Longitudinal neuroimaging measures of volumetric change across the frontotemporal dementia spectrum

Frontotemporal dementia (FTD) is a common cause of young onset dementia, encompassing several clinical, genetic and pathological subgroups. Currently there are no treatments, but there are promising candidates in development. However, proven biomarkers of disease progression in FTD are lacking and urgently needed to facilitate these trials. Investigating large sporadic and genetic FTD cohorts, this thesis provides a comprehensive comparison of longitudinal neuroimaging measures of structural change within the clinical, genetic and pathological FTD subgroups. Effect size and sample size estimates are computed to explore the feasibility of these brain measures as surrogate markers of disease progression in order to detect disease-modifying treatment effects. The first project compares 17 automated techniques for extracting whole-brain atrophy measures. Many of the techniques showed great promise, producing sample sizes of substantially less than 100 patients required to detect a disease-modifying effect. Significant differences in performance were found between both techniques and patient subgroups, highlighting the importance of informed biomarker choice in matching the optimal marker to the patient group to be enrolled in a trial. In the following chapters, I explored lobar and subcortical change across the disease spectrum. The different patient subgroups presented with unique profiles of change but, interestingly, automated measures of temporal lobe, caudate and thalamic atrophy proved to be particularly sensitive markers of change, producing low sample size estimates across the FTD subgroups. Importantly, I found significantly increased rates of amygdala, hippocampus, caudate and thalamic atrophy in differing patterns across presymptomatic mutation carriers, providing the first comprehensive assessment of the utility of such markers for early therapeutic intervention at this ideal stage before symptoms develop. In summary, this work expands current knowledge and builds on the limited longitudinal investigations currently available in FTD, as well as providing valuable information about the potential of non-invasive biomarkers for sporadic and genetic FTD trials

Neuroimaging biomarkers of neurodegenerative diseases and dementia

Neurodegenerative disorders leading to dementia are common diseases that affect many older and some young adults. Neuroimaging methods are important tools for assessing and monitoring pathological brain changes associated with progressive neurodegenerative conditions. In this review, the authors describe key findings from neuroimaging studies (magnetic resonance imaging and radionucleotide imaging) in neurodegenerative disorders, including Alzheimer's disease (AD) and prodromal stages, familial and atypical AD syndromes, frontotemporal dementia, amyotrophic lateral sclerosis with and without dementia, Parkinson's disease with and without dementia, dementia with Lewy bodies, Huntington's disease, multiple sclerosis, HIV-associated neurocognitive disorder, and prion protein associated diseases (i.e., Creutzfeldt-Jakob disease). The authors focus on neuroimaging findings of in vivo pathology in these disorders, as well as the potential for neuroimaging to provide useful information for differential diagnosis of neurodegenerative disorders

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

Study of longitudinal neurodegeneration biomarkers to support the early diagnosis of Alzheimer’s disease

