Rolf Kötter

Radboud University Nijmegen, Nymegen, Gelderland, Netherlands

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Publications (89)330.59 Total impact

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    ABSTRACT: Primate sensory systems subserve complex neurocomputational functions. Consequently, these systems are organised anatomically in a distributed fashion, commonly linking areas to form specialised processing streams. Each stream is related to a specific function, as evidenced from studies of the visual cortex, which features rather prominent segregation into spatial and non-spatial domains. It has been hypothesised that other sensory systems, including auditory, are organised in a similar way on the cortical level. Recent studies offer rich qualitative evidence for the dual stream hypothesis. Here we provide a new paradigm to quantitatively uncover these patterns in the auditory system, based on an analysis of multiple anatomical studies using multivariate techniques. As a test case, we also apply our assessment techniques to more ubiquitously-explored visual system. Importantly, the introduced framework opens the possibility for these techniques to be applied to other neural systems featuring a dichotomised organisation, such as language or music perception.
    Brain and Language 06/2014; 135C:73-84. · 3.39 Impact Factor
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    ABSTRACT: Primate sensory systems subserve complex neurocomputational functions. Consequently, these systems are organised anatomically in a distributed fashion, commonly linking areas to form specialised processing streams. Each stream is related to a specific function, as evidenced from studies of the visual cortex, which features rather prominent segregation into spatial and non-spatial domains. It has been hypothesised that other sensory systems, including auditory, are organised in a similar way on the cortical level. Recent studies offer rich qualitative evidence for the dual stream hypothesis. Here we provide a new paradigm to quantitatively uncover these patterns in the auditory system, based on an analysis of multiple anatomical studies using multivariate techniques. As a test case, we also apply our assessment techniques to more ubiquitously-explored visual system. Importantly, the introduced framework opens the possibility for these techniques to be applied to other neural systems featuring a dichotomised organisation, such as language or music perception.
    Brain and Language 01/2014; 135:73–84. · 3.39 Impact Factor
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    ABSTRACT: The Scalable Brain Atlas (SBA) is a collection of web services that provide unified access to a large collection of brain atlas templates for different species. Its main component is an atlas viewer that displays brain atlas data as a stack of slices in which stereotaxic coordinates and brain regions can be selected. These are subsequently used to launch web queries to resources that require coordinates or region names as input. It supports plugins which run inside the viewer and respond when a new slice, coordinate or region is selected. It contains 14 atlases in five species, and plugins to compute coordinate transformations, display anatomical connectivity and fiducial points, and retrieve properties, descriptions, definitions and 3d reconstructions of brain regions. The ambition of SBA is to provide a unified representation of all publicly available brain atlases directly in the web browser, while remaining a responsive and light weight resource that specializes in atlas comparisons, searches, coordinate transformations and interactive displays.
    12/2013;
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    ABSTRACT: Brain activity can be measured with several non-invasive neuroimaging modalities, but each modality has inherent limitations with respect to resolution, contrast and interpretability. It is hoped that multimodal integration will address these limitations by using the complementary features of already available data. However, purely statistical integration can prove problematic owing to the disparate signal sources. As an alternative, we propose here an advanced neural population model implemented on an anatomically sound cortical mesh with freely adjustable connectivity, which features proper signal expression through a realistic head model for the electroencephalogram (EEG), as well as a haemodynamic model for functional magnetic resonance imaging based on blood oxygen level dependent contrast (fMRI BOLD). It hence allows simultaneous and realistic predictions of EEG and fMRI BOLD from the same underlying model of neural activity. As proof of principle, we investigate here the influence on simulated brain activity of strengthening visual connectivity. In the future we plan to fit multimodal data with this neural population model. This promises novel, model-based insights into the brain's activity in sleep, rest and task conditions.
