{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T16:03:08Z","timestamp":1777737788967,"version":"3.51.4"},"reference-count":46,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2023,12,8]],"date-time":"2023-12-08T00:00:00Z","timestamp":1701993600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2023,12,8]],"date-time":"2023-12-08T00:00:00Z","timestamp":1701993600000},"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":["BMC Med Imaging"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Objectives<\/jats:title>\n                    <jats:p>This study aims to investigate the potential of radiomics with multiple parameters from conventional T1 weighted imaging (T1WI) and susceptibility weighted imaging (SWI) in distinguishing between idiopathic Parkinson\u2019s disease (PD) and multiple system atrophy (MSA).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>A total of 201 participants, including 57 patients with PD, 74 with MSA, and 70 healthy control (HCs) individuals, underwent T1WI and SWI scans. From the 12 subcortical nuclei (e.g. red nucleus, substantia nigra, subthalamic nucleus, putamen, globus pallidus, and caudate nucleus), 2640 radiomic features were extracted from both T1WI and SWI scans. Three classification models - logistic regression (LR), support vector machine (SVM), and light gradient boosting machine (LGBM) - were used to distinguish between MSA and PD, as well as among MSA, PD, and HC. These classifications were based on features extracted from T1WI, SWI, and a combination of T1WI and SWI. Five-fold cross-validation was used to evaluate the performance of the models with metrics such as sensitivity, specificity, accuracy, and area under the receiver operating curve (AUC). During each fold, the ANOVA and least absolute shrinkage and selection operator (LASSO) methods were used to identify the most relevant subset of features for the model training process.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The LGBM model trained by the features combination of T1WI and SWI exhibited the most outstanding differential performance in both the three-class classification task of MSA vs. PD vs. HC and the binary classification task of MSA vs. PD, with an accuracy of 0.814 and 0.854, and an AUC of 0.904 and 0.881, respectively. The texture-based differences (GLCM) of the SN and the shape-based differences of the GP were highly effective in discriminating between the three classes and two classes, respectively.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Radiomic features combining T1WI and SWI can achieve a satisfactory differential diagnosis for PD, MSA, and HC groups, as well as for PD and MSA groups, thus providing a useful tool for clinical decision-making based on routine MRI sequences.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1186\/s12880-023-01169-1","type":"journal-article","created":{"date-parts":[[2023,12,8]],"date-time":"2023-12-08T09:02:36Z","timestamp":1702026156000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":20,"title":["Multi-parametric radiomics of conventional T1 weighted and susceptibility-weighted imaging for differential diagnosis of idiopathic Parkinson\u2019s disease and multiple system atrophy"],"prefix":"10.1186","volume":"23","author":[{"given":"Shuting","family":"Bu","sequence":"first","affiliation":[]},{"given":"Huize","family":"Pang","sequence":"additional","affiliation":[]},{"given":"Xiaolu","family":"Li","sequence":"additional","affiliation":[]},{"given":"Mengwan","family":"Zhao","sequence":"additional","affiliation":[]},{"given":"Juzhou","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Yu","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Hongmei","family":"Yu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,12,8]]},"reference":[{"issue":"5","key":"1169_CR1","doi-asserted-by":"publisher","first-page":"404","DOI":"10.1212\/WNL.0000000000001807","volume":"85","author":"S Koga","year":"2015","unstructured":"Koga S, Aoki N, Uitti RJ, et al. When DLB, PD, and PSP masquerade as MSA: an autopsy study of 134 patients. Neurology. 