{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,18]],"date-time":"2026-10-18T12:08:10Z","timestamp":1792325290876,"version":"4.4.0"},"reference-count":33,"publisher":"Public Library of Science (PLoS)","issue":"1","license":[{"start":{"date-parts":[[2021,1,26]],"date-time":"2021-01-26T00:00:00Z","timestamp":1611619200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS ONE"],"abstract":"<jats:sec id=\"sec001\">\n<jats:title>Background<\/jats:title>\n<jats:p>Deep brain stimulation (DBS) of the subthalamic nucleus (STN) is an effective treatment for improving the motor symptoms of advanced Parkinson\u2019s disease (PD). Accurate positioning of the stimulation electrodes is necessary for better clinical outcomes.<\/jats:p>\n<\/jats:sec>\n<jats:sec id=\"sec002\">\n<jats:title>Objective<\/jats:title>\n<jats:p>We applied deep learning techniques to microelectrode recording (MER) signals to better predict motor function improvement, represented by the UPDRS part III scores, after bilateral STN DBS in patients with advanced PD. If we find the optimal stimulation point with MER by deep learning, we can improve the clinical outcome of STN DBS even under restrictions such as general anesthesia or non-cooperation of the patients.<\/jats:p>\n<\/jats:sec>\n<jats:sec id=\"sec003\">\n<jats:title>Methods<\/jats:title>\n<jats:p>In total, 696 4-second left-side MER segments from 34 patients with advanced PD who underwent bilateral STN DBS surgery under general anesthesia were included. We transformed the original signal into three wavelets of 1\u201350 Hz, 50\u2013500 Hz, and 500\u20135,000 Hz. The wavelet-transformed MER was used for input data of the deep learning. The patients were divided into two groups, good response and moderate response groups, according to DBS on to off ratio of UPDRS part III score for the off-medication state, 6 months postoperatively. The ratio were used for output data in deep learning. The Visual Geometry Group (VGG)-16 model with a multitask learning algorithm was used to estimate the bilateral effect of DBS. Different ratios of the loss function in the task-specific layer were applied considering that DBS affects both sides differently.<\/jats:p>\n<\/jats:sec>\n<jats:sec id=\"sec004\">\n<jats:title>Results<\/jats:title>\n<jats:p>When we divided the MER signals according to the frequency, the maximal accuracy was higher in the 50\u2013500 Hz group than in the 1\u201350 Hz and 500\u20135,000 Hz groups. In addition, when the multitask learning method was applied, the stability of the model was improved in comparison with single task learning. The maximal accuracy (80.21%) occurred when the right-to-left loss ratio was 5:1 or 6:1. The area under the curve (AUC) was 0.88 in the receiver operating characteristic (ROC) curve.<\/jats:p>\n<\/jats:sec>\n<jats:sec id=\"sec005\">\n<jats:title>Conclusion<\/jats:title>\n<jats:p>Clinical improvements in PD patients who underwent bilateral STN DBS could be predicted based on a multitask deep learning-based MER analysis.<\/jats:p>\n<\/jats:sec>","DOI":"10.1371\/journal.pone.0244133","type":"journal-article","created":{"date-parts":[[2021,1,26]],"date-time":"2021-01-26T18:26:52Z","timestamp":1611685612000},"page":"e0244133","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":18,"title":["Clinical outcome prediction from analysis of microelectrode recordings using deep learning in subthalamic deep brain stimulation for Parkinson`s disease"],"prefix":"10.1371","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2386-4680","authenticated-orcid":true,"given":"Kwang Hyon","family":"Park","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sukkyu","family":"Sun","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yong Hoon","family":"Lim","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hye Ran","family":"Park","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jae Meen","family":"Lee","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kawngwoo","family":"Park","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Beomseok","family":"Jeon","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hee-Pyoung","family":"Park","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2112-426X","authenticated-orcid":true,"given":"Hee Chan","family":"Kim","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3007-8653","authenticated-orcid":true,"given":"Sun Ha","family":"Paek","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2021,1,26]]},"reference":[{"issue":"1","key":"pone.0244133.ref001","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1016\/S1474-4422(08)70291-6","article-title":"Deep brain stimulation of the subthalamic nucleus for the treatment of Parkinson's disease","volume":"8","author":"AL Benabid","year":"2009","journal-title":"Lancet Neurol"},{"issue":"3","key":"pone.0244133.ref002","doi-asserted-by":"crossref","first-page":"639","DOI":"10.3171\/2010.3.JNS091385","article-title":"Direct visualization of deep brain stimulation targets in Parkinson disease with the use of 7-tesla magnetic resonance imaging","volume":"113","author":"Z-H Cho","year":"2010","journal-title":"J Neurosurg"},{"issue":"6","key":"pone.0244133.ref003","doi-asserted-by":"crossref","first-page":"351","DOI":"10.1159\/000449206","article-title":"Review on Factors Affecting Targeting Accuracy of Deep Brain Stimulation Electrode Implantation between 2001 and 2015","volume":"94","author":"Z Li","year":"2016","journal-title":"Stereotact Funct Neurosurg"},{"issue":"1","key":"pone.0244133.ref004","doi-asserted-by":"crossref","first-page":"175","DOI":"10.3171\/jns.2007.106.1.175","article-title":"Detection of the subthalamic nucleus in microelectrographic recordings in Parkinson disease using the high-frequency (> 500 hz) neuronal background. 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