The effect of intellectual ability on functional activation in a neurodevelopmental disorder: preliminary evidence from multiple fMRI studies in Williams syndrome

BACKGROUND: Williams syndrome (WS) is a rare genetic disorder caused by the deletion of approximately 25 genes at 7q11.23 that involves mild to moderate intellectual disability (ID). When using functional magnetic resonance imaging (fMRI) to compare individuals with ID to typically developing individuals, there is a possibility that differences in IQ contribute to between-group differences in BOLD signal. If IQ is correlated with BOLD signal, then group-level analyses should adjust for IQ, or else IQ should be matched between groups. If, however, IQ is not correlated with BOLD signal, no such adjustment or criteria for matching (and exclusion) based on IQ is necessary. METHODS: In this study, we aimed to test this hypothesis systematically using four extant fMRI datasets in WS. Participants included 29 adult subjects with WS (17 men) demonstrating a wide range of standardized IQ scores (composite IQ mean = 67, SD = 17.2). We extracted average BOLD activation for both cognitive and task-specific anatomically defined regions of interest (ROIs) in each individual and correlated BOLD with composite IQ scores, verbal IQ scores and non-verbal IQ scores in Spearman rank correlation tests. RESULTS: Of the 312 correlations performed, only six correlations (2%) in four ROIs reached statistical significance at a P value < 0.01, but none survived correction for multiple testing. All six correlations were positive. Therefore, none supports the hypothesis that IQ is negatively correlated with BOLD response. CONCLUSIONS: These data suggest that the inclusion of subjects with below normal IQ does not introduce a confounding factor, at least for some types of fMRI studies with low cognitive load. By including subjects who are representative of IQ range for the targeted disorder, findings are more likely to generalize to that population

Diffusion tensor tractography in children with sensory processing disorder: potentials for devising machine learning classifiers

The “sensory processing disorder” (SPD) refers to brain's inability to organize sensory input for appropriate use. In this study, we determined the diffusion tensor imaging (DTI) microstructural and connectivity correlates of SPD, and apply machine learning algorithms for identification of children with SPD based on DTI/tractography metrics. A total of 44 children with SPD and 41 typically developing children (TDC) were prospectively recruited and scanned. In addition to fractional anisotropy (FA), mean diffusivity (MD), and radial diffusivity (RD), we applied probabilistic tractography to generate edge density (ED) and track density (TD) from DTI maps. For identification of children with SPD, accurate classification rates from a combination of DTI microstructural (FA, MD, AD, and RD), connectivity (TD) and connectomic (ED) metrics with different machine learning algorithms – including naïve Bayes, random forest, support vector machine, and neural networks – were determined. In voxel-wise analysis, children with SPD had lower FA, ED, and TD but higher MD and RD compared to TDC – predominantly in posterior white matter tracts including posterior corona radiata, posterior thalamic radiation, and posterior body and splenium of corpus callosum. In stepwise penalized logistic regression, the only independent variable distinguishing children with SPD from TDC was the average TD in the splenium (p < 0.001). Among different combinations of machine learning algorithms and DTI/connectivity metrics, random forest models using tract-based TD yielded the highest accuracy in classification of SPD – 77.5% accuracy, 73.8% sensitivity, and 81.6% specificity. Our findings demonstrate impaired microstructural and connectivity/connectomic integrity in children with SPD, predominantly in posterior white matter tracts, and with reduced TD of the splenium of corpus callosum as the most distinctive pattern. Applying machine learning algorithms, these connectivity metrics can be used to devise novel imaging biomarkers for neurodevelopmental disorders

Sensory over-responsivity: parent report, direct assessment measures, and neural architecture

