Linear Mixed Models Minimise False Positive Rate and Enhance Precision of Mass Univariate Vertex-Wise Analyse of Grey-Matter

International audienceWe evaluated the statistical power, family wise error rate (FWER) and precision of several competing methods that perform mass-univariate vertex-wise analyses of grey-matter (thickness and surface area). In particular, we compared several generalised linear models (GLMs, current state of the art) to linear mixed models (LMMs) that have proven superior in genomics. We used phenotypes simulated from real vertex-wise data and a large sample size (N=8,662) which may soon become the norm in neuroimaging. No method ensured a FWER<5% (at a vertex or cluster level) after applying Bonferroni correction for multiple testing. LMMs should be preferred to GLMs as they minimise the false positive rate and yield smaller clusters of associations. Associations on real phenotypes must be interpreted with caution, and replication may be warranted to conclude about an association

Large-scale inference in the focally damaged human brain

Clinical outcomes in focal brain injury reflect the interactions between two distinct anatomically distributed patterns: the functional organisation of the brain and the structural distribution of injury. The challenge of understanding the functional architecture of the brain is familiar; that of understanding the lesion architecture is barely acknowledged. Yet, models of the functional consequences of focal injury are critically dependent on our knowledge of both. The studies described in this thesis seek to show how machine learning-enabled high-dimensional multivariate analysis powered by large-scale data can enhance our ability to model the relation between focal brain injury and clinical outcomes across an array of modelling applications. All studies are conducted on internationally the largest available set of MR imaging data of focal brain injury in the context of acute stroke (N=1333) and employ kernel machines at the principal modelling architecture. First, I examine lesion-deficit prediction, quantifying the ceiling on achievable predictive fidelity for high-dimensional and low-dimensional models, demonstrating the former to be substantially higher than the latter. Second, I determine the marginal value of adding unlabelled imaging data to predictive models within a semi-supervised framework, quantifying the benefit of assembling unlabelled collections of clinical imaging. Third, I compare high- and low-dimensional approaches to modelling response to therapy in two contexts: quantifying the effect of treatment at the population level (therapeutic inference) and predicting the optimal treatment in an individual patient (prescriptive inference). I demonstrate the superiority of the high-dimensional approach in both settings

Right ventricular biomechanics in pulmonary hypertension

As outcome in pulmonary hypertension is strongly associated with progressive right ventricular dysfunction, the work in this thesis seeks to determine the regional distribution of forces on the right ventricle, its geometry, and deformations subsequent to load. This thesis contributes to the understanding of how circulating biomarkers of energy metabolism and stress-response pathways are related to adverse cardiac remodelling and functional decompensation. A numerical model of the heart was used to derive a three-dimensional representation of right ventricular morphology, function and wall stress in pulmonary hypertension patients. This approach was tested by modelling the effect of pulmonary endarterectomy in patients with chronic thromboembolic disease. The relationship between the cardiac phenotype and 10 circulating metabolites, known to be associated with all-cause mortality, was assessed using mass univariate regression. Increasing afterload (mean pulmonary artery pressure) was significantly associated with hypertrophy of the right ventricular inlet and dilatation, indicative of global eccentric remodelling, and decreased systolic excursion. Right ventricular ejection fraction was found to be negatively associated with 3-hydroxy-3-methylglutarate, N-formylmethionine, and fumarate. Wall stress was related to all-cause mortality and its decrease after pulmonary endarterectomy was associated with a fall in brain natriuretic peptide. Six metabolites were associated with elevated end-systolic wall stress: dehydroepiandrosterone sulfate, N2,N2-dimethylguanosine, N1-methylinosine, 3-hydroxy-3-methylglutarate, N-acetylmethionine, and N-formylmethionine. Metabolic profiles related to energy metabolism and stress-response are associated with elevations in right ventricular end-systolic wall stress that have prognostic significance in pulmonary hypertension patients. These results show that statistical parametric mapping can give regional information on the right ventricle and that metabolic phenotyping, as well as predicting outcomes, provides markers informative of the biomechanical status of the right ventricle in pulmonary hypertension.Open Acces

