Developmental dyslexia is often accompanied by altered phonological processing of speech. Underlying neural changes have typically been characterized in terms of stimulus- and/or task-related responses within individual brain regions or their functional connectivity. Less is known about potential changes in the more global functional organization of brain networks. Here we recorded electroencephalography (EEG) in typical and dyslexic readers while they listened to (a) a random sequence of syllables and (b) a series of tri-syllabic real words. The network topology of the phase synchronization of evoked cortical oscillations was investigated in four frequency bands (delta, theta, alpha and beta) using minimum spanning tree graphs. We found that, compared to syllable tracking, word tracking triggered a shift toward a more integrated network topology in the theta band in both groups. Importantly, this change was significantly stronger in the dyslexic readers, who also showed increased reliance on a right frontal cluster of electrodes for word tracking. The current findings point towards an altered effect of word-level processing on the functional brain network organization that may be associated with less efficient phonological and reading skills in dyslexia.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.1016/j.neuroimage.2022.119142 | DOI Listing |
Sci Rep
January 2025
Beijing Neurosurgical Institute, Capital Medical University, Beijing, China.
Patients with Moyamoya disease (MMD) exhibit significant alterations in brain structure and function, but knowledge regarding gray matter networks is limited. The study enrolled 136 MMD patients and 99 healthy controls (HCs). Clinical characteristics and gray matter network topology were analyzed.
View Article and Find Full Text PDFISA Trans
January 2025
College of Mechanical and Vehicle Engineering, Hunan University, Changsha, 410082, Hunan, China. Electronic address:
Approximation-free control effectively addresses uncertainty and disturbances without relying on approximation techniques such as fuzzy logic systems (FLS) and neural networks (NNs). However, singularity problems-where signals exceed preset boundaries under dynamic operating conditions-remain a challenge. This paper proposes an improved approximation-free control (I-AFC) method for the multi-agent system, which introduces a novel singularity compensator, providing a low-complexity design with exceptional adaptability while reducing the risk of singularity issues under changing working conditions (random initial values, system parameter variations, and changes in topology graph and followers' dynamics).
View Article and Find Full Text PDFMed Image Anal
January 2025
Department of Psychiatry, University of North Carolina at Chapel Hill, USA; Department of Computer Science, University of North Carolina at Chapel Hill, USA.
The recent advances in neuroimaging technology allow us to understand how the human brain is wired in vivo and how functional activity is synchronized across multiple regions. Growing evidence shows that the complexity of the functional connectivity is far beyond the widely used mono-layer network. Indeed, the hierarchical processing information among distinct brain regions and across multiple channels requires using a more advanced multilayer model to understand the synchronization across the brain that underlies functional brain networks.
View Article and Find Full Text PDFEur J Neurosci
January 2025
Department of Radiology, Henan Provincial People's Hospital and Zhengzhou University People's Hospital, Zhengzhou, China.
The occurrence and persistence of tinnitus result from the interaction of multiple neural networks. This study aims to explore the alterations in brain network topology associated with the transition of tinnitus from recent-onset to chronic. Twenty-eight patients with chronic tinnitus, 28 patients with recent-onset tinnitus and 28 sex- and age-matched healthy controls (HC) were enrolled in this study.
View Article and Find Full Text PDFPLoS Comput Biol
January 2025
Biorobotics Laboratory, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland.
This study introduces a novel neuromechanical model employing a detailed spiking neural network to explore the role of axial proprioceptive sensory feedback, namely stretch feedback, in salamander locomotion. Unlike previous studies that often oversimplified the dynamics of the locomotor networks, our model includes detailed simulations of the classes of neurons that are considered responsible for generating movement patterns. The locomotor circuits, modeled as a spiking neural network of adaptive leaky integrate-and-fire neurons, are coupled to a three-dimensional mechanical model of a salamander with realistic physical parameters and simulated muscles.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!