7
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Extensive Tonotopic Mapping across Auditory Cortex Is Recapitulated by Spectrally Directed Attention and Systematically Related to Cortical Myeloarchitecture

      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

          Auditory selective attention is vital in natural soundscapes. But it is unclear how attentional focus on the primary dimension of auditory representation—acoustic frequency—might modulate basic auditory functional topography during active listening. In contrast to visual selective attention, which is supported by motor-mediated optimization of input across saccades and pupil dilation, the primate auditory system has fewer means of differentially sampling the world. This makes spectrally-directed endogenous attention a particularly crucial aspect of auditory attention. Using a novel functional paradigm combined with quantitative MRI, we establish in male and female listeners that human frequency-band-selective attention drives activation in both myeloarchitectonically estimated auditory core, and across the majority of tonotopically mapped nonprimary auditory cortex. The attentionally driven best-frequency maps show strong concordance with sensory-driven maps in the same subjects across much of the temporal plane, with poor concordance in areas outside traditional auditory cortex. There is significantly greater activation across most of auditory cortex when best frequency is attended, versus ignored; the same regions do not show this enhancement when attending to the least-preferred frequency band. Finally, the results demonstrate that there is spatial correspondence between the degree of myelination and the strength of the tonotopic signal across a number of regions in auditory cortex. Strong frequency preferences across tonotopically mapped auditory cortex spatially correlate with R 1-estimated myeloarchitecture, indicating shared functional and anatomical organization that may underlie intrinsic auditory regionalization.

          SIGNIFICANCE STATEMENT Perception is an active process, especially sensitive to attentional state. Listeners direct auditory attention to track a violin's melody within an ensemble performance, or to follow a voice in a crowded cafe. Although diverse pathologies reduce quality of life by impacting such spectrally directed auditory attention, its neurobiological bases are unclear. We demonstrate that human primary and nonprimary auditory cortical activation is modulated by spectrally directed attention in a manner that recapitulates its tonotopic sensory organization. Further, the graded activation profiles evoked by single-frequency bands are correlated with attentionally driven activation when these bands are presented in complex soundscapes. Finally, we observe a strong concordance in the degree of cortical myelination and the strength of tonotopic activation across several auditory cortical regions.

          Related collections

          Most cited references73

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

          Improved Localizadon of Cortical Activity by Combining EEG and MEG with MRI Cortical Surface Reconstruction: A Linear Approach.

          Abstract We describe a comprehensive linear approach to the problem of imaging brain activity with high temporal as well as spatial resolution based on combining EEG and MEG data with anatomical constraints derived from MRI images. The "inverse problem" of estimating the distribution of dipole strengths over the cortical surface is highly underdetermined, even given closely spaced EEG and MEG recordings. We have obtained much better solutions to this problem by explicitly incorporating both local cortical orientation as well as spatial covariance of sources and sensors into our formulation. An explicit polygonal model of the cortical manifold is first constructed as follows: (1) slice data in three orthogonal planes of section (needle-shaped voxels) are combined with a linear deblurring technique to make a single high-resolution 3-D image (cubic voxels), (2) the image is recursively flood-filled to determine the topology of the gray-white matter border, and (3) the resulting continuous surface is refined by relaxing it against the original 3-D gray-scale image using a deformable template method, which is also used to computationally flatten the cortex for easier viewing. The explicit solution to an error minimization formulation of an optimal inverse linear operator (for a particular cortical manifold, sensor placement, noise and prior source covariance) gives rise to a compact expression that is practically computable for hundreds of sensors and thousands of sources. The inverse solution can then be weighted for a particular (averaged) event using the sensor covariance for that event. Model studies suggest that we may be able to localize multiple cortical sources with spatial resolution as good as PET with this technique, while retaining a much finer grained picture of activity over time.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            High-resolution intersubject averaging and a coordinate system for the cortical surface.

            The neurons of the human cerebral cortex are arranged in a highly folded sheet, with the majority of the cortical surface area buried in folds. Cortical maps are typically arranged with a topography oriented parallel to the cortical surface. Despite this unambiguous sheetlike geometry, the most commonly used coordinate systems for localizing cortical features are based on 3-D stereotaxic coordinates rather than on position relative to the 2-D cortical sheet. In order to address the need for a more natural surface-based coordinate system for the cortex, we have developed a means for generating an average folding pattern across a large number of individual subjects as a function on the unit sphere and of nonrigidly aligning each individual with the average. This establishes a spherical surface-based coordinate system that is adapted to the folding pattern of each individual subject, allowing for much higher localization accuracy of structural and functional features of the human brain.
              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
                J Neurosci
                J. Neurosci
                jneuro
                jneurosci
                J. Neurosci
                The Journal of Neuroscience
                Society for Neuroscience
                0270-6474
                1529-2401
                13 December 2017
                13 December 2017
                : 37
                : 50
                : 12187-12201
                Affiliations
                [1] 1Department of Psychological Sciences, Birkbeck College, University of London, London, WC1E 7HX, United Kingdom,
                [2] 2Birkbeck/University of London Centre for Neuroimaging, London, WC1H 0AP, United Kingdom,
                [3] 3Department of Experimental Psychology, University College London, London, WC1H 0AP, United Kingdom,
                [4] 4Department of Psychology, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213,
                [5] 5Center for the Neural Basis of Cognition, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213,
                [6] 6Scientific Imaging and Brain Research Center, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213,
                [7] 7Wellcome Trust Center for Neuroimaging, Institute of Neurology, University College London, London, WC1N 3BG, United Kingdom, and
                [8] 8Department of Psychology, San Diego State University, San Diego, California San Diego, California 92182-4611
                Author notes
                Correspondence should be addressed to either of the following: Dr. Frederic K. Dick, Birkbeck/University of London Centre for NeuroImaging, 26 Bedford Way, London, WC1H 0AP, United Kingdom, f.dick@ 123456bbk.ac.uk ; or Dr. Lori L. Holt, Department of Psychology, Baker Hall, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA 15213, loriholt@ 123456cmu.edu

                Author contributions: F.K.D. and L.L.H. designed research; F.K.D., M.I.L., T.A.K., and L.L.H. performed research; M.F.C. and M.I.S. contributed unpublished reagents/analytic tools; F.K.D., M.I.L., and L.L.H. analyzed data; F.K.D., M.F.C., M.I.S., and L.L.H. wrote the paper.

                Author information
                http://orcid.org/0000-0002-2933-3912
                http://orcid.org/0000-0002-7601-9360
                http://orcid.org/0000-0003-0374-1659
                http://orcid.org/0000-0002-2345-8626
                http://orcid.org/0000-0002-7598-7829
                http://orcid.org/0000-0002-8732-4977
                Article
                1436-17
                10.1523/JNEUROSCI.1436-17.2017
                5729191
                29109238
                3da52dbb-e330-4ba7-826b-0c735f7b4f60
                Copyright © 2017 Dick et al.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License Creative Commons Attribution 4.0 International, which permits unrestricted use, distribution and reproduction in any medium provided that the original work is properly attributed.

                History
                : 24 May 2017
                : 4 October 2017
                : 6 October 2017
                Categories
                Research Articles
                Systems/Circuits

                attention,auditory,cortical mapping
                attention, auditory, cortical mapping

                Comments

                Comment on this article