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首页> 外文期刊>NeuroImage >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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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

机译：具有权重稀疏控制和预训练的深度神经网络可提取分层特征并增强分类性能：来自精神分裂症的全脑静止状态功能连接模式的证据

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摘要

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 layersodes, (2) the use of L1-norm regularization, (3) the use of the pre-training, (4) the use of frame wise 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. (C) 2015 Elsevier Inc. All rights reserved.
机译：从静止状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（例如支持向量机（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）已显示出从较低到较高的隐藏层系统地提取图像和语音数据的较低到较高级别信息的能力，从而显着提高了分类准确性。这项研究的目的是采用DNN对精神分裂症（SZ）患者与健康对照（HCs）的全脑静止状态FC模式分类，并识别与SZ相关的异常FC模式。我们假设通过DNN学习的从低到高的特征将显着提高分类精度，并提出了一种自适应学习算法，以通过L1范数正则化显式控制每个隐藏层的权重稀疏性。此外，权重通过基于堆叠自动编码器的预训练进行初始化，以进一步提高分类性能。系统对分类精度进行了系统评估，该函数取决于（1）隐藏层/节点的数量，（2）使用L1-范数正则化，（3）使用预训练，（4）使用逐帧移位（FD）移除，以及（5）使用解剖/功能分割。使用没有FD去除的解剖学上散开区域的FC模式，通过采用L1范数正则化和预训练的三个隐藏层和50个隐藏节点，实现了14.2％的错误率，该错误率大大低于SVM的错误率（22.3％）。此外，发现训练后的DNN权重（即学习的特征）代表了与HC相比SZ中异常FC模式的分层组织。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，通过使用峰度/模量度量对其量化，并且较高隐藏层的特征显示出将SZ与HC区分的整体/全局FC模式。我们提出的方案和通过使用DNN分类器和全脑FC数据获得的发现表明，这种方法显示出提高的能力，可以学习大脑成像数据中的隐藏模式，这对于开发SZ和其他神经精神疾病的诊断工具以及识别相关的异常FC模式。 （C）2015 Elsevier Inc.保留所有权利。

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