Interpretable whole-brain prediction analysis with GraphNet
Interpretable whole-brain prediction analysis with GraphNet
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DOI：
10.1016/j.neuroimage.2012.12.062
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发表时间：
2013-05-15
期刊：
影响因子：
5.7
通讯作者：
Taylor, Jonathan E.
中科院分区：
文献类型：
--
作者：
Grosenick, Logan;Klingenberg, Brad;Taylor, Jonathan E.
多变量机器学习方法越来越多地被用于分析神经成像数据，通常取代了更传统的、一次拟合一个体素的数据的“大规模单变量”技术。在功能磁共振成像(FMRI)文献中，这导致了“现成”分类和回归方法的广泛应用。这些通用的方法允许研究人员使用现成的算法，从神经活动的分布式模式中准确地解码感知、认知或行为状态。然而，当应用于相关的全脑功能磁共振数据时，这些方法存在系数不稳定、对异常值敏感、产生稠密解的问题，如果没有任意的阈值，则很难解释这些解。在这里，我们开发了图形约束弹性网络(GraphNet)的变体，GraphNet是一种快速的全脑回归和分类方法，用于空间和时间相关的数据，自动生成可解释的系数图(Grosenick等人，2009b)。GraphNet方法通过将结构化图形约束(基于关于系数平滑或连通性的知识)与自动选择重要变量的全局稀疏诱导先验相结合，产生稀疏但结构化的解决方案。由于GraphNet方法可以有效地将回归或分类模型匹配到全脑、多时间点数据集，并相对于感兴趣体积(VOI)方法提高分类精度，因此它们消除了固有的有偏见的VOI分析的需要，并允许全脑匹配，而不会出现困扰大量单变量和漫游VOI(“探照灯”)方法的多重比较问题。由于fMRI数据不太可能是正态分布的，我们(1)扩展了GraphNet以包括对异常值不敏感的稳健损失函数，(2)为它们配备了渐近保证正确变量选择的“自适应”惩罚，以及(3)开发了一种新的稀疏结构化支持向量图分类器(SVGN)。当应用于以前发表的数据时(Knutson等人，2007年)，这些高效的全脑方法显著提高了分类精度，而不是以前报道的基于VOI的相同数据分析(Grosenick等人，2008年；Knutson等人，2007年)，同时发现了原始VOI方法中没有记录的任务相关区域。关键是，GraphNet的估计符合Knutson等人的估计。(2007)数据很好地适用于三年多后就同一任务收集的样本外数据，但具有不同的主题和刺激(Karmarkar等人，提交供出版)。通过从多个时间点(>；100,000个“特征”)获取的全脑数据中稳健而高效地选择重要体素，这些方法实现了对大脑区域的数据驱动选择，从而准确预测个体内部和跨个体的单次试验行为。(C)2013 Elsevier Inc.保留所有权利。
Multivariate machine learning methods are increasingly used to analyze neuroimaging data, often replacing more traditional "mass univariate" techniques that fit data one voxel at a time. In the functional magnetic resonance imaging (fMRI) literature, this has led to broad application of "off-the-shelf" classification and regression methods. These generic approaches allow investigators to use ready-made algorithms to accurately decode perceptual, cognitive, or behavioral states from distributed patterns of neural activity. However, when applied to correlated whole-brain fMRI data these methods suffer from coefficient instability, are sensitive to outliers, and yield dense solutions that are hard to interpret without arbitrary thresholding. Here, we develop variants of the Graph-constrained Elastic-Net (GraphNet), a fast, whole-brain regression and classification method developed for spatially and temporally correlated data that automatically yields interpretable coefficient maps (Grosenick et al., 2009b). GraphNet methods yield sparse but structured solutions by combining structured graph constraints (based on knowledge about coefficient smoothness or connectivity) with a global sparsity-inducing prior that automatically selects important variables. Because GraphNet methods can efficiently fit regression or classification models to whole-brain, multiple time-point data sets and enhance classification accuracy relative to volume-of-interest (VOI) approaches, they eliminate the need for inherently biased VOI analyses and allow whole-brain fitting without the multiple comparison problems that plague mass univariate and roaming VOI ("searchlight") methods. As fMRI data are unlikely to be normally distributed, we (1) extend GraphNet to include robust loss functions that confer insensitivity to outliers, (2) equip them with "adaptive" penalties that asymptotically guarantee correct variable selection, and (3) develop a novel sparse structured Support Vector GraphNet classifier (SVGN). When applied to previously published data (Knutson et al., 2007), these efficient whole-brain methods significantly improved classification accuracy over previously reported VOI-based analyses on the same data (Grosenick et al., 2008; Knutson et al., 2007) while discovering task-related regions not documented in the original VOI approach. Critically, GraphNet estimates fit to the Knutson et al. (2007) data generalize well to out-of-sample data collected more than three years later on the same task but with different subjects and stimuli (Karmarkar et al., submitted for publication). By enabling robust and efficient selection of important voxels from whole-brain data taken over multiple time points (>100,000 "features"), these methods enable data-driven selection of brain areas that accurately predict single-trial behavior within and across individuals. (C) 2013 Elsevier Inc. All rights reserved.