    Philosophical Transactions of The Royal Society A Mathematical Physical and Engineering Sciences 10/2011; 369(1952):3785-801. · 2.89 Impact Factor
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    ABSTRACT: Although gait disturbances are present in a substantial portion of patients with cerebral small vessel disease (SVD), their pathogenesis has not been clarified as they are not entirely explained by the white matter lesions (WMLs) and lacunar infarcts. The role of cortical thickness in these patients remains largely unknown. We aimed to assess the regions of cortical thickness associated with distinct gait parameters in patients with SVD, and whether these associations were dependent on WMLs and lacunar infarcts. MRI data were obtained from 415 subjects with SVD, aged between 50 and 85 years. We assessed cortical thickness using surface-based cortical thickness analysis, and gait performance using the GAITRite system. Cortical thickness of predominantly the orbitofrontal and ventrolateral prefrontal cortex, the inferior parietal lobe, cingulate areas and visual association cortices was positively related to stride length. Thickness of the primary and supplementary motor cortices and the cingulate cortex was positively related to cadence, while thickness of the orbitofrontal and ventrolateral prefrontal cortex, anterior cingulate cortex and especially the inferior parietal lobe and superior temporal gyrus was negatively related to stride width. The associations with stride length and width were partially explained by the subcortical WMLs and lacunar infarcts. Cortical thickness may therefore be important in gait disturbances in individuals with SVD, with different cortical patterns for specific gait parameters. We suggest that cortical atrophy is part of the disease processes in patients with SVD.
    NeuroImage 08/2011; 59(2):1478-84. · 6.25 Impact Factor
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    ABSTRACT: Progress in functional neuroimaging of the brain increasingly relies on the integration of data from complementary imaging modalities in order to improve spatiotemporal resolution and interpretability. However, the usefulness of merely statistical combinations is limited, since neural signal sources differ between modalities and are related non-trivially. We demonstrate here that a mean field model of brain activity can simultaneously predict EEG and fMRI BOLD with proper signal generation and expression. Simulations are shown using a realistic head model based on structural MRI, which includes both dense short-range background connectivity and long-range specific connectivity between brain regions. The distribution of modeled neural masses is comparable to the spatial resolution of fMRI BOLD, and the temporal resolution of the modeled dynamics, importantly including activity conduction, matches the fastest known EEG phenomena. The creation of a cortical mean field model with anatomically sound geometry, extensive connectivity, and proper signal expression is an important first step towards the model-based integration of multimodal neuroimages.
    Brain Topography 04/2010; 23(2):139-49. · 3.67 Impact Factor
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    ABSTRACT: Although it is being successfully implemented for exploration of the genome, discovery science has eluded the functional neuroimaging community. The core challenge remains the development of common paradigms for interrogating the myriad functional systems in the brain without the constraints of a priori hypotheses. Resting-state functional MRI (R-fMRI) constitutes a candidate approach capable of addressing this challenge. Imaging the brain during rest reveals large-amplitude spontaneous low-frequency (<0.1 Hz) fluctuations in the fMRI signal that are temporally correlated across functionally related areas. Referred to as functional connectivity, these correlations yield detailed maps of complex neural systems, collectively constituting an individual's "functional connectome." Reproducibility across datasets and individuals suggests the functional connectome has a common architecture, yet each individual's functional connectome exhibits unique features, with stable, meaningful interindividual differences in connectivity patterns and strengths. Comprehensive mapping of the functional connectome, and its subsequent exploitation to discern genetic influences and brain-behavior relationships, will require multicenter collaborative datasets. Here we initiate this endeavor by gathering R-fMRI data from 1,414 volunteers collected independently at 35 international centers. We demonstrate a universal architecture of positive and negative functional connections, as well as consistent loci of inter-individual variability. Age and sex emerged as significant determinants. These results demonstrate that independent R-fMRI datasets can be aggregated and shared. High-throughput R-fMRI can provide quantitative phenotypes for molecular genetic studies and biomarkers of developmental and pathological processes in the brain. To initiate discovery science of brain function, the 1000 Functional Connectomes Project dataset is freely accessible at www.nitrc.org/projects/fcon_1000/.
    Proceedings of the National Academy of Sciences 03/2010; 107(10):4734-9. · 9.74 Impact Factor
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    ABSTRACT: Emerging noninvasive neuroimaging techniques allow for the morphometric analysis of patterns of gray and white matter degeneration in vivo, which may help explain and predict the occurrence of cognitive impairment and Alzheimer's disease. A single center prospective follow-up study (Radboud University Nijmegen Diffusion tensor and Magnetic resonance imaging Cohort study (RUN DMC)) was performed involving 503 nondemented elderly individuals (50-85 years) with a history of symptomatic cerebral small vessel disease (SVD). Age was associated with a global reduction in cortical thickness, and this relationship was strongest for ventrolateral prefrontal cortex, auditory cortex, Wernicke's area, superior temporal lobe, and primary visual cortex. Right and left hemispheres differed in the thickness of language-related areas. White matter (WM) lesions were generally negatively correlated with cortical thickness, primarily in individuals over the age of 60, with the notable exception of Brodmann areas 4 and 5, which were positively correlated in age groups 50-60 and 60-70, respectively. The observed pattern of age-related decline may explain problems in memory and executive functions, which are already well documented in individuals with SVD. The additional gray matter loss affecting visual and auditory cortex, and specifically the head region of primary motor cortex, may indicate morphological correlates of impaired sensory and motor functions. The paradoxical positive relationship between WM lesion volume and cortical thickness in some areas may reflect early compensatory hypertrophy. This study raises a further interest in the mechanisms underlying cerebral gray and white matter degeneration in association with SVD, which will require further investigation with diffusion weighted and longitudinal MR studies.
