{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,28]],"date-time":"2026-03-28T04:22:09Z","timestamp":1774671729840,"version":"3.50.1"},"reference-count":129,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2023,4,27]],"date-time":"2023-04-27T00:00:00Z","timestamp":1682553600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2023,4,27]],"date-time":"2023-04-27T00:00:00Z","timestamp":1682553600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["npj Digit. Med."],"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Brain stimulation (BStim) encompasses multiple modalities (e.g., deep brain stimulation, responsive neurostimulation) that utilize electrodes implanted in deep brain structures to treat neurological disorders. Currently, BStim is primarily used to treat movement disorders such as Parkinson\u2019s, though indications are expanding to include neuropsychiatric disorders like depression and schizophrenia. Traditional BStim systems are \u201copen-loop\u201d and deliver constant electrical stimulation based on manually-determined parameters. Advancements in BStim have enabled development of \u201cclosed-loop\u201d systems that analyze neural biomarkers (e.g., local field potentials in the sub-thalamic nucleus) and adjust electrical modulation in a dynamic, patient-specific, and energy efficient manner. These closed-loop systems enable real-time, context-specific stimulation adjustment to reduce symptom burden. Machine learning (ML) has emerged as a vital component in designing these closed-loop systems as ML models can predict \/ identify presence of disease symptoms based on neural activity and adaptively learn to modulate stimulation. We queried the US National Library of Medicine PubMed database to understand the role of ML in developing closed-loop BStim systems to treat epilepsy, movement disorders, and neuropsychiatric disorders. Both neural and non-neural network ML algorithms have successfully been leveraged to create closed-loop systems that perform comparably to open-loop systems. For disorders in which the underlying neural pathophysiology is relatively well understood (e.g., Parkinson\u2019s, essential tremor), most work has involved refining ML models that can classify neural signals as aberrant or normal. The same is seen for epilepsy, where most current research has focused on identifying optimal ML model design and integrating closed-loop systems into existing devices. For neuropsychiatric disorders, where the underlying pathologic neural circuitry is still being investigated, research is focused on identifying biomarkers (e.g., local field potentials from brain nuclei) that ML models can use to identify onset of symptoms and stratify severity of disease.<\/jats:p>","DOI":"10.1038\/s41746-023-00779-x","type":"journal-article","created":{"date-parts":[[2023,4,27]],"date-time":"2023-04-27T11:02:01Z","timestamp":1682593321000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":41,"title":["Landscape and future directions of machine learning applications in closed-loop brain stimulation"],"prefix":"10.1038","volume":"6","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4585-3504","authenticated-orcid":false,"given":"Anirudha S.","family":"Chandrabhatla","sequence":"first","affiliation":[]},{"given":"I. Jonathan","family":"Pomeraniec","sequence":"additional","affiliation":[]},{"given":"Taylor M.","family":"Horgan","sequence":"additional","affiliation":[]},{"given":"Elizabeth K.","family":"Wat","sequence":"additional","affiliation":[]},{"given":"Alexander","family":"Ksendzovsky","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,4,27]]},"reference":[{"key":"779_CR1","doi-asserted-by":"publisher","first-page":"239821281881601","DOI":"10.1177\/2398212818816017","volume":"2","author":"L Pycroft","year":"2018","unstructured":"Pycroft, L., Stein, J. & Aziz, T. Deep brain stimulation: an overview of history, methods, and future developments. Brain Neurosci. Adv. 2, 2398212818816017 (2018).","journal-title":"Brain Neurosci. Adv."},{"key":"779_CR2","doi-asserted-by":"publisher","first-page":"707","DOI":"10.1177\/0306312713483678","volume":"43","author":"J Gardner","year":"2013","unstructured":"Gardner, J. A history of deep brain stimulation: technological innovation and the role of clinical assessment tools. Soc. Stud. Sci. 43, 707\u2013728 (2013).","journal-title":"Soc. Stud. Sci."},{"key":"779_CR3","doi-asserted-by":"publisher","first-page":"148","DOI":"10.1038\/s41582-018-0128-2","volume":"15","author":"AM Lozano","year":"2019","unstructured":"Lozano, A. M. et al. Deep brain stimulation: current challenges and future directions. Nat. Rev. Neurol. 15, 148\u2013160 (2019).","journal-title":"Nat. Rev. Neurol."},{"key":"779_CR4","doi-asserted-by":"publisher","first-page":"209","DOI":"10.31661\/jbpe.v8i2.898","volume":"8","author":"P Ghasemi","year":"2018","unstructured":"Ghasemi, P., Sahraee, T. & Mohammadi, A. Closed- and open-loop deep brain stimulation: methods, challenges, current and future aspects. J. Biomed. Phys. Eng. 8, 209\u2013216 (2018).","journal-title":"J. Biomed. Phys. Eng."},{"key":"779_CR5","unstructured":"Epilepsy. World Health Organization https:\/\/www.who.int\/news-room\/fact-sheets\/detail\/epilepsy."},{"key":"779_CR6","doi-asserted-by":"publisher","first-page":"475","DOI":"10.1111\/epi.12550","volume":"55","author":"RS Fisher","year":"2014","unstructured":"Fisher, R. S. et al. ILAE official report: a practical clinical definition of epilepsy. Epilepsia 55, 475\u2013482 (2014).","journal-title":"Epilepsia"},{"key":"779_CR7","doi-asserted-by":"publisher","first-page":"1015","DOI":"10.1212\/WNL.0b013e3181f25b16","volume":"75","author":"S Petrovski","year":"2010","unstructured":"Petrovski, S. et al. Neuropsychiatric symptomatology predicts seizure recurrence in newly treated patients. Neurology 75, 1015\u20131021 (2010).","journal-title":"Neurology"},{"key":"779_CR8","doi-asserted-by":"publisher","first-page":"402","DOI":"10.1212\/01.wnl.0000252941.50833.4a","volume":"68","author":"MJ Brodie","year":"2007","unstructured":"Brodie, M. J. et al. Comparison of levetiracetam and controlled-release carbamazepine in newly diagnosed epilepsy. Neurology 68, 402\u2013408 (2007).","journal-title":"Neurology"},{"key":"779_CR9","doi-asserted-by":"publisher","first-page":"1255","DOI":"10.1046\/j.1528-1157.2001.04501.x","volume":"42","author":"P Kwan","year":"2001","unstructured":"Kwan, P. & Brodie, M. J. Effectiveness of first antiepileptic drug. Epilepsia 42, 1255\u20131260 (2001).","journal-title":"Epilepsia"},{"key":"779_CR10","unstructured":"Schachter, S. C. Overview of the management of epilepsy in adults. In UpToDate (ed Post, T. W.) (UpToDate, 2021)."