Biomedical subjects

Nicole A Lazar

Publications and source records attributed to Nicole A Lazar.

5 recordsLinked to original sources

PubMed2004

Estimation and classification of fMRI hemodynamic response patterns.

In this paper, we propose an approach to modeling functional magnetic resonance imaging (fMRI) data that combines hierarchical polynomial models, Bayes estimation, and clustering. A cubic polynomial is used to fit the voxel time courses of event-related design experiments. The coefficients of the polynomials are estimated by Bayes estimation, in a two-level hierarchical model, which allows us to borrow strength from all voxels. The voxel-specific Bayes polynomial coefficients are then transformed to the times and magnitudes of the minimum and maximum points on the hemodynamic response curve, which are in turn used to classify the voxels as being activated or not. The procedure is demonstrated on real data from an event-related design experiment of visually guided saccades and shown to be an effective alternative to existing methods.

Bayes Theorem↗

PubMed2004

Assessing the sensitivity of fMRI group maps.

Group maps created from individual functional maps provide useful summaries of patterns of brain activation. Different methods for combining information have been proposed in the statistical literature and have been recently applied to fMRI data. Since these group maps are statistics, it is natural to ask how robust they are, that is, are they sensitive to the effects of unusual subjects? "Unusual" might be in terms of extent, location, or strength of activation. Our approach in this paper is to jackknife group maps formed by different combining procedures; the jackknife method, which involves deleting each observation (subject) in turn and recalculating the statistic (the group map), is commonly used for the purpose of assessing sensitivity. We examine the theoretical properties of four combining methods. In addition, via a collection of measures defined on the difference between group maps based on the entire sample and based on the jackknifed samples, we evaluate the robustness of these same methods on data from an fMRI experiment. Results indicate that there is a type of tradeoff in the combining techniques we consider, between robustness and conservativeness: methods that are liberal, in that they allow for the discovery of many active voxels, tend also to be more sensitive to the influences of individual subjects.

Aging↗

PubMed2002

Thresholding of statistical maps in functional neuroimaging using the false discovery rate.

Finding objective and effective thresholds for voxelwise statistics derived from neuroimaging data has been a long-standing problem. With at least one test performed for every voxel in an image, some correction of the thresholds is needed to control the error rates, but standard procedures for multiple hypothesis testing (e.g., Bonferroni) tend to not be sensitive enough to be useful in this context. This paper introduces to the neuroscience literature statistical procedures for controlling the false discovery rate (FDR). Recent theoretical work in statistics suggests that FDR-controlling procedures will be effective for the analysis of neuroimaging data. These procedures operate simultaneously on all voxelwise test statistics to determine which tests should be considered statistically significant. The innovation of the procedures is that they control the expected proportion of the rejected hypotheses that are falsely rejected. We demonstrate this approach using both simulations and functional magnetic resonance imaging data from two simple experiments.

Adult↗

PubMed2002

Combining brains: a survey of methods for statistical pooling of information.

More than one subject is scanned in a typical functional brain imaging experiment. How can the scientist make best use of the acquired data to map the specific areas of the brain that become active during the performance of different tasks? It is clear that we can gain both scientific and statistical power by pooling the images from multiple subjects; furthermore, for the comparison of groups of subjects (clinical patients vs healthy controls, children of different ages, left-handed people vs right-handed people, as just some examples), it is essential to have a "group map" to represent each population and to form the basis of a statistical test. While the importance of combining images for these purposes has been recognized, there has not been an organized attempt on the part of neuroscientists to understand the different statistical approaches to this problem, which have various strengths and weaknesses. In this paper we review some popular methods for combining information, and demonstrate the surveyed techniques on a sample data set. Given a combination of brain images, the researcher needs to interpret the result and decide on areas of activation; the question of thresholding is critical here and is also explored.

Adult↗

PubMedDate not supplied

Maturation of cognitive processes from late childhood to adulthood.

To characterize cognitive maturation through adolescence, processing speed, voluntary response suppression, and spatial working memory were measured in 8- to 30-year-old (N = 245) healthy participants using oculomotor tasks. Development progressed with a steep initial improvement in performance followed by stabilization in adolescence. Adult-level mature performance began at approximately 15, 14, and 19 years of age for processing speed, response inhibition, and working memory, respectively. Although processes developed independently, processing speed influenced the development of working memory whereas the development of response suppression and working memory were interdependent. These results indicate that processing speed, voluntary response suppression, and working memory mature through late childhood and into adolescence. How brain maturation specific to adolescence may support cognitive maturation is discussed.

Adolescent↗