Neurocontrol: Methods, models and technologies for manipulating dynamics in the brain

Ritt, Jason T.; Ching, ShiNung

doi:10.1109/acc.2015.7171915

Cited by 13 publications

(11 citation statements)

References 166 publications

(141 reference statements)

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“…Should the reader instead be searching for an excellent treatment of various control methods for single neurons or for ensembles of neurons, we direct them to the recent textbook by Schiff (2012), and to references therein. For further details on emerging control technologies in the brainespecially invasive electrical and optical stimulation at rapid timescales (milliseconds or below)-and associated modelling approaches, please see the recent review by Ritt and Ching (2015).…”

mentioning

confidence: 99%

Colloquium: Control of dynamics in brain networks

2018

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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.

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confidence: 99%

Colloquium: Control of dynamics in brain networks

2018

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“…Model predictive control has been applied to Parkinson's disease (PD) using an extended autoregressive model [27], and various forms of delayed feedback have also been investigated [28,29]. Further review can be found for instance in [30,31].…”

Section: Introductionmentioning

confidence: 99%

Optimizing deep brain stimulation based on isostable amplitude in essential tremor patient models

et al. 2021

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Objective. Deep brain stimulation is a treatment for medically refractory essential tremor. To improve the therapy, closed-loop approaches are designed to deliver stimulation according to the system's state, which is constantly monitored by recording a pathological signal associated with symptoms (e.g. brain signal or limb tremor). Since the space of possible closed-loop stimulation strategies is vast and cannot be fully explored experimentally, how to stimulate according to the state should be informed by modeling. A typical modeling goal is to design a stimulation strategy that aims to maximally reduce the Hilbert amplitude of the pathological signal in order to minimize symptoms. Isostables provide a notion of amplitude related to convergence time to the attractor, which can be beneficial in model-based control problems. However, how isostable and Hilbert amplitudes compare when optimizing the amplitude response to stimulation in models constrained by data is unknown. Approach. We formulate a simple closed-loop stimulation strategy based on models previously fitted to phase-locked deep brain stimulation data from essential tremor patients. We compare the performance of this strategy in suppressing oscillatory power when based on Hilbert amplitude and when based on isostable amplitude. We also compare performance to phase-locked stimulation and open-loop high-frequency stimulation. Main results. For our closed-loop phase space stimulation strategy, stimulation based on isostable amplitude is significantly more effective than stimulation based on Hilbert amplitude when amplitude field computation time is limited to minutes. Performance is similar when there are no constraints, however constraints on computation time are expected in clinical applications. Even when computation time is limited to minutes, closed-loop phase space stimulation based on isostable amplitude is advantageous compared to phase-locked stimulation, and is more efficient than high-frequency stimulation. Significance. Our results suggest a potential benefit to using isostable amplitude more broadly for model-based optimization of stimulation in neurological disorders.

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“…with electrical stimulation, Histed et al, 2009) or populations of neurons are uniformly stimulated to enhance or suppress activity (e.g. with optogenetic stimulation; Cardin et al, 2010;Ritt and Ching, 2015). In contrast, neural representations are diverse and are distributed across the population.…”

Section: Introductionmentioning

confidence: 99%

“…Indeed, control of neural systems has been successful when the structure of the system is well mapped (Ahmadian et al, 2011;Li et al, 2013), such as controlling the specific neurons responsible for locomotion in C. elegans (Yan et al, 2017). However, controlling complex systems, such as large-scale neural populations, is poorly understood (Ritt and Ching, 2015;Tang and Bassett, 2018).…”

Section: Introductionmentioning

confidence: 99%

Learning to Control the Brain through Adaptive Closed-Loop Patterned Stimulation

Tafazoli

MacDowell

Che

et al. 2020

Preprint

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Stimulation of neural activity is an important scientific and clinical tool, causally testing hypotheses and treating neurodegenerative and neuropsychiatric diseases. However, current stimulation approaches cannot flexibly control the pattern of activity in populations of neurons. To address this, we developed an adaptive, closed-loop stimulation (ACLS) system that uses patterned, multi-site electrical stimulation to control the pattern of activity in a population of neurons. Importantly, ACLS is a learning system; it monitors the response to stimulation and iteratively updates the stimulation pattern to produce a specific neural response. In silico and in vivo experiments showed ACLS quickly learns to produce specific patterns of neural activity (~15 minutes) and was robust to noise and drift in neural responses. In visual cortex of awake mice, ACLS learned electrical stimulation patterns that produced responses similar to the natural response evoked by visual stimuli. Similar to how repetition of a visual stimulus causes an adaptation in the neural response, the response to electrical stimulation was adapted when it was preceded by the associated visual stimulus. Altogether, our results show ACLS can learn, in real-time, to generate specific patterns of neural activity, providing a framework for using closed-loop learning to control neural activity.

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Neurocontrol: Methods, models and technologies for manipulating dynamics in the brain

Cited by 13 publications

References 166 publications

Colloquium: Control of dynamics in brain networks

Colloquium: Control of dynamics in brain networks

Optimizing deep brain stimulation based on isostable amplitude in essential tremor patient models

Learning to Control the Brain through Adaptive Closed-Loop Patterned Stimulation

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