La substance grise périaqueducale (PAG) est une région cérébrale impliquée dans l’expression des réponses émotionnelles chez les mammifères et est décrite comme la structure de la stratégie d’adaptation comportementale (coping style) chez le rat et le chat. La PAG est composée de plusieurs subdivisions qui se distinguent par des spécificités fonctionnelles et anatomiques. En particulier, elles présentent des spécificités de connexions avec le reste du cerveau. Afin d’examiner la place de la PAG dans le circuit neuronal des émotions chez le mouton, animal grégaire, nous avons décrit les connexions de la PAG par traçage de voies et tractographie par imagerie par résonance magnétique de diffusion. Nous avons ainsi montré que la PAG ovine est composée de subdivisions qui ont des connexions avec des structures cérébrales impliquées dans les émotions. Ces résultats, cohérents avec ceux obtenus chez d’autres mammifères, placent la PAG dans le circuit neuronal des émotions. Notre étude montre également que l’organisation des connexions de la PAG ovine est d’avantage similaire à celles décrites chez des espèces sociales qu’à celles décrites chez des espèces territoriales et/ou prédatrices. Suite aux connaissances acquises dans ces études, nous pouvons maintenant initier des études fonctionnelles et ainsi confirmer le rôle de la PAG ovine dans les processus émotionnels. / The periaqueductal gray matter (PAG) is a brain region involved in the expression of emotional responses in mammals and is described as the structure of the coping style of behaviours in rats and cats. The PAG is composed of several subdivisions that are distinguished by functional and anatomical specificities. Particularly, they have connections specificities with the rest of the brain. In order to examine the place of the PAG in the neuronal circuit of emotions in sheep, a gregarious species, we described the PAG connections using neuronal tracer and tractography by diffusion magnetic resonance imaging. In this way, we have shown that the sheep PAG is composed of subdivisions which have connections with brain structures involved in emotions. These results, consistent with those obtained in other mammals, place PAG in the neuronal circuit of emotions. Our study also shows that the organization of the sheep PAG connections is more similar to those described in social species than those described in territorial and/or predatory species. Following the knowledge obtained in these studies, now we can initiate functional studies and thus confirm the role of the sheep PAG in emotional processes.

The reading network is only partially understood and even less is known regarding how the network functions when reading is impaired. Dyslexia is characterized by poor phonological processing and affects roughly 5-12% of the population. The Dorsal-Ventral and Cerebellar-Deficit models propose distinct behavioral and structural differences in young adults with dyslexia. Behavioral assessments were used to determine if deficits for young adults with dyslexia were restricted to the literacy domain or dispersed among reading and associated behavioral domains. Diffusion tensor imaging (DTI) was used determine the extent to which white matter pathways and gray matter regions differ structurally in young adults with dyslexia. The present study also investigated whether brain-behavior relationships exist and are consistent with the theoretical models of reading in this population. Findings show that young adults with dyslexia exhibited deficits in both literacy and associated behavioral domains, including verbal working memory and motor function. Structural findings showed increased fractional anisotropy in the left anterior region (the aslant) and decreased fractional anisotropy in left posterior regions (inferior occipital fasciculus and vertical occipital fasciculus) of the reading network for young adults with dyslexia. Brain-behavior associations were found between the right inferior frontal gyrus and decoding for those with dyslexia. These findings provide support for the use of an altered reading network by young adults with dyslexia, as outlined by the Dorsal-Ventral model of reading. Limited structural and behavior findings support of the Cerebellar-Deficit model of reading, findings that warrant additional investigation.

