A Deep Learning Approach to Predict Autism Spectrum Disorder Using Multisite Resting-State fMRI

Subah, Faria Zarin; Deb, Kaushik; Dhar, Pranab Kumar; Koshiba, Takeshi

doi:10.3390/app11083636

Cited by 57 publications

(29 citation statements)

References 54 publications

(60 reference statements)

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“…A brain atlas containing these ROIs is used; therefore, only the BOLD time series voxels of this ROIS were adopted. Among the numerous predefined atlases, Bootstrap Analysis of Stable Clusters (BASC ) was chosen, since it was the map with best performance for distinguishing ASD patients by deep learning model, according to [22]. It was proposed in [47] and generated from group brain parcellation by BASC method, which is a k-means clustering-based algorithm that identifies brain networks with coherent activity in resting-state fMRI [48].…”

Section: A Data and Data Preprocessingmentioning

confidence: 99%

Diagnosis of autism spectrum disorder based on functional brain networks and machine learning

Alves

Toutain

Aguiar

et al. 2022

Preprint

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Autism spectrum disorder is a multifactorial neurodevelopmental disorder with high genetic heterogeneity. Studies of brain networks in autism can provide new insights into the dynamics of information processing in individuals who suffer from such a condition. This paper proposes a method for automatic diagnosis of autism based on fMRI time series and machine learning algorithms. We verify that the left ventral posterior cingulate cortex region reduces the functional connectivity of the brain area in patients with autism spectrum disorder. Also, the brain networks of patients with autism spectrum disorder show more segregation, lower distribution of information, and less connectivity. Our methodology accurately differentiates control and autistic subjects providing an area under the curve close to higher than 95%.

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Section: A Data and Data Preprocessingmentioning

confidence: 99%

Diagnosis of autism spectrum disorder based on functional brain networks and machine learning

Alves

Toutain

Aguiar

et al. 2022

Preprint

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show abstract

“…Although there is no established explanation for the negative correlation between brain regions, it would lose a lot of the information to only use the positive FC to construct the initial graph. Many studies have evaluated different ways to construct functional networks [ 29 , 30 , 55 ]; however, it is unclear how it would affect the performance of deep learning. In the process of constructing the functional graph from FC, we defined the weight of the edge as the connection strength.…”

Section: Discussionmentioning

confidence: 99%

Identification of Young High-Functioning Autism Individuals Based on Functional Connectome Using Graph Isomorphism Network: A Pilot Study

Yang

Jin

et al. 2022

Brain Sciences

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Accumulated studies have determined the changes in functional connectivity in autism spectrum disorder (ASD) and spurred the application of machine learning for classifying ASD. Graph Neural Network provides a new method for network analysis in brain disorders to identify the underlying network features associated with functional deficits. Here, we proposed an improved model of Graph Isomorphism Network (GIN) that implements the Weisfeiler-Lehman (WL) graph isomorphism test to learn the graph features while taking into account the importance of each node in the classification to improve the interpretability of the algorithm. We applied the proposed method on multisite datasets of resting-state functional connectome from Autism Brain Imaging Data Exchange (ABIDE) after stringent quality control. The proposed method outperformed other commonly used classification methods on five different evaluation metrics. We also identified salient ROIs in visual and frontoparietal control networks, which could provide potential neuroimaging biomarkers for ASD identification.

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“…Resting-state functional magnetic resonance imaging (fMRI) data from a multisite data set named Autism Brain Imaging Exchange (ABIDE) were used in [48] for the detection of ASD. In [56] proposed, a deep neural network (DNN) classifier was proposed to detect ASD using functional connectivity features of resting state fMRI data, such as the ABIDE dataset. Most recently, photos taken by ASD have been shown to have different characteristics than controls in [8], [60].…”

Section: E Othersmentioning

confidence: 99%

Discriminative Few Shot Learning of Facial Dynamics in Interview Videos for Autism Trait Classification

Zhang

Ruan

Wang

et al. 2023

IEEE Trans. Affective Comput.

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Autism is a prevalent neurodevelopmental disorder characterized by impairments in social and communicative behaviors. Possible connections between autism and facial expression recognition have recently been studied in the literature. However, most works are based on facial images or short videos. Few works aim at Autism Diagnostic Observation Schedule (ADOS) videos due to their complexity (e.g., interaction between interviewer and interviewee) and length (e.g., usually last for hours). In this paper, we attempt to fill this gap by developing a novel discriminative few shot learning method to analyze hourlong video data and exploring the fusion of facial dynamics for the trait classification of ASD. Leveraging well-established computer vision tools from spatio-temporal feature extraction and marginal fisher analysis to few-shot learning and scene-level fusion, we have constructed a three-category system to classify an individual into Autism, Autism Spectrum, and Non-Spectrum. For the first time, we have shown that certain interview scenes carry more discriminative information for ASD trait classification than others. Experimental results are reported to demonstrate the potential of the proposed automatic ASD trait classification system (achieving 91.72% accuracy on the Caltech ADOS video dataset) and the benefits of few-shot learning and scene-level fusion strategy by extensive ablation studies.

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A Deep Learning Approach to Predict Autism Spectrum Disorder Using Multisite Resting-State fMRI

Cited by 57 publications

References 54 publications

Diagnosis of autism spectrum disorder based on functional brain networks and machine learning

Diagnosis of autism spectrum disorder based on functional brain networks and machine learning

Identification of Young High-Functioning Autism Individuals Based on Functional Connectome Using Graph Isomorphism Network: A Pilot Study

Discriminative Few Shot Learning of Facial Dynamics in Interview Videos for Autism Trait Classification

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