Moderate To Severe Autism Spectrum Disorder – Many previous studies have reported abnormalities in political communication (FC) in autism spectrum disorders (ASD), but small effect sizes prevent us from using these characteristics for the diagnostic objective. Here, canonical correlation analysis (CCA) and hierarchical clustering were used to divide the high-functioning ASD group (ie, the ASD research group) into subgroups. The support vector machine (SVM) model was trained 10 times to predict the Autism Diagnostic Observation Schedule (ADOS) score in the ASD detection group (r = 0.30, P < 0.001, n = 260). Independent sample (ie, ASD control group) (r = 0.35, P = 0.031, n = 29). Neuroimaging-based segmentation obtained two groups representing severe and mild autism. Although we identified FCs showing differences in strength from ASD-severe to ASD-free in controls, the pattern could not be observed in distributions based on ADOS scores. the same. We also identified ASD-specific FCs, similar to a distribution based on ADOS scores. The present study replicated this to show that fcs (rsfMRI) FCs can serve as neural biomarkers for segmenting high-functioning autistic individuals based on symptom severity and show benefits in normal distribution based on the ADOS. given. Dead. The results also indicate a compensatory role for the frontal cortical network in mild ASD patients, indicating potential targets for future clinical treatment.
Autism spectrum disorder (ASD) is a neurodevelopmental condition characterized by impairments in the quality of social interactions as well as limited and repetitive behaviors (American Psychiatric Association, 2013), and includes affects about 1% of children worldwide (Kim et al., Baio 2011; et al. ny.., 2018). Although recent conceptualizations of ASD consider symptoms to range from mild to severe, the classification of ASD based on the severity of symptoms is often used for the purpose of diagnosis (Lord et al., 2000). Currently, ASD diagnosis is driven entirely by behavioral indicators, which have been criticized for the high degree of heterogeneity in phenotypic presentation and etiology (Ecker and Murphy, 2014). For example, a large proportion of children with ASD (11-60%) have mild intellectual disability, which means an intelligence quotient (IQ) below 70 (Baio et al., 2018; Lord et al., 2018) of rare types of de novo mutations (Sanders et al., 2015; Weiner et al., 2017). Recent developments in neuroscience have allowed us to distinguish, for example, autistic patients from controls in ASD (Ecker and Murphy, 2014; Eilam-Stock et al., 2014; Cheng et al., 2015 , 2017; Jack and Pelphrey, 201et; al. ., 2019). The signals of the medial temporal, prefrontal, and parietal regions (Cheng et al., 2015; Holiga et al., 2019) are rich in functional connectivity (FC) that defines. However, no previous MRI study has found an effect size large enough to indicate that brain structure or brain function can be used as a predictor. This has prompted a focus on identifying standardized biomarkers to disaggregate this heterogeneous condition into more homogeneous groups (Loth et al., 2016). Previous studies have explored the use of neural features from fMRI data to identify autism spectrum disorders (Groen et al., 2010; Lombardo et al., 2018), but few of them directly targeted diagnostic scales such as the Autism Diagnostic Observational Scale. have. Schedule (ADOS). Furthermore, few studies have evaluated the concordance between biomarker-based stratification of ASD patients and differences in clinical symptom profiles or severity.
Moderate To Severe Autism Spectrum Disorder
In this study, we first investigated whether resting-state functional brain networks (IQ ≥ 70) can be used as stratification and biomarkers for autism severity (as measured by the ADOS). Baio et al. ). ., 2018; Lord et al., 2018) using a variety of statistical methods including canonical correlation analysis (CCA), hierarchical clustering (Jia et al., 2016; Drysdale et al., 2017) ) and the support vector machine (SVM) (Chang and Lin , 2011). We then further investigated specific neural biomarkers among the stratified ASD groups as well as the ASD group compared to controls.
