[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123344-en":3,"doc-seo-123344-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},123344,1374391974468,"Eden","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Evaluating machine learning pipelines for multimodal neuroimaging in small cohorts - an ALS case study","Advancements in machine learning support the analysis of multimodal neuroimaging and can enable biomarker discovery to improve diagnosis across neurological disorders. For rare and heterogeneous diseases, limited sample sizes make pipeline development and optimization difficult. This study systematically evaluates how machine-learning pipeline choices—scaling, feature selection, dimensionality reduction, and hyperparameter optimization—affect classification performance using multimodal MRI from 16 ALS patients and 14 healthy controls. Subject-wise feature normalization improves results, while other refinements yield only modest or marginal gains, motivating a shift toward cohort expansion and better data utilization.","TYPE Original Research PUBLISHED 13 June 2025  \nDOI 10. 3389/fninf.2025.1568116  \nOPEN ACCESS  \nEDITED BY  \nPawel Oswiecimka,  \nPolish Academy of Sciences, Poland  \nREVIEWED BY  \nYing Wang,  \nHarbin Medical University, China Ayman Mostafa,  \nJouf University, Saudi Arabia  \n*CORRESPONDENCE  \nShailesh Appukuttan  \n [shailesh.appukuttan@univ-amu.fr](shailesh.appukuttan@univ-amu.fr)  \nRECEIVED 28 January 2025  \nACCEPTED 15 May 2025  \nPUBLISHED 13 June 2025  \nCITATION  \nAppukuttan S, Grapperon A-M, El Mendili MM, Dary H, Guye M, Verschueren A, Ranjeva J-P, Attarian S, Zaaraoui W and Gilson M (2025) Evaluating machine learning pipelines for multimodal neuroimaging in small cohorts: an ALS case study.  \nFront. Neuroinform. 19:1568116 .  \ndoi: 10.3389/fninf.2025.1568116  \nCOPYRIGHT  \n© 2025 Appukuttan, Grapperon, El Mendili, Dary, Guye, Verschueren, Ranjeva, Attarian, Zaaraoui and Gilson. This is an open-access article distributed under the terms of the  \nCreative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.  \nEvaluating machine learning pipelines for multimodal neuroimaging in small cohorts:  \nan ALS case study  \nShailesh Appukuttan1,2*, Aude-Marie Grapperon1,3 ,  \nMounir Mohamed El Mendili1 , Hugo Dary1 , Maxime Guye1 , Annie Verschueren3 , Jean-Philippe Ranjeva1 , Shahram Attarian3 , Wafaa Zaaraoui1 and Matthieu Gilson2  \n1Aix Marseille Univ, CNRS, CRMBM, Marseille, France, 2Aix Marseille Univ, CNRS, INT, Marseille, France, 3APHM, Hopital de la Timone, Referral Centre for Neuromuscular Diseases and ALS, Marseille, France  \nAdvancements in machine learning hold great promise for the analysis of multimodal neuroimaging data. They can help identify biomarkers and improve diagnosis for various neurological disorders. However, the application of such techniques for rare and heterogeneous diseases remains challenging due to small-cohorts available for acquiring data. E􀀀orts are therefore commonly directed toward improving the classification models, in an e􀀀ort to optimize outcomes given the limited data. In this study, we systematically evaluated the impact of various machine learning pipeline configurations, including scaling methods, feature selection, dimensionality reduction, and hyperparameter optimization. The e􀀈cacy of such components in the pipeline was evaluated on classification performance using multimodal MRI data from a cohort of 16 ALS patients and 14 healthy controls. Our findings reveal that, while certain pipeline components, such as subject-wise feature normalization, help improve classification outcomes, the overall influence of pipeline refinementson performance is modest. Feature selection and dimensionality reduction steps were found to have limited utility, and the choice of hyperparameter optimization strategies produced only marginal gains. Our results suggest that, for smallcohort studies, the emphasis should shift from extensive tuning of these pipelines to addressing data-related limitations, such as progressively expanding cohort size, integrating additional modalities, and maximizing the information extracted from existing datasets. This study provides a methodological framework to guide future research and emphasizes the need for dataset enrichment to improve clinical utility.  \nKEYWORDS  \namyotrophic lateral sclerosis, machine learning, multimodal MRI, small cohort, classification, pipeline optimization  \n1 Introduction  \nBy allowing for the non-invasive visualization of anatomical and functional problems in the brain, medical imaging has transformed our understanding of neurological illnesses. Magnetic Resonance Imaging (MRI) is one such imaging technique that has become an indispensable t","cbCaimxVthM6fCab","https://ap.wps.com/l/cbCaimxVthM6fCab","pdf",3367221,1,17,"English","en",105,"# Introduction\n## Machine learning and multimodal neuroimaging\n## Challenge of small cohorts in rare diseases","[{\"question\":\"What is the main goal of the study?\",\"answer\":\"To systematically evaluate how different machine-learning pipeline configurations affect classification performance for multimodal neuroimaging in small cohorts, using an ALS case study.\"},{\"question\":\"Which pipeline components are assessed?\",\"answer\":\"The study evaluates scaling methods, feature selection, dimensionality reduction, and hyperparameter optimization as parts of the overall machine-learning pipeline.\"},{\"question\":\"What do the results suggest for small-cohort studies?\",\"answer\":\"The overall impact of pipeline refinement is modest; emphasis should shift toward addressing data limitations such as expanding cohort size, integrating additional modalities, and extracting more information from existing datasets.\"}]","Evaluating machine learning pipelines for multimodal neuroimaging in small cohorts - an ALS case study | PDF",1785816035,43,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"evaluating-machine-learning-pipelines-for-multimodal-neuroimaging-in-small-cohorts-an-als-case-study","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/evaluating-machine-learning-pipelines-for-multimodal-neuroimaging-in-small-cohorts-an-als-case-study/123344/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What is the main goal of the study?","Question",{"text":75,"@type":76},"To systematically evaluate how different machine-learning pipeline configurations affect classification performance for multimodal neuroimaging in small cohorts, using an ALS case study.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which pipeline components are assessed?",{"text":80,"@type":76},"The study evaluates scaling methods, feature selection, dimensionality reduction, and hyperparameter optimization as parts of the overall machine-learning pipeline.",{"name":82,"@type":73,"acceptedAnswer":83},"What do the results suggest for small-cohort studies?",{"text":84,"@type":76},"The overall impact of pipeline refinement is modest; 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