Modelling prognostic trajectories in Alzheimer’s disease

Progression to dementia due to Alzheimer’s Disease (AD) is a long and protracted process that involves multiple pathways of disease pathophysiology. Predicting these dynamic changes has major implications for timely and effective clinical management in AD. There are two reasons why at present we lack appropriate tools to make such predictions. First, a key feature of AD is the interactive nature of the relationships between biomarkers, such as accumulation of β-amyloid -a peptide that builds plaques between nerve cells-, tau -a protein found in the axons of nerve cells- and widespread neurodegeneration. Current models fail to capture these relationships because they are unable to successfully reduce the high dimensionality of biomarkers while exploiting informative multivariate relationships. Second, current models focus on simply predicting in a binary manner whether an individual will develop dementia due to AD or not, without informing clinicians about their predicted disease trajectory. This can result in administering inefficient treatment plans and hindering appropriate stratification for clinical trials. In this thesis, we overcome these challenges by using applied machine learning to build predictive models of patient disease trajectories in the earliest stages of AD. Specifically, to exploit the multi-dimensionality of biomarker data, we used a novel feature generation methodology Partial Least Squares regression with recursive feature elimination (PLSr-RFE). This method applies a hybrid-feature selection and feature construction method that captures co-morbidities in cognition and pathophysiology, resulting in an index of Alzheimer’s disease atrophy from structural MRI. We validated our choice of biomarker and the efficacy of our methodology by showing that the learnt pattern of grey matter atrophy is highly predictive of tau accumulation in an independent sample. Next, to go beyond predicting binary outcomes to deriving individualised prognostic scores of cognitive decline due to AD, we used a novel trajectory modelling approach (Generalised Metric Learning Vector Quantization – Scalar projection) that mines multimodal data from large AD research cohorts. Using this approach, we derive individualised prognostic scores of cognitive decline due to AD, revealing interactive cognitive, and biological factors that improve prediction accuracy. Next, we extended our machine learning framework to classify and stage early AD individuals based on future pathological tau accumulation. Our results show that the characteristic spreading pattern of tau in early AD can be predicted by baseline biomarkers, particularly when stratifying groups using multimodal data. Further, we showed that our prognostic index predicts individualised rates of future tau accumulation with high accuracy and regional specificity in an independent sample of cognitively unimpaired individuals. Overall, our work used machine learning to combine continuous information from AD biomarkers predicting pathophysiological changes at different stages in the AD cascade. The approaches presented in this thesis provide an excellent framework to support personalised clinical interventions and guide effective drug discovery trials

Discovering Biomarkers of Alzheimer's Disease by Statistical Learning Approaches

In this work, statistical learning approaches are exploited to discover biomarkers for Alzheimer's disease (AD). The contributions has been made in the fields of both biomarker and software driven studies. Surprising discoveries were made in the field of blood-based biomarker search. With the inclusion of existing biological knowledge and a proposed novel feature selection method, several blood-based protein models were discovered to have promising ability to separate AD patients from healthy individuals. A new statistical pattern was discovered which can be potential new guideline for diagnosis methodology. In the field of brain-based biomarker, the positive contribution of covariates such as age, gender and APOE genotype to a AD classifier was verified, as well as the discovery of panel of highly informative biomarkers comprising 26 RNA transcripts. The classifier trained by the panetl of genes shows excellent capacity in discriminating patients from control. Apart from biomarker driven studies, the development of statistical packages or application were also involved. R package metaUnion was designed and developed to provide advanced meta-analytic approach applicable for microarray data. This package overcomes the defects appearing in previous meta-analytic packages { 1) the neglection of missing data, 2) the in exibility of feature dimension 3) the lack of functions to support post-analysis summary. R package metaUnion has been applied in a published study as part of the integrated genomic approaches and resulted in significant findings. To provide benchmark references about significance of features for dementia researchers, a web-based platform AlzExpress was built to provide researchers with granular level of differential expression test and meta-analysis results. A combination of fashionable big data technologies and robust data mining algorithms make AlzExpress flexible, scalable and comprehensive platform of valuable bioinformatics in dementia research.Plymouth Universit

