How good is good enough in path analysis of fMRI data?
How good is good enough in path analysis of fMRI data?
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DOI：
10.1006/nimg.2000.0544
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发表时间：
2000-04-01
期刊：
影响因子：
5.7
通讯作者：
Sharma, T
中科院分区：
文献类型：
--
作者：
Bullmore, ET;Horwitz, B;Sharma, T
研究了基于功能磁共振成像（fMRI）数据的区域间相关矩阵的路径分析模型拟合优度的评价问题。我们认为，模型评估的基础上测试的零假设，模型预测的相关矩阵等于人口相关矩阵是有问题的，因为P值的条件渐近分布的结果（这可能是无效的fMRI数据采集时间小于10分钟），以及任意规格的残差方差和有效的自由度在每个区域的fMRI时间序列。我们介绍了一种替代方法的基础上，自动识别的最佳拟合模型，可以找到占的数据的算法。该算法从零模型开始，其中所有的路径系数为零，并迭代地解除约束的系数，具有最大的拉格朗日乘子在每一步，直到一个模型被确定为具有最大的优良度的一个简约的拟合指数。在自举之后重复该过程，数据生成最佳模型的拟合优度的置信区间。如果理论上优选的模型的优度在该置信区间内，我们可以凭经验说理论模型可能是最佳模型。这种相对论和基于数据的模型评估策略说明了从20个正常志愿者在定期性能（5分钟）的任务要求语义决策和无声排练的功能MR图像的分析。一个模型，包括单向连接从额叶到顶叶皮层，旨在代表顺序参与的排练和监测组件的发音循环，被发现是不可辩驳的假设检验和置信限度内的最佳模型，可以拟合到数据。(C)北京大学出版社.
This paper is concerned with the problem of evaluating goodness-of-fit of a path analytic model to an interregional correlation matrix derived from functional magnetic resonance imaging (fMRI) data. We argue that model evaluation based on testing the null hypothesis that the correlation matrix predicted by the model equals the population correlation matrix is problematic because P values are conditional on asymptotic distributional results (which may not be valid for fMRI data acquired in less than 10 min), as well as arbitrary specification of residual variances and effective degrees of freedom in each regional fMRI time series. We introduce an alternative approach based on an algorithm for automatic identification of the best fitting model that can be found to account for the data. The algorithm starts from the null model, in which all path coefficients are zero, and iteratively unconstrains the coefficient which has the largest Lagrangian multiplier at each step until a model is identified which has maximum goodness by a parsimonious fit index. Repeating this process after bootstrapping the data generates a confidence interval for goodness-of-fit of the best model. If the goodness of the theoretically preferred model is within this confidence interval we can empirically say that the theoretical model could be the best model. This relativistic and data-based strategy for model evaluation is illustrated by analysis of functional MR images acquired from 20 normal volunteers during periodic performance (for 5 min) of a task demanding semantic decision and subvocal rehearsal. A model including unidirectional connections from frontal to parietal cortex, designed to represent sequential engagement of rehearsal and monitoring components of the articulatory loop, is found to be irrefutable by hypothesis-testing and within confidence limits for the best model that could be fitted to the data. (C) 2000 Academic Press.