{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,23]],"date-time":"2026-10-23T19:50:05Z","timestamp":1792785005211,"version":"4.5.0"},"reference-count":79,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2024,9,9]],"date-time":"2024-09-09T00:00:00Z","timestamp":1725840000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2024,9,9]],"date-time":"2024-09-09T00:00:00Z","timestamp":1725840000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"funder":[{"name":"DARE - DigitAl lifelong pRevEntion","award":["B53C22006420001"],"award-info":[{"award-number":["B53C22006420001"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Sci Rep"],"DOI":"10.1038\/s41598-024-70165-4","type":"journal-article","created":{"date-parts":[[2024,9,9]],"date-time":"2024-09-09T03:02:41Z","timestamp":1725850961000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":119,"title":["Enhancing early Parkinson\u2019s disease detection through multimodal deep learning and explainable AI: insights from the PPMI database"],"prefix":"10.1038","volume":"14","author":[{"given":"Vincenzo","family":"Dentamaro","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Donato","family":"Impedovo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Luca","family":"Musti","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Giuseppe","family":"Pirlo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Paolo","family":"Taurisano","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,9,9]]},"reference":[{"key":"70165_CR1","doi-asserted-by":"publisher","first-page":"4887","DOI":"10.1097\/MS9.0000000000001142","volume":"85","author":"P Prajjwal","year":"2023","unstructured":"Prajjwal, P. et al. Parkinson\u2019s disease updates: Addressing the pathophysiology, risk factors, genetics, diagnosis, along with the medical and surgical treatment. Ann. Med. Surg. 85, 4887\u20134902 (2023).","journal-title":"Ann. Med. Surg."},{"key":"70165_CR2","doi-asserted-by":"publisher","first-page":"501","DOI":"10.3233\/JPD-191683","volume":"9","author":"S Cerri","year":"2019","unstructured":"Cerri, S., Mus, L. & Blandini, F. Parkinson\u2019s disease in women and men: What\u2019s the difference?. J. Parkinsons Dis. 9, 501\u2013515 (2019).","journal-title":"J. Parkinsons Dis."},{"key":"70165_CR3","doi-asserted-by":"publisher","first-page":"917","DOI":"10.1016\/j.jagp.2017.03.012","volume":"25","author":"YLE Chan","year":"2017","unstructured":"Chan, Y. L. E. et al. Post-traumatic stress disorder and risk of parkinson disease: A nationwide longitudinal study. Am. J. Geriatr. Psychiatry 25, 917\u2013923 (2017).","journal-title":"Am. J. Geriatr. Psychiatry"},{"key":"70165_CR4","doi-asserted-by":"publisher","DOI":"10.3389\/fpsyg.2013.00336","author":"I Sotgiu","year":"2013","unstructured":"Sotgiu, I. & Rusconi, M. L. Investigating emotions in Parkinson\u2019s disease: What we know and what we still don\u2019t know. Front. Psychol. https:\/\/doi.org\/10.3389\/fpsyg.2013.00336 (2013).","journal-title":"Front. Psychol."},{"key":"70165_CR5","doi-asserted-by":"publisher","first-page":"1456","DOI":"10.1002\/mds.27439","volume":"33","author":"J Sieurin","year":"2018","unstructured":"Sieurin, J. et al. Occupational stress and risk for Parkinson\u2019s disease: A nationwide cohort study. Mov. Disord. 33, 1456\u20131464 (2018).","journal-title":"Mov. Disord."},{"key":"70165_CR6","doi-asserted-by":"publisher","DOI":"10.3389\/fnins.2020.573509","author":"YJ Tsai","year":"2020","unstructured":"Tsai, Y. J. et al. Cold Exposure after exercise impedes the neuroprotective effects of exercise on thermoregulation and UCP4 expression in an MPTP-induced Parkinsonian mouse model. Front. Neurosci. https:\/\/doi.org\/10.3389\/fnins.2020.573509 (2020).","journal-title":"Front. Neurosci."},{"key":"70165_CR7","doi-asserted-by":"publisher","first-page":"1191","DOI":"10.3389\/fneur.2018.01191","volume":"9","author":"A Luca","year":"2018","unstructured":"Luca, A., Nicoletti, A., Mostile, G. & Zappia, M. The Parkinsonian personality: More than just a \u201ctrait\u201d. Front. Neurol. 