Multivariate prediction of human behavior from resting state data is gaining increasing popularity in the neuroimaging community, with far-reaching translational implications in neurology and psychiatry. However, the high dimensionality of neuroimaging data increases the risk of overfitting, calling for the use of dimensionality reduction methods to build robust predictive models. In this work, we assess the ability of four well-known dimensionality reduction techniques to extract relevant features from resting state functional connectivity matrices of stroke patients, which are then used to build a predictive model of the associated deficits based on cross-validated regularized regression. In particular, we investigated the prediction ability over different neuropsychological scores referring to language, verbal memory, and spatial memory domains. Principal Component Analysis (PCA) and Independent Component Analysis (ICA) were the two best methods at extracting representative features, followed by Dictionary Learning (DL) and Non-Negative Matrix Factorization (NNMF). Consistent with these results, features extracted by PCA and ICA were found to be the best predictors of the neuropsychological scores across all the considered cognitive domains. For each feature extraction method, we also examined the impact of the regularization method, model complexity (in terms of number of features that entered in the model) and quality of the maps that display predictive edges in the resting state networks. We conclude that PCA-based models, especially when combined with L1 (LASSO) regularization, provide optimal balance between prediction accuracy, model complexity, and interpretability.

中文翻译：

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从脑卒中患者功能连接性数据预测神经心理学得分的特征提取方法比较

从静止状态数据对人类行为的多变量预测在神经影像界越来越受欢迎，在神经病学和精神病学领域具有深远的转化意义。但是，神经影像数据的高维数增加了过拟合的风险，因此需要使用降维方法来建立可靠的预测模型。在这项工作中，我们评估了四种著名的降维技术从中风患者的静止状态功能连接矩阵中提取相关特征的能力，然后将其用于基于交叉验证的正则回归建立相关缺陷的预测模型。特别是，我们调查了有关语言，言语记忆和空间记忆域的不同神经心理学评分的预测能力。主成分分析（PCA）和独立成分分析（ICA）是提取代表性特征的两种最佳方法，其次是字典学习（DL）和非负矩阵因式分解（NNMF）。与这些结果一致的是，发现PCA和ICA提取的特征是跨所有考虑的认知领域的神经心理学得分的最佳预测因子。对于每种特征提取方法，我们还检查了正则化方法，模型复杂度（根据输入模型的特征数量）和在静止状态网络中显示预测性边缘的地图的质量的影响。我们得出的结论是，基于PCA的模型，尤其是与L1（LASSO）正则化结合使用时，可以在预测准确性，模型复杂性和可解释性之间实现最佳平衡。