Multistability in large scale models of brain activity

Golos, Mathieu; Jirsa, Viktor K.; Daucé, Emmanuel

doi:10.1186/1471-2202-14-s1-p84

Cited by 19 publications

(27 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Their advantage is that one has access to a wide array of theoretical observations that can offer intuition about the system's (controlled) dynamics, particularly around an operating point (Gu et al, 2015). The disadvantage is that they cannot speak to neural processes that transition from one dynamical regime (limit cycles, fixed points, attractors) to another (Deco and Jirsa, 2012;Golos et al, 2015;Muldoon et al, 2016). In these cases, developing additional methods for control of nonlinear systems may be necessary.…”

Section: Open Questions In Control and Cognitionmentioning

confidence: 99%

Colloquium: Control of dynamics in brain networks

2018

View full text Add to dashboard Cite

The ability to effectively control brain dynamics holds great promise for the enhancement of cognitive function in humans, and the betterment of their quality of life. Yet, successfully controlling dynamics in neural systems is challenging, in part due to the immense complexity of the brain and the large set of interactions that can drive any single change. While we have gained some understanding of the control of single neurons, the control of large-scale neural systems-networks of multiply interacting components-remains poorly understood. Efforts to address this gap include the construction of tools for the control of brain networks, mostly adapted from control and dynamical systems theory. Informed by current opportunities for practical intervention, these theoretical contributions provide models that draw from a wide array of mathematical approaches. We present recent developments for effective strategies of control in dynamic brain networks, and we also describe potential mechanisms that underlie such processes. We review efforts in the control of general neurophysiological processes with implications for brain development and cognitive function, as well as the control of altered neurophysiological processes in medical contexts such as anesthesia administration, seizure suppression, and deep-brain stimulation for Parkinson's disease. We conclude with a forward-looking discussion regarding how emerging results from network control-especially approaches that deal with nonlinear dynamics or more realistic trajectories for control transitionscould be used to directly address pressing questions in neuroscience.

show abstract

Section: Open Questions In Control and Cognitionmentioning

confidence: 99%

Colloquium: Control of dynamics in brain networks

2018

View full text Add to dashboard Cite

show abstract

“…It has been suggested to manifest ongoing cognition at rest (Gonzalez-Castillo et al, 2015) with an immediate impact on cognitive performance (Bassett et al, 2011;Braun et al, 2015;Shine et al, 2016;Cohen, 2018) and attentional or awareness levels (Kucyi et al, 2017;Cavanna et al, 2018;Lim et al, 2018). It may reflect the sampling of an internal repertoire of alternative dynamical states (Hansen et al, 2015;Golos et al, 2015;Cabral et al, 2017a;Glomb et al, 2017). From a biomarking perspective, pathological conditions, such as Alzheimer's dementia, schizophrenia and other mental disorders (Jones et al, 2012;Damaraju et al, 2014;Braun et al, 2018), or differences in general attributes like gender (Yaesoubi et al, 2015) or development and aging (Hutchison & Morton, 2015;Qin et al, 2015;Davison et al, 2016;Schlesinger et al, 2016;Chen et al, 2017;Viviano et al, 2017), may alter dFC more than they affect time-averaged FC.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Functional Connectivity between Order and Randomness and its Evolution across the Human Adult Lifespan

Battaglia

Boudou

Hansen

et al. 2017

Preprint

View full text Add to dashboard Cite

The aging brain undergoes alterations of both Structural Connectivity (SC) and timeaveraged Functional Connectivity in the resting state (rs FC). Here, we show by means of functional MRI (fMRI) human brain imaging that aging also profoundly impacts on the spontaneous temporal reconfiguration of this rs FC. Analyzing time-dependent correlations between human rs fMRI blood oxygen level dependent (BOLD) time series, we describe Functional Connectivity Dynamics (FCD) as a switching between epochs of meta-stable FC and transients of fast network reconfiguration. We find that the flux of FCD markedly slows down and becomes more "viscous" across the adult lifespan (18-80 yrs), also accounting for the wide inter-subject variability of performance observed in cognitive screening tasks. Such remodeling of FCD discloses qualitatively novel effects of aging that cannot be captured by variations of SC or of static FC, opening the way to improved imaging-based characterizations of the functional mechanisms underlying cognitive aging.

