The Diagnosis of Autism Spectrum Disorder Based on the Random Neural Network Cluster

Bi, Xia-an; Liu, Yingchao; Jiang, Qin; Qing, Shu; Sun, Qi; Dai, Jianhua

doi:10.3389/fnhum.2018.00257

Cited by 37 publications

(33 citation statements)

References 52 publications

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“…Several RNN-based methods were proposed to fully utilize the temporal information in the rs-fMRI time-series data. Bi et al ( 2018 ) designed a random NN cluster, which combined multiple NNs into a model, to improve the classification performance in the diagnosis of ASD. Compared to five different NNs, the random Elman cluster obtained the highest accuracy.…”

Section: Applications In Brain Disorder Analysis With Medical Imagmentioning

confidence: 99%

A Survey on Deep Learning for Neuroimaging-Based Brain Disorder Analysis

et al. 2020

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Deep learning has recently been used for the analysis of neuroimages, such as structural magnetic resonance imaging (MRI), functional MRI, and positron emission tomography (PET), and it has achieved significant performance improvements over traditional machine learning in computer-aided diagnosis of brain disorders. This paper reviews the applications of deep learning methods for neuroimaging-based brain disorder analysis. We first provide a comprehensive overview of deep learning techniques and popular network architectures by introducing various types of deep neural networks and recent developments. We then review deep learning methods for computer-aided analysis of four typical brain disorders, including Alzheimer's disease, Parkinson's disease, Autism spectrum disorder, and Schizophrenia, where the first two diseases are neurodegenerative disorders and the last two are neurodevelopmental and psychiatric disorders, respectively. More importantly, we discuss the limitations of existing studies and present possible future directions.

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Section: Applications In Brain Disorder Analysis With Medical Imagmentioning

confidence: 99%

A Survey on Deep Learning for Neuroimaging-Based Brain Disorder Analysis

et al. 2020

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“…Recently, using neural networks and deep learning methods such as autoencoders, Deep Neural Network (DNN), Long Short Term Memory (LSTM), and Convolutional Neural Network (CNN) have also become very popular for diagnosing ASD (Dvornek et al, 2017; Guo et al, 2017; Bi et al, 2018a; Brown et al, 2018; Khosla et al, 2018; Li et al, 2018). Brown et al (2018) obtained 68.7% classification accuracy on 1, 013 subjects composed of 539 healthy control and 474 with ASD, by proposing an element-wise layer for DNNs which incorporated the data-driven structural priors.…”

Section: Introductionmentioning

confidence: 99%

ASD-DiagNet: A Hybrid Learning Approach for Detection of Autism Spectrum Disorder Using fMRI Data

Eslami

Mirjalili

Fong

et al. 2019

Front. Neuroinform.

245

128

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Heterogeneous mental disorders such as Autism Spectrum Disorder (ASD) are notoriously difficult to diagnose, especially in children. The current psychiatric diagnostic process is based purely on the behavioral observation of symptomology (DSM-5/ICD-10) and may be prone to misdiagnosis. In order to move the field toward more quantitative diagnosis, we need advanced and scalable machine learning infrastructure that will allow us to identify reliable biomarkers of mental health disorders. In this paper, we propose a framework called ASD-DiagNet for classifying subjects with ASD from healthy subjects by using only fMRI data. We designed and implemented a joint learning procedure using an autoencoder and a single layer perceptron (SLP) which results in improved quality of extracted features and optimized parameters for the model. Further, we designed and implemented a data augmentation strategy, based on linear interpolation on available feature vectors, that allows us to produce synthetic datasets needed for training of machine learning models. The proposed approach is evaluated on a public dataset provided by Autism Brain Imaging Data Exchange including 1, 035 subjects coming from 17 different brain imaging centers. Our machine learning model outperforms other state of the art methods from 10 imaging centers with increase in classification accuracy up to 28% with maximum accuracy of 82%. The machine learning technique presented in this paper, in addition to yielding better quality, gives enormous advantages in terms of execution time (40 min vs. 7 h on other methods). The implemented code is available as GPL license on GitHub portal of our lab (https://github.com/pcdslab/ASD-DiagNet).

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“…Compared with previously developed classification methods [22, 57, 58], our strategy is easier to implement because modular information is highly accessible. In this study, we develop a new clustering algorithm to find functional modules but it is possible to use other cortex parcellation schemes [10, 11, 59], including anatomical parcellations which has been integrated in many brain image analysis tools.…”

Section: Resultsmentioning

confidence: 99%

“…In this study, we develop a new clustering algorithm to find functional modules but it is possible to use other cortex parcellation schemes [10, 11, 59], including anatomical parcellations which has been integrated in many brain image analysis tools. Some previously used feature selection strategies are quite complicated, especially when neural networks are involved [22, 57, 58, 60]. Our strategy takes less time and can also achieve high performance.…”

Section: Resultsmentioning

confidence: 99%

A network clustering based feature selection strategy for classifying autism spectrum disorder

et al. 2019

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BackgroundAdvanced non-invasive neuroimaging techniques offer new approaches to study functions and structures of human brains. Whole-brain functional networks obtained from resting state functional magnetic resonance imaging has been widely used to study brain diseases like autism spectrum disorder (ASD). Auto-classification of ASD has become an important issue. Existing classification methods for ASD are based on features extracted from the whole-brain functional networks, which may be not discriminant enough for good performance.MethodsIn this study, we propose a network clustering based feature selection strategy for classifying ASD. In our proposed method, we first apply symmetric non-negative matrix factorization to divide brain networks into four modules. Then we extract features from one of four modules called default mode network (DMN) and use them to train several classifiers for ASD classification.ResultsThe computational experiments show that our proposed method achieves better performances than those trained with features extracted from the whole brain network.ConclusionIt is a good strategy to train the classifiers for ASD based on features from the default mode subnetwork.

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The Diagnosis of Autism Spectrum Disorder Based on the Random Neural Network Cluster

Cited by 37 publications

References 52 publications

A Survey on Deep Learning for Neuroimaging-Based Brain Disorder Analysis

A Survey on Deep Learning for Neuroimaging-Based Brain Disorder Analysis

ASD-DiagNet: A Hybrid Learning Approach for Detection of Autism Spectrum Disorder Using fMRI Data

A network clustering based feature selection strategy for classifying autism spectrum disorder

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