2015;85(5):404\u201312. https:\/\/doi.org\/10.1212\/WNL.0000000000001807.","journal-title":"Neurology"},{"issue":"7","key":"1169_CR2","doi-asserted-by":"publisher","first-page":"710","DOI":"10.1016\/S1474-4422(15)00058-7","volume":"14","author":"PA Low","year":"2015","unstructured":"Low PA, Reich SG, Jankovic J, et al. Natural history of multiple system atrophy in the USA: a prospective cohort study. Lancet Neurol. 2015;14(7):710\u20139. https:\/\/doi.org\/10.1016\/S1474-4422(15)00058-7.","journal-title":"Lancet Neurol"},{"issue":"2","key":"1169_CR3","doi-asserted-by":"publisher","first-page":"60","DOI":"10.14802\/jmd.11012","volume":"4","author":"JH Lee","year":"2011","unstructured":"Lee JH, Baik SK. Putaminal hypointensity in the parkinsonian variant of multiple system atrophy: simple visual assessment using susceptibility-weighted imaging. J Mov Disord. 2011;4(2):60\u20133. https:\/\/doi.org\/10.14802\/jmd.11012.","journal-title":"J Mov Disord"},{"issue":"8","key":"1169_CR4","doi-asserted-by":"publisher","first-page":"3174","DOI":"10.1007\/s00330-017-4743-x","volume":"27","author":"N Wang","year":"2017","unstructured":"Wang N, Yang H, Li C, Fan G, Luo X. Using \u2018swallow-tail\u2019 sign and putaminal hypointensity as biomarkers to distinguish multiple system atrophy from idiopathic Parkinson's disease: a susceptibility-weighted imaging study. Eur Radiol. 2017;27(8):3174\u201380. https:\/\/doi.org\/10.1007\/s00330-017-4743-x.","journal-title":"Eur Radiol"},{"issue":"10","key":"1169_CR5","doi-asserted-by":"publisher","first-page":"2848","DOI":"10.1016\/j.ejrad.2011.12.012","volume":"81","author":"S Kasahara","year":"2012","unstructured":"Kasahara S, Miki Y, Kanagaki M, et al. \u201cHot cross bun\u201d sign in multiple system atrophy with predominant cerebellar ataxia: a comparison between proton density-weighted imaging and T2-weighted imaging. Eur J Radiol. 2012;81(10):2848\u201352. https:\/\/doi.org\/10.1016\/j.ejrad.2011.12.012.","journal-title":"Eur J Radiol"},{"issue":"4","key":"1169_CR6","doi-asserted-by":"publisher","first-page":"1443","DOI":"10.1016\/j.neuroimage.2003.12.005","volume":"21","author":"MF Schocke","year":"2004","unstructured":"Schocke MF, Seppi K, Esterhammer R, et al. Trace of diffusion tensor differentiates the Parkinson variant of multiple system atrophy and Parkinson's disease. NeuroImage. 2004;21(4):1443\u201351. https:\/\/doi.org\/10.1016\/j.neuroimage.2003.12.005.","journal-title":"NeuroImage"},{"issue":"7","key":"1169_CR7","doi-asserted-by":"publisher","first-page":"709","DOI":"10.1212\/WNL.0000000000002985","volume":"87","author":"RG Burciu","year":"2016","unstructured":"Burciu RG, Chung JW, Shukla P, et al. Functional MRI of disease progression in Parkinson disease and atypical parkinsonian syndromes. Neurology. 2016;87(7):709\u201317. https:\/\/doi.org\/10.1212\/WNL.0000000000002985.","journal-title":"Neurology"},{"issue":"1","key":"1169_CR8","doi-asserted-by":"publisher","first-page":"424","DOI":"10.1016\/j.neurobiolaging.2014.07.010","volume":"36","author":"F Nemmi","year":"2015","unstructured":"Nemmi F, Sabatini U, Rascol O, P\u00e9ran P. Parkinson's disease and local atrophy in subcortical nuclei: insight from shape analysis. Neurobiol Aging. 2015;36(1):424\u201333. https:\/\/doi.org\/10.1016\/j.neurobiolaging.2014.07.010.","journal-title":"Neurobiol Aging"},{"issue":"4","key":"1169_CR9","doi-asserted-by":"publisher","first-page":"653","DOI":"10.1111\/ene.14132","volume":"27","author":"X Xu","year":"2020","unstructured":"Xu X, Han Q, Lin J, Wang L, Wu F, Shang H. Grey matter abnormalities in Parkinson's disease: a voxel-wise meta-analysis. Eur J Neurol. 