10
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Temporal sequences of brain activity at rest are constrained by white matter structure and modulated by cognitive demands

      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 diverse set of white matter connections supports seamless transitions between cognitive states. However, it remains unclear how these connections guide the temporal progression of large-scale brain activity patterns in different cognitive states. Here, we analyze the brain’s trajectories across a set of single time point activity patterns from functional magnetic resonance imaging data acquired during the resting state and an n-back working memory task. We find that specific temporal sequences of brain activity are modulated by cognitive load, associated with age, and related to task performance. Using diffusion-weighted imaging acquired from the same subjects, we apply tools from network control theory to show that linear spread of activity along white matter connections constrains the probabilities of these sequences at rest, while stimulus-driven visual inputs explain the sequences observed during the n-back task. Overall, these results elucidate the structural underpinnings of cognitively and developmentally relevant spatiotemporal brain dynamics.

          Abstract

          Eli J. Cornblath et al use tools from linear network control theory to show that white matter connectivity constrains transitions between brain activity patterns at rest to favor transitions with small energy requirements, while visual inputs overcome these constraints during a cognitive task. These findings highlight the importance of accounting for both internal white matter network dynamics and external inputs in models of brain activity.

          Related collections

          Most cited references 45

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

          The adolescent brain.

          Adolescence is a developmental period characterized by suboptimal decisions and actions that give rise to an increased incidence of unintentional injuries and violence, alcohol and drug abuse, unintended pregnancy and sexually transmitted diseases. Traditional neurobiological and cognitive explanations for adolescent behavior have failed to account for the nonlinear changes in behavior observed during adolescence, relative to childhood and adulthood. This review provides a biologically plausible conceptualization of the neural mechanisms underlying these nonlinear changes in behavior, as a heightened responsiveness to incentives while impulse control is still relatively immature during this period. Recent human imaging and animal studies provide a biological basis for this view, suggesting differential development of limbic reward systems relative to top-down control systems during adolescence relative to childhood and adulthood. This developmental pattern may be exacerbated in those adolescents with a predisposition toward risk-taking, increasing the risk for poor outcomes.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            Structural and functional brain development and its relation to cognitive development.

            Despite significant gains in the fields of pediatric neuroimaging and developmental neurobiology, surprisingly little is known about the developing human brain or the neural bases of cognitive development. This paper addresses MRI studies of structural and functional changes in the developing human brain and their relation to changes in cognitive processes over the first few decades of human life. Based on post-mortem and pediatric neuroimaging studies published to date, the prefrontal cortex appears to be one of the last brain regions to mature. Given the prolonged physiological development and organization of the prefrontal cortex during childhood, tasks believed to involve this region are ideal for investigating the neural bases of cognitive development. A number of normative pediatric fMRI studies examining prefrontal cortical activity in children during memory and attention tasks are reported. These studies, while largely limited to the domain of prefrontal functioning and its development, lend support for continued development of attention and memory both behaviorally and physiologically throughout childhood and adolescence. Specifically, the magnitude of activity observed in these studies was greater and more diffuse in children relative to adults. These findings are consistent with the view that increasing cognitive capacity during childhood may coincide with a gradual loss rather than formation of new synapses and presumably a strengthening of remaining synaptic connections. It is clear that innovative methods like fMRI together with MRI-based morphometry and nonhuman primate studies will transform our current understanding of human brain development and its relation to behavioral development.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found
              Is Open Access

              Benchmarking of participant-level confound regression strategies for the control of motion artifact in studies of functional connectivity.

              Since initial reports regarding the impact of motion artifact on measures of functional connectivity, there has been a proliferation of participant-level confound regression methods to limit its impact. However, many of the most commonly used techniques have not been systematically evaluated using a broad range of outcome measures. Here, we provide a systematic evaluation of 14 participant-level confound regression methods in 393 youths. Specifically, we compare methods according to four benchmarks, including the residual relationship between motion and connectivity, distance-dependent effects of motion on connectivity, network identifiability, and additional degrees of freedom lost in confound regression. Our results delineate two clear trade-offs among methods. First, methods that include global signal regression minimize the relationship between connectivity and motion, but result in distance-dependent artifact. In contrast, censoring methods mitigate both motion artifact and distance-dependence, but use additional degrees of freedom. Importantly, less effective de-noising methods are also unable to identify modular network structure in the connectome. Taken together, these results emphasize the heterogeneous efficacy of existing methods, and suggest that different confound regression strategies may be appropriate in the context of specific scientific goals.
                Bookmark

                Author and article information

                Contributors
                dsb@seas.upenn.edu
                Journal
                Commun Biol
                Commun Biol
                Communications Biology
                Nature Publishing Group UK (London )
                2399-3642
                22 May 2020
                22 May 2020
                2020
                : 3
                Affiliations
                [1 ]ISNI 0000 0004 1936 8972, GRID grid.25879.31, Department of Neuroscience, , Perelman School of Medicine, ; Philadelphia, PA 19104 USA
                [2 ]ISNI 0000 0004 1936 8972, GRID grid.25879.31, Department of Bioengineering, , School of Engineering & Applied Science, ; Philadelphia, PA 19104 USA
                [3 ]ISNI 0000 0004 1936 8972, GRID grid.25879.31, Department of Psychiatry, , Perelman School of Medicine, ; Philadelphia, PA 19104 USA
                [4 ]ISNI 0000 0004 1936 8972, GRID grid.25879.31, Department of Biostatistics, Epidemiology, & Informatics, , Perelman School of Medicine, ; Philadelphia, PA 19104 USA
                [5 ]Department of Physics & Astronomy, College of Arts & Sciences, Philadelphia, PA 19104 USA
                [6 ]ISNI 0000 0004 1936 8972, GRID grid.25879.31, Department of Neurology, , Perelman School of Medicine, ; Philadelphia, PA 19104 USA
                [7 ]ISNI 0000 0004 1936 8972, GRID grid.25879.31, Department of Electrical & Systems Engineering, , School of Engineering & Applied Science, ; Philadelphia, PA 19104 USA
                [8 ]ISNI 0000 0004 1936 8972, GRID grid.25879.31, Department of Radiology, Perelman School of Medicine, , University of Pennsylvania, ; Philadelphia, PA 19104 USA
                [9 ]ISNI 0000 0001 1941 1940, GRID grid.209665.e, Santa Fe Institute, ; Santa Fe, NM 87501 USA
                Article
                961
                10.1038/s42003-020-0961-x
                7244753
                32444827
                © The Author(s) 2020

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                Funding
                Funded by: FundRef https://doi.org/10.13039/100000025, U.S. Department of Health & Human Services | NIH | National Institute of Mental Health (NIMH);
                Award ID: R01-MH107235
                Award Recipient :
                Categories
                Article
                Custom metadata
                © The Author(s) 2020

                network models, working memory

                Comments

                Comment on this article