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Executive functioning (EF) is a higher order cognitive process that is thought to depend on a network organization facilitating integration across subnetworks, in the context of which the central role of the fronto-parietal network (FPN) has been described across imaging and neurophysiological modalities. However, the potentially complementary unimodal information on the relevance of the FPN for EF has not yet been integrated. We employ a multilayer framework to allow for integration of different modalities into one 'network of networks.' We used diffusion MRI, resting-state functional MRI, MEG, and neuropsychological data obtained from 33 healthy adults to construct modality-specific single-layer networks as well as a single multilayer network per participant. We computed single-layer and multilayer eigenvector centrality of the FPN as a measure of integration in this network and examined their associations with EF. We found that higher multilayer FPN centrality, but not single-layer FPN centrality, was related to better EF. We did not find a statistically significant change in explained variance in EF when using the multilayer approach as compared to the single-layer measures. Overall, our results show the importance of FPN integration for EF and underline the promise of the multilayer framework toward better understanding cognitive functioning.
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http://dx.doi.org/10.1162/netn_a_00284 | DOI Listing |
Int J Comput Assist Radiol Surg
September 2025
Division of Plastic and Reconstructive Surgery, Neonatal and Pediatric Craniofacial Airway Orthodontics, Department of Surgery, Stanford University School of Medicine, 770 Welch Road, Palo Alto, CA, 94394, USA.
Background: Alveolar molding plate treatment (AMPT) plays a critical role in preparing neonates with cleft lip and palate (CLP) for the first reconstruction surgery (cleft lip repair). However, determining the number of adjustments to AMPT in near-normalizing cleft deformity prior to surgery is a challenging task, often affecting the treatment duration. This study explores the use of machine learning in predicting treatment duration based on three-dimensional (3D) assessments of the pre-treatment maxillary cleft deformity as part of individualized treatment planning.
View Article and Find Full Text PDFComput Methods Biomech Biomed Engin
September 2025
School of Medicine, Tzu Chi University, Hualien, Taiwan.
This study explores deep feature representations from photoplethysmography (PPG) signals for coronary artery disease (CAD) identification in 80 participants (40 with CAD). Finger PPG signals were processed using multilayer perceptron (MLP) and convolutional neural network (CNN) autoencoders, with performance assessed via 5-fold cross-validation. The CNN autoencoder model achieved the best results (recall 96.
View Article and Find Full Text PDFJ Food Sci
September 2025
College of Food and Bioengineering, Zhengzhou University of Light Industry, Zhengzhou, China.
Primary agricultural products are closely related to our daily lives, as they serve not only as raw materials for food processing but also as products directly purchased by consumers. These products face the issue of freshness decline and spoilage during both production and consumption. Freshness degradation induces sensory deterioration and nutritional loss and promotes harmful substance accumulation, causing gastrointestinal issues or even endangering life.
View Article and Find Full Text PDFMethods
September 2025
Center of Bioinformatics, College of Life Sciences, Northwest A&F University, Yangling, Shaanxi 712100, China. Electronic address:
Parkinson's disease is a prevalent neurodegenerative disease, in which genetic mutations in many genes play an important role in its pathogenesis. Among these, a mutation in the PINK1 gene, a mitochondrial-targeted serine/threonine putative kinase 1 that protects cells from stress-induced mitochondrial dysfunction, is implicated in autosomal recessive Parkinsonism. However, the exact etiology is not well understood.
View Article and Find Full Text PDFCarbohydr Polym
November 2025
Key Lab of Guangdong Province for High Property and Functional Polymer Materials, School of Materials Science and Engineering, South China University of Technology, Guangzhou 510640, China. Electronic address:
Inspired by spider silk, polyphenolic nanodots (PTa) loaded multi-layer MXene (mMXene-PTa) through hydrogen and coordination bonds was prepared by self-polymerizing tannic acid on mMXene and used as a new crosslinker for polyvinyl alcohol (PVA). Together with starch (ST), mMXene-PTa was compounded with PVA and exfoliated to fabricate PVA/ST/mMXene-PTa nanocomposite. The phenolic hydroxyl groups in PTa formed high-density H-bonds with PVA and ST, creating an organic-inorganic dynamic crosslinking network with mMXene-PTa as nodes.
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