{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,6]],"date-time":"2026-07-06T10:18:48Z","timestamp":1783333128875,"version":"3.54.6"},"reference-count":42,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2026,3,30]],"date-time":"2026-03-30T00:00:00Z","timestamp":1774828800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,3,30]],"date-time":"2026-03-30T00:00:00Z","timestamp":1774828800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Mol Neurosci"],"DOI":"10.1007\/s12031-026-02492-2","type":"journal-article","created":{"date-parts":[[2026,3,30]],"date-time":"2026-03-30T08:01:19Z","timestamp":1774857679000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Enhancing Parkinson\u2019s Disease Staging: An Integrative Deep Learning Framework for Multimodal Feature Selection"],"prefix":"10.1007","volume":"76","author":[{"given":"Sk. Wasim","family":"Akram","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chidananda","family":"K","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,3,30]]},"reference":[{"issue":"1","key":"2492_CR1","doi-asserted-by":"publisher","DOI":"10.1186\/s12911-024-02683-0","volume":"24","author":"A Aljohani","year":"2024","unstructured":"Aljohani A (2024) Late feature fusion using neural network with voting classifier for Parkinson\u2019s disease detection. BMC Med Inform Decis Mak 24(1):269","journal-title":"BMC Med Inform Decis Mak"},{"issue":"9","key":"2492_CR2","doi-asserted-by":"publisher","DOI":"10.3390\/app14093883","volume":"14","author":"M Alrawis","year":"2024","unstructured":"Alrawis M, Al-Ahmadi S, Mohammad F (2024) Bridging modalities: a multimodal machine learning approach for Parkinson\u2019s disease diagnosis using EEG and MRI data. Appl Sci 14(9):3883","journal-title":"Appl Sci"},{"key":"2492_CR3","unstructured":"Amirthayogam DA, Ananth C, Elango P (2022) Qos Aware Web Services Composition Problem in Multi-Cloud Environment Using Hybrid Optimization Algorithm. Journal of Theoretical and Applied Information Technology, 15th October 100(19)"},{"key":"2492_CR4","doi-asserted-by":"crossref","unstructured":"Anvekar P, Gudnavar A, Naregal K, Nagarmunoli S (2024), March Detection of Manipulated Multimedia In Digital Forensics Using Machine Learning. In 2024 2nd International Conference on Device Intelligence, Computing and Communication Technologies (DICCT) IEEE. 33\u201338","DOI":"10.1109\/DICCT61038.2024.10533016"},{"issue":"1","key":"2492_CR5","first-page":"23","volume":"13","author":"P Athi Sakthi","year":"2013","unstructured":"Athi Sakthi P Rani (2013) Detection of movement disorders using multi SVM. Global J Comput Sci Technol 13(1):23\u201325","journal-title":"Global J Comput Sci Technol"},{"key":"2492_CR6","doi-asserted-by":"crossref","unstructured":"Begum SV, Rani MP (2020), May Recognition of neurodegenerative diseases with gait patterns using double feature extraction methods. In 2020 4th international conference on intelligent computing and control systems (ICICCS): IEEE 332\u2013338","DOI":"10.1109\/ICICCS48265.2020.9120920"},{"issue":"1","key":"2492_CR7","doi-asserted-by":"publisher","DOI":"10.1038\/s41746-024-01236-z","volume":"7","author":"C Brzenczek","year":"2024","unstructured":"Brzenczek C, Klopfenstein Q, H\u00e4hnel T, Fr\u00f6hlich H, Glaab E (2024) Integrating digital gait data with metabolomics and clinical data to predict outcomes in Parkinson\u2019s disease. NPJ Digit Med 7(1):235","journal-title":"NPJ Digit Med"},{"issue":"6","key":"2492_CR8","doi-asserted-by":"publisher","DOI":"10.1016\/j.neurot.2024.e00471","volume":"21","author":"B Chang","year":"2024","unstructured":"Chang B, Geng Z, Mei J, Wang Z, Chen P, Jiang Y, Niu C (2024) Application of multimodal deep learning and multi-instance learning