    Human Brain Mapping 03/2010; 31(12):1983-92. · 6.88 Impact Factor
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    Frontiers in Neuroinformatics 01/2010; 4.
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    ABSTRACT: In a recent paper (Reid et al., 2009) we introduced a method to calculate optimal hierarchies in the visual network that utilizes continuous, rather than discrete, hierarchical levels, and permits a range of acceptable values rather than attempting to fit fixed hierarchical distances. There, to obtain a hierarchy, the sum of deviations from the constraints that define the hierarchy was minimized using linear optimization. In the short time since publication of that paper we noticed that many colleagues misinterpreted the meaning of the term "optimal hierarchy". In particular, a majority of them were under the impression that there was perhaps only one optimal hierarchy, but a substantial difficulty in finding that one. However, there is not only more than one optimal hierarchy but also more than one option for defining optimality. Continuing the line of this work we look at additional options for optimizing the visual hierarchy: minimizing the number of violated constraints and minimizing the maximal size of a constraint violation using linear optimization and mixed integer programming. The implementation of both optimization criteria is explained in detail. In addition, using constraint sets based on the data from Felleman and Van Essen (1991), optimal hierarchies for the visual network are calculated for both optimization methods.
    Frontiers in Neuroinformatics 01/2010; 4:7.
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    ABSTRACT: The bewildering complexity of cortical microcircuits at the single cell level gives rise to surprisingly robust emergent activity patterns at the level of laminar and columnar local field potentials (LFPs) in response to targeted local stimuli. Here we report the results of our multivariate data-analytic approach based on simultaneous multi-site recordings using micro-electrode-array chips for investigation of the microcircuitary of rat somatosensory (barrel) cortex. We find high repeatability of stimulus-induced responses, and typical spatial distributions of LFP responses to stimuli in supragranular, granular, and infragranular layers, where the last form a particularly distinct class. Population spikes appear to travel with about 33 cm/s from granular to infragranular layers. Responses within barrel related columns have different profiles than those in neighbouring columns to the left or interchangeably to the right. Variations between slices occur, but can be minimized by strictly obeying controlled experimental protocols. Cluster analysis on normalized recordings indicates specific spatial distributions of time series reflecting the location of sources and sinks independent of the stimulus layer. Although the precise correspondences between single cell activity and LFPs are still far from clear, a sophisticated neuroinformatics approach in combination with multi-site LFP recordings in the standardized slice preparation is suitable for comparing normal conditions to genetically or pharmacologically altered situations based on real cortical microcircuitry.
    Neural networks: the official journal of the International Neural Network Society 10/2009; · 1.88 Impact Factor
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    ABSTRACT: Functionally relevant large scale brain dynamics operates within the framework imposed by anatomical connectivity and time delays due to finite transmission speeds. To gain insight on the reliability and comparability of large scale brain network simulations, we investigate the effects of variations in the anatomical connectivity. Two different sets of detailed global connectivity structures are explored, the first extracted from the CoCoMac database and rescaled to the spatial extent of the human brain, the second derived from white-matter tractography applied to diffusion spectrum imaging (DSI) for a human subject. We use the combination of graph theoretical measures of the connection matrices and numerical simulations to explicate the importance of both connectivity strength and delays in shaping dynamic behaviour. Our results demonstrate that the brain dynamics derived from the CoCoMac database are more complex and biologically more realistic than the one based on the DSI database. We propose that the reason for this difference is the absence of directed weights in the DSI connectivity matrix.
