{"id":2925,"date":"2018-09-16T20:00:04","date_gmt":"2018-09-17T00:00:04","guid":{"rendered":"https:\/\/www.med.unc.edu\/bigs2?page_id=2925&#038;preview_id=2925"},"modified":"2020-10-13T18:12:27","modified_gmt":"2020-10-13T22:12:27","slug":"marm","status":"publish","type":"page","link":"https:\/\/www.med.unc.edu\/bigs2\/marm\/","title":{"rendered":"MARM: Multiscale Adaptive Regression Model, a DTI-Statistics Toolkit"},"content":{"rendered":"<div id=\"content1\">\n<div id=\"main\">\n<div id=\"right\">\n<div id=\"right_text\">\n<div class=\"box\" style=\"text-align: left\">Neuroimaging studies aim to analyze imaging data with complex spatial patterns\u00a0in a large number of locations (called voxels) on a two-dimensional (2D)\u00a0surface or in a 3D volume. Conventional analyses of imaging data include two\u00a0sequential steps: spatially smoothing imaging data and then independently\u00a0fitting a statistical model at each voxel. However, conventional analyses\u00a0suffer from the same amount of smoothing throughout the whole image, the\u00a0arbitrary choice of smoothing extent, and low statistical power in detecting\u00a0spatial patterns. We propose a multiscale adaptive regression model (MARM) to\u00a0integrate the propagation?separation (PS) approach (Polzehl and Spokoiny, 2000,\u00a02006) with statistical modeling at each voxel for spatial and adaptive analysis\u00a0of neuroimaging data from multiple subjects. MARM has three features: being\u00a0spatial, being hierarchical, and being adaptive. We use a multiscale adaptive\u00a0estimation and testing procedure (MAET) to utilize imaging observations from\u00a0the neighboring voxels of the current voxel to adaptively calculate parameter\u00a0estimates and test statistics. Theoretically, we establish consistency and\u00a0asymptotic normality of the adaptive estimates and the asymptotic distribution\u00a0of the adaptive test statistics.<\/div>\n<\/div>\n<\/div>\n<\/div>\n<div id=\"main\">\n<div id=\"right\">\n<div id=\"right_text\">\n<div class=\"box\">\n<p><strong><a href=\"http:\/\/www.nitrc.org\/projects\/sspm\/\">Software Download<\/a><\/strong><\/p>\n<p><strong>Citation<\/strong>: Yimei Li, Hongtu Zhu, Dinggang Shen, Weili Lin, John H. Gilmore and Joseph G\u00a0Ibrahim. Multiscale Adaptive Regression Models for Neuroimaging Data.\u00a0<em>Journal of the Royal Statistical Society Series B: Statistical Methodology<\/em>, 2011.<\/p>\n<\/div>\n<\/div>\n<\/div>\n<div id=\"footer\">\n<p>&nbsp;<\/p>\n<\/div>\n<\/div>\n<p><!-- footer ends--><\/p>\n<\/div>\n","protected":false},"excerpt":{"rendered":"<p>Neuroimaging studies aim to analyze imaging data with complex spatial patterns\u00a0in a large number of locations (called voxels) on a two-dimensional (2D)\u00a0surface or in a 3D volume. Conventional analyses of imaging data include two\u00a0sequential steps: spatially smoothing imaging data and then independently\u00a0fitting a statistical model at each voxel. However, conventional analyses\u00a0suffer from the same amount &hellip; <a href=\"https:\/\/www.med.unc.edu\/bigs2\/marm\/\" aria-label=\"Read more about MARM: Multiscale Adaptive Regression Model, a DTI-Statistics Toolkit\">Read more<\/a><\/p>\n","protected":false},"author":55348,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_acf_changed":false,"footnotes":"","_links_to":"","_links_to_target":""},"class_list":["post-2925","page","type-page","status-publish","hentry","odd"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v26.8 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>MARM: Multiscale Adaptive Regression Model, a DTI-Statistics Toolkit - BIG-S2<\/title>\n<meta name=\"robots\" content=\"noindex, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"MARM: Multiscale Adaptive Regression Model, a DTI-Statistics Toolkit - BIG-S2\" \/>\n<meta property=\"og:description\" content=\"Neuroimaging studies aim to analyze imaging data with complex spatial patterns\u00a0in a large number of locations (called voxels) on a two-dimensional (2D)\u00a0surface or in a 3D volume. Conventional analyses of imaging data include two\u00a0sequential steps: spatially smoothing imaging data and then independently\u00a0fitting a statistical model at each voxel. However, conventional analyses\u00a0suffer from the same amount &hellip; Read more\" \/>\n<meta property=\"og:url\" content=\"https:\/\/www.med.unc.edu\/bigs2\/marm\/\" \/>\n<meta property=\"og:site_name\" content=\"BIG-S2\" \/>\n<meta property=\"article:modified_time\" content=\"2020-10-13T22:12:27+00:00\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data1\" content=\"1 minute\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"WebPage\",\"@id\":\"https:\/\/www.med.unc.edu\/bigs2\/marm\/\",\"url\":\"https:\/\/www.med.unc.edu\/bigs2\/marm\/\",\"name\":\"MARM: