[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118711-en":3,"doc-seo-118711-105":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},118711,1099513958607,"Jiven","https://ap-avatar.wpscdn.com/avatar/100002390cf8733938c?x-image-process=image/resize,m_fixed,w_180,h_180&k=1778829742770036399",7,"Healthcare","Neuroimaging in Machine Learning for Brain Disorders - Chapter 8 - Overview","Medical imaging is central to detecting, diagnosing, and monitoring brain disorders, enabling both clinical decision-making and research on anatomical, functional, and molecular changes. This chapter explains core neuroimaging modalities such as MRI, CT, PET, and SPECT, including their underlying principles and the information each modality can provide. It details the key processing steps required to extract features and illustrates how those features are used in machine learning studies for brain disorders, including deep learning and feature extraction workflows.","Neuroimaging in Machine Learning for Brain Disorders  \nNinon Burgos  \n To cite this version:  \nNinon Burgos. Neuroimaging in Machine Learning for Brain Disorders. Olivier Colliot. Machine Learning for Brain Disorders, Springer, In press. hal-03814787  \nHAL Id: hal-03814787 [https://hal.inria.fr/hal-03814787](https://hal.inria.fr/hal-03814787)  \nSubmitted on 14 Oct 2022  \nHAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers.  \nL’archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non,émanant des établissements d’enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.  \nChapter 8  \nNeuroimaging in Machine Learning for Brain Disorders  \nNinon Burgos*,1  \n1 Sorbonne Universit´e, Institut du Cerveau – Paris Brain Institute-ICM, CNRS, Inria, Inserm, AP-HP, Hˆopital de la Piti´e-Salpˆetri`ere, F-75013, Paris, France  \n* Corresponding author: e-mail address: [ninon.burgos@cnrs.fr](ninon.burgos@cnrs.fr)  \nAbstract  \nMedical imaging plays an important role in the detection, diagnosis and treatment monitoring of brain disorders. Neuroimaging includes different modalities such as magnetic resonance imaging (MRI), X-ray computed tomography (CT), positron emission tomography (PET) or single-photon emission computed tomography (SPECT) .  \nFor each of these modalities, we will explain the basic principles of the technology, describe the type of information the images can provide, list the key processing steps necessary to extract features and provide examples of their use in machine learning studies for brain disorders.  \nKeywords: Magnetic resonance imaging, Computed tomography, Positron emission tomography, Single-photon emission computed tomography, Neuroimaging, Medical imaging, Machine learning, Deep learning, Feature extraction, Preprocessing  \nTo appear in  \nO. Colliot (Ed.), Machine Learning for Brain Disorders, Springer  \n1. Introduction  \nMedical imaging plays a key role in brain disorders. In clinical care, it is vital for detection, diagnosis and treatment monitoring. It is also an essential tool for research to characterise the anatomical, functional and molecular alterations in brain disorders, to better understand the pathophysiology, or to evaluate the effects of new treatments in clinical trials for instance. Medical imaging of the brain is referred to as neuroimaging and involves different modalities such as X-ray computed tomography (CT), magnetic resonance imaging (MRI), positron emission tomography (PET) or single-photon emission computed tomography (SPECT) .  \nMost neuroimaging modalities have been developed in the 1970s (Figure 1) . The first CT image of a brain was acquired in 1971 [1 , 2] . This technology results from the discovery of X-rays by Wilhelm R¨ontgen in 1895 [3] . A few years later, PET [4] and then SPECT [5 , 6] cameras were developed. Both modalities result from the discovery of natural radioactivity in 1896 by Henri Becquerel [7] . The first MR image of a brain goes back to 1978 [8] following the discovery of nuclear magnetic resonance in 1946 by Felix Bloch [9] . Some of these imaging modalities were later combined into hybrid scanners. The first prototype combining PET and CT was introduced into the clinical arena in 1998 [10] while the first PET and MR images of a brain simultaneously acquired were reported in 2007 [11 , 12] . The first commercial SPECT/CT system dates back to 1999 [13] while SPECT/MR systems are still under development [14] .  \nCT and MRI are the modalities of choice when studying brain anatomy while SPECT and PET are used to image particular biological processes. Note that MRI is a versatile modality that allows studying both the structure and ","cbCaidkiAKLkvKtQ","https://ap.wps.com/l/cbCaidkiAKLkvKtQ","pdf",5927413,1,36,"English","en",105,"# Introduction\n## Key neuroimaging modalities\n## Feature extraction and machine learning pipeline","[{\"question\":\"Which neuroimaging modalities are covered in the chapter?\",\"answer\":\"The chapter addresses MRI, CT, PET, and SPECT. It also distinguishes common clinical research roles for each modality.\"},{\"question\":\"What does the chapter focus on for machine learning use?\",\"answer\":\"It explains the basic principles of each modality, the type of information images provide, and the key processing steps needed to extract features for machine learning studies.\"},{\"question\":\"Why is neuroimaging important for brain disorders?\",\"answer\":\"Neuroimaging supports detection, diagnosis, and treatment monitoring in clinical care, and it helps research characterize brain alterations and evaluate new treatments in trials.\"}]","Neuroimaging in Machine Learning for Brain Disorders - Chapter 8 - Overview | PDF",1785719858,91,{"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},"neuroimaging-in-machine-learning-for-brain-disorders-chapter-8-overview","",{"@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/healthcare/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/neuroimaging-in-machine-learning-for-brain-disorders-chapter-8-overview/118711/",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","2026-08-03",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},"Which neuroimaging modalities are covered in the chapter?","Question",{"text":75,"@type":76},"The chapter addresses MRI, CT, PET, and SPECT. It also distinguishes common clinical research roles for each modality.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What does the chapter focus on for machine learning use?",{"text":80,"@type":76},"It explains the basic principles of each modality, the type of information images provide, and the key processing steps needed to extract features for machine learning studies.",{"name":82,"@type":73,"acceptedAnswer":83},"Why is neuroimaging important for brain disorders?",{"text":84,"@type":76},"Neuroimaging supports detection, diagnosis, and treatment monitoring in clinical care, and it helps research characterize brain alterations and evaluate new treatments in trials.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,118,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":116,"slug":117},40,"healthcare",{"id":119,"doc_module":4,"doc_module_name":46,"category_name":120,"show_sort_weight":121,"slug":122},8,"Research & Report",30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]