Automated diagnoses of attention deficit hyperactive disorder using magnetic resonance imaging

Abstract

Successful automated diagnoses of attention deficit hyperactive disorder (ADHD) using imaging and functional biomarkers would have fundamental consequences on the public health impact of the disease. In this work, we show results on the predictability of ADHD using imaging biomarkers and discuss the scientific and diagnostic impacts of the research. We created a prediction model using the landmark ADHD 200 data set focusing on resting state functional connectivity (rs-fc) and structural brain imaging. We predicted ADHD status and subtype, obtained by behavioral examination, using imaging data, intelligence quotients and other covariates. The novel contributions of this manuscript include a thorough exploration of prediction and image feature extraction methodology on this form of data, including the use of singular value decompositions (SVDs), CUR decompositions, random forest, gradient boosting, bagging, voxel-based morphometry, and support vector machines as well as important insights into the value, and potentially lack thereof, of imaging biomarkers of disease. The key results include the CUR-based decomposition of the rs-fc-fMRI along with gradient boosting and the prediction algorithm based on a motor network parcellation and random forest algorithm. We conjecture that the CUR decomposition is largely diagnosing common population directions of head motion. Of note, a byproduct of this research is a potential automated method for detecting subtle in-scanner motion. The final prediction algorithm, a weighted combination of several algorithms, had an external test set specificity of 94% with sensitivity of 21%. The most promising imaging biomarker was a correlation graph from a motor network parcellation. In summary, we have undertaken a large-scale statistical exploratory prediction exercise on the unique ADHD 200 data set. The exercise produced several potential leads for future scientific exploration of the neurological basis of ADHD.

Keywords: gradient boosting; random forest; singular value decomposition; voxel-based morphometry.

Figure 1

Motor cortex parcellation.

Figure 2

264 seed voxels.

Figure 3

Demographic information.

Figure 4

Dot plot of composite intelligence quotients (average of all available IQ measurements per subject) by data contributing site color coded by disease subtype for the internal training set and internal test set.

Figure 5

Voxels chosen by CUR decomposition.

Figure 6

Dot plot of composite intelligence quotients (average of all available IQ measurements per subject) by data contributing site color coded by disease subtype.

Figure 7

Plot of correlations between the dorsomedial and dorsolateral M1 parcels by disease subtype. A reference line is drawn at zero while the inter-subject means (small horizontal line) and 95% confidence intervals (small vertical lines) are given to the left of each group.