65
views
0
recommends
+1 Recommend
1 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: not found

      Precision functional mapping of individual human brains

      research-article

      Read this article at

      ScienceOpenPublisherPMC
          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.

          Summary

          Human functional MRI (fMRI) research primarily focuses on analyzing data averaged across groups, which limits the detail, specificity, and clinical utility of fMRI resting-state functional connectivity (RSFC) and task activation maps. To push our understanding of functional brain organization to the level of individual humans, we assembled a novel MRI dataset containing five hours of RSFC data, six hours of task fMRI, multiple structural MRIs, and neuropsychological tests from each of ten adults. Using these data, we generated ten high fidelity, individual-specific functional connectomes. This individual connectome approach revealed several new types of spatial and organizational variability in brain networks, including unique network features and topologies that corresponded with structural and task-derived brain features. We are releasing this highly-sampled, individual-focused dataset as a resource for neuroscientists, and we propose precision individual connectomics as a model for future work examining the organization of healthy and diseased individual human brains.

          Related collections

          Most cited references50

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

          A hybrid approach to the skull stripping problem in MRI.

          We present a novel skull-stripping algorithm based on a hybrid approach that combines watershed algorithms and deformable surface models. Our method takes advantage of the robustness of the former as well as the surface information available to the latter. The algorithm first localizes a single white matter voxel in a T1-weighted MRI image, and uses it to create a global minimum in the white matter before applying a watershed algorithm with a preflooding height. The watershed algorithm builds an initial estimate of the brain volume based on the three-dimensional connectivity of the white matter. This first step is robust, and performs well in the presence of intensity nonuniformities and noise, but may erode parts of the cortex that abut bright nonbrain structures such as the eye sockets, or may remove parts of the cerebellum. To correct these inaccuracies, a surface deformation process fits a smooth surface to the masked volume, allowing the incorporation of geometric constraints into the skull-stripping procedure. A statistical atlas, generated from a set of accurately segmented brains, is used to validate and potentially correct the segmentation, and the MRI intensity values are locally re-estimated at the boundary of the brain. Finally, a high-resolution surface deformation is performed that accurately matches the outer boundary of the brain, resulting in a robust and automated procedure. Studies by our group and others outperform other publicly available skull-stripping tools. Copyright 2004 Elsevier Inc.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            A dual-networks architecture of top-down control.

            Complex systems ensure resilience through multiple controllers acting at rapid and slower timescales. The need for efficient information flow through complex systems encourages small-world network structures. On the basis of these principles, a group of regions associated with top-down control was examined. Functional magnetic resonance imaging showed that each region had a specific combination of control signals; resting-state functional connectivity grouped the regions into distinct 'fronto-parietal' and 'cingulo-opercular' components. The fronto-parietal component seems to initiate and adjust control; the cingulo-opercular component provides stable 'set-maintenance' over entire task epochs. Graph analysis showed dense local connections within components and weaker 'long-range' connections between components, suggesting a small-world architecture. The control systems of the brain seem to embody the principles of complex systems, encouraging resilient performance.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              Multi-task connectivity reveals flexible hubs for adaptive task control

              Extensive evidence suggests the human ability to adaptively implement a wide variety of tasks is preferentially due to the operation of a fronto-parietal brain network. We hypothesized that this network’s adaptability is made possible by ‘flexible hubs’ – brain regions that rapidly update their pattern of global functional connectivity according to task demands. We utilized recent advances in characterizing brain network organization and dynamics to identify mechanisms consistent with the flexible hub theory. We found that the fronto-parietal network’s brain-wide functional connectivity pattern shifted more than other networks’ across a variety of task states, and that these connectivity patterns could be used to identify the current task. Further, these patterns were consistent across practiced and novel tasks, suggesting reuse of flexible hub connectivity patterns facilitates adaptive (novel) task performance. Together, these findings support a central role for fronto-parietal flexible hubs in cognitive control and adaptive implementation of task demands generally.
                Bookmark

                Author and article information

                Journal
                8809320
                1600
                Neuron
                Neuron
                Neuron
                0896-6273
                1097-4199
                29 July 2017
                27 July 2017
                16 August 2017
                16 August 2018
                : 95
                : 4
                : 791-807.e7
                Affiliations
                [1 ]VISN 17 Center of Excellence for Research on Returning War Veterans, Waco, TX, 76711
                [2 ]Center for Vital Longevity, School of Behavioral and Brain Sciences, University of Texas at Dallas, Dallas, TX, 75235
                [3 ]Department of Neurology, Washington University School of Medicine, St. Louis, MO, 63110
                [4 ]Laboratory of Brain and Cognition, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, 20892
                [5 ]Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, 63110
                [6 ]Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, 63110
                [7 ]Department of Psychology, New York University, New York, NY 10003
                [8 ]Program in Occupational Therapy, Washington University School of Medicine, St. Louis, MO, 63110
                [9 ]Department of Psychological and Brain Sciences, Washington University in St. Louis, St. Louis, MO, 63130
                [10 ]Department of Neuroscience, Washington University School of Medicine, St. Louis, MO, 63110
                [11 ]Department of Pediatrics, Washington University School of Medicine, St. Louis, MO, 63110
                [12 ]Department of Biomedical Engineering, Washington University in St. Louis, St. Louis, MO, 63130
                [13 ]Department of Neurological Surgery, Washington University School of Medicine, St. Louis, MO, 63110
                [14 ]Department of Psychology and Neuroscience, Baylor University, Waco, TX 76789
                Author notes
                [15]

                Lead Contact

                [*]

                Contributed equally to this work

                [**]

                Contributed equally to this work

                Article
                PMC5576360 PMC5576360 5576360 nihpa892846
                10.1016/j.neuron.2017.07.011
                5576360
                28757305
                393f7ef6-4fc7-4e84-afdd-6451a730f7cd
                History
                Categories
                Article

                brain networks,fMRI,individual variability,functional connectivity,myelin mapping

                Comments

                Comment on this article