show abstract

“…The autonomous LV equations have been used to generate reproducible transient sequences in neural circuits [60][61][62][63][64] , but global brain dynamics do not converge or stabilize around a fixed set of invariants, and might be described as a continuous flow of quick and irregular oscillations 27,65 . Thus, instead of describing dynamics in terms of asymptotic behavior, an alternative 3/19 consist of introducing the dynamical informational structures (DISs), defined as the ISs indexed continuously by time (see Fig.2).…”

Section: Measuring Energy Levels From Fmri Datamentioning

confidence: 99%

“…Equally used for modelling mutualistic systems in Theoretical Ecology and Economy 20, 21 , Informational Structure can relate the topology of complex networks and their dynamics. In mutualistic systems when achieving robustness and life abundance -the so-called architecture of biodiversity 22, 23 -the dynamics is determined not only by the topology 24 but also by modularity 25 and the strength of the parameters 26 .These kinds of relationships are also at the heart of many important open questions in neuroscience 27,28 . Informational Structures and their continuous time-dependence on the strength of connections can allow to understand the dynamics of the system as a coherent process, whose information is structured, and potentially providing new insights into sudden bifurcations 29 .Here, we were interested in characterising human sleep, which is traditionally subdivided into different stages that alternate in the course of the night 30, 31 , mainly non-rapid-eye-movement (NREM) and rapid-eye-movement (REM) sleep.…”

mentioning

confidence: 99%

“…These kinds of relationships are also at the heart of many important open questions in neuroscience 27,28 . Informational Structures and their continuous time-dependence on the strength of connections can allow to understand the dynamics of the system as a coherent process, whose information is structured, and potentially providing new insights into sudden bifurcations 29 .…”

mentioning

confidence: 99%

See 1 more Smart Citation

Dynamical informational structures characterize the different human brain states of wakefulness and deep sleep

Galadí

Pereira

Perl

et al. 2019

Preprint

View full text Add to dashboard Cite

The dynamical activity of the human brain describes an extremely complex energy landscape changing over time and its characterisation is central unsolved problem in neuroscience. We propose a novel mathematical formalism for characterizing how the landscape of attractors sustained by a dynamical system evolves in time. This mathematical formalism is used to distinguish quantitatively and rigorously between the different human brain states of wakefulness and deep sleep. In particular, by using a whole-brain dynamical ansatz integrating the underlying anatomical structure with the local node dynamics based on a Lotka-Volterra description, we compute analytically the global attractors of this cooperative system and their associated directed graphs, here called the informational structures. The informational structure of the global attractor of a dynamical system describes precisely the past and future behaviour in terms of a directed graph composed of invariant sets (nodes) and their corresponding connections (links). We characterize a brain state by the time variability of these informational structures. This theoretical framework is potentially highly relevant for developing reliable biomarkers of patients with e.g. neuropsychiatric disorders or different levels of coma. of the phase space described by a set of selected invariant global solutions of the associated dynamical system, such as stationary points (equilibria), connecting orbits among them, periodic solutions and limit cycles. The informational structure of the GA is defined as a directed graph composed of nodes associated with those invariants and links establishing their connections (see Methods and Supplementary Information for a formal rigorous definition). Informational Structure has been used to show the dependence between the topology, the value of the parameters and the state with respect to its level of integration 14 , as such pointing for the small world configuration of the brain 16 (although see recent controversies 17 ).Previous research has applied Informational Structure to population dynamics in complex networks 18,19 . Equally used for modelling mutualistic systems in Theoretical Ecology and Economy 20, 21 , Informational Structure can relate the topology of complex networks and their dynamics. In mutualistic systems when achieving robustness and life abundance -the so-called architecture of biodiversity 22, 23 -the dynamics is determined not only by the topology 24 but also by modularity 25 and the strength of the parameters 26 .These kinds of relationships are also at the heart of many important open questions in neuroscience 27,28 . Informational Structures and their continuous time-dependence on the strength of connections can allow to understand the dynamics of the system as a coherent process, whose information is structured, and potentially providing new insights into sudden bifurcations 29 .Here, we were interested in characterising human sleep, which is traditionally subdivided into different stages that alternate in the course...

show abstract

Multistability in large scale models of brain activity

Cited by 19 publications

References 4 publications

Colloquium: Control of dynamics in brain networks

Colloquium: Control of dynamics in brain networks

Dynamic Functional Connectivity between Order and Randomness and its Evolution across the Human Adult Lifespan

Dynamical informational structures characterize the different human brain states of wakefulness and deep sleep

Contact Info

Product

Resources

About