Basic prediction mechanisms as a precursor for schizophrenia studies

Traditionally, early visual cortex (V1-3) was thought of as merely a relay centre for feedforward retinal input, providing entry to the cortical visual processing steam. However, in addition to feedforward retinal input, V1 receives a large amount of intracortical information through feedback and lateral connections. Human visual perception is constructed from combining feedforward inputs with these feedback and lateral contributions. Feedback connections allow the visual cortical response to feedforward information to be affected by expectation, knowledge, and context; even at the level of early visual cortex. In Chapter 1 we discuss the feedforward and feedback visual processing streams. We consider historical philosophical and scientific propositions about constructive vision. We introduce modern theories of constructive vision, which suggest that vision is an active process that aims to infer or predict the cause of sensory inputs. We discuss how V1 therefore represents not only retinal input but also high-level effects related to constructive predictive perception. Visual illusions are a ‘side effect’ of constructive and inferential visual perception. For the vast majority of stimulus inputs, integration with context and knowledge facilitates clearer, more veridical perception. In illusion these constructive mechanisms produce incorrect percepts. Illusory effects can be observed in early visual cortex, even when there is no change in the feedforward visual input. We suggest that illusions therefore provide us with a tool to probe feedforward and feedback integration, as they exploit the difference between retinal stimulation and resulting perception. Thus, illusions allow us to see the changes in activation and perception induced only by feedback without changes in feedforward input. We discuss a few specific examples of illusion generation through feedback and the accompanying effects on V1 processing. In Schizophrenia, the integration of feedback and feedforward information is thought to be dysfunctional, with unbalanced contributions of the two sources. This is evidenced by disrupted contextual binding in visual perception and corresponding deficits in contextual illusion perception. We propose that illusions can provide a window into constructive and inferential visual perception in Schizophrenia. Use of illusion paradigms could help elucidate the deficits existing within feedback and feedforward integration. If we can establish clear effects of illusory feedback to V1 in a typical population, we can apply this knowledge to clinical subjects to observe the differences in feedback and feedforward information. Chapter 2 describes a behavioural study of the rubber hand illusion. We probe how multimodal illusory experience arises under varying reliabilities of visuotactile feedforward input. We recorded Likert ratings of illusion experience from subjects, after their hidden hand was stimulated either synchronously or asynchronously with a visible rubber hand (200, 300, 400, or 600ms visuotactile asynchronicity). We used two groups, assessed by a questionnaire measuring a subject’s risk of developing Schizophrenia - moderate/high scorers and a control group of zero-scorers. We therefore consider how schizotypal symptoms contribute to rubber hand illusory experience and interact with visuotactile reliability. Our results reveal that the impact of feedforward information on higher level illusory body schema is modulated by its reliability. Less reliable feedforward inputs (increasing asynchronicity) reduce illusion perception. Our data suggests that some illusions may not be affected on a spectrum of schizotypal traits but only in the full schizophrenic disorder, as we found no effect of group on illusion perception. In Chapter 3 we present an fMRI investigation of the rubber hand illusion in typical participants. Cortical feedback allows information about other modalities and about cognitive states to be represented at the level of V1. Using a multimodal illusion, we investigated whether crossmodal and illusory states could be represented in early visual cortex in the absence of differential visual input. We found increased BOLD activity in motion area V5 and global V1 when the feedforward tactile information and the illusory outcome were incoherent (for example when the subject was experiencing the illusion during asynchronous stimulation). This is suggestive of increased predictive error, supporting predictive coding models of cognitive function. Additionally, we reveal that early visual cortex contains pattern representations specific to the illusory state, irrespective of tactile stimulation and under identical feedforward visual input. In Chapter 4 we use the motion-induced blindness illusion to demonstrate that feedback modulates stimulus representations in V1 during illusory disappearance. We recorded fMRI data from subjects viewing a 2D cross array rotating around a central axis, passing over an oriented Gabor patch target (45°/ 135°). We attempted to decode the target orientation from V1 when the target was either visible or invisible to subjects. Target information could be decoded during target visibility but not during motion-induced blindness. This demonstrates that the target representation in V1 is distorted or destroyed when the target is perceptually invisible. This illusion therefore has effects not only at higher cortical levels, as previously shown, but also in early sensory areas. The representation of the stimulus in V1 is related to perceptual awareness. Importantly, Chapter 4 demonstrated that intracortical processing can disturb constant feedforward information and overwrite feedforward representations. We suggest that the distortion observed occurs through feedback from V5 about the cross array in motion, overwriting feedforward orientation information. The flashed face distortion illusion is a relatively newly discovered illusion in which quickly presented faces become monstrously distorted. The neural underpinnings of the illusion remain unclear; however it has been hypothesised to be a face-specific effect. In Chapter 5 we challenged this account by exploiting two hallmarks of face-specific processing - the other-race effect and left visual field superiority. In two experiments, two ethnic groups of subjects viewed faces presented bilaterally in the visual periphery. We varied the race of the faces presented (same or different than subject), the visual field that the faces were presented in, and the duration of successive presentations (250, 500, 750 or 1000ms per face before replacement). We found that perceived distortion was not affected by stimulus race, visual field, or duration of successive presentations (measured by forced choice in experiment 1 and Likert scale in experiment 2). We therefore provide convincing evidence that FFD is not face-specific and instead suggest that it is an object-general effect created by comparisons between successive stimuli. These comparisons are underlined by a fed back higher level model which dictates that objects cannot immediately replace one another in the same retinotopic space without movement. In Chapter 6 we unify these findings. We discuss how our data show fed back effects on perception to produce visual illusion; effects which cannot be explained through purely feedforward activity processing. We deliberate how lateral connections and attention effects may contribute to our results. We describe known neural mechanisms which allow for the integration of feedback and feedforward information. We discuss how this integration allows V1 to represent the content of visual awareness, including during some of the illusions presented in this thesis. We suggest that a unifying theory of brain computation, Predictive Coding, may explain why feedback exerts top-down effects on feedforward processing. Lastly we discuss how our findings, and others that demonstrate feedback and prediction effects, could help develop the study and understanding of schizophrenia, including our understanding of the underlying neurological pathologies

