Ana S. Lukic

Dimensionality Estimation for Optimal Detection of Functional Networks in BOLD fMRI data

Estimation of the intrinsic dimensionality of fMRI data is an important part of data analysis that helps to separate the signal of interest from noise. We have studied multiple methods of dimensionality estimation proposed in the literature and used these estimates to select a subset of principal components that was subsequently processed by linear discriminant analysis (LDA). Using simulated multivariate Gaussian data, we show that the dimensionality that optimizes signal detection (in terms of the receiver operating characteristic (ROC) metric) goes through a transition from many dimensions to a single dimension as a function of the signal-to-noise ratio. This transition happens when the loci of activation are organized into a spatial network and the variance of the networked, task-related signals is high enough for the signal to be easily detected in the data. We show that reproducibility of activation maps is a metric that captures this switch in intrinsic dimensionality. Except for reproducibility, all of the methods of dimensionality estimation we considered failed to capture this transition: optimization of Bayesian evidence, minimum description length, supervised and unsupervised LDA prediction, and Steins unbiased risk estimator. This failure results in sub-optimal ROC performance of LDA in the presence of a spatially distributed network, and may have caused LDA to underperform in many of the reported comparisons in the literature. Using real fMRI data sets, including multi-subject group and within-subject longitudinal analysis we demonstrate the existence of these dimensionality transitions in real data.

Bayesian Kernel Methods for Analysis of Functional Neuroimages

We propose an approach to analyzing functional neuroimages in which (1) regions of neuronal activation are described by a superposition of spatial kernel functions, the parameters of which are estimated from the data and (2) the presence of activation is detected by means of a generalized likelihood ratio test (GLRT). Kernel methods have become a staple of modern machine learning. Herein, we show that these techniques show promise for neuroimage analysis. In an on-off design, we model the spatial activation pattern as a sum of an unknown number of kernel functions of unknown location, amplitude, and/or size. We employ two Bayesian methods of estimating the kernel functions. The first is a maximum a posteriori (MAP) estimation method based on a reversible-jump Markov-chain Monte-Carlo (RJMCMC) algorithm that searches for both the appropriate model complexity and parameter values. The second is a relevance vector machine (RVM), a kernel machine that is known to be effective in controlling model complexity (and thus discouraging overfitting). In each method, after estimating the activation pattern, we test for local activation using a GLRT. We evaluate the results using receiver operating characteristic (ROC) curves for simulated neuroimaging data and example results for real fMRI data. We find that, while RVM and RJMCMC both produce good results, RVM requires far less computation time, and thus appears to be the more promising of the two approaches.

A spatially robust ICA algorithm for multiple fMRI data sets

In this paper we derive an independent-component analysis (ICA) method for analyzing two or more data sets simultaneously. Our model extracts independent components common to all data sets and independent data-set-specific components. We use time-delayed autocorrelations to obtain independent signal components and base our algorithm on prediction analysis. We applied this method to functional brain mapping using functional magnetic resonance imaging (fMRI). The results of our 3-subject analysis demonstrate the robustness of the algorithm to the spatial misalignment intrinsic in multiple-subject fMRI data sets.

An ICA algorithm for analyzing multiple data sets

We derive an independent-component analysis (ICA) method for analyzing two or more data sets simultaneously. Our model permits there to be components individual to the various data sets, and others that are common to all the sets. We explore the assumed time autocorrelation of independent signal components and base our algorithm on prediction analysis. We illustrate the algorithm using a simple image separation example. Our aim is to apply this method to functional brain mapping using functional magnetic resonance imaging (fMRI).

Dissociation of Down syndrome and Alzheimer's disease effects with imaging.

Down Syndrome (DS) adults experience accumulation of Alzheimers disease (AD)–like amyloid plaques and tangles and a high incidence of dementia and could provide an enriched population to study AD‐targeted treatments. However, to evaluate effects of therapeutic intervention, it is necessary to dissociate the contributions of DS and AD from overall phenotype. Imaging biomarkers offer the potential to characterize and stratify patients who will worsen clinically but have yielded mixed findings in DS subjects.

PET Imaging of Tau Pathology and Relationship to Amyloid, Longitudinal MRI, and Cognitive Change in Down Syndrome: Results from the Down Syndrome Biomarker Initiative (DSBI)

