Literature DB >> 24262909

Creating the feedback loop: closed-loop neurostimulation.

Adam O Hebb1, Jun Jason Zhang, Mohammad H Mahoor, Christos Tsiokos, Charles Matlack, Howard Jay Chizeck, Nader Pouratian.   

Abstract

Current DBS therapy delivers a train of electrical pulses at set stimulation parameters. This open-loop design is effective for movement disorders, but therapy may be further optimized by a closed loop design. The technology to record biosignals has outpaced our understanding of their relationship to the clinical state of the whole person. Neuronal oscillations may represent or facilitate the cooperative functioning of brain ensembles, and may provide critical information to customize neuromodulation therapy. This review addresses advances to date, not of the technology per se, but of the strategies to apply neuronal signals to trigger or modulate stimulation systems.
Copyright © 2014 Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Closed-loop; Control systems; Deep brain stimulation; Local field potentials; Machine learning; Oscillations; Subthalamic nucleus

Mesh:

Year:  2013        PMID: 24262909      PMCID: PMC4058859          DOI: 10.1016/j.nec.2013.08.006

Source DB:  PubMed          Journal:  Neurosurg Clin N Am        ISSN: 1042-3680            Impact factor:   2.509


  108 in total

Review 1.  Analysis of dynamic brain oscillations: methodological advances.

Authors:  Michel Le Van Quyen; Anatol Bragin
Journal:  Trends Neurosci       Date:  2007-06-07       Impact factor: 13.837

2.  Faster self-organizing fuzzy neural network training and a hyperparameter analysis for a brain-computer interface.

Authors:  Damien Coyle; Girijesh Prasad; Thomas Martin McGinnity
Journal:  IEEE Trans Syst Man Cybern B Cybern       Date:  2009-05-29

3.  Distributed cortical adaptation during learning of a brain-computer interface task.

Authors:  Jeremiah D Wander; Timothy Blakely; Kai J Miller; Kurt E Weaver; Lise A Johnson; Jared D Olson; Eberhard E Fetz; Rajesh P N Rao; Jeffrey G Ojemann
Journal:  Proc Natl Acad Sci U S A       Date:  2013-06-10       Impact factor: 11.205

4.  Closed-loop deep brain stimulation is superior in ameliorating parkinsonism.

Authors:  Boris Rosin; Maya Slovik; Rea Mitelman; Michal Rivlin-Etzion; Suzanne N Haber; Zvi Israel; Eilon Vaadia; Hagai Bergman
Journal:  Neuron       Date:  2011-10-20       Impact factor: 17.173

5.  EEG data classification through signal spatial redistribution and optimized linear discriminants.

Authors:  David Gutiérrez; Diana I Escalona-Vargas
Journal:  Comput Methods Programs Biomed       Date:  2009-06-11       Impact factor: 5.428

6.  Comparison of designs towards a subject-independent brain-computer interface based on motor imagery.

Authors:  Fabien Lotte; Cuntai Guan; Kai Keng Ang
Journal:  Conf Proc IEEE Eng Med Biol Soc       Date:  2009

7.  Brain-computer interfaces and neurorehabilitation.

Authors:  Roberta Carabalona; Paolo Castiglioni; Furio Gramatica
Journal:  Stud Health Technol Inform       Date:  2009

Review 8.  Adaptive deep brain stimulation (aDBS) controlled by local field potential oscillations.

Authors:  Alberto Priori; Guglielmo Foffani; Lorenzo Rossi; Sara Marceglia
Journal:  Exp Neurol       Date:  2012-09-27       Impact factor: 5.330

9.  Automatic recognition of epileptic seizures in the EEG.

Authors:  J Gotman
Journal:  Electroencephalogr Clin Neurophysiol       Date:  1982-11

10.  Differences among implanted pulse generator waveforms cause variations in the neural response to deep brain stimulation.

Authors:  Christopher R Butson; Cameron C McIntyre
Journal:  Clin Neurophysiol       Date:  2007-06-19       Impact factor: 3.708

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  11 in total

1.  Surface EEG-Transcranial Direct Current Stimulation (tDCS) Closed-Loop System.

Authors:  Jorge Leite; Leon Morales-Quezada; Sandra Carvalho; Aurore Thibaut; Deniz Doruk; Chiun-Fan Chen; Steven C Schachter; Alexander Rotenberg; Felipe Fregni
Journal:  Int J Neural Syst       Date:  2017-04-11       Impact factor: 5.866

Review 2.  Toward Electrophysiology-Based Intelligent Adaptive Deep Brain Stimulation for Movement Disorders.

Authors:  Andrea A Kühn; R Mark Richardson; Wolf-Julian Neumann; Robert S Turner; Benjamin Blankertz; Tom Mitchell
Journal:  Neurotherapeutics       Date:  2019-01       Impact factor: 7.620

Review 3.  High Frequency Deep Brain Stimulation and Neural Rhythms in Parkinson's Disease.

Authors:  Zack Blumenfeld; Helen Brontë-Stewart
Journal:  Neuropsychol Rev       Date:  2015-11-25       Impact factor: 7.444

Review 4.  Technology for deep brain stimulation at a gallop.

Authors:  Alberto Priori
Journal:  Mov Disord       Date:  2015-05-23       Impact factor: 10.338

5.  Adaptive deep brain stimulation in a freely moving Parkinsonian patient.

Authors:  Manuela Rosa; Mattia Arlotti; Gianluca Ardolino; Filippo Cogiamanian; Sara Marceglia; Alessio Di Fonzo; Francesca Cortese; Paolo M Rampini; Alberto Priori
Journal:  Mov Disord       Date:  2015-05-21       Impact factor: 10.338

6.  Long-Term Task- and Dopamine-Dependent Dynamics of Subthalamic Local Field Potentials in Parkinson's Disease.

Authors:  Sara J Hanrahan; Joshua J Nedrud; Bradley S Davidson; Sierra Farris; Monique Giroux; Aaron Haug; Mohammad H Mahoor; Anne K Silverman; Jun Jason Zhang; Adam Olding Hebb
Journal:  Brain Sci       Date:  2016-11-29

Review 7.  Advances in closed-loop deep brain stimulation devices.

Authors:  Mahboubeh Parastarfeizabadi; Abbas Z Kouzani
Journal:  J Neuroeng Rehabil       Date:  2017-08-11       Impact factor: 4.262

8.  Model-Based Evaluation of Closed-Loop Deep Brain Stimulation Controller to Adapt to Dynamic Changes in Reference Signal.

Authors:  Fei Su; Karthik Kumaravelu; Jiang Wang; Warren M Grill
Journal:  Front Neurosci       Date:  2019-09-10       Impact factor: 4.677

Review 9.  Pedunculopontine Nucleus Degeneration Contributes to Both Motor and Non-Motor Symptoms of Parkinson's Disease.

Authors:  Nicole Elaine Chambers; Kathryn Lanza; Christopher Bishop
Journal:  Front Pharmacol       Date:  2020-01-15       Impact factor: 5.810

10.  A high-performance 8 nV/√Hz 8-channel wearable and wireless system for real-time monitoring of bioelectrical signals.

Authors:  Konstantinos Petkos; Simos Koutsoftidis; Thomas Guiho; Patrick Degenaar; Andrew Jackson; Stephen E Greenwald; Peter Brown; Timothy Denison; Emmanuel M Drakakis
Journal:  J Neuroeng Rehabil       Date:  2019-12-10       Impact factor: 4.262

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