    Journal of neuroscience methods 08/2009; 183(1):86-94. · 2.30 Impact Factor
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    ABSTRACT: A growing body of neuroimaging research has documented that, in the absence of an explicit task, the brain shows temporally coherent activity. This so-called "resting state" activity or, more explicitly, the default-mode network, has been associated with daydreaming, free association, stream of consciousness, or inner rehearsal in humans, but similar patterns have also been found under anesthesia and in monkeys. Spatiotemporal activity patterns in the default-mode network are both complex and consistent, which raises the question whether they are the expression of an interesting cognitive architecture or the consequence of intrinsic network constraints. In numerical simulation, we studied the dynamics of a simplified cortical network using 38 noise-driven (Wilson-Cowan) oscillators, which in isolation remain just below their oscillatory threshold. Time delay coupling based on lengths and strengths of primate corticocortical pathways leads to the emergence of 2 sets of 40-Hz oscillators. The sets showed synchronization that was anticorrelated at <0.1 Hz across the sets in line with a wide range of recent experimental observations. Systematic variation of conduction velocity, coupling strength, and noise level indicate a high sensitivity of emerging synchrony as well as simulated blood flow blood oxygen level-dependent (BOLD) on the underlying parameter values. Optimal sensitivity was observed around conduction velocities of 1-2 m/s, with very weak coupling between oscillators. An additional finding was that the optimal noise level had a characteristic scale, indicating the presence of stochastic resonance, which allows the network dynamics to respond with high sensitivity to changes in diffuse feedback activity.
    Proceedings of the National Academy of Sciences 07/2009; 106(25):10302-7. · 9.74 Impact Factor
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    ABSTRACT: Although information flow in the neocortex has an apparent hierarchical organization, there is much ambiguity with respect to the definition of such a hierarchy, particularly in higher cortical regions. This ambiguity has been addressed by utilizing observable anatomical criteria, based upon tract tracing experiments, to constrain the definition of hierarchy [Felleman D.J. and van Essen D.C., 1991. Distributed hierarchical processing in the primate. Cereb. Cortex. 1(1), 1-47.]. There are, however, a high number of equally optimal hierarchies that fit these constraints [Hilgetag C.C., O'Neill M.A., Young M.P., 1996. Indeterminate organization of the visual system. Science. 271(5250), 776-777.]. Here, we propose a refined constraint set for optimization which utilizes continuous, rather than discrete, hierarchical levels, and permits a range of acceptable values rather than attempting to fit fixed hierarchical distances. Using linear programming to obtain hierarchies across a number of range sizes, we find a clear hierarchical pattern for both the original and refined versions of the Felleman and Van Essen [Felleman D.J. and van Essen D.C., 1991. Distributed hierarchical processing in the primate. Cereb. Cortex. 1(1), 1-47.] visual network. We also obtain an optimal hierarchy from a refined set of anatomical criteria which allows for the direct specification of hierarchical distance from the laminar distribution of labelled cells (Barone P., Batardiere A., Knoblauch K., Kennedy H., 2000. Laminar distribution of neurons in extrastriate areas projecting to visual areas V1 and V4 correlates with the hierarchical rank and indicates the operation of a distance rule. J. Neurosci. 20(9), 3263-3281.), and discuss the limitations and further possible refinements of such an approach.
    NeuroImage 05/2009; 47(2):611-7. · 6.25 Impact Factor
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    ABSTRACT: In this era of complete genomes, our knowledge of neuroanatomical circuitry remains surprisingly sparse. Such knowledge is critical, however, for both basic and clinical research into brain function. Here we advocate for a concerted effort to fill this gap, through systematic, experimental mapping of neural circuits at a mesoscopic scale of resolution suitable for comprehensive, brainwide coverage, using injections of tracers or viral vectors. We detail the scientific and medical rationale and briefly review existing knowledge and experimental techniques. We define a set of desiderata, including brainwide coverage; validated and extensible experimental techniques suitable for standardization and automation; centralized, open-access data repository; compatibility with existing resources; and tractability with current informatics technology. We discuss a hypothetical but tractable plan for mouse, additional efforts for the macaque, and technique development for human. We estimate that the mouse connectivity project could be completed within five years with a comparatively modest budget.
