Main navigation | Main content
HOME » PROGRAMS/ACTIVITIES » Annual Thematic Program
Fred L. Bookstein (University of Michigan)
New Applications of Geometry to Analysis of Brain Imagery
Recently a new methodological discipline, morphometrics, has emerged as a combination of geometry, computer science, statistics, and mathematical biology. The new techniques bridge two domains of mathematics usually applied separately in medical imaging: one, the domain of image acquisition, emphasizing physical noi se models and the associated signal-processing task, and the other, the domain of biometrical pattern analysis, emphasizing the ties of image information to the larger world of covariance structures, group averages, and scientific hypotheses . This talk will sketch the flavor of the new discipline in two applications to empirical investigations into ``endophrenology," the correlation of brain form w ith behavior in disease. One application is an algorithm for usefully parametrizin g stable localized shape difference in the presence of substantial biological variation. The other demonstrates a powerful modeling protocol for identifying "the midline of the corpus callosum," an approximately symmetric structure the shape of which turns out to be strongly informative about embryological conditi ons associated with the diagnosis of Fetal Alcohol Syndrome. In sketching these applications my talk will touch on a range of classical topics including Riemann ian geometry, analytic geometry of planes, interpolation theory, and singularity theory.
William Eddy (Carnegie Mellon University)
Combining Information Across Subjects
It has become common practice to average t statistics when pooling subjects in a series of fMRI experiments. This talk will decry this practice. I will also provide a brief review of other more preferable methods. I will specifically advocate standard meta-analytic methods when they are possible and Fisher's method when better methods are not available. A single common example will be used to illustrate each of the methods discussed.
Reflections on the Inverse EEG and MEG Problems
I discuss the problem of the three-dimensional reconstruction of the electrical activity of the brain from electroencephalography (EEG) and magnetoencephalography (MEG). I propose a variational approach based upon three main methods and ideas. The first one is the optimal control of systems governed by elliptic partial differential equations, the second is the regularization of the solutions while preserving the discontinuities (the edges), and the third one is the use of geometric information obtained from magnetic resonance images (MRI) to constrain the solutions in an anatomically "reasonable'' way. Some preliminary results of implementations of these ideas are presented.
Sheng He (University of Minnesota)
Visual Surfaces Representation in Human MT+ and SPL
Surface extraction is of great importance to visual function and visually guided action. Neurons in primate MT/V5 have been shown to be both direction and disparity selective. Using fMRI, we demonstrate that human MT+ respond more to two planes of oppositely moving random dots if the two planes are placed in different stereo depths. We also tested if the disparity sensitive areas are capable of encoding global surface structures. Results suggest that in addition to the MT+ areas, the Superior Parietal lobule (SPL) is most sensitive to the global structure of visual surface.
Monica Hurdal (Florida State University)
Quasi-conformal Flat Maps of the Human Cerebellum
There is great interest in trying to create flat maps of the cortical surface. It is believed that flat maps can assist in identifying and localizing functional foci obtained from PET and functional MRI data. Most current approaches attempt to minimize or reduce a combination of linear, areal and angular distortion between the flattened surface of the cortex and the original cortical sheet. However, it is impossible to flatten a surface without introducing linear and areal distortion. Nevertheless, the Riemann Mapping Theorem from mathematics states that angle-preserving (conformal) maps exist. A novel computational implementation will be discussed which creates conformal flat maps of the cortical surface using circle packings. This approach offers a number of advantages over existing approaches such as no cuts need to be introduced into the surface, the maps are mathematically unique, maps can be created in the Euclidean and hyperbolic planes and on a sphere, and canonical coordinate systems can be imposed on these maps.
Seong-Gi Kim/Kamil Ugurbil (University of Minnesota)
Are fMRI Responses Localized to Sub-millimeter Functional Structures?
Recently introduced fMRI techniques provide the capability of visualizing elevat ed neuronal activity with spatiotemporal specificity and resolution that has not be en previously available with other non-invasive methods. Such a capability is essential for a system level understanding of human brain function, which posses ses unique attributes that are distinct from animal models and cannot be investigate d with invasive techniques available for animal studies. Despite the indispensable role of the BOLD fMRI technique in mapping human brain function, ability of mapp ing sub-millimeter functional structures such as cortical columns is controversial. We have investigated spatial specificity and resolution of fMRI using well-established anesthetized animal models, rat forepaw stimulation and cat orientation column. We found that columnar structures can be mapped by using metabolism-based early negative BOLD signal or draining vessels-free perfusion-based fMRI.
Jack Lancaster (University of Texas Health Sciences Center at San Antonio)
Representative Brain Models for 3-D MR Brain Images
Our long-term goal is to develop 3-D brain models that retain consistent anatomical features of the group of brains from which they are derived. Such brain models could serve as representative brains for comparisons between groups of interest, i.e. testing for anatomical differences between normal and disease groups. An analysis method based on 3-D deformation fields is proposed for developing representative brain models. The hypothesis is that a target brain (our brain model) that represents the least deformation effort for a group is the best overall representative brain for the group. The process can be thought of as a two step procedure, where the first step is to find the best target brain within the group of brain images, followed by an optimization process that transforms this best target brain into an optimal brain model for the group. A target quality score to measure deformation effort and anatomical variability was devised to guide this processing. The complexity of the processing scheme is O(n^2) where n is the number of brains in the group and number of deformation fields to calculate. Even with high-speed regional warping methods such as octree spatial normalization this would be a lengthy process for large groups of brains. A fast algorithm (O(n)) was found to provide similar results. A review of the processing steps for the development of representative brain models will be presented.
