Combining DTI and fMRI to investigate language lateralisation

Hemispheric lateralisation in the human brain has been a focus of interest in different fields of neurosciences since a long time (Galaburda, LeMay, Kemper, & Geschwind, 1978; Rubino, 1970). One of the most studied and earliest observed lateralised brain functions is language. Reported in the nineteenth by the French physician and anatomist Paul Broca (1861) and by the German anatomist and neuropathologist Carl Wernicke (1874), language was found to be more impaired following tumours or strokes in the left hemisphere. In recent years, a number of studies have employed diffusion tensor imaging (DTI) to characterize left hemisphere language-related white matter pathways (Barrick, Lawes, Mackay, & Clark, 2007; Bernal & Altman, 2010; Catani et al., 2007; Glasser & Rilling, 2008; Hagmann et al., 2006; Parker et al., 2005; Propper et al., 2010; Upadhyay, Hallock, Ducros, Kim, & Ronen, 2008; Vernooij et al., 2007). In addition, lesion and fMRI studies in healthy subjects have indicated that speech comprehension and production are lateralised to the left brain hemisphere (A. U. Turken & Dronkers, 2011). The main aim of the present doctoral work is to better delineate the relationship between anatomical and functional correlates of hemispheric dominance in the perisylvian language network. To this purpose a multi-modal neuroimaging approach including DTI and fMRI on a population of 23 healthy individuals was applied. In the first study, a virtual in vivo interactive dissection of the three subcomponents of the arcuate fasciculus was carried out and measures of perisylvian white matter integrity were derived from tract-specific dissection. Consistently with previous studies (Barrick, et al., 2007; Buchel et al., 2004; Catani, et al., 2007; Powell et al., 2006), a significant leftward asymmetry in the fractional anysotropy (FA) value of the long direct segment of the arcuate fasciculus (AF) has been found. In addition, I found another significant leftward lateralisation in the streamlines (SL) of the posterior segment and a rightward distribution of the SL index of the anterior segment of the AF. Finally, I found no evidence of a significant relationship between the leftward lateralisation indeces and any measures of language and verbal memory performance in my group. In the second study, I implemented functional connectivity analysis to test whether leftward lateralisation of connectivity indeces between perisylvian regions can be observed in individuals performing a language-related task. The main finding of the functional connectivity analysis is a significant rightward lateralisation (left, 0.347 ± 0.183; right, 0.493 ± 0.228; P = 0.037) in the anterior connection, between the the inferior frontal gyrus (IFG) and the inferior parietal lobe (IPG). In the third study, I combined DTI and fMRI data to examine whether a significant relationship is present between these measures of perisylvian connectivity and it significantly differs between hemispheres. The correlation analysis demonstrated significant negative relations between the mean FA values in the long segment of the AF and the strength of inter-regional coupling between the IFG and the middle temporal gyrus (MTG) in the left hemisphere, and between the mean FA values in the anterior segment of the AF and the strength of regional coupling between IFG and IPL in the right hemisphere. Finally, there were no significant correlations between laterality indices estimated on FA and functional connectivity values.

Visual analytics methods for shape analysis of biomedical images exemplified on rodent skull morphology

In morphometrics and its application fields like medicine and biology experts are interested in causal relations of variation in organismic shape to phylogenetic, ecological, geographical, epidemiological or disease factors - or put more succinctly by Fred L. Bookstein, morphometrics is "the study of covariances of biological form". In order to reveal causes for shape variability, targeted statistical analysis correlating shape features against external and internal factors is necessary but due to the complexity of the problem often not feasible in an automated way. Therefore, a visual analytics approach is proposed in this thesis that couples interactive visualizations with automated statistical analyses in order to stimulate generation and qualitative assessment of hypotheses on relevant shape features and their potentially affecting factors. To this end long established morphometric techniques are combined with recent shape modeling approaches from geometry processing and medical imaging, leading to novel visual analytics methods for shape analysis. When used in concert these methods facilitate targeted analysis of characteristic shape differences between groups, co-variation between different structures on the same anatomy and correlation of shape to extrinsic attributes. Here a special focus is put on accurate modeling and interactive rendering of image deformations at high spatial resolution, because that allows for faithful representation and communication of diminutive shape features, large shape differences and volumetric structures. The utility of the presented methods is demonstrated in case studies conducted together with a collaborating morphometrics expert. As exemplary model structure serves the rodent skull and its mandible that are assessed via computed tomography scans

