Literature DB >> 35234664

Concurrent stimulation and sensing in bi-directional brain interfaces: a multi-site translational experience.

Juan Ansó1, Moaad Benjaber2, Brandon Parks3, Samuel Parker4, Carina Renate Oehrn1, Matthew Petrucci5, Ro'ee Gilron1, Simon Little6, Robert Wilt1, Helen Bronte-Stewart5, Aysegul Gunduz7, David Borton4, Philip A Starr1, Timothy Denison8.   

Abstract

Objective. To provide a design analysis and guidance framework for the implementation of concurrent stimulation and sensing during adaptive deep brain stimulation (aDBS) with particular emphasis on artifact mitigations.Approach. We defined a general architecture of feedback-enabled devices, identified key components in the signal chain which might result in unwanted artifacts and proposed methods that might ultimately enable improved aDBS therapies. We gathered data from research subjects chronically-implanted with an investigational aDBS system, Summit RC + S, to characterize and explore artifact mitigations arising from concurrent stimulation and sensing. We then used a prototype investigational implantable device, DyNeuMo, and a bench-setup that accounts for tissue-electrode properties, to confirm our observations and verify mitigations. The strategies to reduce transient stimulation artifacts and improve performance during aDBS were confirmed in a chronic implant using updated configuration settings.Main results.We derived and validated a 'checklist' of configuration settings to improve system performance and areas for future device improvement. Key considerations for the configuration include (a) active instead of passive recharge, (b) sense-channel blanking in the amplifier, (c) high-pass filter settings, (d) tissue-electrode impedance mismatch management, (e) time-frequency trade-offs in the classifier, (f) algorithm blanking and transition rate limits. Without proper channel configuration, the aDBS algorithm was susceptible to limit-cycles of oscillating stimulation independent of physiological state. By applying the checklist, we could optimize each block's performance characteristics within the overall system. With system-level optimization, a 'fast' aDBS prototype algorithm was demonstrated to be feasible without reentrant loops, and with noise performance suitable for subcortical brain circuits.Significance. We present a framework to study sources and propose mitigations of artifacts in devices that provide chronic aDBS. This work highlights the trade-offs in performance as novel sensing devices translate to the clinic. Finding the appropriate balance of constraints is imperative for successful translation of aDBS therapies.Clinical trial:Institutional Review Board and Investigational Device Exemption numbers: NCT02649166/IRB201501021 (University of Florida), NCT04043403/IRB52548 (Stanford University), NCT03582891/IRB1824454 (University of California San Francisco). IDE #180 097. Creative Commons Attribution license.

Entities:  

Keywords:  adaptive deep brain stimulation; algorithms; artifacts; chronic implant; closed loop; embedded; neural sensing

Mesh:

Year:  2022        PMID: 35234664      PMCID: PMC9095704          DOI: 10.1088/1741-2552/ac59a3

Source DB:  PubMed          Journal:  J Neural Eng        ISSN: 1741-2552            Impact factor:   5.043


  58 in total

Review 1.  Electrical stimulation of excitable tissue: design of efficacious and safe protocols.

Authors:  Daniel R Merrill; Marom Bikson; John G R Jefferys
Journal:  J Neurosci Methods       Date:  2005-02-15       Impact factor: 2.390

Review 2.  A novel closed-loop stimulation system in the control of focal, medically refractory epilepsy.

Authors:  K N Fountas; J R Smith
Journal:  Acta Neurochir Suppl       Date:  2007

3.  A Chronically Implantable Neural Coprocessor for Investigating the Treatment of Neurological Disorders.

Authors:  Scott Stanslaski; Jeffrey Herron; Tom Chouinard; Duane Bourget; Ben Isaacson; Vaclav Kremen; Enrico Opri; William Drew; Benjamin H Brinkmann; Aysegul Gunduz; Tom Adamski; Gregory A Worrell; Timothy Denison
Journal:  IEEE Trans Biomed Circuits Syst       Date:  2018-11-07       Impact factor: 3.833

Review 4.  Techniques and devices to restore cognition.

Authors:  Mijail Demian Serruya; Michael J Kahana
Journal:  Behav Brain Res       Date:  2008-04-20       Impact factor: 3.332

5.  Adaptive deep brain stimulation for Parkinson's disease demonstrates reduced speech side effects compared to conventional stimulation in the acute setting.

Authors:  Simon Little; Elina Tripoliti; Martijn Beudel; Alek Pogosyan; Hayriye Cagnan; Damian Herz; Sven Bestmann; Tipu Aziz; Binith Cheeran; Ludvic Zrinzo; Marwan Hariz; Jonathan Hyam; Patricia Limousin; Tom Foltynie; Peter Brown
Journal:  J Neurol Neurosurg Psychiatry       Date:  2016-08-16       Impact factor: 10.154

Review 6.  Adaptive Deep Brain Stimulation for Movement Disorders: The Long Road to Clinical Therapy.

Authors:  Anders Christian Meidahl; Gerd Tinkhauser; Damian Marc Herz; Hayriye Cagnan; Jean Debarros; Peter Brown
Journal:  Mov Disord       Date:  2017-06       Impact factor: 10.338

7.  Bayesian adaptive dual control of deep brain stimulation in a computational model of Parkinson's disease.

Authors:  Logan L Grado; Matthew D Johnson; Theoden I Netoff
Journal:  PLoS Comput Biol       Date:  2018-12-06       Impact factor: 4.475

8.  Epilepsy Personal Assistant Device-A Mobile Platform for Brain State, Dense Behavioral and Physiology Tracking and Controlling Adaptive Stimulation.

Authors:  Tal Pal Attia; Daniel Crepeau; Vaclav Kremen; Mona Nasseri; Hari Guragain; Steven W Steele; Vladimir Sladky; Petr Nejedly; Filip Mivalt; Jeffrey A Herron; Matt Stead; Timothy Denison; Gregory A Worrell; Benjamin H Brinkmann
Journal:  Front Neurol       Date:  2021-07-29       Impact factor: 4.003

9.  Closed-Loop Deep Brain Stimulation for Refractory Chronic Pain.

Authors:  Prasad Shirvalkar; Tess L Veuthey; Heather E Dawes; Edward F Chang
Journal:  Front Comput Neurosci       Date:  2018-03-26       Impact factor: 2.380

10.  Artefact-free recording of local field potentials with simultaneous stimulation for closed-loop Deep-Brain Stimulation.

Authors:  Jean Debarros; Lea Gaignon; Shenghong He; Alek Pogosyan; Moaad Benjaber; Timothy Denison; Peter Brown; Huiling Tan
Journal:  Annu Int Conf IEEE Eng Med Biol Soc       Date:  2020-07
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  1 in total

1.  Computationally efficient neural network classifiers for next generation closed loop neuromodulation therapy - a case study in epilepsy.

Authors:  Ali Kavoosi; Robert Toth; Moaad Benjaber; Mayela Zamora; Antonio Valentin; Andrew Sharott; Timothy Denison
Journal:  Annu Int Conf IEEE Eng Med Biol Soc       Date:  2022-07
  1 in total

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