INFERRING FUNCTIONAL NETWORK-BASED SIGNATURES VIA STRUCTURALLY-WEIGHTED LASSO MODEL.
INFERRING FUNCTIONAL NETWORK-BASED SIGNATURES VIA STRUCTURALLY-WEIGHTED LASSO MODEL.
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DOI：
10.1109/isbi.2013.6556638
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发表时间：
2013
期刊：
影响因子：
--
通讯作者：
Liu T
中科院分区：
文献类型：
--
作者：
Zhu D;Shen D;Liu T
目前大多数功能/有效连接分析的研究方法都集中在成对连接上，而不能处理网络规模的功能交互。本文基于静息状态功能磁共振成像（R-fMRI）数据，提出了一种结构加权LASSO （SW-LASSO）回归模型来表示多个兴趣区域（roi）之间的功能相互作用。由扩散张量成像（DTI）数据导出的结构连通性约束将指导权重的选择，从而调整不同roi对应的不同系数的惩罚水平。采用默认模式网络（Default Mode Network， DMN）作为测试平台，我们的研究结果表明，习得的SW-LASSO具有较好的区分轻度认知障碍（Mild Cognitive Impairment， MCI）受试者与正常对照的能力，具有表征不同状态下大脑功能特征的潜力，可以作为基于功能网络的特征。
Most current research approaches for functional/effective connectivity analysis focus on pair-wise connectivity and cannot deal with network-scale functional interactions. In this paper, we propose a structurally-weighted LASSO (SW-LASSO) regression model to represent the functional interaction among multiple regions of interests (ROIs) based on resting state fMRI (R-fMRI) data. The structural connectivity constraints derived from diffusion tenor imaging (DTI) data will guide the selection of the weights which adjust the penalty levels of different coefficients corresponding to different ROIs. Using the Default Mode Network (DMN) as a test-bed, our results indicate that the learned SW-LASSO has good capability of differentiating Mild Cognitive Impairment (MCI) subjects from their normal controls and has promising potential to characterize the brain functions among different condition, thus serving as the functional network-based signature.