Using DeepGCN to identify the autism spectrum disorder from multi-site resting-state data

Cao, Menglin; Yang, Ming–Hsuan; Qin, Chi; Zhu, Xiaofei; Wang, Jue; Liu, Tian

doi:10.1016/j.bspc.2021.103015

Cited by 68 publications

(35 citation statements)

References 31 publications

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“…We show three different results, one for each of the paper contributions. We compare our results with SOTA DL methods [6], [15], [22], [23], [26], [32], [47], [52]- [55] depending on the task, and ML methods such as support vector machine (SVM) and logistic regression (LR). To be fair to the other papers, we report directly from the results mentioned in the papers.…”

Section: Resultsmentioning

confidence: 99%

“…After denoising, 152 subjects were discarded based on head motion following [23] which results into 942 subjects. ABIDE1 [38] was pre-processed using cpac [45], out of 1112 subjects 871 were selected following [6], [46], [47]. To divide the data into regions, we use Shaefer [48] and Harvard Oxford (HO) [49] atlas depending on the experiment.…”

Section: B Datasetsmentioning

confidence: 99%

“…Even with region-based data our model performs better than existing methods on HCP and FBIRN dataset. As our model does not use phenotypic information about subjects, our model lacks behind [6], [47] on ABIDE.…”

Section: A Classificationmentioning

confidence: 99%

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Deep Dynamic Effective Connectivity Estimation from Multivariate Time Series

Mahmood,

Fu,

Calhoun

et al. 2022

Preprint

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Recently, methods that represent data as a graph, such as graph neural networks (GNNs) have been successfully used to learn data representations and structures to solve classification and link prediction problems. The applications of such methods are vast and diverse, but most of the current work relies on the assumption of a static graph. This assumption does not hold for many highly dynamic systems, where the underlying connectivity structure is non-stationary and is mostly unobserved. Using a static model in these situations may result in suboptimal performance. In contrast, modeling changes in graph structure with time can provide information about the system whose applications go beyond classification. Most work of this type does not learn effective connectivity and focuses on crosscorrelation between nodes to generate undirected graphs. An undirected graph is unable to capture direction of an interaction which is vital in many fields, including neuroscience. To bridge this gap, we developed dynamic effective connectivity estimation via neural network training (DECENNT), a novel model to learn an interpretable directed and dynamic graph induced by the downstream classification/prediction task. DECENNT outperforms state-of-the-art (SOTA) methods on five different tasks and infers interpretable task-specific dynamic graphs. The dynamic graphs inferred from functional neuroimaging data align well with the existing literature and provide additional information. Additionally, the temporal attention module of DECENNT identifies time-intervals crucial for predictive downstream task from multivariate time series data.

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Section: Resultsmentioning

confidence: 99%

Section: B Datasetsmentioning

confidence: 99%

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Deep Dynamic Effective Connectivity Estimation from Multivariate Time Series

Mahmood,

Fu,

Calhoun

et al. 2022

Preprint

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“…The AAL atlas with 116 brain regions was used for the above-mentioned two methods. Parisot et al and Cao et al employed fMRI data and phenotypic information of ROIs based on the HO atlas and constructed a GCN model to identify ASD from NC (Parisot et al, 2018 ; Cao et al, 2021 ). Shrivastava et al used the Craddock 400 (CC400) (Desikan et al, 2006 ) atlas for ROI partition and developed a convolutional neural network (CNN) for ASD diagnosis (Shrivastava et al, 2020 ).…”

Section: Discussionmentioning

confidence: 99%

Multi-Scale Graph Representation Learning for Autism Identification With Functional MRI

Wang¹,

Cao²,

Qiao³

et al. 2022

Front. Neuroinform.

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Resting-state functional MRI (rs-fMRI) has been widely used for the early diagnosis of autism spectrum disorder (ASD). With rs-fMRI, the functional connectivity networks (FCNs) are usually constructed for representing each subject, with each element representing the pairwise relationship between brain region-of-interests (ROIs). Previous studies often first extract handcrafted network features (such as node degree and clustering coefficient) from FCNs and then construct a prediction model for ASD diagnosis, which largely requires expert knowledge. Graph convolutional networks (GCNs) have recently been employed to jointly perform FCNs feature extraction and ASD identification in a data-driven manner. However, existing studies tend to focus on the single-scale topology of FCNs by using one single atlas for ROI partition, thus ignoring potential complementary topology information of FCNs at different spatial scales. In this paper, we develop a multi-scale graph representation learning (MGRL) framework for rs-fMRI based ASD diagnosis. The MGRL consists of three major components: (1) multi-scale FCNs construction using multiple brain atlases for ROI partition, (2) FCNs representation learning via multi-scale GCNs, and (3) multi-scale feature fusion and classification for ASD diagnosis. The proposed MGRL is evaluated on 184 subjects from the public Autism Brain Imaging Data Exchange (ABIDE) database with rs-fMRI scans. Experimental results suggest the efficacy of our MGRL in FCN feature extraction and ASD identification, compared with several state-of-the-art methods.

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“…As a general description, the graph model provides a versatile manner for integration of multimodal information and relation discovery among patients due to the inherent characteristic of the graph [12]. Up to now, graph-based methods, particularly graph convolutional networks (GCNs) [13], [14], have been applied in various biomedical applications and disease prediction field, such as Alzheimer prediction [11], [15]- [17], Autism prediction [18], [19], and cancer prognosis prediction [20].…”

Section: Introductionmentioning

confidence: 99%

Multi-modal Graph Learning for Disease Prediction

Zheng¹,

Zhu²,

Liu³

et al. 2022

Preprint

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Benefiting from the powerful expressive capability of graphs, graph-based approaches have been popularly applied to handle multi-modal medical data and achieved impressive performance in various biomedical applications. For disease prediction tasks, most existing graph-based methods tend to define the graph manually based on specified modality (e.g., demographic information), and then integrated other modalities to obtain the patient representation by Graph Representation Learning (GRL). However, constructing an appropriate graph in advance is not a simple matter for these methods. Meanwhile, the complex correlation between modalities is ignored. These factors inevitably yield the inadequacy of providing sufficient information about the patient's condition for a reliable diagnosis. To this end , we propose an end-to-end Multi-modal Graph Learning framework (MMGL) for disease prediction with multi-modality. To effectively exploit the rich information across multi-modality associated with the disease, modality-aware representation learning is proposed to aggregate the features of each modality by leveraging the correlation and complementarity between the modalities. Furthermore, instead of defining the graph manually, the latent graph structure is captured through an effective way of adaptive graph learning. It could be jointly optimized with the prediction model, thus revealing the intrinsic connections among samples. Our model is also applicable to the scenario of inductive learning for those unseen data. An extensive group of experiments on two disease prediction tasks demonstrates that the proposed MMGL achieves more favorable performance. The code of MMGL is available at https://github.com/SsGood/MMGL.

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Using DeepGCN to identify the autism spectrum disorder from multi-site resting-state data

Cited by 68 publications

References 31 publications

Deep Dynamic Effective Connectivity Estimation from Multivariate Time Series

Deep Dynamic Effective Connectivity Estimation from Multivariate Time Series

Multi-Scale Graph Representation Learning for Autism Identification With Functional MRI

Multi-modal Graph Learning for Disease Prediction

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