10
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: not found

      Reconfiguration of Dynamic Functional Connectivity in Sensory and Perceptual System in Schizophrenia

      Read this article at

      ScienceOpenPublisherPubMed
      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

          Schizophrenia is thought as a self-disorder with dysfunctional brain connectivity. This self-disorder is often attributed to high-order cognitive impairment. Yet due to the frequent report of sensorial and perceptual deficits, it has been hypothesized that self-disorder in schizophrenia is dysfunctional communication between sensory and cognitive processes. To further verify this assumption, the present study comprehensively examined dynamic reconfigurations of resting-state functional connectivity (rsFC) in schizophrenia at voxel level, region level, and network levels (102 patients vs. 124 controls). We found patients who show consistently increased rsFC variability in sensory and perceptual system, including visual network, sensorimotor network, attention network, and thalamus at all the three levels. However, decreased variability in high-order networks, such as default mode network and frontal–parietal network were only consistently observed at region and network levels. Taken together, these findings highlighted the rudimentary role of elevated instability of information communication in sensory and perceptual system and attenuated whole-brain integration of high-order network in schizophrenia, which provided novel neural evidence to support the hypothesis of disrupted perceptual and cognitive function in schizophrenia. The foci of effects also highlighted that targeting perceptual deficits can be regarded as the key to enhance our understanding of pathophysiology in schizophrenia and promote new treatment intervention.

          Related collections

          Most cited references54

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

          Movement-related effects in fMRI time-series.

          This paper concerns the spatial and intensity transformations that are required to adjust for the confounding effects of subject movement during functional MRI (fMRI) activation studies. An approach is presented that models, and removes, movement-related artifacts from fMRI time-series. This approach is predicated on the observation that movement-related effects are extant even after perfect realignment. Movement-related effects can be divided into those that are a function of position of the object in the frame of reference of the scanner and those that are due to movement in previous scans. This second component depends on the history of excitation experienced by spins in a small volume and consequent differences in local saturation. The spin excitation history thus will itself be a function of previous positions, suggesting an autoregression-moving average model for the effects of previous displacements on the current signal. A model is described as well as the adjustments for movement-related components that ensue. The empirical analyses suggest that (in extreme situations) over 90% of fMRI signal can be attributed to movement, and that this artifactual component can be successfully removed.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            A comprehensive assessment of regional variation in the impact of head micromovements on functional connectomics.

            Functional connectomics is one of the most rapidly expanding areas of neuroimaging research. Yet, concerns remain regarding the use of resting-state fMRI (R-fMRI) to characterize inter-individual variation in the functional connectome. In particular, recent findings that "micro" head movements can introduce artifactual inter-individual and group-related differences in R-fMRI metrics have raised concerns. Here, we first build on prior demonstrations of regional variation in the magnitude of framewise displacements associated with a given head movement, by providing a comprehensive voxel-based examination of the impact of motion on the BOLD signal (i.e., motion-BOLD relationships). Positive motion-BOLD relationships were detected in primary and supplementary motor areas, particularly in low motion datasets. Negative motion-BOLD relationships were most prominent in prefrontal regions, and expanded throughout the brain in high motion datasets (e.g., children). Scrubbing of volumes with FD>0.2 effectively removed negative but not positive correlations; these findings suggest that positive relationships may reflect neural origins of motion while negative relationships are likely to originate from motion artifact. We also examined the ability of motion correction strategies to eliminate artifactual differences related to motion among individuals and between groups for a broad array of voxel-wise R-fMRI metrics. Residual relationships between motion and the examined R-fMRI metrics remained for all correction approaches, underscoring the need to covary motion effects at the group-level. Notably, global signal regression reduced relationships between motion and inter-individual differences in correlation-based R-fMRI metrics; Z-standardization (mean-centering and variance normalization) of subject-level maps for R-fMRI metrics prior to group-level analyses demonstrated similar advantages. Finally, our test-retest (TRT) analyses revealed significant motion effects on TRT reliability for R-fMRI metrics. Generally, motion compromised reliability of R-fMRI metrics, with the exception of those based on frequency characteristics - particularly, amplitude of low frequency fluctuations (ALFF). The implications of our findings for decision-making regarding the assessment and correction of motion are discussed, as are insights into potential differences among volume-based metrics of motion. Copyright © 2013 Elsevier Inc. All rights reserved.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              Self-projection and the brain.

              When thinking about the future or the upcoming actions of another person, we mentally project ourselves into that alternative situation. Accumulating data suggest that envisioning the future (prospection), remembering the past, conceiving the viewpoint of others (theory of mind) and possibly some forms of navigation reflect the workings of the same core brain network. These abilities emerge at a similar age and share a common functional anatomy that includes frontal and medial temporal systems that are traditionally associated with planning, episodic memory and default (passive) cognitive states. We speculate that these abilities, most often studied as distinct, rely on a common set of processes by which past experiences are used adaptively to imagine perspectives and events beyond those that emerge from the immediate environment.
                Bookmark

                Author and article information

                Contributors
                Journal
                Cerebral Cortex
                Oxford University Press (OUP)
                1047-3211
                1460-2199
                August 2019
                July 22 2019
                October 01 2018
                August 2019
                July 22 2019
                October 01 2018
                : 29
                : 8
                : 3577-3589
                Affiliations
                [1 ]The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, Center for Information in Medicine, School of life Science and technology, University of Electronic Science and Technology of China, 2006 Xiyuan Avenue, Chengdu, China
                [2 ]Department of Psychiatry, The Fourth People’s Hospital of Chengdu, Chengdu, China
                [3 ]Department of Experimental and Applied Psychology, Faculty of Psychological and Educational Sciences, Vrije Universiteit Brussel, Brussels, Belgium
                [4 ]Department of Data Analysis, Faculty of Psychology and Educational Sciences, Ghent University, Henri Dunantlaan 2, Ghent, Belgium
                Article
                10.1093/cercor/bhy232
                30272139
                ed98ac7b-1c1d-4758-a8da-f685ca8577e1
                © 2018

                https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model

                History

                Comments

                Comment on this article