No Evidence for Semantic Prediction Deficits in Individuals With Cerebellar Degeneration.

Cerebellar involvement in language processing has received considerable attention in the neuroimaging and neuropsychology literatures. Building off the motor control literature, one account of this involvement centers on the idea of internal models. In the context of language, this hypothesis suggests that the cerebellum is essential for building semantic models that, in concert with the cerebral cortex, help anticipate or predict linguistic input. To date, supportive evidence has primarily come from neuroimaging studies showing that cerebellar activation increases in contexts in which semantic predictions are generated and violated. Taking a neuropsychological approach, we put the internal model hypothesis to the test, asking if individuals with cerebellar degeneration (n = 14) show reduced sensitivity to semantic prediction. Using a sentence verification task, we compare reaction time to sentences that vary in terms of cloze probability. We also evaluated a more constrained variant of the prediction hypothesis, asking if the cerebellum facilitates the generation of semantic predictions when the content of a sentence refers to a dynamic rather than static mental transformation. The results failed to support either hypothesis: Compared to matched control participants (n = 17), individuals with cerebellar degeneration showed a similar reduction in reaction time for sentences with high cloze probability and no selective impairment in predictions involving dynamic transformations. These results challenge current theorizing about the role of the cerebellum in language processing, pointing to a misalignment between neuroimaging and neuropsychology research on this topic.

SOMAScience: A Novel Platform for Multidimensional, Longitudinal Pain Assessment.

Chronic pain is one of the most significant health issues in the United States, affecting more than 20% of the population. Despite its contribution to the increasing health crisis, reliable predictors of disease development, progression, or treatment outcomes are lacking. Self-report remains the most effective way to assess pain, but measures are often acquired in sparse settings over short time windows, limiting their predictive ability. In this paper, we present a new mobile health platform called SOMAScience. SOMAScience serves as an easy-to-use research tool for scientists and clinicians, enabling the collection of large-scale pain datasets in single- and multicenter studies by facilitating the acquisition, transfer, and analysis of longitudinal, multidimensional, self-report pain data. Data acquisition for SOMAScience is done through a user-friendly smartphone app, SOMA, that uses experience sampling methodology to capture momentary and daily assessments of pain intensity, unpleasantness, interference, location, mood, activities, and predictions about the next day that provide personal insights into daily pain dynamics. The visualization of data and its trends over time is meant to empower individual users' self-management of their pain. This paper outlines the scientific, clinical, technological, and user considerations involved in the development of SOMAScience and how it can be used in clinical studies or for pain self-management purposes. Our goal is for SOMAScience to provide a much-needed platform for individual users to gain insight into the multidimensional features of their pain while lowering the barrier for researchers and clinicians to obtain the type of pain data that will ultimately lead to improved prevention, diagnosis, and treatment of chronic pain.