Institution
Kennedy Krieger Institute
Nonprofit•Baltimore, Maryland, United States•
About: Kennedy Krieger Institute is a nonprofit organization based out in Baltimore, Maryland, United States. It is known for research contribution in the topics: Autism & Population. The organization has 2237 authors who have published 4493 publications receiving 236619 citations.
Topics: Autism, Population, Autism spectrum disorder, Poison control, Attention deficit hyperactivity disorder
Papers published on a yearly basis
Papers
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Rutgers University1, New York University2, University of Oxford3, Harvard University4, Bangor University5, University of Copenhagen6, National Institutes of Health7, Oregon Health & Science University8, Yale University9, Nathan Kline Institute for Psychiatric Research10, Medical College of Wisconsin11, University of Oulu12, Radboud University Nijmegen13, National Yang-Ming University14, Cleveland Clinic15, Duke University16, Max Planck Society17, Emory University18, University of Queensland19, University of Michigan20, Kennedy Krieger Institute21, Washington University in St. Louis22, Technische Universität München23, Leiden University24, University of Texas at Dallas25, Charité26, University of Pittsburgh27, Southeast University28, Otto-von-Guericke University Magdeburg29, Massachusetts Institute of Technology30, University of Western Ontario31, Medical University of Vienna32, Beijing Normal University33
TL;DR: The 1000 Functional Connectomes Project (Fcon_1000) as discussed by the authors is a large-scale collection of functional connectome data from 1,414 volunteers collected independently at 35 international centers.
Abstract: Although it is being successfully implemented for exploration of the genome, discovery science has eluded the functional neuroimaging community. The core challenge remains the development of common paradigms for interrogating the myriad functional systems in the brain without the constraints of a priori hypotheses. Resting-state functional MRI (R-fMRI) constitutes a candidate approach capable of addressing this challenge. Imaging the brain during rest reveals large-amplitude spontaneous low-frequency (<0.1 Hz) fluctuations in the fMRI signal that are temporally correlated across functionally related areas. Referred to as functional connectivity, these correlations yield detailed maps of complex neural systems, collectively constituting an individual's "functional connectome." Reproducibility across datasets and individuals suggests the functional connectome has a common architecture, yet each individual's functional connectome exhibits unique features, with stable, meaningful interindividual differences in connectivity patterns and strengths. Comprehensive mapping of the functional connectome, and its subsequent exploitation to discern genetic influences and brain-behavior relationships, will require multicenter collaborative datasets. Here we initiate this endeavor by gathering R-fMRI data from 1,414 volunteers collected independently at 35 international centers. We demonstrate a universal architecture of positive and negative functional connections, as well as consistent loci of inter-individual variability. Age and sex emerged as significant determinants. These results demonstrate that independent R-fMRI datasets can be aggregated and shared. High-throughput R-fMRI can provide quantitative phenotypes for molecular genetic studies and biomarkers of developmental and pathological processes in the brain. To initiate discovery science of brain function, the 1000 Functional Connectomes Project dataset is freely accessible at www.nitrc.org/projects/fcon_1000/.
2,787 citations
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TL;DR: A novel approach for drawing group inferences using ICA of fMRI data is introduced, and its application to a simple visual paradigm that alternately stimulates the left or right visual field is presented.
Abstract: Independent component analysis (ICA) is a promising analysis method that is being increasingly applied to fMRI data. A principal advantage of this approach is its applicability to cognitive paradigms for which detailed models of brain activity are not available. Independent component analysis has been successfully utilized to analyze single-subject fMRI data sets, and an extension of this work would be to provide for group inferences. However, unlike univariate methods (e.g., regression analysis, Kolmogorov-Smirnov statistics), ICA does not naturally generalize to a method suitable for drawing inferences about groups of subjects. We introduce a novel approach for drawing group inferences using ICA of fMRI data, and present its application to a simple visual paradigm that alternately stimulates the left or right visual field. Our group ICA analysis revealed task-related components in left and right visual cortex, a transiently task-related component in bilateral occipital/parietal cortex, and a non-task-related component in bilateral visual association cortex. We address issues involved in the use of ICA as an fMRI analysis method such as: (1) How many components should be calculated? (2) How are these components to be combined across subjects? (3) How should the final results be thresholded and/or presented? We show that the methodology we present provides answers to these questions and lay out a process for making group inferences from fMRI data using independent component analysis.
2,729 citations
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New York University1, Nathan Kline Institute for Psychiatric Research2, MIND Institute3, Katholieke Universiteit Leuven4, University of Utah5, Yale University6, University of California, Los Angeles7, Massachusetts Institute of Technology8, Trinity College, Dublin9, Ben-Gurion University of the Negev10, Carnegie Mellon University11, Ludwig Maximilian University of Munich12, Oregon Health & Science University13, Indiana University14, California Institute of Technology15, San Diego State University16, University of Groningen17, Netherlands Institute for Neuroscience18, University of Wisconsin-Madison19, Cornell University20, University of Pittsburgh21, Stanford University22, University of Michigan23, Kennedy Krieger Institute24, Johns Hopkins University25
TL;DR: W Whole-brain analyses reconciled seemingly disparate themes of both hypo- and hyperconnectivity in the ASD literature; both were detected, although hypoconnectivity dominated, particularly for corticocortical and interhemispheric functional connectivity.
