Doctor of Philosophy

dissertationRecent developments in magnetic resonance imaging (MRI) provide an in vivo and noninvasive tool for studying the human brain. In particular, the detection of anisotropic diffusion in biological tissues provides the foundation for diffusion-weighted imaging (DWI), an MRI modality. This modality opens new opportunities for discoveries of the brain's structural connections. Clinically, DWI is often used to analyze white matter tracts to understand neuropsychiatric disorders and the connectivity of the central nervous system. However, due to imaging time required, DWI used in clinical studies has a low angular resolution. In this dissertation, we aim to accurately track and segment the white matter tracts and estimate more representative models from low angular DWI. We first present a novel geodesic approach to segmentation of white matter tracts from diffusion tensor imaging (DTI), estimated from DWI. Geodesic approaches treat the geometry of brain white matter as a manifold, often using the inverse tensor field as a Riemannian metric. The white matter pathways are then inferred from the resulting geodesics. A serious drawback of current geodesic methods is that geodesics tend to deviate from the major eigenvectors in high-curvature areas in order to achieve the shortest path. We propose a method for learning an adaptive Riemannian metric from the DTI data, where the resulting geodesics more closely follow the principal eigenvector of the diffusion tensors even in high-curvature regions. Using the computed geodesics, we develop an automatic way to compute binary segmentations of the white matter tracts. We demonstrate that our method is robust to noise and results in improved geodesics and segmentations. Then, based on binary segmentations, we present a novel Bayesian approach for fractional segmentation of white matter tracts and simultaneous estimation of a multitensor diffusion model. By incorporating a prior that assumes the tensor fields inside each tract are spatially correlated, we are able to reliably estimate multiple tensor compartments in fiber crossing regions, even with low angular diffusion-weighted imaging. This reduces the effects of partial voluming and achieves a more reliable analysis of diffusion measurements

Cerebral white matter analysis using diffusion imaging

Thesis (Ph. D.)--Harvard-MIT Division of Health Sciences and Technology, 2006.Includes bibliographical references (p. 183-198).In this thesis we address the whole-brain tractography segmentation problem. Diffusion magnetic resonance imaging can be used to create a representation of white matter tracts in the brain via a process called tractography. Whole brain tractography outputs thousands of trajectories that each approximate a white matter fiber pathway. Our method performs automatic organization, or segmention, of these trajectories into anatomical regions and gives automatic region correspondence across subjects. Our method enables both the automatic group comparison of white matter anatomy and of its regional diffusion properties, and the creation of consistent white matter visualizations across subjects. We learn a model of common white matter structures by analyzing many registered tractography datasets simultaneously. Each trajectory is represented as a point in a high-dimensional spectral embedding space, and common structures are found by clustering in this space. By annotating the clusters with anatomical labels, we create a model that we call a high-dimensional white matter atlas.(cont.) Our atlas creation method discovers structures corresponding to expected white matter anatomy, such as the corpus callosum, uncinate fasciculus, cingulum bundles, arcuate fasciculus, etc. We show how to extend the spectral clustering solution, stored in the atlas, using the Nystrom method to perform automatic segmentation of tractography from novel subjects. This automatic tractography segmentation gives an automatic region correspondence across subjects when all subjects are labeled using the atlas. We show the resulting automatic region correspondences, demonstrate that our clustering method is reproducible, and show that the automatically segmented regions can be used for robust measurement of fractional anisotropy.by Lauren Jean O'Donnell.Ph.D

Interleukin-6, age, and corpus callosum integrity.