    PLoS Computational Biology 04/2009; 5(3):e1000334. · 4.87 Impact Factor
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    ABSTRACT: Brain atlases are widely used in experimental neuroscience as tools for locating and targeting specific brain structures. Delineated structures in a given atlas, however, are often difficult to interpret and to interface with database systems that supply additional information using hierarchically organized vocabularies (ontologies). Here we discuss the concept of volume-to-ontology mapping in the context of macroscopical brain structures. We present Java tools with which we have implemented this concept for retrieval of mapping and connectivity data on the macaque brain from the CoCoMac database in connection with an electronic version of "The Rhesus Monkey Brain in Stereotaxic Coordinates" authored by George Paxinos and colleagues. The software, including our manually drawn monkey brain template, can be downloaded freely under the GNU General Public License. It adds value to the printed atlas and has a wider (neuro-)informatics application since it can read appropriately annotated data from delineated sections of other species and organs, and turn them into 3D registered stacks. The tools provide additional features, including visualization and analysis of connectivity data, volume and centre-of-mass estimates, and graphical manipulation of entire structures, which are potentially useful for a range of research and teaching applications.
    Neuroinformatics 02/2009; 7(1):7-22. · 3.14 Impact Factor
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    BMC Neuroscience 01/2009; · 3.00 Impact Factor
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    Rolf Kötter
    Frontiers in Neuroscience 01/2009; 3(2):163-4.
  • NeuroImage 01/2009; 47. · 6.25 Impact Factor
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    ABSTRACT: Traditionally brain function is studied through measuring physiological responses in controlled sensory, motor, and cognitive paradigms. However, even at rest, in the absence of overt goal-directed behavior, collections of cortical regions consistently show temporally coherent activity. In humans, these resting state networks have been shown to greatly overlap with functional architectures present during consciously directed activity, which motivates the interpretation of rest activity as day dreaming, free association, stream of consciousness, and inner rehearsal. In monkeys, it has been shown though that similar coherent fluctuations are present during deep anesthesia when there is no consciousness. Here, we show that comparable resting state networks emerge from a stability analysis of the network dynamics using biologically realistic primate brain connectivity, although anatomical information alone does not identify the network. We specifically demonstrate that noise and time delays via propagation along connecting fibres are essential for the emergence of the coherent fluctuations of the default network. The spatiotemporal network dynamics evolves on multiple temporal scales and displays the intermittent neuroelectric oscillations in the fast frequency regimes, 1-100 Hz, commonly observed in electroencephalographic and magnetoencephalographic recordings, as well as the hemodynamic oscillations in the ultraslow regimes, <0.1 Hz, observed in functional magnetic resonance imaging. The combination of anatomical structure and time delays creates a space-time structure in which the neural noise enables the brain to explore various functional configurations representing its dynamic repertoire.
    PLoS Computational Biology 10/2008; 4(10):e1000196. · 4.87 Impact Factor

Publication Stats

5k Citations
330.59 Total Impact Points

Institutions

  • 2007–2014
    • Radboud University Nijmegen
      • • Department of Neuroinformatics
      • • Donders Institute for Brain, Cognition, and Behaviour
      • • Department of Cognitive Neuroscience
      Nymegen, Gelderland, Netherlands
  • 2011
    • University of Birmingham
      • School of Psychology
      Birmingham, ENG, United Kingdom
  • 2007–2011
    • Radboud University Medical Centre (Radboudumc)
      • • Department of Neurology
      • • Department of Cognitive Neuroscience
      Nymegen, Gelderland, Netherlands
  • 2010
    • Justus-Liebig-Universität Gießen
      • Department of Psychology
      Gießen, Hesse, Germany
  • 1999–2010
    • Newcastle University
      • • School of Computing Science
      • • School of Psychology
      Newcastle upon Tyne, ENG, United Kingdom
  • 2009
    • University Pompeu Fabra
      Barcino, Catalonia, Spain
  • 2008
    • Institut de Recherche sur les Phénomènes Hors Equilibre
      Marsiglia, Provence-Alpes-Côte d'Azur, France
  • 2004–2005
    • Indiana University Bloomington
      • Department of Psychological and Brain Sciences
      Bloomington, IN, United States
  • 1998–2005
    • Heinrich-Heine-Universität Düsseldorf
      • C. u. O. Vogt-Institut für Hirnforschung
      Düsseldorf, North Rhine-Westphalia, Germany
  • 2002
    • University of Oxford
      • Department of Experimental Psychology
      Oxford, ENG, United Kingdom
  • 2001
    • San Diego Supercomputer Center
      Los Angeles, California, United States
  • 1994–1995
    • University of Otago
      • Department of Medicine (Dunedin)
      Dunedin, Otago, New Zealand