Sparse logistic regression for whole-brain classification of fMRI data.
Sparse logistic regression for whole-brain classification of fMRI data.
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DOI：
10.1016/j.neuroimage.2010.02.040
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发表时间：
2010-06
期刊：
影响因子：
5.7
通讯作者：
Menon, Vinod
中科院分区：
文献类型：
--
作者：
Ryali, Srikanth;Supekar, Kaustubh;Abrams, Daniel A.;Menon, Vinod
多变量模式识别方法越来越多地被用于识别多区域大脑活动模式，这些模式利用功能磁共振成像数据将一种认知条件或实验组与另一种认知条件或实验组区分开来。这些方法的性能往往是有限的，因为在分析功能磁共振成像数据中考虑的区域的数量相比，观察（试验或参与者）的数量很大。现有的方法，旨在解决这一问题的维度是不太理想的，因为他们要么过拟合的数据或计算上是棘手的。在这里，我们描述了一种基于逻辑回归的新方法，该方法使用L1和L2范数正则化的组合，可以更准确地估计多个条件或组中的区分性大脑区域。使用快速估计程序计算的L1范数确保了快速、稀疏和可推广的解决方案; L2范数确保了相关的大脑区域包含在所得的解决方案中，这是fMRI数据分析的一个关键方面，通常被现有方法忽视。我们首先评估我们的方法在模拟数据上的性能，然后研究其在区分匹配良好的音乐和语音刺激方面的有效性。我们还比较了我们的程序与其他方法，单独使用L1范数正则化或基于支持向量机的特征消除。在模拟数据上，我们的方法在宽范围的对比度噪声比和特征流行率上的表现明显优于现有方法。在实验fMRI数据上，我们的方法在选择性地隔离分布式额颞网络方面更有效，该网络区分了已知参与语音和音乐处理的大脑区域。这些研究结果表明，我们的方法不仅是计算效率，但它也实现了识别相关的歧视性大脑区域和准确分类功能磁共振成像数据的双重目标。
Multivariate pattern recognition methods are increasingly being used to identify multiregional brain activity patterns that collectively discriminate one cognitive condition or experimental group from another, using fMRI data. The performance of these methods is often limited because the number of regions considered in the analysis of fMRI data is large compared to the number of observations (trials or participants). Existing methods that aim to tackle this dimensionality problem are less than optimal because they either over-fit the data or are computationally intractable. Here, we describe a novel method based on logistic regression using a combination of L1 and L2 norm regularization that more accurately estimates discriminative brain regions across multiple conditions or groups. The L1 norm, computed using a fast estimation procedure, ensures a fast, sparse and generalizable solution; the L2 norm ensures that correlated brain regions are included in the resulting solution, a critical aspect of fMRI data analysis often overlooked by existing methods. We first evaluate the performance of our method on simulated data and then examine its effectiveness in discriminating between well-matched music and speech stimuli. We also compared our procedures with other methods which use either L1-norm regularization alone or support vector machine based feature elimination. On simulated data, our methods performed significantly better than existing methods across a wide-range of contrast-to-noise ratios and feature prevalence rates. On experimental fMRI data, our methods were more effective in selectively isolating a distributed fronto-temporal network that distinguished between brain regions known to be involved in speech and music processing. These findings suggest that our method is not only computationally efficient, but it also achieves the twin objectives of identifying relevant discriminative brain regions and accurately classifying fMRI data.
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