Right hemispheric damage (RHD) caused by strokes often induce attentional disorders such as hemispatial neglect. Most patients with neglect over time have a reduction in their ipsilesional spatial attentional bias. Despite this improvement in spatial bias, many patients remain disabled. The cause of this chronic disability is not fully known, but even in the absence of a directional spatial attentional bias, patients with RHD may have an impaired ability to accurately and precisely allocate their spatial attention. This inaccuracy and variable directional allocation of spatial attention may be revealed by repeated performance on a spatial attentional task, such as line bisection (LBT). Participants with strokes of their right versus left (LHD) hemisphere along with healthy controls (HC) performed 24 consecutive trials of 24 cm horizontal line bisections. A vector analysis of the magnitude and direction of deviations from midline, as well as their standard deviations (SD), were calculated. The results demonstrated no significant difference between the LHD, RHD and HC groups in overall spatial bias (mean bisection including magnitude and direction); however, the RHD group had a significantly larger variability of their spatial errors (SD), and made larger errors (from midline) than did the LHD and HC groups. There was a curvilinear relationship between the RHD participants' performance variability and their severity of their inaccuracy. Therefore, when compared to HC and LHD, the RHD subjects' performance on the LBT is more variable and inaccurate.
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http://dx.doi.org/10.1017/S1355617715000338 | 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.
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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.
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