A review of methods for classification and recognition of ASD using fMRI data

Feng, Wei; Liu, Guangyuan; Zeng, Kelong; Zeng, Minchen; Liu, Ying

doi:10.1016/j.jneumeth.2021.109456

Cited by 18 publications

(13 citation statements)

References 36 publications

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“…Most of the routine brain MRI results of children with autism are negative, which is not helpful for the early diagnosis of children with autism ( 16 , 17 ). In this study, we performed multimodal MRI scans on the brains of children with autism, and obtained the microstructures of the brains through image post-processing to understand whether the brain microstructure of children with autism is abnormal and obtain the criteria for early objective imaging diagnosis of the brain of children with autism.…”

Section: Discussionmentioning

confidence: 99%

Application of Quantitative Magnetic Resonance Imaging in the Diagnosis of Autism in Children

Tang

Nie²,

Liu

et al. 2022

Front. Med.

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ObjectiveTo explore the application of quantitative magnetic resonance imaging in the diagnosis of autism in children.MethodsSixty autistic children aged 2–3 years and 60 age- and sex-matched healthy children participated in the study. All the children were scanned using head MRI conventional sequences, 3D-T1, diffusion kurtosis imaging (DKI), enhanced T2*- weighted magnetic resonance angiography (ESWAN) and 3D-pseudo continuous Arterial Spin-Labeled (3D-pcASL) sequences. The quantitative susceptibility mapping (QSM), cerebral blood flow (CBF), and brain microstructure of each brain area were compared between the groups, and correlations were analyzed.ResultsThe iron content and cerebral blood flow in the frontal lobe, temporal lobe, hippocampus, caudate nucleus, substantia nigra, and red nucleus of the study group were lower than those in the corresponding brain areas of the control group (P < 0.05). The mean kurtosis (MK), radial kurtosis (RK), and axial kurtosis (AK) values of the frontal lobe, temporal lobe, putamen, hippocampus, caudate nucleus, substantia nigra, and red nucleus in the study group were lower than those of the corresponding brain areas in the control group (P < 0.05). The mean diffusivity (MD) and fractional anisotropy of kurtosis (FAK) values of the frontal lobe, temporal lobe and hippocampus in the control group were lower than those in the corresponding brain areas in the study group (P < 0.05). The values of CBF, QSM, and DKI in frontal lobe, temporal lobe and hippocampus could distinguish ASD children (AUC > 0.5, P < 0.05), among which multimodal technology (QSM, CBF, DKI) had the highest AUC (0.917) and DKI had the lowest AUC (0.642).ConclusionQuantitative magnetic resonance imaging (including QSM, 3D-pcASL, and DKI) can detect abnormalities in the iron content, cerebral blood flow and brain microstructure in young autistic children, multimodal technology (QSM, CBF, DKI) could be considered as the first choice of imaging diagnostic technology.Clinical Trial Registration[http://www.chictr.org.cn/searchprojen.aspx], identifier [ChiCTR2000029699].

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Section: Discussionmentioning

confidence: 99%

Application of Quantitative Magnetic Resonance Imaging in the Diagnosis of Autism in Children

Tang

Nie²,

Liu

et al. 2022

Front. Med.

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“…In clinical practice, most routine MRI findings are negative, which is detrimental to the early diagnosis of autism ( 25 , 26 ). However, DKI, a kind of diffusion-weighted imaging, can provide abundant physiologic information about brain tissues ( 27 ).…”

Section: Discussionmentioning

confidence: 99%

Application of TBSS-based machine learning models in the diagnosis of pediatric autism

Zhao²,

Sun³

et al. 2023

Front. Neurol.

