Correspondence between fMRI and SNP data by group sparse canonical correlation analysis
نویسندگان
چکیده
منابع مشابه
Correspondence between fMRI and SNP data by group sparse canonical correlation analysis
Both genetic variants and brain region abnormalities are recognized as important factors for complex diseases (e.g., schizophrenia). In this paper, we investigated the correspondence between single nucleotide polymorphism (SNP) and brain activity measured by functional magnetic resonance imaging (fMRI) to understand how genetic variation influences the brain activity. A group sparse canonical c...
متن کاملGroup Study of Simulated Driving fMRI Data by Multiset Canonical Correlation Analysis
In this work, we apply a novel statistical method, multiset canonical correlation analysis (M-CCA), to study a group of functional magnetic resonance imaging (fMRI) datasets acquired during simulated driving task. The M-CCA method jointly decomposes fMRI datasets from different subjects/sessions into brain activation maps and their associated time courses, such that the correlation in each grou...
متن کاملSparse Weighted Canonical Correlation Analysis
Given two data matrices X and Y , Sparse canonical correlation analysis (SCCA) is to seek two sparse canonical vectors u and v to maximize the correlation between Xu and Y v. However, classical and sparse CCA models consider the contribution of all the samples of data matrices and thus cannot identify an underlying specific subset of samples. To this end, we propose a novel Sparse weighted cano...
متن کاملSparse Kernel Canonical Correlation Analysis
We review the recently proposed method of Relevance Vector Machines which is a supervised training method related to Support Vector Machines. We also review the statistical technique of Canonical Correlation Analysis and its implementation in a Feature Space. We show how the technique of Relevance Vectors may be applied to the method of Kernel Canonical Correlation Analysis to gain a very spars...
متن کاملStructured Sparse Canonical Correlation Analysis
In this paper, we propose to apply sparse canonical correlation analysis (sparse CCA) to an important genome-wide association study problem, eQTL mapping. Existing sparse CCA models do not incorporate structural information among variables such as pathways of genes. This work extends the sparse CCA so that it could exploit either the pre-given or unknown group structure via the structured-spars...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Medical Image Analysis
سال: 2014
ISSN: 1361-8415
DOI: 10.1016/j.media.2013.10.010