Alzheimer’s Disease (AD) is a progressive and neurodegenerative disorder characterized by pathological brain changes starting several years before clinical symptoms appear. Earlier and accurate identification of those brain structures changes can help to improve diagnosis and monitoring, allowing that future treatments target the disease in its earliest stages, before irreversible brain damage or mental decline takes place. The brain of AD subjects shrinks significantly as the disease progress. Furthermore, ageing is the major risk factor for sporadic AD, older brains being more susceptible than young or middle-aged ones. However, seemingly healthy elderly brains lose matter in regions related to AD. Likewise, similar changes can also be found in subjects having mild cognitive impairment (MCI), which is a symptomatic pre-dementia phase of AD. This work proposes two methods based on statistical learning methods, which are focused on characterising the ageing-related changes in brain structures of healthy elderly controls (HC), MCI and AD subjects, and addressing the estimation of the current diagnosis (ECD) of HC, MCI and AD, as well as the prediction of future diagnosis (PFD) of these groups mainly focused on the early diagnosis of conversion from MCI to AD. Data correspond to longitudinal neurodegeneration measurements from Magnetic Resonance Imaging (MRI) images. These biomarkers were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) and the Open Access Series of Imaging Studies (OASIS). ADNI data includes MRI biomarkers available at a 5-year follow up on HC, MCI and AD subjects, while OASIS data only includes biomarkers measured at baseline on HC and AD. In the first method, called M-res, variant (vr) and quasi-variant (qvr) biomarkers were identified on HC subjects by using a Linear Mixed Effects (LME) approach on males and females, separately. Then, we built an ageing-based null model, which would characterise the normal atrophy and growth patterns of vr and qvr biomarkers, as well as the correlation between them. By using the null model on those subjects who had been clinically diagnosed as HC, MCI or AD, normal age-related changes were estimated, and then, their deviation scores (residuals) from the observed MRI-based biomarkers were computed. In contrast to M-res, the second method, called M-raw, is focused on directly analyzing the raw MRI-based biomarkers values stratified by five-year age groups. M-raw includes a differential diagnosis-specific feature selection (FS) method, which is applied before classification. In both methods, the differential diagnosis problem was addressed by building Support Vector Machines (SVM) models to carry out three main experiments—AD vs. HC, MCI vs. HC, and AD vs. MCI. In M-res, the SVM models were trained by using as input the residuals computed for the vr biomarkers plus the age, whereas in M-raw, we used the pool of selected features plus age, gender and years of education. The advancement of early disease prediction was calculated as the average number of years advanced in the PFD of the subjects concerning the last known clinical diagnosis. Finally, the ability of both methods to correctly discriminate AD vs. HC subjects was evaluated and compared by testing them on OASIS subjects observed at baseline. Results confirm accelerated or reduced estimates of decline in all cortical biomarkers with increasing age and a frontotemporal pattern of atrophy in HC subjects, as well as in MCI and AD. Regarding the ECD problem, all SVM models obtained better results than comparable methods in the literature for most classification quality indicators, especially on AD vs. HC. Both methods also improve the PFD given the current clinical tests, both in prediction quality indicators and the amount of time by which the diagnosis is advanced.La enfermedad de Alzheimer (AD) es un trastorno progresivo y neurodegenerativo caracterizado por cambios patológicos en el cerebro que comienzan varios años antes de aparecer los primeros síntomas clínicos. La identificación temprana y precisa de estos cambios ayuda a mejorar el diagnóstico y la monitorización, permitiendo que la enfermedad sea abordada en sus primeras etapas, antes de producirse un deterioro morfológico y mental irreversible. El cerebro de los sujetos con AD se reduce significativamente a medida que avanza la enfermedad, siendo el envejecimiento el principal factor de riesgo para la AD esporádica, donde los cerebros de la gente mayor son más susceptibles que los más jóvenes. Sin embargo, ha sido observado que los cerebros de los adultos mayores y de los sujetos en una fase anterior con deterioro cognitivo leve (MCI) pierden materia en regiones relacionadas con AD. Esta tesis propone dos métodos basados en métodos de aprendizaje estadísticos, que se centran en caracterizar los cambios relacionados con el envejecimiento en estructuras cerebrales de controles sanos de edad avanzada (HC), MCI y AD, y en abordar la estimación del diagnóstico actual (ECD) de estos grupos, así como la predicción de su diagnóstico futuro (PFD), principalmente en el diagnóstico precoz de la conversión de MCI a AD. Los datos utilizados corresponden a biomarcadores de neurodegeneración longitudinal obtenidas de imágenes de Resonancia Magnética (MRI). Estos biomarcadores se obtuvieron a partir de los estudios Alzheimer?s Disease Neuroimaging Initiative (ADNI) y Open Access Series of Imaging Studies (OASIS). Los datos de ADNI incluyeron biomarcadores de MRI disponibles en un seguimiento de 5 años en sujetos HC, MCI y AD, mientras que los datos de OASIS solo incluyeron biomarcadores medidos al inicio del estudio en HC y AD. En el primer método, denominado M-res, los biomarcadores que cambiaron significativamente (vr) y los que cambiaron en una reducida escala (qvr) fueron identificados en sujetos HC utilizando modelos lineales de efectos mixtos (LME). Asimismo, modelos nulos basados en el normal envejecimiento del cerebro fueron construidos para cada género. A través de estos ellos se buscó caracterizar la atrofia normal y los patrones de crecimiento de los biomarcadores vr y qvr, así como la correlación entre ellos. Estos modelos fueron utilizados en los sujetos HC, MCI y AD restantes para inferir los valores normales de los biomarcadores vr y luego calcular sus desviaciones (residuos) respecto a los biomarcadores observados. A diferencia de M-res, el segundo método denominado M-raw, se centra en el análisis de los valores directos de los biomarcadores MRI, estratificados por grupos de edad de cinco años. M-raw incluye un método de selección de características específicas del diagnóstico diferencial aplicado antes de la clasificación. En ambos métodos, se entrenaron máquinas soporte vectorial (SVM) para abordar tres experimentos: AD vs. HC, MCI vs. HC y AD vs. MCI. En M-res, los modelos SVM fueron entrenados a partir de los residuos calculados para los biomarcadores vr más la edad, mientras que en M-raw, se utilizó el grupo de características seleccionadas más la edad, el sexo y los años de educación. El avance de la predicción temprana de la enfermedad fue calculada como el promedio de años avanzados en el PFD con respecto al último diagnóstico clínico conocido. Los resultados confirman una reducción en todos los biomarcadores corticales a medida que la edad avanza, siendo el cambio de algunas regiones más acelerados que otras. Asimismo, se observó un patrón de atrofia frontotemporal en los tres grupos de sujetos. Con respecto al problema ECD, todos los modelos SVM obtuvieron mejor desempeño en la clasificación que los métodos comparables en la literatura, especialmente en AD vs. HC. Ambos métodos también mejoraron la PFD, tanto en los indicadores de calidad de predicción como en el tiempo de avance en el diagnóstico (hasta 1.87 años antes en sujetos de 80-84 años).Postprint (published version

