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60
Sep 22, 2013
09/13
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Shivakumar Viswanathan; Matthew Cieslak; Scott T. Grafton
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Information mapping is a popular application of Multivoxel Pattern Analysis (MVPA) to fMRI. Information maps are constructed using the so called searchlight method, where the spherical multivoxel neighborhood of every voxel (i.e., a searchlight) in the brain is evaluated for the presence of task-relevant response patterns. Despite their widespread use, information maps present several challenges for interpretation. One such challenge has to do with inferring the size and shape of a multivoxel...
Source: http://arxiv.org/abs/1210.6317v1
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13
Jun 29, 2018
06/18
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Danielle S. Bassett; Ankit N. Khambhati; Scott T. Grafton
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Neuroengineering is faced with unique challenges in repairing or replacing complex neural systems that are composed of many interacting parts. These interactions form intricate patterns over large spatiotemporal scales, and produce emergent behaviors that are difficult to predict from individual elements. Network science provides a particularly appropriate framework in which to study and intervene in such systems, by treating neural elements (cells, volumes) as nodes in a graph and neural...
Topics: Quantitative Biology, Neurons and Cognition, Biological Physics, Physics
Source: http://arxiv.org/abs/1612.08059
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7.0
Jun 30, 2018
06/18
by
Danielle S. Bassett; Muzhi Yang; Nicholas F. Wymbs; Scott T. Grafton
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Distributed networks of brain areas interact with one another in a time-varying fashion to enable complex cognitive and sensorimotor functions. Here we use novel network analysis algorithms to test the recruitment and integration of large-scale functional neural circuitry during learning. Using functional magnetic resonance imaging (fMRI) data acquired from healthy human participants, from initial training through mastery of a simple motor skill, we investigate changes in the architecture of...
Topics: Nonlinear Sciences, Quantitative Biology, Adaptation and Self-Organizing Systems, Neurons and...
Source: http://arxiv.org/abs/1403.6034
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4.0
Jun 30, 2018
06/18
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Shi Gu; Fabio Pasqualetti; Matthew Cieslak; Scott T. Grafton; Danielle S. Bassett
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Cognitive function is driven by dynamic interactions between large-scale neural circuits or networks, enabling behavior. Fundamental principles constraining these dynamic network processes have remained elusive. Here we use network control theory to offer a mechanistic explanation for how the brain moves between cognitive states drawn from the network organization of white matter microstructure. Our results suggest that densely connected areas, particularly in the default mode system,...
Topics: Systems and Control, Quantitative Biology, Computing Research Repository, Neurons and Cognition
Source: http://arxiv.org/abs/1406.5197
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4.0
Jun 30, 2018
06/18
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Petko Bogdanov; Nazli Dereli; Danielle S. Bassett; Scott T. Grafton; Ambuj K. Singh
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It has become increasingly popular to study the brain as a network due to the realization that functionality cannot be explained exclusively by independent activation of specialized regions. Instead, across a large spectrum of behaviors, function arises due to the dynamic interactions between brain regions. The existing literature on functional brain networks focuses mainly on a battery of network properties characterizing the "resting state" using for example the modularity,...
Topics: Quantitative Biology, Neurons and Cognition
Source: http://arxiv.org/abs/1407.5590
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12
Jun 28, 2018
06/18
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Weiyu Huang; Leah Goldsberry; Nicholas F. Wymbs; Scott T. Grafton; Danielle S. Bassett; Alejandro Ribeiro
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This paper presents methods to analyze functional brain networks and signals from graph spectral perspectives. The notion of frequency and filters traditionally defined for signals supported on regular domains such as discrete time and image grids has been recently generalized to irregular graph domains, and defines brain graph frequencies associated with different levels of spatial smoothness across the brain regions. Brain network frequency also enables the decomposition of brain signals into...
Topics: Quantitative Biology, Neurons and Cognition, Computational Engineering, Finance, and Science,...
Source: http://arxiv.org/abs/1512.00037
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5.0
Jun 29, 2018
06/18
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Shi Gu; Matthew Cieslak; Benjamin Baird; Sarah F. Muldoon; Scott T. Grafton; Fabio Pasqualetti; Danielle S. Bassett
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A critical mystery in neuroscience lies in determining how anatomical structure impacts the complex functional dynamics of human thought. How does large-scale brain circuitry constrain states of neuronal activity and transitions between those states? We address these questions using a maximum entropy model of brain dynamics informed by white matter tractography. We demonstrate that the most probable brain states -- characterized by minimal energy -- display common activation profiles across...