Visual Exploration And Information Analytics Of High-Dimensional Medical Images

Data visualization has transformed how we analyze increasingly large and complex data sets. Advanced visual tools logically represent data in a way that communicates the most important information inherent within it and culminate the analysis with an insightful conclusion. Automated analysis disciplines - such as data mining, machine learning, and statistics - have traditionally been the most dominant fields for data analysis. It has been complemented with a near-ubiquitous adoption of specialized hardware and software environments that handle the storage, retrieval, and pre- and postprocessing of digital data. The addition of interactive visualization tools allows an active human participant in the model creation process. The advantage is a data-driven approach where the constraints and assumptions of the model can be explored and chosen based on human insight and confirmed on demand by the analytic system. This translates to a better understanding of data and a more effective knowledge discovery. This trend has become very popular across various domains, not limited to machine learning, simulation, computer vision, genetics, stock market, data mining, and geography. In this dissertation, we highlight the role of visualization within the context of medical image analysis in the field of neuroimaging. The analysis of brain images has uncovered amazing traits about its underlying dynamics. Multiple image modalities capture qualitatively different internal brain mechanisms and abstract it within the information space of that modality. Computational studies based on these modalities help correlate the high-level brain function measurements with abnormal human behavior. These functional maps are easily projected in the physical space through accurate 3-D brain reconstructions and visualized in excellent detail from different anatomical vantage points. Statistical models built for comparative analysis across subject groups test for significant variance within the features and localize abnormal behaviors contextualizing the high-level brain activity. Currently, the task of identifying the features is based on empirical evidence, and preparing data for testing is time-consuming. Correlations among features are usually ignored due to lack of insight. With a multitude of features available and with new emerging modalities appearing, the process of identifying the salient features and their interdependencies becomes more difficult to perceive. This limits the analysis only to certain discernible features, thus limiting human judgments regarding the most important process that governs the symptom and hinders prediction. These shortcomings can be addressed using an analytical system that leverages data-driven techniques for guiding the user toward discovering relevant hypotheses. The research contributions within this dissertation encompass multidisciplinary fields of study not limited to geometry processing, computer vision, and 3-D visualization. However, the principal achievement of this research is the design and development of an interactive system for multimodality integration of medical images. The research proceeds in various stages, which are important to reach the desired goal. The different stages are briefly described as follows: First, we develop a rigorous geometry computation framework for brain surface matching. The brain is a highly convoluted structure of closed topology. Surface parameterization explicitly captures the non-Euclidean geometry of the cortical surface and helps derive a more accurate registration of brain surfaces. We describe a technique based on conformal parameterization that creates a bijective mapping to the canonical domain, where surface operations can be performed with improved efficiency and feasibility. Subdividing the brain into a finite set of anatomical elements provides the structural basis for a categorical division of anatomical view points and a spatial context for statistical analysis. We present statistically significant results of our analysis into functional and morphological features for a variety of brain disorders. Second, we design and develop an intelligent and interactive system for visual analysis of brain disorders by utilizing the complete feature space across all modalities. Each subdivided anatomical unit is specialized by a vector of features that overlap within that element. The analytical framework provides the necessary interactivity for exploration of salient features and discovering relevant hypotheses. It provides visualization tools for confirming model results and an easy-to-use interface for manipulating parameters for feature selection and filtering. It provides coordinated display views for visualizing multiple features across multiple subject groups, visual representations for highlighting interdependencies and correlations between features, and an efficient data-management solution for maintaining provenance and issuing formal data queries to the back end

Interactive Visualization of Multimodal Brain Connectivity: Applications in Clinical and Cognitive Neuroscience

