Deletion of Glutamate Delta-1 Receptor in Mouse Leads to Aberrant Emotional and Social Behaviors

The delta family of ionotropic glutamate receptors consists of glutamate δ1 (GluD1) and glutamate δ2 (GluD2) receptors. While the role of GluD2 in the regulation of cerebellar physiology is well understood, the function of GluD1 in the central nervous system remains elusive. We demonstrate for the first time that deletion of GluD1 leads to abnormal emotional and social behaviors. We found that GluD1 knockout mice (GluD1 KO) were hyperactive, manifested lower anxiety-like behavior, depression-like behavior in a forced swim test and robust aggression in the resident-intruder test. Chronic lithium rescued the depression-like behavior in GluD1 KO. GluD1 KO mice also manifested deficits in social interaction. In the sociability test, GluD1 KO mice spent more time interacting with an inanimate object compared to a conspecific mouse. D-Cycloserine (DCS) administration was able to rescue social interaction deficits observed in GluD1 KO mice. At a molecular level synaptoneurosome preparations revealed lower GluA1 and GluA2 subunit expression in the prefrontal cortex and higher GluA1, GluK2 and PSD95 expression in the amygdala of GluD1 KO. Moreover, DCS normalized the lower GluA1 expression in prefrontal cortex of GluD1 KO. We propose that deletion of GluD1 leads to aberrant circuitry in prefrontal cortex and amygdala owing to its potential role in presynaptic differentiation and synapse formation. Furthermore, these findings are in agreement with the human genetic studies suggesting a strong association of GRID1 gene with several neuropsychiatric disorders including schizophrenia, bipolar disorder, autism spectrum disorders and major depressive disorder

Mnesic imbalance: a cognitive theory about autism spectrum disorders

Autism is characterized by impairments in social interaction, communicative capacity and behavioral flexibility. Some cognitive theories can be useful for finding a relationship between these irregularities and the biological mechanisms that may give rise to this disorder. Among such theories are mentalizing deficit, weak central coherence and executive dysfunction, but none of them has been able to explain all three diagnostic symptoms of autism. These cognitive disorders may be related among themselves by faulty learning, since several research studies have shown that the brains of autistic individuals have abnormalities in the cerebellum, which plays a role in procedural learning. In keeping with this view, one may postulate the possibility that declarative memory replaces faulty procedural memory in some of its functions, which implies making conscious efforts in order to perform actions that are normally automatic. This may disturb cognitive development, resulting in autism symptoms. Furthermore, this mnesic imbalance is probably involved in all autism spectrum disorders. In the present work, this theory is expounded, including preliminary supporting evidence

Behavioural and Developmental Interventions for Autism Spectrum Disorder: A Clinical Systematic Review

Background: Much controversy exists regarding the clinical efficacy of behavioural and developmental interventions for improving the core symptoms of autism spectrum disorders (ASD). We conducted a systematic review to summarize the evidence on the effectiveness of behavioural and developmental interventions for ASD. Methods and Findings: Comprehensive searches were conducted in 22 electronic databases through May 2007. Further information was obtained through hand searching journals, searching reference lists, databases of theses and dissertations, and contacting experts in the field. Experimental and observational analytic studies were included if they were written in English and reported the efficacy of any behavioural or developmental intervention for individuals with ASD. Two independent reviewers made the final study selection, extracted data, and reached consensus on study quality. Results were summarized descriptively and, where possible, meta-analyses of the study results were conducted. One-hundred-and-one studies at predominantly high risk of bias that reported inconsistent results across various interventions were included in the review. Meta-analyses of three controlled clinical trials showed that Lovaas treatment was superior to special education on measures of adaptive behaviour, communication and interaction, comprehensive language, daily living skills, expressive language, overall intellectual functioning and socialization. High-intensity Lovaas was superior to low-intensity Lovaas on measures of intellectual functioning in two retrospective cohort studies. Pooling the results of two randomized controlle

Early Indicators of Autism Spectrum Disorders in the Second Year of Life

Three groups of 18 children were selected for this study, one group with autism spectrum disorders (ASD), one group with developmental delays in which ASD was ruled out (DD), and one group with typical development (TD), from a pool of 3026 children who were screened with the Communication and Symbolic Behavior Scales Developmental Profile (CSBS DP, Wetherby & Prizant, 2002) Infant-Toddler Checklist under 24 months of age. The CSBS DP Behavior Sample was videotaped on selected children as a second-level evaluation during the second year of life. The Infant-Toddler Checklist had a sensitivity and specificity of 88.9% for this sample of children. Significant group differences were found on the Infant-Toddler Checklist and the Behavior Sample, however, these differences did not distinguish children with ASD and DD with high accuracy. The videotapes of the Behavior Sample were reanalyzed to identify red flags of ASD. Nine red flags differentiated children in the ASD group from both the DD and TD groups and four red flags differentiated children in the ASD Group from the TD group but not the DD group. These 13 red flags were found to discriminate the three groups with a correct classification rate of 94.4%.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/44624/1/10803_2004_Article_492544.pd

Baby Cry Detection: Deep Learning and Classical Approaches

International audienc