SFARI | Topic: Circuits, Cognition & Behavior

Parvalbumin down-regulation as a common pathophysiological mechanism in autism spectrum disorders

Studies in individuals with autism and in autism mouse models suggest that alterations in parvalbumin expression in interneurons may represent a unifying molecular hub for autism pathobiology. By manipulating parvalbumin levels in transgenic mice, Beat Schwaller expects to shed new light on the role of parvalbumin-expressing interneurons in autism.

Interacting neural ensembles in orbitofrontal cortex for social and feeding behaviour.

Dendritic spines in early postnatal fragile X mice are insensitive to novel sensory experience.

Thalamic regulation of switching between cortical representations enables cognitive flexibility.

Deletion of autism risk gene Shank3 disrupts prefrontal connectivity.

Large-scale associations between the leukocyte transcriptome and BOLD responses to speech differ in autism early language outcome subtypes.

Haploinsufficiency of the intellectual disability gene SETD5 disturbs developmental gene expression and cognition.

Partial loss of psychiatric risk gene Mir137 in mice causes repetitive behavior and impairs sociability and learning via increased Pde10a.

Characterization of predictive abilities in individuals with autism using web-based interception games

Dagmar Sternad, in collaboration with Pawan Sinha and Robert Joseph, proposes to experimentally test the Predictive Impairment in Autism hypothesis, which posits that the multifaceted manifestations of autism in several domains have a common core: an impaired ability to make predictions. Focusing on the motor domain, Sternad’s team has developed a suite of novel, child-friendly, virtual games that examine motor performance in interception tasks with different degrees of predictive challenges. They plan to provide a quantitative behavioral characterization of motor-predictive impairments in a genetically defined group of children recruited from the SPARK cohort.

New mathematical approaches to dissect neuronal circuit alterations in Rett syndrome using EEG data

Electroencephalogram (EEG) scalp recordings are one of the most common measures of brain activity, but EEG data lacks cellular resolution. Stefano Panzeri aims to develop new mathematical models based on the principles underlying the theory of neural network dynamics to estimate the activity of specific types of neurons from EEG data. These tools can be used to better track the underlying neuronal processes that contribute to the severity and progression of autism spectrum disorders.