Biomedical subjects

Anqi Qiu

Publications and source records attributed to Anqi Qiu.

3 recordsLinked to original sources

PubMed2006

Estimating linear cortical magnification in human primary visual cortex via dynamic programming.

Human primary visual cortex is organized retinotopically, with adjacent locations in cortex representing adjacent locations on the retina. The spatial sampling in cortex is highly nonuniform: the amount of cortex devoted to a unit area of retina decreases with increasing retinal eccentricity. This sampling property can be quantified by the linear cortical magnification factor, which is expressed in terms of millimeters of cortex per degree of visual angle. In this paper, we present a new method using dynamic programming and fMRI retinotopic eccentricity mapping to estimate the linear cortical magnification factor in human primary visual cortex (V1). We localized cortical activity while subjects viewed each of seven stationary contrast- reversing radial checkerboard rings of equal thickness that tiled the visual field from 1.62 to 12.96 degrees of eccentricity. Imaging data from all epochs of each ring were contrasted with data from fixation epochs on a subject-by-subject basis. The resulting t statistic maps were then superimposed on a local coordinate system constructed from the gray/white matter boundary surface of each individual subject's occipital lobe, separately for each ring. Smoothed maps of functional activity on the cortical surface were constructed using orthonormal bases of the Laplace-Beltrami operator that incorporate the geometry of the cortical surface. This allowed us to stably track the ridge of maximum activation due to each ring via dynamic programming optimization over all possible paths on the cortical surface. We estimated the linear cortical magnification factor by calculating geodesic distances between activation ridges on the cortical surface in a population of five normal subjects. The reliability of these estimates was assessed by comparing results based on data from one quadrant to those based on data from the full hemifield along with a split-half reliability analysis.

Brain Mapping↗

PubMed2005

A stochastic model for studying the laminar structure of cortex from MRI.

The human cerebral cortex is a laminar structure about 3 mm thick, and is easily visualized with current magnetic resonance (MR) technology. The thickness of the cortex varies locally by region, and is likely to be influenced by such factors as development, disease and aging. Thus, accurate measurements of local cortical thickness are likely to be of interest to other researchers. We develop a parametric stochastic model relating the laminar structure of local regions of the cerebral cortex to MR image data. Parameters of the model include local thickness, and statistics describing white, gray and cerebrospinal fluid (CSF) image intensity values as a function of the normal distance from the center of a voxel to a local coordinate system anchored at the gray/white matter interface. Our fundamental data object, the intensity-distance histogram (IDH), is a two-dimensional (2-D) generalization of the conventional 1-D image intensity histogram, which indexes voxels not only by their intensity value, but also by their normal distance to the gray/white interface. We model the IDH empirically as a marked Poisson process with marking process a Gaussian random field model of image intensity indexed against normal distance. In this paper, we relate the parameters of the IDH model to the local geometry of the cortex. A maximum-likelihood framework estimates the parameters of the model from the data. Here, we show estimates of these parameters for 10 volumes in the posterior cingulate, and 6 volumes in the anterior and posterior banks of the central sulcus. The accuracy of the estimates is quantified via Cramer-Rao bounds. We believe that this relatively crude model can be extended in a straightforward fashion to other biologically and theoretically interesting problems such as segmentation, surface area estimation, and estimating the thickness distribution in a variety of biologically relevant contexts.

Algorithms↗

PubMed2003

Gabor analysis of auditory midbrain receptive fields: spectro-temporal and binaural composition.

The spectro-temporal receptive field (STRF) is a model representation of the excitatory and inhibitory integration area of auditory neurons. Recently it has been used to study spectral and temporal aspects of monaural integration in auditory centers. Here we report the properties of monaural STRFs and the relationship between ipsi- and contralateral inputs to neurons of the central nucleus of cat inferior colliculus (ICC) of cats. First, we use an optimal singular-value decomposition method to approximate auditory STRFs as a sum of time-frequency separable Gabor functions. This procedure extracts nine physiologically meaningful parameters. The STRFs of approximately 60% of collicular neurons are well described by a time-frequency separable Gabor STRF model, whereas the remaining neurons exhibited obliquely oriented or multiple excitatory/inhibitory subfields that require a nonseparable Gabor fitting procedure. Parametric analysis reveals distinct spectro-temporal tradeoffs in receptive field size and modulation filtering resolution. Comparisons between an identical model used to study spatio-temporal integration areas of visual neurons further shows that auditory and visual STRFs share numerous structural properties. We then use the Gabor STRF model to compare quantitatively receptive field properties of contra- and ipsilateral inputs to the ICC. We show that most interaural STRF parameters are highly correlated bilaterally. However, the spectral and temporal phases of ipsi- and contralateral STRFs often differ significantly. This suggests that activity originating from each ear share various spectro-temporal response properties such as their temporal delay, bandwidth, and center frequency but have shifted or interleaved patterns of excitation and inhibition. These differences in converging monaural receptive fields expand binaural processing capacity beyond interaural time and intensity aspects and may enable colliculus neurons to detect disparities in the spectro-temporal composition of the binaural input.

Acoustic Stimulation↗