CLoSES: A platform for closed-loop intracranial stimulation in humans.


Journal

NeuroImage
ISSN: 1095-9572
Titre abrégé: Neuroimage
Pays: United States
ID NLM: 9215515

Informations de publication

Date de publication:
12 2020
Historique:
received: 01 03 2020
revised: 15 07 2020
accepted: 25 08 2020
pubmed: 4 9 2020
medline: 2 3 2021
entrez: 4 9 2020
Statut: ppublish

Résumé

Targeted interrogation of brain networks through invasive brain stimulation has become an increasingly important research tool as well as therapeutic modality. The majority of work with this emerging capability has been focused on open-loop approaches. Closed-loop techniques, however, could improve neuromodulatory therapies and research investigations by optimizing stimulation approaches using neurally informed, personalized targets. Implementing closed-loop systems is challenging particularly with regard to applying consistent strategies considering inter-individual variability. In particular, during intracranial epilepsy monitoring, where much of this research is currently progressing, electrodes are implanted exclusively for clinical reasons. Thus, detection and stimulation sites must be participant- and task-specific. The system must run in parallel with clinical systems, integrate seamlessly with existing setups, and ensure safety features are in place. In other words, a robust, yet flexible platform is required to perform different tests with a single participant and to comply with clinical requirements. In order to investigate closed-loop stimulation for research and therapeutic use, we developed a Closed-Loop System for Electrical Stimulation (CLoSES) that computes neural features which are then used in a decision algorithm to trigger stimulation in near real-time. To summarize CLoSES, intracranial electroencephalography (iEEG) signals are acquired, band-pass filtered, and local and network features are continuously computed. If target features are detected (e.g. above a preset threshold for a certain duration), stimulation is triggered. Not only could the system trigger stimulation while detecting real-time neural features, but we incorporated a pipeline wherein we used an encoder/decoder model to estimate a hidden cognitive state from the neural features. CLoSES provides a flexible platform to implement a variety of closed-loop experimental paradigms in humans. CLoSES has been successfully used with twelve patients implanted with depth electrodes in the epilepsy monitoring unit. During cognitive tasks (N=5), stimulation in closed loop modified a cognitive hidden state on a trial by trial basis. Sleep spindle oscillations (N=6) and sharp transient epileptic activity (N=9) were detected in near real-time, and stimulation was applied during the event or at specified delays (N=3). In addition, we measured the capabilities of the CLoSES system. Total latency was related to the characteristics of the event being detected, with tens of milliseconds for epileptic activity and hundreds of milliseconds for spindle detection. Stepwise latency, the actual duration of each continuous step, was within the specified fixed-step duration and increased linearly with the number of channels and features. We anticipate that probing neural dynamics and interaction between brain states and stimulation responses with CLoSES will lead to novel insights into the mechanism of normal and pathological brain activity, the discovery and evaluation of potential electrographic biomarkers of neurological and psychiatric disorders, and the development and testing of patient-specific stimulation targets and control signals before implanting a therapeutic device.

Identifiants

pubmed: 32882382
pii: S1053-8119(20)30800-4
doi: 10.1016/j.neuroimage.2020.117314
pmc: PMC7805582
mid: NIHMS1657550
pii:
doi:

Types de publication

Journal Article Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, Non-P.H.S.

Langues

eng

Sous-ensembles de citation

IM

Pagination

117314

Subventions

Organisme : NINDS NIH HHS
ID : K24 NS088568
Pays : United States
Organisme : NINDS NIH HHS
ID : R01 NS062092
Pays : United States
Organisme : NINDS NIH HHS
ID : R01 NS079533
Pays : United States

Informations de copyright

Copyright © 2020. Published by Elsevier Inc.

Auteurs

Rina Zelmann (R)

Department of Neurology, Massachusetts General Hospital, Boston, MA, USA. Electronic address: rzelmann@mgh.harvard.edu.

Angelique C Paulk (AC)

Department of Neurology, Massachusetts General Hospital, Boston, MA, USA.

Ishita Basu (I)

Department of Psychiatry, Massachusetts General Hospital, Boston, MA, USA; Department of Neurosurgery, Massachusetts General Hospital, Boston, MA, USA; Department of Neurosurgery, University of Cincinnati, OH, USA.

Anish Sarma (A)

Department of Neuroscience, Brown University, Providence, RI, USA.

Ali Yousefi (A)

Department of Psychiatry, Massachusetts General Hospital, Boston, MA, USA; Department of Computer Science, Worcester Polytechnic Institute, Worcester, MA, USA.

Britni Crocker (B)

Department of Neurology, Massachusetts General Hospital, Boston, MA, USA; Massachusetts Institute of Technology, Cambridge, MA, USA.

Emad Eskandar (E)

Department of Neurosurgery, Massachusetts General Hospital, Boston, MA, USA; Department of Neurosurgery, Albert Einstein College of Medicine, NY, USA.

Ziv Williams (Z)

Department of Neurosurgery, Massachusetts General Hospital, Boston, MA, USA.

G Rees Cosgrove (GR)

Department of Neurosurgery, Brigham and Women's Hospital, Boston, MA, USA.

Daniel S Weisholtz (DS)

Department of Neurology, Brigham and Women's Hospital, Boston, MA, USA.

Darin D Dougherty (DD)

Department of Psychiatry, Massachusetts General Hospital, Boston, MA, USA.

Wilson Truccolo (W)

Department of Neuroscience, Brown University, Providence, RI, USA.

Alik S Widge (AS)

Department of Psychiatry, Massachusetts General Hospital, Boston, MA, USA; Department of Psychiatry, University of Minnesota, MI, USA.

Sydney S Cash (SS)

Department of Neurology, Massachusetts General Hospital, Boston, MA, USA.

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Classifications MeSH