Selected Publications

This study investigates multimodal structural MR imaging biomarkers of development trajectories in pediatric bipolar disorder. T1-weighted and diffusion-weighted MR imaging was conducted to investigate cross-sectional group differences with age between typically developing controls (N=26) and youths diagnosed with bipolar disorder (N=26). Region-based analysis was used to examine cortical thickness of gray matter and diffusion tensor parameters in superficial white matter, and tractography-based analysis was used to examine deep white matter fiber bundles. Patients and controls showed significantly different maturation trajectories across brain areas; however, the magnitude of differences varied by region. The rate of cortical thinning with age was greater in patients than controls in the left frontal pole. While controls showed increasing fractional anisotropy (FA) and axial diffusivity (AD) with age, patients showed an opposite trend of decreasing FA and AD with age in fronto-temporal-striatal regions located in both superficial and deep white matter. The findings support fronto-temporal-striatal alterations in the developmental trajectories of youths diagnosed with bipolar disorder, and further, show the value of multimodal computational techniques in the assessment of neuropsychiatric disorders. These preliminary results warrant further investigation into longitudinal changes and the effects on treatment in the brain areas identified in this study.
In Psychiatry Research: Neuroimaging 2018

Exploring neuroanatomical sex differences using a multivariate statistical learning approach can yield insights that cannot be derived with univariate analysis. While gross differences in total brain volume are well-estab- lished, uncovering the more subtle, regional sex-related differences in neuroanatomy requires a multivariate ap- proach that can accurately model spatial complexity as well as the interactions between neuroanatomical fea- tures. Here, we developed a multivariate statistical learning model using a support vector machine (SVM) clas- si er to predict sex from MRI-derived regional neuroanatomical features from a single-site study of 967 healthy youth from the Philadelphia Neurodevelopmental Cohort (PNC). Then, we validated the multivariate model on an independent dataset of 682 healthy youth from the multi-site Pediatric Imaging, Neurocognition and Genet- ics (PING) cohort study. The trained model exhibited an 83% cross-validated prediction accuracy, and correctly predicted the sex of 77% of the subjects from the independent multi-site dataset. Results showed that cortical thickness of the middle occipital lobes and the angular gyri are major predictors of sex. Results also demonstrated the inferential bene ts of going beyond classical regression approaches to capture the interactions among brain features in order to better characterize sex differences in male and female youths. We also identi ed speci c cortical morphological measures and parcellation techniques, such as cortical thickness as derived from the De- strieux atlas, that are better able to discriminate between males and females in comparison to other brain atlases (Desikan-Killiany, Brodmann and subcortical atlases).
In NeuroImage. 2018

This paper presents a comparative evaluation of methods for automated voxel-based spatial mapping in diffusion tensor imaging studies. Such methods are an essential step in computational pipelines and provide anatomically comparable measurements across a population in atlas-based studies. To better understand their strengths and weaknesses, we tested a total of eight methods for voxel-based spatial mapping in two types of diffusion tensor templates. The methods were evaluated with respect to scan-rescan reliability and an application to normal aging. Looking forward, these results can potentially help to interpret results from existing white matter imaging studies, as well as provide a resource to help in planning future studies to maximize reliability and sensitivity with regard to the scientific goals at hand.
In NeuroImage 2017

The basal ganglia is part of a complex system of neuronal circuits that play a key role in the integration and execution of motor, cognitive and emotional function in the human brain. Deep Brain Stimulation (DBS) of the subthalamic nucleus and the globus pallidus pars interna provides an efficient treatment to reduce symptoms and levodopa-induced side effects in Parkinson’s Disease patients. While the underlying mechanism of action of DBS is still unknown, the potential modulation of white matter tracts connecting the surgical targets has become an active area of research. With the introduction of advanced diffusion MRI acquisition sequences and sophisticated post-processing techniques, the architecture of the human brain white matter can be explored in-vivo. The goal of this study is to investigate the white matter connectivity between the subthalamic nucleus and the globus pallidus. Two multi-fiber tractography methods were used to reconstruct pallido-subthalamic, subthalamo-pallidal and pyramidal fibers in five healthy subjects datasets of the Human Connectome Project. The anatomical accuracy of the identified tracts was evaluated by two expert neuroanatomists. Both multi-fiber approaches enabled the detection of complex fiber architecture in the basal ganglia. The evaluation by the neuroanatomists showed that the identified tracts were in agreement with the expected anatomy. False-negative tracts demonstrated the current limitations of the methods for clinical decision-making. Multi-fiber tractography methods combined with state-of-the art diffusion MRI data have the potential to help identify white matter tracts connecting DBS targets in functional neurosurgery intervention.
In Front. Neuroanat. 2016

