Historically, the development of valid and reliable methods for assessing higher-order cognitive abilities (e.g., rule learning and transfer) has been difficult in rodent models. To date, limited evidence supports the existence of higher cognitive abilities such as rule generation and complex decision-making in mice, rats, and rabbits. To this end, we sought to develop a task that would require mice to learn and transfer a rule. We trained mice to visually discriminate a series of images (image set, six total) of increasing complexity following three stages: (1) learn a visual target, (2) learn a rule (ignore any new images around the target), and finally (3) apply this rule in abstract form to a comparable but new image set. To evaluate learning for each stage, we measured (1) days (and performance by day) to discriminate the original target at criterion, (2) days (and performance by day) to get back to criterion when images in the set were altered by the introduction of distractors (rule learning), and (3) overall days (and performance by day) to criterion when experienced versus naïve cohorts of mice were tested on the same image set (rule transfer). Twenty-seven wild-type male C57 mice were tested using Bussey-Saksida touchscreen operant conditioning boxes (Lafayette Instruments). Two comparable black-white image sets were delivered sequentially (counterbalanced for order) to two identical cohorts of mice. Results showed that all mice were able to effectively learn their initial target image and could recall it >80 d later. We also found that mice were able to quickly learn and apply a "rule" : Ignore new distractors and continue to identify their visual target embedded in more complex images. The presence of rule learning was supported because performance criterion thresholds were regained much faster than initial learning when distractors were introduced. On the other hand, mice appeared unable to transfer this rule to a new set of stimuli. This is supported because visual discrimination curves for a new image set were no better than an initial (naïve) learning by a matched cohort of mice. Overall results have important implications for phenotyping research and particularly for the modeling of complex disorders in mice.
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http://dx.doi.org/10.1101/lm.053771.123 | DOI Listing |
Front Cardiovasc Med
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Department of Artificial Intelligence and Informatics, Mayo Clinic, Jacksonville, FL, United States.
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January 2025
Department of Neuroscience, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via Mario Negri 2, 20156, Milan, Italy.
Deposition of abnormally phosphorylated tau aggregates is a central event leading to neuronal dysfunction and death in Alzheimer's disease (AD) and other tauopathies. Among tau aggregates, oligomers (TauOs) are considered the most toxic. AD brains show significant increase in TauOs compared to healthy controls, their concentration correlating with the severity of cognitive deficits and disease progression.
View Article and Find Full Text PDFHealth Sci Rep
January 2025
Research Center for Environmental Determinants of Health (RCEDH), Health Institute Kermanshah University of Medical Sciences Kermanshah Iran.
Background And Aims: Infertility, as defined by the World Health Organization, is the inability to conceive after 12 months of regular, unprotected intercourse. This study aimed to identify factors influencing infertility by applying data mining techniques, specifically rule-mining methods, to analyze diverse patient data and uncover relevant insights. This approach involves a thorough analysis of patients' clinical characteristics, dietary habits, and overall conditions to identify complex patterns and relationships that may contribute to infertility.
View Article and Find Full Text PDFPhys Ther Res
November 2024
Graduate School of Humanities and Social Sciences, Hiroshima University, Japan.
Objective: This study aimed to derive a clinical prediction rule (CPR) that can predict changes in health-related quality of life at 5 months for patients with knee osteoarthritis (KOA) undergoing conservative treatment.
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Int J Med Inform
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Rheumatology and Allergy Clinical Epidemiology Research Center and Division of Rheumatology, Allergy, and Immunology, and Mongan Institute, Department of Medicine, Massachusetts General Hospital Boston MA USA. Electronic address:
Background: ANCA-associated vasculitis (AAV) is a rare but serious disease. Traditional case-identification methods using claims data can be time-intensive and may miss important subgroups. We hypothesized that a deep learning model analyzing electronic health records (EHR) can more accurately identify AAV cases.
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