Biomedical subjects

Yan Zhuo

Publications and source records attributed to Yan Zhuo.

4 recordsLinked to original sources

PubMed2003

Contributions of the visual ventral pathway to long-range apparent motion.

Objects displaced intermittently across the visual field will nonetheless give an illusion of continuous motion [called apparent motion (AM)] under many common conditions. It is believed that form perception is of minor importance in determining AM, and that AM is mediated by motion-sensitive areas in the "where" pathway of the cortex. However, form and motion typically interact in specific ways when natural objects move through the environment. We used functional magnetic resonance imaging to measure cortical activation to long-range AM, compared to short-range AM and flicker, while we varied stability of structural differences between forms. Long-range AM activated the anterior-temporal lobe in the visual ventral pathway, and the response varied according to the form stability. The results suggest that long-range AM is associated with neural systems for form perception.

Brain Mapping↗

PubMed2003

Spatiotemporal activation of the two visual pathways in form discrimination and spatial location: a brain mapping study.

To address the question of the relationship between the two visual pathways, a ventral stream for object and form vision and a dorsal stream for spatial and motion vision, we measured the spatiotemporal activation patterns in the two pathways responding to an integrated visuospatial task to which form discrimination and spatial location were assigned simultaneously. The two cognitive components of form discrimination and spatial location were interwoven in the task; however, the fMRI data demonstrated that such a task still activated both ventral GTi/GF (the inferior temporal gyrus/the fusiform gyrus) and dorsal Ga/PCu (the angular gyrus/Precuneus), which are supposed to mediate form discrimination and spatial location, respectively. In addition, the source waveforms of the fMRI foci based on the source analysis of the fMRI-seeded dipole modeling and the moving dipole modeling indicated that in responding to the task combining simultaneously form perception and spatial location, the activity in Ga/PCu begins earlier than that in GTi/GF, but it peaks later and lasts longer.

Adult↗

PubMed2003

Analysis of fMRI data by blind separation of data in a tiny spatial domain into independent temporal component.

Independent Component Analysis (ICA) is a promising tool for the analysis of functional magnetic resonance imaging (fMRI) time series. In these studies, mostly assumed is a spatially independent component map of fMRI data (spatial ICA). In this paper, we assume that the temporal courses of the signal and noises are independent within a Tiny spatial domain (temporal ICA). Then with fast-ICA algorithm, spatially neighboring fMRI data were blindly separated into several temporal courses and were preassumed to be formed by a signal time course and several noise time courses where the signal has the largest correlation coefficient with the reference signal. The final functional imaging was completed for the signals obtained from each voxel. Simulations showed that compared with the spatial ICA method, the new temporal ICA method is more effective than the spatial ICA in detecting weak signal in a fMRI dataset. As background noise, the simulations include simulated Gaussian noise and fMRI data without stimulation. Finally, vivo fMRI tests showed that the excited areas evoked by a visual stimuli are mainly in the region of the primary visual cortex and that evoked by auditory stimuli are mainly in the region of the primary temporal cortex.

Algorithms↗

PubMed2002

[A method based on independent component analysis for processing fMRI data].

Independent component analysis (ICA) is a new technique in statistical signal processing to extract independent components from multidimensional measurements of mixed signals. In this paper, for the processing of functional magnetic resonance imaging(fMRI) data, two signals of near voxels are used as the mixed signals and are separated by ICA. The correlation coefficients between the reference signal and the separated signals are calculated and those voxels whose correlation coefficients are greater than a threshold are considered to be the activated voxels by the stimulation, and so the functional localization of the stimulation is completed. The validity of the method was primarily proved by trial of real brain functional magnetic resonance imaging data.

Algorithms↗