BACKGROUND: Sensory processing difficulties are common across neurodevelopmental disorders. Thus, reliable measures are needed to understand the biological underpinnings of these differences. This study aimed to define a scoring methodology specific to auditory (AOR) and tactile (TOR) over-responsivity. Second, in a pilot cohort using MRI Diffusion Tensor Imaging, we performed a proof of concept study of whether children with AOR showed measurable differences in their white matter integrity. METHODS: This study included children with AOR and TOR from a mixed neurodevelopmental disorder cohort including autism and sensory processing dysfunction (n = 176) as well as neurotypical children (n = 128). We established cohorts based on sensory over-responsivity using parent report (Short Sensory Profile (SSP)) and direct assessment (Sensory Processing-Three Dimensions: Assessment (SP-3D:A)) measures. With a subset of the children (n = 39), group comparisons, based on AOR phenotype, were conducted comparing the white matter fractional anisotropy in 23 regions of interest. RESULTS: Using direct assessment, 31% of the children with neurodevelopmental disorders had AOR and 27% had TOR. The inter-test agreement between SSP and SP-3D:A for AOR was 65% and TOR was 50%. Children with AOR had three white matter tracts showing decreased fractional anisotropy relative to children without AOR. CONCLUSIONS: This study identified cut-off scores for AOR and TOR using the SSP parent report and SP-3D:A observation. A combination of questionnaire and direct observation measures should be used in clinical and research settings. The SSP parent report and SP-3D:A direct observation ratings overlapped moderately for sensory related behaviors. Based on these preliminary structural neuroimaging results, we suggest a putative neural network may contribute to AOR

Social touch and human development.

Social touch is a powerful force in human development, shaping social reward, attachment, cognitive, communication, and emotional regulation from infancy and throughout life. In this review, we consider the question of how social touch is defined from both bottom-up and top-down perspectives. In the former category, there is a clear role for the C-touch (CT) system, which constitutes a unique submodality that mediates affective touch and contrasts with discriminative touch. Top-down factors such as culture, personal relationships, setting, gender, and other contextual influences are also important in defining and interpreting social touch. The critical role of social touch throughout the lifespan is considered, with special attention to infancy and young childhood, a time during which social touch and its neural, behavioral, and physiological contingencies contribute to reinforcement-based learning and impact a variety of developmental trajectories. Finally, the role of social touch in an example of disordered development -autism spectrum disorder-is reviewed

Regional Brain Differences in Cortical Thickness, Surface Area and Subcortical Volume in Individuals with Williams Syndrome

Williams syndrome (WS) is a rare genetic neurodevelopmental disorder characterized by increased non-social anxiety, sensitivity to sounds and hypersociability. Previous studies have reported contradictory findings with regard to regional brain variation in WS, relying on only one type of morphological measure (usually volume) in each study. The present study aims to contribute to this body of literature and perhaps elucidate some of these discrepancies by examining concurrent measures of cortical thickness, surface area and subcortical volume between WS subjects and typically-developing (TD) controls. High resolution MRI scans were obtained on 31 WS subjects and 50 typically developing control subjects. We derived quantitative regional estimates of cortical thickness, cortical surface area, and subcortical volume using FreeSurfer software. We evaluated between-group ROI differences while controlling for total intracranial volume. In post-hoc exploratory analyses within the WS group, we tested for correlations between regional brain variation and Beck Anxiety Inventory scores. Consistent with our hypothesis, we detected complex patterns of between-group cortical variation, which included lower surface area in combination with greater thickness in the following cortical regions: post central gyrus, cuneus, lateral orbitofrontal cortex and lingual gyrus. Additional cortical regions showed between-group differences in one (but not both) morphological measures. Subcortical volume was lower in the basal ganglia and the hippocampus in WS versus TD controls. Exploratory correlations revealed that anxiety scores were negatively correlated with gray matter surface area in insula, OFC, rostral middle frontal, superior temporal and lingual gyrus. Our results were consistent with previous reports showing structural alterations in regions supporting the socio-affective and visuospatial impairments in WS. However, we also were able to effectively capture novel and complex patterns of cortical differences using both surface area and thickness. In addition, correlation results implicate specific brain regions in levels of anxiety in WS, consistent with previous reports investigating general anxiety disorders in the general population