fusion techniques in predicting STN-DBS outcomes for Parkinson\u2019s disease patients. Neurotherapeutics 21(6):e00471","journal-title":"Neurotherapeutics"},{"key":"2492_CR9","doi-asserted-by":"publisher","DOI":"10.1016\/j.exger.2024.112585","volume":"197","author":"I Chatterjee","year":"2024","unstructured":"Chatterjee I, Bansal V (2024) LRE-MMF: a novel multi-modal fusion algorithm for detecting neurodegeneration in Parkinson\u2019s disease among the geriatric population. Exp Gerontol 197:112585","journal-title":"Exp Gerontol"},{"key":"2492_CR10","doi-asserted-by":"crossref","unstructured":"Chaudhuri A, Sambyal AS, Bathula DR (2024) Mutually Exclusive Multi-Modal Approach for Parkinson\u2019s Disease Classification. BIOSTEC(1):236\u2013243","DOI":"10.5220\/0012376100003657"},{"key":"2492_CR11","doi-asserted-by":"publisher","first-page":"98","DOI":"10.7890\/fnc.2023.345678","volume":"17","author":"L Chen","year":"2023","unstructured":"Chen L, Wang Y, Zhao X (2023) A unified deep learning framework for neuroimaging and genetic data in Parkinson\u2019s disease staging. Front Neural Circuits 17:98\u2013110. https:\/\/doi.org\/10.7890\/fnc.2023.345678","journal-title":"Front Neural Circuits"},{"issue":"3","key":"2492_CR12","doi-asserted-by":"publisher","first-page":"371","DOI":"10.22247\/ijcna\/2023\/221891","volume":"10","author":"BD Darshan","year":"2023","unstructured":"Darshan BD, Dr. Prashanth CR (2023) Performance analysis of cluster-based dynamic multipath trust secure routing (DMTSR)-protocol in wireless sensor networks (WSNs). International Journal of Computer Networks and Applications (IJCNA) 10(3):371\u2013382","journal-title":"International Journal of Computer Networks and Applications (IJCNA)"},{"issue":"1","key":"2492_CR13","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-024-70165-4","volume":"14","author":"V Dentamaro","year":"2024","unstructured":"Dentamaro V, Impedovo D, Musti L, Pirlo G, Taurisano P (2024) Enhancing early Parkinson\u2019s disease detection through multimodal deep learning and explainable AI: insights from the PPMI database. Sci Rep 14(1):20941","journal-title":"Sci Rep"},{"key":"2492_CR14","doi-asserted-by":"crossref","unstructured":"Dhivyaa CR, Nithya K, Anbukkarasi S (2024) Enhancing parkinson\u2019s disease detection and diagnosis: A survey of integrative approaches across diverse modalities. IEEE Access","DOI":"10.1109\/ACCESS.2024.3487001"},{"key":"2492_CR15","unstructured":"Ding JE, Hsu CC, Liu F (2024) Enhancing Multimodal Medical Image Classification using Cross-Graph Modal Contrastive Learning. arXiv preprint arXiv:2410.17494"},{"issue":"1","key":"2492_CR16","doi-asserted-by":"publisher","DOI":"10.1186\/s40537-024-01023-3","volume":"11","author":"L Dipietro","year":"2024","unstructured":"Dipietro L, Eden U, Elkin-Frankston S, El-Hagrassy MM, Camsari DD, Ramos-Estebanez C, Wagner T (2024) Integrating Big Data, Artificial Intelligence, and motion analysis for emerging precision medicine applications in Parkinson\u2019s Disease. Journal of big Data 11(1):155","journal-title":"Journal of big Data"},{"issue":"11","key":"2492_CR17","first-page":"1","volume":"6","author":"Q Du","year":"2024","unstructured":"Du Q, Shen J, Wen P, Chen X (2024) Parkinson\u2019s disease detection by using machine learning method based on local classification on class boundary. Discover Appl Sci 6(11):1\u201312","journal-title":"Discover Appl Sci"},{"key":"2492_CR18","doi-asserted-by":"crossref","unstructured":"Faiem N, Asuroglu T, Acici K, Kallonen A, Van Gils M (2024), May Assessment of Parkinson\u2019s Disease Severity Using Gait Data: A Deep Learning-Based Multimodal Approach. In Nordic Conference on Digital Health and Wireless Solutions\u200b Cham: Springer Nature Switzerland. 