describe a simple procedure, based on restricted maximum likelihood (ReML) estimates of covariance components, that enables full mixed-effects analyses in the context of statistical parametric mapping. Using this procedure, we compare the results of a full mixed-effects analysis with those obtained from the simpler two-stage procedure and comment on the situations when the two approaches may give different results.

Brain↗

PubMed2004

Comparing dynamic causal models.

This article describes the use of Bayes factors for comparing dynamic causal models (DCMs). DCMs are used to make inferences about effective connectivity from functional magnetic resonance imaging (fMRI) data. These inferences, however, are contingent upon assumptions about model structure, that is, the connectivity pattern between the regions included in the model. Given the current lack of detailed knowledge on anatomical connectivity in the human brain, there are often considerable degrees of freedom when defining the connectional structure of DCMs. In addition, many plausible scientific hypotheses may exist about which connections are changed by experimental manipulation, and a formal procedure for directly comparing these competing hypotheses is highly desirable. In this article, we show how Bayes factors can be used to guide choices about model structure, both concerning the intrinsic connectivity pattern and the contextual modulation of individual connections. The combined use of Bayes factors and DCM thus allows one to evaluate competing scientific theories about the architecture of large-scale neural networks and the neuronal interactions that mediate perception and cognition.

Attention↗

PubMed2004

Modelling functional integration: a comparison of structural equation and dynamic causal models.

The brain appears to adhere to two fundamental principles of functional organisation, functional integration and functional specialisation, where the integration within and among specialised areas is mediated by effective connectivity. In this paper, we review two different approaches to modelling effective connectivity from fMRI data, structural equation models (SEMs) and dynamic causal models (DCMs). In common to both approaches are model comparison frameworks in which inferences can be made about effective connectivity per se and about how that connectivity can be changed by perceptual or cognitive set. Underlying the two approaches, however, are two very different generative models. In DCM, a distinction is made between the 'neuronal level' and the 'hemodynamic level'. Experimental inputs cause changes in effective connectivity expressed at the level of neurodynamics, which in turn cause changes in the observed hemodynamics. In SEM, changes in effective connectivity lead directly to changes in the covariance structure of the observed hemodynamics. Because changes in effective connectivity in the brain occur at a neuronal level DCM is the preferred model for fMRI data. This review focuses on the underlying assumptions and limitations of each model and demonstrates their application to data from a study of attention to visual motion.

Algorithms↗

PubMed2001

Advanced database methodology for the Collation of Connectivity data on the Macaque brain (CoCoMac).

The need to integrate massively increasing amounts of data on the mammalian brain has driven several ambitious neuroscientific database projects that were started during the last decade. Databasing the brain's anatomical connectivity as delivered by tracing studies is of particular importance as these data characterize fundamental structural constraints of the complex and poorly understood functional interactions between the components of real neural systems. Previous connectivity databases have been crucial for analysing anatomical brain circuitry in various species and have opened exciting new ways to interpret functional data, both from electrophysiological and from functional imaging studies. The eventual impact and success of connectivity databases, however, will require the resolution of several methodological problems that currently limit their use. These problems comprise four main points: (i) objective representation of coordinate-free, parcellation-based data, (ii) assessment of the reliability and precision of individual data, especially in the presence of contradictory reports, (iii) data mining and integration of large sets of partially redundant and contradictory data, and (iv) automatic and reproducible transformation of data between incongruent brain maps. Here, we present the specific implementation of the 'collation of connectivity data on the macaque brain' (CoCoMac) database (http://www.cocomac.org). The design of this database addresses the methodological challenges listed above, and focuses on experimental and computational neuroscientists' needs to flexibly analyse and process the large amount of published experimental data from tracing studies. In this article, we explain step-by-step the conceptual rationale and methodology of CoCoMac and demonstrate its practical use by an analysis of connectivity in the prefrontal cortex.

Animals↗

PubMed2001

Multimodal characterisation of cortical areas by multivariate analyses of receptor binding and connectivity data.

