Nicole A. Lazar

FUNCTIONAL BRAIN ABNORMALITIES ARE RELATED TO CLINICAL RECOVERY AND TIME TO RETURN-TO-PLAY IN ATHLETES

OBJECTIVE The relationship between athlete reports of symptoms, neurophysiological activation, and neuropsychological functioning is investigated in a sample of high school athletes. METHODS All athletes were evaluated using functional magnetic resonance imaging (fMRI), a computer-based battery of neurocognitive tests, and a subjective symptom scale. Athletes were evaluated within approximately 1 week of injury and again after clinical recovery using all assessment modalities. RESULTS This study found that abnormal fMRI results during the first week of recovery predicted clinical recovery. As a group, athletes who demonstrated hyperactivation on fMRI scans at the time of their first fMRI scan demonstrated a more prolonged clinical recovery than athletes who did not demonstrate hyperactivation at the time of their first fMRI scan. CONCLUSION These results demonstrate the relationship between neurophysiological, neuropsychological, and subjective symptom data in a relatively large sample composed primarily of concussed high school athletes. fMRI represents an important evolving technology for the understanding of brain recovery after concussion and may help shape return-to-play guidelines in the future.

Multi-objective optimal experimental designs for event-related fMRI studies

In this article, we propose an efficient approach to find optimal experimental designs for event-related functional magnetic resonance imaging (ER-fMRI). We consider multiple objectives, including estimating the hemodynamic response function (HRF), detecting activation, circumventing psychological confounds and fulfilling customized requirements. Taking into account these goals, we formulate a family of multi-objective design criteria and develop a genetic-algorithm-based technique to search for optimal designs. Our proposed technique incorporates existing knowledge about the performance of fMRI designs, and its usefulness is shown through simulations. Although our approach also works for other linear combinations of parameters, we primarily focus on the case when the interest lies either in the individual stimulus effects or in pairwise contrasts between stimulus types. Under either of these popular cases, our algorithm outperforms the previous approaches. We also find designs yielding higher estimation efficiencies than m-sequences. When the underlying model is with white noise and a constant nuisance parameter, the stimulus frequencies of the designs we obtained are in good agreement with the optimal stimulus frequencies derived by Liu and Frank, 2004, NeuroImage 21: 387-400. In addition, our approach is built upon a rigorous model formulation.

Understanding the role of facial asymmetry in human face identification

Face recognition has important applications in forensics (criminal identification) and security (biometric authentication). The problem of face recognition has been extensively studied in the computer vision community, from a variety of perspectives. A relatively new development is the use of facial asymmetry in face recognition, and we present here the results of a statistical investigation of this biometric. We first show how facial asymmetry information can be used to perform three different face recognition tasks—human identification (in the presence of expression variations), classification of faces by expression, and classification of individuals according to sex. Initially, we use a simple classification method, and conduct a feature analysis which shows the particular facial regions that play the dominant role in achieving these three entirely different classification goals. We then pursue human identification under expression changes in greater depth, since this is the most important task from a practical point of view. Two different ways of improving the performance of the simple classifier are then discussed: (i) feature combinations and (ii) the use of resampling techniques (bagging and random subspaces). With these modifications, we succeed in obtaining near perfect classification results on a database of 55 individuals, a statistically significant improvement over the initial results as seen by hypothesis tests of proportions.

Selection of Working Correlation Structure in Generalized Estimating Equations via Empirical Likelihood

Generalized estimating equations (GEE) are a popular class of models for analyzing discrete longitudinal data, and do not require the specification of a full likelihood. The GEE estimator for the regression parameter will be the most efficient if the working correlation matrix is correctly specified. Hence it is desirable to choose a working correlation matrix that is the closest to the underlying structure among a set of working structures. In the absence of a parametric likelihood, traditional likelihood-based model selection methods cannot be used for comparing GEE models. Combining the reliability of nonparametric methods with the flexibility and effectiveness of likelihood approaches, empirical likelihood (EL) has the potential to become a model selection tool for GEE. We propose an EL approach to select the working correlation structure in GEE. Our approach is compared to existing methods based on quasi-likelihood or resampling procedures. The effectiveness of the proposed method is demonstrated by simulations. Supplemental materials for this article are available online.

A Capstone Course for Undergraduate Statistics Majors

Many undergraduate statistics students receive limited exposure to real data and the challenges of real data analysis. To help improve our undergraduate program at the University of Georgia, we developed a Statistics Capstone Course. The course has three main threads: (1) teaching advanced/modern statistical methods to undergraduate statistics students; (2) giving these students an intensive, year-long data-analysis experience; and (3) providing the students with an opportunity to improve their written and oral communication skills. In this article, we describe the philosophy behind the Capstone Course, detail its implementation, and informally evaluate the success of our endeavor.