Depression & cognition in the elderly : neuroimaging perspective

This thesis examines the relationship between depression and brain structure in the elderly with (Study I, III) and without (Study II, IV) cognitive impairment (Alzheimer’s disease and mild cognitive impairment). Individuals from four independent cohorts were included. Participants had either a depressive episode (Study II, III) or depressive symptoms, as measured with different depression scales (Study I, IV). Studies I and II have cross-sectional design, and studies III and IV are longitudinal. Main outcomes were cortical thickness of the brain and volumes of different structures (hippocampus, ventral diencephalon, including hypothalamus and corpus callosum), or atrophy rate of the thickness and volumes (Study IV). We found in all the cohorts that depressive symptoms were associated with cortical thinning in the same region – the left temporoparietal junction. Depression-related thinning was observed in three cohorts (Studies I, IV) in superior temporal cortex and temporal pole. In two non-demented cohorts (Studies II, IV) angular cortex was also involved in depression. Longitudinal analysis revealed that thinning in these regions is secondary to depressive symptoms (study IV). In two cohorts (Study I, II) fusiform cortex was involved in depression. In study IV, we also were able to assess thinning which developed in parallel with depressive symptoms. It covered medial superior frontal cortex and lingual cortex. The number of depressive episodes was associated with cortical thinning in the left temporal pole in women (Study II) and reduced volume of the right ventral diencephalon in both – men and women (Study III). We have found moderating effect of gender on the relationship between cortical thickness and depression onset. Women with late-onset depression (>65 years) but not men had the widespread thinning in the prefrontal cortex compared to early-onset depressed. The volume of the right hippocampus and thickness of the superior frontal cortex were positively associated with a level of global cognition measured with the mini-mental state examination (MMSE) This effect was more pronounced in the subgroup of late-onset depressed (Study II). The volume of the right ventral diencephalon was associated with cognitive decline (MCI or dementia diagnosis) one year later in the elderly with a depressive episode (study III). Adding baseline MMSE to the classifier increased its accuracy. Total and phosphorylated tau were associated with cortical thinning in the cluster covering right posterior cingulate cortex and precuneus and cluster covering right parahippocampal and fusiform gyri in the AD patients with depressive symptoms from the KI cohort (Study I). No association has been found in non-depressed AD patients. Higher baseline saliva cortisol levels in non-demented individuals (Study IV) were associated with widespread cortical atrophy in temporal, prefrontal and parietal cortex bilaterally and the right hippocampus, independently of age and MMSE. To sum-up, depression was associated with thinning (Studies I, II) and subsequent atrophy (Study IV) in the superior temporal, supramarginal, temporal pole, lingual, fusiform and parahippocampal cortex. Cortical thinning in the superior frontal and lingual regions developed in parallel or prior to the depressive symptoms. The afore-mentioned regions are involved in social perception (processing of the information about others, experience positive emotions related to other people and building an integrative picture of another person), and are among the first to be impaired in Alzheimer’s disease. Elevated cortisol explained atrophy in these and a number of other regions, including the hippocampus, suggesting that depression and Alzheimer’s disease may be connected via cortisol-related brain damage. Depression-related atrophy in the ventral diencephalon leads to impaired cognitive performance. Assessment of cognitive function during the depressive episode, combined with brain structural measurements may have a prognostic value. Future studies should evaluate if a detailed neurocognitive assessment of elderly patients during the depressive episode would help to identify those at high risk of dementia. It is also important to test if stress-reduction interventions in individuals at-risk of Alzheimer’s disease would be effective in its prevention