Diffusion tensor imaging (DTI) is a relatively new technology of magnetic resonance imaging, which enables us to observe the insight structure of the human body in vivo and non-invasively. It displays water molecule movement by a 3×3 diffusion tensor at each voxel. Tensor field processing, visualisation and tractography are all based on the diffusion tensors. The accuracy of estimating diffusion tensor is essential in DTI. This research focuses on exploring the potential improvements at the tensor estimation of DTI. We analyse the noise arising in the measurement of diffusion signals. We present robust methods, least median squares (LMS) and least trimmed squares (LTS) regressions, with forward search algorithm that reduce or eliminate outliers to the desired level. An investigation of the criterion to detect outliers is provided in theory and practice. We compare the results with the generalised non-robust models in simulation studies and applicants and also validated various regressions in terms of FA, MD and orientations. We show that the robust methods can handle the data with up to 50% corruption. The robust regressions have better estimations than generalised models in the presence of outliers. We also consider the multiple tensors problems. We review the recent techniques of multiple tensor problems. Then we provide a new model considering neighbours' information, the Bayesian single and double tensor models using neighbouring tensors as priors, which can identify the double tensors effectively. We design a framework to estimate the diffusion tensor field with detecting whether it is a single tensor model or multiple tensor model. An output of this framework is the Bayesian neighbour (BN) algorithm that improves the accuracy at the intersection of multiple fibres. We examine the dependence of the estimators on the FA and MD and angle between two principal diffusion orientations and the goodness of fit. The Bayesian models are applied to the real data with validation. We show that the double tensors model is more accurate on distinct fibre orientations, more anisotropic or similar mean diffusivity tensors. The final contribution of this research is in covariance tensor estimation. We define the median covariance matrix in terms of Euclidean and various non-Euclidean metrics taking its symmetric semi-positive definiteness into account. We compare with estimation methods, Euclidean, power Euclidean, square root Euclidean, log-Euclidean, Riemannian Euclidean and Procrustes median tensors. We provide an analysis of the different metric between different median covariance tensors. We also provide the weighting functions and define the weighted non-Euclidean covariance tensors. We finish with manifold-valued data applications that improve the illustration of DTI images in tensor field processing with defined non-weighted and weighted median tensors. The validation of non-Euclidean methods is studied in the tensor field processing. We show that the root square median estimator is preferable in general, which can effectively exclude outliers and clearly shows the important structures of the brain. The power Euclidean median estimator is recommended when producing FA map.

Effective memory representations must be specific to prevent interference between episodes that may overlap in terms of place, time, or items present. Pattern separation, a computational process performed by the hippocampus overcomes this interference by establishing non-overlapping memory representations. This project explores pattern separation and how it is impacted by age and sleep. Experiment 1. Structures of the medial temporal lobe (MTL) are known to be involved in declarative memory processes. However, little is known about how age-related changes in MTL structures, white matter integrity, and functional connectivity affect pattern separation processes in the MTL. In the present study, we used magnetic resonance imaging (MRI) to measure the volumes of MTL regions of interest, including hippocampal subfields (dentate gyrus, CA3, CA1, and subiculum) in healthy older and younger adults. Additionally, we used diffusion tensor imaging to measure white matter integrity for both groups. Finally, we used functional MRI to acquire resting functional connectivity measures for both groups. We show that, along with age, the volume of left CA3/dentate gyrus predicts memory performance. Differences in fractional anisotropy and the strength of resting functional connections between the hippocampus and other cortical structures implicated in memory processing were not significant predictors of performance. As previous studies have only hinted, it seems that the size of left CA3/dentate gyrus contributes more to successful discrimination between similar mnemonic representations than other hippocampal sub-fields, MTL structures, and other neuroimaging correlates. Accordingly, the implications of aging and atrophy on lure discrimination capacities are discussed. Experiment 2. Although it is widely accepted that declarative memories are consolidated during sleep, the effects of sleep on pattern separation have yet to be elucidated. We used whole-brain, high-resolution functional neuroimaging to investigate the effects of sleep on a task that places high demands on pattern separation. Sleep had a selective effect on memory specificity and not general recognition memory. Activity in brain regions related to attention, visual acuity, and visual recall demonstrated an interaction between sleep and delay. Surprisingly, there was no effect of sleep on hippocampal activity using a group-level analysis. To further understand the role of the hippocampus on our task, we performed a representational similarity analysis. We investigated whether hippocampal activity associated with looking at novel stimuli correlated more with similar-looking (lure) stimuli or repeated stimuli. Results indicate that while a single night's sleep does not significantly impact hippocampal responses, the hippocampus does treat lure stimuli similarly as it does novel stimuli.

This thesis describes the research which has been undertaken into a particular area of policy making in the UK, that of the process of designing and implementing programmes aimed at helping industrial firms to become more competitive. Investigations have focused on how the design process is conducted within the Department of Trade and Industry (DTI), which has lead responsibility for industry in Whitehall. The research had Uvo primary aims. First was to provide a detailed description of the process of designing programmes. Based on the research findings it is proposed that the process comprises the components of'Issue Identification', 'Programme Implementation', and 'Evaluation and Feedback'. The thesis discusses the private nature of the work involved in programme design, and that consequently researchers are often unable to directly observe the activities comprising the process. It is suggested that the veil of secrecy surrounding the development of programmes has prevented substantial debate of this research topic. As a civil servant employed in the DTI, the author has been able to review the activities involved 'first hand', and uncover numerous aspects of the process previously not investigated. Based on the analysis of five case study examples, a systems model has been developed which provides a detailed description o f the structure of the design process, and the mechanisms that are employed. The second aim of the research was to develop proposals for improving current arrangements, towards achieving better value for money in the design and operation of support programmes. The thesis describes how a Business Process Re-engineering approach was adopted to exploit the detailed knowledge of the design system which had been gained, with the aim of discovering deficiencies in the current process and developing proposals for. overcoming problems. Investigations showed that the current guidance provided to officials employed in programme design is inadequate in fully supporting them in the task. It is suggested that this deficiency can be overcome through the introduction of a new set of comprehensive guidance, to be contained in an alternative document referred to as the Handbook for Programme Design and Operation. The handbook, it is proposed, would comprise good practice advice across the broad range of activities involved in programme design. Proposals for further improving the design process through the introduction of effective knowledge management were also developed, and these are again set out in the thesis.