Nicholas Lange (Laboratory for Statistical Neuroimaging and Molecular Pharmacology Laboratory, Department of Psychiatry, McLean Hospital and Harvard Medical School)
Analysis of Drug Effects on Spatial Patterns of Gene Expression in MR Neuroimaging and Microscopy
In this talk, I will discuss recent findings on automatic identification of c-fos gene expression in rat thalamus, amygdala and nucleus accumbens post administration of typical and atypical antipsychotics, a benzodiazepine and a sedative, all compared to a control condition. I will provide background for these types of experiments, including some current work in pharmacological magnetic resonance imaging (phMRI). Relevant quantitative analytical considerations lead one to question certain general methodological approaches taken currently in similar animal and human microscopy. Additional recent results will also be presented on one such issue: How to count neurons in human neocortex -- presenting a challenge to so-called ``unbiased" optical disector stereological methods.
Anthony Randal McIntosh (Rotman Research Institute - Baycrest Ctr, University of Toronto) http://psych.utoronto.ca/~mcintosh mcintosh@psych.utoronto.ca
Incoroporating Neurobiology into the Analysis of Brain Imaging Data
Many approaches to the analysis of brain imaging data have adopted conventional parametric statistics (e.g., t-test, ANOVA, MANOVA) for evaluation of brain imaging data. There is a great deal of information that can be lost with such approaches since they were developed for different purposes. We have recently applied methods that are more suited for analysis of systems that show characteristics similar to the brain, such as high interdependency and high dimensionality. I will present two of these methods, path analysis and partial least squares, and discuss the new directions that analytic approaches need to go in order to capitalize on the wealth of information brain imaging can provide.
Michael Miller (John Hopkins University)
Computational Anatomy: An Emerging Discipline
This talk with introduce geodesics on diffeomorphic transformations for anatomic al variation and on geometric and image transformation for growth and neoplasm development. Its role in image matching and measuring anatomical deformation wil l be shown through various example in brain imaging.
Partha Mitra (Bell Labs) mitra@bell-labs.com
A Frequency Localised Framework for Inference in fMRI
Although fMRI time series have been subjected to a very large number of studies, the noise models in use are generally unreliable and inaccurate, as shown by the general scepticism about P-values. We argue that since the noise sources in question, which are primarily physiological in origin, are well segregated in the frequency domain, most of the problems are obviated by confining the inference to the frequency range relevant to the hemodynamic response. We have developed and tested a methodology for statistical inference as well as for exploratory analysis based on projections of the time series data onto frequency localised basis sets, following the framework of multitaper spectral analysis. The associated statistical tests are both straightforward and provide somewhat more sensible P-values, since Gaussian distributional assumptions are more closely valid for narrowband processes due to the central limit theorem.
Charles A. Nelson (University of Minnesota), canelson@tc.umn.edu
A Cognitive Neuroscience Perspective on Memory Development
In this talk I will illustrate the various approaches we have adopted as a means of studying memory in infants and young children. Here I will draw from our work with both typically developing children and those who have might have incurred neurological insults as a means of examing the ontogeny of memory. Work using both event-related potentials and functional magnetic resoance imaging will be highlighted.
Stephen Strother (Minneapolis VA Medical Center)
The Quantitative Evaluation of Functional Neuroimaging Experiments: The NPAIRS Data Analysis Framework
We introduce the NPAIRS (Non-parametric Prediction, Activation, Influence and Reproducibility reSampling) data analysis framework for evaluating the interaction between activation tasks and the methodological choices involved in data acquisition, preprocessing, data-analysis model selection and associated software tools. NPAIRS provides a real-data driven alternative to simulations and ROC curves by examining the relationship between model prediction accuracy and activation image signal-to-noise ratios (SNR)--we plot training-test set predictions of the experimental design variables (e.g., scan state labels, covariates etc.; Morch et al.,1997, Hansen et al.,1999) versus the reproducibility SNR metrics in Strother et al. (1997). For a given spatial scale we propose that methodological choices should be optimized by maximizing the prediction accuracy and the reproducibility SNR of the extracted activation images. NPAIRS also provides a Z-score activation-image incorporating random subject effects for any data-analysis model and a measure of each subject's relative influence (Strother et al., 1998, 1999). We demonstrate NPAIRS using the wide range of activation signal-to-noise ratios and associated PAIR statistics obtained from [O-15]water PET studies of twelve age and sex matched groups performing different motor tasks (8 subjects/group). If time permits preliminary results using NPAIRS to rank the importance of within-subject alignment, temporal detrending and spatial smoothing in a fMRI task will also be presented.
Keith Worsley (McGill University)
A General Statistical Analysis for fMRI data
Many methods are available for the statistical analysis of fMRI data that range from a simple linear model for the response and a global autoregressive model for the temporal errors (SPM), to a more sophisticated non-linear model for the response with a local state space model for the temporal errors (Purdon, et al., 1998). We have written Matlab programs (http://www.bic.mni.mcgill.ca/users/keith) that seek a compromise between validity, generality, simplicity and execution speed. The method is based on linear models with local AR(p) errors. The AR(p) model is fitted via the Yule-Walker equations with a simple bias correction, then the parameters are regularized by spatial smoothing. The resulting effects are then combined across runs in the same session, across sessions in the same subject, and across subjects within a population by further linear models that perform a random effects analysis in which the residual degrees of freedom are increased using a form of regularization by spatial smoothing.
|
|
|
|
|