},{"key":"779_CR11","doi-asserted-by":"publisher","first-page":"525","DOI":"10.1016\/S1474-4422(08)70109-1","volume":"7","author":"S Spencer","year":"2008","unstructured":"Spencer, S. & Huh, L. Outcomes of epilepsy surgery in adults and children. Lancet Neurol. 7, 525\u2013537 (2008).","journal-title":"Lancet Neurol."},{"key":"779_CR12","doi-asserted-by":"publisher","first-page":"465","DOI":"10.1111\/epi.16452","volume":"61","author":"R Yardi","year":"2020","unstructured":"Yardi, R. et al. Long-term outcomes of reoperations in epilepsy surgery. Epilepsia 61, 465\u2013478 (2020).","journal-title":"Epilepsia"},{"key":"779_CR13","doi-asserted-by":"publisher","first-page":"922","DOI":"10.1001\/jama.2012.220","volume":"307","author":"J Engel","year":"2012","unstructured":"Engel, J. et al. Early surgical therapy for drug-resistant temporal lobe epilepsy: a randomized trial. J. Am. Med Assoc. 307, 922\u2013930 (2012).","journal-title":"J. Am. Med Assoc."},{"key":"779_CR14","doi-asserted-by":"publisher","first-page":"970","DOI":"10.1111\/j.1528-1167.2012.03430.x","volume":"53","author":"AM McIntosh","year":"2012","unstructured":"McIntosh, A. M. et al. Long-term seizure outcome and risk factors for recurrence after extratemporal epilepsy surgery. Epilepsia 53, 970\u2013978 (2012).","journal-title":"Epilepsia"},{"key":"779_CR15","doi-asserted-by":"publisher","first-page":"1388","DOI":"10.1016\/S0140-6736(11)60890-8","volume":"378","author":"J de Tisi","year":"2011","unstructured":"de Tisi, J. et al. The long-term outcome of adult epilepsy surgery, patterns of seizure remission, and relapse: a cohort study. Lancet 378, 1388\u20131395 (2011).","journal-title":"Lancet"},{"key":"779_CR16","doi-asserted-by":"publisher","first-page":"1188","DOI":"10.1093\/brain\/awh449","volume":"128","author":"JF T\u00e9llez-Zenteno","year":"2005","unstructured":"T\u00e9llez-Zenteno, J. F., Dhar, R. & Wiebe, S. Long-term seizure outcomes following epilepsy surgery: a systematic review and meta-analysis. Brain 128, 1188\u20131198 (2005).","journal-title":"Brain"},{"key":"779_CR17","doi-asserted-by":"publisher","first-page":"285","DOI":"10.1001\/jama.2014.17426","volume":"313","author":"BC Jobst","year":"2015","unstructured":"Jobst, B. C. & Cascino, G. D. Resective epilepsy surgery for drug-resistant focal epilepsy: a review. J. Am. Med Assoc. 313, 285\u2013293 (2015).","journal-title":"J. Am. Med Assoc."},{"key":"779_CR18","first-page":"2164","volume":"319","author":"R Voelker","year":"2018","unstructured":"Voelker, R. Electrical stimulation for epilepsy. J. Am. Med Assoc. 319, 2164 (2018).","journal-title":"J. Am. Med Assoc."},{"key":"779_CR19","doi-asserted-by":"publisher","first-page":"273","DOI":"10.1111\/epi.13964","volume":"59","author":"MCH Li","year":"2018","unstructured":"Li, M. C. H. & Cook, M. J. Deep brain stimulation for drug-resistant epilepsy. Epilepsia 59, 273\u2013290 (2018).","journal-title":"Epilepsia"},{"key":"779_CR20","doi-asserted-by":"publisher","first-page":"899","DOI":"10.1111\/j.1528-1167.2010.02536.x","volume":"51","author":"R Fisher","year":"2010","unstructured":"Fisher, R. et al. Electrical stimulation of the anterior nucleus of thalamus for treatment of refractory epilepsy. Epilepsia 51, 899\u2013908 (2010).","journal-title":"Epilepsia"},{"key":"779_CR21","doi-asserted-by":"publisher","first-page":"1306","DOI":"10.1111\/epi.16895","volume":"62","author":"V Salanova","year":"2021","unstructured":"Salanova, V. et al. The SANT\u00c9 study at 10 years of follow-up: effectiveness, safety, and sudden unexpected death in epilepsy. Epilepsia 62, 1306\u20131317 (2021).","journal-title":"Epilepsia"},{"key":"779_CR22","doi-asserted-by":"publisher","first-page":"563","DOI":"10.1586\/17434440.2014.947274","volume":"11","author":"FT Sun","year":"2014","unstructured":"Sun, F. T. & Morrell, M. J. The RNS System: responsive cortical stimulation for the treatment of refractory partial epilepsy. Expert Rev. Med. Devices 11, 563\u2013572 (2014).","journal-title":"Expert Rev. Med. Devices"},{"key":"779_CR23","doi-asserted-by":"publisher","first-page":"432","DOI":"10.1111\/epi.12534","volume":"55","author":"CN Heck","year":"2014","unstructured":"Heck, C. N. et al. Two-year seizure reduction in adults with medically intractable partial onset epilepsy treated with responsive neurostimulation: final results of the RNS System Pivotal trial. Epilepsia 55, 432\u2013441 (2014).","journal-title":"Epilepsia"},{"key":"779_CR24","doi-asserted-by":"publisher","first-page":"65","DOI":"10.1016\/j.cnp.2018.03.001","volume":"3","author":"K Gururangan","year":"2018","unstructured":"Gururangan, K., Razavi, B. & Parvizi, J. Diagnostic utility of eight-channel EEG for detecting generalized or hemispheric seizures and rhythmic periodic patterns. Clin. Neurophysiol. Pract. 3, 65\u201373 (2018).","journal-title":"Clin. Neurophysiol. Pract."},{"key":"779_CR25","doi-asserted-by":"publisher","first-page":"453","DOI":"10.1097\/WNP.0000000000000812","volume":"39","author":"ES Swarnalingam","year":"2020","unstructured":"Swarnalingam, E. S., RamachandranNair, R., Choong, K. L. M. & Jones, K. C. Non-neurophysiologist physicians and nurses can detect subclinical seizures in children using a panel of quantitative EEG trends and a seizure detection algorithm. J. Clin. Neurophysiol. 39, 453\u2013458 (2020).","journal-title":"J. Clin. Neurophysiol."},{"key":"779_CR26","doi-asserted-by":"publisher","first-page":"359","DOI":"10.1097\/WNP.0000000000000368","volume":"34","author":"E Amorim","year":"2017","unstructured":"Amorim, E. et al. Performance of spectrogram-based seizure identification of adult EEGs by critical care nurses and neurophysiologists. J. Clin. Neurophysiol. 