Autism spectrum disorder characterization in children by capturing local-regional brain changes in MRI

To design a multiscale descriptor capable of capturing complex local-regional unfolding patterns to support quantitation and diagnosis of autism spectrum disorders (ASD) using T1-weighted structural magnetic resonance images (MRI) with voxel size of 1 × 1 × 1 mm.

The proposed image descriptor uses an adapted multiscale representation, the Curvelet transform, interpretable in terms of texture (local) and shape (regional) to characterize brain regions, and a Generalized Gaussian Distribution (GGD) to reduce feature dimensionality. In this approach, each MRI is first parcelled into 3D anatomical regions. Each resultant region is represented by a single 2D image where slices are placed next to each other. Each 2D image is characterized by mapping it to the Curvelet space and each of the different Curvelet sub-bands is described by the set of GGD parameters. To assess the discriminant power of the proposed descriptor, a classification model per brain region was built to differentiate ASD patients from control subjects. Models were constructed with support vector machines and evaluated using two samples from heterogeneous databases, namely Autism Brain Imaging Data Exchange - ABIDE I (34 ASD and 34 controls, mean age 11.46 ± 2.03 and 11.53 ± 1.79 yr, respectively, male population) and ABIDE II (42 ASD and 41 controls, mean age 10.09 ± 1.37 and 10.52 ± 1.27 yr, respectively, male population), for a total of 151 individuals.

When the model was trained with ABIDE II sample and tested with ABIDE I on a hold-out validation, an area under receiver operator curve (AUC) of 0.69 was computed. When each sample was independently used under a cross-validation scheme, the estimated AUC was 0.75 ± 0.02 for ABIDE I and 0.77 ± 0.01 for ABIDE II. This analysis determined a set of discriminant regions widely reported in the literature as characteristic of ASD.

The presented image descriptor demonstrated differences at local and regional level when high differences were observed in the Curvelet sub-bands. The method is simple in conceptual terms, robust to several sources of noise, and has a very low computational cost.