Statistical analysis for longitudinal MR imaging of dementia

Serial Magnetic Resonance (MR) Imaging can reveal structural atrophy in the brains of subjects with neurodegenerative diseases such as Alzheimer’s Disease (AD). Methods of computational neuroanatomy allow the detection of statistically significant patterns of brain change over time and/or over multiple subjects. The focus of this thesis is the development and application of statistical and supporting methodology for the analysis of three-dimensional brain imaging data. There is a particular emphasis on longitudinal data, though much of the statistical methodology is more general. New methods of voxel-based morphometry (VBM) are developed for serial MR data, employing combinations of tissue segmentation and longitudinal non-rigid registration. The methods are evaluated using novel quantitative metrics based on simulated data. Contributions to general aspects of VBM are also made, and include a publication concerning guidelines for reporting VBM studies, and another examining an issue in the selection of which voxels to include in the statistical analysis mask for VBM of atrophic conditions. Research is carried out into the statistical theory of permutation testing for application to multivariate general linear models, and is then used to build software for the analysis of multivariate deformation- and tensor-based morphometry data, efficiently correcting for the multiple comparison problem inherent in voxel-wise analysis of images. Monte Carlo simulation studies extend results available in the literature regarding the different strategies available for permutation testing in the presence of confounds. Theoretical aspects of longitudinal deformation- and tensor-based morphometry are explored, such as the options for combining within- and between-subject deformation fields. Practical investigation of several different methods and variants is performed for a longitudinal AD study

Neural mechanisms of visual awareness and their modulation by social threat

The human brain can extract an enormous wealth of visual information from our surroundings. However, only a fraction of this immense data set ever becomes available to the observer’s awareness. How and why certain information is selected for awareness are questions under active investigation. Following two introductory chapters, this thesis contains six inter-related experimental chapters, through which I will explore two key outstanding questions in this field, using bistable perceptual paradigms to study conscious and non-conscious visual processing in healthy human volunteers. The first major theme focuses on adding new insight into the brain regions and networks that facilitate transfer between non-conscious and conscious modes of visual processing. In Chapters 3 and 4 I will use fMRI, both in task-related and resting-state conditions, to delineate areas, and their interactions (in terms of effective connectivity), which are relevant for transition between different conscious perceptual experiences. In Chapter 5 I will focus on one specific region in the proposed perceptual transition-related network (the frontal eye field) and explore its causal role in access to awareness using repetitive TMS. The second key question explored in this thesis is how social cues in the visual environment influence non-conscious visual processing as well as transition to conscious vision. In Chapter 6 I will study behavioural effects of non-conscious social cues from faces, and the relationship of these effects to focal brain anatomy. Based on finding slower perceptuomotor performance when non-conscious faces contain threatening cues in Chapter 6, I hypothesise that a defensive freezing response is engaged in such situations. The final two experimental chapters will explore the correlates of putative human freezing in the context of non-conscious social threat: using fMRI and psychophysiological measurements to study effects on perceptual transition in Chapter 7, and relating TMS-induced motor-evoked potentials and concurrent psychophysiological measurements to non-conscious perceptuomotor performance in Chapter 8. Taken together, the presented findings shed new light on the network of brain regions involved in transition between non-conscious and conscious modes of visual processing. In addition, they uncover novel mechanisms through which socially relevant visual cues shape our awareness of the visual world, with particular emphasis on the engagement of defensive responses by socially threatening stimuli. The concluding chapter discusses the implications of these findings and explores relevant avenues for future work

Action control in uncertain environments

A long-standing dichotomy in neuroscience pits automatic or reflexive drivers of behaviour against deliberate or reflective processes. In this thesis I explore how this concept applies to two stages of action control: decision-making and response inhibition. The first part of this thesis examines the decision-making process itself during which actions need to be selected that maximise rewards. Decisions arise through influences from model-free stimulus-response associations as well as model-based, goal-directed thought. Using a task that quantifies their respective contributions, I describe three studies that manipulate the balance of control between these two systems. I find that a pharmacological manipulation with levodopa increases model-based control without affecting model-free function; disruption of dorsolateral prefrontal cortex via magnetic stimulation disrupts model-based control; and direct current stimulation to the same prefrontal region has no effect on decision-making. I then examine how the intricate anatomy of frontostriatal circuits subserves reinforcement learning using functional, structural and diffusion magnetic resonance imaging (MRI). A second stage of action control discussed in this thesis is post-decision monitoring and adjustment of action. Specifically, I develop a response inhibition task that dissociates reactive, bottom-up inhibitory control from proactive, top-down forms of inhibition. Using functional MRI I show that, unlike the strong neural segregation in decision-making systems, neural mechanisms of reactive and proactive response inhibition overlap to a great extent in their frontostriatal circuitry. This leads to the hypothesis that neural decline, for 4 example in the context of ageing, might affect reactive and proactive control similarly. I test this in a large population study administered through a smartphone app. This shows that, against my prediction, reactive control reliably declines with age but proactive control shows no such decline. Furthermore, in line with data on gender differences in age-related neural degradation, reactive control in men declines faster with age than that of women

