2025-01-29T15:20:23Z
2025-01-29T15:20:23Z
2024-11
2025-01-29T15:20:24Z
Background and purpose: In recent years, there has been a growing interest in the study of resting neural networks in different neurological and mental disorders. While previous studies suggest that the default mode network (DMN) may be altered in dyscalculia, the study of resting-state networks in the development of numerical skills, especially in children with developmental dyscalculia (DD), is scarce and relatively recent. Based on this, this study examines differences in resting-state functional connectivity (rs-FC) data of children with DD using functional connectivity multivariate pattern analysis (fc-MVPA), a data-driven methodology that summarizes properties of the entire connectome. Methods: We performed fc-MVPA on resting state images of a sample composed of a group of children with DD (n = 19, 8.06 ± 0.87 years) and an age- and sex-matched control group of typically developing children (n = 23, 7.76 ± 0.46 years). Results: Analysis of fc-MVPA showed significant differences between group connectivity profiles in two clusters allocated in both the right and left medial temporal gyrus. Post hoc effect size results revealed a decreased rs-FC between each temporal pole and the DMN in children with DD and an increased rs-FC between each temporal pole and the sensorimotor network. Conclusions: Our results suggest an aberrant information flow between resting-state networks in children with DD, demonstrating the importance of these networks for arithmetic development.
Artículo
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Cervell; Neurologia; Malalties mentals; Matemàtica; Brain; Neurology; Mental illness; Mathematics
Wiley
Reproducció del document publicat a: https://doi.org/10.1111/jon.13236
Journal of Neuroimaging, 2024, vol. 34, num.6, p. 694-703
https://doi.org/10.1111/jon.13236
cc-by-nc-nd (c) Mateu-Estivill, Roger et al., 2024
http://creativecommons.org/licenses/by-nc-nd/4.0/