9, 1191 (2018).","journal-title":"Front. Neurol."},{"key":"70165_CR8","doi-asserted-by":"publisher","first-page":"831","DOI":"10.3233\/JPD-191823","volume":"10","author":"M Bailey","year":"2020","unstructured":"Bailey, M., Anderson, S. & Hall, D. A. Parkinson\u2019s disease in African Americans: A review of the current literature. J. Parkinsons Dis. 10, 831\u2013841 (2020).","journal-title":"J. Parkinsons Dis."},{"key":"70165_CR9","doi-asserted-by":"publisher","unstructured":"Kouli, A., Torsney, K. M. & Kuan, W.-L. Parkinson\u2019s disease: Etiology, neuropathology, and pathogenesis. In Parkinson\u2019s Disease: Pathogenesis and Clinical Aspects 3\u201326 (2018) https:\/\/doi.org\/10.15586\/CODONPUBLICATIONS.PARKINSONSDISEASE.2018.CH1.","DOI":"10.15586\/CODONPUBLICATIONS.PARKINSONSDISEASE.2018.CH1"},{"key":"70165_CR10","doi-asserted-by":"publisher","first-page":"373","DOI":"10.2217\/nmt-2021-0011","volume":"11","author":"LE Tueth","year":"2021","unstructured":"Tueth, L. E. & Duncan, R. P. Musculoskeletal pain in Parkinson\u2019s disease: A narrative review. Neurodegener. Dis. Manag. 11, 373\u2013385 (2021).","journal-title":"Neurodegener. Dis. Manag."},{"key":"70165_CR11","doi-asserted-by":"publisher","first-page":"1417","DOI":"10.2169\/internalmedicine.1960-18","volume":"58","author":"H Iwaki","year":"2019","unstructured":"Iwaki, H. et al. Using spontaneous eye-blink rates to predict the motor status of patients with Parkinson\u2019s disease. Intern. Med. 58, 1417 (2019).","journal-title":"Intern. Med."},{"key":"70165_CR12","doi-asserted-by":"publisher","first-page":"323","DOI":"10.3233\/JPD-181312","volume":"8","author":"E Goubault","year":"2018","unstructured":"Goubault, E. et al. Cardinal motor features of Parkinson\u2019s disease coexist with peak-dose choreic-type drug-induced dyskinesia. J. Parkinsons Dis. 8, 323 (2018).","journal-title":"J. Parkinsons Dis."},{"key":"70165_CR13","doi-asserted-by":"publisher","first-page":"806","DOI":"10.1002\/mdc3.12552","volume":"4","author":"M Thomas","year":"2017","unstructured":"Thomas, M., Lenka, A. & Kumar Pal, P. Handwriting analysis in Parkinson\u2019s disease: Current status and future directions. Mov. Disord. Clin. Pract. 4, 806\u2013818 (2017).","journal-title":"Mov. Disord. Clin. Pract."},{"key":"70165_CR14","doi-asserted-by":"publisher","first-page":"1331816","DOI":"10.3389\/fnhum.2024.1331816","volume":"18","author":"T Knowles","year":"2024","unstructured":"Knowles, T., Adams, S. G. & Jog, M. Effects of speech rate modifications on phonatory acoustic outcomes in Parkinson\u2019s disease. Front. Hum. Neurosci. 18, 1331816 (2024).","journal-title":"Front. Hum. Neurosci."},{"key":"70165_CR15","doi-asserted-by":"publisher","first-page":"145","DOI":"10.1136\/jnnp.54.2.145","volume":"54","author":"JFV Caekebeke","year":"1991","unstructured":"Caekebeke, J. F. V., Jennekens-Schinkel, A., Van der Linden, M. E., Buruma, O. J. S. & Roos, R. A. C. The interpretation of dysprosody in patients with Parkinson\u2019s disease. J. Neurol. Neurosurg. Psychiatry 54, 145 (1991).","journal-title":"J. Neurol. Neurosurg. Psychiatry"},{"key":"70165_CR16","doi-asserted-by":"publisher","first-page":"3169","DOI":"10.1007\/s00415-019-09382-1","volume":"267","author":"C Raccagni","year":"2020","unstructured":"Raccagni, C. et al. Gait and postural disorders in parkinsonism: A clinical approach. J. Neurol. 267, 3169\u20133176 (2020).","journal-title":"J. Neurol."},{"key":"70165_CR17","doi-asserted-by":"publisher","first-page":"310","DOI":"10.1136\/practneurol-2013-000741","volume":"14","author":"A Todorova","year":"2014","unstructured":"Todorova, A., Jenner, P. & RayC haudhuri, K. Non-motor Parkinson\u2019s: Integral to motor Parkinson\u2019s, yet often neglected. Pract. Neurol. 14, 310\u2013322 (2014).","journal-title":"Pract. Neurol."