This paper presents and evaluates a method for kernel regression estimation of fiber orientations and associated volume fractions for diffusion MR tractography and population-based atlas construction in clinical imaging studies of brain white matter. This is a model-based image processing technique in which representative fiber models are estimated from collections of component fiber models in model-valued image data. This extends prior work in nonparametric image processing and multi-compartment processing to provide computational tools for image interpolation, smoothing, and fusion with fiber orientation mixtures. In contrast to related work on multi-compartment processing, this approach is based on directional measures of divergence and includes data-adaptive extensions for model selection and bilateral filtering. This is useful for reconstructing complex anatomical features in clinical datasets analyzed with the ball-and-sticks model, and our framework's data-adaptive extensions are potentially useful for general multi-compartment image processing.
In NeuroImage 2016

The reconstruction of the corticospinal tract in the human brain is a clinically important task for both surgical planning and population studies. Diffusion MRI tractography provides an in-vivo and patient-specific technique for mapping the tract’s geometry; however, crossing fibers present a challenge for the standard tensor model. In this paper, we explore the use multi-fiber models that have been shown to overcome some of these issues, and we apply methods for potentially improving on previous work with model-based processing. We conduct experiments with three real clinical dataset including normal and tumor-infiltrated corticospinal tracts and the arcuate fasciculus. We show our results with visualizations of the fiber bundles alongside volumetric data and tumor surface models. We found the multi-fiber reconstructions included lateral projections of the corticospinal tract in most cases and frontal projections of the arcuate fasciculus in one case. Our results suggest this approach could be considered for clinical applications of corticospinal tract modeling.
In MICCAI DTI Challenge 2015

We present and evaluate a bilateral filter for smoothing diffusion MRI fiber orientations with preservation of anatomical boundaries and support for multiple fibers per voxel. Two challenges in the process are the geometric structure of fiber orientations and the combinatorial problem of matching multiple fibers across voxels. To address these issues, we define distances and local estimators of weighted collections of multi-fiber models and show that these provide a basis for an efficient bilateral filtering algorithm for orientation data. We evaluate our approach with experiments testing the effect on tractography-based reconstruction of fiber bundles and response to synthetic noise in computational phantoms and clinical human brain data. We found this to significantly reduce the effects of noise and to avoid artifacts introduced by linear filtering. This approach has potential applications to diffusion MR tractography, brain connectivity mapping, and cardiac modeling.

This paper presents a method for estimating models for such operations by clustering fiber orientations. Our approach is applied to ball-and-stick diffusion models, which include an isotropic tensor and multiple sticks encoding fiber volume and orientation. We consider operations which can be generalized to a weighted combination of fibers and present a method for representing such combinations with a mixture-of-Watsons model, learning its parameters by Expectation Maximization. We evaluate this approach with two experiments. First, we show it is effective for filtering in the presence of synthetic noise. Second, we demonstrate interpolation and averaging by construction of a tractography atlas, showing improved reconstruction of white matter pathways. These experiments indicate that our method is useful in estimating multi-fiber ball-and-stick diffusion volumes resulting from a range of image analysis operations.
In MICCAI 2013

This paper presents an algorithmic approach to clustering such spatial and orientation data and apply it to brain white matter supervoxel segmentation. This approach is an exten- sion of the DP-means algorithm to support axial data, and we present its theoretical connection to probabilistic models, including the Gaussian and Watson distributions. We evaluate our method with the analysis of synthetic data and an application to diffusion tensor atlas segmentation. We find our approach to be efficient and effective for the automatic extraction of regions of interest that respect the structure of brain white matter. The resulting supervoxel segmentation could be used to map regional anatomical changes in clinical studies or serve as a domain for more complex modeling.

We present a diffeomorphic approach for constructing intrinsic shape atlases of sulci on the human cortex. Sulci are represented as square-root velocity functions of continuous open curves in R3, and their shapes are studied as functional representations of an infinite-dimensional sphere. This spherical manifold has some advantageous properties – it is equipped with a Riemannian metric on the tangent space and facilitates computational analyses and correspondences between sulcal shapes. Sulcal shape mapping is achieved by computing geodesics in the quotient space of shapes modulo scales, translations, rigid rotations and reparameterizations. The resulting sulcal shape atlas preserves important local geometry inherently present in the sample population. The sulcal shape atlas is integrated in a cortical registration framework and exhibits better geometric matching compared to the conventional euclidean method. We demonstrate experimental results for sulcal shape mapping, cortical surface registration, and sulcal classification for two different surface extraction protocols for separate subject populations.
In IEEE TMI 2012

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Some reflections and a discussion of findings from my graduate work at Brown CS, originally published here