[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126608-en":3,"doc-seo-126608-105":31,"detail-sidebar-cat-0-en-105":92},{"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":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},126608,687207020761,"Patrick","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Detection of Alzheimer’s disease onset using MRI and PET neuroimaging - longitudinal data analysis and machine learning","The study focuses on detecting Alzheimer’s disease onset and improving progression estimation to support earlier intervention. It evaluates longitudinal data analysis and machine learning methods using MRI and PET neuroimaging, emphasizing feature extraction from complex imaging signals. The work examines vulnerable brain regions, related neuroimaging biomarkers, and threshold values tied to plaques, tangles, and neurodegeneration. It proposes automated approaches to strengthen linkage between biomarkers and more accurate onset detection.","Downloaded from [http://journals.lww.com/nrronline by BhDMf5ePHKav1zEoum1tQfN4a](http://journals.lww.com/nrronline by BhDMf5ePHKav1zEoum1tQfN4a)+kJLhEZgbsIHo4XMi0hCyw CX 1AWnYQp/ I l Qr HD3i 3 D0OdRyi 7TvS Fl4Cf3VC1y0abggQZXdgGj2MwlZLeI= on 03/17/2023  \nDetection of Alzheimer’s disease onset using MRI and PET neuroimaging: longitudinal data analysis and machine learning  \nIroshan Aberathne1, Don Kulasiri1, *, Sandhya Samarasinghe1  \n\n| [https://doi.org/10.4103/1673-5374.367840](https://doi.org/10.4103/1673-5374.367840) | Abstract\u003Cbr>The scientists are dedicated to studying the detection of Alzheimer’s disease onset to find a cure, or at the very least, medication that can slow the progression of the disease. This article explores the effectiveness of longitudinal data analysis, artificial intelligence, and machine learning approaches based on magnetic resonance imaging and positron emission tomography neuroimaging modalities for progression estimation and the detection of Alzheimer’s disease onset. The significance of feature extraction in highly complex neuroimaging data, identification of vulnerable brain regions, and the determination of the threshold values for plaques, tangles, and neurodegeneration of these regions will extensively be evaluated. Developing automated methods to improve the aforementioned research areas would enable specialists to determine the progression of the disease and find the link between the biomarkers and more accurate detection of Alzheimer’s disease onset.\u003Cbr>Key Words: deep learning; image processing; linear mixed effect model; neuroimaging; neuroimaging data sources; onset of Alzheimer’s disease detection; pattern recognition |  |\n| --- | --- | --- |\n| Date of submission: August 24, 2022 |  |  |\n| Date of decision: December 8, 2022 |  |  |\n| Date of acceptance: January 12, 2023 |  |  |\n| Date of web publication: March 3, 2023 |  |  |\n| From the Contents\u003Cbr>Introduction 2134 Search Strategy 2134 Onset of Alzheimer’s Disease and Disease Continuum 2135 Neuroimaging Biomarkers and Alzheimer’s Disease 2135 Detection Techniques 2137\u003Cbr> Future Directionson Onset of Alzheimer’s Disease Detection 2138   Summary and Concluding Remarks 2139 \u003Cbr>Introduction\u003Cbr>Alzheimer’s disease (AD) is a disorder which destroys the human brain and ultimately leads to dementia (Joubert et al., 2016; Ayodele et al., 2021) . Alois Alzheimer, a German psychiatrist, diagnosed the first AD patient (51 years old) in 1906 (Ammar and Ayed, 2020) . In the long term, due to the significant decline of her memory, the patient ultimately become totally dependent on caregivers, a fate shared by all AD patients (Feng et al., 2020; Popuri et al., 2020; Dashtipour et al., 2021; Huggins et al., 2021) .\u003Cbr>Globally, AD is the most common form of dementia, the percentages range from 50% to 75% . This figure is staggering when compared with other types of dementia, including vascular dementia (20–30%), Lewy Body disease (10–25%), and frontotemporal dementia (10–15%) (Joubert et al., 2016; Ayodele et al., 2021; Dashtipour et al., 2021; Gao, 2021) . In 2019, there were 50 million individuals diagnosed with dementia. It has been estimated that by 2050 there will be 131.5 million sufferers. By 2030, the total, global financial cost of AD is expected to be US$2 trillion (Ammar and Ayed, 2020; Farina et al., 2020; Feng et al., 2020; Popuri et al., 2020; Shirbandi et al., 2021) . By 2050 globally, it is estimated that the number of people suffering from dementia would be 130 million (Gao, 2021) . This may have a positive association with the increasing rate of the elderly population worldwide anda negative relationship with the lack of proper medication or more accurate early detection techniques for AD.