008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field
240806b |||||||| |||| 00| 0 eng d
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number
00002
040 ## - CATALOGING SOURCE
Transcribing agency
LEMON
042 ## - AUTHENTICATION CODE
Authentication code
dc
050 ## - LIBRARY OF CONGRESS CALL NUMBER
Classification number
REF
Item number
05
082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER
Edition number
REF BS0130
Classification number
C5
090 ## - LOCALLY ASSIGNED LC-TYPE CALL NUMBER (OCLC); LOCAL CALL NUMBER (RLIN)
Classification number (OCLC) (R) ; Classification number, CALL (RLIN) (NR)
REF BS0130
Local cutter number (OCLC) ; Book number/undivided call number, CALL (RLIN)
C5
100 10 - MAIN ENTRY--PERSONAL NAME
Personal name
Shah, Ashesh
Relator term
author
9 (RLIN)
2791
245 00 - TITLE STATEMENT
Title
Combining Multimodal Biomarkers to Guide Deep Brain Stimulation Programming in Parkinson Disease
260 ## - PUBLICATION, DISTRIBUTION, ETC.
Date of publication, distribution, etc.
2022-02-23.
500 ## - GENERAL NOTE
General note
/pmc/articles/PMC7614142/
500 ## - GENERAL NOTE
General note
/pubmed/35219571
520 ## - SUMMARY, ETC.
Summary, etc.
BACKGROUND: Deep brain stimulation (DBS) programming of multicontact DBS leads relies on a very time-consuming manual screening procedure, and strategies to speed up this process are needed. Beta activity in subthalamic nucleus (STN) local field potentials (LFP) has been suggested as a promising marker to index optimal stimulation contacts in patients with Parkinson disease. OBJECTIVE: In this study, we investigate the advantage of algorithmic selection and combination of multiple resting and movement state features from STN LFPs and imaging markers to predict three relevant clinical DBS parameters (clinical efficacy, therapeutic window, side-effect threshold). MATERIALS AND METHODS: STN LFPs were recorded at rest and during voluntary movements from multicontact DBS leads in 27 hemispheres. Resting- and movement-state features from multiple frequency bands (alpha, low beta, high beta, gamma, fast gamma, high frequency oscillations [HFO]) were used to predict the clinical outcome parameters. Subanalyses included an anatomical stimulation sweet spot as an additional feature. RESULTS: Both resting- and movement-state features contributed to the prediction, with resting (fast) gamma activity, resting/ movement-modulated beta activity, and movement-modulated HFO being most predictive. With the proposed algorithm, the best stimulation contact for the three clinical outcome parameters can be identified with a probability of almost 90% after considering half of the DBS lead contacts, and it outperforms the use of beta activity as single marker. The combination of electrophysiological and imaging markers can further improve the prediction. CONCLUSION: LFP-guided DBS programming based on algorithmic selection and combination of multiple electrophysiological and imaging markers can be an efficient approach to improve the clinical routine and outcome of DBS patients.
540 ## - TERMS GOVERNING USE AND REPRODUCTION NOTE
Terms governing use and reproduction
https://creativecommons.org/licenses/by/4.0/This is an open access article under the CC BY license (https://creativecommons.org/licenses/by/4.0/).
546 ## - LANGUAGE NOTE
Language note
en
655 7# - INDEX TERM--GENRE/FORM
Genre/form data or focus term
Text
Source of term
local
690 ## - LOCAL SUBJECT ADDED ENTRY--TOPICAL TERM (OCLC, RLIN)