Bayesian Inference for the Directional Brain Network Modeled by High-dimensional Damped Harmonic Osc

来源 :数学统计在医学成像及大数据应用的集成方法研讨会(MSMIA2016) | 被引量 : 0次 | 上传用户:evaclamp
下载到本地 , 更方便阅读
声明 : 本文档内容版权归属内容提供方 , 如果您对本文有版权争议 , 可与客服联系进行内容授权或下架
论文部分内容阅读
  We use ordinary differential equations(ODE)to model the directional interaction,also called effective connectivity,among brain regions.In contrast to existing ODE models that focus on effective connectivity among only a few brain re-gions and that rely on strong prior belief of the existence and strength of connections,we propose a high-dimensional ODE model motivated by statistical considerations to explore connectivity among multiple small brain regions.
其他文献
会议
  Motivated by the needs of selecting important features from big neu-roimaging data,we develop a Bayesian variable screening algorithm for ultra-high dimensi
会议
  Medical imaging data have been widely applied for prognosis,screening,diagnosis,and treatment of various diseases in modern health care.In this study,we con
会议
  Different from the standard treatment discovery framework which is used for finding single treatments for a homogenous group of patients,personal-ized medic
会议
  We propose a bivariate quantile regression method for the bivariate vary-ing coefficient model through a directional approach.The varying coefficients are a
会议
  The completion of tensors,or high-order arrays,attracts significant atten-tion in recent research.Current literature on tensor completion primarily focuses
会议
  With the advantages of fairly good spatial(3 mm)and temporal reso-lution(0.1-2s),no radioactivity,and easy implementation,resting-state functional magnetic
会议
  Many challenging issues,such as statistical variability in a population,arise from the study of structural connectome maps by using diffusion MRI(dMRI)tract
会议
  Modern Clinical research studies commonly acquire complementary multi-modal and multi-source data for each patient in order to obtain a more accurate and ri
会议
  Local dependence is common in high-dimension and non-Euclidean data sequences.We consider the testing and estimation of change-points in such sequences.A ne
会议