ASD-SAENet: A Sparse Autoencoder, and Deep-Neural Network Model for Detecting Autism Spectrum Disorder (ASD) Using fMRI Data.
ASD-SAENet: A Sparse Autoencoder, and Deep-Neural Network Model for Detecting Autism Spectrum Disorder (ASD) Using fMRI Data.
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ASD-SAENET：使用fMRI数据检测自闭症谱系障碍（ASD）的稀疏自动编码器和深神经网络模型。
DOI：
10.3389/fncom.2021.654315
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发表时间：
2021
影响因子：
3.2
通讯作者：
Saeed F
中科院分区：
文献类型：
--
作者：
Almuqhim F;Saeed F
自闭症谱系障碍（ASD）是一种异质性的神经发育障碍，其特征是沟通障碍和社会交往受限。目前的临床方法的缺点是完全基于行为学的观察，以及对ASD潜在的神经机制的理解不足，需要鉴定新的生物标志物，这些生物标志物可以帮助研究大脑发育和功能，并且可以导致ASD的准确和早期检测。在本文中，我们开发了一个名为ASD-SAENet的深度学习模型，用于使用fMRI数据将ASD患者与典型对照受试者进行分类。我们设计并实现了一个稀疏的自动编码器（SAE），从而优化了可用于分类的特征提取。然后将这些特征输入到深度神经网络（DNN）中，从而对更倾向于ASD的fMRI脑部扫描进行上级分类。我们提出的模型进行训练，以优化分类器，同时改善基于重建数据误差和分类器误差的提取特征。我们使用从17个不同研究中心收集的公开可用的自闭症脑成像数据交换（ABIDE）数据集评估了我们提出的深度学习模型，其中包括超过1，035名受试者。我们广泛的实验表明，与其他方法相比，ASD-SAENet对整个数据集表现出相当的准确性（70.8%）和上级特异性（79.1%）。此外，我们的实验证明了与其他最先进的方法相比，在17个成像中心中的12个上的上级结果，在不同的数据采集部位和协议中表现出上级的可推广性。实现的代码可以在我们实验室的GitHub门户网站上获得：https://github.com/pcdslab/ASD-SAENet。
Autism spectrum disorder (ASD) is a heterogenous neurodevelopmental disorder which is characterized by impaired communication, and limited social interactions. The shortcomings of current clinical approaches which are based exclusively on behavioral observation of symptomology, and poor understanding of the neurological mechanisms underlying ASD necessitates the identification of new biomarkers that can aid in study of brain development, and functioning, and can lead to accurate and early detection of ASD. In this paper, we developed a deep-learning model called ASD-SAENet for classifying patients with ASD from typical control subjects using fMRI data. We designed and implemented a sparse autoencoder (SAE) which results in optimized extraction of features that can be used for classification. These features are then fed into a deep neural network (DNN) which results in superior classification of fMRI brain scans more prone to ASD. Our proposed model is trained to optimize the classifier while improving extracted features based on both reconstructed data error and the classifier error. We evaluated our proposed deep-learning model using publicly available Autism Brain Imaging Data Exchange (ABIDE) dataset collected from 17 different research centers, and include more than 1,035 subjects. Our extensive experimentation demonstrate that ASD-SAENet exhibits comparable accuracy (70.8%), and superior specificity (79.1%) for the whole dataset as compared to other methods. Further, our experiments demonstrate superior results as compared to other state-of-the-art methods on 12 out of the 17 imaging centers exhibiting superior generalizability across different data acquisition sites and protocols. The implemented code is available on GitHub portal of our lab at: https://github.com/pcdslab/ASD-SAENet.
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影响因子：
11
作者：
Di Martino, A.;Yan, C-G;Li, Q.;Denio, E.;Castellanos, F. X.;Alaerts, K.;Anderson, J. S.;Assaf, M.;Bookheimer, S. Y.;Dapretto, M.;Deen, B.;Delmonte, S.;Dinstein, I.;Ertl-Wagner, B.;Fair, D. A.;Gallagher, L.;Kennedy, D. P.;Keown, C. L.;Keysers, C.;Lainhart, J. E.;Lord, C.;Luna, B.;Menon, V.;Minshew, N. J.;Monk, C. S.;Mueller, S.;Mueller, R. A.;Nebel, M. B.;Nigg, J. T.;O'Hearn, K.;Pelphrey, K. A.;Peltier, S. J.;Rudie, J. D.;Sunaert, S.;Thioux, M.;Tyszka, J. M.;Uddin, L. Q.;Verhoeven, J. S.;Wenderoth, N.;Wiggins, J. L.;Mostofsky, S. H.;Milham, M. P.
通讯作者：
Milham, M. P.
DOI：
10.15585/mmwr.ss6706a1
发表时间：
2018-04-27
期刊：
Morbidity and mortality weekly report. Surveillance summaries (Washington, D.C. : 2002)
影响因子：
--
作者：
Baio J;Wiggins L;Christensen DL;Maenner MJ;Daniels J;Warren Z;Kurzius-Spencer M;Zahorodny W;Robinson Rosenberg C;White T;Durkin MS;Imm P;Nikolaou L;Yeargin-Allsopp M;Lee LC;Harrington R;Lopez M;Fitzgerald RT;Hewitt A;Pettygrove S;Constantino JN;Vehorn A;Shenouda J;Hall-Lande J;Van Naarden Braun K;Dowling NF
通讯作者：
Dowling NF
影响因子：
4.2
作者：
Heinsfeld, Anibal Solon;Franco, Alexandre Rosa;Meneguzzi, Felipe
通讯作者：
Meneguzzi, Felipe
影响因子：
3.9
作者：
Bradshaw, Jessica;Steiner, Amanda Mossman;Koegel, Lynn Kern
通讯作者：
Koegel, Lynn Kern
影响因子：
3.7
作者：
Just, Marcel Adam;Cherkassky, Vladimir L.;Minshew, Nancy J.
通讯作者：
Minshew, Nancy J.