Purpose: This study aimed to explore the underlying mechanisms of the observed visuomotor deficit in amblyopia.
Methods: Twenty-four amblyopic (25.8 ± 3.8 years; 15 males) and 22 normal participants (25.8 ± 2.1 years; 8 males) took part in the study. The participants were instructed to continuously track a randomly moving Gaussian target on a computer screen using a mouse. In experiment 1, the participants performed the tracking task at six different target sizes. In experiments 2 and 3, they were asked to track a target with the contrast adjusted to individual's threshold. The tracking performance was represented by the kernel function calculated as the cross-correlation between the target and mouse displacements. The peak, latency, and width of the kernel were extracted and compared between the two groups.
Results: In experiment 1, target size had a significant effect on the kernel peak (F(1.649, 46.170) = 200.958, P = 4.420 × 10-22). At the smallest target size, the peak in the amblyopic group was significantly lower than that in the normal group (0.089 ± 0.023 vs. 0.107 ± 0.020, t(28) = -2.390, P = 0.024) and correlated with the contrast sensitivity function (r = 0.739, P = 0.002) in the amblyopic eyes. In experiments 2 and 3, with equally visible stimuli, there were still differences in the kernel between the two groups (all Ps < 0.05).
Conclusions: When stimulus visibility was compensated, amblyopic participants still showed significantly poorer tracking performance.
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http://dx.doi.org/10.1167/iovs.65.5.7 | DOI Listing |
Comput Biol Med
January 2025
Department of Electrical and Computer Engineering, University of Florida, Gainesville, FL, 32610, United States; Department of Medicine, University of Florida, Gainesville, FL, 32610, United States; Department of Health Outcomes and Biomedical Informatics, University of Florida, Gainesville, FL, 32610, United States; Intelligent Clinical Care Center, University of Florida, Gainesville, FL, 32610, United States. Electronic address:
Retinal image registration is essential for monitoring eye diseases and planning treatments, yet it remains challenging due to large deformations, minimal overlap, and varying image quality. To address these challenges, we propose RetinaRegNet, a multi-stage image registration model with zero-shot generalizability across multiple retinal imaging modalities. RetinaRegNet begins by extracting image features using a pretrained latent diffusion model.
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Neurosurgical Simulation and Artificial Intelligence Learning Centre, Department of Neurology and Neurosurgery, Montreal Neurological Institute, McGill University, Montreal , Quebec , Canada.
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View Article and Find Full Text PDFJ Appl Physiol (1985)
January 2025
Department of Electrical Engineering and Automation, Aalto University, Espoo, Finland.
Disruption of the blood supply to a limb in conjunction with active movement boosts muscle growth, aids in rehabilitation, and allows controlled exploration of the sensorimotor system. Yet, the underlying neuromechanical changes have not been observed in great detail. This study aims to report the acute neuromuscular effects of temporary blood flow restriction (BFR) through behavioral changes at the level of motor units (MUs) using high-density surface electromyography on the abductor digiti minimi muscle during 20 trapezoidal and sinusoidal isometric force tracking tasks (5 pre-BFR, 5 during BFR, and 10 post-BFR).
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Divison of Arrhythmia, Cardiology and Vascular Department, St. David's Medical Center, Austin, TX, USA.
Background: The relationship between premature ventricular contractions (PVC) and right ventricular (RV) function is not widely known. Left ventricular (LV) dysfunction due to PVC is known as PVC-induced cardiomyopathy (PIC) and suppressing the PVC substrate would improve LV function. The effect of PVC ablation on changes in RV function in patients with subtle RV subclinical dysfunction remains unknown.
View Article and Find Full Text PDFRev Sci Instrum
January 2025
State Key Laboratory of Particle Detection and Electronics, University of Science and Technology of China, Hefei 230026, China.
Long-Time Coherent Integration (LTCI) utilizes digital integration to combine multiple coherent cycles, thereby improving the signal-to-noise ratio (SNR). Our previous work introduced single-bit LTCI, an approach optimized for FPGA implementation, but faced challenges of output saturation at high SNR levels and inherent limitations in SNR gain (SNRG), which are insufficient for certain applications. This paper presents a threshold tracking method that improves the performance of single-bit LTCI in high-SNR scenarios.
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