Few studies have explored neural mechanisms of reward learning in ASD despite evidence of behavioral impairments of predictive abilities in ASD. To investigate the neural correlates of reward prediction errors in ASD, 16 adults with ASD and 14 typically developing controls performed a prediction error task during fMRI scanning. Results revealed greater activation in the ASD group in the left paracingulate gyrus during signed prediction errors and the left insula and right frontal pole during thresholded unsigned prediction errors. Findings support atypical neural processing of reward prediction errors in ASD in frontostriatal regions critical for prediction coding and reward learning. Results provide a neural basis for impairments in reward learning that may contribute to traits common in ASD (e.g., intolerance of unpredictability).
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http://dx.doi.org/10.1155/2019/5469191 | DOI Listing |
Korean J Ophthalmol
January 2025
Department of Ophthalmology, Seoul National University Bundang Hospital, Seongnam, Korea.
Purpose: To evaluate the accuracy of toric intraocular lens (IOL) axis prediction between two preoperative measurement devices: the optical biometry (IOLMaster 500 or 700) and the dual Scheimpflug topography (Galilei G4).
Methods: Medical records of 64 eyes from 44 patients who underwent phacoemulsification and posterior chamber toric IOL (Zeiss AT TORBI 709M) implantation between July 2017 and January 2022 were reviewed. All patients underwent preoperative evaluation by optical biometry (IOLMaster 500 or IOLMaster 700) and Galilei G4.
Nat Rev Neurosci
January 2025
Intramural Research Program, National Institute on Drug Abuse, Baltimore, MD, USA.
Transient changes in the firing of midbrain dopamine neurons have been closely tied to the unidimensional value-based prediction error contained in temporal difference reinforcement learning models. However, whereas an abundance of work has now shown how well dopamine responses conform to the predictions of this hypothesis, far fewer studies have challenged its implicit assumption that dopamine is not involved in learning value-neutral features of reward. Here, we review studies in rats and humans that put this assumption to the test, and which suggest that dopamine transients provide a much richer signal that incorporates information that goes beyond integrated value.
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January 2025
Department of Computer Science and Information Technology, Benazir Bhutto Shaheed University Lyari, Karachi, 75660, Pakistan.
Deep learning-based medical image analysis has shown strong potential in disease categorization, segmentation, detection, and even prediction. However, in high-stakes and complex domains like healthcare, the opaque nature of these models makes it challenging to trust predictions, particularly in uncertain cases. This sort of uncertainty can be crucial in medical image analysis; diabetic retinopathy is an example where even slight errors without an indication of confidence can have adverse impacts.
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January 2025
Faculty of Life and Allied Health Sciences, MS Ramiah University of Applied Sciences (RUAS), MSR Nagar, New BEL Road, Bangalore, 560054, India.
Background Breast cancer represents a significant public health concern in India, accounting for 28% of all cancer diagnoses and imposing a substantial economic burden. This study introduces a novel approach to forecasting the number of breast cancer cases (based on prevalence rates) and estimating the associated economic impact in India using the autoregressive integrated moving average (ARIMA) model. Methods Data on the prevalence of breast cancer in India from 2000 to 2021 were obtained from the Global Burden of Disease (GBD) database.
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January 2025
Department of Statistics, Faculty of Science, Fasa University, Fasa, 74616-86131, Iran.
Air pollution is a significant challenge in metropolitan areas, where increasing amounts of air pollutants threaten public health and environmental safety. The present study aims to forecast the concentrations of various air pollutants, including CO, O, NO, SO, PM, and PM, from 2013 to 2023 in the Tehran megacity, Iran, via deep learning (DL) models and evaluate their effectiveness over conventional machine learning (ML) methods. Key driving variables, including temperature, relative humidity, dew point, wind speed, and air pressure, were considered.
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