Bloomington, IN, United States

Indiana University Bloomington
Bloomington, IN, United States

Indiana University Bloomington is a public research university located in Bloomington, Indiana, United States. With over 40,000 students, IU Bloomington is the flagship institution of the Indiana University system and its largest university.It is a member of the Association of American Universities and has numerous schools and programs the comprise part of IU, including the Jacobs School of Music, the IU School of Informatics and Computing, the Kelley School of Business, the School of Public Health, the School of Nursing, the School of Public and Environmental Affairs, the Maurer School of Law, the IU School of Library and Information Science, and the IU School of Education.With a Fall 2014 total campus enrollment of 42,634 students, IU Bloomington is the largest university campus in the state. While 55.2% of the student body was from Indiana, students from 49 of the 50 states, Washington D.C., and 165 foreign nations were also enrolled. The university is home to an extensive student life program, with about 17 percent of undergraduates joining the Greek system. Indiana athletic teams compete in Division I of the NCAA and are known as the Indiana Hoosiers. The university is a member of the Big Ten Conference. Wikipedia.

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van den Heuvel M.P.,University Utrecht | Sporns O.,Indiana University Bloomington
Trends in Cognitive Sciences | Year: 2013

Virtually all domains of cognitive function require the integration of distributed neural activity. Network analysis of human brain connectivity has consistently identified sets of regions that are critically important for enabling efficient neuronal signaling and communication. The central embedding of these candidate 'brain hubs' in anatomical networks supports their diverse functional roles across a broad range of cognitive tasks and widespread dynamic coupling within and across functional networks. The high level of centrality of brain hubs also renders them points of vulnerability that are susceptible to disconnection and dysfunction in brain disorders. Combining data from numerous empirical and computational studies, network approaches strongly suggest that brain hubs play important roles in information integration underpinning numerous aspects of complex cognitive function. © 2013 Elsevier Ltd.

Kruschke J.K.,Indiana University Bloomington
Journal of Experimental Psychology: General | Year: 2013

Bayesian estimation for 2 groups provides complete distributions of credible values for the effect size, group means and their difference, standard deviations and their difference, and the normality of the data. The method handles outliers. The decision rule can accept the null value (unlike traditional t tests) when certainty in the estimate is high (unlike Bayesian model comparison using Bayes factors). The method also yields precise estimates of statistical power for various research goals. The software and programs are free and run on Macintosh, Windows, and Linux platforms. © 2012 American Psychological Association.

Sporns O.,Indiana University Bloomington
NeuroImage | Year: 2013

The human connectome refers to a map of the brain's structural connections, rendered as a connection matrix or network. This article attempts to trace some of the historical origins of the connectome, in the process clarifying its definition and scope, as well as its putative role in illuminating brain function. Current efforts to map the connectome face a number of significant challenges, including the issue of capturing network connectivity across multiple spatial scales, accounting for individual variability and structural plasticity, as well as clarifying the role of the connectome in shaping brain dynamics. Throughout, the article argues that these challenges require the development of new approaches for the statistical analysis and computational modeling of brain network data, and greater collaboration across disciplinary boundaries, especially with researchers in complex systems and network science. © 2013 Elsevier Inc.

Sporns O.,Indiana University Bloomington
NeuroImage | Year: 2012

Connectivity is fundamental for understanding the nature of brain function. The intricate web of synaptic connections among neurons is critically important for shaping neural responses, representing statistical features of the sensory environment, coordinating distributed resources for brain-wide processing, and retaining a structural record of the past in order to anticipate future events and infer their relations. The importance of brain connectivity naturally leads to the adoption of the theoretical framework of networks and graphs. Network science approaches have been productively deployed in other domains of science and technology and are now beginning to make contributions across many areas of neuroscience. This article offers a personal perspective on the confluence of networks and neuroimaging, charting the origins of some of its major intellectual themes. © 2011 Elsevier Inc.

