Biomedical subjects

Anqi Qiu

Publications and source records attributed to Anqi Qiu.

5 recordsLinked to original sources

PubMed2006

Diffeomorphic metric surface mapping in subregion of the superior temporal gyrus.

This paper describes the application of large deformation diffeomorphic metric mapping to cortical surfaces based on the shape and geometric properties of subregions of the superior temporal gyrus in the human brain. The anatomical surfaces of the cortex are represented as triangulated meshes. The diffeomorphic matching algorithm is implemented by defining a norm between the triangulated meshes, based on the algorithms of Vaillant and Glaunès. The diffeomorphic correspondence is defined as a flow of the extrinsic three dimensional coordinates containing the cortical surface that registers the initial and target geometry by minimizing the norm. The methods are demonstrated in 40 high-resolution MRI cortical surfaces of planum temporale (PT) constructed from subsets of the superior temporal gyrus (STG). The effectiveness of the algorithm is demonstrated via the Euclidean positional distance, distance of normal vectors, and curvature before and after the surface matching as well as the comparison with a landmark matching algorithm. The results demonstrate that both the positional and shape variability of the anatomical configurations are being represented by the diffeomorphic maps.

Adult↗

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↗

PubMed2006

Smooth functional and structural maps on the neocortex via orthonormal bases of the Laplace-Beltrami operator.

Functional and structural maps, such as a curvature, cortical thickness, and functional magnetic resonance imaging (MRI) maps, indexed over the local coordinates of the cortical manifold play an important role in neuropsychiatric studies. Due to the highly convoluted nature of the cerebral cortex and image quality, these functions are generally uninterpretable without proper methods of association and smoothness onto the local coordinate system. In this paper, we generalized the spline smoothing problem (Wahba, 1990) from a sphere to any arbitrary two-dimensional (2-D) manifold with boundaries. We first seek a numerical solution to orthonormal basis functions of the Laplace-Beltrami (LB) operator with Neumann boundary conditions for a 2-D manifold M then solve the spline smoothing problem in a reproducing kernel Hilbert space (r.k.h.s.) of real-valued functions on manifold M with kernel constructed from the basis functions. The explicit discrete LB representation is derived using the finite element method calculated directly on the manifold coordinates so that finding discrete LB orthonormal basis functions is equivalent to solving an algebraic eigenvalue problem. And then smoothed functions in r.k.h.s can be represented as a linear combination of the basis functions. We demonstrate numerical solutions of spherical harmonics on a unit sphere and brain orthonormal basis functions on a planum temporale manifold. Then synthetic data is used to quantify the goodness of the smoothness compared with the ground truth and discuss how many basis functions should be incorporated in the smoothing. We present applications of our approach to smoothing sulcal mean curvature, cortical thickness, and functional statistical maps on submanifolds of the neocortex.

Algorithms↗

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↗