},{"key":"70165_CR18","doi-asserted-by":"publisher","DOI":"10.3389\/fneur.2018.00170","author":"H Tibar","year":"2018","unstructured":"Tibar, H. et al. Non-motor symptoms of Parkinson\u2019s disease and their impact on quality of life in a cohort of Moroccan patients. Front. Neurol. https:\/\/doi.org\/10.3389\/fneur.2018.00170 (2018).","journal-title":"Front. Neurol."},{"key":"70165_CR19","doi-asserted-by":"publisher","first-page":"434","DOI":"10.1097\/WCO.0000000000000112","volume":"27","author":"JG Goldman","year":"2014","unstructured":"Goldman, J. G. & Postuma, R. Premotor and non-motor features of Parkinson\u2019s disease. Curr. Opin. Neurol. 27, 434 (2014).","journal-title":"Curr. Opin. Neurol."},{"key":"70165_CR20","doi-asserted-by":"publisher","first-page":"103","DOI":"10.3390\/biology9050103","volume":"9","author":"C V\u00e1radi","year":"2020","unstructured":"V\u00e1radi, C. Clinical features of Parkinson\u2019s disease: The evolution of critical symptoms. Biology 9, 103 (2020).","journal-title":"Biology"},{"key":"70165_CR21","doi-asserted-by":"publisher","first-page":"385","DOI":"10.1016\/S1474-4422(21)00030-2","volume":"20","author":"E Tolosa","year":"2021","unstructured":"Tolosa, E., Garrido, A., Scholz, S. W. & Poewe, W. Challenges in the diagnosis of Parkinson\u2019s disease. Lancet Neurol. 20, 385 (2021).","journal-title":"Lancet Neurol."},{"key":"70165_CR22","doi-asserted-by":"publisher","first-page":"687","DOI":"10.1586\/ern.12.49","volume":"12","author":"MA Thenganatt","year":"2012","unstructured":"Thenganatt, M. A. & Louis, E. D. Distinguishing essential tremor from Parkinson\u2019s disease: Bedside tests and laboratory evaluations. Expert Rev. Neurother. 12, 687\u2013696 (2012).","journal-title":"Expert Rev. Neurother."},{"key":"70165_CR23","doi-asserted-by":"publisher","first-page":"566","DOI":"10.1212\/WNL.0000000000002350","volume":"86","author":"G Rizzo","year":"2016","unstructured":"Rizzo, G. et al. Accuracy of clinical diagnosis of Parkinson disease: A systematic review and meta-analysis. Neurology 86, 566\u2013576 (2016).","journal-title":"Neurology"},{"key":"70165_CR24","doi-asserted-by":"publisher","first-page":"558","DOI":"10.1002\/mds.29317","volume":"38","author":"S Virameteekul","year":"2023","unstructured":"Virameteekul, S., Revesz, T., Jaunmuktane, Z., Warner, T. T. & De Pablo-Fern\u00e1ndez, E. Clinical diagnostic accuracy of Parkinson\u2019s disease: Where do we stand?. Mov. Disord. 38, 558\u2013566 (2023).","journal-title":"Mov. Disord."},{"key":"70165_CR25","doi-asserted-by":"publisher","DOI":"10.1186\/s40035-022-00286-1","author":"P Mahlknecht","year":"2022","unstructured":"Mahlknecht, P., Marini, K., Werkmann, M., Poewe, W. & Seppi, K. Prodromal Parkinson\u2019s disease: Hype or hope for disease-modification trials?. Transl. Neurodegener. https:\/\/doi.org\/10.1186\/s40035-022-00286-1 (2022).","journal-title":"Transl. Neurodegener."},{"key":"70165_CR26","doi-asserted-by":"publisher","DOI":"10.3389\/fnins.2020.00259","author":"D Pan","year":"2020","unstructured":"Pan, D. et al. Early detection of Alzheimer\u2019s disease using magnetic resonance imaging: A novel approach combining convolutional neural networks and ensemble learning. Front. Neurosci. https:\/\/doi.org\/10.3389\/fnins.2020.00259 (2020).","journal-title":"Front. Neurosci."},{"key":"70165_CR27","doi-asserted-by":"publisher","first-page":"234","DOI":"10.1016\/j.compbiomed.2018.09.008","volume":"102","author":"S Bhat","year":"2018","unstructured":"Bhat, S., Acharya, U. R., Hagiwara, Y., Dadmehr, N. & Adeli, H. Parkinson\u2019s disease: Cause factors, measurable indicators, and early diagnosis. Comput. Biol. Med. 102, 234\u2013241 (2018).","journal-title":"Comput. Biol. Med."