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Spinocerebellar ataxia type 1 (SCA1) is an adult-onset, dominantly inherited neurodegenerative disease caused by the expanded polyQ tract in the protein ATAXIN1 (ATXN1) and characterized by progressive motor and cognitive impairments. There are no disease-modifying treatments or cures for SCA1. Brain-derived neurotrophic factor (BDNF) plays important role in cerebellar physiology and has shown therapeutic potential for cerebellar pathology in the transgenic mouse model of SCA1, ATXN1[82Q] line that overexpress mutant ATXN1 under a cerebellar Purkinje-cell-specific promoter. Here we demonstrate decreased expression of brain derived neurotrophic factor (BDNF) in the cerebellum and medulla of patients with SCA1. Early stages of disease seem most amenable to therapy. Thus, we next quantified Bdnf expression in Atxn1154Q/2Q mice, a knock-in mouse model of SCA1, during the early symptomatic disease stage in four clinically relevant brain regions: cerebellum, medulla, hippocampus and motor cortex. We found that during the early stages of disease, Bdnf mRNA expression is reduced in the hippocampus and cerebellum, while it is increased in the cortex and brainstem. Importantly, we observed that pharmacological delivery of recombinant BDNF improved motor and cognitive performance, and mitigated pathology in the cerebellum and hippocampus of Atxn1154Q/2Q mice. Our findings demonstrate brain-region specific deficiency of BDNF in SCA1 and show that reversal of low BDNF levels offers the potential for meaningful treatment of motor and cognitive deficits in SCA1.

During the present decade a large body of research has employed confirmatory factor analysis (CFA) to evaluate the factor structure of the Strengths and Difficulties Questionnaire (SDQ) across multiple languages and cultures. However, because CFA can produce strongly biased estimations when the population cross-loadings differ meaningfully from zero, it may not be the most appropriate framework to model the SDQ responses. With this in mind, the current study sought to assess the factorial structure of the SDQ using the more flexible exploratory structural equation modeling approach. Using a large-scale Spanish sample composed of 67,253 youths aged between 10 and 18 years ( M = 14.16, SD = 1.07), the results showed that CFA provided a severely biased and overly optimistic assessment of the underlying structure of the SDQ. In contrast, exploratory structural equation modeling revealed a generally weak factorial structure, including questionable indicators with large cross-loadings, multiple error correlations, and significant wording variance. A subsequent Monte Carlo study showed that sample sizes greater than 4,000 would be needed to adequately recover the SDQ loading structure. The findings from this study prevent recommending the SDQ as a screening tool and suggest caution when interpreting previous results in the literature based on CFA modeling.