98%
921
2 minutes
20
Background: Theoretical models posit abnormalities in cortico-striatal pathways in two of the most common neurodevelopmental disorders (Developmental dyslexia, DD, and Attention deficit hyperactive disorder, ADHD), but it is still unclear what distinct cortico-striatal dysfunction might distinguish language disorders from others that exhibit very different symptomatology. Although impairments in tasks that depend on the cortico-striatal network, including reinforcement learning (RL), have been implicated in both disorders, there has been little attempt to dissociate between different types of RL or to compare learning processes in these two types of disorders. The present study builds upon prior research indicating the existence of two learning manifestations of RL and evaluates whether these processes can be differentiated in language and attention deficit disorders. We used a two-step RL task shown to dissociate model-based from model-free learning in human learners.
Results: Our results show that, relative to neurotypicals, DD individuals showed an impairment in model-free but not in model-based learning, whereas in ADHD the ability to use both model-free and model-based learning strategies was significantly compromised.
Conclusions: Thus, learning impairments in DD may be linked to a selective deficit in the ability to form action-outcome associations based on previous history, whereas in ADHD some learning deficits may be related to an incapacity to pursue rewards based on the tasks' structure. Our results indicate how different patterns of learning deficits may underlie different disorders, and how computation-minded experimental approaches can differentiate between them.
Download full-text PDF |
Source |
---|---|
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10029183 | PMC |
http://dx.doi.org/10.1186/s12993-023-00207-w | DOI Listing |
BMC Oral Health
September 2025
Oral and Maxillofacial Radiology Department, Cairo university, Cairo, Egypt.
Aim: The purpose of this study was to assess the accuracy of a customized deep learning model based on CNN and U-Net for detecting and segmenting the second mesiobuccal canal (MB2) of maxillary first molar teeth on cone beam computed tomography (CBCT) scans.
Methodology: CBCT scans of 37 patients were imported into 3D slicer software to crop and segment the canals of the mesiobuccal (MB) root of the maxillary first molar. The annotated data were divided into two groups: 80% for training and validation and 20% for testing.
BMC Med Ethics
September 2025
Dept of Neurobiology, Care Sciences, and Society, Karolinska Institutet, Stockholm, Sweden.
BMC Med Educ
September 2025
Department of Learning, Informatics, Management & Ethics (LIME) Widerströmska huset, Karolinska Institutet, Stockholm, Sweden.
Background: Live tissue training (LTT) refers to the use of live anaesthetised animals for the purpose of medical education. It is a type of simulation training that is contentious, and there is an ethical imperative for educators to justify the use of animals. This should include scrutinising educational practices.
View Article and Find Full Text PDFInfect Dis Poverty
September 2025
Faculty of Medicine and Pharmaceutical Sciences, University of Douala, Douala, Cameroon.
Background: Little is documented on key community-based One Health (OH) approach implementation, pro-activeness and effectiveness of interactions and strategies against Mpox outbreak public health emergency in international concern (PHEIC) in various African countries in order to stamp out the persisting Mpox outbreak threat and burden. Prioritizing critical community-based interventions and lessons learned from previous COVID-19, Mpox, Ebola, COVID-19, Rift Valley Fever and Marburg virus outbreaks revealed critical shortcomings in funding, surveillance, and community engagement that plague public health initiatives across the continent. The article provides critical insights and benefits of community-based One Health approaches implementation against Mpox outbreak management in Africa.
View Article and Find Full Text PDF