Imaging gene and environmental effects on cerebellum in Attention-Deficit/Hyperactivity Disorder and typical development

AbstractThis study investigates the effects of XKR4, a recently identified candidate gene for Attention-Deficit/Hyperactivity Disorder (ADHD), birth weight, and their interaction on brain volume in ADHD. XKR4 is expressed in cerebellum and low birth weight has been associated both with changes in cerebellum and with ADHD, probably due to its relation with prenatal adversity. Anatomical MRI scans were acquired in 58 children with ADHD and 64 typically developing controls and processed to obtain volumes of cerebrum, cerebellum and gray and white matter in each structure. DNA was collected from saliva. Analyses including data on birth weight were conducted in a subset of 37 children with ADHD and 51 controls where these data were retrospectively collected using questionnaires. There was an interaction between genotype and birth weight for cerebellum gray matter volume (p=.020). The combination of homozygosity for the G-allele (the allele previously found to be overtransmitted in ADHD) and higher birth weight was associated with smaller volume. Furthermore, birth weight was positively associated with cerebellar white matter volume in controls, but not ADHD (interaction: p=.021). The interaction of genotype with birth weight affecting cerebellum gray matter is consistent with models that emphasize increased influence of genetic risk-factors in an otherwise favorable prenatal environment. The absence of an association between birth weight and cerebellum white matter volume in ADHD suggests that other genetic or environmental effects may be at play, unrelated to XKR4. These results underscore the importance of considering environmental effects in imaging genetics studies

Differential Brain Development with Low and High IQ in Attention-Deficit/Hyperactivity Disorder

Attention-Deficit/Hyperactivity Disorder (ADHD) and intelligence (IQ) are both heritable phenotypes. Overlapping genetic effects have been suggested to influence both, with neuroimaging work suggesting similar overlap in terms of morphometric properties of the brain. Together, this evidence suggests that the brain changes characteristic of ADHD may vary as a function of IQ. This study investigated this hypothesis in a sample of 108 children with ADHD and 106 typically developing controls, who participated in a cross-sectional anatomical MRI study. A subgroup of 64 children also participated in a diffusion tensor imaging scan. Brain volumes, local cortical thickness and average cerebral white matter microstructure were analyzed in relation to diagnostic group and IQ. Dimensional analyses investigated possible group differences in the relationship between anatomical measures and IQ. Second, the groups were split into above and below median IQ subgroups to investigate possible differences in the trajectories of cortical development. Dimensionally, cerebral gray matter volume and cerebral white matter microstructure were positively associated with IQ for controls, but not for ADHD. In the analyses of the below and above median IQ subgroups, we found no differences from controls in cerebral gray matter volume in ADHD with below-median IQ, but a delay of cortical development in a number of regions, including prefrontal areas. Conversely, in ADHD with above-median IQ, there were significant reductions from controls in cerebral gray matter volume, but no local differences in the trajectories of cortical development

Deficits in Cognitive Control, Timing and Reward Sensitivity Appear to be Dissociable in ADHD

<div><p>Recent neurobiological models of ADHD suggest that deficits in different neurobiological pathways may independently lead to symptoms of this disorder. At least three independent pathways may be involved: a dorsal frontostriatal pathway involved in cognitive control, a ventral frontostriatal pathway involved in reward processing and a frontocerebellar pathway related to temporal processing. Importantly, we and others have suggested that disruptions in these three pathways should lead to separable deficits at the cognitive level. Furthermore, if these truly represent separate biological pathways to ADHD, these cognitive deficits should segregate between individuals with ADHD. The present study tests these hypotheses in a sample of children, adolescents and young adults with ADHD and controls. 149 Subjects participated in a short computerized battery assessing cognitive control, timing and reward sensitivity. We used Principal Component Analysis to find independent components underlying the variance in the data. The segregation of deficits between individuals was tested using Loglinear Analysis. We found four components, three of which were predicted by the model: Cognitive control, reward sensitivity and timing. Furthermore, 80% of subjects with ADHD that had a deficit were deficient on only one component. Loglinear Analysis statistically confirmed the independent segregation of deficits between individuals. We therefore conclude that cognitive control, timing and reward sensitivity were separable at a cognitive level and that deficits on these components segregated between individuals with ADHD. These results support a neurobiological framework of separate biological pathways to ADHD with separable cognitive deficits.</p> </div

Calculation of the reaction time (RT) distribution shift measure for the reward sensitivity task.

