Editorial: State-dependent brain computation

Ritter, Petra; Jirsa, Viktor K.; McIntosh, Anthony R.; Breakspear, Michael

doi:10.3389/fncom.2015.00077

Cited by 10 publications

(6 citation statements)

References 18 publications

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“…From the very start, neuroimaging experiments have -albeit mostly involuntarily -explored the link between spontaneous and task-related activity by pervasive use of subtraction paradigms. Yet, it is only with the rise of resting state paradigms that a better understanding has arisen of the link between them [23,62].…”

Section: Beyond Resting: the Brain Is Slow Under Stimuli/task Processingmentioning

confidence: 99%

The Rediscovery of Slowness: Exploring the Timing of Cognition

Kringelbach

McIntosh

Ritter

et al. 2015

Trends in Cognitive Sciences

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108

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Slowness of thought is not necessarily a handicap but could be a signature of optimal brain function. Emerging evidence shows that neuroanatomical and dynamical constraints of the human brain shape its functionality in optimal ways, characterized by slowness during task-based cognition in the context of spontaneous resting-state activity. This activity can be described mechanistically by whole-brain computational modeling that relates directly to optimality in the context of theories arguing for metastability in the brain. We discuss the role for optimal processing of information in the context of cognitive, task-related activity, and propose that combining multi-modal neuroimaging and explicit whole-brain models focused on the timing of functional dynamics can help to uncover fundamental rules of brain function in health and disease.

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Section: Beyond Resting: the Brain Is Slow Under Stimuli/task Processingmentioning

confidence: 99%

The Rediscovery of Slowness: Exploring the Timing of Cognition

Kringelbach

McIntosh

Ritter

et al. 2015

Trends in Cognitive Sciences

Self Cite

108

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show abstract

“…Resting-state brain networks also show complex spatio-temporal dynamics, in terms of transitions between states characterized by high and low functional connectivities, which resemble transiently existing attractor structures [10]. Such state-dependent fluctuations may play an important role in task-related brain computations [11] such as the interaction of motion and sensation (cf [12]).…”

Section: Introductionmentioning

confidence: 99%

Attractor metadynamics in terms of target points in slow-fast systems: adiabatic versus symmetry protected flow in a recurrent neural network

2018

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In dynamical systems with distinct time scales the time evolution in phase space may be influenced strongly by the fixed points of the fast subsystem. Orbits then typically follow these points, performing in addition rapid transitions between distinct branches on the time scale of the fast variables. As the branches guide the dynamics of a system along the manifold of former fixed points, they are considered transiently attracting states and the intermittent transitions between branches correspond to state switching within transient-state dynamics. A full characterization of the set of former fixed points, the critical manifold, tends to be difficult in high-dimensional dynamical systems such as large neural networks. Here we point out that an easily computable subset of the critical manifold, the set of target points, can be used as a reference for the investigation of high-dimensional slow-fast systems. The set of target points corresponds in this context to the adiabatic projection of a given orbit to the critical manifold. Applying our framework to a simple recurrent neural network, we find that the scaling relation of the Euclidean distance between the trajectory and its target points with the control parameter of the slow time scale allows to distinguish an adiabatic regime from a state that is effectively independent from target points.

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“…Two hundred phasic inhibitory inputs were modeled using Equation (9). The firing frequency of phasic inhibitory input was set to 5 Hz.…”

Section: Rs Neuron Modelmentioning

confidence: 99%

“…Changes in this ratio alter neuronal excitability and can have a significant impact on the oscillatory states that a neural circuit is capable of producing, as well as on the computations it can perform. 4,[8][9][10][11] Behaviorally important examples include the rapid changes in practically all relevant ions during transitions between sleep and wake states, 2 and the long-term decreases in intracellular chloride concentrations ([Cl -] i ) seen over the initial stages of normal development. [12][13][14][15][16] The blood-brain barrier has numerous specializations to keep the concentration of ions in the extracellular space within certain physiological bounds.…”

Section: Introductionmentioning

confidence: 99%

Biophysical Modeling Suggests Optimal Drug Combinations for Improving the Efficacy of GABA Agonists after Traumatic Brain Injuries

Sudhakar

Choi

Ahmed

2019

Journal of Neurotrauma

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Traumatic brain injuries (TBI) lead to dramatic changes in the surviving brain tissue. Altered ion concentrations, coupled with changes in the expression of membrane-spanning proteins, create a post-TBI brain state that can lead to further neuronal loss caused by secondary excitotoxicity. Several GABA receptor agonists have been tested in the search for neuroprotection immediately after an injury, with paradoxical results. These drugs not only fail to offer neuroprotection, but can also slow down functional recovery after TBI. Here, using computational modeling, we provide a biophysical hypothesis to explain these observations. We show that the accumulation of intracellular chloride ions caused by a transient upregulation of Na +-K +-2Cl-(NKCC1) co-transporters as observed following TBI, causes GABA receptor agonists to lead to excitation and depolarization block, rather than the expected hyperpolarization. The likelihood of prolonged, excitotoxic depolarization block is further exacerbated by the extremely high levels of extracellular potassium seen after TBI. Our modeling results predict that the neuroprotective efficacy of GABA receptor agonists can be substantially enhanced when they are combined with NKCC1 co-transporter inhibitors. This suggests a rational, biophysically principled method for identifying drug combinations for neuroprotection after TBI.

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Editorial: State-dependent brain computation

Cited by 10 publications

References 18 publications

The Rediscovery of Slowness: Exploring the Timing of Cognition

The Rediscovery of Slowness: Exploring the Timing of Cognition

Attractor metadynamics in terms of target points in slow-fast systems: adiabatic versus symmetry protected flow in a recurrent neural network

Biophysical Modeling Suggests Optimal Drug Combinations for Improving the Efficacy of GABA Agonists after Traumatic Brain Injuries

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