Topics: Quantitative Biology, Neurons and Cognition
Source: http://arxiv.org/abs/1607.01959
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104
Sep 22, 2013
09/13
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Danielle S. Bassett; Nicholas F. Wymbs; M. Puck Rombach; Mason A. Porter; Peter J. Mucha; Scott T. Grafton
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As a person learns a new skill, distinct synapses, brain regions, and networks are engaged and change over time. To better understand the dynamic processes that integrate information across a set of regions to enable the emergence of novel behaviour, we measure brain activity during motor sequencing and characterise network properties based on coherent activity between brain regions. Using recently developed algorithms to detect time-evolving communities of brain regions that control learning,...
Source: http://arxiv.org/abs/1210.3555v1
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5.0
Jun 29, 2018
06/18
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Sarah Feldt Muldoon; Fabio Pasqualetti; Shi Gu; Matthew Cieslak; Scott T. Grafton; Jean M. Vettel; Danielle S. Bassett
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The ability to modulate brain states using targeted stimulation is increasingly being employed to treat neurological disorders and to enhance human performance. Despite the growing interest in brain stimulation as a form of neuromodulation, much remains unknown about the network-level impact of these focal perturbations. To study the system wide impact of regional stimulation, we employ a data-driven computational model of nonlinear brain dynamics to systematically explore the effects of...
Topics: Quantitative Biology, Systems and Control, Neurons and Cognition, Computing Research Repository
Source: http://arxiv.org/abs/1601.00987
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4.0
Jun 29, 2018
06/18
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Shi Gu; Richard F. Betzel; Matthew Cieslak; Philip R. Delio; Scott T. Grafton; Fabio Pasqualetti; Danielle S. Bassett
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The complexity of neural dynamics stems in part from the complexity of the underlying anatomy. Yet how the organization of white matter architecture constrains how the brain transitions from one cognitive state to another remains unknown. Here we address this question from a computational perspective by defining a brain state as a pattern of activity across brain regions. Drawing on recent advances in network control theory, we model the underlying mechanisms of brain state transitions as...
Topics: Quantitative Biology, Neurons and Cognition
Source: http://arxiv.org/abs/1607.01706
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Sep 19, 2013
09/13
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Danielle S. Bassett; Nicholas F. Wymbs; Mason A. Porter; Peter J. Mucha; Jean M. Carlson; Scott T. Grafton
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Human learning is a complex phenomenon requiring flexibility to adapt existing brain function and precision in selecting new neurophysiological activities to drive desired behavior. These two attributes -- flexibility and selection -- must operate over multiple temporal scales as performance of a skill changes from being slow and challenging to being fast and automatic. Such selective adaptability is naturally provided by modular structure, which plays a critical role in evolution, development,...
Source: http://arxiv.org/abs/1010.3775v2
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6.0
Jun 29, 2018
06/18
by
Ari E. Kahn; Marcelo G. Mattar; Jean M. Vettel; Nicholas F. Wymbs; Scott T. Grafton; Danielle S. Bassett
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Human skill learning requires fine-scale coordination of distributed networks of brain regions that are directly linked to one another by white matter tracts to allow for effective information transmission. Yet how individual differences in these anatomical pathways may impact individual differences in learning remains far from understood. Here, we test the hypothesis that individual differences in the organization of structural networks supporting task performance predict individual...
Topics: Quantitative Biology, Neurons and Cognition
Source: http://arxiv.org/abs/1605.04033
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3.0
Jun 28, 2018
06/18
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Cassiano O. Becker; Sergio Pequito; George J. Pappas; Michael B. Miller; Scott T. Grafton; Danielle S. Bassett; Victor M. Preciado
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Understanding the relationship between the dynamics of neural processes and the anatomical substrate of the brain is a central question in neuroscience. On the one hand, modern neuroimaging technologies, such as diffusion tensor imaging, can be used to construct structural graphs representing the architecture of white matter streamlines linking cortical and subcortical structures. On the other hand, temporal patterns of neural activity can be used to construct functional graphs representing...
