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      Adolescent Brain Maturation and Cortical Folding: Evidence for Reductions in Gyrification

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          Abstract

          Evidence from anatomical and functional imaging studies have highlighted major modifications of cortical circuits during adolescence. These include reductions of gray matter (GM), increases in the myelination of cortico-cortical connections and changes in the architecture of large-scale cortical networks. It is currently unclear, however, how the ongoing developmental processes impact upon the folding of the cerebral cortex and how changes in gyrification relate to maturation of GM/WM-volume, thickness and surface area. In the current study, we acquired high-resolution (3 Tesla) magnetic resonance imaging (MRI) data from 79 healthy subjects (34 males and 45 females) between the ages of 12 and 23 years and performed whole brain analysis of cortical folding patterns with the gyrification index (GI). In addition to GI-values, we obtained estimates of cortical thickness, surface area, GM and white matter (WM) volume which permitted correlations with changes in gyrification. Our data show pronounced and widespread reductions in GI-values during adolescence in several cortical regions which include precentral, temporal and frontal areas. Decreases in gyrification overlap only partially with changes in the thickness, volume and surface of GM and were characterized overall by a linear developmental trajectory. Our data suggest that the observed reductions in GI-values represent an additional, important modification of the cerebral cortex during late brain maturation which may be related to cognitive development.

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          Most cited references63

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          An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest.

          In this study, we have assessed the validity and reliability of an automated labeling system that we have developed for subdividing the human cerebral cortex on magnetic resonance images into gyral based regions of interest (ROIs). Using a dataset of 40 MRI scans we manually identified 34 cortical ROIs in each of the individual hemispheres. This information was then encoded in the form of an atlas that was utilized to automatically label ROIs. To examine the validity, as well as the intra- and inter-rater reliability of the automated system, we used both intraclass correlation coefficients (ICC), and a new method known as mean distance maps, to assess the degree of mismatch between the manual and the automated sets of ROIs. When compared with the manual ROIs, the automated ROIs were highly accurate, with an average ICC of 0.835 across all of the ROIs, and a mean distance error of less than 1 mm. Intra- and inter-rater comparisons yielded little to no difference between the sets of ROIs. These findings suggest that the automated method we have developed for subdividing the human cerebral cortex into standard gyral-based neuroanatomical regions is both anatomically valid and reliable. This method may be useful for both morphometric and functional studies of the cerebral cortex as well as for clinical investigations aimed at tracking the evolution of disease-induced changes over time, including clinical trials in which MRI-based measures are used to examine response to treatment.
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            Cortical surface-based analysis. I. Segmentation and surface reconstruction.

            Several properties of the cerebral cortex, including its columnar and laminar organization, as well as the topographic organization of cortical areas, can only be properly understood in the context of the intrinsic two-dimensional structure of the cortical surface. In order to study such cortical properties in humans, it is necessary to obtain an accurate and explicit representation of the cortical surface in individual subjects. Here we describe a set of automated procedures for obtaining accurate reconstructions of the cortical surface, which have been applied to data from more than 100 subjects, requiring little or no manual intervention. Automated routines for unfolding and flattening the cortical surface are described in a companion paper. These procedures allow for the routine use of cortical surface-based analysis and visualization methods in functional brain imaging. Copyright 1999 Academic Press.
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              Measuring the thickness of the human cerebral cortex from magnetic resonance images.

              Accurate and automated methods for measuring the thickness of human cerebral cortex could provide powerful tools for diagnosing and studying a variety of neurodegenerative and psychiatric disorders. Manual methods for estimating cortical thickness from neuroimaging data are labor intensive, requiring several days of effort by a trained anatomist. Furthermore, the highly folded nature of the cortex is problematic for manual techniques, frequently resulting in measurement errors in regions in which the cortical surface is not perpendicular to any of the cardinal axes. As a consequence, it has been impractical to obtain accurate thickness estimates for the entire cortex in individual subjects, or group statistics for patient or control populations. Here, we present an automated method for accurately measuring the thickness of the cerebral cortex across the entire brain and for generating cross-subject statistics in a coordinate system based on cortical anatomy. The intersubject standard deviation of the thickness measures is shown to be less than 0.5 mm, implying the ability to detect focal atrophy in small populations or even individual subjects. The reliability and accuracy of this new method are assessed by within-subject test-retest studies, as well as by comparison of cross-subject regional thickness measures with published values.
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                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, USA )
                1932-6203
                2014
                15 January 2014
                : 9
                : 1
                : e84914
                Affiliations
                [1 ]Department of Neurophysiology, Max-Planck Institute for Brain Research, Frankfurt am Main, Germany
                [2 ]Ernst Strüngmann Institute (ESI) for Neuroscience in Cooperation with Max Planck Society, Frankfurt am Main, Germany
                [3 ]Department of Neurocognitive Psychology, Institute of Psychology, Johann Wolfgang Goethe University, Frankfurt am Main, Germany
                [4 ]Department of Psychiatry, Psychosomatic Medicine and Psychotherapy, Johann Wolfgang Goethe University, Frankfurt am Main, Germany
                [5 ]Department of Brain and Cognitive Sciences, Seoul National University, Seoul, Republic of Korea
                [6 ]School of Computing Science and Institute of Neuroscience, Newcastle University, Newcastle, United Kingdom
                [7 ]Frankfurt Institute for Advanced Studies, Johann Wolfgang Goethe University, Frankfurt am Main, Germany
                [8 ]Institute of Neuroscience and Psychology, University of Glasgow, Glasgow, United Kingdom
                University of Montreal, Canada
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                Conceived and designed the experiments: DK HM FR ARJ. Performed the experiments: MC CH DK PU SS. Analyzed the data: PU WS DK EG SS MC. Contributed reagents/materials/analysis tools: MC CH HM DK. Wrote the paper: WS DK PU EG MK.

                Article
                PONE-D-13-27415
                10.1371/journal.pone.0084914
                3893168
                24454765
                e21fdf44-cbf4-4487-9c8a-c0180866080d
                Copyright @ 2014

                This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 3 July 2013
                : 28 November 2013
                Page count
                Pages: 10
                Funding
                This work was supported by the Max Planck Society (P. J. Uhlhaas) and by National Research Foundation of Korea funded by the Ministry of Education, Science and Technology (R32-10142, C.E. Han). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article
                Biology
                Anatomy and Physiology
                Neurological System
                Neuroanatomy
                Neuroscience
                Neuroimaging
                fMRI
                Developmental Neuroscience
                Neuroanatomy
                Medicine
                Anatomy and Physiology
                Neurological System
                Neuroanatomy
                Neurology
                Neuroimaging
                Radiology
                Diagnostic Radiology
                Magnetic Resonance Imaging
                Social and Behavioral Sciences
                Psychology
                Developmental Psychology
                Neuropsychology

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                Uncategorized

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