The contribution of inflammation to deleterious aging outcomes is increasingly recognized; however, little is known about the complex relationship between interleukin-6 (IL-6) and brain structure, or how this association might change with increasing age. We examined the association between IL-6, white matter integrity, and cognition in 151 community dwelling older adults, and tested whether age moderated these associations. Blood levels of IL-6 and vascular risk (e.g., homocysteine), as well as health history information, were collected. Processing speed assessments were administered to assess cognitive functioning, and we employed tract-based spatial statistics to examine whole brain white matter and regions of interest. Given the association between inflammation, vascular risk, and corpus callosum (CC) integrity, fractional anisotropy (FA) of the genu, body, and splenium represented our primary dependent variables. Whole brain analysis revealed an inverse association between IL-6 and CC fractional anisotropy. Subsequent ROI linear regression and ridge regression analyses indicated that the magnitude of this effect increased with age; thus, older individuals with higher IL-6 levels displayed lower white matter integrity. Finally, higher IL-6 levels were related to worse processing speed; this association was moderated by age, and was not fully accounted for by CC volume. This study highlights that at older ages, the association between higher IL-6 levels and lower white matter integrity is more pronounced; furthermore, it underscores the important, albeit burgeoning role of inflammatory processes in cognitive aging trajectories

Edema-informed anatomically constrained particle filter tractography

International audienceIn this work, we propose an edema-informed anatomically constrained tractography paradigm that enables reconstructing larger spatial extent of white matter bundles as well as increased cortical coverage in the presence of edema. These improvements will help surgeons maximize the extent of the resection while minimizing the risk of cogni-tive deficits. The new paradigm is based on a segmentation of the brain into gray matter, white matter, corticospinal fluid, edema and tumor regions which utilizes a tumor growth model. Using this segmentation, a valid tracking domain is generated and, in combination with anatomically constrained particle filter tractography, allows streamlines to cross the edema region and reach the cortex. Using subjects with brain tumors, we show that our edema-informed anatomically constrained tractogra-phy paradigm increases the cortico-cortical connections that cross edema-contaminated regions when compared to traditional fractional anisotropy thresholded tracking

Grey and white matter correlates of recent and remote autobiographical memory retrieval:Insights from the dementias

The capacity to remember self-referential past events relies on the integrity of a distributed neural network. Controversy exists, however, regarding the involvement of specific brain structures for the retrieval of recently experienced versus more distant events. Here, we explored how characteristic patterns of atrophy in neurodegenerative disorders differentially disrupt remote versus recent autobiographical memory. Eleven behavioural-variant frontotemporal dementia, 10 semantic dementia, 15 Alzheimer's disease patients and 14 healthy older Controls completed the Autobiographical Interview. All patient groups displayed significant remote memory impairments relative to Controls. Similarly, recent period retrieval was significantly compromised in behavioural-variant frontotemporal dementia and Alzheimer's disease, yet semantic dementia patients scored in line with Controls. Voxel-based morphometry and diffusion tensor imaging analyses, for all participants combined, were conducted to investigate grey and white matter correlates of remote and recent autobiographical memory retrieval. Neural correlates common to both recent and remote time periods were identified, including the hippocampus, medial prefrontal, and frontopolar cortices, and the forceps minor and left hippocampal portion of the cingulum bundle. Regions exclusively implicated in each time period were also identified. The integrity of the anterior temporal cortices was related to the retrieval of remote memories, whereas the posterior cingulate cortex emerged as a structure significantly associated with recent autobiographical memory retrieval. This study represents the first investigation of the grey and white matter correlates of remote and recent autobiographical memory retrieval in neurodegenerative disorders. Our findings demonstrate the importance of core brain structures, including the medial prefrontal cortex and hippocampus, irrespective of time period, and point towards the contribution of discrete regions in mediating successful retrieval of distant versus recently experienced events

Investigating microstructural variation in the human hippocampus using non-negative matrix factorization

