In recent years, there has been growing enthusiasm that functional magnetic resonance imaging (MRI) could achieve clinical utility for a broad range of neuropsychiatric disorders. However, several barriers remain. For example, the acquisition of large-scale datasets capable of clarifying the marked heterogeneity that exists in psychiatric illnesses will need to be realized. In addition, there continues to be a need for the development of image processing and analysis methods capable of separating signal from artifact. As a prototypical hyperkinetic disorder, and movement-related artifact being a significant confound in functional imaging studies, ADHD offers a unique challenge. As part of the ADHD-200 Global Competition and this special edition of Frontiers, the ADHD-200 Consortium demonstrates the utility of an aggregate dataset pooled across five institutions in addressing these challenges. The work aimed to (1) examine the impact of emerging techniques for controlling for “micro-movements,” and (2) provide novel insights into the neural correlates of ADHD subtypes. Using support vector machine (SVM)-based multivariate pattern analysis (MVPA) we show that functional connectivity patterns in individuals are capable of differentiating the two most prominent ADHD subtypes. The application of graph-theory revealed that the Combined (ADHD-C) and Inattentive (ADHD-I) subtypes demonstrated some overlapping (particularly sensorimotor systems), but unique patterns of atypical connectivity. For ADHD-C, atypical connectivity was prominent in midline default network components, as well as insular cortex; in contrast, the ADHD-I group exhibited atypical patterns within the dlPFC regions and cerebellum. Systematic motion-related artifact was noted, and highlighted the need for stringent motion correction. Findings reported were robust to the specific motion correction strategy employed. These data suggest that resting-state functional connectivity MRI (rs-fcMRI) data can be used to characterize individual patients with ADHD and to identify neural distinctions underlying the clinical heterogeneity of ADHD.
Keywords: ADHD, functional connectivity, support vector machines, RDoC, research domain criteria
Citation: Fair DA, Nigg JT, Iyer S, Bathula D, Mills KL, Dosenbach NUF, Schlaggar BL, Mennes M, Gutman D, Bangaru S, Buitelaar JK, Dickstein DP, Di Martino A, Kennedy DN, Kelly C, Luna B, Schweitzer JB, Velanova K, Wang Y-F, Mostofsky S, Castellanos FX and Milham MP (2013) Distinct neural signatures detected for ADHD subtypes after controlling for micro-movements in resting state functional connectivity MRI data. Front. Syst. Neurosci. 6:80. doi: 10.3389/fnsys.2012.00080
Received: 09 July 2012; Paper pending published: 24 September 2012;
Accepted: 30 December 2012; Published online: 04 February 2013.
Edited by:Ranulfo Romo, Universidad Nacional Autónoma de México, Mexico
Reviewed by:Valentin Dragoi, University of Texas Medical School at Houston, USA
Copyright © 2013 Fair, Nigg, Iyer, Bathula, Mills, Dosenbach, Schlaggar, Mennes, Gutman, Bangaru, Buitelaar, Dickstein, Di Martino, Kennedy, Kelly, Luna, Schweitzer, Velanova, Wang, Mostofsky, Castellanos and Milham. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in other forums, provided the original authors and source are credited and subject to any copyright notices concerning any third-party graphics etc.
*Correspondence: Damien A. Fair, Department of Psychiatry, Oregon Health and Science University, 3181 SW Sam Jackson Park Road UHN88, Portland, OR 97239, USA. e-mail: firstname.lastname@example.org
Michael P. Milham, Child Mind Institute, Center for the Developing Brain, 445 Park Avenue, New York, NY 10022, USA. e-mail: email@example.com