Human psychophysical and electrophysiological evidence suggests at least two separate visual motion pathways, one tuned to a lower and one tuned to a broader and partly overlapping range of higher speeds. It remains unclear whether these two different channels are represented by different cortical areas or by sub-populations within a single area. We recorded evoked potentials at 59 scalp locations to the onset of a slow (3.5 degrees /s) and fast (32 degrees /s) moving test pattern, preceded by either a slow or fast adapting pattern that moved in either the same direction or opposite to the test motion. Baseline potentials were recorded for slow and fast moving test patterns after adaptation to a static pattern. Comparison of adapted responses with baseline responses revealed that the N2 peak around 180 ms after test stimulus onset was modulated by the preceding adaptation. This modulation depended on both direction and speed. Source localization of baseline potentials as well as direction-independent motion adaptation revealed cortical areas activated by fast motion to be more dorsal, medial and posterior compared with neural structures underlying slow motion processing. For both speeds, the direction-dependent component of this adaptation modulation occurred in the same area, located significantly more dorsally compared with neural structures that were adapted in a direction-independent manner. These results demonstrate for the first time the cortical separation of more ventral areas selectively activated by visual motion at low speeds (and not high speeds) and dorsal motion-sensitive cortical areas that are activated by both high and low speeds.
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Biomed Phys Eng Express
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Children's Hospital of Eastern Switzerland, Claudiusstrasse 6, St.Gallen, 9006, SWITZERLAND.
Mapping the myomagnetic field of a straight and easily accessible muscle after electrical stimulation using triaxial optically pumped magnetometers (OPMs) to assess potential benefits for magnetomyography (MMG). Approach: Six triaxial OPMs were arranged in two rows with three sensors each along the abductor digiti minimi (ADM) muscle. The upper row of sensors was inclined by 45° with respect to the lower row and all sensors were aligned closely to the skin surface without direct contact.
View Article and Find Full Text PDFPLoS One
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School of Information and Communication Engineering, Beijing University of Information Science and Technology, Bei Jing City, China.
To enhance the intelligent classification of computer vulnerabilities and improve the efficiency and accuracy of network security management, this study delves into the application of a comprehensive classification system that integrates the Memristor Neural Network (MNN) and an improved Temporal Convolutional Neural Network (TCNN) in network security management. This system not only focuses on the precise classification of vulnerability data but also emphasizes its core role in strengthening the network security management framework. Firstly, the study designs and implements a neural network model based on memristors.
View Article and Find Full Text PDFPLoS One
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School of Business Economics, European Union University, Montreux, Switzerland.
As people's material living standards continue to improve, the types and quantities of household garbage they generate rapidly increase. Therefore, it is urgent to develop a reasonable and effective method for garbage classification. This is important for resource recycling and environmental improvement and contributes to the sustainable development of production and the economy.
View Article and Find Full Text PDFJ Mater Chem B
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Tissue regeneration after a wound occurs through three main overlapping and interrelated stages namely inflammatory, proliferative, and remodelling phases, respectively. The inflammatory phase is key for successful tissue reconstruction and triggers the proliferative phase. The macrophages in the non-healing wounds remain in the inflammatory loop, but their phenotypes can be changed interactions with nanofibre-based scaffolds mimicking the organisation of the native structural support of healthy tissues.
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Background: Sudden sensorineural hearing loss (SSNHL) is associated with abnormal changes in the brain's central nervous system. Previous studies on the brain networks of SSNHL have primarily focused on functional connectivity within the brain. However, in addition to functional connectivity, structural connectivity also plays a crucial role in brain networks.
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