Synchronization Patterns in Networks of Kuramoto Oscillators: A Geometric Approach for Analysis and Control

Tiberi, Lorenzo; Favaretto, Chiara; Innocenti, Mario; Bassett, Danielle S.; Pasqualetti, Fabio

doi:10.48550/arxiv.1709.06193

Cited by 2 publications

(3 citation statements)

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“…Some cross-sectional work illustrates the link between anatomical network controllability and fMRI dynamics across neurodevelopment (Tang et al, 2017). While the current study used a simplified model of dynamics that has been demonstrated to predict the controllability of Wilson-Cowan (Muldoon et al, 2016) and Kuramoto (Tiberi et al, 2017) oscillators coupled by empirically measured anatomical brain networks, it is important to note that theoretical predictions about controllability would be further strengthened by evaluating empirically measured neural activity in response to exogenous brain stimulation. For example, demonstrating that integrated or segregated or difficult to reach BOLD or EEG states are influenced by TMS as a function of boundary and modal controllability, respectively, would support the theoretical notions described here.…”

Section: Discussionmentioning

confidence: 99%

“…In simulation studies, linear controllabilty statistics were related to predicted effects in dynamics simulated using Wilson-Cowan (Muldoon et al, 2016) oscillators in anatomical brain networks. In addition, they predicted topological changes in network dynamics simulated using Kuramoto oscillators (Tiberi et al, 2017). Thus, we focus on mathematically well defined linear control statistics due to their parsimony and pragmatic utility in applied contexts, such as neuromodulation research.…”

Section: Network Controllabilitymentioning

confidence: 99%

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Network Controllability in the Inferior Frontal Gyrus Relates to Controlled Language Variability and Susceptibility to TMS

et al. 2018

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In language production, humans are confronted with considerable word selection demands. Often, we must select a word from among similar, acceptable, and competing alternative words to construct a sentence that conveys an intended meaning. In recent years, the left inferior frontal gyrus (LIFG) has been identified as being critical to this ability. Despite a recent emphasis on network approaches to understanding language, how the LIFG interacts with the brain's complex networks to facilitate controlled language performance remains unknown. Here, we take a novel approach to understanding word selection as a network control process in the brain. Using an anatomical brain network derived from high-resolution diffusion spectrum imaging, we computed network controllability underlying the site of transcranial magnetic stimulation (TMS) in the LIFG between administrations of language tasks that vary in response (cognitive control) demands: open-response tasks (word generation) versus closed response tasks (number naming). We found that a statistic that quantifies the LIFG's theoretically predicted control of communication across modules in the human connectome explains TMS-induced changes in open-response language task performance only. Moreover, we found that a statistic that quantifies the LIFG's theoretically predicted control of difficult-to-reach states explains vulnerability to TMS in the closed-ended (but not open-ended) response task. These findings establish a link among network controllability, cognitive function, and TMS effects. This work illustrates that network control statistics applied to anatomical connectivity data demonstrate relationships with cognitive variability during controlled language tasks and TMS effects.

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Section: Discussionmentioning

confidence: 99%

Section: Network Controllabilitymentioning

confidence: 99%

Network Controllability in the Inferior Frontal Gyrus Relates to Controlled Language Variability and Susceptibility to TMS

et al. 2018

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“…This should further include the contributions of noise to the system's dynamics. While linear models used in NCT support inferences that can be validated in nonlinear demonstrations (Muldoon et al, 2016, Tiberi, Favaretto, Innocenti, Bassett, & Pasqualetti, 2017, the relationships that characterize neural interactions are diversely nonlinear, and this diversity drives much of the complex dynamics observed in real neural systems (Deco, Jirsa, & McIntosh, 2011). In addition, neural systems are noisy (McDonnell & Ward, 2011), and neural noise can drive important dynamic features such as information processing (McDonnell & Ward, 2011) and multistability (i.e., multiple stable states in neural networks (Deco et al, 2011).…”

Section: Nonlinearity and Noisementioning

confidence: 99%

Clarifying cognitive control and the controllable connectome

Medaglia

2018

WIRES Cognitive Science

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Cognitive control researchers aim to describe the processes that support adaptive cognition to achieve specific goals. Control theorists consider how to influence the state of systems to reach certain user-defined goals. In brain networks, some conceptual and lexical similarities between cognitive control and control theory offer appealing avenues for scientific discovery. However, these opportunities also come with the risk of conceptual confusion. Here, I suggest that each field of inquiry continues to produce novel and distinct insights. Then, I describe opportunities for synergistic research at the intersection of these subdisciplines with a critical stance that reduces the risk of conceptual confusion. Through this exercise, we can observe that both cognitive neuroscience and systems engineering have much to contribute to cognitive control research in human brain networks. This article is categorized under: Neuroscience > Cognition Computer Science > Neural Networks Neuroscience > Clinical Neuroscience.

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Synchronization Patterns in Networks of Kuramoto Oscillators: A Geometric Approach for Analysis and Control

Cited by 2 publications

References 0 publications

Network Controllability in the Inferior Frontal Gyrus Relates to Controlled Language Variability and Susceptibility to TMS

Network Controllability in the Inferior Frontal Gyrus Relates to Controlled Language Variability and Susceptibility to TMS

Clarifying cognitive control and the controllable connectome

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