{"doi":"10.1016/j.ynirp.2024.100227","title":"MRI-guided clustering of patients with mild dementia due to Alzheimer's disease using self-organizing maps","abstract":null,"journal":"NeuroImage: Reports","year":2024,"id":596836,"datarank":0.26876392038420827,"base_score":1.791759469228055,"endowment":1.791759469228055,"self_citation_contribution":0.26876392038420827,"citation_network_contribution":0.0,"self_endowment_contribution":0.26876392038420827,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":5,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":null,"is_data_producer":false,"deposit_databanks":null,"is_oa":false,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":null,"fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":1528693,"name":"Bhargav T. Nallapu","orcid":null,"position":1,"is_corresponding":false},{"id":209824,"name":"Richard B. Lipton","orcid":null,"position":2,"is_corresponding":false},{"id":612094,"name":"Ellen Grober","orcid":"0000-0001-7998-5778","position":3,"is_corresponding":false},{"id":695614,"name":"Ali Ezzati","orcid":"0000-0003-1307-4589","position":4,"is_corresponding":false},{"id":895198,"name":"Kellen K. Petersen","orcid":"0000-0003-3195-3456","position":0,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"resolved":true,"title":"MRI-guided clustering of patients with mild dementia due to Alzheimer's disease using self-organizing maps","abstract":"<h4>Introduction</h4>Alzheimer's disease (AD) is a phenotypically and pathologically heterogenous neurodegenerative disorder. This heterogeneity can be studied and disentangled using data-driven clustering techniques.<h4>Methods</h4>We implemented a self-organizing map clustering algorithm on baseline volumetric MRI measures from nine brain regions of interest (ROIs) to cluster 1041 individuals enrolled in the placebo arm of the EXPEDITION3 trial. Volumetric MRI differences were compared among clusters. Demographics as well as baseline and longitudinal cognitive performance metrics were used to evaluate cluster characteristics.<h4>Results</h4>Three distinct clusters, with an overall silhouette coefficient of 0.491, were identified based on MRI volumetrics. Cluster 1 (N = 400) had the largest baseline volumetric measures across all ROIs and the best cognitive performance at baseline. Cluster 2 (N = 269) had larger hippocampal and medial temporal lobe volumes, but smaller parietal lobe volumes in comparison with the third cluster (N = 372). Significant between-group mean differences were observed between Clusters 1 and 2 (difference, 2.38; 95% CI, 1.85 to 2.91; P < 0.001), Clusters 1 and 3 (difference, 1.93; 95% CI, 1.41 to 2.44; P < 0.001), but not between Clusters 2 and 3 (difference, 0.45; 95% CI, -0.11 to 1.02; P = 0.146) in ADAS-14.<h4>Conclusions</h4>Volumetric MRI can be used to identify homogenous clusters of amyloid positive individuals with mild dementia. The groups identified differ in baseline and longitudinal characteristics. Cluster 1 shows little ADAS-14 change over the first 40 weeks of study on placebo treatment and may be unsuitable for identifying early benefits of treatment.","is_dataset_classified":null,"base_score":0.0,"endowment":0.0,"datacite_reuse_total":0,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"39886010","pmcid":"PMC11781377","openalex_id":null,"authors":[],"funders":[{"funder_name":"National Institute on Aging","grant_id":"K23 AG063993","title":null},{"funder_name":"National Institute on Aging","grant_id":"AG03949","title":null},{"funder_name":"Alzheimer&apos;s Association","grant_id":"2019-AACSF-641329","title":null},{"funder_name":"NIA NIH HHS","grant_id":"RF1 AG057531","title":null},{"funder_name":"NIA NIH HHS","grant_id":"R56 AG057548","title":null},{"funder_name":"NIA NIH HHS","grant_id":"RF1 AG054548","title":null},{"funder_name":"NIA NIH HHS","grant_id":"R01 AG048642","title":null},{"funder_name":"NIA NIH HHS","grant_id":"R01 AG060933","title":null},{"funder_name":"NIA NIH HHS","grant_id":"R21 AG056920","title":null},{"funder_name":"NINDS NIH HHS","grant_id":"U24 NS113847","title":null},{"funder_name":"FDA HHS","grant_id":"UG3 FD006795","title":null},{"funder_name":"NIA NIH HHS","grant_id":"P01 AG003949","title":null},{"funder_name":"NIA NIH HHS","grant_id":"R01 AG062622","title":null},{"funder_name":"NINDS NIH HHS","grant_id":"U10 NS077308","title":null},{"funder_name":"National Institutes of Health","grant_id":"1RF1AG057531-01","title":"COSMIC: An international consortium to identify risk and protective factors and biomarkers of cognitive ageing and dementia in diverse ethno-racial groups and geographical settings"},{"funder_name":"National Institutes of Health","grant_id":"6R56AG057548-02","title":"The biological underpinnings of Motoric Cognitive Risk syndrome: a multi-center study"},{"funder_name":"National Institutes of Health","grant_id":"5U10NS077308-03","title":"Einstein Center for Excellence for Clinical Trials in Neuroscience"},{"funder_name":"National Institutes of Health","grant_id":"1U24NS113847-01","title":"Early Phase Pain Investigation Clinical Network: Greater New York Clinical Center"},{"funder_name":"National Institutes of Health","grant_id":"5K23AG063993-03","title":"Predictive analytics for cognitive decline and Alzheimer’s disease"},{"funder_name":"National Institutes of Health","grant_id":"1RF1AG054548-01","title":"MRI Measures of Cerebrovascular Injury and AD Atrophy in a Study of Latinos"},{"funder_name":"National Institutes of Health","grant_id":"5R21AG056920-02","title":"Correction of Bias in Estimating Risk of AD and Cognitive and Mobile Decline Using Auxiliary Information"},{"funder_name":"National Institutes of Health","grant_id":"1UG3FD006795-01","title":"Migraine Clinical Outcome Assessment System (MiCOAS)"},{"funder_name":"National Institutes of Health","grant_id":"5P01AG003949-29","title":"Neuropathology Core"}],"total_grants":23,"fwci":null,"citation_percentile":null,"influential_citations":0,"citation_trend":[],"oa_status":"gold","license":"CC BY NC","oa_locations":[{"url":"https://api.elsevier.com/content/article/PII:S2666956024000333?httpAccept=text/xml","host_type":"publisher"},{"url":"https://api.elsevier.com/content/article/PII:S2666956024000333?httpAccept=text/plain","host_type":"publisher"},{"url":"https://europepmc.org/articles/PMC11781377","host_type":"Europe_PMC"},{"url":"https://europepmc.org/articles/PMC11781377?pdf=render","host_type":"Europe_PMC"},{"url":"https://doi.org/10.1016/j.ynirp.2024.100227","host_type":""},{"url":"https://pubmed.ncbi.nlm.nih.gov/39886010","host_type":""},{"url":"http://dx.doi.org/10.1016/j.ynirp.2024.100227","host_type":""},{"url":"https://doaj.org/article/6c544ce44de2425788dfecc50d6f8bc7","host_type":""}],"fields_of_study":["0301 basic medicine","03 medical and health sciences","0302 clinical medicine"],"mesh_terms":[],"keywords":["Subtypes","Alzheimer’s disease","Machine Learning","Unsupervised Learning","Structural Mri","Mild Dementia","Neurosciences. 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