Localizing and Comparing Weight Maps Generated from Linear Kernel Machine Learning Models

J. Schrouff, J. Crémers, G. Garraux, Luca Baldassarre, J. Miranda, C. Phillips
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引用次数: 37

Abstract

Recently, machine learning models have been applied to neuroimaging data, allowing to make predictions about a variable of interest based on the pattern of activation or anatomy over a set of voxels. These pattern recognition based methods present undeniable assets over classical (univariate) techniques, by providing predictions for unseen data, as well as the weights of each voxel in the model. However, the obtained weight map cannot be thresholded to perform regionally specific inference, leading to a difficult localization of the variable of interest. In this work, we provide local averages of the weights according to regions defined by anatomical or functional atlases (e.g. Brodmann atlas). These averages can then be ranked, thereby providing a sorted list of regions that can be (to a certain extent) compared with univariate results. Furthermore, we defined a "ranking distance", allowing for the quantitative comparison between localized patterns. These concepts are illustrated with two datasets.
由线性核机器学习模型生成的权重图的定位和比较
最近,机器学习模型已被应用于神经成像数据,允许基于一组体素的激活模式或解剖模式对感兴趣的变量进行预测。这些基于模式识别的方法通过提供对未见数据的预测以及模型中每个体素的权重,比经典(单变量)技术具有无可否认的优势。然而,获得的权重图不能被阈值化来执行区域特定的推理,导致感兴趣的变量难以定位。在这项工作中,我们根据解剖或功能地图集(例如Brodmann地图集)定义的区域提供了权重的局部平均值。然后可以对这些平均值进行排序,从而提供一个排序的区域列表,可以(在一定程度上)与单变量结果进行比较。此外,我们定义了一个“排序距离”,允许在局部模式之间进行定量比较。这些概念用两个数据集来说明。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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