34, 359\u2013364 (2017).","journal-title":"J. Clin. Neurophysiol."},{"key":"779_CR27","doi-asserted-by":"publisher","first-page":"S29","DOI":"10.1016\/j.yebeh.2011.08.031","volume":"22","author":"A Kharbouch","year":"2011","unstructured":"Kharbouch, A., Shoeb, A., Guttag, J. & Cash, S. S. An algorithm for seizure onset detection using intracranial EEG. Epilepsy Behav. 22, S29\u2013S35 (2011).","journal-title":"Epilepsy Behav."},{"key":"779_CR28","doi-asserted-by":"publisher","first-page":"157","DOI":"10.1142\/S0129065709001938","volume":"19","author":"A Shoeb","year":"2009","unstructured":"Shoeb, A., Pang, T., Guttag, J. & Schachter, S. Non-invasive computerized system for automatically initiating vagus nerve stimulation following patient-specific detection of seizures or epileptiform discharges. Int. J. Neural Syst. 19, 157\u2013172 (2009).","journal-title":"Int. J. Neural Syst."},{"key":"779_CR29","doi-asserted-by":"publisher","unstructured":"Manzouri, F., Heller, S., D\u00fcmpelmann, M., Woias, P. & Schulze-Bonhage, A. A comparison of machine learning classifiers for energy-efficient implementation of seizure detection. Front. Syst. Neurosci. 12 (2018). https:\/\/doi.org\/10.3389\/fnsys.2018.00043.","DOI":"10.3389\/fnsys.2018.00043"},{"key":"779_CR30","doi-asserted-by":"publisher","first-page":"392","DOI":"10.1088\/1741-2560\/5\/4\/004","volume":"5","author":"DE Snyder","year":"2008","unstructured":"Snyder, D. E., Echauz, J., Grimes, D. B. & Litt, B. The statistics of a practical seizure warning system. J. Neural Eng. 5, 392\u2013401 (2008).","journal-title":"J. Neural Eng."},{"key":"779_CR31","doi-asserted-by":"publisher","first-page":"603868","DOI":"10.3389\/fneur.2021.603868","volume":"12","author":"AC Constantino","year":"2021","unstructured":"Constantino, A. C. et al. Expert-level intracranial electroencephalogram ictal pattern detection by a deep learning neural network. Front Neurol. 12, 603868 (2021).","journal-title":"Front Neurol."},{"key":"779_CR32","doi-asserted-by":"crossref","unstructured":"Jankovic, J. Etiology and pathogenesis of Parkinson disease. In UpToDate (ed Post, T. W.) (UpToDate, 2021).","DOI":"10.1016\/B978-0-323-31071-0.00005-6"},{"key":"779_CR33","unstructured":"M. A. Spindler & D Tarsy. Initial pharmacologic treatment of Parkinson disease. In UpToDate (ed Post, T. W.) (UpToDate, 2021)."},{"key":"779_CR34","unstructured":"K. L. Chou & D. Tarsy. Device-assisted and lesioning procedures for Parkinson disease. In UpToDate (ed Post, T. W.) (UpToDate, 2021)."},{"key":"779_CR35","doi-asserted-by":"publisher","first-page":"499","DOI":"10.3389\/fnins.2020.00499","volume":"14","author":"A Mohammed","year":"2020","unstructured":"Mohammed, A., Bayford, R. & Demosthenous, A. A framework for adapting deep brain stimulation using parkinsonian state estimates. Front. Neurosci. 14, 499 (2020).","journal-title":"Front. Neurosci."},{"key":"779_CR36","doi-asserted-by":"publisher","first-page":"046058","DOI":"10.1088\/1741-2552\/abfc1d","volume":"18","author":"D Sand","year":"2021","unstructured":"Sand, D. et al. Machine learning-based personalized subthalamic biomarkers predict ON-OFF levodopa states in Parkinson patients. J. Neural Eng. 18, 046058 (2021).","journal-title":"J. Neural Eng."},{"key":"779_CR37","doi-asserted-by":"publisher","first-page":"046042","DOI":"10.1088\/1741-2552\/abaca3","volume":"17","author":"M Ahn","year":"2020","unstructured":"Ahn, M. et al. Rapid motor fluctuations reveal short-timescale neurophysiological biomarkers of Parkinson\u2019s disease. J. Neural Eng. 17, 046042 (2020).","journal-title":"J. Neural Eng."},{"key":"779_CR38","doi-asserted-by":"publisher","first-page":"216","DOI":"10.1109\/TNSRE.2017.2754879","volume":"26","author":"S Niketeghad","year":"2018","unstructured":"Niketeghad, S., Hebb, A. O., Nedrud, J., Hanrahan, S. J. & Mahoor, M. H. Motor task detection from human STN using interhemispheric connectivity. IEEE Trans. Neural Syst. Rehabilit. Eng. 26, 216\u2013223 (2018).","journal-title":"IEEE Trans. Neural Syst. Rehabilit. Eng."},{"key":"779_CR39","doi-asserted-by":"publisher","first-page":"582","DOI":"10.1093\/brain\/awz417","volume":"143","author":"S Khawaldeh","year":"2020","unstructured":"Khawaldeh, S. et al. Subthalamic nucleus activity dynamics and limb movement prediction in Parkinson\u2019s disease. Brain 143, 582\u2013596 (2020).","journal-title":"Brain"},{"key":"779_CR40","first-page":"4140","volume":"2012","author":"P Shukla","year":"2012","unstructured":"Shukla, P., Basu, I., Graupe, D., Tuninetti, D. & Slavin, K. V. A neural network-based design of an on-off adaptive control for Deep Brain Stimulation in movement disorders. Annu. Int. Conf. IEEE Eng. Med. Biol. Soc. 2012, 4140\u20134143 (2012).","journal-title":"Annu. Int. Conf. IEEE Eng. Med. Biol. Soc."},{"key":"779_CR41","doi-asserted-by":"publisher","first-page":"5151895","DOI":"10.1155\/2017\/5151895","volume":"2017","author":"MS Islam","year":"2017","unstructured":"Islam, M. S., Mamun, K. A. & Deng, H. Decoding of human movements based on deep brain local field potentials using ensemble neural networks. Comput. Intell. Neurosci. 2017, 5151895 (2017).","journal-title":"Comput. Intell. Neurosci."},{"key":"779_CR42","doi-asserted-by":"publisher","first-page":"274","DOI":"10.1016\/j.clinph.2019.09.021","volume":"131","author":"L Yao","year":"2019","unstructured":"Yao, L., Brown, P. & Shoaran, M. Improved detection of Parkinsonian resting tremor with feature engineering and Kalman filtering. Clin. Neurophysiol. 131, 274\u2013284 (2019).","journal-title":"Clin. Neurophysiol."},{"key":"779_CR43","unstructured":"A. Deik & D. Tarsy. Essential tremor: Treatment and prognosis. In UpToDate (ed Post, T. W.) (UpToDate, 2021)."},{"key":"779_CR44","doi-asserted-by":"publisher","first-page":"541625","DOI":"10.3389\/fnhum.2020.541625","volume":"14","author":"S Casta\u00f1o-Candamil","year":"2020","unstructured":"Casta\u00f1o-Candamil, S. et al. A pilot study on data-driven adaptive deep brain stimulation in chronically implanted essential tremor patients. Front. Hum. Neurosci. 14, 541625 (2020).","journal-title":"Front. Hum. Neurosci."