title: Distinct neural signatures detected for ADHD subtypes after controlling for micro-movements in resting state functional connectivity MRI data.
abstract: 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.
authors: Department of Behavioral Neuroscience, Advanced Imaging Research Center, Oregon Health and Science University Portland, OR, USA ; Department of Psychiatry, Advanced Imaging Research Center, Oregon Health and Science University Portland, OR, USA.
authors: Damien A
authors: DA
authors: Fair
authors: Joel T
authors: JT
authors: Nigg
authors: Swathi
authors: S
authors: Iyer
authors: Deepti
authors: D
authors: Bathula
authors: Kathryn L
authors: KL
authors: Mills
authors: Nico U F
authors: NU
authors: Dosenbach
authors: Bradley L
authors: BL
authors: Schlaggar
authors: Maarten
authors: M
authors: Mennes
authors: David
authors: D
authors: Gutman
authors: Saroja
authors: S
authors: Bangaru
authors: Jan K
authors: JK
authors: Buitelaar
authors: Daniel P
authors: DP
authors: Dickstein
authors: Adriana
authors: A
authors: Di Martino
authors: David N
authors: DN
authors: Kennedy
authors: Clare
authors: C
authors: Kelly
authors: Beatriz
authors: B
authors: Luna
authors: Julie B
authors: JB
authors: Schweitzer
authors: Katerina
authors: K
authors: Velanova
authors: Yu-Feng
authors: YF
authors: Wang
authors: Stewart
authors: S
authors: Mostofsky
authors: F Xavier
authors: FX
authors: Castellanos
authors: Michael P
authors: MP
authors: Milham
keywords: ADHD; RDoC; functional connectivity; research domain criteria; support vector machines
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