Given the difficulty in predicting outcomes in persons with stroke-induced aphasia (PWA), neuroimaging-based biomarkers of recovery could provide invaluable predictive power to stroke models. However, the neural patterns that constitute beneficial neural organization of language in PWA remain debated. Thus, in this work, we propose a novel network theory of aphasia recovery and test our overarching hypothesis, i.e., that task-specific language processing in PWA requires the dynamic engagement of intact tissue within a bilateral network of anatomically-segregated but functionally and structurally connected language-specific and domain-general brain regions. We first present two studies in which we examined left frontotemporal connectivity during different language tasks (i.e., picture naming and semantic feature verification). Results suggest that PWA heavily rely on left middle frontal gyrus (LMFG)-driven connectivity for tasks requiring lexical-semantic processing and semantic control whereas controls prefer models with input to either LMFG or left inferior frontal gyrus (LIFG). Both studies also revealed several significant associations between spared tissue, connectivity and language skills in PWA. In the third study, we examined bilateral frontotemporoparietal connectivity and tested a lesion- and connectivity-based hierarchical model of chronic aphasia recovery. Between-group comparisons showed controls exhibited stronger left intra-hemispheric task-modulated connectivity than did PWA. Connectivity and language deficit patterns most closely matched predictions for patients with primarily anterior damage whereas connectivity results for patients with other lesion types were best explained by the nature of the semantic task. In the last study, we investigated the utility of lesion classification based on gray matter (GM) only versus combined GM plus white matter (WM) metrics. Results suggest GM only classification was sufficient for characterizing aphasia and anomia severity but the GM+WM classification better predicted naming treatment outcomes. We also found that fractional anisotropy of left WM association tracts predicted baseline naming and treatment outcomes independent of total lesion volume. Finally, results of a preliminary multimodal prediction analysis suggest that combined structural and functional metrics reflecting the integrity of regions and connections comprise optimal predictive models of behavior in PWA. To conclude this dissertation, we discuss how multimodal network models of aphasia recovery can guide future investigations. / 2020-10-23T00:00:00Z

Introduction: Schizotypy is a personality trait that shares some of the characteristics of clinical disorders such as schizophrenia. Similarities are found in expression of psychotic-like experiences and presence of attenuated negative signs. Furthermore, schizotypal samples are associated with impairments in cognitive tasks, albeit in a less comprised form. For these reasons and others, schizotypy is considered a part of the extended-phenotype of schizophrenia and as such can be utilised as an analogue sample without some of theconfounds associated with illness. Objective: The aim of the PhD is to examine the relationship of schizotypal features and brain function and structure in a sample of adolescents and young adults (age 16-25 years). This will attempt to provide further evidence for the placement of schizotypy on the continuum, along with insights into pathophysiological mechanisms involved in schizophrenia and related disorders. Methods: The study involved three main phases: recruitment via an online survey, further neuropsychological testing and brain imaging on selected high schizotypes and controls. The thesis comprises 5 papers/experiments. Paper 1 utilises confirmatory factor analysis (CFA) to examine the factorial structure of the schizotypal personality questionnaire (SPQ) in a community sample aged 16-25 years. It also examined the effects of demographics on schizotypal levels. Paper 2 examined the association between schizotypy and measures of sustained attention and spatial working memory both in a total sample, and in samples split by age and by sex. Paper 3 further examined the association between schizotypy and cognition laboratory tests of attention, executive function and verbal learning/memory. Paper 4 tested the same participants on measures of functional brain asymmetry. Paper 5 used diffusion tensor imaging (DTI) to examine white matter structures in a sample of high schizotypes and controls. Results: Paper 1 confirmed that the SPQ is most appropriately modelled by a four-factor structure in an adolescent and young adult sample. Demographic effects on SPQ subscales scores mirrored those seen in clinical samples. Paper 2 found that where small associations between schizotypy and sustained attention/spatial working memory function occurred, these were in relation to either age of sex. Paper 3 demonstrated an association between increased schizotypal features and a slight reduction in performance on verbal learning/memory, but no association with tasks of executive function or attention. In Paper 4, schizotypy was associated with a left-hemifield bias on a computerised line bisection task. Paper 5 found that a group of high schizotypes had an increase in tract coherence in the uncinate fasciculus compared to controls. Furthermore, increasing subclinical hallucinatory experiences were associated with increased tract coherence in the right hemisphere arcuate fasciculus. Conclusions: Schizotypy was associated with changes in brain function and structure similar to that demonstrated in more serious mental illness, although to a lesser degree. The current studies suggested that schizotypy is associated with relatively intact prefrontal function, but slight performance bias on measures of medial temporal lobe function. There was also evidence for structural brain changes in schizotypes, with these being indicative of either a protective factor, or a marker of a pathological process. Correlations between hallucinatory experiences and white matter tracts between language regions support theories implicating hyperconnectivity and presentation of symptoms in clinical groups. The functional and structural data collected from this study suggests that the ‘schizotypal’ brain may represent an ‘early’ stage of pathology, but which is likely to be compensated enough such that transition to serious mental illness is unlikely. Further studies could examine similarities and differences between the schizotypal profile and clinical conditions, which would provide further insights into aetiological mechanisms in schizophrenia/psychosis.