Magnetic resonance imaging (MRI) has become a readily available prognostic and diagnostic method, providing invaluable information for the clinical treatment of neurological diseases. Multimodal neuroimaging allows integration of complementary data from various aspects such as functional and anatomical properties; thus, it has the potential to overcome the limitations of each individual modality. Specifically, functional and diffusion MRI are two non-invasive neuroimaging techniques customized to capture brain activity and microstructural properties, respectively. Data from these two modalities is inherently complex, and interactive visualization can assist with data comprehension. The current thesis presents the design, development, and validation of visualization and computation approaches that address the need for integration of brain connectivity from functional and structural domains. Two contexts were considered to develop these approaches: neuroscience exploration and minimally invasive neurosurgical planning. The goal was to provide novel visualization algorithms and gain new insights into big and complex data (e.g., brain networks) by visual analytics. This goal was achieved through three steps: 3D Graphical Collision Detection: One of the primary challenges was the timely rendering of grey matter (GM) regions and white matter (WM) fibers based on their 3D spatial maps. This challenge necessitated pre-scanning those objects to generate a memory array containing their intersections with memory units. This process helped faster retrieval of GM and WM virtual models during the user interactions. Neuroscience Enquiry (MultiXplore): A software interface was developed to display and react to user inputs by means of a connectivity matrix. This matrix displays connectivity information and is capable to accept selections from users and display the relevant ones in 3D anatomical view (with associated anatomical elements). In addition, this package can load multiple matrices from dynamic connectivity methods and annotate brain fibers. Neurosurgical Planning (NeuroPathPlan): A computational method was provided to map the network measures to GM and WM; thus, subject-specific eloquence metric can be derived from related resting state networks and used in objective assessment of cortical and subcortical tissue. This metric was later compared to apriori knowledge based decisions from neurosurgeons. Preliminary results show that eloquence metric has significant similarities with expert decisions

Diffusion tensor imaging in elderly patients with idiopathic normal pressure hydrocephalus or Parkinson’s disease: diagnosis of gait abnormalities

BACKGROUND: Gait abnormalities in the elderly, characterized by short steps and frozen gait, can be caused by several diseases, including idiopathic normal pressure hydrocephalus (INPH), and Parkinson’s disease (PD). We analyzed the relationship between these two conditions and their association with gait abnormalities using laboratory test data and findings from diffusion tensor imaging (DTI). METHODS: The study involved 10 patients with INPH, 18 with PD, and 10 healthy individuals (control group). Fractional anisotropy (FA) of five brain areas was measured and compared among the three groups. In addition, the association of INPH and PD with gait capability, frontal lobe function, and FA of each brain area was evaluated. RESULTS: The INPH group had significantly lower FA for anterior thalamic radiation (ATR) and forceps minor (Fmin) as compared to the PD group. The gait capability correlated with ATR FA in the INPH and PD groups. We found that adding DTI to the diagnosis assisted the differential diagnosis of INPH from PD, beyond what could be inferred from ventricular size alone. CONCLUSIONS: We expect that DTI will provide a useful tool to support the differential diagnosis of INPH and PD and their respective severities

Structural and cognitive correlates of body mass index in healthy older adults

Obesity, commonly measured with body mass index (BMI), is associated with numerous deleterious health conditions and may be a modifier of age-related alterations in brain integrity. Research suggests that white matter integrity observed using diffusion tensor imaging (DTI) is altered with high BMI, but the integrity of specific tracts remains poorly understood. Additionally, no studies have examined fractional anisotropy (FA) of tract integrity in conjunction with neuropsychological evaluation with elevated BMI among older adults. It was hypothesized that elevated BMI would be independently associated with lower white matter integrity and cognitive performance. It was also hypothesized that age and BMI would interact in relation to measures of white matter integrity and cognitive performance. Sixty two healthy older adults aged 51 to 81 were evaluated using DTI and neuropsychological evaluation. Associations were examined between BMI, FA in tracts connecting frontal and temporal lobes, and cognitive ability in domains of executive function, processing speed, and memory. Hierarchical linear regressions were utilized to determine the impact of BMI on FA and cognitive function after accounting for demographics, followed by a test for a BMI by age interaction on FA and cognitive indices. Secondary analyses assessed the sensitivity of DTI diffusivity metrics to elevated BMI, and related tract FA to cognitive performance. After controlling for initial demographic relationships, elevated BMI was associated with lower FA in the uncinate fasciculus, though there was no evidence of an age by BMI interaction relating to FA in this tract. No relationships between BMI and cognition were observed. Secondary analyses did not suggest that DTI diffusivity metrics provide unique information about tract integrity related to high BMI. Overall, results suggest elevated BMI is associated with altered integrity of the uncinate fasciculus. This white matter tract connects frontal and temporal lobes and is involved in memory function. There was no evidence of an age by BMI interaction on FA of the uncinate fasciculus, and BMI did not relate to cognitive performance. Future studies should examine measures of cardiovascular health and systemic inflammation to identify factors influencing relationships between BMI and white matter integrity