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Mikail Rubinov

Bio: Mikail Rubinov is an academic researcher from Howard Hughes Medical Institute. The author has contributed to research in topics: Connectome & Resting state fMRI. The author has an hindex of 26, co-authored 38 publications receiving 11045 citations. Previous affiliations of Mikail Rubinov include Vanderbilt University & University of Sydney.

Papers
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Journal ArticleDOI
TL;DR: Construction of brain networks from connectivity data is discussed and the most commonly used network measures of structural and functional connectivity are described, which variously detect functional integration and segregation, quantify centrality of individual brain regions or pathways, and test resilience of networks to insult.

9,291 citations

Journal ArticleDOI
TL;DR: This study generalizes measures of modularity and centrality to fully connected and weighted complex networks, describes the detection of degenerate high-modularity partitions of these networks, and introduces a weighted-connectivity null model of these Networks.

807 citations

Journal ArticleDOI
TL;DR: Nonlinear measures of cortical interactions and graph‐theoretical metrics of network topography are combined to investigate this schizophrenia “disconnection hypothesis” by analyzing the spatiotemporal structure of resting state scalp EEG data from 40 young subjects with a recent first episode of schizophrenia and 40 healthy matched controls.
Abstract: A disturbance in the interactions between distributed cortical regions may underlie the cogni- tive and perceptual dysfunction associated with schizophrenia. In this article, nonlinear measures of cortical interactions and graph-theoretical metrics of network topography are combined to investigate this schizophrenia ''disconnection hypothesis.'' This is achieved by analyzing the spatiotemporal struc- ture of resting state scalp EEG data previously acquired from 40 young subjects with a recent first epi- sode of schizophrenia and 40 healthy matched controls. In each subject, a method of mapping the to- pography of nonlinear interactions between cortical regions was applied to a widely distributed array of these data. The resulting nonlinear correlation matrices were converted to weighted graphs. The path length (a measure of large-scale network integration), clustering coefficient (a measure of ''cliqu- ishness''), and hub structure of these graphs were used as metrics of the underlying brain network ac- tivity. The graphs of both groups exhibited high levels of local clustering combined with comparatively short path lengths—features consistent with a ''small-world'' topology—as well as the presence of strong, central hubs. The graphs in the schizophrenia group displayed lower clustering and shorter path lengths in comparison to the healthy group. Whilst still ''small-world,'' these effects are consistent with a subtle randomization in the underlying network architecture—likely associated with a greater number of links connecting disparate clusters. This randomization may underlie the cognitive distur- bances characteristic of schizophrenia. Hum Brain Mapp 30:403-416, 2009. V C 2007 Wiley-Liss, Inc.

454 citations

Journal ArticleDOI
TL;DR: It is concluded that there is a real risk of motion-related bias in connectivity analysis of fMRI data, but that this risk is generally manageable, by effective time series denoising strategies designed to attenuate synchronized signal transients induced by abrupt head movements.

330 citations

Journal ArticleDOI
TL;DR: It is found that spike-timing-dependent synaptic plasticity enabled a rapid phase transition from random subcritical dynamics to ordered supercritical dynamics, and modular connectivity and low wiring cost broadened this transition, and enabled a regime indicative of self-organized criticality.
Abstract: Self-organized criticality refers to the spontaneous emergence of self-similar dynamics in complex systems poised between order and randomness. The presence of self-organized critical dynamics in the brain is theoretically appealing and is supported by recent neurophysiological studies. Despite this, the neurobiological determinants of these dynamics have not been previously sought. Here, we systematically examined the influence of such determinants in hierarchically modular networks of leaky integrate-and-fire neurons with spike-timing-dependent synaptic plasticity and axonal conduction delays. We characterized emergent dynamics in our networks by distributions of active neuronal ensemble modules (neuronal avalanches) and rigorously assessed these distributions for power-law scaling. We found that spike-timing-dependent synaptic plasticity enabled a rapid phase transition from random subcritical dynamics to ordered supercritical dynamics. Importantly, modular connectivity and low wiring cost broadened this transition, and enabled a regime indicative of self-organized criticality. The regime only occurred when modular connectivity, low wiring cost and synaptic plasticity were simultaneously present, and the regime was most evident when between-module connection density scaled as a power-law. The regime was robust to variations in other neurobiologically relevant parameters and favored systems with low external drive and strong internal interactions. Increases in system size and connectivity facilitated internal interactions, permitting reductions in external drive and facilitating convergence of postsynaptic-response magnitude and synaptic-plasticity learning rate parameter values towards neurobiologically realistic levels. We hence infer a novel association between self-organized critical neuronal dynamics and several neurobiologically realistic features of structural connectivity. The central role of these features in our model may reflect their importance for neuronal information processing.

