Abstract
Although emerging evidence has implicated structural/functional abnormalities of patients with Autism Spectrum Disorder(ASD), definitive neuroimaging markers remain obscured due to inconsistent or incompatible findings, especially for structural imaging. Furthermore, brain differences defined by statistical analysis are difficult to implement individual prediction. The present study has employed the machine learning techniques under the unified framework in neuroimaging to identify the neuroimaging markers of patients with ASD and distinguish them from typically developing controls(TDC). To enhance the interpretability of the machine learning model, the study has processed three levels of assessments including model-level assessment, feature-level assessment, and biology-level assessment. According to these three levels assessment, the study has identified neuroimaging markers of ASD including the opercular part of bilateral inferior frontal gyrus, the orbital part of right inferior frontal gyrus, right rolandic operculum, right olfactory cortex, right gyrus rectus, right insula, left inferior parietal gyrus, bilateral supramarginal gyrus, bilateral angular gyrus, bilateral superior temporal gyrus, bilateral middle temporal gyrus, and left inferior temporal gyrus. In addition, negative correlations between the communication skill score in the Autism Diagnostic Observation Schedule (ADOS_G) and regional gray matter (GM) volume in the gyrus rectus, left middle temporal gyrus, and inferior temporal gyrus have been detected. A significant negative correlation has been found between the communication skill score in ADOS_G and the orbital part of the left inferior frontal gyrus. A negative correlation between verbal skill score and right angular gyrus and a significant negative correlation between non-verbal communication skill and right angular gyrus have been found. These findings in the study have suggested the GM alteration of ASD and correlated with the clinical severity of ASD disease symptoms. The interpretable machine learning framework gives sight to the pathophysiological mechanism of ASD but can also be extended to other diseases.
Highlights
Autism Spectrum Disorder, known as ASD, is a complex neuro-developmental disorder and has been characterized by a series of symptoms including early-onset difficulties in social communication as well as restricted, repetitive behaviors and interests (Pagnozzi et al, 2018)
It can be seen that these clusters have covered most brain regions found by traditional statistical analysis, except FFG.R,CAL.L,INS.L, bilateral MOG, and PCG.R
For these brain regions failed to be detected in model-level assessment, the possible reason is that the differences are not obvious, and the areas of the clusters containing these brain regions are small in statistical analysis, e.g., the cluster of CAL.L only has 93 volxes, MOG.R and PCG.R only have 63 and 56 voxels, respectively
Summary
Autism Spectrum Disorder, known as ASD, is a complex neuro-developmental disorder and has been characterized by a series of symptoms including early-onset difficulties in social communication as well as restricted, repetitive behaviors and interests (Pagnozzi et al, 2018). The symptoms of ASD generally occur within the first 3 years of life and tend to last even one’s whole life (Hazlett et al, 2017). It is reported that patients with ASD are far more likely to encounter premature death than healthy controls (Hirvikoski et al, 2015). For each patient with ASD, the average lifetime social cost is approximately $3.6 million (Cakir et al, 2020)
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