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Motivation deficits, such as apathy, are pervasive in both neurological and psychiatric diseases. Even when they are not the core symptom, they reduce quality of life, compromise functional outcome and increase the burden for caregivers. They are currently assessed with clinical scales that do not give any mechanistic insight susceptible to guide therapeutic intervention. Here, we present another approach that consists of phenotyping the behaviour of patients in motivation tests, using computational models. These formal models impose a precise and operational definition of motivation that is embedded in decision theory. Motivation can be defined as the function that orients and activates the behaviour according to two attributes: a content (the goal) and a quantity (the goal value). Decision theory offers a way to quantify motivation, as the cost that patients would accept to endure in order to get the benefit of achieving their goal. We then review basic and clinical studies that have investigated the trade-off between the expected cost entailed by potential actions and the expected benefit associated with potential rewards. These studies have shown that the trade-off between effort and reward involves specific cortical, subcortical and neuromodulatory systems, such that it may be shifted in particular clinical conditions, and reinstated by appropriate treatments. Finally, we emphasize the promises of computational phenotyping for clinical purposes. Ideally, there would be a one-to-one mapping between specific neural components and distinct computational variables and processes of the decision model. Thus, fitting computational models to patients' behaviour would allow inferring of the dysfunctional mechanism in both cognitive terms (e.g. hyposensitivity to reward) and neural terms (e.g. lack of dopamine). This computational approach may therefore not only give insight into the motivation deficit but also help personalize treatment.
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http://dx.doi.org/10.1093/brain/awx278 | DOI Listing |
J Orthop Res
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Department of Kinesiology, College of Health Sciences, University of Rhode Island, Kingston, Rhode Island, USA.
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Boehringer Ingelheim Pharma GmbH & Co. KG, Binger Straße 173, 55216, Ingelheim am Rhein, Germany.
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Large language models (LLMs) have been successfully used for data extraction from free-text radiology reports. Most current studies were conducted with LLMs accessed via an application programming interface (API). We evaluated the feasibility of using open-source LLMs, deployed on limited local hardware resources for data extraction from free-text mammography reports, using a common data element (CDE)-based structure.
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Sports injury surveillance programs have been vital in advancing the understanding of injury epidemiology across various athlete populations. Surveillance-based epidemiological measures of injury occurrence are ubiquitous in the sports medicine literature, and the injury rate is one such commonly used measure. Traditional approaches to calculating injury rates have predominantly relied on frequentist methods, which, while informative, have limitations in addressing certain practical questions.
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