Abstract
The ADHD-200 Global Competition provides an excellent opportunity for building diagnostic classifiers of Attention-Deficit/Hyperactivity Disorder (ADHD) based on resting-state functional MRI (rs-fMRI) and structural MRI data. Here, we introduce a simple method to classify ADHD based on morphological information without using functional data. Our test results show that the accuracy of this approach is competitive with methods based on rs-fMRI data. We used isotropic local binary patterns on three orthogonal planes (LBP-TOP) to extract features from MR brain images. Subsequently, support vector machines (SVM) were used to develop classification models based on the extracted features. In this study, a total of 436 male subjects (210 with ADHD and 226 controls) were analyzed to show the discriminative power of the method. To analyze the properties of this approach, we tested disparate LBP-TOP features from various parcellations and different image resolutions. Additionally, morphological information using a single brain tissue type (i.e., gray matter (GM), white matter (WM), and CSF) was tested. The highest accuracy we achieved was 0.6995. The LBP-TOP was found to provide better discriminative power using whole-brain data as the input. Datasets with higher resolution can train models with increased accuracy. The information from GM plays a more important role than that of other tissue types. These results and the properties of LBP-TOP suggest that most of the disparate feature distribution comes from different patterns of cortical folding. Using LBP-TOP, we provide an ADHD classification model based only on anatomical information, which is easier to obtain in the clinical environment and which is simpler to preprocess compared with rs-fMRI data.
Highlights
Attention-Deficit/Hyperactivity Disorder (ADHD) is a multifactorial and clinically heterogeneous disorder, which is highly prevalent in children worldwide
We found that the brain morphological changes described by a 3D texture analysis can be used to distinguish children with ADHD from typically developing children (TDC)
Our results demonstrate that using features based on local binary patterns on three orthogonal planes (LBP-TOP) data to train the linear support vector machines (SVM) can result in greater discriminative power than using features based on rs-fMRI data
Summary
Attention-Deficit/Hyperactivity Disorder (ADHD) is a multifactorial and clinically heterogeneous disorder, which is highly prevalent in children worldwide. It is estimated that 5–10% of school-age children and 4% of adults suffer from ADHD (Biederman, 2005). The negative impact of ADHD on patients, their families, and society make ADHD a major public health problem (Ferguson, 2000). An objective biological tool to diagnose ADHD is still unavailable. Foreseeing the importance, the organizers of the ADHD-200 Global Competition have collected functional and anatomical ADHD MRI datasets of an unprecedented scale, which are accessible via the Internet (http://fcon_1000.projects.nitrc.org/indi/adhd200/). This work provides an important opportunity for researchers all over the world to study brain changes in ADHD subjects based on numerous brain MRI images
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