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      Co-activation patterns across multiple tasks reveal robust anti-correlated functional networks

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          Abstract

          Whether antagonistic brain states constitute a fundamental principle of human brain organization has been debated over the past decade. Some argue that intrinsically anti-correlated brain networks in resting-state functional connectivity are an artifact of preprocessing. Others argue that anti-correlations are biologically meaningful predictors of how the brain will respond to different stimuli. Here, we investigated the co-activation patterns across the whole brain in various tasks and test whether brain regions demonstrate anti-correlated activity similar to those observed at rest. We examined brain activity in 47 task contrasts from the Human Connectome Project ( N = 680) and found robust antagonistic interactions between networks. Regions of the default network exhibited the highest degree of cortex-wide negative connectivity. The negative co-activation patterns across tasks showed good correspondence to that derived from resting-state data processed with global signal regression (GSR). Interestingly, GSR-processed resting-state data was a significantly better predictor of task-induced modulation than data processed without GSR. Finally, in a cohort of 25 patients with depression, we found that task-based anti-correlations between the dorsolateral prefrontal cortex (DLPFC) and subgenual anterior cingulate cortex were associated with clinical efficacy of transcranial magnetic stimulation therapy targeting the DLPFC. Overall, our findings indicate that anti-correlations are a biologically meaningful phenomenon and may reflect an important principle of functional brain organization.

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          The organization of the human cerebral cortex estimated by intrinsic functional connectivity.

          Information processing in the cerebral cortex involves interactions among distributed areas. Anatomical connectivity suggests that certain areas form local hierarchical relations such as within the visual system. Other connectivity patterns, particularly among association areas, suggest the presence of large-scale circuits without clear hierarchical relations. In this study the organization of networks in the human cerebrum was explored using resting-state functional connectivity MRI. Data from 1,000 subjects were registered using surface-based alignment. A clustering approach was employed to identify and replicate networks of functionally coupled regions across the cerebral cortex. The results revealed local networks confined to sensory and motor cortices as well as distributed networks of association regions. Within the sensory and motor cortices, functional connectivity followed topographic representations across adjacent areas. In association cortex, the connectivity patterns often showed abrupt transitions between network boundaries. Focused analyses were performed to better understand properties of network connectivity. A canonical sensory-motor pathway involving primary visual area, putative middle temporal area complex (MT+), lateral intraparietal area, and frontal eye field was analyzed to explore how interactions might arise within and between networks. Results showed that adjacent regions of the MT+ complex demonstrate differential connectivity consistent with a hierarchical pathway that spans networks. The functional connectivity of parietal and prefrontal association cortices was next explored. Distinct connectivity profiles of neighboring regions suggest they participate in distributed networks that, while showing evidence for interactions, are embedded within largely parallel, interdigitated circuits. We conclude by discussing the organization of these large-scale cerebral networks in relation to monkey anatomy and their potential evolutionary expansion in humans to support cognition.
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            A default mode of brain function.

            A baseline or control state is fundamental to the understanding of most complex systems. Defining a baseline state in the human brain, arguably our most complex system, poses a particular challenge. Many suspect that left unconstrained, its activity will vary unpredictably. Despite this prediction we identify a baseline state of the normal adult human brain in terms of the brain oxygen extraction fraction or OEF. The OEF is defined as the ratio of oxygen used by the brain to oxygen delivered by flowing blood and is remarkably uniform in the awake but resting state (e.g., lying quietly with eyes closed). Local deviations in the OEF represent the physiological basis of signals of changes in neuronal activity obtained with functional MRI during a wide variety of human behaviors. We used quantitative metabolic and circulatory measurements from positron-emission tomography to obtain the OEF regionally throughout the brain. Areas of activation were conspicuous by their absence. All significant deviations from the mean hemisphere OEF were increases, signifying deactivations, and resided almost exclusively in the visual system. Defining the baseline state of an area in this manner attaches meaning to a group of areas that consistently exhibit decreases from this baseline, during a wide variety of goal-directed behaviors monitored with positron-emission tomography and functional MRI. These decreases suggest the existence of an organized, baseline default mode of brain function that is suspended during specific goal-directed behaviors.
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              A multi-modal parcellation of human cerebral cortex