Topics: Quantitative Biology, Neurons and Cognition, Applications, Statistics
Source: http://arxiv.org/abs/1512.02602
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5.0
Jun 29, 2018
06/18
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Andrew C. Murphy; Shi Gu; Ankit N. Khambhati; Nicholas F. Wymbs; Scott T. Grafton; Theodore D. Satterthwaite; Danielle S. Bassett
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A major challenge in neuroimaging is understanding the mapping of neurophysiological dynamics onto cognitive functions. Traditionally, these maps have been constructed by examining changes in the activity magnitude of regions related to task performance. Recently, network neuroscience has produced methods to map connectivity patterns among many regions to certain cognitive functions by drawing on tools from network science and graph theory. However, these two different views are rarely...
Topics: Quantitative Biology, Neurons and Cognition
Source: http://arxiv.org/abs/1611.07962
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5.0
Jun 30, 2018
06/18
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Pranav G. Reddy; Marcelo G. Mattar; Andrew C. Murphy; Nicholas F. Wymbs; Scott T. Grafton; Theodore D. Satterthwaite; Danielle S. Bassett
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Learning requires the traversal of inherently distinct cognitive states to produce behavioral adaptation. Yet, tools to explicitly measure these states with non-invasive imaging -- and to assess their dynamics during learning -- remain limited. Here, we describe an approach based on a novel application of graph theory in which points in time are represented by network nodes, and similarities in brain states between two different time points are represented as network edges. We use a graph-based...
Topics: Quantitative Biology, Neurons and Cognition
Source: http://arxiv.org/abs/1701.07646
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5.0
Jun 30, 2018
06/18
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Elizabeth N. Davison; Kimberly J. Schlesinger; Danielle S. Bassett; Mary-Ellen Lynall; Michael B. Miller; Scott T. Grafton; Jean M. Carlson
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eye 5
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Activity in the human brain moves between diverse functional states to meet the demands of our dynamic environment, but fundamental principles guiding these transitions remain poorly understood. Here, we capitalize on recent advances in network science to analyze patterns of functional interactions between brain regions. We use dynamic network representations to probe the landscape of brain reconfigurations that accompany task performance both within and between four cognitive states: a...
Topics: Quantitative Biology, Neurons and Cognition
Source: http://arxiv.org/abs/1407.8234
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5.0
Jun 29, 2018
06/18
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Elizabeth N. Davison; Benjamin O. Turner; Kimberly J. Schlesinger; Michael B. Miller; Scott T. Grafton; Danielle S. Bassett; Jean M. Carlson
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eye 5
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Individual differences in brain functional networks may be related to complex personal identifiers, including health, age, and ability. Understanding and quantifying these differences is a necessary first step towards developing predictive methods derived from network topology. Here, we present a method to quantify individual differences in brain functional dynamics by applying hypergraph analysis, a method from dynamic network theory. Using a summary metric derived from the hypergraph...
Topics: Quantitative Biology, Neurons and Cognition
Source: http://arxiv.org/abs/1606.09545
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4.0
Jun 30, 2018
06/18
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Konstantinos Slavakis; Shiva Salsabilian; David S. Wack; Sarah F. Muldoon; Henry E. Baidoo-Williams; Jean M. Vettel; Matthew Cieslak; Scott T. Grafton
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This paper advocates Riemannian multi-manifold modeling in the context of network-wide non-stationary time-series analysis. Time-series data, collected sequentially over time and across a network, yield features which are viewed as points in or close to a union of multiple submanifolds of a Riemannian manifold, and distinguishing disparate time series amounts to clustering multiple Riemannian submanifolds. To support the claim that exploiting the latent Riemannian geometry behind many...
Topics: Learning, Machine Learning, Statistics, Computing Research Repository
Source: http://arxiv.org/abs/1701.07767
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85
Sep 20, 2013
09/13
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Alexander V. Mantzaris; Danielle S. Bassett; Nicholas F. Wymbs; Ernesto Estrada; Mason A. Porter; Peter J. Mucha; Scott T. Grafton; Desmond J. Higham
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We study functional activity in the human brain using functional Magnetic Resonance Imaging and recently developed tools from network science. The data arise from the performance of a simple behavioural motor learning task. Unsupervised clustering of subjects with respect to similarity of network activity measured over three days of practice produces significant evidence of `learning', in the sense that subjects typically move between clusters (of subjects whose dynamics are similar) as time...
Source: http://arxiv.org/abs/1207.5047v1
