The long-lasting neuromodulatory effects of repetitive transcranial magnetic stimulation (rTMS) are of great interest for therapeutic applications in various neurological and psychiatric disorders, due to which functional connectivity among brain regions is profoundly disturbed. Classic TMS studies selectively alter neural activity in specific brain regions and observe neural activity changes on nonperturbed areas to infer underlying connectivity and its changes. Less has been indicated in direct measures of functional connectivity and/or neural network and on how connectivity/network alterations occur. Here, we developed a novel analysis framework to directly investigate both neural activity and connectivity changes induced by rTMS from resting-state EEG (rsEEG) acquired in a group of subjects with a chronic disorder of imbalance, known as the mal de debarquement syndrome (MdDS). Resting-state activity in multiple functional brain areas was identified through a data-driven blind source separation analysis on rsEEG data, and the connectivity among them was characterized using a phase synchronization measure. Our study revealed that there were significant long-lasting changes in resting-state neural activity, in theta, low alpha, and high alpha bands and neural networks in theta, low alpha, high alpha and beta bands, over broad cortical areas 4 to 5 h after the last application of rTMS in a consecutive five-day protocol. Our results of rsEEG connectivity further indicated that the changes, mainly in the alpha band, over the parietal and occipital cortices from pre- to post-TMS sessions were significantly correlated, in both magnitude and direction, to symptom changes in this group of subjects with MdDS. This connectivity measure not only suggested that rTMS can generate positive treatment effects in MdDS patients, but also revealed new potential targets for future therapeutic trials to improve treatment effects. It is promising that the new connectivity measure from rsEEG can be used to understand the variability in treatment response to rTMS in brain disorders with impaired functional connectivity and, eventually, to determine individually tailored stimulation parameters and treatment procedures in rTMS.
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http://dx.doi.org/10.1109/TBME.2014.2313575 | DOI Listing |
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Department of Rehabilitation, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China.
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January 2025
Exercise Biochemistry Laboratory, Center of Physical Education and Sports, Federal University of Santa Maria, Santa Maria, Brazil; Department of Sports Methods and Techniques, Federal University of Santa Maria, Santa Maria, Brazil. Electronic address:
Traumatic brain injury (TBI) is a burdensome condition frequently associated with an increased risk of psychiatric disorders. Although the exact molecular signaling pathways have not yet been fully defined, the compromised integrity of functional brain networks in regions such as the prefrontal cortex and anterior cingulate cortex has been linked to persistent symptoms, including depression, fatigue, and sleep disorders. Understanding how TBI affects neural physiology enables the development of effective interventions.
View Article and Find Full Text PDFJ Affect Disord
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Department of Psychology, University of Turin, Turin, Italy. Electronic address:
Dysfunctional parenting (DP) is a factor of vulnerability and a predictive risk factor for psychopathology. Although previous research has shown specific functional and structural brain alterations, the neural basis of DP remains understudied. We therefore investigated EEG functional connectivity changes within the Salience Network before and after the exposure to attachment-related stimuli in individuals with high and low perceived DP.
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January 2025
Key Laboratory of Quantum Materials and Devices of Ministry of Education, School of Physics, Southeast University, Nanjing, 21189, China.
Directly generating material structures with optimal properties is a long-standing goal in material design. Traditional generative models often struggle to efficiently explore the global chemical space, limiting their utility to localized space. Here, we present a framework named Material Generation with Efficient Global Chemical Space Search (MAGECS) that addresses this challenge by integrating the bird swarm algorithm and supervised graph neural networks, enabling effective navigation of generative models in the immense chemical space towards materials with target properties.
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January 2025
School of Electrical and Electronic Engineering, Hanoi University of Science and Technology, Hanoi, Viet Nam. Electronic address:
In the field of medical science, skin segmentation has gained significant importance, particularly in dermatology and skin cancer research. This domain demands high precision in distinguishing critical regions (such as lesions or moles) from healthy skin in medical images. With growing technological advancements, deep learning models have emerged as indispensable tools in addressing these challenges.
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