26
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      The suppression of scale-free fMRI brain dynamics across three different sources of effort: aging, task novelty and task difficulty

      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

          There is growing evidence that fluctuations in brain activity may exhibit scale-free (“fractal”) dynamics. Scale-free signals follow a spectral-power curve of the form P( f ) ∝  f −β , where spectral power decreases in a power-law fashion with increasing frequency. In this study, we demonstrated that fractal scaling of BOLD fMRI signal is consistently suppressed for different sources of cognitive effort. Decreases in the Hurst exponent ( H), which quantifies scale-free signal, was related to three different sources of cognitive effort/task engagement: 1) task difficulty, 2) task novelty, and 3) aging effects. These results were consistently observed across multiple datasets and task paradigms. We also demonstrated that estimates of H are robust across a range of time-window sizes. H was also compared to alternative metrics of BOLD variability (SD BOLD) and global connectivity (Gconn), with effort-related decreases in H producing similar decreases in SD BOLD and Gconn. These results indicate a potential global brain phenomenon that unites research from different fields and indicates that fractal scaling may be a highly sensitive metric for indexing cognitive effort/task engagement.

          Related collections

          Most cited references38

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

          Controlled and automatic human information processing: I. Detection, search, and attention.

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

            Partial Least Squares (PLS) methods for neuroimaging: a tutorial and review.

            Partial Least Squares (PLS) methods are particularly suited to the analysis of relationships between measures of brain activity and of behavior or experimental design. In neuroimaging, PLS refers to two related methods: (1) symmetric PLS or Partial Least Squares Correlation (PLSC), and (2) asymmetric PLS or Partial Least Squares Regression (PLSR). The most popular (by far) version of PLS for neuroimaging is PLSC. It exists in several varieties based on the type of data that are related to brain activity: behavior PLSC analyzes the relationship between brain activity and behavioral data, task PLSC analyzes how brain activity relates to pre-defined categories or experimental design, seed PLSC analyzes the pattern of connectivity between brain regions, and multi-block or multi-table PLSC integrates one or more of these varieties in a common analysis. PLSR, in contrast to PLSC, is a predictive technique which, typically, predicts behavior (or design) from brain activity. For both PLS methods, statistical inferences are implemented using cross-validation techniques to identify significant patterns of voxel activation. This paper presents both PLS methods and illustrates them with small numerical examples and typical applications in neuroimaging. Copyright © 2010 Elsevier Inc. All rights reserved.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              The temporal structures and functional significance of scale-free brain activity.

              Scale-free dynamics, with a power spectrum following P proportional to f(-beta), are an intrinsic feature of many complex processes in nature. In neural systems, scale-free activity is often neglected in electrophysiological research. Here, we investigate scale-free dynamics in human brain and show that it contains extensive nested frequencies, with the phase of lower frequencies modulating the amplitude of higher frequencies in an upward progression across the frequency spectrum. The functional significance of scale-free brain activity is indicated by task performance modulation and regional variation, with beta being larger in default network and visual cortex and smaller in hippocampus and cerebellum. The precise patterns of nested frequencies in the brain differ from other scale-free dynamics in nature, such as earth seismic waves and stock market fluctuations, suggesting system-specific generative mechanisms. Our findings reveal robust temporal structures and behavioral significance of scale-free brain activity and should motivate future study on its physiological mechanisms and cognitive implications. Copyright 2010 Elsevier Inc. All rights reserved.
                Bookmark

                Author and article information

                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group
                2045-2322
                08 August 2016
                2016
                : 6
                : 30895
                Affiliations
                [1 ]Keenan Research Centre of the Li Ka Shing Knowledge Institute, St. Michael’s Hospital , 30 Bond St, Toronto, ON, M5B 1W8, Canada
                [2 ]Rotman Research Institute, Baycrest Hospital , 3560 Bathurst St, Toronto, ON, M6A 2E1, Canada
                [3 ]Department of Medical Biophysics, University of Toronto , 101 College St, Suite 15-701, Toronto, ON, M5G 1L7, Canada
                [4 ]School of Nursing, University of Michigan , 426 N Ingalls St, Ann Arbor, MI, 48104, USA
                [5 ]Department of Psychology, University of Michigan , 400 N Ingalls St, Ann Arbor, MI, 48109, USA
                [6 ]Department of Radiology, University of Washington , 4245 Roosevelt Way NE, Seattle, WA, 98105, USA
                [7 ]College of Nursing, Chungnam National University , Jung-gu, Munhwa-ro 266, Daejeon, 301-747, South Korea
                [8 ]Biomedical Engineering, University of Michigan , 2200 Bonisteel Blvd, Ann Arbor, MI, 48109, USA
                [9 ]Institute of Medical Science, University of Toronto , 1 King’s College Cir, Toronto, ON, M5S 1A8, Canada
                [10 ]Department of Psychology, University of Chicago , 5848 S University Ave, Chicago, IL, 60637, USA
                [11 ]Grossman Institute for Neuroscience, Quantitative Biology and Human Behavior , 5812 S Ellis Ave, Chicago, IL, 60637, USA
                Author notes
                Article
                srep30895
                10.1038/srep30895
                4976369
                27498696
                e418ba07-6ebb-49b3-bb23-6af8855800c7
                Copyright © 2016, The Author(s)

                This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

                History
                : 17 December 2015
                : 10 July 2016
                Categories
                Article

                Uncategorized
                Uncategorized

                Comments

                Comment on this article