13
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Micro and Macro Pattern Analyses of fMRI Data Support Both Early and Late Interaction of Numerical and Spatial Information

      research-article

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Numbers and space are two semantic primitives that interact with each other. Both recruit brain regions along the dorsal pathway, notably parietal cortex. This makes parietal cortex a candidate for the origin of numerical–spatial interaction. The underlying cognitive architecture of the interaction is still under scrutiny. Two classes of explanations can be distinguished. The early interaction approach assumes that numerical and spatial information are integrated into a single representation at a semantic level. A second approach postulates independent semantic representations. Only at the stage of response selection and preparation these two streams interact. In this study we used a numerical landmark task to identify the locus of the interaction between numbers and space. While lying in an MR scanner participants decided on the smaller of two numerical intervals in a visually presented number triplet. The spatial position of the middle number was varied; hence spatial intervals were congruent or incongruent with the numerical intervals. Responses in incongruent trials were slower and less accurate than in congruent trials. By combining across-vertex correlations (micro pattern) with a cluster analysis (macro pattern) we identified large-scale networks that were devoted to number processing, eye movements, and sensory–motor functions. Using support vector classification in different regions of interest along the intraparietal sulcus, the frontal eye fields, and supplementary motor area we were able to distinguish between congruent and incongruent trials in each of the networks. We suggest that the identified networks participate in the integration of numerical and spatial information and that the exclusive assumption of either an early or a late interaction between numerical and spatial information does not do justice to the complex interaction between both dimensions.

          Related collections

          Most cited references41

          • Record: found
          • Abstract: not found
          • Article: not found

          The mental representation of parity and number magnitude.

            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            Interactions between number and space in parietal cortex.

            Since the time of Pythagoras, numerical and spatial representations have been inextricably linked. We suggest that the relationship between the two is deeply rooted in the brain's organization for these capacities. Many behavioural and patient studies have shown that numerical-spatial interactions run far deeper than simply cultural constructions, and, instead, influence behaviour at several levels. By combining two previously independent lines of research, neuroimaging studies of numerical cognition in humans, and physiological studies of spatial cognition in monkeys, we propose that these numerical-spatial interactions arise from common parietal circuits for attention to external space and internal representations of numbers.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              Smoothing and cluster thresholding for cortical surface-based group analysis of fMRI data.

              Cortical surface-based analysis of fMRI data has proven to be a useful method with several advantages over 3-dimensional volumetric analyses. Many of the statistical methods used in 3D analyses can be adapted for use with surface-based analyses. Operating within the framework of the FreeSurfer software package, we have implemented a surface-based version of the cluster size exclusion method used for multiple comparisons correction. Furthermore, we have a developed a new method for generating regions of interest on the cortical surface using a sliding threshold of cluster exclusion followed by cluster growth. Cluster size limits for multiple probability thresholds were estimated using random field theory and validated with Monte Carlo simulation. A prerequisite of RFT or cluster size simulation is an estimate of the smoothness of the data. In order to estimate the intrinsic smoothness of group analysis statistics, independent of true activations, we conducted a group analysis of simulated noise data sets. Because smoothing on a cortical surface mesh is typically implemented using an iterative method, rather than directly applying a Gaussian blurring kernel, it is also necessary to determine the width of the equivalent Gaussian blurring kernel as a function of smoothing steps. Iterative smoothing has previously been modeled as continuous heat diffusion, providing a theoretical basis for predicting the equivalent kernel width, but the predictions of the model were not empirically tested. We generated an empirical heat diffusion kernel width function by performing surface-based smoothing simulations and found a large disparity between the expected and actual kernel widths.
                Bookmark

                Author and article information

                Journal
                Front Hum Neurosci
                Front. Hum. Neurosci.
                Frontiers in Human Neuroscience
                Frontiers Research Foundation
                1662-5161
                21 October 2011
                2011
                : 5
                : 115
                Affiliations
                [1] 1simpleSection Neuropsychology, Neurological Clinic, University Hospital Aachen Aachen, Germany
                [2] 2simpleInterdisciplinary Center for Clinical Research Aachen, University Hospital Aachen Aachen, Germany
                [3] 3simpleDepartment of Educational Psychology, Institute for Psychology, Johann Wolfgang Goethe University Frankfurt, Germany
                [4] 4simpleCenter for Individual Development and Adaptive Education of Children at Risk Frankfurt, Germany
                Author notes

                Edited by: Filip Van Opstal, Ghent University, Belgium

                Reviewed by: Ruth Seurinck, Ghent University, Belgium; Maria Grazia Di Bono, University of Padua, Italy

                *Correspondence: André Knops, Section Neuropsychology, Department of Neurology, Brain and Number Group, University Hospital, RWTH Aachen University, Aachen, Germany. e-mail: knops.andre@ 123456gmail.com

                Jan Willem Koten Jr. and André Knops have contributed equally to this work.

                Article
                10.3389/fnhum.2011.00115
                3199539
                22028688
                6f1fce85-a379-4793-a143-8d05bbedd944
                Copyright © 2011 Koten Jr., Lonnemann, Willmes and Knops.

                This is an open-access article subject to a non-exclusive license between the authors and Frontiers Media SA, which permits use, distribution and reproduction in other forums, provided the original authors and source are credited and other Frontiers conditions are complied with.

                History
                : 13 July 2011
                : 27 September 2011
                Page count
                Figures: 3, Tables: 0, Equations: 0, References: 53, Pages: 12, Words: 11011
                Categories
                Neuroscience
                Original Research

                Neurosciences
                early interaction,late interaction,multi-voxel pattern analysis,numerical landmark task,interaction between number and space,cluster analysis

                Comments

                Comment on this article