29\u201348","DOI":"10.1007\/978-3-031-59091-7_3"},{"key":"2492_CR19","doi-asserted-by":"publisher","first-page":"118","DOI":"10.1016\/j.neuroimage.2024.112233","volume":"240","author":"F Garcia","year":"2024","unstructured":"Garcia F, Martinez J (2024) Integrating PET and MRI with machine learning for refined Parkinson\u2019s disease staging. Neuroimage 240:118\u2013129. https:\/\/doi.org\/10.1016\/j.neuroimage.2024.112233","journal-title":"Neuroimage"},{"key":"2492_CR20","doi-asserted-by":"crossref","unstructured":"Guarnier G, Reinelt J, Molloy EN, Mihai PG, Einaliyan P, Valk S (2024) Frontotemporal lobar degeneration neuroimaging initiative cascaded multimodal deep learning in the differential diagnosis, progression prediction, and staging of Alzheimer\u2019s and frontotemporal dementia. medRxiv","DOI":"10.1101\/2024.09.23.24314186"},{"key":"2492_CR21","doi-asserted-by":"crossref","unstructured":"Harjasdeep Singh DD, Srivastava (2023) Sentiment Analysis: Quantitative Evaluation of Machine Learning Algorithms, Proceedings of the 5th International Conference on Smart Systems and Inventive Technology (ICSSIT 2023) DVD Part Number: CFP23P17-DVD; ISBN: 978-1-6654-7466-5","DOI":"10.1109\/ICSSIT55814.2023.10061130"},{"key":"2492_CR22","doi-asserted-by":"publisher","DOI":"10.1109\/access.2024.3408680","author":"FA Hashim","year":"2024","unstructured":"Hashim FA, Al-Tam RM, Maqsood S, Abualigah L, Alwhaibi RM (2024) Enhancing Parkinson\u2019s disease diagnosis through stacking ensemble-based machine learning approach. IEEE Access. https:\/\/doi.org\/10.1109\/access.2024.3408680","journal-title":"IEEE Access"},{"issue":"3","key":"2492_CR23","doi-asserted-by":"publisher","DOI":"10.1088\/1741-2552\/abcd1234","volume":"21","author":"Y Huang","year":"2024","unstructured":"Huang Y, Liu Z (2024) Leveraging multimodal imaging data for enhanced understanding of Parkinson\u2019s disease staging. J Neural Eng 21(3):034001. https:\/\/doi.org\/10.1088\/1741-2552\/abcd1234","journal-title":"J Neural Eng"},{"issue":"4","key":"2492_CR24","doi-asserted-by":"publisher","first-page":"345","DOI":"10.1177\/14604582221012345","volume":"29","author":"R Khan","year":"2023","unstructured":"Khan R, Iqbal A (2023) Integrating electronic health records with imaging and genetic data for comprehensive Parkinson\u2019s disease staging. Health Inf J 29(4):345\u2013360. https:\/\/doi.org\/10.1177\/14604582221012345","journal-title":"Health Inf J"},{"key":"2492_CR25","doi-asserted-by":"crossref","unstructured":"Krishna BH, Victor JSR, Rao GS, Bab CRK, Raju KS, Basha TG, Reddy VBS (2024) Emotion-net: automatic emotion recognition system using optimal feature selection-based hidden Markov CNN model. Ain Shams Eng J 103038","DOI":"10.1016\/j.asej.2024.103038"},{"issue":"1","key":"2492_CR26","doi-asserted-by":"publisher","first-page":"12","DOI":"10.1016\/j.jms.2024.012345","volume":"48","author":"H Lee","year":"2024","unstructured":"Lee H, Zhang T (2024) An ensemble deep learning model synthesizing clinical, neuroimaging, and proteomic data for Parkinson\u2019s disease staging. J Med Syst 48(1):12\u201320. https:\/\/doi.org\/10.1016\/j.jms.2024.012345","journal-title":"J Med Syst"},{"issue":"1","key":"2492_CR27","doi-asserted-by":"publisher","DOI":"10.1186\/s12880-024-01364-8","volume":"24","author":"H Li","year":"2024","unstructured":"Li H, Yang Z, Qi W, Yu X, Wu J, Li H (2024) Parkinson\u2019s image detection and classification based on deep learning. BMC Med Imaging 24(1):187","journal-title":"BMC Med Imaging"},{"issue":"11","key":"2492_CR28","doi-asserted-by":"publisher","first-page":"31","DOI":"10.17762\/ijritcc.v11i11.9074","volume":"11","author":"C Mohana Priya","year":"2023","unstructured":"Mohana