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. 2021 Dec;14(12):2512-2523.
doi: 10.1002/aur.2626. Epub 2021 Oct 13.

Unified framework for early stage status prediction of autism based on infant structural magnetic resonance imaging

Kun Gao  1 , Yue Sun  1 , Sijie Niu  1   2 , Li Wang  1
Affiliations

Unified framework for early stage status prediction of autism based on infant structural magnetic resonance imaging

Kun Gao et al. Autism Res. 2021 Dec.

Abstract

Autism, or autism spectrum disorder (ASD), is a developmental disability that is diagnosed at about 2 years of age based on abnormal behaviors. Existing neuroimaging-based methods for the prediction of ASD typically focus on functional magnetic resonance imaging (fMRI); however, most of these fMRI-based studies include subjects older than 5 years of age. Due to challenges in the application of fMRI for infants, structural magnetic resonance imaging (sMRI) has increasingly received attention in the field for early status prediction of ASD. In this study, we propose an automated prediction framework based on infant sMRI at about 24 months of age. Specifically, by leveraging an infant-dedicated pipeline, iBEAT V2.0 Cloud, we derived segmentation and parcellation maps from infant sMRI. We employed a convolutional neural network to extract features from pairwise maps and a Siamese network to distinguish whether paired subjects were from the same or different classes. As compared to T1w imaging without segmentation and parcellation maps, our proposed approach with segmentation and parcellation maps yielded greater sensitivity, specificity, and accuracy of ASD prediction, which was validated using two datasets with different imaging protocols/scanners and was confirmed by receiver operating characteristic analysis. Furthermore, comparison with state-of-the-art methods demonstrated the superior effectiveness and robustness of the proposed method. Finally, attention maps were generated to identify subject-specific autism effects, supporting the reasonability of the predictive results. Collectively, these findings demonstrate the utility of our unified framework for the early-stage status prediction of ASD by sMRI. LAY SUMMARY: The status prediction of autism spectrum disorder (ASD) at an early age is highly desirable, as early intervention may significantly reduce autism symptoms. However, current methods for diagnosing young children are limited to behavioral assays. In this study, we propose an automated method for ASD status prediction at the age of 24 months that uses infant structural magnetic resonance imaging to identify neural features.

Keywords: autism Spectrum disorder (ASD); deep learning algorithm; early-stage status prediction; infant structural MRI; subject-specific autism attention.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

FIGURE 1
FIGURE 1
Overview of the study framework. Parallel paths were used to process raw data from a training set and a testing set from iBEAT V2.0 Cloud. After optimization with the training set, the testing set was used to distinguish autism spectrum disorder and normal control groups
FIGURE 2
FIGURE 2
The pipeline of the deep learning method for automatic autism spectrum disorder (ASD) diagnosis. (a) Segmentation and parcellation maps were obtained from iBEAT V2.0 and applied as parallel attention paths. (b) Convolutional neural network was used for feature extraction and fusion; (c) Subject‐specific autism attention module was used to identify ASD‐associated features; (d) Siamese classifier was used to integrate sex information and develop a matrix for calculating the contrastive loss
FIGURE 3
FIGURE 3
Large inter‐site data heterogeneity for two infant subjects acquired by different scanners. The left subject from the Infant Brain Imaging Study network was acquired by a Siemens 3T scanner, whereas the right subject from Autism Centers of Excellence was acquired by a GE 3T scanner. The large inter‐site data heterogeneity limits the application of models trained on a dataset with a set of specific imaging parameters to other datasets with different imaging parameters (protocols/scanners)
FIGURE 4
FIGURE 4
Assessment of different combinations of T1w images, segmentation, and parcellation maps. The methods, each of which was trained on dataset A, were evaluated in terms of sensitivity (SEN), specificity (SPE), and accuracy (ACC). The metric shows the performance of the model trained with T1w intensity images (the first bars), a combination of intensity images, segmentation and parcellation maps (the second bars), and the proposed segmentation + parcellation maps (the third bars)
FIGURE 5
FIGURE 5
T1w images, segmentation maps, and parcellations maps for three representative subjects from dataset A. The first column is the T1w images affected by imaging noise and Gibbs artifacts. The second and third columns are the segmentation map and parcellation map, respectively. The images in the last two columns were generated by iBEAT V2.0 cloud with the guidance of prior anatomy knowledge
FIGURE 6
FIGURE 6
Receiver operating characteristic curves for datasets A and B with/without the sex information. Green lines represent models trained without sex information, and orange lines represent models trained with sex information. (a) Was generated by 10‐fold experimental cross‐validation of dataset A, while (b) was generated by applying the model trained on dataset A to dataset B directly
FIGURE 7
FIGURE 7
Attention maps generated by the proposed method. The amygdala, hippocampus, and cerebellum were identified by our method as abnormal regions associated with autism spectrum disorder, shown in the first and second rows respectively. In the right panels, red and blue indicate high and low discriminative power, respectively
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