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Search Results: 1 - 10 of 17274 matches for " Andrew Zalesky "
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Network scaling effects in graph analytic studies of human resting-state fMRI data
Alex Fornito,Andrew Zalesky,Edward T. Bullmore
Frontiers in Systems Neuroscience , 2010, DOI: 10.3389/fnsys.2010.00022
Abstract: Graph analysis has become an increasingly popular tool for characterizing topological properties of brain connectivity networks. Within this approach, the brain is modeled as a graph comprising N nodes connected by M edges. In functional magnetic resonance imaging (fMRI) studies, the nodes typically represent brain regions and the edges some measure of interaction between them. These nodes are commonly defined using a variety of regional parcellation templates, which can vary both in the volume sampled by each region, and the number of regions parcellated. Here, we sought to investigate how such variations in parcellation templates affect key graph analytic measures of functional brain organization using resting-state fMRI in 30 healthy volunteers. Seven different parcellation resolutions (84, 91, 230, 438, 890, 1314, and 4320 regions) were investigated. We found that gross inferences regarding network topology, such as whether the brain is small-world or scale-free, were robust to the template used, but that both absolute values of, and individual differences in, specific parameters such as path length, clustering, small-worldness, and degree distribution descriptors varied considerably across the resolutions studied. These findings underscore the need to consider the effect that a specific parcellation approach has on graph analytic findings in human fMRI studies, and indicate that results obtained using different templates may not be directly comparable.
Efficient Determination of Gibbs Estimators with Submodular Energy Functions
Boris Zalesky
Mathematics , 2003,
Abstract: Combinatorial algorithms for minimization of functions of many variables, which take their values in finite totally ordered sets, are developed. For that the decomposition of the functions by Boolean polynomials is used. The modified SFM algorithm for minimization of submodular Boolean polynomials is described. The representation of some subclass of submodular Boolean polynomials by graphs is derived in order to make possible use of the graph cut technique.
Dwelling Quietly in the Rich Club: Brain Network Determinants of Slow Cortical Fluctuations
Leonardo L. Gollo,Andrew Zalesky,R. Matthew Hutchison,Martijn van den Heuvel,Michael Breakspear
Quantitative Biology , 2015,
Abstract: For more than a century, cerebral cartography has been driven by investigations of structural and morphological properties of the brain across spatial scales and the temporal/functional phenomena that emerge from these underlying features. The next era of brain mapping will be driven by studies that consider both of these components of brain organization simultaneously -- elucidating their interactions and dependencies. Using this guiding principle, we explored the origin of slowly fluctuating patterns of synchronization within the topological core of brain regions known as the rich club, implicated in the regulation of mood and introspection. We find that a constellation of densely interconnected regions that constitute the rich club (including the anterior insula, amygdala, and precuneus) play a central role in promoting a stable, dynamical core of spontaneous activity in the primate cortex. The slow time scales are well matched to the regulation of internal visceral states, corresponding to the somatic correlates of mood and anxiety. In contrast, the topology of the surrounding "feeder" cortical regions show unstable, rapidly fluctuating dynamics likely crucial for fast perceptual processes. We discuss these findings in relation to psychiatric disorders and the future of connectomics.
Dynamic Changes in Brain Functional Connectivity during Concurrent Dual-Task Performance
Luca Cocchi, Andrew Zalesky, Ulrike Toepel, Thomas J. Whitford, Marzia De-Lucia, Micah M. Murray, Olivia Carter
PLOS ONE , 2011, DOI: 10.1371/journal.pone.0028301
Abstract: This study investigated the spatial, spectral, temporal and functional proprieties of functional brain connections involved in the concurrent execution of unrelated visual perception and working memory tasks. Electroencephalography data was analysed using a novel data-driven approach assessing source coherence at the whole-brain level. Three connections in the beta-band (18–24 Hz) and one in the gamma-band (30–40 Hz) were modulated by dual-task performance. Beta-coherence increased within two dorsofrontal-occipital connections in dual-task conditions compared to the single-task condition, with the highest coherence seen during low working memory load trials. In contrast, beta-coherence in a prefrontal-occipital functional connection and gamma-coherence in an inferior frontal-occipitoparietal connection was not affected by the addition of the second task and only showed elevated coherence under high working memory load. Analysis of coherence as a function of time suggested that the dorsofrontal-occipital beta-connections were relevant to working memory maintenance, while the prefrontal-occipital beta-connection and the inferior frontal-occipitoparietal gamma-connection were involved in top-down control of concurrent visual processing. The fact that increased coherence in the gamma-connection, from low to high working memory load, was negatively correlated with faster reaction time on the perception task supports this interpretation. Together, these results demonstrate that dual-task demands trigger non-linear changes in functional interactions between frontal-executive and occipitoparietal-perceptual cortices.