Developing neuroimaging biomarkers of blast-induced traumatic brain injury

In the past two decades, the awareness of the physical and emotional effects and sequalae of traumatic brain injuries (TBI) has grown considerably, especially in the case of soldiers returning from their deployment in Iraq and Afghanistan, after sustaining blast-induced TBI (bTBI). While the understanding of bTBI and how it compares to civilian non-blast TBI is essential for proper prevention, diagnosis and treatment, it is currently limited, especially in human in-vivo studies. Developing neuroimaging biomarkers of bTBI is key in understanding primary blast injury mechanism. I therefore investigated the patterns of white matter and grey matter injuries that are specific to bTBI and aren¶t commonl\ seen in civilians Zho suffered from head trauma using advanced neuroimaging techniques. However, because of significant methodological issues and limitations, I developed and tested a new pipeline capable of running the analysis of white matter abnormalities in soldiers, called subject-specific diffusion segmentation (SSDS). I also used standard methodologies to investigate changes at the level of the grey matter structures, and more particularly the limbic system. Finally, I trained a machine learning algorithm that builds decision trees with the aim of classifying between patients with TBI and controls, and between different TBI mechanisms as an example of what could potentially be applied in the context of bTBI. I found three main neuroimaging biomarkers specific to bTBI. The first one is a microstructural white matter abnormality at the level of the middle cerebellar peduncle, characterized by a decrease of diffusivity measures. The second is also a decrease in diffusivity properties, at the level of the white matter boundary, and the third one is a loss of hippocampal volume, with no association to post-traumatic stress disorder. Finally, I demonstrated that SSDS can be used in tandem with a machine learning algorithm for potential diagnosis of TBI with high accuracy. These findings provide mechanistic insights into bTBI and the effect of primary blast injuries on the human brain. This work also identifies important neuroimaging biomarkers that might facilitate prevention and diagnosis in soldiers who suffered from bTBI.Open Acces