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Machine learning (ML) and deep learning (DL) are powerful techniques that allow for analysis and classification of large MRI datasets. With the growing accessibility of high-powered computing and large data storage, there has been an explosive interest in their uses for assisting clinical analysis and interpretation. Though these methods can provide insights into the data which are not possible through human analysis alone, they require significantly large datasets for training which can difficult for anyone (researcher and clinician) to obtain on their own. The growing use of publicly available, multi-site databases helps solve this problem. Inadvertently, however, these databases can sometimes contain outliers or incorrectly labeled data as the subjects may or may not have subclinical or underlying pathology unbeknownst to them or to those who did the data collection. Due to the outlier sensitivity of ML and DL techniques, inclusion of such data can lead to poor classification rates and subsequent low specificity and sensitivity. Thus, the focus of this work was to evaluate large brain MRI datasets, specifically diffusion tensor imaging (DTI), for the presence of anomalies and to validate and compare different methods of anomaly detection. A total of 1029 male and female subjects ages 22 to 35 were downloaded from a global imaging repository and divided into 6 cohorts depending on their age and sex. Care was made to minimize variance due to hardware and hence only data from a specific vendor (General Electric Healthcare) and MRI B0 field strength (i.e. 3 Tesla) were obtained. The raw DTI data (i.e. in this case DICOM images) was first preprocessed into scalar metrics (i.e. FA, RD, AD, MD) and warped to MNI152 T1 1mm standardized space using the FMRIB software library (FSL). Subsequently data was segmented into regions of interest (ROI) using the JHU DTI-based white-matter atlas and a mean was calculated for each ROI defined by that atlas. The ROI data was standardized and a Z-score, for each ROI over all subjects, was calculated. Four different algorithms were used for anomaly detection, including Z-score outlier detection, maximum likelihood estimator (MLE) and minimum covariance determinant (MCD) based Mahalanobis distance outlier detection, one-class support vector machine (OCSVM) outlier detection, and OCSVM novelty detection trained on MCD based Mahalanobis distance data. The best outlier detector was found to be MCD based Mahalanobis distance, with the OCSVM novelty detector performing exceptionally well on the MCD based Mahalanobis distance data. From the results of this study, it is clear that these global databases contain outliers within their healthy control datasets, further reinforcing the need for the inclusion of outlier or novelty detection as part of the preprocessing pipeline for ML and DL related studies. / Thesis / Master of Applied Science (MASc) / Artificial intelligence (AI) refers to the ability of a computer or robot to mimic human traits such as problem solving or learning. Recently there has been an explosive interest in its uses for assisting in clinical analysis. However, successful use of these methods require a significantly large training set which can often contain outliers or incorrectly labeled data. Due to the sensitivity of these techniques to outliers, this often leads to poor classification rates as well as low specificity and sensitivity. The focus of this work was to evaluate different methods of outlier detection and investigate the presence of anomalies in large brain MRI datasets. The results of this study show that these large brain MRI datasets contain anomalies and provide a method best fit for identifying them.