There have recently been considerable advances in our understanding of the neuronal mechanisms underlying multitasking, but the role of multimodal integration for this faculty has remained rather unclear. We examined this issue by comparing different modality combinations in a multitasking (stop-change) paradigm. In-depth neurophysiological analyses of event-related potentials (ERPs) were conducted to complement the obtained behavioral data. Specifically, we applied signal decomposition using second order blind identification (SOBI) to the multi-subject ERP data and source localization. We found that both general multimodal information integration and modality-specific aspects (potentially related to task difficulty) modulate behavioral performance and associated neurophysiological correlates. Simultaneous multimodal input generally increased early attentional processing of visual stimuli (i.e. P1 and N1 amplitudes) as well as measures of cognitive effort and conflict (i.e. central P3 amplitudes). Yet, tactile-visual input caused larger impairments in multitasking than audio-visual input. General aspects of multimodal information integration modulated the activity in the premotor cortex (BA 6) as well as different visual association areas concerned with the integration of visual information with input from other modalities (BA 19, BA 21, BA 37). On top of this, differences in the specific combination of modalities also affected performance and measures of conflict/effort originating in prefrontal regions (BA 6).
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http://dx.doi.org/10.1038/s41598-017-04828-w | DOI Listing |
Adv Healthc Mater
January 2025
Faculty of Biomedical Engineering, Technion-Israel Institute of Technology, Haifa, 3200003, Israel.
Volumetric muscle loss (VML) refers to muscle tissue loss exceeding 20% within a functional area due to trauma or surgery, often leading to physical disabilities. VML treatment relies on the transplantation of autologous flaps harvested from a healthy-donor site while minimizing the probability of immune rejection. However, this approach often leads to donor-site morbidity and relies on a restricted supply of muscle tissue.
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January 2025
Division of Biophotonics and Imaging, Biomedical Technology Wing, Sree Chitra Tirunal Institute for Medical Sciences and Technology (SCTIMST), Trivandrum 695012, India.
Targeting the unique characteristics of the tumor microenvironment (TME) has emerged as a highly promising strategy for cancer therapy. Chemodynamic therapy (CDT), which leverages the TME's intrinsic properties to convert HO into cytotoxic hydroxyl radicals (˙OH), has attracted significant attention. However, the effectiveness of CDT is often limited by the catalytic efficiency of the materials used.
View Article and Find Full Text PDFBMJ Oncol
January 2025
Department of Immunotherapeutics and Biotechnology, Texas Tech University Health Sciences Center, Jerry H Hodge School of Pharmacy, Abilene, Texas, USA.
In 2025, it will be 30 years since the initial clinical approval of pegylated liposomal doxorubicin (PLD) by the Food and Drug Administration. PLD predated the field of nanomedicine and became a model nanomedicine setting key pharmacological principles (prolonged circulation, slow drug release and the enhanced permeability and retention (EPR) effect) for clinical application of other nano-drugs in cancer therapy. The impressive reduction of cardiotoxicity conferred by PLD is the most valuable clinical asset.
View Article and Find Full Text PDFNeural Netw
January 2025
The Key Laboratory for Computer Systems of State Ethnic Affairs Commission, Southwest Minzu University, Chengdu, Sichuan 610225, China. Electronic address:
The brain is a complex system with multiple scales and hierarchies, making it challenging to identify abnormalities in individuals with mental disorders. The dynamic segregation and integration of activities across brain regions enable flexible switching between local and global information processing modes. Modeling these scale dynamics within and between brain regions can uncover hidden correlates of brain structure and function in mental disorders.
View Article and Find Full Text PDFPLoS One
January 2025
College of Education for the Future, Beijing Normal University, Zhuhai, Guangdong, China.
Personalized sports training plans are essential for addressing individual athlete needs, but traditional methods often need to integrate diverse data types, limiting adaptability and effectiveness. Existing machine learning (ML) and rule-based approaches cannot dynamically generate context-specific training programs, reducing their applicability in real-world scenarios. This study aims to develop a Generative Adversarial Network (GAN)- based framework to create context-specific training plans by integrating numeric attributes (e.
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