Defining language impairments in a subgroup of children with autism spectrum disorder

Autism spectrum disorder (ASD) is diagnosed on the basis of core impairments in pragmatic language skills, which are found across all ages and subtypes. In contrast, there is significant heterogeneity in language phenotypes, ranging from nonverbal to superior linguistic abilities, as defined on standardized tests of vocabulary and grammatical knowledge. The majority of children are verbal but impaired in language, relative to age-matched peers. One hypothesis is that this subgroup has ASD and co-morbid specific language impairment (SLI). An experiment was conducted comparing children with ASD to children with SLI and typically developing controls on aspects of language processing that have been shown to be impaired in children with SLI: repetition of nonsense words. Patterns of performance among the children with ASD and language impairment were similar to those with SLI, and contrasted with the children with ASD and no language impairment and typical controls, providing further evidence for the hypothesis that a subgroup of children with ASD has co-morbid SLI. The findings are discussed in the context of brain imaging studies that have explored the neural bases of language impairment in ASD and SLI, and overlap in the genes associated with elevated risk for these disorders.M01 RR00533 - NCRR NIH HHS; R01 DC10290 - NIDCD NIH HHS; U19 DC03610 - NIDCD NIH HH

Avoidance as a strategy of (not) coping: qualitative interviews with carers of Huntington's Disease patients

Peer reviewedPublisher PD

Genome-wide and Ordered-Subset linkage analyses provide support for autism loci on 17q and 19p with evidence of phenotypic and interlocus genetic correlates

BACKGROUND: Autism is a neurobehavioral spectrum of phenotypes characterized by deficits in the development of language and social relationships and patterns of repetitive, rigid and compulsive behaviors. Twin and family studies point to a significant genetic etiology, and several groups have performed genomic linkage screens to identify susceptibility loci. METHODS: We performed a genome-wide linkage screen in 158 combined Tufts, Vanderbilt and AGRE (Autism Genetics Research Exchange) multiplex autism families using parametric and nonparametric methods with a categorical autism diagnosis to identify loci of main effect. Hypothesizing interdependence of genetic risk factors prompted us to perform exploratory studies applying the Ordered-Subset Analysis (OSA) approach using LOD scores as the trait covariate for ranking families. We employed OSA to test for interlocus correlations between loci with LOD scores ≥1.5, and empirically determined significance of linkage in optimal OSA subsets using permutation testing. Exploring phenotypic correlates as the basis for linkage increases involved comparison of mean scores for quantitative trait-based subsets of autism between optimal subsets and the remaining families. RESULTS: A genome-wide screen for autism loci identified the best evidence for linkage to 17q11.2 and 19p13, with maximum multipoint heterogeneity LOD scores of 2.9 and 2.6, respectively. Suggestive linkage (LOD scores ≥1.5) at other loci included 3p, 6q, 7q, 12p, and 16p. OSA revealed positive correlations of linkage between the 19p locus and 17q, between 19p and 6q, and between 7q and 5p. While potential phenotypic correlates for these findings were not identified for the chromosome 7/5 combination, differences indicating more rapid achievement of "developmental milestones" was apparent in the chromosome 19 OSA-defined subsets for 17q and 6q. OSA was used to test the hypothesis that 19p linkage involved more rapid achievement of these milestones and it revealed significantly increased LOD* scores at 19p13. CONCLUSIONS: Our results further support 19p13 as harboring an autism susceptibility locus, confirm other linkage findings at 17q11.2, and demonstrate the need to analyze more discreet trait-based subsets of complex phenotypes to improve ability to detect genetic effects

Robust automated detection of microstructural white matter degeneration in Alzheimer’s disease using machine learning classification of multicenter DTI data

Diffusion tensor imaging (DTI) based assessment of white matter fiber tract integrity can support the diagnosis of Alzheimer’s disease (AD). The use of DTI as a biomarker, however, depends on its applicability in a multicenter setting accounting for effects of different MRI scanners. We applied multivariate machine learning (ML) to a large multicenter sample from the recently created framework of the European DTI study on Dementia (EDSD). We hypothesized that ML approaches may amend effects of multicenter acquisition. We included a sample of 137 patients with clinically probable AD (MMSE 20.6±5.3) and 143 healthy elderly controls, scanned in nine different scanners. For diagnostic classification we used the DTI indices fractional anisotropy (FA) and mean diffusivity (MD) and, for comparison, gray matter and white matter density maps from anatomical MRI. Data were classified using a Support Vector Machine (SVM) and a Naïve Bayes (NB) classifier. We used two cross-validation approaches, (i) test and training samples randomly drawn from the entire data set (pooled cross-validation) and (ii) data from each scanner as test set, and the data from the remaining scanners as training set (scanner-specific cross-validation). In the pooled cross-validation, SVM achieved an accuracy of 80% for FA and 83% for MD. Accuracies for NB were significantly lower, ranging between 68% and 75%. Removing variance components arising from scanners using principal component analysis did not significantly change the classification results for both classifiers. For the scanner-specific cross-validation, the classification accuracy was reduced for both SVM and NB. After mean correction, classification accuracy reached a level comparable to the results obtained from the pooled cross-validation. Our findings support the notion that machine learning classification allows robust classification of DTI data sets arising from multiple scanners, even if a new data set comes from a scanner that was not part of the training sample