},{"key":"70165_CR28","doi-asserted-by":"publisher","unstructured":"Shang, L. & Wang, C. Three-Dimensional Reconstruction and Protection of Mining Heritage Based on Lidar Remote Sensing and Deep Learning. https:\/\/doi.org\/10.1155\/2022\/2412394 (2022).","DOI":"10.1155\/2022\/2412394"},{"key":"70165_CR29","doi-asserted-by":"publisher","DOI":"10.1142\/S0218001423560049","author":"Z Mu","year":"2023","unstructured":"Mu, Z. et al. Algorithm analysis of face recognition robot based on deep learning. Int. J. Pattern Recogn. Artif. Intell. https:\/\/doi.org\/10.1142\/S0218001423560049 (2023).","journal-title":"Int. J. Pattern Recogn. Artif. Intell."},{"key":"70165_CR30","doi-asserted-by":"publisher","first-page":"185","DOI":"10.1007\/s42235-018-0015-y","volume":"15","author":"F Cavallo","year":"2018","unstructured":"Cavallo, F. et al. Emotion modelling for social robotics applications: A review. J. Bionic Eng. 15, 185\u2013203 (2018).","journal-title":"J. Bionic Eng."},{"issue":"6","key":"70165_CR31","doi-asserted-by":"publisher","first-page":"3639","DOI":"10.1007\/s11831-022-09710-1","volume":"29","author":"S Saravanan","year":"2022","unstructured":"Saravanan, S. et al. A systematic review of artificial intelligence (AI) based approaches for the diagnosis of Parkinson\u2019s disease. Arch. Comput. Methods Eng. 29(6), 3639\u20133653 (2022).","journal-title":"Arch. Comput. Methods Eng."},{"key":"70165_CR32","doi-asserted-by":"publisher","unstructured":"Zhang, X., Yang, Y., Wang, H., Ning, S. & Wang, H. Deep neural networks with broad views for Parkinson\u2019s disease screening. In Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019 1018\u20131022 https:\/\/doi.org\/10.1109\/BIBM47256.2019.8983000 (2019).","DOI":"10.1109\/BIBM47256.2019.8983000"},{"key":"70165_CR33","doi-asserted-by":"publisher","DOI":"10.1186\/s42466-020-00092-y","author":"J Prasuhn","year":"2020","unstructured":"Prasuhn, J., Heldmann, M., M\u00fcnte, T. F. & Br\u00fcggemann, N. A machine learning-based classification approach on Parkinson\u2019s disease diffusion tensor imaging datasets. Neurol. Res. Pract. https:\/\/doi.org\/10.1186\/s42466-020-00092-y (2020).","journal-title":"Neurol. Res. Pract."},{"key":"70165_CR34","doi-asserted-by":"publisher","unstructured":"Vanegas, M. I., Ghilardi, M. F., Kelly, S. P. & Blangero, A. Machine learning for EEG-based biomarkers in Parkinson\u2019s disease. In Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018 2661\u20132665 https:\/\/doi.org\/10.1109\/BIBM.2018.8621498 (2019).","DOI":"10.1109\/BIBM.2018.8621498"},{"key":"70165_CR35","doi-asserted-by":"publisher","first-page":"1041","DOI":"10.1016\/j.clinph.2021.01.021","volume":"132","author":"VJ Geraedts","year":"2021","unstructured":"Geraedts, V. J. et al. Machine learning for automated EEG-based biomarkers of cognitive impairment during deep brain stimulation screening in patients with Parkinson\u2019s disease. Clin. Neurophysiol. 132, 1041\u20131048 (2021).","journal-title":"Clin. Neurophysiol."},{"key":"70165_CR36","doi-asserted-by":"publisher","unstructured":"Shaban, M., Cahoon, S., Khan, F. & Polk, M. Exploiting the differential wavelet domain of resting-state EEG using a deep-CNN for screening Parkinson\u2019s disease. In 2021 IEEE Symposium Series on Computational Intelligence, SSCI 2021 - Proceedings https:\/\/doi.org\/10.1109\/SSCI50451.2021.9660178 (2021).","DOI":"10.1109\/SSCI50451.2021.9660178"},{"key":"70165_CR37","doi-asserted-by":"publisher","first-page":"1225","DOI":"10.1007\/s00521-016-2756-z","volume":"30","author":"R Yuvaraj","year":"2018","unstructured":"Yuvaraj, R., Rajendra Acharya, U. & Hagiwara, Y. A novel Parkinson\u2019s Disease Diagnosis Index using higher-order spectra features in EEG signals. Neural Comput. Appl. 30, 1225\u20131235 (2018).","journal-title":"Neural Comput. Appl."