{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T16:37:28Z","timestamp":1787071048943,"version":"build-2736575974"},"reference-count":40,"publisher":"Ovid Technologies (Wolters Kluwer Health)","issue":"11","license":[{"start":{"date-parts":[[2025,11,1]],"date-time":"2025-11-01T00:00:00Z","timestamp":1761955200000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-sa\/4.0"}],"content-domain":{"domain":["lww.com","ovid.com"],"crossmark-restriction":true},"short-container-title":[],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background:<\/jats:title>\n                    <jats:p>Alzheimer\u2019s disease (AD) is a progressive and irreversible brain disorder, characterized by a gradual decline in cognitive and memory function, with memory loss being one of the most prominent symptoms. Accurate and early diagnosis of AD is essential for effective management and treatment. Structural magnetic resonance imaging (sMRI) and positron emission tomography (PET) are widely utilized neuroimaging modalities for diagnosing AD due to their ability to provide complementary structural and functional insights into brain abnormalities.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods:<\/jats:title>\n                    <jats:p>This study introduces a novel computer-aided diagnosis system that integrates sMRI and PET data using Fuzzy Cognitive Maps (FCM) to improve diagnostic accuracy. The research is conducted using the ADNI dataset, where preprocessing of sMRI and PET images is performed using FSL and statistical parametric mapping tools, respectively. In a key innovation, features extracted from both modalities are fused and dimensionality reduction is achieved through an Autoencoder model. The reduced feature set is then classified using FCM, Support Vector Machine, k-Nearest Neighbors, and Multilayer Perceptron.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results:<\/jats:title>\n                    <jats:p>The FCM-based approach demonstrates superior performance, achieving the highest accuracy of 93.71%, surpassing other classifiers tested.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions:<\/jats:title>\n                    <jats:p>This study underscores the effectiveness of integrating FCM with multimodal neuroimaging data and highlights its potential for enhancing the early and reliable diagnosis of AD.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.4103\/jmss.jmss_3_25","type":"journal-article","created":{"date-parts":[[2025,11,1]],"date-time":"2025-11-01T07:00:06Z","timestamp":1761980406000},"update-policy":"https:\/\/doi.org\/10.1097\/lww.0000000000001000","source":"Crossref","is-referenced-by-count":0,"title":["A Fuzzy Cognitive Map-based Framework for Alzheimer\u2019s Disease Diagnosis Using Multimodal Magnetic Resonance Imaging-Positron Emission Tomography Registration"],"prefix":"10.4103","volume":"15","author":[{"given":"Seyed Assef","family":"Mahdavi","sequence":"first","affiliation":[{"name":"Department of Biomedical Engineering, Central Tehran Branch, Islamic Azad University, Tehran, Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Keivan","family":"Maghooli","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fardad","family":"Farokhi","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering, Central Tehran Branch, Islamic Azad University, Tehran, Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"276","published-online":{"date-parts":[[2025,11]]},"reference":[{"key":"R1-20251101","doi-asserted-by":"crossref","first-page":"v1000","DOI":"10.12688\/f1000research.14506.1","article-title":"Current understanding of Alzheimer's disease diagnosis and treatment","volume":"7","author":"Weller","year":"2018","journal-title":"F1000Res"},{"key":"R2-20251101","doi-asserted-by":"crossref","first-page":"1097","DOI":"10.1001\/archneur.1985.04060100083029","article-title":"Diagnosis of Alzheimer's disease","volume":"42","author":"Khachaturian","year":"1985","journal-title":"Arch Neurol"},{"key":"R3-20251101","doi-asserted-by":"crossref","first-page":"484","DOI":"10.1016\/S1474-4422(21)00066-1","article-title":"Clinical diagnosis of Alzheimer's disease: Recommendations of the International Working Group","volume":"20","author":"Dubois","year":"2021","journal-title":"Lancet Neurol"},{"key":"R4-20251101","doi-asserted-by":"crossref","first-page":"120644","DOI":"10.1016\/j.talanta.2019.120644","article-title":"Applications of biosensors in Alzheimer's disease diagnosis","volume":"210","author":"Brazaca","year":"2020","journal-title":"Talanta"},{"key":"R5-20251101","first-page":"CD010783","article-title":"Mini-Mental State Examination (MMSE) for the detection of Alzheimer's disease and other dementias in people with mild cognitive impairment (MCI)","author":"Arevalo-Rodriguez","year":"2015","journal-title":"Cochrane Database Syst Rev 2015"},{"key":"R6-20251101","first-page":"CD010860","article-title":"Mini-Cog for the diagnosis of Alzheimer's disease dementia and other dementias within a community setting","author":"Fage","year":"2015","journal-title":"Cochrane Database Syst Rev"},{"key":"R7-20251101","doi-asserted-by":"crossref","first-page":"101","DOI":"10.1016\/j.compmedimag.2018.09.009","article-title":"Alzheimer's Disease Neuroimaging Initiative. 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