},{"key":"779_CR45","doi-asserted-by":"publisher","first-page":"016004","DOI":"10.1088\/1741-2552\/aae67f","volume":"16","author":"B Houston","year":"2018","unstructured":"Houston, B., Thompson, M., Ko, A. & Chizeck, H. A machine-learning approach to volitional control of a closed-loop deep brain stimulation system. J. Neural Eng. 16, 016004 (2018).","journal-title":"J. Neural Eng."},{"key":"779_CR46","doi-asserted-by":"publisher","first-page":"858","DOI":"10.1016\/j.brs.2019.02.011","volume":"12","author":"H Tan","year":"2019","unstructured":"Tan, H. et al. Decoding voluntary movements and postural tremor based on thalamic LFPs as a basis for closed-loop stimulation for essential tremor. Brain Stimul. 12, 858\u2013867 (2019).","journal-title":"Brain Stimul."},{"key":"779_CR47","doi-asserted-by":"publisher","first-page":"eaay7680","DOI":"10.1126\/scitranslmed.aay7680","volume":"12","author":"E Opri","year":"2020","unstructured":"Opri, E. et al. Chronic embedded cortico-thalamic closed-loop deep brain stimulation for the treatment of essential tremor. Sci. Transl. Med. 12, eaay7680 (2020).","journal-title":"Sci. Transl. Med."},{"key":"779_CR48","doi-asserted-by":"publisher","first-page":"863","DOI":"10.1002\/mds.28513","volume":"36","author":"S He","year":"2021","unstructured":"He, S. et al. Closed-loop deep brain stimulation for essential tremor based on thalamic local field potentials. Mov. Disord. 36, 863\u2013873 (2021).","journal-title":"Mov. Disord."},{"key":"779_CR49","doi-asserted-by":"publisher","unstructured":"LeMoyne, R. et al. Implementation of a smartphone wireless accelerometer platform for establishing deep brain stimulation treatment efficacy of essential tremor with machine learning. In 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) 6772\u20136775 (2015). https:\/\/doi.org\/10.1109\/EMBC.2015.7319948.","DOI":"10.1109\/EMBC.2015.7319948"},{"key":"779_CR50","doi-asserted-by":"publisher","unstructured":"Shukla, P., Basu, I. & Tuninetti, D. Towards closed-loop deep brain stimulation: Decision tree-based Essential Tremor patient\u2019s state classifier and tremor reappearance predictor. In 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society 2605\u20132608 (2014). https:\/\/doi.org\/10.1109\/EMBC.2014.6944156.","DOI":"10.1109\/EMBC.2014.6944156"},{"key":"779_CR51","unstructured":"Jankovic, J. Tourette syndrome: Management. In UpToDate (ed Post, T. W.) (UpToDate, 2021)."},{"key":"779_CR52","doi-asserted-by":"publisher","first-page":"18","DOI":"10.5498\/wjp.v6.i1.18","volume":"6","author":"TT Quach","year":"2016","unstructured":"Quach, T. T., Lerch, J. K., Honnorat, J., Khanna, R. & Duchemin, A.-M. Neuronal networks in mental diseases and neuropathic pain: Beyond brain derived neurotrophic factor and collapsin response mediator proteins. World J. Psychiatry 6, 18\u201330 (2016).","journal-title":"World J. Psychiatry"},{"key":"779_CR53","doi-asserted-by":"publisher","first-page":"822614","DOI":"10.3389\/fncel.2021.822614","volume":"15","author":"S Koizumi","year":"2022","unstructured":"Koizumi, S. Glial purinergic signals and psychiatric disorders. Front. Cell Neurosci. 15, 822614 (2022).","journal-title":"Front. Cell Neurosci."},{"key":"779_CR54","doi-asserted-by":"publisher","first-page":"87","DOI":"10.3389\/fncel.2019.00087","volume":"13","author":"MV Foga\u00e7a","year":"2019","unstructured":"Foga\u00e7a, M. V. & Duman, R. S. Cortical GABAergic dysfunction in stress and depression: new insights for therapeutic interventions. Front. Cell. Neurosci. 13, 87 (2019).","journal-title":"Front. Cell. Neurosci."},{"key":"779_CR55","doi-asserted-by":"publisher","first-page":"47","DOI":"10.1016\/j.tins.2011.11.004","volume":"35","author":"RS Duman","year":"2012","unstructured":"Duman, R. S. & Voleti, B. Signaling pathways underlying the pathophysiology and treatment of depression: novel mechanisms for rapid-acting agents. Trends Neurosci. 35, 47\u201356 (2012).","journal-title":"Trends Neurosci."},{"key":"779_CR56","doi-asserted-by":"publisher","first-page":"358","DOI":"10.1111\/pcn.13300","volume":"75","author":"Y Hirano","year":"2021","unstructured":"Hirano, Y. & Uhlhaas, P. J. Current findings and perspectives on aberrant neural oscillations in schizophrenia. Psychiatry Clin. Neurosci. 75, 358\u2013368 (2021).","journal-title":"Psychiatry Clin. Neurosci."},{"key":"779_CR57","doi-asserted-by":"publisher","first-page":"421","DOI":"10.1038\/nature13595","volume":"511","author":"Schizophrenia Working Group of the Psychiatric Genomics Consortium.","year":"2014","unstructured":"Schizophrenia Working Group of the Psychiatric Genomics Consortium. Biological insights from 108 schizophrenia-associated genetic loci. Nature 511, 421\u2013427 (2014).","journal-title":"Nature"},{"key":"779_CR58","doi-asserted-by":"publisher","first-page":"2057","DOI":"10.1038\/mp.2017.230","volume":"23","author":"P Steullet","year":"2018","unstructured":"Steullet, P. et al. The thalamic reticular nucleus in schizophrenia and bipolar disorder: role of parvalbumin-expressing neuron networks and oxidative stress. Mol. Psychiatry 23, 2057\u20132065 (2018).","journal-title":"Mol. Psychiatry"},{"key":"779_CR59","doi-asserted-by":"publisher","first-page":"3216","DOI":"10.1073\/pnas.1609194114","volume":"114","author":"AM Apergis-Schoute","year":"2017","unstructured":"Apergis-Schoute, A. M. et al. Neural basis of impaired safety signaling in obsessive compulsive disorder. Proc. Natl Acad. Sci. USA 114, 3216\u20133221 (2017).","journal-title":"Proc. Natl Acad. Sci. USA"},{"key":"779_CR60","doi-asserted-by":"publisher","first-page":"444","DOI":"10.1038\/mp.2016.232","volume":"23","author":"M Ullrich","year":"2018","unstructured":"Ullrich, M. et al. OCD-like behavior is caused by dysfunction of thalamo-amygdala circuits and upregulated TrkB\/ERK-MAPK signaling as a result of SPRED2 deficiency. Mol. Psychiatry 23, 444\u2013458 (2018).","journal-title":"Mol. Psychiatry"},{"key":"779_CR61","doi-asserted-by":"publisher","first-page":"104574","DOI":"10.1016\/j.neubiorev.2022.104574","volume":"135","author":"J Liu","year":"2022","unstructured":"Liu, J. et al. Abnormal resting-state functional connectivity in patients with obsessive-compulsive disorder: A systematic review and meta-analysis. Neurosci. Biobehav. Rev. 135, 104574 (2022).","journal-title":"Neurosci. Biobehav. Rev."