{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,23]],"date-time":"2025-10-23T21:02:50Z","timestamp":1761253370104,"version":"3.37.3"},"reference-count":41,"publisher":"Oxford University Press (OUP)","issue":"24","license":[{"start":{"date-parts":[[2019,5,16]],"date-time":"2019-05-16T00:00:00Z","timestamp":1557964800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"funder":[{"name":"Science and Technology Planning Project of Guangdong","award":["201904010417","2015B010131011"],"award-info":[{"award-number":["201904010417","2015B010131011"]}]},{"name":"Major Program of National Natural Science Foundation of China","award":["U15012561016942"],"award-info":[{"award-number":["U15012561016942"]}]},{"name":"Science and Technology Planning Project of Guangzhou","award":["201904010417"],"award-info":[{"award-number":["201904010417"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"NSFC","doi-asserted-by":"publisher","award":["81601562"],"award-info":[{"award-number":["81601562"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100007333","name":"ADNI","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100007333","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["U01 AG024904"],"award-info":[{"award-number":["U01 AG024904"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"name":"DOD ADNI"},{"DOI":"10.13039\/100000005","name":"Department of Defense","doi-asserted-by":"publisher","award":["W81XWH-12-2-0012"],"award-info":[{"award-number":["W81XWH-12-2-0012"]}],"id":[{"id":"10.13039\/100000005","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019,12,15]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>The detection of potential biomarkers of Alzheimer\u2019s disease (AD) is crucial for its early prediction, diagnosis and treatment. Voxel-wise genome-wide association study (VGWAS) is a commonly used method in imaging genomics and usually applied to detect AD biomarkers in imaging and genetic data. However, existing VGWAS methods entail large computational cost and disregard spatial correlations within imaging data. A novel method is proposed to solve these issues.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>We introduce a novel method to incorporate spatial correlations into a VGWAS framework for the detection of potential AD biomarkers. To consider the characteristics of AD, we first present a modification of a simple linear iterative clustering method for spatial grouping in an anatomically meaningful manner. Second, we propose a spatial\u2013anatomical similarity matrix to incorporate correlations among voxels. Finally, we detect the potential AD biomarkers from imaging and genetic data by using a fast VGWAS method and test our method on 708 subjects obtained from an Alzheimer\u2019s Disease Neuroimaging Initiative dataset. Results show that our method can successfully detect some new risk genes and clusters of AD. The detected imaging and genetic biomarkers are used as predictors to classify AD\/normal control subjects, and a high accuracy of AD\/normal control classification is achieved. To the best of our knowledge, the association between imaging and genetic data has yet to be systematically investigated while building statistical models for classifying AD subjects to create a link between imaging genetics and AD. Therefore, our method may provide a new way to gain insights into the underlying pathological mechanism of AD.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>https:\/\/github.com\/Meiyan88\/SASM-VGWAS.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btz401","type":"journal-article","created":{"date-parts":[[2019,5,7]],"date-time":"2019-05-07T22:40:06Z","timestamp":1557268806000},"page":"5271-5280","source":"Crossref","is-referenced-by-count":6,"title":["Incorporating spatial\u2013anatomical similarity into the VGWAS framework for AD biomarker detection"],"prefix":"10.1093","volume":"35","author":[{"given":"Meiyan","family":"Huang","sequence":"first","affiliation":[{"name":"Guangdong Provincial Key Laboratory of Medical Image Processing, School of Biomedical Engineering, Southern Medical University , Guangzhou 510515, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuwei","family":"Yu","sequence":"additional","affiliation":[{"name":"Guangdong Provincial Key Laboratory of Medical Image Processing, School of Biomedical Engineering, Southern Medical University , Guangzhou 510515, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wei","family":"Yang","sequence":"additional","affiliation":[{"name":"Guangdong Provincial Key Laboratory of Medical Image Processing, School of Biomedical Engineering, Southern Medical University , Guangzhou 510515, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qianjin","family":"Feng","sequence":"additional","affiliation":[{"name":"Guangdong Provincial Key Laboratory of Medical Image Processing, School of Biomedical Engineering, Southern Medical University , Guangzhou 510515, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"name":"Alzheimer\u2019s Disease Neuroimaging Initiative","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2019,5,16]]},"reference":[{"key":"2023020108351421900_btz401-B1","doi-asserted-by":"crossref","first-page":"2274","DOI":"10.1109\/TPAMI.2012.120","article-title":"SLIC superpixels compared to state-of-the-art superpixel methods","volume":"34","author":"Achanta","year":"2012","journal-title":"IEEE Trans. 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