},{"key":"70165_CR38","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3390\/biomedicines9010012","volume":"9","author":"CY Chien","year":"2020","unstructured":"Chien, C. Y., Hsu, S. W., Lee, T. L., Sung, P. S. & Lin, C. C. Using artificial neural network to discriminate Parkinson\u2019s disease from other Parkinsonisms by focusing on putamen of dopamine transporter SPECT images. Biomedicines 9, 1\u201311 (2020).","journal-title":"Biomedicines"},{"key":"70165_CR39","doi-asserted-by":"publisher","first-page":"4792","DOI":"10.3390\/molecules25204792","volume":"25","author":"SY Hsu","year":"2020","unstructured":"Hsu, S. Y. et al. Classification of the multiple stages of Parkinson\u2019s disease by a deep convolution neural network based on 99mTc-TRODAT-1 SPECT images. Molecules 25, 4792 (2020).","journal-title":"Molecules"},{"key":"70165_CR40","doi-asserted-by":"publisher","first-page":"1888","DOI":"10.2967\/jnumed.116.186403","volume":"58","author":"PT Meyer","year":"2017","unstructured":"Meyer, P. T., Frings, L., R\u00fccker, G. & Hellwig, S. 18F-FDG PET in Parkinsonism: Differential diagnosis and evaluation of cognitive impairment. J. Nucl. Med. 58, 1888\u20131898 (2017).","journal-title":"J. Nucl. Med."},{"key":"70165_CR41","doi-asserted-by":"publisher","first-page":"7003","DOI":"10.1007\/s00330-021-07779-z","volume":"31","author":"A Piccardo","year":"2021","unstructured":"Piccardo, A. et al. The role of the deep convolutional neural network as an aid to interpreting brain [18F]DOPA PET\/CT in the diagnosis of Parkinson\u2019s disease. Eur. Radiol. 31, 7003\u20137011 (2021).","journal-title":"Eur. Radiol."},{"key":"70165_CR42","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2021.115283","volume":"182","author":"J Goyal","year":"2021","unstructured":"Goyal, J., Khandnor, P. & Aseri, T. C. A hybrid approach for Parkinson\u2019s disease diagnosis with resonance and time-frequency based features from speech signals. Expert Syst. Appl. 182, 115283 (2021).","journal-title":"Expert Syst. Appl."},{"key":"70165_CR43","doi-asserted-by":"publisher","first-page":"64","DOI":"10.1016\/j.patrec.2023.03.011","volume":"168","author":"R Khaskhoussy","year":"2023","unstructured":"Khaskhoussy, R. & Ayed, Y. B. Improving Parkinson\u2019s disease recognition through voice analysis using deep learning. Pattern Recogn. Lett. 168, 64\u201370 (2023).","journal-title":"Pattern Recogn. Lett."},{"key":"70165_CR44","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2019.113075","volume":"143","author":"I El Maachi","year":"2020","unstructured":"El Maachi, I., Bilodeau, G. A. & Bouachir, W. Deep 1D-Convnet for accurate Parkinson disease detection and severity prediction from gait. Expert Syst. Appl. 143, 113075 (2020).","journal-title":"Expert Syst. Appl."},{"key":"70165_CR45","doi-asserted-by":"publisher","unstructured":"Bobi\u0107, V., Duri\u0107-Jovi\u010di\u0107, M., Draga\u0161evi\u0107-Mi\u0161kovi\u0107, N., Kosti\u0107, V. S. & Kva\u0161\u010dev, G. Comparison of two deep learning models for the recognition of parkinson\u2019s disease gait patterns. In Proceedings - 10th International Conference on Electrical, Electronic and Computing Engineering, IcETRAN 2023 https:\/\/doi.org\/10.1109\/ICETRAN59631.2023.10192156 (2023).","DOI":"10.1109\/ICETRAN59631.2023.10192156"},{"key":"70165_CR46","doi-asserted-by":"publisher","first-page":"234","DOI":"10.1016\/j.future.2020.11.020","volume":"117","author":"I Kamran","year":"2021","unstructured":"Kamran, I., Naz, S., Razzak, I. & Imran, M. Handwriting dynamics assessment using deep neural network for early identification of Parkinson\u2019s disease. Future Gener. Comput. Syst. 117, 234\u2013244 (2021).","journal-title":"Future Gener. Comput. Syst."},{"key":"70165_CR47","doi-asserted-by":"publisher","first-page":"7034","DOI":"10.3390\/s21217034","volume":"21","author":"HW Loh","year":"2021","unstructured":"Loh, H. W. et al. Application of deep learning models for automated identification of Parkinson\u2019s disease: A review (2011\u20132021). Sensors 21, 7034 (2021).","journal-title":"Sensors"},{"key":"70165_CR48","doi-asserted-by":"publisher","first-page":"8708","DOI":"10.3934\/mbe.2023382","volume":"20","author":"K Yan","year":"2023","unstructured":"Yan, K., Li, T., Lobo Marques, J. A., Gao, J. & Fong, S. J. A review on multimodal machine learning in medical diagnostics. Math. Biosci. Eng. 20, 8708\u20138726 (2023).","journal-title":"Math. Biosci. Eng."