251 citations


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Journal ArticleDOI
TL;DR: This article reviews studies investigating complex brain networks in diverse experimental modalities and provides an accessible introduction to the basic principles of graph theory and highlights the technical challenges and key questions to be addressed by future developments in this rapidly moving field.
Abstract: Recent developments in the quantitative analysis of complex networks, based largely on graph theory, have been rapidly translated to studies of brain network organization. The brain's structural and functional systems have features of complex networks--such as small-world topology, highly connected hubs and modularity--both at the whole-brain scale of human neuroimaging and at a cellular scale in non-human animals. In this article, we review studies investigating complex brain networks in diverse experimental modalities (including structural and functional MRI, diffusion tensor imaging, magnetoencephalography and electroencephalography in humans) and provide an accessible introduction to the basic principles of graph theory. We also highlight some of the technical challenges and key questions to be addressed by future developments in this rapidly moving field.

9,700 citations

01 Aug 2000
TL;DR: Assessment of medical technology in the context of commercialization with Bioentrepreneur course, which addresses many issues unique to biomedical products.
Abstract: BIOE 402. Medical Technology Assessment. 2 or 3 hours. Bioentrepreneur course. Assessment of medical technology in the context of commercialization. Objectives, competition, market share, funding, pricing, manufacturing, growth, and intellectual property; many issues unique to biomedical products. Course Information: 2 undergraduate hours. 3 graduate hours. Prerequisite(s): Junior standing or above and consent of the instructor.

4,833 citations

Journal ArticleDOI
17 Nov 2011-Neuron
TL;DR: In this article, the authors studied functional brain organization in healthy adults using resting state functional connectivity MRI and proposed two novel brain wide graphs, one of 264 putative functional areas, the other a modification of voxelwise networks that eliminates potentially artificial short-distance relationships.

3,517 citations

Journal ArticleDOI
04 Jul 2013-PLOS ONE
TL;DR: This work has developed a graph-theoretical network visualization toolbox, called BrainNet Viewer, to illustrate human connectomes as ball-and-stick models, and helps researchers to visualize brain networks in an easy, flexible and quick manner.
Abstract: The human brain is a complex system whose topological organization can be represented using connectomics. Recent studies have shown that human connectomes can be constructed using various neuroimaging technologies and further characterized using sophisticated analytic strategies, such as graph theory. These methods reveal the intriguing topological architectures of human brain networks in healthy populations and explore the changes throughout normal development and aging and under various pathological conditions. However, given the huge complexity of this methodology, toolboxes for graph-based network visualization are still lacking. Here, using MATLAB with a graphical user interface (GUI), we developed a graph-theoretical network visualization toolbox, called BrainNet Viewer, to illustrate human connectomes as ball-and-stick models. Within this toolbox, several combinations of defined files with connectome information can be loaded to display different combinations of brain surface, nodes and edges. In addition, display properties, such as the color and size of network elements or the layout of the figure, can be adjusted within a comprehensive but easy-to-use settings panel. Moreover, BrainNet Viewer draws the brain surface, nodes and edges in sequence and displays brain networks in multiple views, as required by the user. The figure can be manipulated with certain interaction functions to display more detailed information. Furthermore, the figures can be exported as commonly used image file formats or demonstration video for further use. BrainNet Viewer helps researchers to visualize brain networks in an easy, flexible and quick manner, and this software is freely available on the NITRC website (www.nitrc.org/projects/bnv/).

3,048 citations

Journal ArticleDOI
TL;DR: The inception of this journal has been foreshadowed by an ever-increasing number of publications on functional connectivity, causal modeling, connectomics, and multivariate analyses of distributed patterns of brain responses.
Abstract: Over the past 20 years, neuroimaging has become a predominant technique in systems neuroscience. One might envisage that over the next 20 years the neuroimaging of distributed processing and connectivity will play a major role in disclosing the brain's functional architecture and operational principles. The inception of this journal has been foreshadowed by an ever-increasing number of publications on functional connectivity, causal modeling, connectomics, and multivariate analyses of distributed patterns of brain responses. I accepted the invitation to write this review with great pleasure and hope to celebrate and critique the achievements to date, while addressing the challenges ahead.

2,822 citations