BACKGROUND Adults with Down syndrome (DS) represent an enriched population for the development of Alzheimers disease (AD), which could aid the study of therapeutic interventions, and in turn, could benefit from discoveries made in other AD populations. OBJECTIVES 1) Understand the relationship between tau pathology and age, amyloid deposition, neurodegeneration (MRI and FDG PET), and cognitive and functional performance; 2) detect and differentiate AD-specific changes from DS-specific brain changes in longitudinal MRI. METHODS Twelve non-demented adults, ages 30 to 60, with DS were enrolled in the Down Syndrome Biomarker Initiative (DSBI), a 3-year, observational, cohort study to demonstrate the feasibility of conducting AD intervention/prevention trials in adults with DS. We collected imaging data with 18F-AV-1451 tau PET, AV-45 amyloid PET, FDG PET, and volumetric MRI, as well as cognitive and functional measures and additional laboratory measures. RESULTS All amyloid negative subjects imaged were tau-negative. Among the amyloid positive subjects, three had tau in regions associated with Braak stage VI, two at stage V, and one at stage II. Amyloid and tau burden correlated with age. The MRI analysis produced two distinct volumetric patterns. The first differentiated DS from normal (NL) and AD, did not correlate with age or amyloid, and was longitudinally stable. The second pattern reflected AD-like atrophy and differentiated NL from AD. Tau PET and MRI atrophy correlated with several cognitive and functional measures. CONCLUSIONS Tau accumulation is associated with amyloid positivity and age, as well as with progressive neurodegeneration measurable using FDG and MRI. Tau correlates with cognitive decline, as do AD-specific hypometabolism and atrophy.

Quantification of pupil parameters in diseased and normal eyes with near infrared iris transillumination imaging.

BACKGROUND AND OBJECTIVE To investigate near infrared iris transillumination (NIRit) imaging as a new method to quantify pupil shape, size, and position because the imaging modality can uniquely provide simultaneous information regarding iris structural details that influence pupil characteristics and because exploration of related techniques could promote discovery helpful to clinical research and care. PATIENTS AND METHODS Digital NIRit images of normal and diseased eyes were used along with computer-assisted techniques to quantify four primary pupil parameters, including pupil roundness (PR), pupil ovalness (PO), pupil size (PS), and pupil eccentricity (PE). A combined measure of PR and PO was also developed (the pupil circularity index [PCI]). Repeatability of the measures was studied and example analyses were performed. RESULTS Pupil measures could be calculated for right eyes of 307 subjects (164 normal, 143 other), with fewer than 0.5% exclusions due to image quality. Repeatability study did not show significant bias (P < .05) for any of the four primary measures. Example analyses could show age-associated differences in pupil shape (≥ 50 year olds had less regular pupils than < 50 year olds: median PCI = 0.009 vs 0.006; P < .01) and that a group of pigment dispersion syndrome subjects (n = 27) had less regular pupils than a group of matched controls (PO = 0.9966 vs 0.9990; P < .05). CONCLUSION Digital NIRit imaging can provide novel, reliable, and informative methods to quantify pupil characteristics while providing simultaneous information about iris structure that may influence these parameters.

An evaluation of methods for detecting brain activations from PET or fMRI images

Brain activation studies based on PET or fMRI seek to explore neuroscience questions by using statistical techniques to analyze the acquired images, Currently, the predominant viewpoint toward quantifying the detection performance of these statistical methods is to model their output using random field theory, then to ascribe statistical significance (false-positive probability) based on the model. In this paper, we pursue instead an empirical strategy, based on receiver operating characteristics (ROC) analysis, as a first step toward a more-complete evaluation of the performance of brain-activation detection methods, including the power (true-positive probability) of various tests, Using a phantom model derived from parameters measured from PET neuroimaging studies, we compare three methods for detecting brain activation. We consider one method based on pixel-by-pixel image comparisons (the t-test) and two methods based on pixel covariances (correlation thresholding and singular value decomposition thresholding). The simple geometry of our phantom model allows us to construct an optimal detector, the generalized likelihood ratio test (GLRT), for comparison with the simpler detection procedures. In this study the methods based on pixel covariances were found to perform better than the more widely used t-test. Among the covariance-based methods, none was found to be uniformly superior to the others. The performance of the GLRT served as an upper bound against which to compare the other methods. Our results suggest that correlation-based detectors are a promising direction for further investigation.

Novel observations and potential applications using digital infrared iris imaging.

Digital infrared iris photography using a modified digital camera system was performed on approximately 300 subjects seen during routine clinical care and research at one facility. Because this image database offered an opportunity to gain new insight into the potential utility of infrared iris imaging, it was surveyed for unique image patterns. Then, a selection of photographs was compiled that would illustrate the spectrum of this imaging experience. Potentially informative image patterns were observed in subjects with cataracts, diabetic retinopathy, Posner-Schlossman syndrome, iridociliary cysts, long anterior lens zonules, nevi, oculocutaneous albinism, pigment dispersion syndrome, pseudophakia, suspected vascular anomaly, and trauma. Image patterns were often unanticipated regardless of preexisting information and suggest that infrared iris imaging may have numerous potential clinical and research applications, some of which may still not be recognized. These observations suggest further development and study of this technology.

Effect of Spatial Alignment Transformations in PCA and ICA of Functional Neuroimages

It has been previously observed that independent component analysis (ICA), if applied to data pooled in a particular way, may lessen the need for spatial alignment of scans in a functional neuroimaging study. In this paper, we seek to determine analytically the conditions under which this observation is true, not only for spatial ICA, but also for temporal ICA and for principal component analysis (PCA). In each case, we find conditions that the spatial alignment operator must satisfy to ensure invariance of the results. We illustrate our findings using functional magnetic-resonance imaging (fMRI) data. Our analysis is applicable to both intersubject and intrasubject spatial normalization.