2020;27(4):653\u20139. https:\/\/doi.org\/10.1111\/ene.14132.","journal-title":"Eur J Neurol"},{"key":"1169_CR10","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1016\/j.pscychresns.2018.03.004","volume":"275","author":"C Owens-Walton","year":"2018","unstructured":"Owens-Walton C, Jakabek D, Li X, et al. Striatal changes in Parkinson disease: an investigation of morphology, functional connectivity and their relationship to clinical symptoms. Psychiatry Res Neuroimaging. 2018;275:5\u201313. https:\/\/doi.org\/10.1016\/j.pscychresns.2018.03.004.","journal-title":"Psychiatry Res Neuroimaging"},{"key":"1169_CR11","doi-asserted-by":"publisher","first-page":"100","DOI":"10.1016\/j.neurobiolaging.2016.09.015","volume":"49","author":"NW Sterling","year":"2017","unstructured":"Sterling NW, Du G, Lewis MM, et al. Cortical gray and subcortical white matter associations in Parkinson's disease. Neurobiol Aging. 2017;49:100\u20138. https:\/\/doi.org\/10.1016\/j.neurobiolaging.2016.09.015.","journal-title":"Neurobiol Aging"},{"issue":"4","key":"1169_CR12","doi-asserted-by":"publisher","first-page":"1681","DOI":"10.1002\/hbm.22282","volume":"35","author":"RA Menke","year":"2014","unstructured":"Menke RA, Szewczyk-Krolikowski K, Jbabdi S, et al. Comprehensive morphometry of subcortical grey matter structures in early-stage Parkinson's disease. Hum Brain Mapp. 2014;35(4):1681\u201390. https:\/\/doi.org\/10.1002\/hbm.22282.","journal-title":"Hum Brain Mapp"},{"key":"1169_CR13","doi-asserted-by":"publisher","first-page":"19","DOI":"10.1016\/j.jns.2019.04.014","volume":"401","author":"M Tsuda","year":"2019","unstructured":"Tsuda M, Asano S, Kato Y, Murai K, Miyazaki M. Differential diagnosis of multiple system atrophy with predominant parkinsonism and Parkinson's disease using neural networks. J Neurol Sci. 2019;401:19\u201326. https:\/\/doi.org\/10.1016\/j.jns.2019.04.014.","journal-title":"J Neurol Sci"},{"issue":"3","key":"1169_CR14","doi-asserted-by":"publisher","first-page":"1165","DOI":"10.1002\/hbm.22694","volume":"36","author":"PJ Planetta","year":"2015","unstructured":"Planetta PJ, Kurani AS, Shukla P, et al. Distinct functional and macrostructural brain changes in Parkinson's disease and multiple system atrophy. Hum Brain Mapp. 2015;36(3):1165\u201379. https:\/\/doi.org\/10.1002\/hbm.22694.","journal-title":"Hum Brain Mapp"},{"key":"1169_CR15","doi-asserted-by":"publisher","first-page":"101999","DOI":"10.1016\/j.nicl.2019.101999","volume":"24","author":"S Mazzucchi","year":"2019","unstructured":"Mazzucchi S, Frosini D, Costagli M, et al. Quantitative susceptibility mapping in atypical Parkinsonisms. NeuroImage Clin. 2019;24:101999. https:\/\/doi.org\/10.1016\/j.nicl.2019.101999.","journal-title":"NeuroImage Clin"},{"key":"1169_CR16","doi-asserted-by":"publisher","first-page":"109394","DOI":"10.1016\/j.ejrad.2020.109394","volume":"133","author":"MP Fedeli","year":"2020","unstructured":"Fedeli MP, Contarino VE, Siggillino S, et al. Iron deposition in Parkinsonisms: a quantitative susceptibility mapping study in the deep grey matter. Eur J Radiol. 2020;133:109394. https:\/\/doi.org\/10.1016\/j.ejrad.2020.109394.","journal-title":"Eur J Radiol"},{"issue":"8","key":"1169_CR17","doi-asserted-by":"publisher","first-page":"3950","DOI":"10.1093\/cercor\/bhab061","volume":"31","author":"Y Ding","year":"2021","unstructured":"Ding Y, Zhao K, Che T, et al. Quantitative Radiomic features as new biomarkers for Alzheimer's disease: an amyloid PET study. Cereb Cortex (New York, NY: 1991). 