Heat-induced seizures, premature mortality, and hyperactivity in a novel Scn1a nonsense model for Dravet syndrome

Dravet syndrome (Dravet) is a severe congenital developmental genetic epilepsy caused by de novo mutations in the SCN1A gene. Nonsense mutations are found in ∼20% of the patients, and the R613X mutation was identified in multiple patients. Here we characterized the epileptic and non-epileptic phenotypes of a novel preclinical Dravet mouse model harboring the R613X nonsense Scn1a mutation. Scn1aWT/R613X mice, on a mixed C57BL/6J:129S1/SvImJ background, exhibited spontaneous seizures, susceptibility to heat-induced seizures, and premature mortality, recapitulating the core epileptic phenotypes of Dravet. In addition, these mice, available as an open-access model, demonstrated increased locomotor activity in the open-field test, modeling some non-epileptic Dravet-associated phenotypes. Conversely, Scn1aWT/R613X mice, on the pure 129S1/SvImJ background, had a normal life span and were easy to breed. Homozygous Scn1aR613X/R613X mice (pure 129S1/SvImJ background) died before P16. Our molecular analyses of hippocampal and cortical expression demonstrated that the premature stop codon induced by the R613X mutation reduced Scn1a mRNA and NaV1.1 protein levels to ∼50% in heterozygous Scn1aWT/R613X mice (on either genetic background), with marginal expression in homozygous Scn1aR613X/R613X mice. Together, we introduce a novel Dravet model carrying the R613X Scn1a nonsense mutation that can be used to study the molecular and neuronal basis of Dravet, as well as the development of new therapies associated with SCN1A nonsense mutations in Dravet

Adult reversal of cognitive phenotypes in neurodevelopmental disorders

Recent findings in mice suggest that it is possible to reverse certain neurodevelopmental disorders in adults. Changes in development, previously thought to be irreparable in adults, were believed to underlie the neurological and psychiatric phenotypes of a range of common mental health problems with a clear developmental component. As a consequence, most researchers have focused their efforts on understanding the molecular and cellular processes that alter development with the hope that early intervention could prevent the emergent pathology. Unexpectedly, several different animal model studies published recently, including animal models of autism, suggest that it may be possible to reverse neurodevelopmental disorders in adults: Addressing the underlying molecular and cellular deficits in adults could in several cases dramatically improve the neurocognitive phenotypes in these animal models. The findings reviewed here provide hope to millions of individuals afflicted with a wide range of neurodevelopmental disorders, including autism, since they suggest that it may be possible to treat or even cure them in adults

Statistical Learning is Constrained to Less Abstract Patterns in Complex Sensory Input (but not the least)

The influence of statistical information on behavior (either through learning or adaptation) is quickly becoming foundational to many domains of cognitive psychology and cognitive neuroscience, from language comprehension to visual development. We investigate a central problem impacting these diverse fields: when encountering input with rich statistical information, are there any constraints on learning? This paper examines learning outcomes when adult learners are given statistical information across multiple levels of abstraction simultaneously: from abstract, semantic categories of everyday objects to individual viewpoints on these objects. After revealing statistical learning of abstract, semantic categories with scrambled individual exemplars (Exp. 1), participants viewed pictures where the categories as well as the individual objects predicted picture order (e.g., bird1—dog1, bird2—dog2). Our findings suggest that participants preferentially encode the relationships between the individual objects, even in the presence of statistical regularities linking semantic categories (Exps. 2 and 3). In a final experiment we investigate whether learners are biased towards learning object-level regularities or simply construct the most detailed model given the data (and therefore best able to predict the specifics of the upcoming stimulus) by investigating whether participants preferentially learn from the statistical regularities linking individual snapshots of objects or the relationship between the objects themselves (e.g., bird_picture1— dog_picture1, bird_picture2—dog_picture2). We find that participants fail to learn the relationships between individual snapshots, suggesting a bias towards object-level statistical regularities as opposed to merely constructing the most complete model of the input. This work moves beyond the previous existence proofs that statistical learning is possible at both very high and very low levels of abstraction (categories vs. individual objects) and suggests that, at least with the current categories and type of learner, there are biases to pick up on statistical regularities between individual objects even when robust statistical information is present at other levels of abstraction. These findings speak directly to emerging theories about how systems supporting statistical learning and prediction operate in our structure-rich environments. Moreover, the theoretical implications of the current work across multiple domains of study is already clear: statistical learning cannot be assumed to be unconstrained even if statistical learning has previously been established at a given level of abstraction when that information is presented in isolation

Statistical learning is constrained to less abstract patterns in complex sensory input (but not the least)

The influence of statistical information on behavior (either through learning or adaptation) is quickly becoming foundational to many domains of cognitive psychology and cognitive neuroscience, from language comprehension to visual development. We investigate a central problem impacting these diverse fields: when encountering input with rich statistical information, are there any constraints on learning? This paper examines learning outcomes when adult learners are given statistical information across multiple levels of abstraction simultaneously: from abstract, semantic categories of everyday objects to individual viewpoints on these objects. After revealing statistical learning of abstract, semantic categories with scrambled individual exemplars (Exp. 1), participants viewed pictures where the categories as well as the individual objects predicted picture order (e.g., bird(1)—dog(1), bird(2)—dog(2)). Our findings suggest that participants preferentially encode the relationships between the individual objects, even in the presence of statistical regularities linking semantic categories (Exps. 2 and 3). In a final experiment we investigate whether learners are biased towards learning object-level regularities or simply construct the most detailed model given the data (and therefore best able to predict the specifics of the upcoming stimulus) by investigating whether participants preferentially learn from the statistical regularities linking individual snapshots of objects or the relationship between the objects themselves (e.g., bird_picture(1)— dog_picture(1), bird_picture(2)—dog_picture(2)). We find that participants fail to learn the relationships between individual snapshots, suggesting a bias towards object-level statistical regularities as opposed to merely constructing the most complete model of the input. This work moves beyond the previous existence proofs that statistical learning is possible at both very high and very low levels of abstraction (categories vs. individual objects) and suggests that, at least with the current categories and type of learner, there are biases to pick up on statistical regularities between individual objects even when robust statistical information is present at other levels of abstraction. These findings speak directly to emerging theories about how systems supporting statistical learning and prediction operate in our structure-rich environments. Moreover, the theoretical implications of the current work across multiple domains of study is already clear: statistical learning cannot be assumed to be unconstrained even if statistical learning has previously been established at a given level of abstraction when that information is presented in isolation