Assumption-Free Assessment of Corpus Callosum Shape: Benchmarking and Application

Shape analysis provides a unique insight into biological processes. This paper evaluates the properties, performance, and utility of elliptical Fourier (eFourier) analysis to operationalise global shape, focussing on the human corpus callosum. 8000 simulated corpus callosum contours were generated, systematically varying in terms of global shape (midbody arch, splenium size), local complexity (surface smoothness), and nonshape characteristics (e.g., rotation). 2088 real corpus callosum contours were manually traced from the PATH study. Performance of eFourier was benchmarked in terms of its capacity to capture and then reconstruct shape and systematically operationalise that shape via principal components analysis. We also compared the predictive performance of corpus callosum volume, position in Procrustes-aligned Landmark tangent space, and position in eFourier n-dimensional shape space in relation to the Symbol Digit Modalities Test. Jaccard index for original vs. reconstructed from eFourier shapes was excellent (M=0.98). The combination of eFourier and PCA performed particularly well in reconstructing known n-dimensional shape space but was disrupted by the inclusion of local shape manipulations. For the case study, volume, eFourier, and landmark measures were all correlated. Mixed effect model results indicated all methods detected similar features, but eFourier estimates were most predictive, and of the two shape operationalization techniques had the least error and better model fit. Elliptical Fourier analysis, particularly in combination with principal component analysis, is a powerful, assumption-free and intuitive method of quantifying global shape of the corpus callosum and shows great promise for shape analysis in neuroimaging more broadly.Te study was supported by NHMRC of Australia Grant No. 1002160, 1063907 and ARC Grant 130101705. Tis research was partly undertaken on the National Computational Infrastructure (NCI) facility in Canberra, Australia, which is supported by the Australian Commonwealth Government

Visual and visuomotor interhemispheric transfer time in older adults

Older adults typically experience reductions in the structural integrity of the anterior channels of the corpus callosum. Despite preserved structural integrity in central and posterior channels, many studies have reported that interhemispheric transfer, a function attributed to these regions, is detrimentally affected by aging. In this study, we use a constrained event-related potential analysis in the theta and alpha frequency bands to determine whether interhemispheric transfer is affected in older adults. The crossed-uncrossed difference and lateralized visual evoked potentials were used to assess interhemispheric transfer in young (18–27) and older adults (63–80). We observed no differences in the crossed-uncrossed difference measure between young and older groups. Older adults appeared to have elongated transfer in the theta band potentials, but this effect was driven by shortened contralateral peak latencies, rather than delayed ipsilateral latencies. In the alpha band, there was a trend toward quicker transfer in older adults. We conclude that older adults do not experience elongated interhemispheric transfer in the visuomotor or visual domains and that these functions are likely attributed to posterior sections of the corpus callosum, which are unaffected by aging

White matter connectivity, cognition, symptoms and genetic risk factors in Schizophrenia

