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, 18, 744-752

Hemispheric Brain Asymmetry Differences in Youths With Attention-Deficit/Hyperactivity Disorder


Hemispheric Brain Asymmetry Differences in Youths With Attention-Deficit/Hyperactivity Disorder

P K Douglas et al. Neuroimage Clin.


Introduction: Attention-deficit hyperactive disorder (ADHD) is the most common neurodevelopmental disorder in children. Diagnosis is currently based on behavioral criteria, but magnetic resonance imaging (MRI) of the brain is increasingly used in ADHD research. To date however, MRI studies have provided mixed results in ADHD patients, particularly with respect to the laterality of findings.

Methods: We studied 849 children and adolescents (ages 6-21 y.o.) diagnosed with ADHD (n = 341) and age-matched typically developing (TD) controls with structural brain MRI. We calculated volumetric measures from 34 cortical and 14 non-cortical brain regions per hemisphere, and detailed shape morphometry of subcortical nuclei. Diffusion tensor imaging (DTI) data were collected for a subset of 104 subjects; from these, we calculated mean diffusivity and fractional anisotropy of white matter tracts. Group comparisons were made for within-hemisphere (right/left) and between hemisphere asymmetry indices (AI) for each measure.

Results: DTI mean diffusivity AI group differences were significant in cingulum, inferior and superior longitudinal fasciculus, and cortico-spinal tracts (p < 0.001) with the effect of stimulant treatment tending to reduce these patterns of asymmetry differences. Gray matter volumes were more asymmetric in medication free ADHD individuals compared to TD in twelve cortical regions and two non-cortical volumes studied (p < 0.05). Morphometric analyses revealed that caudate, hippocampus, thalamus, and amygdala were more asymmetric (p < 0.0001) in ADHD individuals compared to TD, and that asymmetry differences were more significant than lateralized comparisons.

Conclusions: Brain asymmetry measures allow each individual to serve as their own control, diminishing variability between individuals and when pooling data across sites. Asymmetry group differences were more significant than lateralized comparisons between ADHD and TD subjects across morphometric, volumetric, and DTI comparisons.


Fig. 1
Fig. 1
(From left to right) Morphometry results for caudate, hippocampus, thalamus, and amygdala with dorsal and ventral views shown in the upper and lower panels, respectively. For each shape, the three dimensional anatomical structure is combined across subjects to create an average anatomical model for each left and right subcortical shape. Differences were computed between ADHD-Free and TD for each hemisphere. The p-values shown have been mapped onto their associated location for group average templates for each anatomical shape and thresholded from p < 0.012 (blue) to p < 0.0001, based on cumulative distribution functions, where red indicates increased asymmetry in the ADHD group.
Fig. 2
Fig. 2
a. Morphometry results for Caudate Asymmetry Index shown for thickness differences (top) and associated p-maps (bottom). Note the critical q-value used to threshold the p-maps was derived from the cumulative distribution function for Asymmetry, shown in Fig. 2b. Here, all morphology changes were mapped onto the group average shape, calculated using all caudates from both hemispheres. b. Cumulative distribution functions (CDFs) calculated across morphology results for the ADHD Left versus Typically Developing (TD) Left Caudate (turquoise), ADHD Right versus TD right caudates (magenta), and both Asymmetry (blue) and absolute Asymmetry (green). The q-value, or point along the x-axis that corresponds to the intersection of the CDF and y = 20x line other than the origin, is a measure of the overall significance of the p-value maps. The q-value of the CDF for both symmetry indices were higher than q-values for either right or left CDFs, suggesting that this analysis method boosts the statistical power to detect group morphology differences between TD and ADHD subjects.
Fig. 3
Fig. 3
Illustrative figure of tractography fibers that showed significant differences between ADHD-Free and TD groups. Axial and sagittal views shown for (a) cingulum, (b) uncinate fasciculus, (c) inferior longitudinal fasciculus, (d) superior longitudinal fasciculus, (e) corticospinal tract, and (f) inferior fronto-occipital fasciculus. All images created were created using DTI data from BrainSuite version 14c with 0.5 track seeds per voxel (Yan et al., 2011; Shattuck et al., 2009) using region of interest locations specified in (Catani, 2006) and 20 mm spheres. Colors indicate the average of the directions along the fiber tracts as follows: green (anterior/posterior), blue (inferior/superior).

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    1. Alexander A.L., Lee J.E., Lazar M., Field A.S. Diffusion tensor imaging of the brain. Neurother. J. Am. Soc. Exp. Neurother. 2007;4:316–329.
    1. Anderson A., Douglas P.K., Kerr W.T., Haynes V.S., Yuille A.L., Xie J., Wu Y.N., Brown J.A., Cohen M.S. Non-negative matrix factorization of multimodal MRI, fMRI and phenotypic data reveals differential changes in default mode subnetworks in ADHD. NeuroImage. 2014;102(Pt 1):207–219. - PMC - PubMed
    1. Bates D., Machler M., Bolker B., Walker S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 2012;67(1):1–48.
    1. Benjamini Y., Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Stat. Soc. Ser. B. 1995;57:289–300.
    1. Biswal B.B., Mennes M., Zuo X.-N., Gohel S., Kelly C., Smith S.M., Beckmann C.F., Adelstein J.S., Buckner R.L., Colcombe S., Dogonowski A.-M., Ernst M., Fair D., Hampson M., Hoptman M.J., Hyde J.S., Kiviniemi V.J., Kötter R., Li S.-J., Lin C.-P., Lowe M.J., Mackay C., Madden D.J., Madsen K.H., Margulies D.S., Mayberg H.S., McMahon K., Monk C.S., Mostofsky S.H., Nagel B.J., Pekar J.J., Peltier S.J., Petersen S.E., Riedl V., Rombouts S.A.R.B., Rypma B., Schlaggar B.L., Schmidt S., Seidler R.D., Siegle G.J., Sorg C., Teng G.-J., Veijola J., Villringer A., Walter M., Wang L., Weng X.-C., Whitfield-Gabrieli S., Williamson P., Windischberger C., Zang Y.-F., Zhang H.-Y., Castellanos F.X., Milham M.P. Toward discovery science of human brain function. Proc. Natl. Acad. Sci. U. S. A. 2010;107:4734–4739. - PMC - PubMed

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