In this work we use non-negative matrix factorization to identify patterns of microstructural variance in the human hippocampus. We utilize high-resolution structural and diffusion magnetic resonance imaging data from the Human Connectome Project to query hippocampus microstructure on a multivariate, voxelwise basis. Application of non-negative matrix factorization identifies spatial components (clusters of voxels sharing similar covariance patterns), as well as subject weightings (individual variance across hippocampus microstructure). By assessing the stability of spatial components as well as the accuracy of factorization, we identified 4 distinct microstructural components. Furthermore, we quantified the benefit of using multiple microstructural metrics by demonstrating that using three microstructural metrics (T1-weighted/T2-weighted signal, mean diffusivity and fractional anisotropy) produced more stable spatial components than when assessing metrics individually. Finally, we related individual subject weightings to demographic and behavioural measures using a partial least squares analysis. Through this approach we identified interpretable relationships between hippocampus microstructure and demographic and behavioural measures. Taken together, our work suggests non-negative matrix factorization as a spatially specific analytical approach for neuroimaging studies and advocates for the use of multiple metrics for data-driven component analyses

Using neurite orientation dispersion and density imaging and tracts constrained by underlying anatomy to differentiate between subjects along the Alzheimer's disease continuum

OBJECTIVE: To assess the involvement of the white matter of the brain in the pathology of Alzheimer’s disease. Using Neurite Orientation Density and Dispersion Imaging (NODDI) and the probabilistic white matter parcellation tool Tracula as a means for understanding whether alterations in the white matter underlie changes in perceived cognitive abilities across the spectrum from health aging to Alzheimer’s disease. METHOD: Data were obtained from 28 participants in the Health Outreach Program for the Elderly (HOPE) at the Boston University Alzheimer’s Disease Center (BU ADC) Clinical Core Registry. MRI scans included an MPRAGE T1 scan, multi-b shell diffusion scan and a High Angular Resolution Diffusion Imaging scan (HARDI). Scans were processed with Freesurfer v6.0 and the NODDI Python2.7 toolkit. The resulting data included the orientation dispersion index (ODI) and Fractional Anisotropy (FA) values for cortical and subcortical regions in the DKT atlas space as well as specific Tracts Constrained by Underlying Anatomy (TRACULA) measurements for 18 specific established white matter tracts. Statistical models using measures of pathway integrity (FA and ODI data) were used to assess relationships with Informant Cognitive Change Index (ICCI), self-described Cognitive Change Index (CCI), and Clinical Dementia Rating (CDR) values. RESULTS: Measures of white matter integrity within several tracts predicted ICCI and CDR well in statistical models. FA and ODI values of the bilateral superior longitudinal fasciculi, inferior longitudinal fasciculi, and the cingulum bundle tracts were all related to ICCI and CDR. None of the known tracts’ FA or ODI values were related to CCI. CONCLUSIONS: Measures of white matter pathway integrity were predictive of ICCI and CDR scores but not CCI. These finding support the notion that self-report of cognitive abilities may be compromised by alterations in insight and reinforce the need for informed study partners and clinical ratings to evaluate potential MCI and AD

Neuroimaging Evidence of Major Morpho-Anatomical and Functional Abnormalities in the BTBR T+TF/J Mouse Model of Autism

BTBR T+tf/J (BTBR) mice display prominent behavioural deficits analogous to the defining symptoms of autism, a feature that has prompted a widespread use of the model in preclinical autism research. Because neuro-behavioural traits are described with respect to reference populations, multiple investigators have examined and described the behaviour of BTBR mice against that exhibited by C57BL/6J (B6), a mouse line characterised by high sociability and low self-grooming. In an attempt to probe the translational relevance of this comparison for autism research, we used Magnetic Resonance Imaging (MRI) to map in both strain multiple morpho-anatomical and functional neuroimaging readouts that have been extensively used in patient populations. Diffusion tensor tractography confirmed previous reports of callosal agenesis and lack of hippocampal commissure in BTBR mice, and revealed a concomitant rostro-caudal reorganisation of major cortical white matter bundles. Intact inter-hemispheric tracts were found in the anterior commissure, ventro-medial thalamus, and in a strain-specific white matter formation located above the third ventricle. BTBR also exhibited decreased fronto-cortical, occipital and thalamic gray matter volume and widespread reductions in cortical thickness with respect to control B6 mice. Foci of increased gray matter volume and thickness were observed in the medial prefrontal and insular cortex. Mapping of resting-state brain activity using cerebral blood volume weighted fMRI revealed reduced cortico-thalamic function together with foci of increased activity in the hypothalamus and dorsal hippocampus of BTBR mice. Collectively, our results show pronounced functional and structural abnormalities in the brain of BTBR mice with respect to control B6 mice. The large and widespread white and gray matter abnormalities observed do not appear to be representative of the neuroanatomical alterations typically observed in autistic patients. The presence of reduced fronto-cortical metabolism is of potential translational relevance, as this feature recapitulates previously-reported clinical observations

