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

From Nonspecific DNA–Protein Encounter Complexes to the Prediction of DNA–Protein Interactions

©2009 Gao, Skolnick. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.doi:10.1371/journal.pcbi.1000341DNA–protein interactions are involved in many essential biological activities. Because there is no simple mapping code between DNA base pairs and protein amino acids, the prediction of DNA–protein interactions is a challenging problem. Here, we present a novel computational approach for predicting DNA-binding protein residues and DNA–protein interaction modes without knowing its specific DNA target sequence. Given the structure of a DNA-binding protein, the method first generates an ensemble of complex structures obtained by rigid-body docking with a nonspecific canonical B-DNA. Representative models are subsequently selected through clustering and ranking by their DNA–protein interfacial energy. Analysis of these encounter complex models suggests that the recognition sites for specific DNA binding are usually favorable interaction sites for the nonspecific DNA probe and that nonspecific DNA–protein interaction modes exhibit some similarity to specific DNA–protein binding modes. Although the method requires as input the knowledge that the protein binds DNA, in benchmark tests, it achieves better performance in identifying DNA-binding sites than three previously established methods, which are based on sophisticated machine-learning techniques. We further apply our method to protein structures predicted through modeling and demonstrate that our method performs satisfactorily on protein models whose root-mean-square Ca deviation from native is up to 5 Å from their native structures. This study provides valuable structural insights into how a specific DNA-binding protein interacts with a nonspecific DNA sequence. The similarity between the specific DNA–protein interaction mode and nonspecific interaction modes may reflect an important sampling step in search of its specific DNA targets by a DNA-binding protein

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

What can Cortical Development in Attention-Deficit/Hyperactivity Disorder Teach us About the Early Developmental Mechanisms Involved?

Studies of Attention-Deficit/Hyperactivity Disorder (ADHD) have shown developmental changes in the cortical mantle. Different dimensions of cortical morphology, such as surface area and thickness, relate to different neurodevelopmental mechanisms. As such, studying multiple dimensions may inform us about the developmental origins of ADHD. Furthermore, results from existing longitudinal samples await replication. Therefore, we conducted a longitudinal study of multiple cortical dimensions in a sizable, independent ADHD sample. We analyzed 297 anatomical MRI scans from two matched groups of 94 subjects with ADHD and 94 controls, aged 6-28 years. We estimated the developmental trajectories of cortical volume, surface, thickness and gyrification for 68 regions using mixed-effects regression analysis. Subjects with ADHD had smaller overall cortical volume, predominantly driven by decreases in frontal lobe volume that were associated with reduced surface area and gyrification. Nearly all decreases were stable across development. Only a few decreases survived stringent Bonferroni correction for multiple comparisons, with the smallest detectable Cohen's d |0.43|. There were no between-group differences in cortical thickness, or in subcortical volumes. Our results suggest that ADHD is associated with developmentally persistent reductions in frontal cortical volume, surface area, and gyrification. This may implicate early neurodevelopmental mechanisms regulating cortical expansion and convolution in ADHD

What can Cortical Development in Attention-Deficit/Hyperactivity Disorder Teach us About the Early Developmental Mechanisms Involved?

Studies of Attention-Deficit/Hyperactivity Disorder (ADHD) have shown developmental changes in the cortical mantle. Different dimensions of cortical morphology, such as surface area and thickness, relate to different neurodevelopmental mechanisms. As such, studying multiple dimensions may inform us about the developmental origins of ADHD. Furthermore, results from existing longitudinal samples await replication. Therefore, we conducted a longitudinal study of multiple cortical dimensions in a sizable, independent ADHD sample. We analyzed 297 anatomical MRI scans from two matched groups of 94 subjects with ADHD and 94 controls, aged 6-28 years. We estimated the developmental trajectories of cortical volume, surface, thickness and gyrification for 68 regions using mixed-effects regression analysis. Subjects with ADHD had smaller overall cortical volume, predominantly driven by decreases in frontal lobe volume that were associated with reduced surface area and gyrification. Nearly all decreases were stable across development. Only a few decreases survived stringent Bonferroni correction for multiple comparisons, with the smallest detectable Cohen's d |0.43|. There were no between-group differences in cortical thickness, or in subcortical volumes. Our results suggest that ADHD is associated with developmentally persistent reductions in frontal cortical volume, surface area, and gyrification. This may implicate early neurodevelopmental mechanisms regulating cortical expansion and convolution in ADHD

What can cortical development in attention-deficit/ hyperactivity disorder teach us about the early developmental mechanisms involved?

