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Can Taichi Reshape the Brain? A Brain Morphometry Study

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      Abstract

      Although research has provided abundant evidence for Taichi-induced improvements in psychological and physiological well-being, little is known about possible links to brain structure of Taichi practice. Using high-resolution MRI of 22 Tai Chi Chuan (TCC) practitioners and 18 controls matched for age, sex and education, we set out to examine the underlying anatomical correlates of long-term Taichi practice at two different levels of regional specificity. For this purpose, parcel-wise and vertex-wise analyses were employed to quantify the difference between TCC practitioners and the controls based on cortical surface reconstruction. We also adopted the Attention Network Test (ANT) to explore the effect of TCC on executive control. TCC practitioners, compared with controls, showed significantly thicker cortex in precentral gyrus, insula sulcus and middle frontal sulcus in the right hemisphere and superior temporal gyrus and medial occipito-temporal sulcus and lingual sulcus in the left hemisphere. Moreover, we found that thicker cortex in left medial occipito-temporal sulcus and lingual sulcus was associated with greater intensity of TCC practice. These findings indicate that long-term TCC practice could induce regional structural change and also suggest TCC might share similar patterns of neural correlates with meditation and aerobic exercise.

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      Most cited references 63

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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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        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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          Measuring the thickness of the human cerebral cortex from magnetic resonance images.

           B Fischl,  Anders Dale (2000)
          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

            Affiliations
            [1 ]Key Laboratory of Behavioral Science, Laboratory for Functional Connectome and Development, Magnetic Resonance Imaging Research Center, Institute of Psychology, Chinese Academy of Sciences, Beijing, China
            [2 ]Key Laboratory of Mental Health, Institute of Psychology, Chinese Academy of Sciences, Beijing, China
            [3 ]Psychiatry Research Center, Beijing Huilongguan Hospital, Beijing, China
            [4 ]Beijing Key Laboratory of Learning and Cognition, Department of Psychology, Capital Normal University, Beijing, China
            [5 ]University of Chinese Academy of Sciences, Beijing, China
            Beijing Normal University, China
            Author notes

            Competing Interests: The senior author (Dr. Xi-Nian Zuo) currently serves as an academic editor for PLOS ONE. This does not alter the authors' adherence to all the PLOS ONE policies on sharing data and materials.

            Conceived and designed the experiments: GXW JL XNZ. Performed the experiments: GXW HMD. Analyzed the data: GXW TX LJ FMF XNZ. Contributed reagents/materials/analysis tools: GXW JL XNZ. Wrote the paper: GXW TX FMF HMD LJ ZY HJL JL XNZ.

            Contributors
            Role: Editor
            Journal
            PLoS One
            PLoS ONE
            plos
            plosone
            PLoS ONE
            Public Library of Science (San Francisco, USA )
            1932-6203
            2013
            9 April 2013
            : 8
            : 4
            23585869
            3621760
            PONE-D-12-34411
            10.1371/journal.pone.0061038
            (Editor)

            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.

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            Pages: 9
            Funding
            This work was supported by the National Natural Science Foundation of China (31200794, 91132728, 81171409, 81030028) and the Open Research Fund of the Key Laboratory of Mental Health, the Knowledge Innovation Program (KSCX2-EW-J-8) from Institute of Psychology, Chinese Academy of Sciences (CAS). The senior author Prof. Xi-Nian Zuo acknowledges the Hundred Talents Program (Y2CX112006) and the Key Research Program (KSZD-EW-TZ-002) of CAS and the Major Joint Fund for International Cooperation and Exchange of the National Natural Science Foundation (81220108014). 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
            Neuroanatomy
            Medicine
            Anatomy and Physiology
            Neurological System
            Neuroanatomy
            Mental Health
            Psychology
            Behavior
            Attention (Behavior)
            Cognitive Psychology
            Neurology
            Neuroimaging
            Radiology
            Diagnostic Radiology
            Magnetic Resonance Imaging
            Sports and Exercise Medicine

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