},{"key":"70165_CR49","doi-asserted-by":"publisher","first-page":"13","DOI":"10.1016\/j.ijmedinf.2016.03.001","volume":"90","author":"R Prashanth","year":"2016","unstructured":"Prashanth, R., Dutta Roy, S., Mandal, P. K. & Ghosh, S. High-accuracy detection of early Parkinson\u2019s disease through multimodal features and machine learning. Int. J. Med. Inform. 90, 13\u201321 (2016).","journal-title":"Int. J. Med. Inform."},{"key":"70165_CR50","doi-asserted-by":"publisher","first-page":"629","DOI":"10.1016\/j.pneurobio.2011.09.005","volume":"95","author":"K Marek","year":"2011","unstructured":"Marek, K. et al. The Parkinson progression marker initiative (PPMI). Prog. Neurobiol. 95, 629\u2013635 (2011).","journal-title":"Prog. Neurobiol."},{"key":"70165_CR51","doi-asserted-by":"publisher","first-page":"e47714","DOI":"10.1371\/journal.pone.0047714","volume":"7","author":"D Long","year":"2012","unstructured":"Long, D. et al. Automatic classification of early Parkinson\u2019s disease with multi-modal MR imaging. PLoS ONE 7, e47714 (2012).","journal-title":"PLoS ONE"},{"key":"70165_CR52","doi-asserted-by":"publisher","unstructured":"Zhu, S. Early diagnosis of Parkinson\u2019s disease by analyzing magnetic resonance imaging brain scans and patient characteristic. In 2022 10th International Conference on Bioinformatics and Computational Biology, ICBCB 2022 116\u2013123 https:\/\/doi.org\/10.1109\/ICBCB55259.2022.9802132 (2022)","DOI":"10.1109\/ICBCB55259.2022.9802132"},{"key":"70165_CR53","doi-asserted-by":"publisher","first-page":"2399","DOI":"10.1109\/TNSRE.2023.3277749","volume":"31","author":"V Skaramagkas","year":"2023","unstructured":"Skaramagkas, V., Pentari, A., Kefalopoulou, Z. & Tsiknakis, M. Multi-modal deep learning diagnosis of Parkinson\u2019s disease\u2014a systematic review. IEEE Trans. Neural Syst. Rehabil. Eng. 31, 2399\u20132423 (2023).","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"70165_CR54","doi-asserted-by":"publisher","first-page":"106308","DOI":"10.1016\/j.compbiomed.2022.106308","volume":"152","author":"L Huang","year":"2023","unstructured":"Huang, L., Ye, X., Yang, M., Pan, L. & Zheng, S. H. MNC-Net: Multi-task graph structure learning based on node clustering for early Parkinson\u2019s disease diagnosis. Comput. Biol. Med. 152, 106308 (2023).","journal-title":"Comput. Biol. Med."},{"key":"70165_CR55","doi-asserted-by":"publisher","first-page":"2011","DOI":"10.1109\/TPAMI.2019.2913372","volume":"42","author":"J Hu","year":"2017","unstructured":"Hu, J., Shen, L., Albanie, S., Sun, G. & Wu, E. Squeeze-and-excitation networks. IEEE Trans. Pattern Anal. Mach. Intell. 42, 2011\u20132023 (2017).","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"70165_CR56","doi-asserted-by":"publisher","first-page":"423","DOI":"10.1109\/TPAMI.2018.2798607","volume":"41","author":"T Baltrusaitis","year":"2017","unstructured":"Baltrusaitis, T., Ahuja, C. & Morency, L. P. Multimodal machine learning: A survey and taxonomy. IEEE Trans. Pattern Anal. Mach. Intell. 41, 423\u2013443 (2017).","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"70165_CR57","doi-asserted-by":"publisher","DOI":"10.1093\/bib\/bbab569","author":"SR Stahlschmidt","year":"2022","unstructured":"Stahlschmidt, S. R., Ulfenborg, B. & Synnergren, J. Multimodal deep learning for biomedical data fusion: A review. Brief Bioinform. https:\/\/doi.org\/10.1093\/bib\/bbab569 (2022).","journal-title":"Brief Bioinform."},{"key":"70165_CR58","doi-asserted-by":"publisher","DOI":"10.1515\/revneuro-2013-0004","author":"NS Narayanan","year":"2013","unstructured":"Narayanan, N. S., Rodnitzky, R. L. & Uc, E. Y. Prefrontal dopamine signaling and cognitive symptoms of Parkinson\u2019s disease. Rev. Neurosci. https:\/\/doi.org\/10.1515\/revneuro-2013-0004 (2013).","journal-title":"Rev. Neurosci."