              Understanding the amazingly complex human cerebral cortex requires a map (or parcellation) of its major subdivisions, known as cortical areas. Making an accurate areal map has been a century-old objective in neuroscience. Using multi-modal magnetic resonance images from the Human Connectome Project (HCP) and an objective semi-automated neuroanatomical approach, we delineated 180 areas per hemisphere bounded by sharp changes in cortical architecture, function, connectivity, and/or topography in a precisely aligned group average of 210 healthy young adults. We characterized 97 new areas and 83 areas previously reported using post-mortem microscopy or other specialized study-specific approaches. To enable automated delineation and identification of these areas in new HCP subjects and in future studies, we trained a machine-learning classifier to recognize the multi-modal ‘fingerprint’ of each cortical area. This classifier detected the presence of 96.6% of the cortical areas in new subjects, replicated the group parcellation, and could correctly locate areas in individuals with atypical parcellations. The freely available parcellation and classifier will enable substantially improved neuroanatomical precision for studies of the structural and functional organization of human cerebral cortex and its variation across individuals and in development, aging, and disease.
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                Author and article information

                Journal
                9215515
                20498
                Neuroimage
                Neuroimage
                NeuroImage
                1053-8119
                1095-9572
                28 March 2021
                29 December 2020
                15 February 2021
                09 April 2021
                : 227
                : 117680
                Affiliations
                [a ]Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, USA
                [b ]Department of Medical Imaging, Zhengzhou University People Hospital & Henan Provincial People’s Hospital, Zhengzhou, Henan, China
                [c ]Department of Radiology, Ludwig Maximilian University of Munich, Munich, Germany
                [d ]Department of Neurosurgery, First Affiliated Hospital of Fujian Medical University, Fuzhou, China
                [e ]Department of Neurosurgery, Comprehensive Epilepsy Center, Sanbo Brain Hospital, Capital Medical University, Beijing, China
                [f ]Department of Medical Imaging, Jinling Hospital, Clinical School of Medical College, Nanjing University, Nanjing, China
                [g ]Department of Neurology, Xuanwu Hospital of Capital Medical University, Beijing, China
                [h ]Marcus Institute for Aging Research, Hebrew SeniorLife, Harvard Medical School, Boston, MA, USA
                [i ]Center for Brain Circuit Therapeutics, Departments of Neurology, Psychiatry, and Radiology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
                [j ]Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
                [k ]Beijing Institute for Brain Disorders, Capital Medical University, Beijing, China
                [l ]Department of Neuroscience, Medical University of South Carolina, Charleston, SC, USA
                Author notes
                [1]

                Contributed equally.

                Author contributions

                H.L., M.L. and M.W. conceived the study; M.L. and H.L. designed the algorithm; M.L., L.D., D.W., J.R., and F.G. performed the analyses with support from S.S. and M.D.F.; M.W., A.P.L, Y.H., Guo.L., Z.Z., Gua.L., and Y.L. provided support and guidance with data interpretation. L.D., H.L., M.L., and F.G. wrote the manuscript with contribution from S.S. and M.D.F. All authors commented on the manuscript.

                [* ]Corresponding author. mywang@ 123456ha.edu.cn (M. Wang).
                Article
                NIHMS1687476
                10.1016/j.neuroimage.2020.117680
                8034806
                33359345
                5757968c-7a18-4a86-ad4b-db21f93d1d87

                This is an open access article under the CC BY-NC-ND license ( http://creativecommons.org/licenses/by-nc-nd/4.0/)

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                Categories
                Article

                Neurosciences
                anti-correlations,functional connectivity,task fmri,global signal regression,transcranial magnetic stimulation

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