In recent years, there has been growing enthusiasm that functional 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 A) examine the impact of emerging techniques for controlling for “micro-movements,” and B) provide novel insights into the neural correlates of ADHD subtypes. Using SVM based 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 rs-fcMRI data can be used to characterize individual patients with ADHD and to identify neural distinctions underlying the clinical heterogeneity of ADHD.
@article{fair_distinct_2013,
title = {Distinct {Neural} {Signatures} {Detected} for {ADHD} {Subtypes} after {Controlling} for {Micro}-{Movements} in {Resting} {State} {Functional} {Connectivity} {MRI} {Data}},
volume = {6},
issn = {1662-5137},
doi = {10.3389/fnsys.2012.00080},
language = {English},
journal = {Frontiers in Systems Neuroscience},
author = {Fair, Damien and Nigg, Joel T. and Iyer, Swathi and Bathula, Deepti and Mills, Kathryn L. and Dosenbach, Nico UF and Schlaggar, Bradley L. and Mennes, Maarten and Gutman, David and Bangaru, Saroja and Buitelaar, Jan K. and Dickstein, Daniel P. and Di Martino, Adriana and Kennedy, David N. and Kelly, Clare and Luna, Beatriz and Schweitzer, Julie B. and Velanova, Katerina and Wang, Yu-Feng and Mostofsky, Stewart H. and Castellanos, Francisco Xavier and Milham, Michael P.},
year = {2013},
keywords = {ADHD, functional connectivity, RDoC, Research Domain Criteria, Support Vector Machines}
}