},{"key":"779_CR62","doi-asserted-by":"publisher","first-page":"905","DOI":"10.3389\/fpsyt.2019.00905","volume":"10","author":"S Senova","year":"2019","unstructured":"Senova, S. et al. Deep brain stimulation for refractory obsessive-compulsive disorder: towards an individualized approach. Front Psychiatry 10, 905 (2019).","journal-title":"Front Psychiatry"},{"key":"779_CR63","unstructured":"Krishnan, R., Roy-Byrne, P. & Solomon, D. Unipolar depression: Neurobiology. In (UpToDate, 2021)."},{"key":"779_CR64","unstructured":"Rush, A. J. Unipolar major depression in adults: choosing initial treatment. In UpToDate (ed. Post, T. W.) (UpToDate, 2020)."},{"key":"779_CR65","doi-asserted-by":"publisher","first-page":"21m13973","DOI":"10.4088\/JCP.21m13973","volume":"82","author":"FL Hitti","year":"2021","unstructured":"Hitti, F. L. et al. Deep brain stimulation of the ventral capsule\/ventral striatum for treatment-resistant depression: a decade of clinical follow-up. J. Clin. Psychiatry 82, 21m13973 (2021).","journal-title":"J. Clin. Psychiatry"},{"key":"779_CR66","doi-asserted-by":"publisher","first-page":"29","DOI":"10.1016\/S2215-0366(19)30415-8","volume":"7","author":"R Ramasubbu","year":"2020","unstructured":"Ramasubbu, R. et al. Long versus short pulse width subcallosal cingulate stimulation for treatment-resistant depression: a randomised, double-blind, crossover trial. Lancet Psychiatry 7, 29\u201340 (2020).","journal-title":"Lancet Psychiatry"},{"key":"779_CR67","doi-asserted-by":"publisher","first-page":"261","DOI":"10.1016\/j.jad.2021.04.081","volume":"290","author":"HJ Hopman","year":"2021","unstructured":"Hopman, H. J. et al. Personalized prediction of transcranial magnetic stimulation clinical response in patients with treatment-refractory depression using neuroimaging biomarkers and machine learning. J. Affect. Disord. 290, 261\u2013271 (2021).","journal-title":"J. Affect. Disord."},{"key":"779_CR68","doi-asserted-by":"publisher","DOI":"10.1038\/s41398-021-01445-0","volume":"11","author":"RV Shah","year":"2021","unstructured":"Shah, R. V. et al. Personalized machine learning of depressed mood using wearables. Transl. Psychiatry 11, 338 (2021).","journal-title":"Transl. Psychiatry"},{"key":"779_CR69","doi-asserted-by":"publisher","first-page":"24","DOI":"10.1177\/15500594211018545","volume":"53","author":"C Uyulan","year":"2021","unstructured":"Uyulan, C. et al. Depression diagnosis modeling with advanced computational methods: frequency-domain eMVAR and deep learning. Clin. EEG Neurosci. 53, 24\u201336 (2021).","journal-title":"Clin. EEG Neurosci."},{"key":"779_CR70","doi-asserted-by":"publisher","first-page":"109209","DOI":"10.1016\/j.jneumeth.2021.109209","volume":"358","author":"RA Movahed","year":"2021","unstructured":"Movahed, R. A., Jahromi, G. P., Shahyad, S. & Meftahi, G. H. A major depressive disorder classification framework based on EEG signals using statistical, spectral, wavelet, functional connectivity, and nonlinear analysis. J. Neurosci. Methods 358, 109209 (2021).","journal-title":"J. Neurosci. Methods"},{"key":"779_CR71","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41398-021-01669-0","volume":"11","author":"MSE Sendi","year":"2021","unstructured":"Sendi, M. S. E. et al. Intraoperative neural signals predict rapid antidepressant effects of deep brain stimulation. Transl. Psychiatry 11, 1\u20137 (2021).","journal-title":"Transl. Psychiatry"},{"key":"779_CR72","doi-asserted-by":"publisher","first-page":"229","DOI":"10.1038\/s41591-020-01175-8","volume":"27","author":"KW Scangos","year":"2021","unstructured":"Scangos, K. W., Makhoul, G. S., Sugrue, L. P., Chang, E. F. & Krystal, A. D. State-dependent responses to intracranial brain stimulation in a patient with depression. Nat. Med. 27, 229\u2013231 (2021).","journal-title":"Nat. Med."},{"key":"779_CR73","unstructured":"Fischer, B. & Buchanan, R. Schizophrenia in adults: Epidemiology and pathogenesis. In UpToDate (ed T. W. Post) (UpToDate, 2021)."},{"key":"779_CR74","unstructured":"Skehan, B. & Dvir, Y. Approach to treating Schizophrenia in children and adolescents. In UpToDate (ed Post, T. W.) (UpToDate, 2020)."},{"key":"779_CR75","doi-asserted-by":"publisher","first-page":"917","DOI":"10.3171\/2015.4.JNS15120","volume":"124","author":"CB Mikell","year":"2016","unstructured":"Mikell, C. B., Sinha, S. & Sheth, S. A. Neurosurgery for schizophrenia: an update on pathophysiology and a novel therapeutic target. J. Neurosurg. 124, 917\u2013928 (2016).","journal-title":"J. Neurosurg."},{"key":"779_CR76","doi-asserted-by":"publisher","first-page":"777","DOI":"10.1136\/jnnp-2017-316946","volume":"89","author":"JM Gault","year":"2018","unstructured":"Gault, J. M. et al. Approaches to neuromodulation for schizophrenia. J. Neurol. Neurosurg. Psychiatry 89, 777\u2013787 (2018).","journal-title":"J. Neurol. Neurosurg. Psychiatry"},{"key":"779_CR77","doi-asserted-by":"publisher","first-page":"110436","DOI":"10.1016\/j.pnpbp.2021.110436","volume":"112","author":"I Corripio","year":"2022","unstructured":"Corripio, I. et al. Target selection for deep brain stimulation in treatment resistant schizophrenia. Prog. Neuropsychopharmacol. Biol. Psychiatry 112, 110436 (2022).","journal-title":"Prog. Neuropsychopharmacol. Biol. Psychiatry"},{"key":"779_CR78","doi-asserted-by":"publisher","first-page":"102568","DOI":"10.1016\/j.ebiom.2019.11.029","volume":"51","author":"I Corripio","year":"2020","unstructured":"Corripio, I. et al. Deep brain stimulation in treatment resistant schizophrenia: a pilot randomized cross-over clinical trial. EBioMedicine 51, 102568 (2020).","journal-title":"EBioMedicine"},{"key":"779_CR79","doi-asserted-by":"publisher","first-page":"651439","DOI":"10.3389\/fnins.2021.651439","volume":"15","author":"Z Zhao","year":"2021","unstructured":"Zhao, Z. et al. Classification of schizophrenia by combination of brain effective and functional connectivity. Front Neurosci. 15, 651439 (2021).","journal-title":"Front Neurosci."