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Parkinson's disease (PD), which is caused by degeneration of dopaminergic neurons in the midbrain, results in a heterogeneous clinical picture including cognitive decline. Since the phasic signal of dopamine neurons is proposed to guide learning by signifying mismatches between subjects' expectations and external events, we here investigated whether akinetic-rigid PD patients without mild cognitive impairment exhibit difficulties in dealing with either relevant (requiring flexibility) or irrelevant (requiring stability) prediction errors. Following our previous study on flexibility and stability in prediction (Trempler et al. J Cogn Neurosci 29(2):298-309, 2017), we then assessed whether deficits would correspond with specific structural alterations in dopaminergic regions as well as in inferior frontal cortex, medial prefrontal cortex, and the hippocampus. Twenty-one healthy controls and twenty-one akinetic-rigid PD patients on and off medication performed a task which required to serially predict upcoming items. Switches between predictable sequences had to be indicated via button press, whereas sequence omissions had to be ignored. Independent of the disease, midbrain volume was related to a general response bias to unexpected events, whereas right putamen volume correlated with the ability to discriminate between relevant and irrelevant prediction errors. However, patients compared with healthy participants showed deficits in stabilisation against irrelevant prediction errors, associated with thickness of right inferior frontal gyrus and left medial prefrontal cortex. Flexible updating due to relevant prediction errors was also affected in patients compared with controls and associated with right hippocampus volume. Dopaminergic medication influenced behavioural performance across, but not within the patients. Our exploratory study warrants further research on deficient prediction error processing and its structural correlates as a core of cognitive symptoms occurring already in early stages of the disease.
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http://dx.doi.org/10.1007/s00429-018-1616-2 | DOI Listing |
PLoS One
September 2025
School of Mechanical Engineering, Northwestern Polytechnical University, Xi'an, China.
Accurate prediction of time-varying dynamic parameters during the milling process is a prerequisite for chatter-free cutting of thin-walled parts. In this paper, a matrix iterative prediction method based on weighted parameters is proposed for the time-varying structural modes during the milling of thin-walled blade structures. The thin-walled blade finite element model is established based on the 4-node plate element, and the time-varying dynamic parameters of the workpiece during the cutting process can be obtained by modifying the thickness of the nodes through the constructed mesh element finite element model It is not necessary to re-divide the mesh elements of the thin-walled parts at each cutting position, thus improving the calculation efficiency of the dynamic parameters of the workpiece.
View Article and Find Full Text PDFIEEE Trans Neural Syst Rehabil Eng
September 2025
Force prediction is crucial for functional rehabilitation of the upper limb. Surface electromyography (sEMG) signals play a pivotal role in muscle force studies, but its non-stationarity challenges the reliability of sEMG-driven models. This problem may be alleviated by fusion with electrical impedance myography (EIM), an active sensing technique incorporating tissue morphology information.
View Article and Find Full Text PDFCereb Cortex
August 2025
Brain and Cognition, KU Leuven, Tiensestraat 102, 3000 Leuven, Belgium.
Centro-parietal electroencephalogram signals (centro-parietal positivity and error positivity) correlate with the reported level of confidence. According to recent computational work these signals reflect evidence which feeds into the computation of confidence, not directly confidence. To test this prediction, we causally manipulated prior beliefs to selectively affect confidence, while leaving objective task performance unaffected.
View Article and Find Full Text PDFNanoscale
September 2025
School of Materials Science and Engineering, Beihang University, Beijing 100191, China.
The challenge of photocatalytic hydrogen production has motivated a targeted search for MXenes as a promising class of materials for this transformation because of their high mobility and high light absorption. High-throughput screening has been widely used to discover new materials, but the relatively high cost limits the chemical space for searching MXenes. We developed a deep-learning-enabled high-throughput screening approach that identified 14 stable candidates with suitable band alignment for water splitting from 23 857 MXenes.
View Article and Find Full Text PDFInt J Comput Assist Radiol Surg
September 2025
Division of Plastic and Reconstructive Surgery, Neonatal and Pediatric Craniofacial Airway Orthodontics, Department of Surgery, Stanford University School of Medicine, 770 Welch Road, Palo Alto, CA, 94394, USA.
Background: Alveolar molding plate treatment (AMPT) plays a critical role in preparing neonates with cleft lip and palate (CLP) for the first reconstruction surgery (cleft lip repair). However, determining the number of adjustments to AMPT in near-normalizing cleft deformity prior to surgery is a challenging task, often affecting the treatment duration. This study explores the use of machine learning in predicting treatment duration based on three-dimensional (3D) assessments of the pre-treatment maxillary cleft deformity as part of individualized treatment planning.
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