},{"key":"70165_CR59","doi-asserted-by":"publisher","DOI":"10.3389\/fneur.2019.00536","author":"S Stuart","year":"2019","unstructured":"Stuart, S., Belluscio, V., Quinn, J. F. & Mancini, M. Pre-frontal cortical activity during walking and turning is reliable and differentiates across young, older adults and people with Parkinson\u2019s disease. Front. Neurol. https:\/\/doi.org\/10.3389\/fneur.2019.00536 (2019).","journal-title":"Front. Neurol."},{"key":"70165_CR60","doi-asserted-by":"publisher","first-page":"962","DOI":"10.1002\/ana.25734","volume":"87","author":"F Irmen","year":"2020","unstructured":"Irmen, F. et al. Left prefrontal connectivity links subthalamic stimulation with depressive symptoms. Ann. Neurol. 87, 962\u2013975 (2020).","journal-title":"Ann. Neurol."},{"key":"70165_CR61","doi-asserted-by":"publisher","first-page":"117","DOI":"10.1111\/cns.13188","volume":"26","author":"C Zhou","year":"2020","unstructured":"Zhou, C. et al. Progressive brain atrophy in Parkinson\u2019s disease patients who convert to mild cognitive impairment. CNS Neurosci. Ther. 26, 117 (2020).","journal-title":"CNS Neurosci. Ther."},{"key":"70165_CR62","doi-asserted-by":"publisher","first-page":"297","DOI":"10.1002\/mds.23443","volume":"26","author":"TO Dalaker","year":"2011","unstructured":"Dalaker, T. O. et al. Ventricular enlargement and mild cognitive impairment in early Parkinson\u2019s disease. Mov. Disord. 26, 297\u2013301 (2011).","journal-title":"Mov. Disord."},{"key":"70165_CR63","doi-asserted-by":"publisher","first-page":"2113","DOI":"10.1016\/j.neurobiolaging.2011.06.014","volume":"33","author":"L Apostolova","year":"2012","unstructured":"Apostolova, L. et al. Hippocampal and ventricular changes in Parkinson\u2019s disease mild cognitive impairment. Neurobiol. Aging 33, 2113 (2012).","journal-title":"Neurobiol. Aging"},{"key":"70165_CR64","doi-asserted-by":"publisher","first-page":"68","DOI":"10.1016\/j.parkreldis.2019.07.005","volume":"66","author":"S Behnke","year":"2019","unstructured":"Behnke, S. et al. Third ventricular width assessed by transcranial ultrasound correlates with cognitive performance in Parkinson\u2019s disease. Parkinsonism Relat. Disord. 66, 68\u201373 (2019).","journal-title":"Parkinsonism Relat. Disord."},{"key":"70165_CR65","doi-asserted-by":"publisher","first-page":"a008888","DOI":"10.1101\/cshperspect.a008888","volume":"2","author":"C Klein","year":"2012","unstructured":"Klein, C. & Westenberger, A. Genetics of Parkinson\u2019s disease. Cold Spring Harb. Perspect. Med. 2, a008888 (2012).","journal-title":"Cold Spring Harb. Perspect. Med."},{"key":"70165_CR66","doi-asserted-by":"publisher","first-page":"2353","DOI":"10.3233\/JPD-223380","volume":"12","author":"A Kolicheski","year":"2022","unstructured":"Kolicheski, A. et al. Early-onset Parkinson\u2019s disease: Creating the right environment for a genetic disorder. J. Parkinsons Dis. 12, 2353 (2022).","journal-title":"J. Parkinsons Dis."},{"key":"70165_CR67","unstructured":"Clarke, C. E. et al. UK Parkinson\u2019s Disease Society Brain Bank Diagnostic Criteria. (2016)."},{"key":"70165_CR68","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0295069","volume":"19","author":"A Soko\u0142owski","year":"2024","unstructured":"Soko\u0142owski, A. et al. Longitudinal brain structure changes in Parkinson\u2019s disease: A replication study. PLoS ONE 19, e0295069 (2024).","journal-title":"PLoS ONE"},{"key":"70165_CR69","doi-asserted-by":"publisher","first-page":"1120","DOI":"10.1093\/brain\/awu036","volume":"137","author":"A Hanganu","year":"2014","unstructured":"Hanganu, A. et al. Mild cognitive impairment is linked with faster rate of cortical thinning in patients with Parkinson\u2019s disease longitudinally. Brain 137, 1120\u20131129 (2014).","journal-title":"Brain"},{"key":"70165_CR70","doi-asserted-by":"publisher","first-page":"702","DOI":"10.1007\/s11682-023-00796-0","volume":"17","author":"J Liu","year":"2023","unstructured":"Liu, J. et al. Altered connectivity in the cognitive control-related prefrontal cortex in Parkinson\u2019s disease with rapid eye movement sleep behavior disorder. Brain Imaging Behav. 