2021;31(8):3950\u201361. https:\/\/doi.org\/10.1093\/cercor\/bhab061.","journal-title":"Cereb Cortex (New York, NY: 1991)"},{"key":"1169_CR18","doi-asserted-by":"publisher","first-page":"587250","DOI":"10.3389\/fnagi.2020.587250","volume":"12","author":"H Pang","year":"2020","unstructured":"Pang H, Yu Z, Li R, Yang H, Fan G. MRI-based Radiomics of basal nuclei in differentiating idiopathic Parkinson's disease from parkinsonian variants of multiple system atrophy: a susceptibility-weighted imaging study. Front Aging Neurosci. 2020;12:587250. https:\/\/doi.org\/10.3389\/fnagi.2020.587250.","journal-title":"Front Aging Neurosci"},{"issue":"11","key":"1169_CR19","doi-asserted-by":"publisher","first-page":"8218","DOI":"10.1007\/s00330-021-07979-7","volume":"31","author":"P Tupe-Waghmare","year":"2021","unstructured":"Tupe-Waghmare P, Rajan A, Prasad S, Saini J, Pal PK, Ingalhalikar M. Radiomics on routine T1-weighted MRI can delineate Parkinson's disease from multiple system atrophy and progressive supranuclear palsy. Eur Radiol. 2021;31(11):8218\u201327. https:\/\/doi.org\/10.1007\/s00330-021-07979-7.","journal-title":"Eur Radiol"},{"issue":"3","key":"1169_CR20","doi-asserted-by":"publisher","first-page":"1611","DOI":"10.1002\/mrm.28522","volume":"85","author":"ZY Shu","year":"2021","unstructured":"Shu ZY, Cui SJ, Wu X, et al. Predicting the progression of Parkinson's disease using conventional MRI and machine learning: an application of radiomic biomarkers in whole-brain white matter. Magn Reson Med. 2021;85(3):1611\u201324. https:\/\/doi.org\/10.1002\/mrm.28522.","journal-title":"Magn Reson Med"},{"issue":"2","key":"1169_CR21","doi-asserted-by":"publisher","first-page":"877","DOI":"10.1007\/s00330-018-5575-z","volume":"29","author":"J Wei","year":"2019","unstructured":"Wei J, Yang G, Hao X, et al. A multi-sequence and habitat-based MRI radiomics signature for preoperative prediction of MGMT promoter methylation in astrocytomas with prognostic implication. Eur Radiol. 2019;29(2):877\u201388. https:\/\/doi.org\/10.1007\/s00330-018-5575-z.","journal-title":"Eur Radiol"},{"issue":"10","key":"1169_CR22","doi-asserted-by":"publisher","first-page":"4786","DOI":"10.21037\/qims-22-115","volume":"12","author":"SM Rezaeijo","year":"2022","unstructured":"Rezaeijo SM, Jafarpoor Nesheli S, Fatan Serj M, Tahmasebi Birgani MJ. Segmentation of the prostate, its zones, anterior fibromuscular stroma, and urethra on the MRIs and multimodality image fusion using U-net model. Quant Imaging Med Surg. 2022;12(10):4786\u2013804. https:\/\/doi.org\/10.21037\/qims-22-115.","journal-title":"Quant Imaging Med Surg"},{"issue":"3","key":"1169_CR23","doi-asserted-by":"publisher","first-page":"181","DOI":"10.1136\/jnnp.55.3.181","volume":"55","author":"AJ Hughes","year":"1992","unstructured":"Hughes AJ, Daniel SE, Kilford L, Lees AJ. Accuracy of clinical diagnosis of idiopathic Parkinson's disease: a clinico-pathological study of 100 cases. J Neurol Neurosurg Psychiatry. 1992;55(3):181\u20134. https:\/\/doi.org\/10.1136\/jnnp.55.3.181.","journal-title":"J Neurol Neurosurg Psychiatry"},{"issue":"9","key":"1169_CR24","doi-asserted-by":"publisher","first-page":"670","DOI":"10.1212\/01.wnl.0000324625.00404.15","volume":"71","author":"S Gilman","year":"2008","unstructured":"Gilman S, Wenning GK, Low PA, et al. Second consensus statement on the diagnosis of multiple system atrophy. Neurology. 2008;71(9):670\u20136. https:\/\/doi.org\/10.1212\/01.wnl.0000324625.00404.15.","journal-title":"Neurology"},{"key":"1169_CR25","doi-asserted-by":"publisher","first-page":"646617","DOI":"10.3389\/fnins.2021.646617","volume":"15","author":"Q Ren","year":"2021","unstructured":"Ren Q, Wang Y, Leng S, et al. Substantia Nigra Radiomics feature extraction of Parkinson's disease based on magnitude images of susceptibility-weighted imaging. Front Neurosci. 