Long-Term Effects of Attentional Performance on Functional Brain Network Topology
Thomas P. K. Breckel, Christiane M. Thiel, Edward T. Bullmore, Andrew Zalesky, Ameera X. Patel, Carsten Giessing
PLOS ONE , 2013, DOI: 10.1371/journal.pone.0074125
Abstract: Individuals differ in their cognitive resilience. Less resilient people demonstrate a greater tendency to vigilance decrements within sustained attention tasks. We hypothesized that a period of sustained attention is followed by prolonged changes in the organization of “resting state” brain networks and that individual differences in cognitive resilience are related to differences in post-task network reorganization. We compared the topological and spatial properties of brain networks as derived from functional MRI data (N = 20) recorded for 6 mins before and 12 mins after the performance of an attentional task. Furthermore we analysed changes in brain topology during task performance and during the switches between rest and task conditions. The cognitive resilience of each individual was quantified as the rate of increase in response latencies over the 32-minute time course of the attentional paradigm. On average, functional networks measured immediately post-task demonstrated significant and prolonged changes in network organization compared to pre-task networks with higher connectivity strength, more clustering, less efficiency, and shorter distance connections. Individual differences in cognitive resilience were significantly correlated with differences in the degree of recovery of some network parameters. Changes in network measures were still present in less resilient individuals in the second half of the post-task period (i.e. 6–12 mins after task completion), while resilient individuals already demonstrated significant reductions of functional connectivity and clustering towards pre-task levels. During task performance brain topology became more integrated with less clustering and higher global efficiency, but linearly decreased with ongoing time-on-task. We conclude that sustained attentional task performance has prolonged, “hang-over” effects on the organization of post-task resting-state brain networks; and that more cognitively resilient individuals demonstrate faster rates of network recovery following a period of attentional effort.
Network flow optimization for restoration of images
Boris A. Zalesky
Journal of Applied Mathematics , 2002, DOI: 10.1155/s1110757x02110035
Abstract: The network flow optimization approach is offered for restoration of gray-scale and color images corrupted by noise. The Ising models are used as a statistical background of the proposed method. We present the new multiresolution network flow minimum cut algorithm, which is especially efficient in identification of the maximum a posteriori (MAP) estimates of corrupted images. The algorithm is able to compute the MAP estimates of large-size images and can be used in a concurrent mode. We also consider the problem of integer minimization of two functions, U1(x)=λ∑i|yi−xi|
Fast algorithms of Bayesian Segmentation of Images
B. A. Zalesky
Mathematics , 2002,
Abstract: The network flow optimization approach is offered for Bayesian segmentation of gray-scale and color images. It is supposed image pixels are characterized by a feature function taking finite number of arbitrary rational values (it can be either intensity values or other characteristics of images). The clusters of homogeneous pixels are described by labels with values in another set of rational numbers. They are assumed to be dependent and distributed according to either the exponential or the Gaussian Gibbs law. Instead traditionally used local neighborhoods of nearest pixels the completely connected graph of dependence of all pixels is employed for the Gibbs prior distributions. The methods developed reduce the problem of segmentation to the problem of determination of the minimum network cuts.
Network Flow Optimization for Restoration of Images
Boris A. Zalesky
Mathematics , 2001,
Abstract: The network flow optimization approach is offered for restoration of grayscale and color images corrupted by noise. The Ising models are used as a statistical background of the proposed method. The new multiresolution network flow minimum cut algorithm, which is especially efficient in identification of the maximum a posteriori estimates of corrupted images, is presented. The algorithm is able to compute the MAP estimates of large size images and can be used in a concurrent mode. We also describe the efficient solutions of the problem of integer minimization of two energy functions for the Ising models of gray-scale and color images.
Decreased Functional Brain Connectivity in Adolescents with Internet Addiction
Soon-Beom Hong, Andrew Zalesky, Luca Cocchi, Alex Fornito, Eun-Jung Choi, Ho-Hyun Kim, Jeong-Eun Suh, Chang-Dai Kim, Jae-Won Kim, Soon-Hyung Yi
PLOS ONE , 2013, DOI: 10.1371/journal.pone.0057831
Abstract: Background Internet addiction has become increasingly recognized as a mental disorder, though its neurobiological basis is unknown. This study used functional neuroimaging to investigate whole-brain functional connectivity in adolescents diagnosed with internet addiction. Based on neurobiological changes seen in other addiction related disorders, it was predicted that connectivity disruptions in adolescents with internet addiction would be most prominent in cortico-striatal circuitry. Methods Participants were 12 adolescents diagnosed with internet addiction and 11 healthy comparison subjects. Resting-state functional magnetic resonance images were acquired, and group differences in brain functional connectivity were analyzed using the network-based statistic. We also analyzed network topology, testing for between-group differences in key graph-based network measures. Results Adolescents with internet addiction showed reduced functional connectivity spanning a distributed network. The majority of impaired connections involved cortico-subcortical circuits (~24% with prefrontal and ~27% with parietal cortex). Bilateral putamen was the most extensively involved subcortical brain region. No between-group difference was observed in network topological measures, including the clustering coefficient, characteristic path length, or the small-worldness ratio. Conclusions Internet addiction is associated with a widespread and significant decrease of functional connectivity in cortico-striatal circuits, in the absence of global changes in brain functional network topology.
Can a Massive Graviton be a Stable Particle  [PDF]
Andrew Beckwith
Journal of Modern Physics (JMP) , 2011, DOI: 10.4236/jmp.2011.25043
Abstract: This document is based on a question asked in the Dark Side of the Universe 2010 conference in Leon, Mexico, when a researcher from India asked the author about how to obtain a stability analysis of massive gravitons. The answer to this question involves an extension of the usual Pauli_Fiertz Langrangian as written by Ortin, with non- zero graviton mass contributing to a relationship between the trace of a revised GR stress-energy tensor (assuming non- zero graviton mass), and the trace of a revised symmetric tensor times a tiny mass for a 4 dimensional graviton. The resulting analysis makes use of Visser’s treatment of a stress en-ergy tensor, with experimental applications discussed in the resulting analysis. If the square of frequency of a massive graviton is real valued and greater than zero, stability can be possibly confirmed experimentally.
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