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The study sample was obtained from the Autism Brain Imaging Data Exchange (ABIDE) I and II (Di Martino et al., 2014), published in 2012 and 2016. Approval from the Andrim Review Board was required. -government (IRB) respectively. All participants’ autism symptoms (measured using the ADOS), as well as state magnetic resonance imaging (rsfMRI) data, were collected from the website of the world’s largest database. More information on data acquisition can be found on the event’s website
, Time correction, motion correction, smoothing (full width at half maximum = 6 mm), artifact reduction using the BrainWavelet toolbox (Patel et al., 2014; Patel and Bullmore, 2016), The Standard space is registered with MNI152. With a voxel size of 2 mm × 2 mm × 2 mm, the functional images were matched to each structural T1 image using boundary-based registration (Greve and Fischl, 2009 ) and then verified by the FSL FLIRT tool.
; Confounding covariates including Friston 24 head movement index (Friston et al., 1996), white matter signal, cerebrospinal fluid signal (obtained by FSL instrument)
), and the global signal was extracted from the oxygen level-dependent (BOLD) signal and band-pass filtered (0.01–0.1 Hz) by AFNI (Cox, 1996). All processed data were visually checked for quality control.
Moderate Learning Difficulties
The Automated Anatomical Labeling Atlas (AAL-2) model, 2nd edition (Rolls et al., 2015) was used to segment the brain into 94 regions of interest (ROIs) (Supplementary Table 1) (Cheng et al., 2019). . The time series of each ROI was extracted by averaging the signals of all included voxels, which spanned 94 functional nodes throughout the brain. For each node in this brain pair analysis, the Pearson correlation coefficient was calculated by Fisher z-transformation of FCs. Finally, z-scores were calculated for each subject’s FC for comparisons across subjects and sites (Supplementary Figures 1, 2). Therefore, for each subject, the constructed brain network consists of 94 brain regions and 4,371 (i.e.,
Subjects selected for the current study included those who (i) had a total IQ score equal to or greater than 70, (ii) had a displacement of less than 0.5 mm, and (iii) had between 6-30 years in the assessment. . To meet the purpose of statistical analysis, we selected participants and divided them into three groups: the ASD group, the control group, and the independent ASD verification group. The ASD group was selected using the following inclusion criteria: (i) had an ABIDE I or II autism diagnosis and (ii) had an ADOS score greater than seven after the diagnostic recommendation ( Lord et al., 2000). , including Asperger’s and Pervasive Developmental Disorder Not Otherwise Specified [ie, consistent with the term ASD as defined in the Diagnostic and Statistical Manual of Mental Disorders (Fifth Edition) (DSM-V)]. Note that one study site calculated the ADOS total score differently than another. Therefore, we rescaled the ADOS total score across all sites as the sum of the ADOS communication subscale scores and the ADOS social interaction subscale scores for the Lord et al. (2000) (Supplementary Table 2); and (iii) avoid unreliable estimates of location effects, from locations with three or more ASD subjects meeting the same criteria.
ASD patients with complete information for all ADOS categories assigned to fMRI in the training mode were divided into a research group, and a control group without at least one subscale of the ADOS. Sample test. A control group of participants was selected from the same setting as the ASD group. Each control subject was recorded as a “healthy control” in ABIDE I or II. The independent ASD control group consisted of (i) participants with ASD without a mean ADOS total score and (ii) participants from sites who did not select one of the two test samples.
As a result of subject selection and quality control, 260 participants in the ASD research group, 574 in the control group, and 29 in the ASD verification group were included in the study. now. Demographic information is summarized in Table 1. There were no differences in IQ, gender, and age between the ASD research and ASD control groups. Confounding factors such as total IQ, gender, age, average migration, and site were repeated in the following analysis unless otherwise noted. The method was implemented using MATLAB software [version: 184.108.40.2064444 (R2018b)].
Discussing Different Types Of Autism In Brief
The detailed data analysis strategy is shown in Figure 1. Before statistical modeling, we calculated the Spearman rank correlation matrix between 4,371 FCs and three ADOS subscales (Communication, Social Relations , and Restricted/Stereotyped Behavior) among participants in the ASD Discovery Cohort. Only FCs with a Spearman rank correlation (threshold P < 0.005) with at least one ADOS subscale were included, which was used to reduce the number of FCs included in the CCA.
A threshold above P < 0.005 was set to select sufficient but not excessive FC. as long as
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