High-definition tDCS of the temporo-parietal cortex enhances access to newly learned words

Learning associations between words and their referents is crucial for language learning in the developing and adult brain and for language re-learning after neurological injury. Non-invasive transcranial direct current stimulation (tDCS) to the posterior temporo-parietal cortex has been suggested to enhance this process. However, previous studies employed standard tDCS set-ups that induce diffuse current flow in the brain, preventing the attribution of stimulation effects to the target region. This study employed high-definition tDCS (HD-tDCS) that allowed the current flow to be constrained to the temporo-parietal cortex, to clarify its role in novel word learning. In a sham-controlled, double-blind, between-subjects design, 50 healthy adults learned associations between legal non-words and unfamiliar object pictures. Participants were stratified by baseline learning ability on a short version of the learning paradigm and pairwise randomized to active (20 mins; N = 25) or sham (40 seconds; N = 25) HD-tDCS. Accuracy was comparable during the baseline and experimental phases in both HD-tDCS conditions. However, active HD-tDCS resulted in faster retrieval of correct word-picture pairs. Our findings corroborate the critical role of the temporo-parietal cortex in novel word learning, which has implications for current theories of language acquisition

Autism as a disorder of neural information processing: directions for research and targets for therapy

The broad variation in phenotypes and severities within autism spectrum disorders suggests the involvement of multiple predisposing factors, interacting in complex ways with normal developmental courses and gradients. Identification of these factors, and the common developmental path into which theyfeed, is hampered bythe large degrees of convergence from causal factors to altered brain development, and divergence from abnormal brain development into altered cognition and behaviour. Genetic, neurochemical, neuroimaging and behavioural findings on autism, as well as studies of normal development and of genetic syndromes that share symptoms with autism, offer hypotheses as to the nature of causal factors and their possible effects on the structure and dynamics of neural systems. Such alterations in neural properties may in turn perturb activity-dependent development, giving rise to a complex behavioural syndrome many steps removed from the root causes. Animal models based on genetic, neurochemical, neurophysiological, and behavioural manipulations offer the possibility of exploring these developmental processes in detail, as do human studies addressing endophenotypes beyond the diagnosis itself

Post-Mortem diagnosis of dementia by informant interview

Abstract The diagnosis of normal cognition or dementia in the Brazilian Brain Bank of the Aging Brain Study Group (BBBABSG) has relied on postmortem interview with an informant. Objectives: To ascertain the sensitivity and specificity of postmortem diagnosis based on informant interview compared against the diagnosis established at a memory clinic. Methods: A prospective study was conducted at the BBBABSG and at the Reference Center for Cognitive Disorders (RCCD), a specialized memory clinic of the Hospital das Clínicas, University of São Paulo Medical School. Control subjects and cognitively impaired subjects were referred from the Hospital das Clínicas to the RCCD where subjects and their informants were assessed. The same informant was then interviewed at the BBBABSG. Specialists' panel consensus, in each group, determined the final diagnosis of the case, blind to other center's diagnosis. Data was compared for frequency of diagnostic equivalence. For this study, the diagnosis established at the RCCD was accepted as the gold standard. Sensitivity and specificity were computed. Results: Ninety individuals were included, 45 with dementia and 45 without dementia (26 cognitively normal and 19 cognitively impaired but non-demented). The informant interview at the BBBABSG had a sensitivity of 86.6% and specificity of 84.4% for the diagnosis of dementia, and a sensitivity of 65.3% and specificity of 93.7% for the diagnosis of normal cognition. Conclusions: The informant interview used at the BBBABSG has a high specificity and sensitivity for the diagnosis of dementia as well as a high specificity for the diagnosis of normal cognition

Rapid automatized naming as an index of genetic liability to autism

This study investigated rapid automatized naming (RAN) ability in high functioning individuals with autism and parents of individuals with autism. Findings revealed parallel patterns of performance in parents and individuals with autism, where both groups had longer naming times than controls. Significant parent-child correlations were also detected, along with associations with language and personality features of the broad autism phenotype (retrospective reports of early language delay, socially reticent personality). Together, findings point towards RAN as a potential marker of genetic liability to autism