Multiscale Adaptive Regression Model, a DTI-Statistics Toolkit - BIG-S2\",\"isPartOf\":{\"@id\":\"https:\/\/www.med.unc.edu\/bigs2\/#website\"},\"datePublished\":\"2018-09-17T00:00:04+00:00\",\"dateModified\":\"2020-10-13T22:12:27+00:00\",\"breadcrumb\":{\"@id\":\"https:\/\/www.med.unc.edu\/bigs2\/marm\/#breadcrumb\"},\"inLanguage\":\"en-US\",\"potentialAction\":[{\"@type\":\"ReadAction\",\"target\":[\"https:\/\/www.med.unc.edu\/bigs2\/marm\/\"]}]},{\"@type\":\"BreadcrumbList\",\"@id\":\"https:\/\/www.med.unc.edu\/bigs2\/marm\/#breadcrumb\",\"itemListElement\":[{\"@type\":\"ListItem\",\"position\":1,\"name\":\"Home\",\"item\":\"https:\/\/www.med.unc.edu\/bigs2\/\"},{\"@type\":\"ListItem\",\"position\":2,\"name\":\"MARM: Multiscale Adaptive Regression Model, a DTI-Statistics Toolkit\"}]},{\"@type\":\"WebSite\",\"@id\":\"https:\/\/www.med.unc.edu\/bigs2\/#website\",\"url\":\"https:\/\/www.med.unc.edu\/bigs2\/\",\"name\":\"BIG-S2\",\"description\":\"Biostatistics and Imaging Genomics analysis lab - Statistics &amp; Signal\",\"potentialAction\":[{\"@type\":\"SearchAction\",\"target\":{\"@type\":\"EntryPoint\",\"urlTemplate\":\"https:\/\/www.med.unc.edu\/bigs2\/?s={search_term_string}\"},\"query-input\":{\"@type\":\"PropertyValueSpecification\",\"valueRequired\":true,\"valueName\":\"search_term_string\"}}],\"inLanguage\":\"en-US\"}]}<\/script>\n<!-- \/ Yoast SEO plugin. -->","yoast_head_json":{"title":"MARM: Multiscale Adaptive Regression Model, a DTI-Statistics Toolkit - BIG-S2","robots":{"index":"noindex","follow":"follow","max-snippet":"max-snippet:-1","max-image-preview":"max-image-preview:large","max-video-preview":"max-video-preview:-1"},"og_locale":"en_US","og_type":"article","og_title":"MARM: Multiscale Adaptive Regression Model, a DTI-Statistics Toolkit - BIG-S2","og_description":"Neuroimaging studies aim to analyze imaging data with complex spatial patterns\u00a0in a large number of locations (called voxels) on a two-dimensional (2D)\u00a0surface or in a 3D volume. Conventional analyses of imaging data include two\u00a0sequential steps: spatially smoothing imaging data and then independently\u00a0fitting a statistical model at each voxel. However, conventional analyses\u00a0suffer from the same amount &hellip; Read more","og_url":"https:\/\/www.med.unc.edu\/bigs2\/marm\/","og_site_name":"BIG-S2","article_modified_time":"2020-10-13T22:12:27+00:00","twitter_card":"summary_large_image","twitter_misc":{"Est. reading time":"1 minute"},"schema":{"@context":"https:\/\/schema.org","@graph":[{"@type":"WebPage","@id":"https:\/\/www.med.unc.edu\/bigs2\/marm\/","url":"https:\/\/www.med.unc.edu\/bigs2\/marm\/","name":"MARM: Multiscale Adaptive Regression Model, a DTI-Statistics Toolkit - BIG-S2","isPartOf":{"@id":"https:\/\/www.med.unc.edu\/bigs2\/#website"},"datePublished":"2018-09-17T00:00:04+00:00","dateModified":"2020-10-13T22:12:27+00:00","breadcrumb":{"@id":"https:\/\/www.med.unc.edu\/bigs2\/marm\/#breadcrumb"},"inLanguage":"en-US","potentialAction":[{"@type":"ReadAction","target":["https:\/\/www.med.unc.edu\/bigs2\/marm\/"]}]},{"@type":"BreadcrumbList","@id":"https:\/\/www.med.unc.edu\/bigs2\/marm\/#breadcrumb","itemListElement":[{"@type":"ListItem","position":1,"name":"Home","item":"https:\/\/www.med.unc.edu\/bigs2\/"},{"@type":"ListItem","position":2,"name":"MARM: Multiscale Adaptive Regression Model, a DTI-Statistics Toolkit"}]},{"@type":"WebSite","@id":"https:\/\/www.med.unc.edu\/bigs2\/#website","url":"https:\/\/www.med.unc.edu\/bigs2\/","name":"BIG-S2","description":"Biostatistics and Imaging Genomics analysis lab - Statistics &amp; Signal","potentialAction":[{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https:\/\/www.med.unc.edu\/bigs2\/?s={search_term_string}"},"query-input":{"@type":"PropertyValueSpecification","valueRequired":true,"valueName":"search_term_string"}}],"inLanguage":"en-US"}]}},"_links_to":[],"_links_to_target":[],"_links":{"self":[{"href":"https:\/\/www.med.unc.edu\/bigs2\/wp-json\/wp\/v2\/pages\/2925","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.med.unc.edu\/bigs2\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/www.med.unc.edu\/bigs2\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/www.med.unc.edu\/bigs2\/wp-json\/wp\/v2\/users\/55348"}],"replies":[{"embeddable":true,"href":"https:\/\/www.med.unc.edu\/bigs2\/wp-json\/wp\/v2\/comments?post=2925"}],"version-history":[{"count":0,"href":"https:\/\/www.med.unc.edu\/bigs2\/wp-json\/wp\/v2\/pages\/2925\/revisions"}],"wp:attachment":[{"href":"https:\/\/www.med.unc.edu\/bigs2\/wp-json\/wp\/v2\/media?parent=2925"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}