We determined how various cognitive abilities, including several measures of a proposed domain-specific number sense, relate to mathematical competence in nearly 100 9-year-old children with normal reading skill. Results are consistent with an extended number processing network and suggest that important processing nodes of this network are phonological processing, verbal knowledge, visuo-spatial short-term and working memory, spatial ability and general executive functioning. The model was highly specific to predicting arithmetic performance. There were no strong relations between mathematical achievement and verbal short-term and working memory, sustained attention, response inhibition, finger knowledge and symbolic number comparison performance. Non-verbal intelligence measures were also non-significant predictors when added to our model. Number sense variables were non-significant predictors in the model and they were also non-significant predictors when entered into regression analysis with only a single visuo-spatial WM measure. Number sense variables were predicted by sustained attention. Results support a network theory of mathematical competence in primary school children and falsify the importance of a proposed modular 'number sense'. We suggest an 'executive memory function centric' model of mathematical processing. Mapping a complex processing network requires that studies consider the complex predictor space of mathematics rather than just focusing on a single or a few explanatory factors.
Download full-text PDF |
Source |
---|---|
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4253132 | PMC |
http://dx.doi.org/10.1111/desc.12144 | DOI Listing |
Bone Joint Res
March 2025
Department of Orthopedics, The Affiliated Changzhou Second People's Hospital of Nanjing Medical University, Changzhou, China.
Aims: Osteoarthritis (OA) is a widespread chronic degenerative joint disease with an increasing global impact. The pathogenesis of OA involves complex interactions between genetic and environmental factors. Despite this, the specific genetic mechanisms underlying OA remain only partially understood, hindering the development of targeted therapeutic strategies.
View Article and Find Full Text PDFCancer Med
March 2025
Universidad Autónoma del Estado de Morelos, Facultad de Medicina, Cuernavaca, Morelos, Mexico.
Introduction: Osteosarcoma, a highly aggressive bone cancer primarily affecting children and young adults, remains a significant challenge in clinical oncology. Metastasis stands as the primary cause of mortality in osteosarcoma patients. However, the mechanisms driving this process remain incompletely understood.
View Article and Find Full Text PDFArterioscler Thromb Vasc Biol
March 2025
Department of Pediatrics, Division of Pediatric Cardiology, Medical College of Wisconsin, Milwaukee (T.B., J.R.K., A.J.K., J.L.).
Background: Heart valve function requires a highly organized ECM (extracellular matrix) network that provides the necessary biomechanical properties needed to withstand pressure changes during each cardiac cycle. Lay down of the valve ECM begins during embryogenesis and continues throughout postnatal stages when it is remodeled into stratified layers and arranged according to blood flow. Alterations in this process can lead to dysfunction and, if left untreated, heart failure.
View Article and Find Full Text PDFAppl Med Artif Intell (2024)
February 2025
Center for Advanced Medical Computing and Analysis, Massachusetts General Hospital and Harvard Medical School, Boston, MA, 02114, USA.
Head motion is a major source of image artifacts in head computed tomography (CT), degrading the image quality and impacting diagnosis. Image-domain-based motion correction is practical for routine use since it doesn't rely on hard-to-obtain CT projection data. However, existing convolutional neural network (CNN)-based methods tend to over-smooth images, particularly in cases of moderate to severe 3D motion artifacts.
View Article and Find Full Text PDFJ Med Imaging (Bellingham)
March 2025
Purdue University, School of Electrical and Computer Engineering, Video and Image Processing Laboratory, West Lafayette, Indiana, United States.
Purpose: The advancement of high-content optical microscopy has enabled the acquisition of very large three-dimensional (3D) image datasets. The analysis of these image volumes requires more computational resources than a biologist may have access to in typical desktop or laptop computers. This is especially true if machine learning tools are being used for image analysis.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!