ابعاد کنترل روانشناختی والدین: ارتباطات با پرخاشگری فیزیکی و رابطه پیش دبستانی در روسیه

The aim of this study is to investigate whether the anatomical organization of large-scale brain systems would change in ADHD patients compared to healthy controls. We utilized a structural covariance network (SCN) mapping approach to investigate large-scale networks in 30 drug-naïve ADHD boys and 30 gender- and age-matched controls. The regions showing significant between-group differences in gray matter (GM) volume were defined as seed regions of interest. Then, the SCNs derived from these seeds were statistically compared between ADHD and controls. Significant regional GM volume decreases (P<0.05, corrected) were observed in the right insula and the right orbito-frontal cortex (OFC) in ADHD relative to controls. Both SCNs derived from these two seeds showed more localized topology in ADHD group. Furthermore, significantly decreased structural connectivity were found between insula and right hippocampus, bilateral olfactory cortex, and between OFC and bilateral caudate nucleus (P<0.05, corrected) in ADHD group. Significantly increased association was observed between insula and left middle temporal gyrus (P<0.05, corrected) in ADHD group. Taken together, our results reveal abnormal regional brain anatomy as well as aberrant structural covariance networks in ADHD, supporting previous findings of dysfunction in distributed network organization in patients with ADHD.

Attention-deficit/hyperactivity disorder (ADHD) is one of the most common neurodevelopmental disorders of childhood. It is characterized primarily by developmentally inappropriate inattention, hyperactivity and/or impulsivity (Biederman, 1997). Although the neurobiology of ADHD remains unknown, accumulating evidence from structural and functional magnetic resonance imaging (MRI) studies indicates the presence of distributed brain region abnormalities in ADHD. Structural and functional abnormalities are consistently reported in the prefrontal cortex, basal ganglia, inferior parietal cortex and cerebellum (Cubillo et al., 2012, Frodl and Skokauskas, 2012 and Hart et al., 2012); however, other brain regions, such as the temporal and occipital cortex, insula and hippocampal gyrus, are also implicated in the pathophysiology of ADHD (Biederman, 2005 and Bush et al., 2005). Recently, functional and anatomical connectivity studies, which explore systems-level relationships among brain regions, have indicated abnormalities in large-scale brain systems in ADHD (Castellanos and Proal, 2012). Functional connectivity, which explores the correlation of regional functional MRI (fMRI) signals across time from different brain regions, has been widely used to explore the pathophysiology of ADHD and has indicated a number of different neural systems abnormalities in ADHD, including abnormalities in prefrontal-striatal circuits and the default-mode network (Cao et al., 2009, Castellanos et al., 2008, Castellanos and Proal, 2012 and Sun et al., 2012). Diffusion tensor imaging (DTI) explores connectivity in vivo by inferring the structural connectivity between brain regions and examining the microstructural integrity of white matter tissue (Le Bihan, 2003). Studies employing DTI have revealed widespread changes in white matter integrity in ADHD, most consistently in the right anterior corona radiata, right forceps minor, bilateral internal capsule and left cerebellum (all of which were previously implicated in the ADHD pathophysiology) (van Ewijk et al., 2012). However, many concerns remain regarding DTI methodology, such as its bias against longer paths, diminished resolution of crossing fibers and sensitivity to algorithm and parameter choice (Evans, 2013). Structural covariance networks (SCNs), derived from voxel-based morphometry (VBM), represent a complementary addition to fMRI and DTI connectivity analyses. SCNs characterize the pattern of covariance between the GM volume of a region of interest and the GM volumes of other regions throughout the entire brain using a general linear model framework (Mechelli et al., 2005, Montembeault et al., 2012, Soriano-Mas et al., 2013 and Zielinski et al., 2010). This covariance of GM volume in different cortical regions is thought to result from mutually trophic influences (Ferrer et al., 1995) or shared experience-related plasticity (Draganski et al., 2004 and Mechelli et al., 2004). Structural covariance is also thought to partially reflect connectivity between cortical regions, such as physical connectivity via white-matter tracts (Evans, 2013). Gong et al. reported about 35–40% of GM thickness correlations are convergent with fiber connections (Gong et al., 2012). In addition, Seeley et al. (2009) reported the SCNs have been shown to recapitulate functional brain network topologies of synchronously activated regions. These findings provide substantial support for the use of SCNs to measure network integrity for cross-sectional group studies. To date, SCNs analyses have successfully explored changes in brain connectivity in different neuropsychiatric disorders, such as autism and obsessive-compulsive disorder (Cardoner et al., 2007, McAlonan et al., 2005 and Nosarti et al., 2011). However, it remains unknown whether the anatomical organization of large-scale brain systems is altered in ADHD patients relative to healthy controls. Here, we utilized an SCN mapping approach to investigate large-scale networks in 30 drug-naive ADHD boys and 30 gender- and age-matched controls. We only included drug-naïve patients in this study because previous studies have suggested that stimulants can significantly influence the structure and function of the brain in ADHD (Bush et al., 2008 and Shaw et al., 2009). We first defined seed regions of interest (ROIs) by identifying regions showing significant between-group differences in GM volume using VBM. Then, we constructed the SCNs on the basis of the correlations among the volume of each seed ROI and GM volumes of other regions throughout the whole brain. Given that interconnecting brain systems have common influences on development and maturation (Cheverud, 1982 and Cheverud, 1984), we expect that ADHD patients may have altered GM networks compared to controls.