1. Ciaramidaro A, Bolte S, Schlitt S, Hainz D, Poustka F, Weber B, Bara BG, Freitag C, Walter H. {{Schizophrenia and Autism as Contrasting Minds: Neural Evidence for the Hypo-Hyper-Intentionality Hypothesis}}. {Schizophrenia bulletin}. 2014 Sep 9.
Both schizophrenia (SCZ) and autism spectrum disorder (ASD) are characterized by mentalizing problems and associated neural dysfunction of the social brain. However, the deficits in mental state attribution are somehow opposed: Whereas patients with SCZ tend to over-attribute intentions to agents and physical events (« hyper-intentionality »), patients with autism treat people as devoid of intentions (« hypo-intentionality »). Here we aimed to investigate whether this hypo-hyper-intentionality hypothesis can be supported by neural evidence during a mentalizing task. Using functional magnetic resonance imaging (fMRI), we investigated the neural responses and functional connectivity during reading others intention. Scanning was performed in 23 individuals with ASD, 18 with paranoid SCZ and 23 gender and IQ matched control subjects. Both clinical groups showed reduced brain activation compared to controls for the contrast intentional vs physical information processing in left posterior superior temporal sulcus (pSTS) and ventral medial prefrontal cortex (vMPFC) for SCZ, and right pSTS in ASD. As predicted, these effects were caused in a group specific way: Relative increased activation for physical information processing in SCZ that was also correlated with positive PANNS score and relative decreased activation for intentional information processing in ASD. Additionally, we could demonstrate opposed connectivity patterns between the right pSTS and vMPFC in the clinical groups, ie, increased for SCZ, decreased for ASD. These findings represent opposed neural signatures in key regions of the social brain as predicted by the hyper-hypo-intentionality hypothesis.
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2. Ellegood J, Anagnostou E, Babineau BA, Crawley JN, Lin L, Genestine M, DiCicco-Bloom E, Lai JK, Foster JA, Penagarikano O, Geschwind DH, Pacey LK, Hampson DR, Laliberte CL, Mills AA, Tam E, Osborne LR, Kouser M, Espinosa-Becerra F, Xuan Z, Powell CM, Raznahan A, Robins DM, Nakai N, Nakatani J, Takumi T, van Eede MC, Kerr TM, Muller C, Blakely RD, Veenstra-VanderWeele J, Henkelman RM, Lerch JP. {{Clustering autism: using neuroanatomical differences in 26 mouse models to gain insight into the heterogeneity}}. {Molecular psychiatry}. 2014 Sep 9.
Autism is a heritable disorder, with over 250 associated genes identified to date, yet no single gene accounts for >1-2% of cases. The clinical presentation, behavioural symptoms, imaging and histopathology findings are strikingly heterogeneous. A more complete understanding of autism can be obtained by examining multiple genetic or behavioural mouse models of autism using magnetic resonance imaging (MRI)-based neuroanatomical phenotyping. Twenty-six different mouse models were examined and the consistently found abnormal brain regions across models were parieto-temporal lobe, cerebellar cortex, frontal lobe, hypothalamus and striatum. These models separated into three distinct clusters, two of which can be linked to the under and over-connectivity found in autism. These clusters also identified previously unknown connections between Nrxn1alpha, En2 and Fmr1; Nlgn3, BTBR and Slc6A4; and also between X monosomy and Mecp2. With no single treatment for autism found, clustering autism using neuroanatomy and identifying these strong connections may prove to be a crucial step in predicting treatment response.Molecular Psychiatry advance online publication, 9 September 2014; doi:10.1038/mp.2014.98.
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3. Mazurek MO, Handen BL, Wodka EL, Nowinski L, Butter E, Engelhardt CR. {{Age at First Autism Spectrum Disorder Diagnosis: The Role of Birth Cohort, Demographic Factors, and Clinical Features}}. {Journal of developmental and behavioral pediatrics : JDBP}. 2014 Sep 9.
OBJECTIVE:: This study sought to identify factors that may be associated with delays in autism spectrum disorder (ASD) diagnosis, including birth cohort, sociodemographic characteristics, and clinical features. METHODS:: Participants included 1716 children and adolescents with ASD enrolled in the Autism Speaks Autism Treatment Network (AS-ATN) between the years 2008 and 2011. Data were collected at enrollment using AS-ATN parent- and clinician-report forms and standardized measures of I.Q., ASD symptoms, adaptive function, and psychiatric symptoms. RESULTS:: Age at first ASD diagnosis was positively correlated with current age, suggesting a birth cohort effect. Sociodemographic and clinical features were also associated with age at diagnosis, even after controlling for current age. Hierarchical linear regression results showed that older current age, lower socioeconomic status (SES), higher I.Q. score, and lower levels of autism symptoms were associated with later age at initial diagnosis. There was also a significant interaction between current age and I.Q., with higher functioning children being diagnosed at younger ages than in previous years. CONCLUSIONS:: Early diagnosis of ASD is critically important for improving access to interventions; however, many children experience diagnostic delays. In this sample, children from the most recent birth cohorts were diagnosed earlier, suggesting that early signs of ASD are being increasingly recognized. However, socioeconomic barriers to diagnosis still seem to exist. Children with less severe ASD symptoms and with higher I.Q. are also diagnosed at later ages. Efforts are still needed to reduce diagnostic disparities for families of low SES and to improve early recognition of more subtle symptoms.