\u003Cbr>Even though scientists have spent considerable amounts of time, money, and effort researching early AD diagnosis using a variety of methods, post-mortem examination is still considered to be the only definitive way to confirm a diagnosis (Hou","cbCaimblF5egkpqZ","https://ap.wps.com/l/cbCaimblF5egkpqZ","pdf",5288516,4,1,7,"English","en",105,"# Introduction\n# Search Strategy\n# Onset of Alzheimer’s Disease and Disease Continuum\n# Neuroimaging Biomarkers and Alzheimer’s Disease\n# Detection Techniques\n# Future Directions on Onset of Alzheimer’s Disease Detection\n# Summary and Concluding Remarks","[{\"question\":\"What imaging modalities are used to detect Alzheimer’s disease onset in this study?\",\"answer\":\"The study uses magnetic resonance imaging (MRI) and positron emission tomography (PET) neuroimaging to observe structural and molecular changes and their patterns.\"},{\"question\":\"How does longitudinal data analysis contribute to progression estimation?\",\"answer\":\"Longitudinal data analysis supports modeling how biomarkers and imaging features change over time, enabling more informed estimates of disease progression and onset detection.\"},{\"question\":\"What aspects of neuroimaging are emphasized for accurate detection?\",\"answer\":\"The article highlights the importance of feature extraction from complex neuroimaging data, identification of vulnerable brain regions, and determining threshold values related to plaques, tangles, and neurodegeneration.\"}]","Detection of Alzheimer’s disease onset using MRI and PET neuroimaging - longitudinal data analysis and machine learning | PDF",1785933713,18,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":29},"detection-of-alzheimers-disease-onset-using-mri-and-pet-neuroimaging-longitudinal-data-analysis-and-machine-learning","",{"@graph":37,"@context":86},[38,54,69],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,49,52],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":48},"https://docshare.wps.com/document/","Document",2,{"item":50,"name":12,"@type":44,"position":51},"https://docshare.wps.com/document/research-report/",3,{"item":53,"name":13,"@type":44,"position":20},"https://docshare.wps.com/document/detection-of-alzheimers-disease-onset-using-mri-and-pet-neuroimaging-longitudinal-data-analysis-and-machine-learning/126608/",{"url":53,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":24,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":42,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-26","2026-08-05",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"What imaging modalities are used to detect Alzheimer’s disease onset in this study?","Question",{"text":76,"@type":77},"The study uses magnetic resonance imaging (MRI) and positron emission tomography (PET) neuroimaging to observe structural and molecular changes and their patterns.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How does longitudinal data analysis contribute to progression estimation?",{"text":81,"@type":77},"Longitudinal data analysis supports modeling how biomarkers and imaging features change over time, enabling more informed estimates of disease progression and onset detection.",{"name":83,"@type":74,"acceptedAnswer":84},"What aspects of neuroimaging are emphasized for accurate detection?",{"text":85,"@type":77},"The article highlights the importance of feature extraction from complex neuroimaging data, identification of vulnerable brain regions, and determining threshold values related to plaques, tangles, and neurodegeneration.","https://schema.org",{"og:url":53,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":53},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,120,123,128,131,135],{"id":21,"doc_module":4,"doc_module_name":47,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":48,"doc_module":4,"doc_module_name":47,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":20,"doc_module":4,"doc_module_name":47,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":47,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":47,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":22,"doc_module":4,"doc_module_name":47,"category_name":117,"show_sort_weight":118,"slug":119},"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":47,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":47,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":47,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":47,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":47,"category_name":137,"show_sort_weight":107,"slug":138},19,"General","general"]