Schizophrenia is a highly heritable complex neuropsychiatric disorder with a lifetime prevalence of around 1%. It is often characterised by impaired white matter structural dysconnectivity. In vivo and post-mortem alterations in white matter microstructure have been reported, along with differences in the topology of the structural connectome; overall these suggest a reduced communication between distal brain regions. Schizophrenia is characterised by persistent cognitive impairments that predate the occurrence of symptoms and have been shown to have a neural foundation reflecting aberrant brain connectivity. So far, 179 independent genome-wide significant single nucleotide polymorphisms (SNPs) have been associated with a diagnosis of schizophrenia. The high heritability and polygenicity of schizophrenia, white matter parameters and cognitive functions provides a great opportunity to investigate the potential relationships between them due to the genetic overlap shared among these factors. This work investigates the psychopathology of schizophrenia from a neurobiological, psychological and genetic perspective. The datasets used here include data from the Scottish Family Mental Health (SFMH) study, the Lothian Birth Cohort 1936 (LBC1936) and UK Biobank. The main goal of this thesis was to study white matter microstructure in schizophrenia using diffusion MRI (dMRI) data. Our first aim was to examine whether processing speed mediated the association between white matter structure and general intelligence in patients diagnosed with schizophrenia in the SFMH study. Secondly, we investigated specific networks from the structural connectome and their topological properties in both healthy controls and patients diagnosed with schizophrenia in the SFMH study. These networks were studied alongside cognition, clinical symptoms and polygenic risk factor for schizophrenia (szPGRS). The third aim of this thesis was to study the effects of szPGRS on the longitudinal trajectories of white matter connectivity (measured using tractography and graph theory metrics) in the LBC1936 over a period of three-years. Finally, we derived the salience network which has been previously associated with schizophrenia and examined the effect of szPGRS on the grey matter nodes associated with this network and their connecting white matter tracts in UK Biobank. With regards to the first aim, we found that processing speed significantly mediates the association between a general factor of white matter structure and general intelligence in schizophrenia. These results suggest that, as in healthy controls, processing speed acts as a key cognitive resource facilitating higher order cognition by allowing multiple cognitive processes to be simultaneously available. Secondly, we found that several graph theory metrics were significantly impaired in patients diagnosed with schizophrenia compared with healthy controls. Moreover, these metrics were significantly associated with intelligence. There was a strong tendency towards significance for a correlation between intelligence and szPGRS that was significantly mediated by graph theory metrics in both healthy controls and schizophrenia patients of the SFMH study. These results are consistent with the hypothesis that intelligence deficits are associated with a genetic risk for schizophrenia, which is mediated via the disruption of distributed brain networks. In the LBC1936 we found that higher szPGRS showed significant associations with longitudinal increases in MD in several white matter tracts. Significant declines over time were observed in graph theory metrics. Overall these findings suggest that szPGRS confer risk for ageing-related degradation of some aspects of structural connectivity. Moreover, we found significant associations between higher szPGRS and decreases in cortical thickness, in particular, in a latent factor for cortical thickness of the salience network. Taken together, our findings suggest that white matter connectivity plays a significant role in the disorder and its psychopathology. The computation of the structural connectome has improved our understanding of the topological characteristics of the brain’s networks in schizophrenia and how it relates to the microstructural level. In particular, the data suggests that white matter structure provides a neuroanatomical substrate for cognition and that structural connectivity mediates the relationship between szPGRS and intelligence. Additionally, these results suggest that szPGRS may have a role in age-related changes in brain structural connectivity, even among individuals who are not diagnosed with schizophrenia. Further work will be required to validate these results and will hopefully examine additional risk factors and biomarkers, with the ultimate aims of improving scientific knowledge about schizophrenia and conceivably of improving clinical practice

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

Commissural white matter disconnectivity in normal ageing and Alzheimer’s disease

The network of commissural white matter fibres responsible for connecting the hemispheres of the brain is known as the corpus callosum (CC). Atrophy to the CC is evident in studies of aging and Alzheimer’s disease (AD), but patterns and functional implications of neurodegeneration are still somewhat unclear. In this thesis, neuroimaging methods were used to further examine how structural and functional CC properties are affected by normal ageing and AD. In Study 1, diffusion tensor imaging (DTI) was used to examine the posterior CC tract bundles in young and older adults. Parietal and temporal midsagittal CC segments were particularly impaired in older adults, while occipital tracts were relatively preserved. Study 2 applied this methodology to study Mild Cognitive Impairment (MCI) and AD. MCI patients exhibited reduced integrity in midsagittal parietal segments compared to controls. AD patients exhibited reductions in parietal and temporal segments, yielding high classification accuracy (95-98%) against controls. Study 3 assessed visual interhemispheric transfer in aging using electroencephalography (EEG). Transfer speed was elongated in older adults, but was driven by earlier activation of the input hemisphere rather than delayed activation of the receiving hemisphere. This was not interpreted as impairment in older age, in line with findings of preserved occipital tracts in Study 1. Study 5 examined EEG functional connectivity methodology. We showed that connectivity was strongest at the dominant EEG frequency, which experiences slowing in older age. Previous studies using conventional frequency bands may therefore be biased against older adults. Study 6 applied these findings to study interhemispheric functional connectivity in older adults, while controlling for age-related frequency slowing. Age-related disconnectivity between frontal sites was evident, reflecting typical anterior-posterior neurodegeneration in older adults (Bennett, Madden, Vaidya, Howard, & Howard, 2010). These studies provide novel spatial and methodological insight into the CC during ageing and AD