Sporns O.,Indiana University Bloomington
Nature Neuroscience | Year: 2014

The confluence of new approaches in recording patterns of brain connectivity and quantitative analytic tools from network science has opened new avenues toward understanding the organization and function of brain networks. Descriptive network models of brain structural and functional connectivity have made several important contributions; for example, in the mapping of putative network hubs and network communities. Building on the importance of anatomical and functional interactions, network models have provided insight into the basic structures and mechanisms that enable integrative neural processes. Network models have also been instrumental in understanding the role of structural brain networks in generating spatially and temporally organized brain activity. Despite these contributions, network models are subject to limitations in methodology and interpretation, and they face many challenges as brain connectivity data sets continue to increase in detail and complexity. © 2014 Nature America, Inc. All rights reserved.

Petersen S.E.,University of Washington | Sporns O.,Indiana University Bloomington
Neuron | Year: 2015

Most accounts of human cognitive architectures have focused on computational accounts of cognition while making little contact with the study of anatomical structures and physiological processes. A renewed convergence between neurobiology and cognition is well under way. A promising area arises from the overlap between systems/cognitive neuroscience on the one side and the discipline of network science on the other. Neuroscience increasingly adopts network tools and concepts to describe the operation of collections of brain regions. Beyond just providing illustrative metaphors, network science offers a theoretical framework for approaching brain structure and function as a multi-scale system composed of networks of neurons, circuits, nuclei, cortical areas, and systems of areas. This paper views large-scale networks at the level of areas and systems, mostly on the basis of data from human neuroimaging, and how this view of network structure and function has begun to illuminate our understanding of the biological basis of cognitive architectures. © 2015 Elsevier Inc.

Kostelecky V.A.,Indiana University Bloomington | Russell N.,Northern Michigan University
Reviews of Modern Physics | Year: 2011

This work tabulates measured and derived values of coefficients for Lorentz and CPT violation in the standard-model extension. Summary tables are extracted listing maximal attained sensitivities in the matter, photon, and gravity sectors. Tables presenting definitions and properties are also compiled. © 2011 American Physical Society.

Sporns O.,Indiana University Bloomington
Nature Methods | Year: 2013

New methods for mapping synaptic connections and recording neural signals generate rich and complex data on the structure and dynamics of brain networks. Making sense of these data will require a concerted effort directed at data analysis and reduction as well as computational modeling. © 2013 Nature America, Inc. All rights reserved.

Radicchi F.,Indiana University Bloomington
Nature Physics | Year: 2015

The function of a real network depends not only on the reliability of its own components, but is affected also by the simultaneous operation of other real networks coupled with it. Whereas theoretical methods of direct applicability to real isolated networks exist, the frameworks developed so far in percolation theory for interdependent network layers are of little help in practical contexts, as they are suited only for special models in the limit of infinite size. Here, we introduce a set of heuristic equations that takes as inputs the adjacency matrices of the layers to draw the entire phase diagram for the interconnected network. We demonstrate that percolation transitions in interdependent networks can be understood by decomposing these systems into uncoupled graphs: the intersection among the layers, and the remainders of the layers. When the intersection dominates the remainders, an interconnected network undergoes a smooth percolation transition. Conversely, if the intersection is dominated by the contribution of the remainders, the transition becomes abrupt even in small networks. We provide examples of real systems that have developed interdependent networks sharing cores of 'high quality edges to prevent catastrophic failures. © 2015 Macmillan Publishers Limited. All rights reserved.

Lynch M.,Indiana University Bloomington
Trends in Genetics | Year: 2010

Understanding the mechanisms of evolution requires information on the rate of appearance of new mutations and their effects at the molecular and phenotypic levels. Although procuring such data has been technically challenging, high-throughput genome sequencing is rapidly expanding knowledge in this area. With information on spontaneous mutations now available in a variety of organisms, general patterns have emerged for the scaling of mutation rate with genome size and for the likely mechanisms that drive this pattern. Support is presented for the hypothesis that natural selection pushes mutation rates down to a lower limit set by the power of random genetic drift rather than by intrinsic physiological limitations, and that this has resulted in reduced levels of replication, transcription, and translation fidelity in eukaryotes relative to prokaryotes. © 2010 Elsevier Ltd.

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