Microstructural Alterations in Hippocampal Subfields Mediate Age-Related Memory Decline in Humans.

Aging, even in the absence of clear pathology of dementia, is associated with cognitive decline. Neuroimaging, especially diffusion-weighted imaging, has been highly valuable in understanding some of these changes in live humans, non-invasively. Traditional tensor techniques have revealed that the integrity of the fornix and other white matter tracts significantly deteriorates with age, and that this deterioration is highly correlated with worsening cognitive performance. However, traditional tensor techniques are still not specific enough to indict explicit microstructural features that may be responsible for age-related cognitive decline and cannot be used to effectively study gray matter properties. Here, we sought to determine whether recent advances in diffusion-weighted imaging, including Neurite Orientation Dispersion and Density Imaging (NODDI) and Constrained Spherical Deconvolution, would provide more sensitive measures of age-related changes in the microstructure of the medial temporal lobe. We evaluated these measures in a group of young (ages 20-38 years old) and older (ages 59-84 years old) adults and assessed their relationships with performance on tests of cognition. We found that the fiber density (FD) of the fornix and the neurite density index (NDI) of the fornix, hippocampal subfields (DG/CA3, CA1, and subiculum), and parahippocampal cortex, varied as a function of age in a cross-sectional cohort. Moreover, in the fornix, DG/CA3, and CA1, these changes correlated with memory performance on the Rey Auditory Verbal Learning Test (RAVLT), even after regressing out the effect of age, suggesting that they were capturing neurobiological properties directly related to performance in this task. These measures provide more details regarding age-related neurobiological properties. For example, a change in fiber density could mean a reduction in axonal packing density or myelination, and the increase in NDI observed might be explained by changes in dendritic complexity or even sprouting. These results provide a far more comprehensive view than previously determined on the possible system-wide processes that may be occurring because of healthy aging and demonstrate that advanced diffusion-weighted imaging is evolving into a powerful tool to study more than just white matter properties

Dentate nucleus connectivity in adult patients with multiple sclerosis: functional changes at rest and correlation with clinical features

Background and objective: The dentate nucleus, which is the largest of the cerebellar nuclei, plays a critical role in movement and cognition. The aim of our study was to assess any changes in dentate functional connectivity (FC) in adult relapsing remitting multiple sclerosis (RR-MS) patients and to investigate possible clinical correlates. Materials and methods: In all, 54 patients and 24 healthy subjects (HS) underwent multimodal magnetic resonance imaging (MRI), including diffusion tensor imaging (DTI), three-dimensional-T1-weighted and resting state (RS) functional images; they also underwent a cognitive evaluation, that is, attention and information processing speed, by means of the Paced Auditory Serial Addition Test (PASAT). Patients were also scored according to Expanded Disability Status Scale (EDSS). RS-MRI data were analysed using FMRIB Software Library (FSL) tools, with the seed-based method to identify dentate FC. Results: When compared with HS, patients exhibited brain atrophy and widespread DTI abnormalities, as well as greater FC between the dentate nucleus and cortical areas, particularly in the frontal and parietal lobes. Within these areas, FC in patients correlated inversely with clinical impairment. Finally, FC correlated inversely with lesion load and microstructural brain damage. Conclusion: Our findings indicate that dentate FC at rest is altered in MS patients. Whether these functional changes are induced by the disease and play a compensatory role remains to be established