Characterising population variability in brain structure through models of whole-brain structural connectivity

Models of whole-brain connectivity are valuable for understanding neurological function. This thesis seeks to develop an optimal framework for extracting models of whole-brain connectivity from clinically acquired diffusion data. We propose new approaches for studying these models. The aim is to develop techniques which can take models of brain connectivity and use them to identify biomarkers or phenotypes of disease. The models of connectivity are extracted using a standard probabilistic tractography algorithm, modified to assess the structural integrity of tracts, through estimates of white matter anisotropy. Connections are traced between 77 regions of interest, automatically extracted by label propagation from multiple brain atlases followed by classifier fusion. The estimates of tissue integrity for each tract are input as indices in 77x77 ”connectivity” matrices, extracted for large populations of clinical data. These are compared in subsequent studies. To date, most whole-brain connectivity studies have characterised population differences using graph theory techniques. However these can be limited in their ability to pinpoint the locations of differences in the underlying neural anatomy. Therefore, this thesis proposes new techniques. These include a spectral clustering approach for comparing population differences in the clustering properties of weighted brain networks. In addition, machine learning approaches are suggested for the first time. These are particularly advantageous as they allow classification of subjects and extraction of features which best represent the differences between groups. One limitation of the proposed approach is that errors propagate from segmentation and registration steps prior to tractography. This can cumulate in the assignment of false positive connections, where the contribution of these factors may vary across populations, causing the appearance of population differences where there are none. The final contribution of this thesis is therefore to develop a common co-ordinate space approach. This combines probabilistic models of voxel-wise diffusion for each subject into a single probabilistic model of diffusion for the population. This allows tractography to be performed only once, ensuring that there is one model of connectivity. Cross-subject differences can then be identified by mapping individual subjects’ anisotropy data to this model. The approach is used to compare populations separated by age and gender

Multimodal imaging : functional, structural, and molecular brain correlates of cognitive aging

Aging is associated with a decline in many (but not all) cognitive abilities. Although it remains largely unknown how changes in brain integrity relate to cognitive deficits, these changes are likely expressed across interrelated functional, structural, and molecular layers. This complexity calls for a multimodal imaging approach in age-related mind-brain research. Hence, in this thesis, different imaging modalities were combined in order to study the neural basis of cognitive aging. Study I investigated functional connectivity patterns among three large-scale functional brain networks (i.e., default mode [DMN], frontoparietal control [FPN], and dorsal attention [DAN] networks) during rest and task in younger and older adults. The FPN was flexible in its affiliation to other networks, given that it was more functionally connected to the DMN during rest and to the DAN during task performance. Age-related differences were stable across states for the FPN, but were only present for connectivity between the DMN and DAN during the task. Taken together, these results suggest that resting-state is not sufficient to uncover the entire functional connectome of the human brain. Study II identified brain iron as a potential source of age-related differences in connectivity. Greater striatal iron content was associated with lower intrinsic functional connectivity of the caudate and putamen. Additionally, more iron was associated with less connectivity between the putamen and the rest of the brain. Functional connectivity within the putamen was also linked to motor ability, indicating that iron-related connectivity features are behaviorally meaningful. Study III explored the relationship between functional and structural connectivity, and showed that increased homotopic functional connectivity in the prefrontal cortex was associated with worse microstructural degeneration of the corpus callosum, and exacerbated working memory decline. However, given that the association between function and structure was weak, results also suggest that homotopic functional connectivity can be resilient to change in the integrity of its structural paths. Study IV found that dopamine and iron in the putamen were positively associated, but only up until middle age. Together with the fact that dopamine requires iron for its synthesis, these results indicate that, for individuals without excessive iron accumulation, more iron is associated with higher dopaminergic activity. Higher iron load in the putamen was also linked to better processing speed for those in middle age. Collectively, the studies show that functional connectivity is influenced by mental state, white-matter changes, and molecular properties, with the latter also being interrelated among themselves. These different features are associated with performance and interact with each other, suggesting that cognitive decline is linked to a multitude of changes in brain integrity, and that age-related alterations in the human brain are complex and multifaceted