Cortical areas are regarded as fundamental structural and functional units within the information processing networks of the brain. Their properties have been described extensively by cyto-, myelo- and chemoarchitectonics, cortical and extracortical connectivity patterns, receptive field mapping, activation properties, lesion effects, and other structural and functional characteristics. Systematic integrative approaches aiming at multimodal characterisations of cortical areas or at the delineation of global features of the cortical network, however, are still scarce and usually limited to a single data modality, such as cytoarchitectonical or tract tracing data. Here we describe a methodological framework for the systematic evaluation, comparison and integration of different data modalities from the brain and demonstrate its practical application and significance in the analysis of receptor binding and connectivity data within the motor and visual cortices of macaque monkeys. The framework builds on algorithmic methods to convert data between different cortical parcellation schemes, as well as on statistical techniques for the exploration of multivariate data sets comprising data of different types and scales. Thereby, we establish a relationship between intrinsic area properties as expressed by quantitative receptor binding, and extrinsic inter-area communication, which relies on anatomical connectivity. Our analyses provide preliminary evidence for a good correspondence of these two data types in the motor cortex, and their partial discrepancy in the visual cortex, raising hypotheses about the different organisational aspects highlighted by receptors and connectivity. The methodological framework presented here is flexible enough to accommodate a wide range of further data modalities, and is specific enough to permit novel insights and predictions concerning brain organisation. Thus, this approach promises to be very useful in the endeavour to characterise multimodal structure-function relationships in the brain.

Animals↗

PubMed2001

Effects of olanzapine on cerebellar functional connectivity in schizophrenia measured by fMRI during a simple motor task.

BACKGROUND: According to current theories, schizophrenia results from altered connectivity in brain circuits for fundamental cognitive operations. Consequently, the poorly understood mechanisms of neuroleptic treatment may be explainable by altered functional interactions within such networks. The 'cognitive dysmetria' model hypothesizes that one key structure in these circuits is the cerebellum. To investigate the effects of olanzapine on cerebellar functional connectivity (CFC), a seed-voxel correlation analysis (SVCA) was used in a functional magnetic resonance imaging (fMRI) study of a simple finger-tapping task. METHODS: fMRI scans were obtained from six schizophrenic patients under both drug-free and olanzapine-treated conditions and from a matched control group of six healthy subjects at corresponding time points. SVCAs were performed for anatomically and functionally standardized seed voxels in the anterior cerebellum. SVCA results were then processed by three different randomization analyses. RESULTS: The analyses revealed that olanzapine caused widespread changes of CFC, including prominent changes in prefrontal cortex and mediodorsal thalamus. Significant changes in motor structures were found after subtractions within both groups and may thus indicate repetition effects rather than drug effects. Olanzapine 'normalized' the patients' CFC patterns for the right, but not for the left cerebellum. CONCLUSION: Even for a simple motor task, olanzapine affects functional interactions between the cerebellum and many non-motor brain regions, including elements of the 'cognitive dysmetria' circuit. Altogether, our findings suggest that olanzapine has a stronger differential effect on neural activity in prefrontal cortex and thalamus than in motor structures.

Adult↗

PubMed2000

Coordinate-independent mapping of structural and functional data by objective relational transformation (ORT).

Neuroscience has produced an enormous amount of structural and functional data. Powerful database systems are required to make these data accessible for computational approaches such as higher-order analyses and simulations. Available databases for key data such as anatomical and functional connectivity between cortical areas, however, are still hampered by methodological problems. These problems arise predominantly from the parcellation problem, the use of incongruent parcellation schemes by different authors. We here present a coordinate-independent mathematical method to overcome this problem: objective relational transformation (ORT). Based on new classifications for brain data and on methods from theoretical computer science, ORT represents a formally defined, transparent transformation method for reproducible, coordinate-independent mapping of brain data to freely chosen parcellation schemes. We describe the methodology of ORT and discuss its strengths and limitations. Using two practical examples, we show that ORT in conjunction with connectivity databases like CoCoMac (http://www.cocomac.org) is an important tool for analyses of cortical organization and structure-function relationships.