Geostatistical analysis in clustering fMRI time series

Clustering of functional magnetic resonance imaging (fMRI) time series--either directly or through characteristic features such as the cross-correlation with the experimental protocol signal--has been extensively used for the identification of active regions in the brain. Both approaches have drawbacks; clustering of the time series themselves may identify voxels with similar temporal behavior that is unrelated to the stimulus, whereas cross-correlation requires knowledge of the stimulus presentation protocol. In this paper we propose the use of autocorrelation structure instead--an idea borrowed from geostatistics; this approach does not suffer from the deficits associated with previous clustering methods. We first formalize the traditional classification methods as three steps: feature extraction, choice of classification metric and choice of classification algorithm. The use of different characteristics to effect the clustering (cross-correlation, autocorrelation, and so forth) relates to the first of these three steps. We then demonstrate the efficacy of autocorrelation clustering on a simple visual task and on resting data. A byproduct of our analysis is the finding that masking prior to clustering, as is commonly done, may degrade the quality of the discovered clusters, and we offer an explanation for this phenomenon.

Incorporating spatial dependence into Bayesian multiple testing of statistical parametric maps in functional neuroimaging.

The analysis of functional neuroimaging data often involves the simultaneous testing for activation at thousands of voxels, leading to a massive multiple testing problem. This is true whether the data analyzed are time courses observed at each voxel or a collection of summary statistics such as statistical parametric maps (SPMs). It is known that classical multiplicity corrections become strongly conservative in the presence of a massive number of tests. Some more popular approaches for thresholding imaging data, such as the Benjamini-Hochberg step-up procedure for false discovery rate control, tend to lose precision or power when the assumption of independence of the data does not hold. Bayesian approaches to large scale simultaneous inference also often rely on the assumption of independence. We introduce a spatial dependence structure into a Bayesian testing model for the analysis of SPMs. By using SPMs rather than the voxel time courses, much of the computational burden of Bayesian analysis is mitigated. Increased power is demonstrated by using the dependence model to draw inference on a real dataset collected in a fMRI study of cognitive control. The model also is shown to lead to improved identification of neural activation patterns known to be associated with eye movement tasks.

The neural correlates of habituation of response to startling tactile stimuli presented in a functional magnetic resonance imaging environment

Functional magnetic resonance imaging (fMRI) provides a means of identifying neural circuitry associated with startle and its modulation in humans. Twelve subjects who demonstrated eyeblink startle in the laboratory were recruited for an fMRI study in which they were scanned while presented with two identical runs consisting of alternating blocks of no stimuli and startling tactile stimuli. Together, behavioral and imaging data are consistent with a pattern of general cortical and thalamic activation induced by startling stimuli that shows habituation both across and within runs. From Run 1 to Run 2, both the eyeblink amplitude and the fMRI signal decreased. Within Run 1, there was a graded decrease in eyeblink amplitude and whole-brain fMRI signal across blocks of startling stimuli. A similar graded decrease was observed in the thalamus signal, as well. Thus, startling tactile stimuli initially induce widespread cortical and thalamic activity, perhaps mediated by the reticular activating system. The activity then habituates in a graded fashion with repeated presentations of the stimuli.

Sparse geostatistical analysis in clustering fMRI time series

Clustering is used in fMRI time series data analysis to find the active regions in the brain related to a stimulus. However, clustering algorithms usually do not work well for ill-balanced data, i.e., when only a small proportion of the voxels in the brain respond to the stimulus. This is the typical situation in fMRI--most voxels do not, in fact, respond to the specific task. We propose a new method of sparse geostatistical analysis in clustering, which first uses sparse principal component analysis (SPCA) to perform data reduction, followed by geostatistical clustering. The proposed method is model-free and data-driven; in particular it does not require prior knowledge of the hemodynamic response function, nor of the experimental paradigm. Our data analysis shows that the spatial and temporal structures of the task-related activation produced by our new approach are more stable compared with other methods (e.g., GLM analysis with geostatistical clustering). Sparse geostatistical analysis appears to be a promising tool for exploratory clustering of fMRI time series.

A Meta-Analysis of fMRI Activation Differences during Episodic Memory in Alzheimer's Disease and Mild Cognitive Impairment

Functional MRI (fMRI) has the potential to be used as a tool to detect biomarkers related to classifying Alzheimers disease (AD) and its prodromal stage, mild cognitive impairment (MCI). Previous meta‐analyses suggest that during episodic memory tasks, MCI patients exhibit hyperactivation in the medial temporal lobe (MTL) while AD patients exhibit hypoactivation, compared to healthy older adults (HOAs). However, these previous studies have methodological weaknesses that limit the generalizability of the results. This quantitative meta‐analysis re‐examines the activation associated with episodic memory in AD and MCI as compared to cognitively normal populations to assess these commonly cited activation differences. A whole‐brain activation likelihood estimation based meta‐analysis was conducted on fMRI studies that examined episodic memory in HOA (n = 200), MCI (n = 131), and AD populations (n = 89; total n = 409). Diffuse activation was exhibited in the HOA sample, while activation was more limited in the clinical populations. Additionally, the HOA sample showed more activation in the right hippocampus compared to the AD sample. The MCI studies showed greater activation in the cerebellum compared to the HOA sample, potentially indicating a compensatory mechanism for verbal encoding. MTL hypoactivation in the AD sample is consistent with previous studies, but more evidence of MCI hyperactivation is needed before considering MTL activation as an early biomarker for the AD disease process.