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Introduction: This study utilized electroencephalography (EEG) to compare brain functional and effective connectivity patterns in children with reading difficulties (RD) and math difficulties (MD) during specific tasks. The aim was to identify neurophysiological distinctions between these two learning disorders, which often exhibit high comorbidity.
Methods: Data from a publicly available dataset of 28 children (11 RD, 17 MD) aged 7-13 years were analyzed. Functional connectivity was quantified using the weighted Phase Lag Index (wPLI), and effective connectivity was assessed with the Directed Transfer Function (DTF).
Results: Functional connectivity analysis revealed significant group differences. The RD group showed significantly higher beta band synchronization in the right temporal lobe compared to the MD group. Conversely, the MD group exhibited significantly greater connectivity in the frontal lobe's delta band and the parietal lobe's theta band. However, no statistically significant differences were observed between the groups regarding effective connectivity.
Discussion: These findings highlight specific task-related brain functional connectivity differences between reading and math learning difficulties, suggesting potential compensatory mechanisms in RD and cognitive control challenges in MD. The lack of significant effective connectivity findings may be attributed to the small sample size, which is a key limitation of the study. This research emphasizes the need for larger samples, refined task designs, and multimodal neuroimaging in future studies.
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http://dx.doi.org/10.3389/fnins.2025.1612884 | DOI Listing |
JMIR Hum Factors
September 2025
Department of Community Health Systems, University of California, San Francisco, School of Nursing, San Francisco, CA, United States.
Background: The COVID-19 pandemic forced the world to quarantine to slow the rate of transmission, causing communities to transition into virtual spaces. Asian American and Pacific Islander communities faced the additional challenge of discrimination that stemmed from racist and xenophobic rhetoric in the media. Limited data exist on technology use among Asian American and Pacific Islander adults during the height of the COVID-19 shelter-in-place period and its effect on their physical and mental health.
View Article and Find Full Text PDFNutr Health
September 2025
Division of General Internal Medicine, Mayo Clinic, Rochester, MN, USA.
BackgroundCoronavirus Disease 2019 (COVID-19) has led to dramatic changes including social distancing, closure of schools, travel bans, and issues of stay-at-home orders. The health-care field has been transformed with elective procedures and on-site visits being deferred. Telemedicine has emerged as a novel mechanism to continue to provide care.
View Article and Find Full Text PDFJ Robot Surg
September 2025
ORSI Academy, Melle, Belgium.
This Letter to the Editor responds to the recent publication by Patel et al. (J Robot Surg. Jul 11;19(1):370, 2025), which outlines a framework and recommendations for telesurgery.
View Article and Find Full Text PDFClin Oral Investig
September 2025
Department of Innovative Technologies in Medicine & Dentistry, "G. D'Annunzio" University, Via Dei Vestini 31, Chieti, Italy.
Objectives: This study aimed to compare the efficacy of the full-thickness palatal graft technique (FTPGT) and the coronally advanced flap with subepithelial connective tissue graft (CAF + SCTG) in achieving complete root coverage (CRC) in single gingival recessions (GR).
Methods: Forty healthy patients with a single RT1 GR were randomized into two groups: 20 treated with CAF + SCTG and 20 with FTPGT. Baseline and 12-month measurements of GR, keratinized tissue width (KTW), probing depth (PD), clinical attachment level (CAL), and gingival thickness (GT) were recorded.
Chaos
September 2025
School of Computational Science and Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332, USA.
Although many real-world time series are complex, developing methods that can learn from their behavior effectively enough to enable reliable forecasting remains challenging. Recently, several machine-learning approaches have shown promise in addressing this problem. In particular, the echo state network (ESN) architecture, a type of recurrent neural network where neurons are randomly connected and only the read-out layer is trained, has been proposed as suitable for many-step-ahead forecasting tasks.
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