Severity and Progression of White Matter Changes in Frontotemporal Dementia Subtypes Using Diffusion Tensor Imaging

The three main subtypes of frontotemporal dementia (FTD): behavioural-variant FTD (bvFTD), primary progressive aphasia-nonfluent variant (nfv-PPA) and semantic variant (sv-PPA) are characterised by progressive brain atrophy in the frontotemporal regions. The brain white matter also undergoes marked pathological alterations but is less studied. This thesis aimed to study i) the patterns of white matter changes in the three FTD subtypes, ii) the progression of white matter changes in the same FTD subtypes, and iii) the white matter changes in bvFTD with or without the C9orf72 gene expansions. Chapter 3 revealed distinctive patterns of white matter changes in each FTD subtype. White matter alterations were observed in orbitofrontal and anterior temporal tracts in bvFTD, bilateral (left>right) frontotemporal tracts in nfv-PPA and circumscribed left temporal lobe in sv-PPA. These white matter changes greatly overlapped with grey matter changes in bvFTD and nfv-PPA but not in sv-PPA. The white matter abnormalities varied depending on the diffusion tensor imaging (DTI) measurements used, with mean diffusivity being the most sensitive metric for all FTD subtypes. Chapter 4 showed the 12-month longitudinal assessment of white matter changes in the same participants. White matter alterations appeared in regions shown at baseline but with additional changes extending beyond the original regions affected in all FTD subtypes. White matter abnormalities extended far beyond sites of grey matter atrophy in the same time period. Fractional anisotropy and radial diffusivity were most sensitive in detecting white matter abnormalities in this longitudinal assessment. Chapter 5 compared bvFTD cases with or without C9orf72 expansion carriers. C9orf72 expansion carriers did not exhibit the typical frontotemporal white matter changes as shown in non-carriers. Stereotypical behaviour was less prevalent in C9orf72 expansion carriers than non-carriers and the left cingulum and anterior thalamic radiation were predictive of stereotypical behavioural scores. Semantic knowledge was less affected in C9orf72 expansion carriers and the left uncinate fasciculus was predictive of changes in semantic knowledge. These predictions significantly differentiated C9orf72 expansion carriers from non-carriers. Investigations using DTI presented in this thesis improved the understanding of white matter changes in FTD and contributed to the characterisation of the different FTD subtypes

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

Neural mechanisms of social-emotional dysfunction in autism spectrum disorder and conduct disorder

Individuals with autism spectrum disorder (ASD) and individuals with conduct disorder (CD) are characterized by notable impairments in social-emotional functioning. In this thesis social-emotional impairments were investigated using a cognitive neuroscience perspective (i.e., studying cognitive mechanisms and associated neural processes and structures). First, we directly compared groups of ASD and CD to test the hypothesized dissociable deficits in understanding other’s emotions in ASD in contrast to deficits in feeling other’s emotions in CD. This was done by comparing brain activity during basic emotion processing to assess cognitive and affective aspects of empathy, and by comparing white matter tracts that may underlie social-emotional processing. Second, we examined the neural processes at the level of social interactions in ASD and in CD, which has been overlooked by prior work, by studying interactive decision-making in response to other’s emotions. The results of the first part of this thesis show that different neural mechanisms underlie social-emotional difficulties in ASD and CD. Results of the second part imply that uncovering the neural correlates of interacting with others might lead to refined models of social-emotional deficits in ASD and CD that are different from previous accounts based on merely observing other’s emotions.The studies described in this thesis were supported by the Netherlands Organization for Scientific Research (NWO) Grant No. 056-23-011.LUMC / Geneeskunde Repositoriu

Comparative Analysis of Connection and Disconnection in the Human Brain Using Diffusion MRI: New Methods and Applications

Institute for Adaptive and Neural ComputationDiffusion magnetic resonance imaging (dmri) is a technique that can be used to examine the diffusion characteristics of water in the living brain. A recently developed application of this technique is tractography, in which information from brain images obtained using dmri is used to reconstruct the pathways which connect regions of the brain together. Proxy measures for the integrity, or coherence, of these pathways have also been defined using dmri-derived information. The disconnection hypothesis suggests that specific neurological impairments can arise from damage to these pathways as a consequence of the resulting interruption of information flow between relevant areas of cortex. The development of dmri and tractography have generated a considerable amount of renewed interest in the disconnectionist thesis, since they promise a means for testing the hypothesis in vivo in any number of pathological scenarios. However, in order to investigate the effects of pathology on particular pathways, it is necessary to be able to reliably locate them in three-dimensional dmri images. The aim of the work described in this thesis is to improve upon the robustness of existing methods for segmenting specific white matter tracts from image data, using tractography, and to demonstrate the utility of the novel methods for the comparative analysis of white matter integrity in groups of subjects. The thesis begins with an overview of probability theory, which will be a recurring theme throughout what follows, and its application to machine learning. After reviewing the principles of magnetic resonance in general, and dmri and tractography in particular, we then describe existing methods for segmenting particular tracts from group data, and introduce a novel approach. Our innovation is to use a reference tract to define the topological characteristics of the tract of interest, and then search a group of candidate tracts in the target brain volume for the best match to this reference. In order to assess how well two tracts match we define a heuristic but quantitative tract similarity measure. In later chapters we demonstrate that this method is capable of successfully segmenting tracts of interest in both young and old, healthy and unhealthy brains; and then describe a formalised version of the approach which uses machine learning methods to match tracts from different subjects. In this case the similarity between tracts is represented as a matching probability under an explicit model of topological variability between equivalent tracts in different brains. Finally, we examine the possibility of comparing the integrity of groups of white matter structures at a level more fine-grained than a whole tract