Studies of Attention-Deficit/Hyperactivity Disorder (ADHD) have shown developmental changes in the cortical mantle. Different dimensions of cortical morphology, such as surface area and thickness, relate to different neurodevelopmental mechanisms. As such, studying multiple dimensions may inform us about the developmental origins of ADHD. Furthermore, results from existing longitudinal samples await replication. Therefore, we conducted a longitudinal study of multiple cortical dimensions in a sizable, independent ADHD sample. We analyzed 297 anatomical MRI scans from two matched groups of 94 subjects with ADHD and 94 controls, aged 6-28 years. We estimated the developmental trajectories of cortical volume, surface, thickness and gyrification for 68 regions using mixed-effects regression analysis. Subjects with ADHD had smaller overall cortical volume, predominantly driven by decreases in frontal lobe volume that were associated with reduced surface area and gyrification. Nearly all decreases were stable across development. Only a few decreases survived stringent Bonferroni correction for multiple comparisons, with the smallest detectable Cohen's d |0.43|. There were no between-group differences in cortical thickness, or in subcortical volumes. Our results suggest that ADHD is associated with developmentally persistent reductions in frontal cortical volume, surface area, and gyrification. This may implicate early neurodevelopmental mechanisms regulating cortical expansion and convolution in ADHD

DRD3 gene and striatum in autism spectrum disorder

A single-nucleotide polymorphism (SNP) of the DRD3 gene (rs167771) was recently associated with autism spectrum disorders (ASD). Different polymorphisms of rs167771 corresponded to varying degrees of stereotyped behaviour. As DRD3 receptors are relatively overexpressed in the striatum, we investigated whether striatal volume was related to these polymorphisms in autism. We assessed volumes of caudate nucleus and putamen in 86 participants with ASD (mean age 15.3 years). MANCOVA showed an association between alleles of the rs167771 SNP and the volume of striatal structures. Furthermore, greater caudate nucleus volume correlated with stereotyped behaviour. These findings support a relationship between DRD3 gene SNPs, striatum and stereotyped behaviour in ASD

What can cortical development in attention-deficit/ hyperactivity disorder teach us about the early developmental mechanisms involved?

Studies of Attention-Deficit/Hyperactivity Disorder (ADHD) have shown developmental changes in the cortical mantle. Different dimensions of cortical morphology, such as surface area and thickness, relate to different neurodevelopmental mechanisms. As such, studying multiple dimensions may inform us about the developmental origins of ADHD. Furthermore, results from existing longitudinal samples await replication. Therefore, we conducted a longitudinal study of multiple cortical dimensions in a sizable, independent ADHD sample. We analyzed 297 anatomical MRI scans from two matched groups of 94 subjects with ADHD and 94 controls, aged 6-28 years. We estimated the developmental trajectories of cortical volume, surface, thickness and gyrification for 68 regions using mixed-effects regression analysis. Subjects with ADHD had smaller overall cortical volume, predominantly driven by decreases in frontal lobe volume that were associated with reduced surface area and gyrification. Nearly all decreases were stable across development. Only a few decreases survived stringent Bonferroni correction for multiple comparisons, with the smallest detectable Cohen's d |0.43|. There were no between-group differences in cortical thickness, or in subcortical volumes. Our results suggest that ADHD is associated with developmentally persistent reductions in frontal cortical volume, surface area, and gyrification. This may implicate early neurodevelopmental mechanisms regulating cortical expansion and convolution in ADHD

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