Treating a novel plasticity defect rescues episodic memory in Fragile X model mice.

Episodic memory, a fundamental component of human cognition, is significantly impaired in autism. We believe we report the first evidence for this problem in the Fmr1-knockout (KO) mouse model of Fragile X syndrome and describe potentially treatable underlying causes. The hippocampus is critical for the formation and use of episodes, with semantic (cue identity) information relayed to the structure via the lateral perforant path (LPP). The unusual form of synaptic plasticity expressed by the LPP (lppLTP) was profoundly impaired in Fmr1-KOs relative to wild-type mice. Two factors contributed to this defect: (i) reduced GluN1 subunit levels in synaptic NMDA receptors and related currents, and (ii) impaired retrograde synaptic signaling by the endocannabinoid 2-arachidonoylglycerol (2-AG). Studies using a novel serial cue paradigm showed that episodic encoding is dependent on both the LPP and the endocannabinoid receptor CB1, and is strikingly impaired in Fmr1-KOs. Enhancing 2-AG signaling rescued both lppLTP and learning in the mutants. Thus, two consequences of the Fragile-X mutation converge on plasticity at one site in hippocampus to prevent encoding of a basic element of cognitive memory. Collectively, the results suggest a clinically plausible approach to treatment.

Information Extraction of Behavior Change Intervention Descriptions.

We describe an information extraction (IE) approach for knowledge base population of behavior change scientific intervention findings. In this paper, we focus on building a system able to characterize the specific intervention techniques that are undertaken within behavior change intervention studies. We have investigated three different configurations of a general information retrieval based framework for information extraction: a) an unsupervised approach that hinges on specification of a query for each attribute to be extracted and a few parameters for rule-based post-processing; b) a semi-supervised approach, which uses a part of the ground-truth annotations as a training set to automatically learn optimal representation of the queries; and c) a supervised approach that replaces the rule-based post processing by a text classifier. To train and evaluate our system, we make use of a ground-truth data set annotated by behavior science experts. This dataset consists of a total of 226 research papers on smoking cessation.

Spaced training improves learning in Ts65Dn and Ube3a mouse models of intellectual disabilities.

Benefits of distributed learning strategies have been extensively described in the human literature, but minimally investigated in intellectual disability syndromes. We tested the hypothesis that training trials spaced apart in time could improve learning in two distinct genetic mouse models of neurodevelopmental disorders characterized by intellectual impairments. As compared to training with massed trials, spaced training significantly improved learning in both the Ts65Dn trisomy mouse model of Down syndrome and the maternally inherited Ube3a mutant mouse model of Angelman syndrome. Spacing the training trials at 1 h intervals accelerated acquisition of three cognitive tasks by Ts65Dn mice: (1) object location memory, (2) novel object recognition, (3) water maze spatial learning. Further, (4) spaced training improved water maze spatial learning by Ube3a mice. In contrast, (5) cerebellar-mediated rotarod motor learning was not improved by spaced training. Corroborations in three assays, conducted in two model systems, replicated within and across two laboratories, confirm the strength of the findings. Our results indicate strong translational relevance of a behavioral intervention strategy for improving the standard of care in treating the learning difficulties that are characteristic and clinically intractable features of many neurodevelopmental disorders.