Multimodal brain image analysis : first international workshop, MBIA 2011, held in conjunction with MICCAI 2011, Toronto, Canada, September 18, 2011 : proceedings最新文献
Pub Date : 2011-09-01DOI: 10.1007/978-3-642-24446-9_4
Li Shen, Sungeun Kim, Y. Qi, M. Inlow, S. Swaminathan, K. Nho, Jing Wan, S. Risacher, L. Shaw, J. Trojanowski, M. Weiner, A. Saykin
{"title":"Identifying Neuroimaging and Proteomic Biomarkers for MCI and AD via the Elastic Net","authors":"Li Shen, Sungeun Kim, Y. Qi, M. Inlow, S. Swaminathan, K. Nho, Jing Wan, S. Risacher, L. Shaw, J. Trojanowski, M. Weiner, A. Saykin","doi":"10.1007/978-3-642-24446-9_4","DOIUrl":"https://doi.org/10.1007/978-3-642-24446-9_4","url":null,"abstract":"","PeriodicalId":90657,"journal":{"name":"Multimodal brain image analysis : first international workshop, MBIA 2011, held in conjunction with MICCAI 2011, Toronto, Canada, September 18, 2011 : proceedings","volume":"103 1","pages":"27-34"},"PeriodicalIF":0.0,"publicationDate":"2011-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78085020","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2011-01-01DOI: 10.1007/978-3-642-24446-9_5
Yan Jin, Yonggang Shi, Shantanu H Joshi, Neda Jahanshad, Liang Zhan, Greig I de Zubicaray, Katie L McMahon, Nicholas G Martin, Margaret J Wright, Arthur W Toga, Paul M Thompson
Genetic analysis of diffusion tensor images (DTI) shows great promise in revealing specific genetic variants that affect brain integrity and connectivity. Most genetic studies of DTI analyze voxel-based diffusivity indices in the image space (such as 3D maps of fractional anisotropy) and overlook tract geometry. Here we propose an automated workflow to cluster fibers using a white matter probabilistic atlas and perform genetic analysis on the shape characteristics of fiber tracts. We apply our approach to large study of 4-Tesla high angular resolution diffusion imaging (HARDI) data from 198 healthy, young adult twins (age: 20-30). Illustrative results show heritability for the shapes of several major tracts, as color-coded maps.
{"title":"Heritability of White Matter Fiber Tract Shapes: A HARDI Study of 198 Twins.","authors":"Yan Jin, Yonggang Shi, Shantanu H Joshi, Neda Jahanshad, Liang Zhan, Greig I de Zubicaray, Katie L McMahon, Nicholas G Martin, Margaret J Wright, Arthur W Toga, Paul M Thompson","doi":"10.1007/978-3-642-24446-9_5","DOIUrl":"https://doi.org/10.1007/978-3-642-24446-9_5","url":null,"abstract":"<p><p>Genetic analysis of diffusion tensor images (DTI) shows great promise in revealing specific genetic variants that affect brain integrity and connectivity. Most genetic studies of DTI analyze voxel-based diffusivity indices in the image space (such as 3D maps of fractional anisotropy) and overlook tract geometry. Here we propose an automated workflow to cluster fibers using a white matter probabilistic atlas and perform genetic analysis on the shape characteristics of fiber tracts. We apply our approach to large study of 4-Tesla high angular resolution diffusion imaging (HARDI) data from 198 healthy, young adult twins (age: 20-30). Illustrative results show heritability for the shapes of several major tracts, as color-coded maps.</p>","PeriodicalId":90657,"journal":{"name":"Multimodal brain image analysis : first international workshop, MBIA 2011, held in conjunction with MICCAI 2011, Toronto, Canada, September 18, 2011 : proceedings","volume":"2011 ","pages":"35-43"},"PeriodicalIF":0.0,"publicationDate":"2011-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4205954/pdf/nihms393571.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"32772939","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2011-01-01DOI: 10.1007/978-3-642-24446-9_1
Xue Yang, Carolyn B Lauzon, Ciprian Crainiceanu, Brian Caffo, Susan M Resnick, Bennett A Landman
Massively univariate regression and inference in the form of statistical parametric mapping have transformed the way in which multi-dimensional imaging data are studied. In functional and structural neuroimaging, the de facto standard "design matrix"-based general linear regression model and its multi-level cousins have enabled investigation of the biological basis of the human brain. With modern study designs, it is possible to acquire multiple three-dimensional assessments of the same individuals - e.g., structural, functional and quantitative magnetic resonance imaging alongside functional and ligand binding maps with positron emission tomography. Current statistical methods assume that the regressors are non-random. For more realistic multi-parametric assessment (e.g., voxel-wise modeling), distributional consideration of all observations is appropriate (e.g., Model II regression). Herein, we describe a unified regression and inference approach using the design matrix paradigm which accounts for both random and non-random imaging regressors.
{"title":"Accounting for Random Regressors: A Unified Approach to Multi-modality Imaging.","authors":"Xue Yang, Carolyn B Lauzon, Ciprian Crainiceanu, Brian Caffo, Susan M Resnick, Bennett A Landman","doi":"10.1007/978-3-642-24446-9_1","DOIUrl":"https://doi.org/10.1007/978-3-642-24446-9_1","url":null,"abstract":"<p><p>Massively univariate regression and inference in the form of statistical parametric mapping have transformed the way in which multi-dimensional imaging data are studied. In functional and structural neuroimaging, the <i>de facto</i> standard \"design matrix\"-based general linear regression model and its multi-level cousins have enabled investigation of the biological basis of the human brain. With modern study designs, it is possible to acquire multiple three-dimensional assessments of the same individuals - e.g., structural, functional and quantitative magnetic resonance imaging alongside functional and ligand binding maps with positron emission tomography. Current statistical methods assume that the regressors are non-random. For more realistic multi-parametric assessment (e.g., voxel-wise modeling), distributional consideration of all observations is appropriate (e.g., Model II regression). Herein, we describe a unified regression and inference approach using the design matrix paradigm which accounts for both random and non-random imaging regressors.</p>","PeriodicalId":90657,"journal":{"name":"Multimodal brain image analysis : first international workshop, MBIA 2011, held in conjunction with MICCAI 2011, Toronto, Canada, September 18, 2011 : proceedings","volume":"7012 ","pages":"1-9"},"PeriodicalIF":0.0,"publicationDate":"2011-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1007/978-3-642-24446-9_1","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"32773945","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Multimodal brain image analysis : first international workshop, MBIA 2011, held in conjunction with MICCAI 2011, Toronto, Canada, September 18, 2011 : proceedings
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