Deep neural network with weight sparsity control and pre-training extracts hierarchical features and enhances classification performance: Evidence from whole-brain resting-state functional connectivity patterns of schizophrenia.
Deep neural network with weight sparsity control and pre-training extracts hierarchical features and enhances classification performance: Evidence from whole-brain resting-state functional connectivity patterns of schizophrenia.
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DOI：
10.1016/j.neuroimage.2015.05.018
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发表时间：
2016-01-01
期刊：
影响因子：
5.7
通讯作者：
Lee JH
中科院分区：
文献类型：
--
作者：
Kim J;Calhoun VD;Shim E;Lee JH
从静息态功能磁共振成像数据获得的功能连接（FC）模式通常用于通过使用模式分类器（如支持向量机（SVM））来研究神经精神疾病。同时，具有多个隐藏层的深度神经网络（DNN）已经显示出其从低到高的隐藏层系统地提取图像和语音数据的低到高级别信息的能力，显着提高了分类精度。本研究的目的是采用DNN对精神分裂症（SZ）患者与健康对照（HC）的全脑静息状态FC模式进行分类，并识别与SZ相关的异常FC模式。我们假设通过DNN学习的低到高级别特征将显着提高分类精度，并提出了一种自适应学习算法，通过L1范数正则化显式控制每个隐藏层的权重稀疏度。此外，通过基于堆栈的自编码器的预训练来初始化权重，以进一步提高分类性能。分类准确性被系统地评估为以下因素的函数：（1）隐藏层/节点的数量，（2）L1范数正则化的使用，（3）预训练的使用，（4）逐帧位移（FD）的使用去除，和（5）解剖/功能分组的使用。使用FC模式从解剖学分割区域没有FD去除，通过采用三个隐藏层和50个隐藏节点与L1范数正则化和预训练，实现了14.2%的错误率，这是大大低于错误率从SVM（22.3%）。此外，训练的DNN权重（即，学习的特征）被发现代表与HC相比SZ中异常FC模式的层次组织。具体而言，从较低的隐藏层提取的节点对表示涉及SZ的稀疏FC模式，这是通过使用峰度/模块化措施和来自较高隐藏层的特征进行量化，显示出区分SZ和HC的整体/全局FC模式。我们提出的方案和报告的结果通过使用DNN分类器和全脑FC数据表明，这种方法显示出更好的学习脑成像数据中隐藏模式的能力，这可能有助于开发SZ和其他神经精神疾病的诊断工具，并识别相关的异常FC模式。
Functional connectivity (FC) patterns obtained from resting-state functional magnetic resonance imaging data are commonly employed to study neuropsychiatric conditions by using pattern classifiers such as the support vector machine (SVM). Meanwhile, a deep neural network (DNN) with multiple hidden layers has shown its ability to systematically extract lower-to-higher level information of image and speech data from lower-to-higher hidden layers, markedly enhancing classification accuracy. The objective of this study was to adopt the DNN for whole-brain resting-state FC pattern classification of schizophrenia (SZ) patients vs. healthy controls (HCs) and identification of aberrant FC patterns associated with SZ. We hypothesized that the lower-to-higher level features learned via the DNN would significantly enhance the classification accuracy, and proposed an adaptive learning algorithm to explicitly control the weight sparsity in each hidden layer via L1-norm regularization. Furthermore, the weights were initialized via stacked autoencoder based pre-training to further improve the classification performance. Classification accuracy was systematically evaluated as a function of (1) the number of hidden layers/nodes, (2) the use of L1-norm regularization, (3) the use of the pre-training, (4) the use of framewise displacement (FD) removal, and (5) the use of anatomical/functional parcellation. Using FC patterns from anatomically parcellated regions without FD removal, an error rate of 14.2% was achieved by employing three hidden layers and 50 hidden nodes with both L1-norm regularization and pre-training, which was substantially lower than the error rate from the SVM (22.3%). Moreover, the trained DNN weights (i.e., the learned features) were found to represent the hierarchical organization of aberrant FC patterns in SZ compared with HC. Specifically, pairs of nodes extracted from the lower hidden layer represented sparse FC patterns implicated in SZ, which was quantified by using kurtosis/modularity measures and features from the higher hidden layer showed holistic/global FC patterns differentiating SZ from HC. Our proposed schemes and reported findings attained by using the DNN classifier and whole-brain FC data suggest that such approaches show improved ability to learn hidden patterns in brain imaging data, which may be useful for developing diagnostic tools for SZ and other neuropsychiatric disorders and identifying associated aberrant FC patterns.