17, 702\u2013714 (2023).","journal-title":"Brain Imaging Behav."},{"key":"70165_CR71","doi-asserted-by":"publisher","first-page":"2969","DOI":"10.3390\/diagnostics13182969","volume":"13","author":"M Wang","year":"2023","unstructured":"Wang, M. et al. Surface-based functional alterations in early-onset and late-onset Parkinson\u2019s disease: A multi-modal MRI study. Diagnostics 13, 2969 (2023).","journal-title":"Diagnostics"},{"key":"70165_CR72","doi-asserted-by":"publisher","first-page":"2129","DOI":"10.1002\/mds.22340","volume":"23","author":"CG Goetz","year":"2008","unstructured":"Goetz, C. G. et al. Movement disorder society-sponsored revision of the unified Parkinson\u2019s disease rating scale (MDS-UPDRS): Scale presentation and clinimetric testing results. Mov. Disord. 23, 2129\u20132170 (2008).","journal-title":"Mov. Disord."},{"key":"70165_CR73","doi-asserted-by":"publisher","unstructured":"Luong, M. T., Pham, H. & Manning, C. D. Effective approaches to attention-based neural machine translation. In Conference Proceedings - EMNLP 2015: Conference on Empirical Methods in Natural Language Processing 1412\u20131421 https:\/\/doi.org\/10.18653\/v1\/d15-1166 (2015).","DOI":"10.18653\/v1\/d15-1166"},{"key":"70165_CR74","unstructured":"Jetley, S., Lord, N. A., Lee, N. & Torr, P. H. S. Learn to pay attention. (2018)."},{"key":"70165_CR75","first-page":"5999","volume":"2017","author":"A Vaswani","year":"2017","unstructured":"Vaswani, A. et al. Attention is all you need. Adv. Neural Inf. Process Syst. 2017, 5999\u20136009 (2017).","journal-title":"Adv. Neural Inf. Process Syst."},{"key":"70165_CR76","doi-asserted-by":"publisher","first-page":"283","DOI":"10.3390\/e23030283","volume":"23","author":"A Hern\u00e1ndez","year":"2021","unstructured":"Hern\u00e1ndez, A. & Amig\u00f3, J. M. Attention mechanisms and their applications to complex systems. Entropy 23, 283 (2021).","journal-title":"Entropy"},{"key":"70165_CR77","unstructured":"Dosovitskiy, A. et al. An image is worth 16x16 words: Transformers for image recognition at scale. In ICLR 2021 - 9th International Conference on Learning Representations (2020)."},{"key":"70165_CR78","unstructured":"Sundararajan, M., Taly, A. & Yan, Q. Axiomatic attribution for deep networks. In 34th International Conference on Machine Learning, ICML 2017 Vol. 7, 5109\u20135118 (2017)."},{"key":"70165_CR79","doi-asserted-by":"publisher","unstructured":"Abnar, S. & Zuidema, W. Quantifying attention flow in transformers. In Proceedings of the Annual Meeting of the Association for Computational Linguistics 4190\u20134197 https:\/\/doi.org\/10.18653\/v1\/2020.acl-main.385 (2020).","DOI":"10.18653\/v1\/2020.acl-main.385"}],"container-title":["Scientific Reports"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41598-024-70165-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-024-70165-4","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-024-70165-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,12]],"date-time":"2024-09-12T07:10:10Z","timestamp":1726125010000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41598-024-70165-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,9,9]]},"references-count":79,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2024,12]]}},"alternative-id":["70165"],"URL":"https:\/\/doi.org\/10.1038\/s41598-024-70165-4","relation":{},"ISSN":["2045-2322"],"issn-type":[{"value":"2045-2322","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,9,9]]},"assertion":[{"value":"21 March 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 August 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 September 2024","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":"20941"}}