222
views
0
recommends
+1 Recommend
0 collections
    8
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      A Baseline for the Multivariate Comparison of Resting-State Networks

      research-article
      1 , 1 , 1 , 1 , 2 , 1 , 3 , 1 , 2 , 1 , 2 , 1 , 1 , 1 , 2 , 1 , 1 , 1 , 4 , 5 , 4 , 1 , 6 , 7 , 4 , 8 , 1 , 6 , 8 , 1 , 1 , 9 , 10 , 1 , 6 , 8 , 1 , 11 , 1 , 6 , 8 , 12 , 13 , 1 , 10 , 14 , 15 , 16 , 1 , 10 , 4 , 17 , 14 , 15 , 1 , 6 , 1 , 4 , 6 , 1 , 2 , 4 , 8 , 15
      Frontiers in Systems Neuroscience
      Frontiers Research Foundation
      fMRI, functional connectivity, resting-state, independent component analysis, connectome

      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

          As the size of functional and structural MRI datasets expands, it becomes increasingly important to establish a baseline from which diagnostic relevance may be determined, a processing strategy that efficiently prepares data for analysis, and a statistical approach that identifies important effects in a manner that is both robust and reproducible. In this paper, we introduce a multivariate analytic approach that optimizes sensitivity and reduces unnecessary testing. We demonstrate the utility of this mega-analytic approach by identifying the effects of age and gender on the resting-state networks (RSNs) of 603 healthy adolescents and adults (mean age: 23.4 years, range: 12–71 years). Data were collected on the same scanner, preprocessed using an automated analysis pipeline based in SPM, and studied using group independent component analysis. RSNs were identified and evaluated in terms of three primary outcome measures: time course spectral power, spatial map intensity, and functional network connectivity. Results revealed robust effects of age on all three outcome measures, largely indicating decreases in network coherence and connectivity with increasing age. Gender effects were of smaller magnitude but suggested stronger intra-network connectivity in females and more inter-network connectivity in males, particularly with regard to sensorimotor networks. These findings, along with the analysis approach and statistical framework described here, provide a useful baseline for future investigations of brain networks in health and disease.

          Related collections

          Most cited references71

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

          The role of the medial frontal cortex in cognitive control.

          Adaptive goal-directed behavior involves monitoring of ongoing actions and performance outcomes, and subsequent adjustments of behavior and learning. We evaluate new findings in cognitive neuroscience concerning cortical interactions that subserve the recruitment and implementation of such cognitive control. A review of primate and human studies, along with a meta-analysis of the human functional neuroimaging literature, suggest that the detection of unfavorable outcomes, response errors, response conflict, and decision uncertainty elicits largely overlapping clusters of activation foci in an extensive part of the posterior medial frontal cortex (pMFC). A direct link is delineated between activity in this area and subsequent adjustments in performance. Emerging evidence points to functional interactions between the pMFC and the lateral prefrontal cortex (LPFC), so that monitoring-related pMFC activity serves as a signal that engages regulatory processes in the LPFC to implement performance adjustments.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            Intrinsic functional connectivity as a tool for human connectomics: theory, properties, and optimization.

            Resting state functional connectivity MRI (fcMRI) is widely used to investigate brain networks that exhibit correlated fluctuations. While fcMRI does not provide direct measurement of anatomic connectivity, accumulating evidence suggests it is sufficiently constrained by anatomy to allow the architecture of distinct brain systems to be characterized. fcMRI is particularly useful for characterizing large-scale systems that span distributed areas (e.g., polysynaptic cortical pathways, cerebro-cerebellar circuits, cortical-thalamic circuits) and has complementary strengths when contrasted with the other major tool available for human connectomics-high angular resolution diffusion imaging (HARDI). We review what is known about fcMRI and then explore fcMRI data reliability, effects of preprocessing, analysis procedures, and effects of different acquisition parameters across six studies (n = 98) to provide recommendations for optimization. Run length (2-12 min), run structure (1 12-min run or 2 6-min runs), temporal resolution (2.5 or 5.0 s), spatial resolution (2 or 3 mm), and the task (fixation, eyes closed rest, eyes open rest, continuous word-classification) were varied. Results revealed moderate to high test-retest reliability. Run structure, temporal resolution, and spatial resolution minimally influenced fcMRI results while fixation and eyes open rest yielded stronger correlations as contrasted to other task conditions. Commonly used preprocessing steps involving regression of nuisance signals minimized nonspecific (noise) correlations including those associated with respiration. The most surprising finding was that estimates of correlation strengths stabilized with acquisition times as brief as 5 min. The brevity and robustness of fcMRI positions it as a powerful tool for large-scale explorations of genetic influences on brain architecture. We conclude by discussing the strengths and limitations of fcMRI and how it can be combined with HARDI techniques to support the emerging field of human connectomics.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              Evidence for a frontoparietal control system revealed by intrinsic functional connectivity.

