Stroke causes behavioral deficits in multiple cognitive domains and there is a growing interest in predicting patient performance from neuroimaging data using machine learning techniques. Here, we investigated a deep learning approach based on convolutional neural networks (CNNs) for predicting the severity of language disorder from 3D lesion images from magnetic resonance imaging (MRI) in a heterogeneous sample of stroke patients. CNN performance was compared to that of conventional (shallow) machine learning methods, including ridge regression (RR) on the images’ principal components and support vector regression. We also devised a hybrid method based on re-using CNN’s high-level features as additional input to the RR model. Predictive accuracy of the four different methods was further investigated in relation to the size of the training set and the level of redundancy across lesion images in the dataset, which was evaluated in terms of location and topological properties of the lesions. The Hybrid model achieved the best performance in most cases, thereby suggesting that the high-level features extracted by CNNs are complementary to principal component analysis features and improve the model’s predictive accuracy. Moreover, our analyses indicate that both the size of training data and image redundancy are critical factors in determining the accuracy of a computational model in predicting behavioral outcome from the structural brain imaging data of stroke patients.

中文翻译：

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根据 MRI 病变图像预测中风患者认知表现的浅层学习方法和深度学习方法的比较

中风会导致多个认知领域的行为缺陷，人们越来越有兴趣使用机器学习技术根据神经影像数据预测患者的表现。在这里，我们研究了一种基于卷积神经网络 (CNN) 的深度学习方法，用于根据中风患者异质样本中磁共振成像 (MRI) 的 3D 病变图像来预测语言障碍的严重程度。将 CNN 性能与传统（浅层）机器学习方法进行了比较，包括图像主成分的岭回归 (RR) 和支持向量回归。我们还设计了一种基于重新使用 CNN 高级特征作为 RR 模型的附加输入的混合方法。进一步研究了四种不同方法的预测准确性与训练集的大小和数据集中病变图像之间的冗余水平的关系，并根据病变的位置和拓扑特性进行了评估。混合模型在大多数情况下取得了最佳性能，这表明 CNN 提取的高级特征与主成分分析特征是互补的，并提高了模型的预测准确性。此外，我们的分析表明，训练数据的大小和图像冗余都是确定计算模型根据中风患者的结构性脑成像数据预测行为结果的准确性的关键因素。