Design of a novel closed-loop deep brain stimulation system for Parkinson's disease and obsessive-compulsive disorder

Karamintziou, Sofia D; Piallat, Brigitte; Chabardès, Stéphan; Polosan, Mircea; David, Olivier; Tsirogiannis, George L.; Deligiannis, Nick G; Stathis, Pantelis; Tagaris, G.; Boviatsis, Efstathios; Sakas, Damianos E.; Polychronaki, G E; Nikita, Konstantina S.

doi:10.1109/ner.2015.7146759

Cited by 3 publications

(2 citation statements)

References 18 publications

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“…If a control strategy only affects cognition for moments, it may have no practical utility. Encouragingly, control strategies can be designed to administer continuous stimulation to treat motor [8] and cognitive symptoms [59] over months and years. Crucially, it may not always be necessary to administer continuous control to achieve long-lasting cognitive effects [60,61].…”

Section: Controlling Specific Mental Functionsmentioning

confidence: 99%

Mind control as a guide for the mind

et al. 2017

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The human brain is a complex network that supports mental function. The nascent field of network neuroscience applies tools from mathematics to neuroimaging data in the hopes of shedding light on cognitive function. A critical question arising from these empirical studies is how to modulate a human brain network to treat cognitive deficits or enhance mental abilities. While historically a number of tools have been employed to modulate mental states (such as cognitive behavioral therapy and brain stimulation), theoretical frameworks to guide these interventions -and to optimize them for clinical use -are fundamentally lacking. One promising and as-yet under-explored approach lies in a sub-discipline of engineering known as network control theory. Here, we posit that network control fundamentally relates to mind control, and that this relationship highlights important areas for future empirical research and opportunities to translate knowledge into practical domains. We clarify the conceptual intersection between neuroanatomy, cognition, and control engineering in the context of network neuroscience. Finally, we discuss the challenges, ethics, and promises of mind control.

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Section: Controlling Specific Mental Functionsmentioning

confidence: 99%

Mind control as a guide for the mind

et al. 2017

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“…Apart from the aforementioned general conclusions, we ought to highlight the fact that, with the exception of irregular patterns, the relative desynchronizing effect of the examined alternative patterns of stimulation with respect to standard DBS was substantially dependent on the recorded neural activity. Thereupon, we implicitly suggest that in any closedloop DBS system configuration, maximal information about the underlying neuronal dynamics captured by real-time recordings should be carefully considered, before proceeding to define optimal stimulation pattern characteristics [89].…”

Section: Disorder-and Neural Activity-specific Effect Of Stimulationmentioning

confidence: 99%

Dominant efficiency of nonregular patterns of subthalamic nucleus deep brain stimulation for Parkinson’s disease and obsessive-compulsive disorder in a data-driven computational model

et al. 2015

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In addition to providing novel insights into the efficiency of low-frequency nonregular patterns of STN-DBS for advanced PD and treatment-refractory OCD, this work points to a possible correlation of a model-based outcome measure with clinical effectiveness of stimulation and may have significant implications for an energy- and therapeutically-efficient configuration of a closed-loop neuromodulation system.

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Algorithmic design of a noise-resistant and efficient closed-loop deep brain stimulation system: A computational approach

Karamintziou¹,

Custódio²,

Piallat³

et al. 2017

PLoS ONE

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Advances in the field of closed-loop neuromodulation call for analysis and modeling approaches capable of confronting challenges related to the complex neuronal response to stimulation and the presence of strong internal and measurement noise in neural recordings. Here we elaborate on the algorithmic aspects of a noise-resistant closed-loop subthalamic nucleus deep brain stimulation system for advanced Parkinson’s disease and treatment-refractory obsessive-compulsive disorder, ensuring remarkable performance in terms of both efficiency and selectivity of stimulation, as well as in terms of computational speed. First, we propose an efficient method drawn from dynamical systems theory, for the reliable assessment of significant nonlinear coupling between beta and high-frequency subthalamic neuronal activity, as a biomarker for feedback control. Further, we present a model-based strategy through which optimal parameters of stimulation for minimum energy desynchronizing control of neuronal activity are being identified. The strategy integrates stochastic modeling and derivative-free optimization of neural dynamics based on quadratic modeling. On the basis of numerical simulations, we demonstrate the potential of the presented modeling approach to identify, at a relatively low computational cost, stimulation settings potentially associated with a significantly higher degree of efficiency and selectivity compared with stimulation settings determined post-operatively. Our data reinforce the hypothesis that model-based control strategies are crucial for the design of novel stimulation protocols at the backstage of clinical applications.

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Design of a novel closed-loop deep brain stimulation system for Parkinson's disease and obsessive-compulsive disorder

Cited by 3 publications

References 18 publications

Mind control as a guide for the mind

Mind control as a guide for the mind

Dominant efficiency of nonregular patterns of subthalamic nucleus deep brain stimulation for Parkinson’s disease and obsessive-compulsive disorder in a data-driven computational model

Algorithmic design of a noise-resistant and efficient closed-loop deep brain stimulation system: A computational approach

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