              Two functionally distinct, and potentially competing, brain networks have been recently identified that can be broadly distinguished by their contrasting roles in attention to the external world versus internally directed mentation involving long-term memory. At the core of these two networks are the dorsal attention system and the hippocampal-cortical memory system, a component of the brain's default network. Here spontaneous blood-oxygenation-level-dependent (BOLD) signal correlations were used in three separate functional magnetic resonance imaging data sets (n = 105) to define a third system, the frontoparietal control system, which is spatially interposed between these two previously defined systems. The frontoparietal control system includes many regions identified as supporting cognitive control and decision-making processes including lateral prefrontal cortex, anterior cingulate cortex, and inferior parietal lobule. Detailed analysis of frontal and parietal cortex, including use of high-resolution data, revealed clear evidence for contiguous but distinct regions: in general, the regions associated with the frontoparietal control system are situated between components of the dorsal attention and hippocampal-cortical memory systems. The frontoparietal control system is therefore anatomically positioned to integrate information from these two opposing brain systems.
                Bookmark

                Author and article information

                Journal
                Front Syst Neurosci
                Front. Syst. Neurosci.
                Frontiers in Systems Neuroscience
                Frontiers Research Foundation
                1662-5137
                04 February 2011
                2011
                : 5
                : 2
                Affiliations
                [1] 1simpleThe Mind Research Network Albuquerque, NM, USA
                [2] 2simpleDepartment of Electrical and Computer Engineering, University of New Mexico Albuquerque, NM, USA
                [3] 3simpleDepartment of Family and Community Medicine, University of New Mexico Albuquerque, NM, USA
                [4] 4simpleDepartment of Psychiatry, University of New Mexico Albuquerque, NM, USA
                [5] 5simpleDepartment of Biological and Medical Psychology, Faculty of Psychology, University of Bergen Bergen, Norway
                [6] 6simpleDepartment of Psychology, University of New Mexico Albuquerque, NM, USA
                [7] 7simpleCenter on Alcoholism Substance Abuse and Addiction, University of New Mexico Albuquerque, NM, USA
                [8] 8simpleDepartment of Neuroscience, University of New Mexico Albuquerque, NM, USA
                [9] 9simpleCenter for BrainHealth, School of Behavioral and Brain Sciences, University of Texas at Dallas Dallas, TX, USA
                [10] 10simpleDepartment of Neurology, University of New Mexico Albuquerque, NM, USA
                [11] 11simpleDepartment of Neurosurgery, University of New Mexico Albuquerque, NM, USA
                [12] 12simpleCenter for Development and Disability, University of New Mexico Albuquerque, NM, USA
                [13] 13simpleDepartment Obstetrics and Gynecology, University of New Mexico Albuquerque, NM, USA
                [14] 14simpleOlin Neuropsychiatry Research Center, The Institute of Living Hartford, CT, USA
                [15] 15simpleDepartment of Psychiatry, Yale University New Haven, CT, USA
                [16] 16simpleDepartment of Neurobiology, Yale University New Haven, CT, USA
                [17] 17simpleBehavioral Health Care Line, New Mexico VA Health Care System Albuquerque, NM, USA
                Author notes

                Edited by: Silvina G. Horovitz, National Institutes of Health, USA

                Reviewed by: Scott K. Holland, Cincinnati Children's Research Foundation, USA

                *Correspondence: Elena A. Allen, The Mind Research Network, 1101 Yale Boulevard NE, Albuquerque, NM 87106, USA. e-mail: eallen@ 123456mrn.org
                Article
                10.3389/fnsys.2011.00002
                3051178
                21442040
                7bb9ad69-8d97-4d18-b6a9-9fc8c2be85c5
                Copyright © 2011 Allen, Erhardt, Damaraju, Gruner, Segall, Silva, Havlicek, Rachakonda, Fries, Kalyanam, Michael, Caprihan, Turner, Eichele, Adelsheim, Bryan, Bustillo, Clark, Feldstein Ewing, Filbey, Ford, Hutchison, Jung, Kiehl, Kodituwakku, Komesu, Mayer, Pearlson, Phillips, Sadek, Stevens, Teuscher, Thoma and Calhoun.

                This is an open-access article subject to an exclusive license agreement between the authors and Frontiers Media SA, which permits unrestricted use, distribution, and reproduction in any medium, provided the original authors and source are credited.

                History
                : 16 June 2010
                : 03 January 2011
                Page count
                Figures: 12, Tables: 2, Equations: 2, References: 123, Pages: 23, Words: 18575
                Categories
                Neuroscience
                Original Research

                Neurosciences
                functional connectivity,independent component analysis,connectome,resting-state,fmri
                Neurosciences
                functional connectivity, independent component analysis, connectome, resting-state, fmri

                Comments

                Comment on this article