Mechanisms of cognitive reserve : computational and experimental explorations

Cognitive reserve is the name given to the latent variable that describes individual differences in the ability to offset cognitive decline in old age. This thesis attempts to provide mechanistic explanations for two major aspects of cognitive reserve. These are neural compensation and neural reserve. Furthermore, behavioural experiments carried out as part of this investigation have extended the knowledge of existing theories as to the age invariance of neural compensation and the relationship between language, other more traditional proxies of cognitive reserve, and executive control. The results of these studies carried out in this thesis have demonstrated a biologically viable mechanism for the monitoring of task demand with resultant control of interhemispheric communication as a method of compensation. Further, this aspect of neural compensation was not found in younger participants. The neural network model in this thesis demonstrated differences over age in the spacing of representations for bilingual and monolingual networks as well as demonstrating increased inhibition in the bilingual network as a result of a negative relationship between weights from the tags of each language to nodes in the hidden layer. Finally, regression analysis using data from two large scale behavioural experiments demonstrated a minimal influence of bilingual language use on performance in executive control tasks. The models in this thesis provide an insight into the mechanisms behind cognitive reserve whilst supporting empirical results. Further, the results from the neural network model allowed predictions to be made with regard to the performance of bilinguals in dual category retrieval tasks. The lack of a relationship between bilingualism and cognitive control is supported by emerging research in the area and suggests that the functionality underlying cognitive reserve may be better described by biological rather than cognitive processes

Characterizing structural neural networks in major depressive disorder using diffusion tensor imaging

Diffusion tensor imaging (DTI) is a noninvasive MRI technique used to assess white matter (WM) integrity, fiber orientation, and structural connectivity (SC) using water diffusion properties. DTI techniques are rapidly evolving and are now having a dramatic effect on depression research. Major depressive disorder (MDD) is highly prevalent and a leading cause of worldwide disability. Despite decades of research, the neurobiology of MDD remains poorly understood. MDD is increasingly viewed as a disorder of neural circuitry in which a network of brain regions involved in mood regulation is dysfunctional. In an effort to better understand the neurobiology of MDD and develop more effective treatments, much research has focused on delineating the structure of this mood regulation network. Although many studies have focused on the structural connectivity of the mood regulation network, findings using DTI are highly variable, likely due to many technical and analytical limitations. Further, structural connectivity pattern analyses have not been adequately utilized in specific clinical contexts where they would likely have high relevance, e.g., the use of white matter deep brain stimulation (DBS) as an investigational treatment for depression. In this dissertation, we performed a comprehensive analysis of structural WM integrity in a large sample of depressed patients and demonstrated that disruption of WM does not play a major role in the neurobiology of MDD. Using graph theory analysis to assess organization of neural network, we elucidated the importance of the WM network in MDD. As an extension of this WM network analysis, we identified the necessary and sufficient WM tracts (circuit) that mediate the response of subcallosal cingulate cortex DBS treatment for depression; this work showed that such analyses may be useful in prospective target selection. Collectively, these findings contribute to better understanding of depression as a neural network disorder and possibly will improve efficacy of SCC DBS.Ph.D