1. Cohen S, Fulcher BD, Rajaratnam SMW, Conduit R, Sullivan JP, Hilaire MAS, Phillips AJ, Loddenkemper T, Kothare SV, McConnell K, Ahearn W, Braga-Kenyon P, Shlesinger A, Potter J, Bird F, Cornish KM, Lockley SW. {{Behaviorally-determined sleep phenotypes are robustly associated with adaptive functioning in individuals with low functioning autism}}. {Sci Rep}. 2017; 7(1): 14228.
Despite sleep disturbance being a common complaint in individuals with autism, specific sleep phenotypes and their relationship to adaptive functioning have yet to be identified. This study used cluster analysis to find distinct sleep patterns and relate them to independent measures of adaptive functioning in individuals with autism. Approximately 50,000 nights of care-giver sleep/wake logs were collected on school-days for 106 individuals with low functioning autism (87 boys, 14.77 +/- 3.11 years) for 0.5-6 years (2.2 +/- 1.5 years) from two residential schools. Using hierarchical cluster analysis, performed on summary statistics of each individual across their recording duration, two clusters of individuals with clearly distinguishable sleep phenotypes were found. The groups were summarized as ‘unstable’ sleepers (cluster 1, n = 41) and ‘stable’ sleepers (cluster 2, n = 65), with the former exhibiting reduced sleep duration, earlier sleep offset, and less stability in sleep timing. The sleep clusters displayed significant differences in properties that were not used for clustering, such as intellectual functioning, communication, and socialization, demonstrating that sleep phenotypes are associated with symptom severity in individuals with autism. This study provides foundational evidence for profiling and targeting sleep as a standard part of therapeutic intervention in individuals with autism.
Lien vers le texte intégral (Open Access ou abonnement)
2. de Verdier K, Fernell E, Ek U. {{Challenges and Successful Pedagogical Strategies: Experiences from Six Swedish Students with Blindness and Autism in Different School Settings}}. {J Autism Dev Disord}. 2017.
The prevalence of autism in children with blindness is much higher than in the general population. There are many challenges regarding the school situation for children with this complex dual disability. This study explored challenges and successful strategies in school for a sample of six Swedish children with blindness and autism, with and without intellectual disability, through qualitative interviews with students, teachers and parents. All students displayed executive functioning deficits, and the teaching situation entailed several challenges. Our research points to the importance of adopting evidence-based practices for ASD, but adapted according to the students lack of vision. For this to be possible, close collaboration between teachers, parents and specialists in the field of visual impairment and autism is necessary.
Lien vers le texte intégral (Open Access ou abonnement)
3. Ueoka I, Kawashima H, Konishi A, Aoki M, Tanaka R, Yoshida H, Maeda T, Ozaki M, Yamaguchi M. {{Novel Drosophila model for psychiatric disorders including autism spectrum disorder by targeting of ATP-binding cassette protein A}}. {Exp Neurol}. 2017; 300: 51-9.
Autism spectrum disorder (ASD) is characterized by persistent deficits in social communication and social interactions, as well as restricted, stereotyped patterns of behavior and interests. In addition, alterations in circadian sleep-wake rhythm are common in young children with ASD. Mutations in ATP binding cassette subfamily A member 13 (ABCA13) have been recently identified in a monkey that displays behavior associated with ASD. ABCA13, a member of the ABCA family of proteins, is predicted to transport lipid molecules and is expressed in the human trachea, testis, bone marrow, hippocampus, cortex, and other tissues. However, its physiological function remains unknown. Drosophila CG1718 shows high homology to human ABCA genes including ABCA13 and is thus designated as Drosophila ABCA (dABCA). To elucidate the physiological role of dABCA, we specifically knocked down dABCA in all neurons of flies and investigated their phenotypes. The pan-neuron-specific knockdown of dABCA resulted in increased social space with the closest neighbor in adult male flies but exerted no effect on their climbing ability, indicating that the increase in social space is not due to a defect in their climbing ability. An activity assay with adult male flies revealed that knockdown of dABCA in all neurons induces early onset of evening activity in adult flies followed by relatively high activity during morning peaks, evening peaks, and midday siesta. These phenotypes are similar to defects observed in human ASD patients, suggesting that the established dABCA knockdown flies are a promising model for ASD. In addition, an increase in satellite boutons in presynaptic terminals of motor neurons was observed in dABCA knockdown third instar larvae, suggesting that dABCA regulates the formation and/or maintenance of presynaptic terminals of motor neurons.
Lien vers le texte intégral (Open Access ou abonnement)
4. Zhang F, Savadjiev P, Cai W, Song Y, Rathi Y, Tunc B, Parker D, Kapur T, Schultz RT, Makris N, Verma R, O’Donnell LJ. {{Whole brain white matter connectivity analysis using machine learning: An application to autism}}. {Neuroimage}. 2017.
In this paper, we propose an automated white matter connectivity analysis method for machine learning classification and characterization of white matter abnormality via identification of discriminative fiber tracts. The proposed method uses diffusion MRI tractography and a data-driven approach to find fiber clusters corresponding to subdivisions of the white matter anatomy. Features extracted from each fiber cluster describe its diffusion properties and are used for machine learning. The method is demonstrated by application to a pediatric neuroimaging dataset from 149 individuals, including 70 children with autism spectrum disorder (ASD) and 79 typically developing controls (TDC). A classification accuracy of 78.33% is achieved in this cross-validation study. We investigate the discriminative diffusion features based on a two-tensor fiber tracking model. We observe that the mean fractional anisotropy from the second tensor (associated with crossing fibers) is most affected in ASD. We also find that local along-tract (central cores and endpoint regions) differences between ASD and TDC are helpful in differentiating the two groups. These altered diffusion properties in ASD are associated with multiple robustly discriminative fiber clusters, which belong to several major white matter tracts including the corpus callosum, arcuate fasciculus, uncinate fasciculus and aslant tract; and the white matter structures related to the cerebellum, brain stem, and ventral diencephalon. These discriminative fiber clusters, a small part of the whole brain tractography, represent the white matter connections that could be most affected in ASD. Our results indicate the potential of a machine learning pipeline based on white matter fiber clustering.
