There remains an open question about the usefulness and the interpretation of Machine learning (MLE) approaches for discrimination of spatial patterns of brain images between samples or activation states. In the last few decades, these approaches have limited their operation to feature extraction and linear classification tasks for between-group inference. In this context, statistical inference is assessed by randomly permuting image labels or by the use of random effect models that consider between-subject variability. These multivariate MLE-based statistical pipelines, whilst potentially more effective for detecting activations than hypotheses-driven methods, have lost their mathematical elegance, ease of interpretation, and spatial localization of the ubiquitous General linear Model (GLM). Recently, the estimation of the conventional GLM has been demonstrated to be connected to an univariate classification task when the design matrix is expressed as a binary indicator matrix. In this paper we explore the complete connection between the univariate GLM and MLE \emph{regressions}. To this purpose we derive a refined statistical test with the GLM based on the parameters obtained by a linear Support Vector Regression (SVR) in the \emph{inverse} problem (SVR-iGLM). Subsequently, random field theory (RFT) is employed for assessing statistical significance following a conventional GLM benchmark. Experimental results demonstrate how parameter estimations derived from each model (mainly GLM and SVR) result in different experimental design estimates that are significantly related to the predefined functional task. Moreover, using real data from a multisite initiative the proposed MLE-based inference demonstrates statistical power and the control of false positives, outperforming the regular GLM.
翻译:关于机器学习(MLE)方法在样本或激活状态之间对大脑图像空间模式进行区分的实用性和解释问题仍然存在一个未决问题。在过去几十年中,这些方法限制了其功能,以突出群体间推断的提取和线性分类任务。在这方面,统计推论是通过随机变换图像标签或使用随机调整图像标签或使用随机随机效应模型来评估的,其中考虑到不同对象之间的变异性。这些基于MLE的多变量统计管道,虽然可能比假设驱动的MR方法更能有效检测激活,但已经失去了其数学优雅度、解释方便度和普通通用直观线性模型(GLM)的空间定位。最近,对常规 GLM的估算已经证明,当设计矩阵被标为常规指标矩阵矩阵时,将它与单词分类任务评估联系起来。我们探讨了单词GLEM 和MLEM 递归正值方法之间的完整联系。为此,我们根据从直径直径直径的直径直径直径直径直径GLLLLLeal 和直径直径直径LLLLLLL值估算的参数来进行精确的统计测试。