2021;15:646617. https:\/\/doi.org\/10.3389\/fnins.2021.646617.","journal-title":"Front Neurosci"},{"issue":"21","key":"1169_CR26","doi-asserted-by":"publisher","first-page":"e104","DOI":"10.1158\/0008-5472.CAN-17-0339","volume":"77","author":"JJM van Griethuysen","year":"2017","unstructured":"van Griethuysen JJM, Fedorov A, Parmar C, et al. Computational Radiomics system to decode the radiographic phenotype. Cancer Res. 2017;77(21):e104\u20137. https:\/\/doi.org\/10.1158\/0008-5472.CAN-17-0339.","journal-title":"Cancer Res"},{"key":"1169_CR27","doi-asserted-by":"publisher","first-page":"107714","DOI":"10.1016\/j.cmpb.2023.107714","volume":"240","author":"MR Salmanpour","year":"2023","unstructured":"Salmanpour MR, Hosseinzadeh M, Rezaeijo SM, Rahmim A. Fusion-based tensor radiomics using reproducible features: application to survival prediction in head and neck cancer. Comput Methods Prog Biomed. 2023;240:107714. https:\/\/doi.org\/10.1016\/j.cmpb.2023.107714.","journal-title":"Comput Methods Prog Biomed"},{"key":"1169_CR28","doi-asserted-by":"publisher","first-page":"404","DOI":"10.1016\/j.clnesp.2022.07.011","volume":"51","author":"A Jahangirimehr","year":"2022","unstructured":"Jahangirimehr A, Abdolahi Shahvali E, Rezaeijo SM, et al. Machine learning approach for automated predicting of COVID-19 severity based on clinical and paraclinical characteristics: serum levels of zinc, calcium, and vitamin D. Clin Nutr ESPEN. 2022;51:404\u201311. https:\/\/doi.org\/10.1016\/j.clnesp.2022.07.011.","journal-title":"Clin Nutr ESPEN"},{"issue":"3","key":"1169_CR29","doi-asserted-by":"publisher","first-page":"325","DOI":"10.1002\/mds.26471","volume":"31","author":"G Barbagallo","year":"2016","unstructured":"Barbagallo G, Sierra-Pe\u00f1a M, Nemmi F, et al. Multimodal MRI assessment of nigro-striatal pathway in multiple system atrophy and Parkinson disease. Mov Disord. 2016;31(3):325\u201334. https:\/\/doi.org\/10.1002\/mds.26471.","journal-title":"Mov Disord"},{"issue":"8","key":"1169_CR30","doi-asserted-by":"publisher","first-page":"915","DOI":"10.1007\/s00702-017-1717-8","volume":"124","author":"B Heim","year":"2017","unstructured":"Heim B, Krismer F, De Marzi R, Seppi K. Magnetic resonance imaging for the diagnosis of Parkinson's disease. J Neural Transm. 2017;124(8):915\u201364. https:\/\/doi.org\/10.1007\/s00702-017-1717-8.","journal-title":"J Neural Transm"},{"issue":"10","key":"1169_CR31","doi-asserted-by":"publisher","first-page":"1691","DOI":"10.3390\/diagnostics13101691","volume":"13","author":"M Hosseinzadeh","year":"2023","unstructured":"Hosseinzadeh M, Gorji A, Fathi Jouzdani A, Rezaeijo SM, Rahmim A, Salmanpour MR. Prediction of cognitive decline in Parkinson's disease using clinical and DAT SPECT imaging features, and hybrid machine learning systems. Diagnostics (Basel, Switzerland). 2023;13(10):1691. https:\/\/doi.org\/10.3390\/diagnostics13101691.","journal-title":"Diagnostics (Basel, Switzerland)"},{"issue":"10","key":"1169_CR32","doi-asserted-by":"publisher","first-page":"1696","DOI":"10.3390\/diagnostics13101696","volume":"13","author":"MR Salmanpour","year":"2023","unstructured":"Salmanpour MR, Rezaeijo SM, Hosseinzadeh M, Rahmim A. Deep versus handcrafted tensor Radiomics features: prediction of survival in head and neck Cancer using machine learning and fusion techniques. Diagnostics (Basel, Switzerland). 