1. Anghelescu I, Dettling M. {{Neuron number in children with autism}}. {JAMA};2012 (Feb 22);307(8):783; author reply 783-784.
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2. Cicchetti DV. {{On Scales of Measurement in Autism Spectrum Disorders (ASD) and Beyond: Where Smitty Went Wrong}}. {J Autism Dev Disord};2012 (Feb 23)
The author examined critically three beliefs of S.S. Stevens pertaining to his quadripartite system of scales of measurement: (1) There are four scales of measurement in common usage (2) These scales and the scientific disciplines that use them can be incrementally graded for levels of reliability and validity or accuracy such that: Nominal scale variables produce the lowest levels of reliability and accuracy; with successively improving levels for Ordinal, Equal Interval, and Equal Ratio scales; and (3) The scale upon which a variable is measured determines the type of statistical test that one is permitted to apply. It was shown that each of the three beliefs is fundamentally flawed.
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3. Goodwin A, Fein D, Naigles LR. {{Comprehension of Wh-Questions Precedes Their Production in Typical Development and Autism Spectrum Disorders}}. {Autism Res};2012 (Feb 22)
Children with autism spectrum disorders (ASD) rarely produce wh-questions (e.g. « What hit the book? ») in naturalistic speech. It is unclear if this is due to social-pragmatic difficulties, or if grammatical deficits are also involved. If grammar is impaired, production of wh-questions by rote memorization might precede comprehension of similar forms. In a longitudinal study, 15 children with ASD and 18 initially language-matched typically developing (TD) toddlers were visited in their homes at 4-month intervals across a 3-year period. The wh-question task was presented via intermodal preferential looking. Silent « hitting » events (e.g. an apple hitting a flower) were followed by test trials in which the apple and flower were juxtaposed on the screen. During test trials, subject-wh- and object-wh-question audios were sequentially presented (e.g. « What hit the flower? » or « What did the apple hit? »). Control audios were also presented (e.g. « Where’s the apple/flower? »). Children’s eye movements were coded off-line, frame by frame. To show reliable comprehension, children should look longer to the named item (i.e. apple or flower) during the « where » questions but less at the named item during the subject-wh and object-wh-questions. To compare comprehension to production, we coded 30-min spontaneous speech samples drawn from mother-child interactions at each visit. Results indicated that comprehension of subject- and object-wh-questions was delayed in children with ASD compared with age-matched TD children, but not when matched on overall language levels. Additionally, both groups comprehended wh-questions before producing similar forms, indicating that development occurred in a similar manner. This paper discusses the implications of our findings for language acquisition in ASD.
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4. Izawa J, Pekny SE, Marko MK, Haswell CC, Shadmehr R, Mostofsky SH. {{Motor Learning Relies on Integrated Sensory Inputs in ADHD, but Over-Selectively on Proprioception in Autism Spectrum Conditions}}. {Autism Res};2012 (Feb 22)
The brain builds an association between action and sensory feedback to predict the sensory consequence of self-generated motor commands. This internal model of action is central to our ability to adapt movements and may also play a role in our ability to learn from observing others. Recently, we reported that the spatial generalization patterns that accompany adaptation of reaching movements were distinct in children with autism spectrum disorder (ASD) as compared with typically developing (TD) children. To test whether the generalization patterns are specific to ASD, here, we compared the patterns of adaptation with those in children with attention deficit hyperactivity disorder (ADHD). Consistent with our previous observations, we found that in ASD, the motor memory showed greater than normal generalization in proprioceptive coordinates compared with both TD children and children with ADHD; children with ASD also showed slower rates of adaptation compared with both control groups. Children with ADHD did not show this excessive generalization to the proprioceptive target, but they did show excessive variability in the speed of movements with an increase in the exponential distribution of responses (tau) as compared with both TD children and children with ASD. The results suggest that slower rate of adaptation and anomalous bias towards proprioceptive feedback during motor learning are characteristics of autism, whereas increased variability in execution is a characteristic of ADHD. Autism Res 2012,**:**-**. (c) 2012 International Society for Autism Research, Wiley Periodicals, Inc.
