| 000 | 03294 am a22003853u 4500 | ||
|---|---|---|---|
| 042 | _adc | ||
| 100 | 1 | 0 |
_aShah, Ashesh _eauthor _92791 |
| 700 | 1 | 0 |
_aNguyen, Thuy-Anh Khoa _eauthor _92792 |
| 700 | 1 | 0 |
_aPeterman, Katrin _eauthor _92793 |
| 700 | 1 | 0 |
_aKhawaldeh, Saed _eauthor _92794 |
| 700 | 1 | 0 |
_aDebove, Ines _eauthor _92795 |
| 700 | 1 | 0 |
_aShah, Syed Ahmar _eauthor _92796 |
| 700 | 1 | 0 |
_aTorrecillos, Flavie _eauthor |
| 700 | 1 | 0 |
_aTan, Huiling _eauthor |
| 700 | 1 | 0 |
_aPogosyan, Alek _eauthor |
| 700 | 1 | 0 |
_aLachenmayer, Martin Lenard _eauthor _92800 |
| 700 | 1 | 0 |
_aMichelis, Joan _eauthor _92801 |
| 700 | 1 | 0 |
_aBrown, Peter _eauthor _92802 |
| 700 | 1 | 0 |
_aPollo, Claudio _eauthor _92803 |
| 700 | 1 | 0 |
_aKrack, Paul _eauthor _92804 |
| 700 | 1 | 0 |
_aNowacki, Andreas _eauthor _92805 |
| 700 | 1 | 0 |
_aTinkhauser, Gerd _eauthor _92806 |
| 245 | 0 | 0 | _aCombining Multimodal Biomarkers to Guide Deep Brain Stimulation Programming in Parkinson Disease |
| 260 | _c2022-02-23. | ||
| 500 | _a/pmc/articles/PMC7614142/ | ||
| 500 | _a/pubmed/35219571 | ||
| 520 | _aBACKGROUND: 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 | _a | ||
| 540 | _ahttps://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 | _aen | ||
| 690 | _aArticle | ||
| 655 | 7 |
_aText _2local |
|
| 786 | 0 | _nNeuromodulation | |
| 856 | 4 | 1 |
_uhttp://dx.doi.org/10.1016/j.neurom.2022.01.017 _zConnect to this object online. |
| 999 |
_c2259 _d2259 |
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