R fMRI analyses included two types of data driven

R-fMRI analyses included two types of data-driven approaches: (1) a set of commonly used regional derivatives that carboxypeptidase a are amenable to univariate voxel-wise analysis, including: Degree Centrality (DC; Zuo et al., 2012), Regional Homogeneity (ReHo; Zang et al., 2004), fractional Amplitude of Low-Frequency Fluctuations (fALFF; Zou et al., 2008); and Voxel-Mirrored Homotopic Connectivity (VMHC; Zuo et al., 2010); and (2) a multivariate analytic framework: Multivariate Distance Matrix Regression (MDMR: Shehzad et al., 2014). MDMR identifies voxels whose whole-brain connectivity patterns vary significantly with verbal WM performance, age, or their interactions and provide a more comprehensive characterization of brain–behavior relationships. See Table 1 for definition/interpretation of each approach. Given continued controversies regarding optimal R-fMRI preprocessing strategies (Power et al., 2014; Yan et al., 2013c), we also evaluated the robustness of our results to preprocessing decisions.
Materials and methods
Results
Discussion
Conflict of interest
Acknowledgments This work was supported in part by NIHU01MH099059 to MPM and gifts from Phyllis Green, Randolph Cowen and Joseph P. Healey to MPM. This work was also partly supported by NIH R01MH081218 and R01HD065282 to FXC. We thank Charlotte E. Michaelcheck for help with imaging data quality control.
Introduction The identification of biomarkers for neurodevelopmental disorders, a high priority for functional connectomics (Castellanos et al., 2013; Di Martino et al., 2014), depends on the development of measures that yield consistent results when repeated over time, i.e., their test–retest reliability must be adequate. A growing literature has worked to establish the test–retest reliability of common resting state functional magnetic resonance imaging (R-fMRI) measures (for review, see Zuo and Xing, 2014). Initial results have been encouraging, showing moderate-to-high short- and long-term test–retest reliability for an array of R-fMRI metrics, including: seed-based functional connectivity (e.g., Shehzad et al., 2009), amplitude of low-frequency fluctuations (ALFF; e.g., Zuo et al., 2010a), independent component analysis (ICA) based-indices (e.g., Thomason et al., 2011; Zuo et al., 2010b), regional homogeneity (ReHo) (Zuo et al., 2013) and voxel-mirrored homotopic connectivity (VMHC; Zuo et al., 2010c). These studies focused almost exclusively on neurotypical adults; only one study specifically examined test–retest reliability in children (Thomason et al., 2011). That study demonstrated high consistency of connectivity networks identified using ICA in typically developing children (TDC). However, questions remain about the generalizability of these findings for a broader array of commonly examined R-fMRI metrics and for children with clinical conditions. Here, we systematically quantified test–retest reliability of a range of R-fMRI metrics in clinical and nonclinical developing participants by leveraging a convenience sample of children with and without Attention-Deficit/Hyperactivity Disorder (ADHD) who completed two scans in the same session (∼25min apart). We focused on R-fMRI measures previously shown to be sensitive to brain development and increasingly investigated in neuropsychiatric disorders (e.g., Di Martino et al., 2014; Collin and van den Heuvel, 2013; Craddock et al., 2013; Dennis and Thompson, 2014; Fox and Greicius, 2010; Hagmann et al., 2012; Uddin et al., 2010). Specifically, we examined VMHC (which characterizes interhemispheric interactions; Zuo et al., 2010c); ReHo (local connectivity; Zang et al., 2004), ALFF (regional variability of the BOLD signal; Zang et al., 2007) and its normalized variant (fALFF; Zou et al., 2008). Finally, based on consistent findings of altered default network integrity in neurodevelopmental disorders, including ADHD (Castellanos et al., 2008; Posner et al., 2014), we examined posterior cingulate cortex (PCC) functional connectivity using seed based correlations.