9
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Structural covariance in subcortical stroke patients measured by automated MRI-based volumetry

      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

          A network-level investigation of the volumetric changes of subcortical stroke patients is still lacking. Here, we explored the alterations of structural covariance caused by subcortical stroke with automated brain volumetry. T1-weighed brain MRI scans were obtained from 63 normal controls (NC), 46 stroke patients with infarct in left internal capsule (CI_L), 33 stroke patients with infarct in right internal capsule (CI_R). We performed automatic anatomical segmentation of the T1-weighted brain images with AccuBrain. Volumetric structural covariance analyses were first performed within the basal ganglia structures that were both identified by voxel-based morphometry with AAL atlas and AccuBrain. Subsequently, we additionally included the infratentorial regions that were particularly quantified by AccuBrain for the structural covariance analyses and investigated the alterations of anatomical connections within these subcortical regions in CI_L and CI_R compared with NC. The association between the regional brain volumetry and motor function was also evaluated in stroke groups. There were significant and extensive volumetric differences in stroke patients. These significant regions were generally symmetric for CI_L and CI_R group depending on the side of stroke, involving both regions close to lesions and remote regions. The structural covariance analyses revealed the synergy volume alteration in subcortical regions both in CI_L and CI_R group. In addition, the alterations of volumetric structural covariance were more extensive in CI_L group than CI_R group. Moreover, we found that the subcortical regions with atrophy contributed to the deficits of motor function in CI_R group but not CI_L group, indicating a lesion-side effect of brain volumetric changes after stroke. These findings indicated that the chronic subcortical stroke patients have extensive disordered anatomical connections involving the whole-brain level network, and the connections patterns depend on the lesion-side.

          Hightlights

          • Chronic subcortical stroke patients show extensive brain volumetric atrophy.

          • Subcortical stroke patients show disordered structural covariance network pattern.

          • Brain volumetric and connections patterns change depend on the lesion-side.

          Related collections

          Most cited references32

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

          The Human Thalamus Is an Integrative Hub for Functional Brain Networks

          The thalamus is globally connected with distributed cortical regions, yet the functional significance of this extensive thalamocortical connectivity remains largely unknown. By performing graph-theoretic analyses on thalamocortical functional connectivity data collected from human participants, we found that most thalamic subdivisions display network properties that are capable of integrating multimodal information across diverse cortical functional networks. From a meta-analysis of a large dataset of functional brain-imaging experiments, we further found that the thalamus is involved in multiple cognitive functions. Finally, we found that focal thalamic lesions in humans have widespread distal effects, disrupting the modular organization of cortical functional networks. This converging evidence suggests that the human thalamus is a critical hub region that could integrate diverse information being processed throughout the cerebral cortex as well as maintain the modular structure of cortical functional networks. SIGNIFICANCE STATEMENT The thalamus is traditionally viewed as a passive relay station of information from sensory organs or subcortical structures to the cortex. However, the thalamus has extensive connections with the entire cerebral cortex, which can also serve to integrate information processing between cortical regions. In this study, we demonstrate that multiple thalamic subdivisions display network properties that are capable of integrating information across multiple functional brain networks. Moreover, the thalamus is engaged by tasks requiring multiple cognitive functions. These findings support the idea that the thalamus is involved in integrating information across cortical networks.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            Structural covariance in the human cortex.

            The morphology of the human cortex varies remarkably across individuals, regardless of overall brain size. It is currently unclear whether related cortical regions covary in gray matter density, as a result of mutually trophic influences or common experience-related plasticity. We acquired a structural magnetic resonance imaging scan from 172 subjects and extracted the regional gray matter densities from 12 readily identifiable regions of interest involved in sensorimotor or higher-order cognitive functions. We then used these values to predict regional densities in the remaining areas of the cortex, using voxel-based morphometry. This revealed patterns of positive and negative covariance that provide insight into the topographical organization of multiple cortical regions. We report that the gray matter density of a region is a good predictor of the density of the homotopic region in the contralateral hemisphere, with the striking exception of primary visual cortex. Whereas some regions express patterns of regional covariance that are mirror symmetrical relative to the interhemispheric fissure, other regions express asymmetric patterns of regional covariance. Finally, patterns of covariance are remarkably consistent between males and females, with the exception of the left amygdala, which is positively associated with the left and right anterior inferior temporal cortex in males and with the right angular gyrus in females. Our study establishes that the density of different cortical regions is coordinated within an individual. The coordinated variations we report are likely to be determined by both genetic and environmental factors and may be the basis for differences in individual behavior.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              Normalization of cerebral volumes by use of intracranial volume: implications for longitudinal quantitative MR imaging.

              MR-based volumetric measures of cerebral structures are increasingly used for diagnostic purposes and to measure progression of atrophy. Variations in individual head size may be corrected by normalization with use of a total intracranial volume (TIV) measurement. The TIV also may be used to correct for voxel size fluctuations in serial studies. The TIV should be measured from the same images used for structural volumetry, usually T1-weighted imaging. The objectives were to show that normalization with TIV reduces interindividual variation, to develop and validate a simple protocol for measuring TIV from T1-weighted MR images, and to apply TIV normalization to serial brain measures in controls and subjects with Alzheimer disease (AD). We measured TIV with a semiautomated segmentation technique on T1- and T2-weighted MR images in 55 controls, 10 AD patients, and two persons at risk of familial AD. Whole-brain volumes also were measured and normalized with TIVs. The TIV normalization of cross-sectional brain volumes significantly reduced interindividual variation; the coefficient of variation (CV) was reduced from 10.0% to 6.0% in controls (P <.001). The TIVs measured on T1-weighted images had low variability (CV, 0.16%) and did not differ significantly from those measured on T2-weighted images (P =.16). The TIV normalization of serial brain-volume measurements reduced interimage differences caused by voxel-scaling variations (CV reduced from 1.3% to 0.5%, P =.002) in 10 controls and five AD patients. Structural volumes should be normalized with a TIV, measured cross-sectionally, to reduce interindividual variation, and longitudinally with a concurrent measurement, to reduce subtle interimage differences. This may have important implications in progression studies.
                Bookmark

                Author and article information

                Contributors
                Journal
                Neuroimage Clin
                Neuroimage Clin
                NeuroImage : Clinical
                Elsevier
                2213-1582
                22 January 2019
                2019
                22 January 2019
                : 22
                : 101682
                Affiliations
                [a ]Department of MRI, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
                [b ]BrainNow Research Institute, Shenzhen, Guangdong Province, China
                [c ]Department of Radiology, Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
                [d ]Department of Neurology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
                [e ]Department of Imaging and Interventional Radiology, The Chinese University of Hong Kong, Shatin, Hong Kong
                Author notes
                [* ]Correspondence to: L. Shi, BrainNow Research Institute, Shenzhen, Guangdong Province, China. shilin@ 123456cuhk.edu.hk
                [** ]Corresponding author. fccchengjl@ 123456zzu.edu.com
                [1]

                These authors contributed equally to the work.

                Article
                S2213-1582(19)30032-4 101682
                10.1016/j.nicl.2019.101682
                6357849
                30710874
                ea3f714a-c476-4cde-858d-a7fdc1926f25
                © 2019 Published by Elsevier Inc.

                This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

                History
                : 12 November 2018
                : 27 December 2018
                : 20 January 2019
                Categories
                Regular Article

                brain segmentation,brain volumetric changes,mri imaging,stroke,structural covariance,lesion-side effect

                Comments

                Comment on this article