2023;13(10):1696. https:\/\/doi.org\/10.3390\/diagnostics13101696.","journal-title":"Diagnostics (Basel, Switzerland)"},{"key":"1169_CR33","doi-asserted-by":"publisher","first-page":"88","DOI":"10.1016\/j.neulet.2017.04.034","volume":"651","author":"B Peng","year":"2017","unstructured":"Peng B, Wang S, Zhou Z, et al. A multilevel-ROI-features-based machine learning method for detection of morphometric biomarkers in Parkinson's disease. Neurosci Lett. 2017;651:88\u201394. https:\/\/doi.org\/10.1016\/j.neulet.2017.04.034.","journal-title":"Neurosci Lett"},{"issue":"2","key":"1169_CR34","doi-asserted-by":"publisher","first-page":"401","DOI":"10.1148\/radiol.2020191235","volume":"296","author":"P Vitali","year":"2020","unstructured":"Vitali P, Pan MI, Palesi F, et al. Substantia Nigra Volumetry with 3-T MRI in De novo and advanced Parkinson disease. Radiology. 2020;296(2):401\u201310. https:\/\/doi.org\/10.1148\/radiol.2020191235.","journal-title":"Radiology"},{"key":"1169_CR35","doi-asserted-by":"publisher","first-page":"102070","DOI":"10.1016\/j.nicl.2019.102070","volume":"24","author":"B Xiao","year":"2019","unstructured":"Xiao B, He N, Wang Q, et al. Quantitative susceptibility mapping based hybrid feature extraction for diagnosis of Parkinson's disease. NeuroImage Clin. 2019;24:102070. https:\/\/doi.org\/10.1016\/j.nicl.2019.102070.","journal-title":"NeuroImage Clin"},{"issue":"4","key":"1169_CR36","doi-asserted-by":"publisher","first-page":"600","DOI":"10.1002\/mds.27307","volume":"33","author":"P P\u00e9ran","year":"2018","unstructured":"P\u00e9ran P, Barbagallo G, Nemmi F, et al. MRI supervised and unsupervised classification of Parkinson's disease and multiple system atrophy. Mov Disord. 2018;33(4):600\u20138. https:\/\/doi.org\/10.1002\/mds.27307.","journal-title":"Mov Disord"},{"issue":"2","key":"1169_CR37","doi-asserted-by":"publisher","first-page":"460","DOI":"10.1002\/mds.28348","volume":"36","author":"L Chougar","year":"2021","unstructured":"Chougar L, Faouzi J, Pyatigorskaya N, et al. Automated categorization of parkinsonian syndromes using magnetic resonance imaging in a clinical setting. Mov Disord. 2021;36(2):460\u201370. https:\/\/doi.org\/10.1002\/mds.28348.","journal-title":"Mov Disord"},{"key":"1169_CR38","doi-asserted-by":"publisher","first-page":"101720","DOI":"10.1016\/j.nicl.2019.101720","volume":"22","author":"HC Baggio","year":"2019","unstructured":"Baggio HC, Abos A, Segura B, et al. Cerebellar resting-state functional connectivity in Parkinson's disease and multiple system atrophy: characterization of abnormalities and potential for differential diagnosis at the single-patient level. NeuroImage Clin. 2019;22:101720. https:\/\/doi.org\/10.1016\/j.nicl.2019.101720.","journal-title":"NeuroImage Clin"},{"issue":"11","key":"1169_CR39","doi-asserted-by":"publisher","first-page":"3662","DOI":"10.3390\/s21113662","volume":"21","author":"H Zhang","year":"2021","unstructured":"Zhang H, Li Y. LightGBM indoor positioning method based on merged Wi-fi and image fingerprints. Sensors (Basel, Switzerland). 2021;21(11):3662. https:\/\/doi.org\/10.3390\/s21113662.","journal-title":"Sensors (Basel, Switzerland)"},{"issue":"9996","key":"1169_CR40","doi-asserted-by":"publisher","first-page":"896","DOI":"10.1016\/S0140-6736(14)61393-3","volume":"386","author":"LV Kalia","year":"2015","unstructured":"Kalia LV, Lang AE. Parkinson's disease. Lancet (London, England). 2015;386(9996):896\u2013912. https:\/\/doi.org\/10.1016\/S0140-6736(14)61393-3.","journal-title":"Lancet (London, England)"},{"key":"1169_CR41","doi-asserted-by":"publisher","first-page":"12","DOI":"10.1016\/j.parkreldis.2020.07.002","volume":"78","author":"MT Chau","year":"2020","unstructured":"Chau MT, Todd G, Wilcox R, Agzarian M, Bezak E. Diagnostic accuracy of the appearance of Nigrosome-1 on magnetic resonance imaging in Parkinson's disease: a systematic review and meta-analysis. Parkinsonism Relat Disord. 