Quantitative PET and SPECT

Since the introduction of personalized medicine, the primary focus of imaging has moved from detection and diagnosis to tissue characterization, the determination of prognosis, prediction of treatment efficacy, and measurement of treatment response. Precision (personalized) imaging heavily relies on the use of hybrid technologies and quantitative imaging biomarkers. The growing number of promising theragnostics require accurate quantification for pre- and post-treatment dosimetry. Furthermore, quantification is required in the pharmacokinetic analysis of new tracers and drugs and in the assessment of drug resistance. Positron Emission Tomography (PET) is, by nature, a quantitative imaging tool, relating the time–activity concentration in tissues and the basic functional parameters governing the biological processes being studied. Recent innovations in single photon emission computed tomography (SPECT) reconstruction techniques have allowed for SPECT to move from relative/semi-quantitative measures to absolute quantification. The strength of PET and SPECT is that they permit whole-body molecular imaging in a noninvasive way, evaluating multiple disease sites. Furthermore, serial scanning can be performed, allowing for the measurement of functional changes over time during therapeutic interventions. This Special Issue highlights the hot topics on quantitative PET and SPECT

The neurobiological basis of gait dysfunction in Parkinson’s disease: A cross-sectional and longitudinal approach

Ph. D. Thesis.Gait impairments are a cardinal feature of Parkinson’s disease (PD) and significantly affect the well-being of patients. However, current therapies are not effective at improving specific aspects of gait in PD nor preventing them from worsening over time. This is largely due to poor understanding of the mechanisms that the brain uses to control discrete gait characteristics in PD. The aim of this thesis was, therefore, to investigate associations between the brain and gait characteristics in PD, using both cross-sectional and longitudinal analytical approaches. Newly diagnosed PD participants (n=99) and age-matched controls (n=47) completed quantitative gait, structural magnetic resonance imaging and clinical assessments soon after diagnosis; additional gait assessments were completed every 18 months for up to six years. Partial correlations and linear regression analyses determined cross-sectional associations between regional brain volumes and gait. Linear mixed-effects models identified gait characteristics that changed over six years in PD, more so than in controls, and assessed the predictive nature of regional brain volumes on gait changes. Original contributions to knowledge were that regional brain volumes selectively associated with discrete gait characteristics in PD; many associations were unique to PD, even in early disease. Brain regions which primarily relate to both motor and non-motor functions correlated with PD gait impairment. Associations with non-motor structures might be attributable to contributions from the cortical cholinergic system, given its role in maintaining gait in PD. This thesis provides evidence for the reliance on alternative and compensatory neural mechanisms during PD gait. Additionally, this thesis demonstrates the first evidence for regional brain volumes predicting disease-specific changes in gait. This not only provides greater understanding of neural underpinnings of gait dysfunction in PD, but demonstrates the potential for regional brain volumes to be considered clinically as an indicator of those at greater risk of mobility loss and fallsWellcome Trust, Parkinson’s U

Linear mixed models minimise false positive rate and enhance precision of mass univariate vertex-wise analyses of grey-matter

We evaluated the statistical power, family wise error rate (FWER) and precision of several competing methods that perform mass-univariate vertex-wise analyses of grey-matter (thickness and surface area). In particular, we compared several generalised linear models (GLMs, current state of the art) to linear mixed models (LMMs) that have proven superior in genomics. We used phenotypes simulated from real vertex-wise data and a large sample size (\mathrm{N}=8,662) which may soon become the norm in neuroimaging. No method ensured a \text{FWER} < 5{\%} (at a vertex or cluster level) after applying Bonferroni correction for multiple testing. LMMs should be preferred to GLMs as they minimise the false positive rate and yield smaller clusters of associations. Associations on real phenotypes must be interpreted with caution, and replication may be warranted to conclude about an association