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5. Lewis JD, Theilmann RJ, Fonov V, Bellec P, Lincoln A, Evans AC, Townsend J. {{Callosal fiber length and interhemispheric connectivity in adults with autism: Brain overgrowth and underconnectivity}}. {Hum Brain Mapp};2012 (Feb 22)
Typical adults show an inverse relation between callosal fiber length and degree of interhemispheric connectivity. This has been hypothesized to be a consequence of the influence of conduction delays and cellular costs during development on axonal pruning, both of which increase with fiber length. Autism spectrum disorder (ASD) provides a test of this hypothesis: Children with ASD are known to have enlarged brains; thus, adults with ASD should show reductions in interhemispheric connectivity proportional to their degree of brain overgrowth during development. This prediction was tested by assessing the relation between both the size and structure of the corpus callosum and callosal fiber length, adjusting for intracranial volume, which is thought to reflect maximum brain size achieved during development. Using tractography to estimate the length of callosal fibers emanating from all areas of cortex, and through which region of the corpus callosum they pass, we show that adults with ASD show an inverse relation between callosal fiber length, adjusted for intracranial volume, and callosum size, and a positive relation between adjusted callosal fiber length and radial diffusivity. The results provide support for the hypothesized impact of fiber length during development. Hum Brain Mapp, 2012. (c) 2011 Wiley Periodicals, Inc.
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6. Mefford HC, Batshaw ML, Hoffman EP. {{Genomics, intellectual disability, and autism}}. {N Engl J Med};2012 (Feb 23);366(8):733-743.
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7. Nygren G, Cederlund M, Sandberg E, Gillstedt F, Arvidsson T, Carina Gillberg I, Westman Andersson G, Gillberg C. {{Erratum to: The Prevalence of Autism Spectrum Disorders in Toddlers: A Population Study of 2-Year-Old Swedish Children}}. {J Autism Dev Disord};2012 (Feb 23)
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8. Raznahan A, Lee Y, Vaituzis C, Tran L, Mackie S, Tiemeier H, Clasen L, Lalonde F, Greenstein D, Pierson R, Giedd JN. {{Allelic Variation Within the Putative Autism Spectrum Disorder Risk Gene Homeobox A1 and Cerebellar Maturation in Typically Developing Children and Adolescents}}. {Autism Res};2012 (Feb 22)
Homeobox A1 (HOXA1) has been proposed as a candidate gene for autism spectrum disorder (ASD) as it regulates embryological patterning of hind-brain structures implicated in autism neurobiology. In line with this notion, a nonsynonymous single nucleotide polymorphism within a highly conserved domain of HOXA1-A218G (rs10951154)-has been linked to both ASD risk, and cross-sectional differences in superior posterior lobar cerebellar anatomy in late adulthood. Despite evidence for early onset and developmentally dynamic cerebellar involvement in ASD, little is known of the relationship between A218G genotype and maturation of the cerebellum over early development. We addressed this issue using 296 longitudinally acquired structural magnetic resonance imaging brain scans from 116 healthy individuals between 5 and 23 years of age. Mixed models were used to compare the relationship between age and semi-automated measures of cerebellar volume in A-homozygotes (AA) and carriers of the G allele (Gcar). Total cerebellar volume increased between ages of 5 and 23 years in both groups. However, this was accelerated in the Gcar relative to the AA group (Genotype-by-age interaction term, P = 0.03), and driven by genotype-dependent differences in the rate of bilateral superior posterior lobar volume change with age (P = 0.002). Resultantly, although superior posterior lobar volume did not differ significantly between genotype groups at age 5 (P = 0.9), by age 23 it was 12% greater in Gcar than AA (P = 0.002). Our results suggest that common genetic variation within this putative ASD risk gene has the capacity to modify the development of cerebellar systems implicated in ASD neurobiology.
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9. Rochman B. {{The end of an epidemic? Why a proposed new definition of autism has parents and advocates worried}}. {Time};2012 (Feb 6);179(5):16.
10. Squillaro T, Alessio N, Cipollaro M, Melone MA, Hayek G, Renieri A, Giordano A, Galderisi U. {{Reduced expression of MECP2 affects cell commitment and maintenance in neurons by triggering senescence, new perspective for Rett syndrome}}. {Mol Biol Cell};2012 (Feb 22)
MECP2 protein binds preferentially to methylated CpGs and regulates gene expression by causing changes in chromatin structure. The mechanism by which impaired MECP2 activity can induce pathological abnormalities in the nervous system of patients with Rett syndrome (RTT) is not clearly understood.To gain further insight into the role of MECP2 in human neurogenesis, we compared the neural differentiation process in mesenchymal stem cells (MSCs) obtained from a RTT patient and from healthy donors. We further analyzed neural differentiation in a human neuroblastoma cell line carrying a partially silenced MECP2 gene.Senescence and reduced expression of neural markers were observed in proliferating and differentiating MSCs from the RTT patient, which suggests that impaired activity of MECP2 protein may impair neural differentiation, as observed in RTT patients.Next, we used an inducible expression system to silence MECP2 in neuroblastoma cells before and after the induction of neural differentiation via retinoic acid treatment. This approach was used to test whether MECP2 inactivation affected the cell fate of neural progenitors and/or neuronal differentiation and maintenance.Overall, our data suggest that neural cell fate and neuronal maintenance may be perturbed by senescence triggered by impaired MECP2 activity either before or after neural differentiation.
