A small number of studies have described verbal selection deficits in Parkinson's disease (PD) when selection must occur among competing alternatives. However, these studies have largely focused on single-word processing, or have utilised sentence stems that carry high contextual constraint, thus reducing selection demands. The present study aimed to determine the influence of variable contextual constraint on the selection of a verbal response in PD. This was achieved using an adaption of the Hayling Sentence Completion Task whereby PD participants and matched controls were required to provide a single word to complete a cloze probability sentence stem that carried a low, medium, or high degree of contextual constraint. Results revealed no main effect of group in terms of response time or accuracy, though a group-by-condition interaction in accuracy was noted. This was characterised by a significant difference in accuracy between low and medium levels of constraint for control participants, but no significant difference for the PD group. Functional MRI data revealed marked between-group differences in underlying neural activity. The control group showed increased recruitment of the dorsal striatum and the vlPFC under conditions that placed greater demands upon selection (i.e. low and medium constraint), and greater activity overall in the left dlPFC and right vlPFC. However, in the PD group, behavioural performance appeared to be maintained despite underlying decreases in frontostriatal activity, suggesting other compensatory mechanisms that may include changes in functional connectivity or an over-medication effect in frontal networks in response to loss of signalling in cortico-subcortical pathways.
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JMIR Med Educ
January 2025
Digital Society Initiative, University of Zurich, Zurich, Switzerland.
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College of Education, United Arab Emirates University, Al Ain, Abu Dhabi, UAE.
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View Article and Find Full Text PDFPLoS One
January 2025
Department of Information Engineering, The Chinese University of Hong Kong, Shatin, N.T., Hong Kong, China.
As education increasingly relies on data-driven methodologies, accurately predicting student performance is essential for implementing timely and effective interventions. The California Student Performance Dataset offers a distinctive basis for analyzing complex elements that affect educational results, such as student demographics, academic behaviours, and emotional health. This study presents the GNN-Transformer-InceptionNet (GNN-TINet) model to overcome the constraints of prior models that fail to effectively capture intricate interactions in multi-label contexts, where students may display numerous performance categories concurrently.
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Université de Caen Normandie, EA4260 CESAMS, Caen, France.
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View Article and Find Full Text PDFBehav Brain Res
March 2025
Department of Psychological and Brain Sciences, Indiana University, USA. Electronic address:
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