Literature DB >> 20889437

Closed-loop control of deep brain stimulation: a simulation study.

Sabato Santaniello1, Giovanni Fiengo, Luigi Glielmo, Warren M Grill.   

Abstract

Deep brain stimulation (DBS) is an effective therapy to treat movement disorders including essential tremor, dystonia, and Parkinson's disease. Despite over a decade of clinical experience the mechanisms of DBS are still unclear, and this lack of understanding makes the selection of stimulation parameters quite challenging. The objective of this work was to develop a closed-loop control system that automatically adjusted the stimulation amplitude to reduce oscillatory neuronal activity, based on feedback of electrical signals recorded from the brain using the same electrode as implanted for stimulation. We simulated a population of 100 intrinsically active model neurons in the Vim thalamus, and the local field potentials (LFPs) generated by the population were used as the feedback (control) variable for closed loop control of DBS amplitude. Based on the correlation between the spectral content of the thalamic activity and tremor (Hua , 1998), (Lenz , 1988), we implemented an adaptive minimum variance controller to regulate the power spectrum of the simulated LFPs and restore the LFP power spectrum present under tremor conditions to a reference profile derived under tremor free conditions. The controller was based on a recursively identified autoregressive model (ARX) of the relationship between stimulation input and LFP output, and showed excellent performances in tracking the reference spectral features through selective changes in the theta (2-7 Hz), alpha (7-13 Hz), and beta (13-35 Hz) frequency ranges. Such changes reflected modifications in the firing patterns of the model neuronal population, and, differently from open-loop DBS, replaced the tremor-related pathological patterns with patterns similar to those simulated in tremor-free conditions. The closed-loop controller generated a LFP spectrum that approximated more closely the spectrum present in the tremor-free condition than did open loop fixed intensity stimulation and adapted to match the spectrum after a change in the neuronal oscillation frequency. This computational study suggests the feasibility of closed-loop control of DBS amplitude to regulate the spectrum of the local field potentials and thereby normalize the aberrant pattern of neuronal activity present in tremor.

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Year:  2010        PMID: 20889437     DOI: 10.1109/TNSRE.2010.2081377

Source DB:  PubMed          Journal:  IEEE Trans Neural Syst Rehabil Eng        ISSN: 1534-4320            Impact factor:   3.802


  53 in total

1.  Engineering the synchronization of neuron action potentials using global time-delayed feedback stimulation.

Authors:  Craig G Rusin; Sarah E Johnson; Jaideep Kapur; John L Hudson
Journal:  Phys Rev E Stat Nonlin Soft Matter Phys       Date:  2011-12-06

2.  Neural origin of evoked potentials during thalamic deep brain stimulation.

Authors:  Alexander R Kent; Warren M Grill
Journal:  J Neurophysiol       Date:  2013-05-29       Impact factor: 2.714

3.  Pathological tremor prediction using surface electromyogram and acceleration: potential use in 'ON-OFF' demand driven deep brain stimulator design.

Authors:  Ishita Basu; Daniel Graupe; Daniela Tuninetti; Pitamber Shukla; Konstantin V Slavin; Leo Verhagen Metman; Daniel M Corcos
Journal:  J Neural Eng       Date:  2013-05-08       Impact factor: 5.379

4.  Local field potential recordings in a non-human primate model of Parkinsons disease using the Activa PC + S neurostimulator.

Authors:  Allison T Connolly; Abirami Muralidharan; Claudia Hendrix; Luke Johnson; Rahul Gupta; Scott Stanslaski; Tim Denison; Kenneth B Baker; Jerrold L Vitek; Matthew D Johnson
Journal:  J Neural Eng       Date:  2015-10-15       Impact factor: 5.379

5.  Design strategies for dynamic closed-loop optogenetic neurocontrol in vivo.

Authors:  M F Bolus; A A Willats; C J Whitmire; C J Rozell; G B Stanley
Journal:  J Neural Eng       Date:  2018-04       Impact factor: 5.379

Review 6.  Brain-machine interfaces from motor to mood.

Authors:  Maryam M Shanechi
Journal:  Nat Neurosci       Date:  2019-09-24       Impact factor: 24.884

7.  Multi-disease Deep Brain Stimulation.

Authors:  Mahboubeh Parastarfeizabadi; Roy V Sillitoe; Abbas Z Kouzani
Journal:  IEEE Access       Date:  2020-12-02       Impact factor: 3.367

8.  A Fuzzy Inference System for Closed-Loop Deep Brain Stimulation in Parkinson's Disease.

Authors:  Carmen Camara; Kevin Warwick; Ricardo Bruña; Tipu Aziz; Francisco del Pozo; Fernando Maestú
Journal:  J Med Syst       Date:  2015-09-18       Impact factor: 4.460

Review 9.  Systems approaches to optimizing deep brain stimulation therapies in Parkinson's disease.

Authors:  Sabato Santaniello; John T Gale; Sridevi V Sarma
Journal:  Wiley Interdiscip Rev Syst Biol Med       Date:  2018-03-20

10.  Origins and suppression of oscillations in a computational model of Parkinson's disease.

Authors:  Abbey B Holt; Theoden I Netoff
Journal:  J Comput Neurosci       Date:  2014-08-07       Impact factor: 1.621

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