Disease progression and genetic risk factors in the primary tauopathies

The primary tauopathies are a group of progressive neurodegenerative diseases within the frontotemporal lobar degeneration spectrum (FTLD) characterised by the accumulation of misfolded, hyperphosphorylated microtubule-associated tau protein (MAPT) within neurons and glial cells. They can be classified according to the underlying ratio of three-repeat (3R) to four-repeat (4R) tau and include Pick’s disease (PiD), which is the only 3R tauopathy, and the 4R tauopathies the most common of which are progressive supranuclear palsy (PSP) and corticobasal degeneration (CBD). There are no disease modifying therapies currently available, with research complicated by the wide variability in clinical presentations for each underlying pathology, with presentations often overlapping, as well as the frequent occurrence of atypical presentations that may mimic other non-FTLD pathologies. Although progress has been made in understanding the genetic contribution to disease risk in the more common 4R tauopathies (PSP and CBD), very little is known about the genetics of the 3R tauopathy PiD. There are two broad aims to this thesis; firstly, to use data-driven generative models of disease progression to try and more accurately stage and subtype patients presenting with PSP and corticobasal syndrome (CBS, the most common presentation of CBD), and secondly to identify genetic drivers of disease risk and progression in PiD. Given the rarity of these disorders, as part of this PhD I had to assemble two large cohorts through international collaboration, the 4R tau imaging cohort and the Pick’s disease International Consortium (PIC), to build large enough sample sizes to enable the required analyses. In Chapter 3 I use a probabilistic event-based modelling (EBM) approach applied to structural MRI data to determine the sequence of brain atrophy changes in clinically diagnosed PSP - Richardson syndrome (PSP-RS). The sequence of atrophy predicted by the model broadly mirrors the sequential spread of tau pathology in PSP post-mortem staging studies, and has potential utility to stratify PSP patients on entry into clinical trials based on disease stage, as well as track disease progression. To better characterise the spatiotemporal heterogeneity of the 4R tauopathies, I go on to use Subtype and Stage Inference (SuStaIn), an unsupervised machine algorithm, to identify population subgroups with distinct patterns of atrophy in PSP (Chapter 4) and CBS (Chapter 5). The SuStaIn model provides data-driven evidence for the existence of two spatiotemporal subtypes of atrophy in clinically diagnosed PSP, giving insights into the relationship between pathology and clinical syndrome. In CBS I identify two distinct imaging subtypes that are differentially associated with underlying pathology, and potentially a third subtype that if confirmed in a larger dataset may allow the differentiation of CBD from both PSP and AD pathology using a baseline MRI scan. In Chapter 6 I investigate the association between the MAPT H1/H2 haplotype and PiD, showing for the first time that the H2 haplotype, known to be strongly protective against developing PSP or CBD, is associated with an increased risk of PiD. This is an important finding and has implications for the future development of MAPT isoform-specific therapeutic strategies for the primary tauopathies. In Chapter 7 I perform the first genome wide association study (GWAS) in PiD, identifying five genomic loci that are nominally associated with risk of disease. The top two loci implicate perturbed GABAergic signalling (KCTD8) and dysregulation of the ubiquitin proteosome system (TRIM22) in the pathogenesis of PiD. In the final chapter (Chapter 8) I investigate the genetic determinants of survival in PiD, by carrying out a Cox proportional hazards genome wide survival study (GWSS). I identify a genome-wide significant association with survival on chromosome 3, within the NLGN1 gene. which encodes a synaptic scaffolding protein located at the neuronal pre-synaptic membrane. Loss of synaptic integrity with resulting dysregulation of synaptic transmission leading to increased pathological tau accumulation is a plausible mechanism though which NLGN1 dysfunction could impact on survival in PiD

Machine Learning As Tool And Theory For Computational Neuroscience

Computational neuroscience is in the midst of constructing a new framework for understanding the brain based on the ideas and methods of machine learning. This is effort has been encouraged, in part, by recent advances in neural network models. It is also driven by a recognition of the complexity of neural computation and the challenges that this poses for neuroscience’s methods. In this dissertation, I first work to describe these problems of complexity that have prompted a shift in focus. In particular, I develop machine learning tools for neurophysiology that help test whether tuning curves and other statistical models in fact capture the meaning of neural activity. Then, taking up a machine learning framework for understanding, I consider theories about how neural computation emerges from experience. Specifically, I develop hypotheses about the potential learning objectives of sensory plasticity, the potential learning algorithms in the brain, and finally the consequences for sensory representations of learning with such algorithms. These hypotheses pull from advances in several areas of machine learning, including optimization, representation learning, and deep learning theory. Each of these subfields has insights for neuroscience, offering up links for a chain of knowledge about how we learn and think. Together, this dissertation helps to further an understanding of the brain in the lens of machine learning