},{"key":"779_CR80","doi-asserted-by":"publisher","first-page":"1123","DOI":"10.1109\/TBME.2020.3011842","volume":"68","author":"K Masychev","year":"2021","unstructured":"Masychev, K., Ciprian, C., Ravan, M., Reilly, J. P. & MacCrimmon, D. Advanced signal processing methods for characterization of schizophrenia. IEEE Trans. Biomed. Eng. 68, 1123\u20131130 (2021).","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"779_CR81","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-021-89690-7","volume":"11","author":"J Trajkovic","year":"2021","unstructured":"Trajkovic, J. et al. Resting state alpha oscillatory activity is a valid and reliable marker of schizotypy. Sci. Rep. 11, 10379 (2021).","journal-title":"Sci. Rep."},{"key":"779_CR82","doi-asserted-by":"publisher","first-page":"E3934","DOI":"10.3390\/jcm9123934","volume":"9","author":"J-Y Kim","year":"2020","unstructured":"Kim, J.-Y., Lee, H. S. & Lee, S.-H. EEG source network for the diagnosis of schizophrenia and the identification of subtypes based on symptom severity\u2014a machine learning approach. J. Clin. Med. 9, E3934 (2020).","journal-title":"J. Clin. Med."},{"key":"779_CR83","unstructured":"Simpson, H. B. Obsessive-compulsive disorder in adults: epidemiology, pathogenesis, clinical manifestations, course, and diagnosis. In UpToDate (ed T. W. Post) (UpToDate, 2021)."},{"key":"779_CR84","unstructured":"Rosenberg, D. Treatment of obsessive-compulsive disorder in children and adolescents. In UpToDate (ed Post, T. W.) (UpToDate, 2020)."},{"key":"779_CR85","doi-asserted-by":"publisher","DOI":"10.1038\/s41398-018-0165-z","volume":"8","author":"P Rappel","year":"2018","unstructured":"Rappel, P. et al. Subthalamic theta activity: a novel human subcortical biomarker for obsessive compulsive disorder. Transl. Psychiatry 8, 118 (2018).","journal-title":"Transl. Psychiatry"},{"key":"779_CR86","doi-asserted-by":"publisher","first-page":"1259","DOI":"10.2147\/NDT.S178207","volume":"15","author":"M Tastevin","year":"2019","unstructured":"Tastevin, M., Spatola, G., R\u00e9gis, J., Lan\u00e7on, C. & Richieri, R. Deep brain stimulation in the treatment of obsessive-compulsive disorder: current perspectives. Neuropsychiatr. Dis. Treat. 15, 1259\u20131272 (2019).","journal-title":"Neuropsychiatr. Dis. Treat."},{"key":"779_CR87","doi-asserted-by":"publisher","first-page":"1186","DOI":"10.1038\/mp.2014.2","volume":"19","author":"W-J Neumann","year":"2014","unstructured":"Neumann, W.-J. et al. Different patterns of local field potentials from limbic DBS targets in patients with major depressive and obsessive compulsive disorder. Mol. Psychiatry 19, 1186\u20131192 (2014).","journal-title":"Mol. Psychiatry"},{"key":"779_CR88","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-017-07792-7","volume":"7","author":"Y Takagi","year":"2017","unstructured":"Takagi, Y. et al. A neural marker of obsessive-compulsive disorder from whole-brain functional connectivity. Sci. Rep. 7, 7538 (2017).","journal-title":"Sci. Rep."},{"key":"779_CR89","first-page":"354","volume":"2020","author":"Y Ding","year":"2020","unstructured":"Ding, Y. et al. Automated detection of enhanced DBS device settings. Companion Publ. 2020 Int Conf. Multimodal Interact. 2020, 354\u2013356 (2020).","journal-title":"Companion Publ. 2020 Int Conf. Multimodal Interact."},{"key":"779_CR90","doi-asserted-by":"publisher","first-page":"1550010","DOI":"10.1142\/S0129065715500100","volume":"25","author":"S Aydin","year":"2015","unstructured":"Aydin, S., Arica, N., Ergul, E. & Tan, O. Classification of obsessive compulsive disorder by EEG complexity and hemispheric dependency measurements. Int. J. Neural Syst. 25, 1550010 (2015).","journal-title":"Int. J. Neural Syst."},{"key":"779_CR91","doi-asserted-by":"publisher","DOI":"10.1186\/s13040-017-0155-3","volume":"10","author":"D Chicco","year":"2017","unstructured":"Chicco, D. Ten quick tips for machine learning in computational biology. BioData Min. 10, 35 (2017).","journal-title":"BioData Min."},{"key":"779_CR92","unstructured":"Raschka, S. Model Evaluation, Model Selection, and Algorithm Selection in Machine Learning. arXiv:1811.12808 [cs, stat]. Preprint at arXiv https:\/\/arxiv.org\/abs\/1811.12808 (2020)"},{"key":"779_CR93","doi-asserted-by":"publisher","first-page":"119","DOI":"10.1016\/j.asoc.2004.12.002","volume":"6","author":"S Ali","year":"2006","unstructured":"Ali, S. & Smith, K. A. On learning algorithm selection for classification. Appl. Soft Comput. 6, 119\u2013138 (2006).","journal-title":"Appl. Soft Comput."},{"key":"779_CR94","doi-asserted-by":"publisher","first-page":"257","DOI":"10.3233\/AIC-2012-0533","volume":"25","author":"L Kotthoff","year":"2012","unstructured":"Kotthoff, L., Gent, I. P. & Miguel, I. An evaluation of machine learning in algorithm selection for search problems. AI Commun. 25, 257\u2013270 (2012).","journal-title":"AI Commun."},{"key":"779_CR95","doi-asserted-by":"publisher","first-page":"157","DOI":"10.1016\/j.bushor.2019.10.005","volume":"63","author":"I Lee","year":"2020","unstructured":"Lee, I. & Shin, Y. J. Machine learning for enterprises: applications, algorithm selection, and challenges. Bus. Horiz. 63, 157\u2013170 (2020).","journal-title":"Bus. Horiz."},{"key":"779_CR96","doi-asserted-by":"publisher","first-page":"428","DOI":"10.1002\/ehf2.12419","volume":"6","author":"SE Awan","year":"2019","unstructured":"Awan, S. E., Bennamoun, M., Sohel, F., Sanfilippo, F. M. & Dwivedi, G. Machine learning\u2010based prediction of heart failure readmission or death: implications of choosing the right model and the right metrics. ESC Heart Fail. 6, 428\u2013435 (2019).","journal-title":"ESC Heart Fail."},{"key":"779_CR97","doi-asserted-by":"publisher","unstructured":"Heaton, J., McElwee, S., Fraley, J. & Cannady, J. Early stabilizing feature importance for TensorFlow deep neural networks. In 2017 International Joint Conference on Neural Networks (IJCNN) 4618\u20134624. https:\/\/doi.org\/10.1109\/IJCNN.2017.7966442 (2017).","DOI":"10.1109\/IJCNN.2017.7966442"},{"key":"779_CR98","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41746-019-0217-7","volume":"3","author":"N Mahadevan","year":"2020","unstructured":"Mahadevan, N. et al. Development of digital biomarkers for resting tremor and bradykinesia using a wrist-worn wearable device. npj Digit. Med. 3, 1\u201312 (2020).","journal-title":"npj Digit. Med."