A BAYESIAN HIERARCHICAL SPATIAL POINT PROCESS MODEL FOR MULTI-TYPE NEUROIMAGING META-ANALYSIS.
A BAYESIAN HIERARCHICAL SPATIAL POINT PROCESS MODEL FOR MULTI-TYPE NEUROIMAGING META-ANALYSIS.
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DOI：
10.1214/14-aoas757
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发表时间：
2014-09
期刊：
影响因子：
--
通讯作者：
Johnson TD
中科院分区：
文献类型：
--
作者：
Kang J;Nichols TE;Wager TD;Johnson TD
神经影像学荟萃分析是一种重要的工具，可以在通常只有20名或更少受试者的研究中找到一致的效果。对大脑映射中的元分析的兴趣也受到最近对所谓的“反向推理”的关注的驱动：传统的“正向推理”识别参与任务的大脑区域，反向推理识别任务所涉及的认知过程。然而，这种反向推理需要一组元分析，每个元分析针对一个可能的认知领域。然而，现有的神经影像学荟萃分析方法有很大的局限性。神经影像学荟萃分析的常用方法不是基于模型的，不提供可解释的参数估计，并且只产生零假设推断;此外，它们通常是针对单个研究组设计的，不能产生反向推断。在这项工作中，我们通过采用非参数贝叶斯方法对来自多个类别或类型的研究的Meta分析数据来解决这些限制。具体地，来自每种类型的研究的焦点被建模为由随机强度函数驱动的聚类过程，该随机强度函数被建模为伽马随机场的核卷积。特定于类型的伽马随机场被链接并建模为所有类型共享的公共伽马随机场的实现，其诱导研究类型之间的相关性并模仿单变量混合效应模型的行为。我们说明了我们的模型模拟研究和Meta分析的五种情绪从219项研究和检查模型拟合的后验预测评估。此外，我们实现了反向推理，通过使用该模型来预测学习类型从一个新的研究。我们评估这种预测性能通过留一交叉验证，有效地实现使用重要性抽样技术。
Neuroimaging meta-analysis is an important tool for finding consistent effects over studies that each usually have 20 or fewer subjects. Interest in meta-analysis in brain mapping is also driven by a recent focus on so-called “reverse inference”: where as traditional “forward inference” identifies the regions of the brain involved in a task, a reverse inference identifies the cognitive processes that a task engages. Such reverse inferences, however, requires a set of meta-analysis, one for each possible cognitive domain. However, existing methods for neuroimaging meta-analysis have significant limitations. Commonly used methods for neuroimaging meta-analysis are not model based, do not provide interpretable parameter estimates, and only produce null hypothesis inferences; further, they are generally designed for a single group of studies and cannot produce reverse inferences. In this work we address these limitations by adopting a non-parametric Bayesian approach for meta analysis data from multiple classes or types of studies. In particular, foci from each type of study are modeled as a cluster process driven by a random intensity function that is modeled as a kernel convolution of a gamma random field. The type-specific gamma random fields are linked and modeled as a realization of a common gamma random field, shared by all types, that induces correlation between study types and mimics the behavior of a univariate mixed effects model. We illustrate our model on simulation studies and a meta analysis of five emotions from 219 studies and check model fit by a posterior predictive assessment. In addition, we implement reverse inference by using the model to predict study type from a newly presented study. We evaluate this predictive performance via leave-one-out cross validation that is efficiently implemented using importance sampling techniques.
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影响因子：
4.8
作者：
Eickhoff, Simon B.;Laird, Angela R.;Grefkes, Christian;Wang, Ling E.;Zilles, Karl;Fox, Peter T.
通讯作者：
Fox, Peter T.
影响因子：
3.7
作者：
Kang J;Johnson TD;Nichols TE;Wager TD
通讯作者：
Wager TD
影响因子：
1.2
作者：
BONDESSON, L
通讯作者：
BONDESSON, L
影响因子：
1
作者：
Moller, J;Syversveen, AR;Waagepetersen, RP
通讯作者：
Waagepetersen, RP
影响因子：
5.6
作者：
Adolphs, R
通讯作者：
Adolphs, R