Cytokine Networks And Immunosurveillance In Cancer

The cytokine milieu in the tumor microenvironment plays a key role in modulating the immune response either in favor of or against tumorigenesis. For many tumors, this complex network of cytokine and immune interactions represent a formidable means of escape from immune surveillance. These cytokine networks are particularly important in pancreatic ductal adenocarcinoma (PDA), where a prominent infiltration of immunosuppressive immune populations could be found. Myeloid-derived suppressor cells (MDSCs) have previously been shown to be potent suppressors of anti-tumor immunity in PDA, but the cytokine networks regulating their recruitment to the tumor microenvironment remain incompletely understood. Here, I found that CXCR2 ligand expression is specifically correlated with enrichment of the granulocytic subset of MDSCs (G-MDSCs) in human PDAs. Using a genetically engineered mouse model of PDA, I showed that CXCR2 is required for G-MDSC trafficking to the tumor microenvironment, but not necessary for their systemic differentiation and expansion. The specific lack of G-MDSCs in the tumor microenvironment led to a T cell-dependent inhibition of tumor growth. Expression of CXCR2 ligands in PDA tumor cells can be potently induced by NF-κB activation. These findings describe a cytokine network in PDA where inflammatory signals in the tumor microenvironment drive the expression of CXCR2 ligands and the recruitment of immunosuppressive G-MDSCs. To discover other potentially important cytokine networks, I developed a novel analysis pipeline to reconstruct and compare cytokine networks from whole tumor gene expression data. Using expression of cytolytic genes as a gauge for anti-tumor immune activity, I found that PDA patients with high cytolytic activity have a slight survival advantage compared to those with lower activity. While macrophages were the most influential in tumors with low cytolytic activity, tumors with high cytolytic activity were characterized by increased activity of NK cells, recruitment of B cells, and increased importance of CD8 T cells, CD4 T helper cells, and B cells, among others. I further highlighted the cytokines that might be associated with these immune populations. Therefore, my analysis identified potentially important components of the cytokine network associated with high and low cytolytic activity. Collectively, the work in this thesis suggests that cytokine networks are crucial for maintaining an immunosuppressive microenvironment in cancer. Furthermore, disrupting key components of these networks can tip the balance in favor of cancer immunosurveillance

The function and structure of the entorhinal cortex in mild cognitive impairment

The function and structure of the entorhinal cortex in mild cognitive impairment Background: The entorhinal cortex (EC) is the first brain region to exhibit neurodegeneration in Alzheimer’s disease (AD). As such, tests of EC function may help aid detection of the disease in its earliest stages. Animal and human studies indicate that the posteromedial (pmEC) and anterolateral (alEC) subdivisions of the EC are involved respectively in navigation and object-location memory. The advent of immersive VR (iVR) technology provides an opportunity to determine whether tests of pmEC and alEC function has value in the detection of AD prior to dementia onset. Aim: To test the hypotheses that measures of pmEC and alEC function and structure differentiate i) patients with mild cognitive impairment (MCI) from age-matched healthy controls and ii) MCI patients at high and low risk of developing AD dementia. Methodology: Participants underwent testing of EC function using novel iVR paradigms of path integration and object-location memory. Total EC, pm-EC and al-EC volumes were segmented from high resolution MRI. A proportion of MCI patients underwent CSF testing for AD biomarkers to separate into biomarker-positive and negative groups (MCI+ and MCI-). The ability of the VR tests to classify pre-dementia AD (i.e. MCI+ patients) was compared with that of a battery of comparator cognitive tests used in current clinical and research practice. 8 Results: Performance on the path integration task was not only impaired in MCI but importantly differentiated MCI+ from MCI- with greater classification accuracy than comparator cognitive tasks. Task performance correlated with pm-EC volume. For the object-location task MCI patients demonstrated larger distance errors than controls, with performance associated with both anterolateral EC volume and total hippocampal volume. However, no difference between MCI+ and MCI- was observed. Conclusion This work demonstrates that iVR-based testing of EC function may improve diagnosis of early AD above and beyond current cognitive tests. The basis of such tests on single cell physiology and their comparability with behavioural tasks used in animal models of disease, confers additional advantages for translational research aimed at understanding the mechanisms linking pathological spread, disruption of cell physiology and behavioural alterations in early AD.MRC Sackler fund