Abstract: Autism spectrum disorders (ASDs) represent a formidable challenge for psychiatry and neuroscience because of their high prevalence, lifelong nature, complexity and substantial heterogeneity. Facing these obstacles requires large-scale multidisciplinary efforts. Although the field of genetics has pioneered data sharing for these reasons, neuroimaging had not kept pace. In response, we introduce the Autism Brain Imaging Data Exchange (ABIDE)-a grassroots consortium aggregating and openly sharing 1112 existing resting-state functional magnetic resonance imaging (R-fMRI) data sets with corresponding structural MRI and phenotypic information from 539 individuals with ASDs and 573 age-matched typical controls (TCs; 7-64 years) (http://fcon_1000.projects.nitrc.org/indi/abide/). Here, we present this resource and demonstrate its suitability for advancing knowledge of ASD neurobiology based on analyses of 360 male subjects with ASDs and 403 male age-matched TCs. We focused on whole-brain intrinsic functional connectivity and also survey a range of voxel-wise measures of intrinsic functional brain architecture. Whole-brain analyses reconciled seemingly disparate themes of both hypo- and hyperconnectivity in the ASD literature; both were detected, although hypoconnectivity dominated, particularly for corticocortical and interhemispheric functional connectivity. Exploratory analyses using an array of regional metrics of intrinsic brain function converged on common loci of dysfunction in ASDs (mid- and posterior insula and posterior cingulate cortex), and highlighted less commonly explored regions such as the thalamus. The survey of the ABIDE R-fMRI data sets provides unprecedented demonstrations of both replication and novel discovery. By pooling multiple international data sets, ABIDE is expected to accelerate the pace of discovery setting the stage for the next generation of ASD studies.
1,939 citations
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TL;DR: It is indicated that innate neuroimmune reactions play a pathogenic role in an undefined proportion of autistic patients, suggesting that future therapies might involve modifying neuroglial responses in the brain.
Abstract: Autism is a neurodevelopmental disorder characterized by impaired communication and social interaction and may be accompanied by mental retardation and epilepsy. Its cause remains unknown, despite evidence that genetic, environmental, and immunological factors may play a role in its pathogenesis. To investigate whether immune-mediated mechanisms are involved in the pathogenesis of autism, we used immunocytochemistry, cytokine protein arrays, and enzyme-linked immunosorbent assays to study brain tissues and cerebrospinal fluid (CSF) from autistic patients and determined the magnitude of neuroglial and inflammatory reactions and their cytokine expression profiles. Brain tissues from cerebellum, midfrontal, and cingulate gyrus obtained at autopsy from 11 patients with autism were used for morphological studies. Fresh-frozen tissues available from seven patients and CSF from six living autistic patients were used for cytokine protein profiling. We demonstrate an active neuroinflammatory process in the cerebral cortex, white matter, and notably in cerebellum of autistic patients. Immunocytochemical studies showed marked activation of microglia and astroglia, and cytokine profiling indicated that macrophage chemoattractant protein (MCP)-1 and tumor growth factor-beta1, derived from neuroglia, were the most prevalent cytokines in brain tissues. CSF showed a unique proinflammatory profile of cytokines, including a marked increase in MCP-1. Our findings indicate that innate neuroimmune reactions play a pathogenic role in an undefined proportion of autistic patients, suggesting that future therapies might involve modifying neuroglial responses in the brain.
1,845 citations
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TL;DR: Diffusion tensor imaging (DTI) is a recently developed MRI technique that can measure macroscopic axonal organization in nervous system tissues and several applications are introduced, including visualization of axonal tracts in myelin and axonal injuries as well as human brain and mouse embryonic development.
1,593 citations
Authors
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Name | H-index | Papers | Citations |
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Eduardo Marbán | 129 | 579 | 49586 |
Godfrey D. Pearlson | 128 | 740 | 58845 |
Marilyn S. Albert | 124 | 467 | 79341 |
Allan L. Reiss | 118 | 529 | 59363 |
Constantine G. Lyketsos | 111 | 567 | 43932 |
John Hart | 108 | 1081 | 54283 |
Stephen Jay Gould | 108 | 464 | 70855 |
Peter C.M. van Zijl | 100 | 424 | 43990 |
Robert Stevens | 98 | 705 | 43881 |
Scott L. Zeger | 95 | 377 | 78179 |
Susumu Mori | 94 | 361 | 38956 |
Joel T. Nigg | 90 | 264 | 31142 |
Robert L. Findling | 88 | 556 | 27905 |
David R. Borchelt | 87 | 258 | 34660 |
Yi Li | 86 | 882 | 33629 |