},{"key":"779_CR99","first-page":"3681","volume":"33","author":"A Ghorbani","year":"2019","unstructured":"Ghorbani, A., Abid, A. & Zou, J. Interpretation of Neural Networks Is Fragile. Proc. AAAI Conf. Artif. Intell. 33, 3681\u20133688 (2019).","journal-title":"Proc. AAAI Conf. Artif. Intell."},{"key":"779_CR100","doi-asserted-by":"publisher","first-page":"1295","DOI":"10.1212\/WNL.0b013e3182302056","volume":"77","author":"MJ Morrell","year":"2011","unstructured":"Morrell, M. J. Responsive cortical stimulation for the treatment of medically intractable partial epilepsy. Neurology 77, 1295\u20131304 (2011).","journal-title":"Neurology"},{"key":"779_CR101","doi-asserted-by":"publisher","first-page":"810","DOI":"10.1212\/WNL.0000000000001280","volume":"84","author":"GK Bergey","year":"2015","unstructured":"Bergey, G. K. et al. Long-term treatment with responsive brain stimulation in adults with refractory partial seizures. Neurology 84, 810\u2013817 (2015).","journal-title":"Neurology"},{"key":"779_CR102","doi-asserted-by":"publisher","first-page":"e1244","DOI":"10.1212\/WNL.0000000000010154","volume":"95","author":"DR Nair","year":"2020","unstructured":"Nair, D. R. et al. Nine-year prospective efficacy and safety of brain-responsive neurostimulation for focal epilepsy. Neurology 95, e1244\u2013e1256 (2020).","journal-title":"Neurology"},{"key":"779_CR103","doi-asserted-by":"publisher","first-page":"667373","DOI":"10.3389\/fnins.2021.667373","volume":"15","author":"W Barry","year":"2021","unstructured":"Barry, W., Arcot Desai, S., Tcheng, T. K. & Morrell, M. J. A high accuracy electrographic seizure classifier trained using semi-supervised labeling applied to a large spectrogram dataset. Front. Neurosci. 15, 667373 (2021).","journal-title":"Front. Neurosci."},{"key":"779_CR104","doi-asserted-by":"publisher","first-page":"105490","DOI":"10.1016\/j.nbd.2021.105490","volume":"159","author":"FI Alanazi","year":"2021","unstructured":"Alanazi, F. I. et al. Neurophysiological responses of globus pallidus internus during the auditory oddball task in Parkinson\u2019s disease. Neurobiol. Dis. 159, 105490 (2021).","journal-title":"Neurobiol. Dis."},{"key":"779_CR105","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.bandl.2016.12.003","volume":"168","author":"J P\u00e9ron","year":"2017","unstructured":"P\u00e9ron, J. et al. Vocal emotion decoding in the subthalamic nucleus: an intracranial ERP study in Parkinson\u2019s disease. Brain Lang. 168, 1\u201311 (2017).","journal-title":"Brain Lang."},{"key":"779_CR106","doi-asserted-by":"publisher","first-page":"1768","DOI":"10.1162\/jocn_a_01450","volume":"31","author":"W Chen","year":"2019","unstructured":"Chen, W. et al. Altered prefrontal theta and gamma activity during an emotional face processing task in Parkinson disease. J. Cogn. Neurosci. 31, 1768\u20131776 (2019).","journal-title":"J. Cogn. Neurosci."},{"key":"779_CR107","doi-asserted-by":"publisher","first-page":"300","DOI":"10.3389\/fnins.2018.00300","volume":"12","author":"RW Bina","year":"2018","unstructured":"Bina, R. W. & Langevin, J.-P. Closed loop deep brain stimulation for PTSD, addiction, and disorders of affective facial interpretation: review and discussion of potential biomarkers and stimulation paradigms. Front. Neurosci. 12, 300 (2018).","journal-title":"Front. Neurosci."},{"key":"779_CR108","doi-asserted-by":"publisher","first-page":"748165","DOI":"10.3389\/fnins.2021.748165","volume":"15","author":"C de Hemptinne","year":"2021","unstructured":"de Hemptinne, C. et al. Prefrontal physiomarkers of anxiety and depression in Parkinson\u2019s disease. Front. Neurosci. 15, 748165 (2021).","journal-title":"Front. Neurosci."},{"key":"779_CR109","doi-asserted-by":"publisher","first-page":"310","DOI":"10.1136\/jnnp.2006.095315","volume":"78","author":"MS Okun","year":"2007","unstructured":"Okun, M. S. et al. Deep brain stimulation in the internal capsule and nucleus accumbens region: responses observed during active and sham programming. J. Neurol. Neurosurg. Psychiatry 78, 310\u2013314 (2007).","journal-title":"J. Neurol. Neurosurg. Psychiatry"},{"key":"779_CR110","doi-asserted-by":"publisher","first-page":"150","DOI":"10.1001\/archgenpsychiatry.2011.1456","volume":"69","author":"PE Holtzheimer","year":"2012","unstructured":"Holtzheimer, P. E. et al. Subcallosal cingulate deep brain stimulation for treatment-resistant unipolar and bipolar depression. Arch. Gen. Psychiatry 69, 150\u2013158 (2012).","journal-title":"Arch. Gen. Psychiatry"},{"key":"779_CR111","doi-asserted-by":"publisher","first-page":"491","DOI":"10.1136\/jnnp-2021-327512","volume":"93","author":"ND Sisterson","year":"2022","unstructured":"Sisterson, N. D., Kokkinos, V., Urban, A., Li, N. & Richardson, R. M. Responsive neurostimulation of the thalamus improves seizure control in idiopathic generalised epilepsy: initial case series. J. Neurol. Neurosurg. Psychiatry 93, 491\u2013498 (2022).","journal-title":"J. Neurol. Neurosurg. Psychiatry"},{"key":"779_CR112","doi-asserted-by":"publisher","first-page":"907716","DOI":"10.3389\/fnhum.2022.907716","volume":"16","author":"AJ Zillgitt","year":"2022","unstructured":"Zillgitt, A. J., Haykal, M. A., Chehab, A. & Staudt, M. D. Centromedian thalamic neuromodulation for the treatment of idiopathic generalized epilepsy. Front. Hum. Neurosci. 16, 907716 (2022).","journal-title":"Front. Hum. Neurosci."},{"key":"779_CR113","doi-asserted-by":"publisher","first-page":"439","DOI":"10.1007\/s11065-015-9302-0","volume":"25","author":"HL Combs","year":"2015","unstructured":"Combs, H. L. et al. Cognition and depression following deep brain stimulation of the subthalamic nucleus and globus pallidus pars internus in Parkinson\u2019s disease: a meta-analysis. Neuropsychol. Rev. 25, 439\u2013454 (2015).","journal-title":"Neuropsychol. Rev."},{"key":"779_CR114","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-021-23311-9","volume":"12","author":"A Boutet","year":"2021","unstructured":"Boutet, A. et al. Predicting optimal deep brain stimulation parameters for Parkinson\u2019s disease using functional MRI and machine learning. Nat. Commun. 12, 3043 (2021).","journal-title":"Nat. Commun."