<p>After removing accidental button presses and outliers (RT<100 ms or RT 2 SD faster or slower than the mean), RTs for each trial-type were rank ordered from fastest to slowest RT. The rank ordered RTs of the rewarded trials were then regressed on the rank ordered RTs of the non-rewarded trials for 5 ct and 15 ct conditions separately. The regression coefficients obtained (B_0vs5ct and B_0vs15ct) represent the shift of the RT distribution: if B<1, the RTs in the rewarded condition were <i>faster</i>, if B>1, the RTs in the rewarded condition were <i>slower</i>.</p

Number of ADHD subjects scoring below the 10^th percentile of the distribution in controls.

<p>ADHD, Attention-Deficit/Hyperactivity Disorder.</p

Venn diagram of deficits in the ADHD group for the predicted cognitive components.

<p>Venn diagram of deficits in the ADHD group for the predicted cognitive components.</p

Participant characteristics.

<p>ADHD, Attention-Deficit/Hyperactivity Disorder; ASR, Adult Self Report; ODD, Oppositional Defiant Disorder; DISC-IV, Diagnostic Interview Schedule for Children-Fourth Edition; CBCL, Child Behavior Checklist; MINI-Plus, Mini International Neuropsychiatric Interview Plus; SES, Socio-Economic Status.</p><p>Reported are: t-tests for continuous variables, Fisher Exact test for gender (due to low cell counts and large cell count differences) and Chi² for subtypes by age group (as Fisher Exact tests cannot be applied to 3×2 tables).</p><p>a. Unavailable for 11 controls and 8 subjects with ADHD.</p><p>b. Data father unavailable in 3 controls and 7 subjects with ADHD, data mother unavailable in 1 control and 7 subjects with ADHD.</p

Data for brain volumes and global white matter microstructure and results of the tests for main effects of group, group by age interactions and dimensional IQ effects.

<p>Abbreviations: ADHD, Attention-Deficit/Hyperactivity Disorder; FA, Fractional Anisotropy.</p><p>Note: covariates for gender, age and slice thickness on T1 were included in all analyses (except the analysis of cerebral FA where there were no differences in slice thickness); a. n_Control = 98, n_ADHD = 90; b. raw ventricular volumes are tabulated. For analyses, these measures were log-transformed due to a deviation from normality; c. n_Control = 96, n_ADHD = 90; d. n_Control = 34, n_ADHD = 30, not split in IQ groups due to small group size; e. This column reports analyses of age effects on the whole diagnostic groups (not split by IQ). Analyses on the group with age<14 years (n_control = 93, n_ADHD = 85) showed the same pattern of results except for Mean Cortical Thickness (p = .028). Both groups showed decreasing thickness with age, but the regression line was steeper in the control group; f. This column reports analyses of IQ effects where IQ is treated as a dimensional measure, with its effects tested on the whole diagnostic groups. As these analyses were performed on continuous measures, three above median IQ outliers were excluded from structural MRI dataset for the IQ analyses (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0035770#s2" target="_blank">Methods</a>).</p

Demographic data.

<p>Abbreviations: ADHD, Attention-Deficit/Hyperactivity Disorder (I = inattentive type, HI = hyperactive/impulsive type, C = combined type); AM, Above Median; BM, Below Median; ODD, Oppositional Defiant Disorder; DISC-IV, Diagnostic Interview Schedule for Children-Fourth Edition; CBCL, Child Behavior Checklist; TRF, Teacher Report Form; SES, Socio-Economic Status.</p>a<p>Four children that met DISC-IV criteria for ODD also met criteria for CD;</p>b<p>CBCL unavailable for 2 Control_{Below-median IQ}, 2 Control_{Above-median IQ}, 11 ADHD_{Below-median IQ}, 2 ADHD_{Above-median IQ} in structural MRI sample, for 1 Control and 3 ADHD in DTI sample; TRF unavailable for 5 Control_{Below-median IQ}, 8 Control_{Above-median IQ}, 12 ADHD_{Below-median IQ}, 5 ADHD_{Above-median IQ} in structural MRI sample, for 1 control and 9 ADHD in DTI sample.</p>c<p>Medication histories were available for 87% of ADHD_{Below-median IQ} and 79% of the ADHD_{Above-median IQ} children in the structural MRI sample and 87% of ADHD children in the DTI sample. Reported is the percentage of established use in the entire (sub)sample. Corrected duration is calculated as: duration of use in months/((age in months) – 60).</p

Differences in the development of cortical thickness or children with ADHD and below median IQ ADHD versus matched controls.

<p>The figure shows t-maps from the comparison of the developmental trajectories of cortical thickness between subgroups of children with ADHD and below median IQ and matched controls. Critical t-values were t = 3.69 for the right hemisphere and t = 4.27 for the left hemisphere. For the two significant prefrontal regions, scatterplots with the best fit are shown for the below median IQ data. Fits for the entire group are also shown as a reference. Abbreviations: ADHD, Attention- Deficit/Hyperactivity Disorder; PFC, prefrontal cortex.</p