Mindfulness-based interventions are showing increasing promise as a treatment for psychological disorders, with improvements in cognition and emotion regulation after intervention. Understanding the changes in functional brain activity and neural plasticity that underlie these benefits from mindfulness interventions is thus of interest in current neuroimaging research. Previous studies have found functional brain changes during resting and task states to be associated with mindfulness both cross-sectionally and longitudinally, particularly in the executive control, default mode and salience networks. However, limited research has combined information from rest and task to study mindfulness-related functional changes in the brain, particularly in the context of intervention studies with active controls. Recent work has found that the reconfiguration efficiency of brain activity patterns between rest and task states is behaviorally relevant in healthy young adults. Thus, we applied this measure to investigate how mindfulness intervention changed functional reconfiguration between rest and a breath-counting task in elderly participants with self-reported sleep difficulties. Improving on previous longitudinal designs, we compared the intervention effects of a mindfulness-based therapy to an active control (sleep hygiene) intervention. We found that mindfulness intervention improved self-reported mindfulness measures and brain functional reconfiguration efficiency in the executive control, default mode and salience networks, though the brain and behavioral changes were not associated with each other. Our findings suggest that neuroplasticity may be induced through regular mindfulness practice, thus bringing the intrinsic functional configuration in participants' brains closer to a state required for mindful awareness.
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http://dx.doi.org/10.1038/s41398-023-02642-9 | DOI Listing |
Sci Rep
January 2025
Department of Electronics Engineering, College of Engineering, Chang Gung University, Taoyuan City, 330, Taiwan.
Reconfigurable modular robots can be used in application domains such as exploration, logistics, and outer space. The robots should be able to assemble and work as a single entity to perform a task that requires high throughput. Selecting an optimum assembly position with minimum distance traveled by robots in an obstacle surrounding the environment is challenging.
View Article and Find Full Text PDFSmall Methods
January 2025
Department of Chemical Engineering, National Taiwan University of Science and Technology, Taipei, 10607, Taiwan.
Electrochemical water splitting is a pivotal process for sustainable hydrogen energy production, relying on efficient hydrogen evolution reaction (HER) catalysts, particularly in acidic environments, where both high activity and durability are crucial. Despite the favorable kinetics of platinum (Pt)-based materials, their performance is hindered under harsh conditions, driving the search for alternatives. Due to their unique structural characteristic, Prussian blue analogs (PBAs) emerge as attractive candidates for designing efficient HER electrocatalysts.
View Article and Find Full Text PDFNat Commun
January 2025
School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, 639798, Singapore.
Hardware implementation of reconfigurable and nonvolatile photoresponsivity is essential for advancing in-sensor computing for machine vision applications. However, existing reconfigurable photoresponsivity essentially depends on the photovoltaic effect of p-n junctions, which photoelectric efficiency is constrained by Shockley-Queisser limit and hinders the achievement of high-performance nonvolatile photoresponsivity. Here, we employ bulk photovoltaic effect of rhombohedral (3R) stacked/interlayer sliding tungsten disulfide (WS) to surpass this limit and realize highly reconfigurable, nonvolatile photoresponsivity with a retinomorphic photovoltaic device.
View Article and Find Full Text PDFNat Commun
January 2025
School of Integrated Circuits and Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, Hubei, 430074, China.
Biological neural circuits demonstrate exceptional adaptability to diverse tasks by dynamically adjusting neural connections to efficiently process information. However, current two-dimension materials-based neuromorphic hardware mainly focuses on specific devices to individually mimic artificial synapse or heterosynapse or soma and encoding the inner neural states to realize corresponding mock object function. Recent advancements suggest that integrating multiple two-dimension material devices to realize brain-like functions including the inter-mutual connecting assembly engineering has become a new research trend.
View Article and Find Full Text PDFLight Sci Appl
January 2025
Electrical and Computer Engineering Program, Computer Electrical Mathematical Science and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia.
Advancements in neuromorphic computing have given an impetus to the development of systems with adaptive behavior, dynamic responses, and energy efficiency characteristics. Although charge-based or emerging memory technologies such as memristors have been developed to emulate synaptic plasticity, replicating the key functionality of neurons-integrating diverse presynaptic inputs to fire electrical impulses-has remained challenging. In this study, we developed reconfigurable metal-oxide-semiconductor capacitors (MOSCaps) based on hafnium diselenide (HfSe).
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