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ObjectiveTo explore the microstructural changes of white matter in children with pediatric autism by using diffusion kurtosis imaging (DKI), and evaluate whether the combination of tract-based spatial statistics (TBSS) and back-propagation neural network (BPNN)/support vector machine (SVM)/logistic regression (LR) was feasible for the classification of pediatric autism.MethodsDKI data were retrospectively collected from 32 children with autism and 27 healthy controls (HCs). Kurtosis fractional anisotropy (FAK), mean kurtosis (MK), axial kurtosis (KA), radial kurtosis (RK), fractional anisotropy (FA), axial diffusivity (DA), mean diffusivity (MD) and Radial diffusivity (DR) were generated by iQuant workstation. TBSS was used to detect the regions of parameters values abnormalities and for the comparison between these two groups. In addition, we also introduced the lateralization indices (LI) to study brain lateralization in children with pediatric autism, using TBSS for additional analysis. The parameters values of the differentiated regions from TBSS were then calculated for each participant and used as the features in SVM/BPNN/LR. All models were trained and tested with leave-one-out cross validation (LOOCV).ResultsCompared to the HCs group, the FAK, DA, and KA values of multi-fibers [such as the bilateral superior longitudinal fasciculus (SLF), corticospinal tract (CST) and anterior thalamic radiation (ATR)] were lower in pediatric autism group (p < 0.05, TFCE corrected). And we also found DA lateralization abnormality in Superior longitudinal fasciculus (SLF) (the LI in HCs group was higher than that in pediatric autism group). However, there were no significant differences in FA, MD, MK, DR, and KR values between HCs and pediatric autism group (P > 0.05, TFCE corrected). After performing LOOCV to train and test three model (SVM/BPNN/LR), we found the accuracy of BPNN (accuracy = 86.44%) was higher than that of LR (accuracy = 76.27%), but no different from SVM (RBF, accuracy = 81.36%; linear, accuracy = 84.75%).ConclusionOur proposed method combining TBSS findings with machine learning (LR/SVM/BPNN), was applicable in the classification of pediatric autism with high accuracy. Furthermore, the FAK, DA, and KA values and Lateralization index (LI) value could be used as neuroimaging biomarkers to discriminate the children with pediatric autism or not.

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“…Based on fMRI data, many machine learning methods and deep learning methods have been proposed for ASD classification. Feng et al [ 8 ] summarized the progress of ASD classification work with the Autism Brain Imaging Data Exchange (ABIDE) dataset in the last three years. Kong et al [ 9 ] proposed an ASD-assisted diagnosis method based on a deep neural network (DNN).…”

Section: Backgoundmentioning

confidence: 99%

A heterogeneous graph convolutional attention network method for classification of autism spectrum disorder

Shao,

Fu,

Chen

2023

BMC Bioinformatics

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Background Autism spectrum disorder (ASD) is a serious developmental disorder of the brain. Recently, various deep learning methods based on functional magnetic resonance imaging (fMRI) data have been developed for the classification of ASD. Among them, graph neural networks, which generalize deep neural network models to graph structured data, have shown great advantages. However, in graph neural methods, because the graphs constructed are homogeneous, the phenotype information of the subjects cannot be fully utilized. This affects the improvement of the classification performance. Methods To fully utilize the phenotype information, this paper proposes a heterogeneous graph convolutional attention network (HCAN) model to classify ASD. By combining an attention mechanism and a heterogeneous graph convolutional network, important aggregated features can be extracted in the HCAN. The model consists of a multilayer HCAN feature extractor and a multilayer perceptron (MLP) classifier. First, a heterogeneous population graph was constructed based on the fMRI and phenotypic data. Then, a multilayer HCAN is used to mine graph-based features from the heterogeneous graph. Finally, the extracted features are fed into an MLP for the final classification. Results The proposed method is assessed on the autism brain imaging data exchange (ABIDE) repository. In total, 871 subjects in the ABIDE I dataset are used for the classification task. The best classification accuracy of 82.9% is achieved. Compared to the other methods using exactly the same subjects in the literature, the proposed method achieves superior performance to the best reported result. Conclusions The proposed method can effectively integrate heterogeneous graph convolutional networks with a semantic attention mechanism so that the phenotype features of the subjects can be fully utilized. Moreover, it shows great potential in the diagnosis of brain functional disorders with fMRI data.

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A review of methods for classification and recognition of ASD using fMRI data

Cited by 18 publications

References 36 publications

Application of Quantitative Magnetic Resonance Imaging in the Diagnosis of Autism in Children

Application of Quantitative Magnetic Resonance Imaging in the Diagnosis of Autism in Children

Application of TBSS-based machine learning models in the diagnosis of pediatric autism

A heterogeneous graph convolutional attention network method for classification of autism spectrum disorder

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