To investigate facilitatory and inhibitory processes during selective attention among adults with good (n=17) and poor (n=14) phonological decoding skills, a go/nogo flanker task was completed while EEG was recorded. Participants responded to a middle target letter flanked by compatible or incompatible flankers. The target was surrounded by a small or large circular cue which was presented simultaneously or 500ms prior. Poor decoders showed a greater RT cost for incompatible stimuli preceded by large cues and less RT benefit for compatible stimuli. Poor decoders also showed reduced modulation of ERPs by cue-size at left hemisphere posterior sites (N1) and by flanker compatibility at right hemisphere posterior sites (N1) and frontal sites (N2), consistent with processing differences in fronto-parietal attention networks. These findings have potential implications for understanding the relationship between spatial attention and phonological decoding in dyslexia.
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http://dx.doi.org/10.1016/j.bandl.2015.10.008 | DOI Listing |
Med Biol Eng Comput
January 2025
Anhui BioX-Vision Biological Technology Co., Ltd, Hefei, 230031, Anhui, China.
The identification and categorization of circulating tumor cells (CTCs) in peripheral blood are imperative for advancing cancer diagnostics and prognostics. The intricacy of various CTCs subtypes, coupled with the difficulty in developing exhaustive datasets, has impeded progress in this specialized domain. To date, no methods have been dedicated exclusively to overcoming the classification challenges of CTCs.
View Article and Find Full Text PDFFront Neurorobot
January 2025
School of Computer Science, Northwestern Polytechnical University, Xi'an, China.
Significant strides have been made in emotion recognition from Electroencephalography (EEG) signals. However, effectively modeling the diverse spatial, spectral, and temporal features of multi-channel brain signals remains a challenge. This paper proposes a novel framework, the Directional Spatial and Spectral Attention Network (DSSA Net), which enhances emotion recognition accuracy by capturing critical spatial-spectral-temporal features from EEG signals.
View Article and Find Full Text PDFObjective: To assist in the rapid clinical identification of brain tumor types while achieving segmentation detection, this study investigates the feasibility of applying the deep learning YOLOv5s algorithm model to the segmentation of brain tumor magnetic resonance images and optimizes and upgrades it on this basis.
Methods: The research institute utilized two public datasets of meningioma and glioma magnetic resonance imaging from Kaggle. Dataset 1 contains a total of 3,223 images, and Dataset 2 contains 216 images.
Front Rehabil Sci
January 2025
Department of Rehabilitation, Shiragikuen Hospital, Kochi, Japan.
A 69-year-old right-handed man, who initially suffered a stroke 8 years ago and experienced two recurrences since then, presented with right hemiplegia and left hemispatial neglect as a post-stroke syndrome in the chronic phase. This report demonstrates the use of active musical instrument playing with Musical Neglect Training (MNT®) to improve severe left-side neglect and activities of daily living (ADLs). In addition to physical and occupational therapy, individual MNT® was incorporated into the patient's rehabilitation plan to improve his hemispatial neglect.
View Article and Find Full Text PDFJ Bone Oncol
February 2025
School of Mathematics and Computer Science, Quanzhou Normal University, Quanzhou, 362001, China.
Objective: Segmenting and reconstructing 3D models of bone tumors from 2D image data is of great significance for assisting disease diagnosis and treatment. However, due to the low distinguishability of tumors and surrounding tissues in images, existing methods lack accuracy and stability. This study proposes a U-Net model based on double dimensionality reduction and channel attention gating mechanism, namely the DCU-Net model for oncological image segmentation.
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