Abstract
Neuroimaging-based diagnostics could potentially assist clinicians to make more accurate diagnoses resulting in faster, more effective treatment. We participated in the 2011 ADHD-200 Global Competition which involved analyzing a large dataset of 973 participants including Attention deficit hyperactivity disorder (ADHD) patients and healthy controls. Each participant's data included a resting state functional magnetic resonance imaging (fMRI) scan as well as personal characteristic and diagnostic data. The goal was to learn a machine learning classifier that used a participant's resting state fMRI scan to diagnose (classify) that individual into one of three categories: healthy control, ADHD combined (ADHD-C) type, or ADHD inattentive (ADHD-I) type. We used participants' personal characteristic data (site of data collection, age, gender, handedness, performance IQ, verbal IQ, and full scale IQ), without any fMRI data, as input to a logistic classifier to generate diagnostic predictions. Surprisingly, this approach achieved the highest diagnostic accuracy (62.52%) as well as the highest score (124 of 195) of any of the 21 teams participating in the competition. These results demonstrate the importance of accounting for differences in age, gender, and other personal characteristics in imaging diagnostics research. We discuss further implications of these results for fMRI-based diagnosis as well as fMRI-based clinical research. We also document our tests with a variety of imaging-based diagnostic methods, none of which performed as well as the logistic classifier using only personal characteristic data.
Highlights
Attention deficit hyperactivity disorder (ADHD) is a psychiatric disorder characterized by impulsiveness, inattention, and hyperactivity
GENERAL DIAGNOSIS PROCEDURE The ADHD-200 Global Competition required three-way diagnostic classification, but we explored binary diagnosis because we suspected that it might be easier than three-way diagnosis
The logistic classifier, linear support vector machine (SVM), quadratic SVM, and cubic SVM classifiers all performed significantly better than chance on both binary and three-way diagnosis using either the personal characteristic data selection 1 (PCs1) or selection 2 (PCs2) features
Summary
Attention deficit hyperactivity disorder (ADHD) is a psychiatric disorder characterized by impulsiveness, inattention, and hyperactivity. Machine learning (artificial intelligence) techniques have been used to diagnose various psychiatric illnesses based on an individual’s fMRI data (Zhu et al, 2005, 2008; Shinkareva et al, 2006; Calhoun et al, 2008; Fu et al, 2008; Marquand et al, 2008; Cecchi et al, 2009; Arribas et al, 2010; Nouretdinov et al, 2011; Shen et al, 2010; Costafreda et al, 2011; Fan et al, 2011) Such fMRI-based diagnosis has the potential to assist psychiatrists in providing improved diagnosis and treatment for psychiatric patients. This approach is consistent with the recent emphasis on personalized medicine in health care delivery
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