2020;78:12\u201320. https:\/\/doi.org\/10.1016\/j.parkreldis.2020.07.002.","journal-title":"Parkinsonism Relat Disord"},{"issue":"8","key":"1169_CR42","doi-asserted-by":"publisher","first-page":"2094","DOI":"10.1007\/s00415-013-6951-x","volume":"260","author":"JH Lee","year":"2013","unstructured":"Lee JH, Han YH, Kang BM, et al. Quantitative assessment of subcortical atrophy and iron content in progressive supranuclear palsy and parkinsonian variant of multiple system atrophy. J Neurol. 2013;260(8):2094\u2013101. https:\/\/doi.org\/10.1007\/s00415-013-6951-x.","journal-title":"J Neurol"},{"issue":"16 Pt 2","key":"1169_CR43","doi-asserted-by":"publisher","first-page":"1403","DOI":"10.1212\/01.wnl.0000285082.18969.3a","volume":"70","author":"AH Rajput","year":"2008","unstructured":"Rajput AH, Sitte HH, Rajput A, Fenton ME, Pifl C, Hornykiewicz O. Globus pallidus dopamine and Parkinson motor subtypes: clinical and brain biochemical correlation. Neurology. 2008;70(16 Pt 2):1403\u201310. https:\/\/doi.org\/10.1212\/01.wnl.0000285082.18969.3a.","journal-title":"Neurology"},{"issue":"12","key":"1169_CR44","doi-asserted-by":"publisher","first-page":"1485","DOI":"10.1002\/mds.20236","volume":"19","author":"LC Pereira","year":"2004","unstructured":"Pereira LC, Palter VN, Lang AE, Hutchison WD, Lozano AM, Dostrovsky JO. Neuronal activity in the globus pallidus of multiple system atrophy patients. Mov Disord. 2004;19(12):1485\u201392. https:\/\/doi.org\/10.1002\/mds.20236.","journal-title":"Mov Disord"},{"key":"1169_CR45","doi-asserted-by":"publisher","first-page":"645287","DOI":"10.3389\/fncir.2021.645287","volume":"15","author":"J Dong","year":"2021","unstructured":"Dong J, Hawes S, Wu J, Le W, Cai H. Connectivity and functionality of the Globus Pallidus externa under Normal conditions and Parkinson's disease. Front Neural Circuits. 2021;15:645287. https:\/\/doi.org\/10.3389\/fncir.2021.645287.","journal-title":"Front Neural Circuits"},{"key":"1169_CR46","doi-asserted-by":"crossref","unstructured":"Arai A. \u201cPutaminal hypo-intensity (PUT-hypo)\u201d on susceptibility-weighted imaging (SWI) in parkinsonism predominant multiple system atrophy (MSA-P): comparison between Parkinson\u2019s disease (PD) and age-matched controls without parkinsonism (991). Neurology. 2020;94 http:\/\/n.neurology.org\/content\/94\/15_Supplement\/991.abstract","DOI":"10.1212\/WNL.94.15_supplement.991"}],"container-title":["BMC Medical Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12880-023-01169-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s12880-023-01169-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12880-023-01169-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,12,8]],"date-time":"2023-12-08T09:04:24Z","timestamp":1702026264000},"score":1,"resource":{"primary":{"URL":"https:\/\/bmcmedimaging.biomedcentral.com\/articles\/10.1186\/s12880-023-01169-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,12,8]]},"references-count":46,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2023,12]]}},"alternative-id":["1169"],"URL":"https:\/\/doi.org\/10.1186\/s12880-023-01169-1","relation":{"has-preprint":[{"id-type":"doi","id":"10.21203\/rs.3.rs-3323376\/v1","asserted-by":"object"}]},"ISSN":["1471-2342"],"issn-type":[{"value":"1471-2342","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,12,8]]},"assertion":[{"value":"4 September 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"1 December 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 December 2023","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"This retrospective study was approved by the Ethics Committee of the First hospital of China Medical University (No.AF-SOP-07-1. 1-01). Informed consent was obtained from all individual participants included in the study. We confirmed that the study was carried out in accordance with relevant guidelines and regulations of Helsinki Declaration.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"204"}}