Investigation of dimensional phenomenology and neurobiology across affective and psychotic disorders

For a long time, traditional classification systems have been used to categorize mental disorders into strict classes based on a set of specific and standardized criteria. Such classifications assume a clear cut off between disorders. However, research using these classification systems fail to identify transdiagnostic markers and “points of rarity” separating mental disorders. Categorical approaches are limited by the large neurobiological overlapping of phenomenology as well as molecular genetics, neuro-anatomy and function, and environmental risk across disorders. Moreover, categorical approaches merely consider characteristics above and below the given categorical thresholds using not otherwise specified diagnoses, not fitting to other officially specified categories. Given the limitations of categorical approaches, dimensional factor models can be used as a valuable framework providing significant progress for the understanding of the neurobiology of the major psychiatric disorders (major depressive disorder, bipolar disorder, schizophrenia spectrum disorder). Previous studies show a range of different factor models, indicating that descriptive psychopathology might be organized in a bifactorial or hierarchical framework. However, there is still a lack of comprehensive factorial models comprising a broad range of symptoms across the major psychiatric disorders. Moreover, the neuro-anatomical and neuro-cognitive correlates of transdiagnostic psychopathological factors remain largely elusive. Categorical studies on overlapping gray matter volume alterations across disorders compared to a healthy control group show paralimbic and heteromodal regions to be commonly altered across disorders. In addition, the transdiagnostic investigation of neuro-cognitive measures shows large overlaps and comparable results across disorders and domains with motor speed being the only domain separating disorders. To overcome the reported obstacles, the studies underlying this dissertation investigate the factorial structure of a broad range of psychopathological symptoms across affective and psychotic disorders. Further, dimensional factors are used to determine the underlying neuro-anatomical and neuro-cognitive correlates of descriptive psychopathology. STUDY I demonstrates a cross-validated factor model comprising five first order and two second order factors, supporting the use of hierarchical models. The extracted first order factors (depression, negative syndrome, positive formal thought disorder, paranoid-hallucinatory syndrome, increased appetite) are present in all diagnostic categories, suggesting a diagnosis-shared phenomenology. STUDY II examines the brain structural correlates of the factors derived from STUDY I. Results include a negative association of the negative syndrome with the bilateral frontal opercula. Positive formal thought disorder is negatively associated with the right middle frontal gyrus and with the left amygdala-hippocampus-complex. The paranoid-hallucinatory syndrome is negatively associated with two whole brain clusters (right fusiform gyrus and left middle frontal gyrus) as well as regions-of-interest including the left angular gyrus, bilateral thalami, left postcentral gyrus and left posterior cingulate gyrus. Investigating the neuro-cognitive correlates of psychopathological factors, STUDY III indicates state of illness-dependent associations in almost all cognitive domains. While positive formal thought disorder and the negative syndrome show most pronounced correlations, no or only weak correlations emerge for the other factors. Finally, STUDY IV investigates formal thought disorder in more detail. Results indicate a three factor model (verbosity, emptiness, disorganization) that is differentially associated with gray and white matter brain structure. The verbosity factor is negatively associated with gray matter volume of the temporo-occipital language junction and positively with the white matter microstructure of the inferior longitudinal fascicle and the posterior part of the cingulum bundle. Emptiness is negatively associated with the gray matter volume of the left hippocampus and thalamus but not with white matter. The disorganization factor associates with the white matter structure of the bilateral anterior thalamic radiation and with the hippocampal part of the right cingulum bundle. In conclusion, this dissertation can be interpreted as a first effort overcoming the limitations given by previous categorical approaches. The psychopathological factor models reported are linked to brain structural and neuro-cognitive measures, supporting the view of diagnosis shared and independent biological mechanisms. The studies of this dissertation open up completely new approaches for pathogenic and etiological research. Dimensional methods as applied in this dissertation constitute the basis for a new taxonomy that can in a next step be used to improve prediction, treatment and therapy of the major psychiatric disorders