},{"key":"779_CR115","doi-asserted-by":"publisher","first-page":"fcac104","DOI":"10.1093\/braincomms\/fcac104","volume":"4","author":"JM Fan","year":"2022","unstructured":"Fan, J. M. et al. Network connectivity predicts effectiveness of responsive neurostimulation in focal epilepsy. Brain Commun. 4, fcac104 (2022).","journal-title":"Brain Commun."},{"key":"779_CR116","doi-asserted-by":"publisher","first-page":"1020","DOI":"10.1038\/s41380-021-01358-w","volume":"27","author":"X Chen","year":"2022","unstructured":"Chen, X. et al. Common and differential connectivity profiles of deep brain stimulation and capsulotomy in refractory obsessive-compulsive disorder. Mol. Psychiatry 27, 1020\u20131030 (2022).","journal-title":"Mol. Psychiatry"},{"key":"779_CR117","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-020-16734-3","volume":"11","author":"N Li","year":"2020","unstructured":"Li, N. et al. A unified connectomic target for deep brain stimulation in obsessive-compulsive disorder. Nat. Commun. 11, 3364 (2020).","journal-title":"Nat. Commun."},{"key":"779_CR118","doi-asserted-by":"crossref","unstructured":"Ans\u00f3, J. et al. Concurrent stimulation and sensing in bi-directional brain interfaces: a multi-site translational experience. J. Neural Eng. 19 (2022). https:\/\/iopscience.iop.org\/article\/10.1088\/1741-2552\/ac59a3.","DOI":"10.1088\/1741-2552\/ac59a3"},{"key":"779_CR119","doi-asserted-by":"publisher","unstructured":"Stanslaski, S. et al. An implantable Bi-directional brain-machine interface system for chronic neuroprosthesis research. In 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society 5494\u20135497. https:\/\/doi.org\/10.1109\/IEMBS.2009.5334562 (2009).","DOI":"10.1109\/IEMBS.2009.5334562"},{"key":"779_CR120","doi-asserted-by":"publisher","first-page":"725797","DOI":"10.3389\/fnins.2021.725797","volume":"15","author":"DD Cummins","year":"2021","unstructured":"Cummins, D. D. et al. Chronic sensing of subthalamic local field potentials: comparison of first and second generation implantable bidirectional systems within a single subject. Front. Neurosci. 15, 725797 (2021).","journal-title":"Front. Neurosci."},{"key":"779_CR121","doi-asserted-by":"publisher","first-page":"605","DOI":"10.3171\/2016.11.JNS161162","volume":"128","author":"NC Swann","year":"2018","unstructured":"Swann, N. C. et al. Chronic multisite brain recordings from a totally implantable bidirectional neural interface: experience in five patients with Parkinson\u2019s disease. J. Neurosurg. 128, 605\u2013616 (2018).","journal-title":"J. Neurosurg."},{"key":"779_CR122","doi-asserted-by":"publisher","first-page":"046006","DOI":"10.1088\/1741-2552\/aabc9b","volume":"15","author":"NC Swann","year":"2018","unstructured":"Swann, N. C. et al. Adaptive deep brain stimulation for Parkinson\u2019s disease using motor cortex sensing. J. Neural Eng. 15, 046006 (2018).","journal-title":"J. Neural Eng."},{"key":"779_CR123","doi-asserted-by":"publisher","first-page":"916627","DOI":"10.3389\/fnhum.2022.916627","volume":"16","author":"SR Stanslaski","year":"2022","unstructured":"Stanslaski, S. R., Case, M. A., Giftakis, J. E., Raike, R. S. & Stypulkowski, P. H. Long term performance of a bi-directional neural interface for deep brain stimulation and recording. Front. Hum. Neurosci. 16, 916627 (2022).","journal-title":"Front. Hum. Neurosci."},{"key":"779_CR124","doi-asserted-by":"publisher","first-page":"220","DOI":"10.1159\/000345493","volume":"91","author":"PH Stypulkowski","year":"2013","unstructured":"Stypulkowski, P. H., Stanslaski, S. R., Denison, T. J. & Giftakis, J. E. Chronic evaluation of a clinical system for deep brain stimulation and recording of neural network activity. Stereotact. Funct. Neurosurg. 91, 220\u2013232 (2013).","journal-title":"Stereotact. Funct. Neurosurg."},{"key":"779_CR125","doi-asserted-by":"publisher","first-page":"994","DOI":"10.1111\/epi.13740","volume":"58","author":"EB Geller","year":"2017","unstructured":"Geller, E. B. et al. Brain-responsive neurostimulation in patients with medically intractable mesial temporal lobe epilepsy. Epilepsia 58, 994\u20131004 (2017).","journal-title":"Epilepsia"},{"key":"779_CR126","unstructured":"Bronte-Stewart, H. M. Bilateral Closed Loop Deep Brain Stimulation for Freezing of Gait Using Neural and Kinematic Feedback. https:\/\/clinicaltrials.gov\/ct2\/show\/NCT04043403 (2021)."},{"key":"779_CR127","unstructured":"P. Shirvalkar. Closed-Loop Deep Brain Stimulation for Refractory Chronic Pain Using Summit RC+S. https:\/\/clinicaltrials.gov\/ct2\/show\/NCT04144972 (2021)."},{"key":"779_CR128","unstructured":"H. Bronte-Stewart. Adaptive DBS Algorithm for Personalized Therapy in Parkinson\u2019s Disease. https:\/\/clinicaltrials.gov\/ct2\/show\/study\/NCT04547712 (2021)."},{"key":"779_CR129","doi-asserted-by":"publisher","first-page":"046017","DOI":"10.1088\/1741-2552\/abed82","volume":"18","author":"P Venkatesh","year":"2021","unstructured":"Venkatesh, P. et al. Quantifying a frequency modulation response biomarker in responsive neurostimulation. J. Neural Eng. 18, 046017 (2021).","journal-title":"J. Neural Eng."}],"container-title":["npj Digital Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41746-023-00779-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41746-023-00779-x","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41746-023-00779-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,4,27]],"date-time":"2023-04-27T12:10:54Z","timestamp":1682597454000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41746-023-00779-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,4,27]]},"references-count":129,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2023,12]]}},"alternative-id":["779"],"URL":"https:\/\/doi.org\/10.1038\/s41746-023-00779-x","relation":{},"ISSN":["2398-6352"],"issn-type":[{"value":"2398-6352","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,4,27]]},"assertion":[{"value":"27 July 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 February 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 April 2023","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The authors declare no competing interests.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"79"}}