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This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Competing interests: The authors have declared that no competing interests exist. Autism Spectrum Disorder ASD is a group of developmental disabilities that manifest in early childhood. ASD is characterized by impaired communication and social skills, repetitive behaviors and fixated interests [ 1 ].
It is highly heterogeneous in its etiology, comorbidity, pathogenesis, genetics and severity [ 2 — 5 ]. Currently ASD diagnosis is primarily based on assessing the behavioral and intellectual abilities of a child.
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This diagnosis procedure can be subjective, time consuming, and inconclusive due to factors such as comorbidity [ 6 ]. Further, since it is based only on behavioral symptoms, it does not provide insight on the underlying etiology and cannot be utilized for early diagnosis and intervention. Structural magnetic resonance imaging sMRI is a non-invasive tool and is used to capture the brain morphometry.
ASD diagnosis based on sMRI can be objective and can be utilized even at the prenatal and neonatal stage [ 7 ]. The successful identification of brain biomarkers of ASD can help characterize the neuroanatomical basis of the ASD heterogeneity in etiology and pathogenesis.
With successful identification of brain biomarkers, sMRI can also be a powerful technique for early diagnosis and intervention. Characterizing ASD in terms of brain morphometry is challenging due to its heterogeneity [ 11 ].
The anatomical differences reported in previous studies are mostly contained in the frontal lobe, parietal lobe, temporal lobe, limbic system and cerebellum [ 12 ], but previously reported regions have not been consistent across studies [ 13 — 15 ]. The variability in differences can be due to the heterogeneity of ASD, differences in methodological approaches, or a combination of both.
Mass univariate techniques such as Voxel Based Morphometry VBM are widely applied in brain imaging studies to detect group level brain anatomical differences. Univariate approaches generally have high exploratory power but are fundamentally limited to the detection of inter-variable relationships. Multivariate pattern recognition techniques MVPT [ 16 ] can perform better on capturing the brain morphometry of a heterogeneous condition like ASD.
MVPTs are capable of detecting subtle and spatially distributed differences in data and thus hold promise for characterizing ASD and its sub groups based on brain morphometry. TDC classification setting.
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Table 1 , below, shows classification accuracies from previous studies and the disparity of results is clear. In this study, we investigate the heterogeneity in ASD brain morphometry as a major reason behind the inconsistent neuroanatomical findings and the disparity in classification accuracies. We investigate if demographics and behavioral measures DB of the subjects can be utilized to mitigate the ASD heterogeneity and, hence help understanding ASD brain abnormalities and predicting ASD using brain morphometry.
We use multiple automatically extracted brain morphometric features and multiple classification techniques for this investigation. Second, we investigate if sub-grouping the subjects by the above mentioned DB measures helps to improve ASD vs. TDC classification. Third, we explore the important features for classification in the sub-groups and how they change with DB measures.
Further, we try to explain the discrepancies in the reported neuroimaging findings on brain abnormalities in ASD and classification accuracies in relation to the heterogeneity in ASD brain morphometry. Finally, we discuss ASD heterogeneity in brain morphometry and future directions for neuroimaging studies to tackle it. Consent to participate was obtained via IRB approval or explicit waiver.
Each sMRI was visually inspected and the sMRIs with significant motion or other artifacts were excluded from analysis. An additional 96 subjects were excluded due to poor image quality, and another 64 subjects were discarded due to FreeSurfer [ 26 ] segmentation failure. Since ASD is highly prevalent in males [ 27 ] and also to avoid the gender effects, female subjects were excluded. Summary of DB measures of the used sample is presented in Table 2.
Scanner information of the individual sites can be obtained from Table 1 in [ 22 ].
We decided to use only male subjects in this study for several reasons. First, we wanted to remove the gender effects on the ASD brain heterogeneity and focus only on the brain morphometry of ASD males.
This decision was motivated by the previous findings that there are significant differences between the brain anatomy of ASD males and females. Brain abnormalities in female ASD subjects reported by studies using only female subjects [ 28 , 29 ] have a very small overlap with the abnormalities reported by the studies performing meta analyses of predominantly male subjects [ 30 , 31 ]. A recent study by Lai et al.
