{"id":2992,"date":"2018-09-18T13:12:39","date_gmt":"2018-09-18T17:12:39","guid":{"rendered":"https:\/\/www.med.unc.edu\/bigs2\/?page_id=2992"},"modified":"2020-10-13T18:47:22","modified_gmt":"2020-10-13T22:47:22","slug":"fadtts-2","status":"publish","type":"page","link":"https:\/\/www.med.unc.edu\/bigs2\/fadtts-2\/","title":{"rendered":"FADTTS: Functional Analysis of Diffusion Tensor Tract Statistics, a DTI-Statistics Toolkit"},"content":{"rendered":"
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FADTTS is a functional analysis pipeline for delineating the structure of the\u00a0variability of multiple di usion properties along major white matter fiber\u00a0bundles and their association with a set of covariates of interest, such as\u00a0age, diagnostic status and gender, in various diffiusion tensor imaging\u00a0studies. The FADTTS integrates five statistical tools: a multivariate varying\u00a0coefficient model for allowing the varying coefficient functions to\u00a0characterize the varying association between fiber bundles di union properties\u00a0and a set of covariates, a weighted least squares estimation to estimate the\u00a0varying coefficient functions, a functional principal component analysis to\u00a0delineate the structure of the variability in fiber bundles diffusion\u00a0properties, a global test statistic to test hypotheses of interest, and a\u00a0simultaneous conffidence band to quantify the uncertainty in the estimated\u00a0coefficient function. FADTTS can be used to facilitate understanding normal brain development,\u00a0the neural bases of neuropsychiatric disorders, and the joint effects of
\nenvironmental and genetic factors on white matter fiber bundles.<\/p>\n

Software Download (with GUI) & Supplementary Documents<\/a><\/strong><\/p>\n

<\/h4>\n

Citation<\/strong>:<\/p>\n

1. Zhu H, Li R, and Kong L. Multivariate varying\u00a0coefficient models for functional responses. Annals of Statistics<\/em>. 2012.<\/p>\n

2. Zhu H, Kong L, Li R, et al. FADTTS: functional analysis of diffusion tensor tract statistics. Neuroimage<\/em>. 2011.<\/p>\n

3. Zhu H, Styner M, Li Y, et al. Multivariate varying coefficient models for DTI tract statistics. Med Image Comput Comput Assist Interv<\/em>. 2010.<\/p>\n

4. Verde AR, Berger JB, Gupta A, et al. UNC-Utah NA-MIC DTI framework: Atlas Based Fiber Tract Analysis with Application to a Study of Nicotine Smoking Addiction. Proc SPIE Int Soc Opt Eng<\/em>. 2013.<\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n

<\/p>\n<\/div>\n","protected":false},"excerpt":{"rendered":"

FADTTS is a functional analysis pipeline for delineating the structure of the\u00a0variability of multiple di usion properties along major white matter fiber\u00a0bundles and their association with a set of covariates of interest, such as\u00a0age, diagnostic status and gender, in various diffiusion tensor imaging\u00a0studies. The FADTTS integrates five statistical tools: a multivariate varying\u00a0coefficient model for allowing … Read more<\/a><\/p>\n","protected":false},"author":1503,"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-2992","page","type-page","status-publish","hentry","odd"],"acf":[],"yoast_head":"\nFADTTS: Functional Analysis of Diffusion Tensor Tract Statistics, 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=\"FADTTS: Functional Analysis of Diffusion Tensor Tract Statistics, a DTI-Statistics Toolkit - BIG-S2\" \/>\n<meta property=\"og:description\" content=\"FADTTS is a functional analysis pipeline for delineating the structure of the\u00a0variability of multiple di usion properties along major white matter fiber\u00a0bundles and their association with a set of covariates of interest, such as\u00a0age, diagnostic status and gender, in various diffiusion tensor imaging\u00a0studies. 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