Divide and Conquer: Sub-Grouping of ASD Improves ASD Detection Based on Brain Morphometry.

Low success (<60%) in autism spectrum disorder (ASD) classification using brain morphometry from the large multi-site ABIDE dataset and inconsistent findings on brain morphometric abnormalities in ASD can be attributed to the ASD heterogeneity. In this study, we show that ASD brain morphometry is highly heterogeneous, and demonstrate that the heterogeneity can be mitigated and classification improved if autism severity (AS), verbal IQ (VIQ) and age are used with morphometric features. Morphometric features from structural MRIs (sMRIs) of 734 males (ASD: 361, controls: 373) of ABIDE were derived using FreeSurfer. Applying the Random Forest classifier, an AUC of 0.61 was achieved. Adding VIQ and age to morphometric features, AUC improved to 0.68. Sub-grouping the subjects by AS, VIQ and age improved the classification with the highest AUC of 0.8 in the moderate-AS sub-group (AS = 7-8). Matching subjects on age and/or VIQ in each sub-group further improved the classification with the highest AUC of 0.92 in the low AS sub-group (AS = 4-5). AUC decreased with AS and VIQ, and was the lowest in the mid-age sub-group (13-18 years). The important features were mainly from the frontal, temporal, ventricular, right hippocampal and left amygdala regions. However, they highly varied with AS, VIQ and age. The curvature and folding index features from frontal, temporal, lingual and insular regions were dominant in younger subjects suggesting their importance for early detection. When the experiments were repeated using the Gradient Boosting classifier similar results were obtained. Our findings suggest that identifying brain biomarkers in sub-groups of ASD can yield more robust and insightful results than searching across the whole spectrum. Further, it may allow identification of sub-group specific brain biomarkers that are optimized for early detection and monitoring, increasing the utility of sMRI as an important tool for early detection of ASD

Important features for classification are different across sub-groups.

<p>Top 10 important features for autism spectrum disorder (ASD) vs. typically developing controls (TDC) classification in each sub-group (by AS, VIQ, age) are presented. Each feature is represented by a colored bar; the length of the bar represents the relative % importance for classification with respect to the top feature. The features have been grouped and color-coded by volume, area, thickness mean, thickness standard deviation, folding index, mean curvature and Gaussian curvature. Before each feature, Cohen’s d and two sample t-test significance (<i>P</i><0.005** and <i>P</i><0.05*) of ASD vs. TDC group difference are presented. The important features for classification varied across the sub-groups demonstrating the heterogeneity in ASD brain morphometry.</p

Subject demographics and behavioral (DB) measures.

<p>Subject demographics and behavioral (DB) measures.</p

Inter-method discrepancies in brain volume estimation may drive inconsistent findings in autism

Previous studies applying automatic preprocessing methods on Structural Magnetic Resonance Imaging (sMRI) report inconsistent neuroanatomical abnormalities in Autism Spectrum Disorder (ASD). In this study we investigate inter-method differences as a possible cause behind these inconsistent findings. In particular, we focus on the estimation of the following brain volumes: gray matter (GM), white matter (WM), cerebrospinal fluid (CSF), and total intra cranial volume (TIV). T1-weighted sMRIs of 417 ASD subjects and 459 typically developing controls (TDC) from the ABIDE dataset were estimated using three popular preprocessing methods: SPM, FSL, and FreeSurfer (FS). Brain volumes estimated by the three methods were correlated but had significant inter-method biases; except TIVSPM versus TIVFS, all inter-method differences were significant. ASD versus TDC group differences in all brain volume estimates were dependent on the method used. SPM showed that TIV, GM, and CSF volumes of ASD were larger than TDC with statistical significance, whereas FS and FSL did not show significant differences in any of the volumes; in some cases, the direction of the differences were opposite to SPM. When methods were compared with each other, they showed differential biases for autism, and several biases were larger than ASD versus TDC differences of the respective methods. After manual inspection, we found inter-method segmentation mismatches in the cerebellum, sub-cortical structures, and inter-sulcal CSF. In addition, to validate automated TIV estimates we performed manual segmentation on a subset of subjects. Results indicate that SPM estimates are closest to manual segmentation, followed by FS while FSL estimates were significantly lower. In summary, we show that ASD versus TDC brain volume differences are method dependent and that these inter-method discrepancies can contribute to inconsistent neuroimaging findings in general. We suggest cross-validation across methods and emphasize the need to develop better methods to increase the robustness of neuroimaging findings

Improvement in classification by sub-grouping based on autism severity (AS), age and Verbal IQ (VIQ).

<p>The AUC scores of the classification in the sub-groups are presented. A point represents the mean and an error bar represents the one standard deviation of the AUC scores from 10 test folds. <b>A)</b> Smaller classes were up-sampled in each training fold to balance the number of ASD & TDC subjects. Sub-grouping improved the classification with the most and least improvements from sub-grouping by AS and age respectively. <b>B)</b> Larger classes were down-sampled matching the demographics of the smaller classes. This scheme further improved the classification performance.</p

Sub-groups definition.

<p>Sub-groups definition.</p

Folding index and curvature features are important for classification in young subjects i.e. in the young-age sub-group.

<p>The important folding index and curvature features from the young-age sub-group and/or whose ASD vs. TDC group differences across all subjects were statistically significant (multiple comparisons uncorrected) are presented. The features are mainly from frontal, temporal, lingual and insular region, and are larger in ASD. However, the group differences decrease with age and even the direction of the group difference flips for 12 out of 17 features.</p

Previous ASD vs. TDC Classification studies.

<p>Previous ASD vs. TDC Classification studies.</p

ASD vs. TDC group difference in ventricular volumes decreases with verbal IQ (VIQ).

<p>The ventricular volumes were normalized by total intracranial volume. Across all subjects, except 3^rd and 4^th, all ventricles and total intracranial volumes were larger in ASD. When the subjects were sub-grouped by VIQ, the group differences were the largest in the low-VIQ sub-group but decreased with VIQ. For some ventricular, the direction of group difference even flipped in the high-VIQ sub-group i.e. volumes were larger in TDC for some ventricles.</p