Second, compared to females, ASD is more prevalent in males [ 27 ]. The recon-all preprocessing workflow of FreeSurfer v. Recon-all is a fully automated workflow that performs all the FreeSurfer cortical reconstruction and sub-cortical segmentation steps in a unified pipeline. It includes several processing stages such as motion correction, non-uniform intensity normalization, Talairach transform computation, intensity normalization, skull stripping, and cortical parcellation steps.
Volume of 40 sub-cortical structures from Aseg atlas [ 35 ] and volume, surface area, Gaussian curvature, mean curvature, folding index, thickness mean and thickness standard deviation of 34 cortical structures from Desikan-Killiany atlas [ 37 ] were derived from each sMRI. In total, brain morphometric features were derived for each subject. The volume features of each subject were normalized by Total Intracranial Volume TIV since percentage or relative brain volumes have been found to be more robust across scanner types and scanner drifts [ 38 ] and thus this should reduce sensitivity to the fact that the data were collected at multiple sites.
Random forest RF [ 39 ] classification models were trained using brain morphometric features for ASD vs. RF was used since it is inherently suitable for parallel processing, has very few hyper parameters to tune, does not require scaling the data, is theoretically resistant to overfitting, provides variable importance and has been found to be very good for a variety of datasets [ 39 , 40 ].
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To avoid the results from being affected by model selection bias, we repeated the experiments using Gradient Boosting Machine GBM [ 41 ] classifier models and the results are presented as S1 and S2 Figs. For all classifiers, hyper parameter tuning was performed with cross-validation and classification performance was estimated by fold cross-validation. RF and GBM classification models are briefly explained below and are explained in detail in the cited publications.
RF is an ensemble of decision trees and its output class is the mode value of the output classes of the individual decision trees. Gini impurity [ 43 ] was minimized while growing decision trees. Gini impurity is the probability that a randomly chosen sample would be incorrectly labeled if the samples were labeled according to the distribution of class labels.
In general, the higher the number of decision trees in a RF, the more reliable is the prediction and the interpretability of the variable importance [ 44 ]. So, a large number of decision trees were used in the RF classification models used in this study. A general boosting technique iteratively combines several weak or base learners into one strong learner. Here a learner means a basic prediction model such as decision tree. At each iteration or boosting step, GBM constructs a new base learner to be the most parallel to the negative gradient of a loss function along the observed data so that the new base learner focuses on the weakness of the model [ 45 ].
In other words, it performs functional approximation of a model by consecutively improving along the negative direction of a loss function. In this study, a binomial loss function was used and decision trees were used as base learners. A grid search on the depth of decision tree 1 to 12 and the subsample ratio 0. To increase the stability of the model and increase the reliability of the variable importance, a large number of decision trees were used in the GBM classification models used in this study.
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To avoid overfitting, a very low learning rate 0. Accuracy is a threshold-based metric and AUC is a ranking based metric. AUC is the probability that a classifier will rank a randomly chosen positive example higher than a randomly chosen negative example with the assumption that the positive example ranks higher than the negative example [ 46 ]. The practical difference or effect size of ASD vs. We reduced the heterogeneity of subjects by adding DB measures AS, VIQ and age to brain morphometric features and we achieved this by following two schemes. First, VIQ and age were used as training features in conjunction with the morphometric features.
In the second scheme, the subjects were sub-grouped by AS, VIQ and age into three sub-groups by each as defined in Table 3. TDC subjects were not used while sub-grouping by AS. In sub-grouping by age, the thresholds 13 and 18 years were chosen because they approximately reflect pre-puberty, adolescence and early adulthood, and also yielded in sub-groups with similar sample sizes.
Subjects greater than 40 years were not used as they were very few in number In each sub-group, ASD vs. TDC classification models were trained using morphometric features. The sub-grouping resulted in an unbalanced classification problem in sub-groups i. This problem was addressed in two ways: up-sampling the smaller class in each training fold and down-sampling the larger class.
AUC has been used to evaluate the performance of classification models in sub-groups since AUC is insensitive to the unbalanced classes [ 50 ]. We first sought to determine whether simply adding age or VIQ as training features would improve brain morphometric classification. When age was added to the brain morphometric features for training the classifier, AUC improved to 0.
In addition, site information was explicitly added to the morphometric features for training the classifier.
One-hot coding method was used to represent the site information, i. In total, 17 binary features for 17 sites representing the site information were added to the morphometric features. After adding site information to the morphometric features for training, AUC did not improve and was same as that from using only the morphometric features.