6.44 DEFICITS IN ANATOMICAL AND FUNCTIONAL CONNECTIVITY IN MEDICATION-NAÏVE CHILDREN WITH ATTENTION-DEFICIT/HYPERACTIVITY DISORDER

• Published in: Journal of the American Academy of Child and Adolescent Psychiatry Vol. 55; no. 10; p. S218
• Main Authors: Choi, Jeewook, MD, Yoo, Jaehyun, MD, Teicher, Martin H., MD, Jeong, Bumseok, MD, PhD
• Format: Journal Article
• Language: English
• Published: Baltimore Elsevier Inc 01.10.2016; Elsevier BV
• Subjects: Algorithms; Anatomy; Attention deficit hyperactivity disorder; Bootstrap method; Brain; Brain structure; Children; Children & youth; Cingulum; Demographics; Drugs; Elimination; Forests; Functional connectivity; Functional impairment; Hippocampus; Hyperactivity; Mental health care; Neural networks; Neuroimaging; Pediatrics; Psychiatry; Rating Scales; Resting; Validity
• ISSN: 0890-8567; 1527-5418
• DOI: 10.1016/j.jaac.2016.09.364

Objectives: Alterations of ADHD subjects extend into widespread brain structures, which were presented by different imaging modalities. This study investigates anatomical and functional connectivity feature sets that can distinguish medication-naive children with ADHD from typically developing children (TDC). Methods: Neuroimaging features included 66 functional connectivities (FC) among 12 resting-state functional networks, diffusion metrics in 20 fiber tracts as anatomical connectivity (AC), and 33 structural features. Single or any kind of combination of FC, AC, or structural features with demographic information were entered in a multiple support vector machine recursive feature elimination algorithm (https://github.com/johncolby/SVM-RFE) to select optimal feature sets for prediction of the diagnostic status of individuals with 19 ADHD from 20 age-, sex-matched TDC. Both out-of-bag error (OOB) as mean prediction error and area under the curve in each set were able to get the results from random forests, which returned the predicted label with the probabilities of all possible outcomes. The final winner set was validated with an independent multimodal dataset (27 ADHD and 13 TDC). Results: The final winner set included both FCs, such as a left central executive network (CEN), with a right CEN or with salience network and ACs such as the axial diffusivity of hippocampal portion of the left cingulum bundle. This winner set had better performance than AC only (Z =-3.295, P < 0.001) and then FC only (Z = -2.091, P = 0.037) and explained 18.05 percent of the variation of the ADHD rating scale. We were able to identify medication-naive ADHD from TDC with 7.69 percent of OOB. This winner set also showed superior performance than both tensor (Z=-2.071, P = 0.038) and marginal significance to FC only by a bootstrap method (D = 1.890, P = 0.059, 5000 replicates) in an independent validation dataset. Conclusions: Our results with multivariate approaches indicate aberrant anatomical and functional brain circuitry that could explain the multiple symptom domains in ADHD. Our results from small sample group sizes having overfitting tendency should be treated with care and be confirmed with large datasets.