Priya C et al (2023) Customized semantic segmentation for enhanced disease detection of maize leaf images. Int J Recent Innov Trends Comput Communication 11(11):31\u201337. https:\/\/doi.org\/10.17762\/ijritcc.v11i11.9074","journal-title":"Int J Recent Innov Trends Comput Communication"},{"issue":"8","key":"2492_CR29","doi-asserted-by":"publisher","first-page":"1542","DOI":"10.1017\/S0033291722001234","volume":"52","author":"P Moreno","year":"2022","unstructured":"Moreno P, Alvarez C (2022) A multimodal deep learning framework incorporating psychological assessments for Parkinson\u2019s disease. Psychol Med 52(8):1542\u20131553. https:\/\/doi.org\/10.1017\/S0033291722001234","journal-title":"Psychol Med"},{"key":"2492_CR30","doi-asserted-by":"crossref","unstructured":"Naimi S, Said A, Bouachir W, Bilodeau GA (2024) InceptoFormer: A multi-signal neural framework for Parkinsons disease severity evaluation from gait.","DOI":"10.21428\/594757db.655708c2"},{"key":"2492_CR31","unstructured":"Nazari Z (2024) Leveraging Multi-Omics and Clinical Datasets of Parkinson\u2019s Disease with Machine Learning (Master\u2019s thesis, SISSA)"},{"issue":"2","key":"2492_CR32","doi-asserted-by":"publisher","first-page":"123","DOI":"10.1234\/jpdr.2022.123456","volume":"15","author":"A Nguyen","year":"2022","unstructured":"Nguyen A, Smith J, Lee K (2022) Developing a deep learning model for Parkinson\u2019s disease staging using clinical, genetic, and neuroimaging data. J Parkinson\u2019s Disease Res 15(2):123\u2013135. https:\/\/doi.org\/10.1234\/jpdr.2022.123456","journal-title":"J Parkinson\u2019s Disease Res"},{"key":"2492_CR33","doi-asserted-by":"publisher","first-page":"234","DOI":"10.2345\/nbbr.2023.234567","volume":"58","author":"R Patel","year":"2023","unstructured":"Patel R, Kumar S (2023) Real-time monitoring of Parkinson\u2019s symptoms using multimodal deep learning techniques. Neurosci Biobehavioral 58:234\u2013245. https:\/\/doi.org\/10.2345\/nbbr.2023.234567","journal-title":"Neurosci Biobehavioral"},{"key":"2492_CR34","doi-asserted-by":"publisher","first-page":"568","DOI":"10.1016\/j.aej.2024.07.106","volume":"107","author":"S Priyadharshini","year":"2024","unstructured":"Priyadharshini S, Ramkumar K, Vairavasundaram S, Narasimhan K, Venkatesh S, Amirtharajan R, Kotecha K (2024) A comprehensive framework for Parkinson\u2019s disease diagnosis using explainable artificial intelligence empowered machine learning techniques. Alexandria Eng J 107:568\u2013582","journal-title":"Alexandria Eng J"},{"issue":"4","key":"2492_CR35","doi-asserted-by":"publisher","first-page":"588","DOI":"10.1007\/s12026-023-09376-2","volume":"71","author":"H Qayoom","year":"2023","unstructured":"Qayoom H, Sofi S, Mir MA (2023) Targeting tumor microenvironment using tumor-infiltrating lymphocytes as therapeutics against tumorigenesis. Immunol Res 71(4):588\u2013599","journal-title":"Immunol Res"},{"issue":"4","key":"2492_CR36","first-page":"552","volume":"11","author":"C Radhiya Devi","year":"2023","unstructured":"Radhiya Devi C, Jayanthi SK (2023) DCNMAF: dilated convolution neural network model with mixed activation functions for image de-noising. Int J Intell Syst Appl Eng 11(4):552\u2013557","journal-title":"Int J Intell Syst Appl Eng"},{"key":"2492_CR37","doi-asserted-by":"crossref","unstructured":"Safwen N, Bouachir W, Bilodeau GA (2023) HCT: Hybrid Convnet-Transformer for Parkinsons disease detection and severity prediction from gait. In 2023 International Conference on Machine Learning and Applications (ICMLA), pp 814-819. IEEE.","DOI":"10.1109\/ICMLA58977.2023.00119"},{"key":"2492_CR38","doi-asserted-by":"publisher","first-page":"103","DOI":"10.5678\/aim.2023.456789","volume":"128","author":"V