Animals↗

PubMed2000

Computational analysis of functional connectivity between areas of primate cerebral cortex.

Recent analyses of association fibre networks in the primate cerebral cortex have revealed a small number of densely intra-connected and hierarchically organized structural systems. Corresponding analyses of data on functional connectivity are required to establish the significance of these structural systems. We therefore built up a relational database by systematically collating published data on the spread of activity after strychnine-induced disinhibition in the macaque cerebral cortex in vivo. After mapping these data to two different parcellation schemes, we used three independent methods of analysis which demonstrate that the cortical network of functional interactions is not homogeneous, but shows a clear segregation into functional assemblies of mutually interacting areas. The assemblies suggest a principal division of the cortex into visual, somatomotor and orbito-temporo-insular systems, while motor and somatosensory areas are inseparably interrelated. These results are largely compatible with corresponding analyses of structural data of mammalian cerebral cortex, and deliver the first functional evidence for 'small-world' architecture of primate cerebral cortex.

Animals↗

PubMed2000

Functional dissociation between medial and lateral prefrontal cortical spatiotemporal activation in negative and positive emotions: a combined fMRI/MEG study.

The orbitofrontal cortex has been cytoarchitectonically and connectionally subdivided into a medial and a lateral part which are assumed to subserve distinct functions in emotional processing. However the exact spatiotemporal mechanisms of negative and positive emotional processing in medial and lateral orbitofrontal cortex remain unclear. We therefore investigated spatiotemporal orbitofrontal and prefrontal cortical activation patterns during emotional stimulation in a combined fMRI/MEG study. We investigated 10 healthy subjects, 5 women and 5 men. Positive and negative pictures from the International Affective Picture system (IAPS) were used for emotional stimulation, whereas neutral and gray pictures were taken as control conditions. fMRI/MEG measurements covered the whole frontal lobe and a time window between -2000 and +200 ms around motor responses (right index finger extension) associated with each picture. Positively and negatively correlated activities were determined in various prefrontal/frontal cortical regions in fMRI. Isocontour maps and single dipoles in MEG were analyzed in 50 ms time windows ranging from -2000 to +200 ms. Dipoles and fMR images were mapped on three-dimensional anatomical MRI so that anatomical localization of single dipoles and regional fMRI activity could be compared. Both negative and positive emotional conditions differed from non-emotional control conditions by strong orbitofrontal and lateral prefrontal activation as well as by the presence of early magnetic fields (-1700 to +1100 ms). Negative emotional processing was characterized by strong medial orbitofrontal activation and earlier (-1700 ms), stronger and more medially oriented orbitofrontal dipoles. In contrast positive emotional processing showed a rather strong activation in lateral prefrontal cortex with later (-1500 ms), weaker and more laterally oriented orbito and prefrontal dipoles. Negative emotional processing can be characterized by strong and early medial orbitofrontal cortical activation, whereas positive emotional processing showed rather later and weaker activation in lateral orbitofrontal/prefrontal cortex. Such a functional dissociation between medial and lateral orbito-frontal/prefrontal cortex during negative and positive emotional processing lends further support to the assumption of a functional subdivision in the orbitofrontal cortex.

Adult↗

PubMedDate not supplied

Useless or helpful? The "limbic system" concept.

The "limbic system" is a widespread and frequently used concept in the neurosciences although it is characterized by many different and often vague or even contradictory definitions. This concept has therefore received an increasing amount of critique during the past years. An appraisal of various opinions - ranging from rejection to limited use and support - led us to suggestions concerning its appropriate use. Most importantly, the diversity of definitions and the weak empirical foundation require careful consideration as to what extent certain aspects of the "limbic system" concept can be useful heuristic tools in scientific reasoning.

Animals↗