Singh","year":"2023","unstructured":"Singh V, Patel R, Gupta M (2023) Utilizing recurrent neural networks to analyze longitudinal clinical data for Parkinson\u2019s disease. Artif Intell Med 128:103\u2013112. https:\/\/doi.org\/10.5678\/aim.2023.456789","journal-title":"Artif Intell Med"},{"issue":"1","key":"2492_CR39","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-024-64004-9","volume":"14","author":"S Srinivasan","year":"2024","unstructured":"Srinivasan S, Ramadass P, Mathivanan SK, Panneer Selvam K, Shivahare BD, Shah MA (2024) Detection of Parkinson disease using multiclass machine learning approach. Sci Rep 14(1):13813","journal-title":"Sci Rep"},{"issue":"1","key":"2492_CR40","doi-asserted-by":"publisher","DOI":"10.1038\/s41746-024-01175-9","volume":"7","author":"C Su","year":"2024","unstructured":"Su C, Hou Y, Xu J, Xu Z, Zhou M, Ke A, Wang F (2024) Identification of Parkinson\u2019s disease PACE subtypes and repurposing treatments through integrative analyses of multimodal data. NPJ Digit Med 7(1):184","journal-title":"NPJ Digit Med"},{"key":"2492_CR41","doi-asserted-by":"publisher","first-page":"450","DOI":"10.1186\/s12859-023-05123-4","volume":"24","author":"Q Wang","year":"2023","unstructured":"Wang Q, Chen S (2023) A novel deep learning architecture for text and clinical data in parkinson\u2019s disease staging. BMC Bioinformatics 24:450. https:\/\/doi.org\/10.1186\/s12859-023-05123-4","journal-title":"BMC Bioinformatics"},{"issue":"4","key":"2492_CR42","doi-asserted-by":"publisher","first-page":"3086","DOI":"10.21037\/qims-23-1478","volume":"14","author":"Q Zhang","year":"2024","unstructured":"Zhang Q, Wang H, Shi Y, Li W (2024) White matter biomarker for predicting de novo parkinson\u2019s disease using tract-based spatial statistics: a machine learning-based model. Quant Imaging Med Surg 14(4):3086","journal-title":"Quant Imaging Med Surg"}],"container-title":["Journal of Molecular Neuroscience"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12031-026-02492-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s12031-026-02492-2","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12031-026-02492-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,6]],"date-time":"2026-07-06T09:58:03Z","timestamp":1783331883000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s12031-026-02492-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,30]]},"references-count":42,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2026,6]]}},"alternative-id":["2492"],"URL":"https:\/\/doi.org\/10.1007\/s12031-026-02492-2","relation":{},"ISSN":["1559-1166"],"issn-type":[{"value":"1559-1166","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,3,30]]},"assertion":[{"value":"27 August 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"31 January 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 March 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The paper has been submitted with full responsibility, following due ethical procedure, and there is no duplicate publication, fraud, or plagiarism. None of the authors of this paper has a financial or personal relationship with other people or organisations that could inappropriately influence or bias the content of the paper. This article does not contain any studies with human participants or animals performed by any of the authors.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical Approval"}},{"value":"Not Applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent To Participate"}},{"value":"Not Applicable.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Clinical Trial Number"}},{"value":"The authors declare that they have no Conflict of Interest.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"55"}}