On the neurobiology of apathy and depression in cerebral small vessel disease

Cerebral small vessel disease (SVD) is a cerebrovascular pathology that affects the small vessels of the brain, resulting in heterogeneous brain tissue changes. These can lead to neuropsychiatric symptoms such as apathy, a loss of motivation, and depression, which is characterised by low mood and a loss of pleasure. Apathy and depression are both prevalent symptoms in SVD, but an understanding of the relationship between underlying disease processes and the expression of these neuropsychiatric symptoms remains poor. This thesis uses magnetic resonance imaging techniques to examine the neurobiological basis of apathy and depression in SVD. We show that apathy is related to focal grey matter damage and distributed white matter microstructural change. These microstructural changes underlie large-scale white matter network disruption, which is related to apathy, but not depression. We then show that depression, as a construct, can be dissociated into distinct symptoms which are associated with overlapping and distinct areas of cortical atrophy over time. This suggests that depression as a general syndrome may be characterised by atrophy in core structures, while different symptoms are associated with atrophy in more specialised areas. Consistent with these patterns of overarching tissue damage, we find that apathy, but not depression, predicts conversion to dementia in patients with SVD. Our findings suggest that different types of SVD-related pathology lead to apathy and depression. Diffuse white matter damage may lead to widespread network disruption, resulting in apathy and cognitive impairment. In contrast, depressive symptoms are associated with focal patterns of grey matter atrophy over time. This highlights the importance of differentiating neuropsychiatric symptoms, and paves the way for targeted treatment approaches.Cambridge International Scholarship (Cambridge Trust)

Neuroimaging correlates of cognitive impairment and dementia in Parkinson's disease.

There has been a gradual shift in the definition of Parkinson's disease, from a movement disorder to a neurodegenerative condition affecting multiple cognitive domains. Mild cognitive impairment (PD-MCI) is a frequent comorbidity in PD that is associated with progression to dementia (PDD) and debilitating consequences for patients and caregivers. At present, the pathophysiology underpinning cognitive impairment in PD is not established, although emerging evidence has suggested that multi-modal imaging biomarkers could be useful in the early diagnosis of PD-MCI and PDD, thereby identifying at-risk patients to enable treatment at the earliest stage possible. Structural MRI studies have revealed prominent grey matter atrophy and disruptions of white matter tracts in PDD, although findings in non-demented PD have been more variable. There is a need for further longitudinal studies to clarify the spatial and temporal progression of morphological changes in PD, as well as to assess their underlying involvement in the evolution of cognitive deficits. In this review, we discuss the aetiology and neuropsychological profiles of PD-MCI and PDD, summarize the putative imaging substrates in light of evidence from multi-modal neuroimaging studies, highlight limitations in the present literature, and suggest recommendations for future research.This work was supported by the NIHR Biomedical Research Unit in Dementia and the Biomedical Research Centre awarded to Cambridge University Hospitals NHS Foundation Trust and the University of Cambridge, and the NIHR Biomedical Research Unit in Dementia and the Biomedical Research Centre awarded to Newcastle upon Tyne Hospitals NHS Foundation Trust and the Newcastle University. Elijah Mak was in receipt of a Gates Cambridge PhD studentship.This is the accepted manuscript. The final version is available at http://www.sciencedirect.com/science/article/pii/S1353802015002151

Cerebral Hemodynamic Disturbances in Motor Neuron Disease

An association between motor neuron disease (MND) and dementia was first realized in the late 1800s, yet substantiating research and a description of dementia as part of the clinical syndrome would not appear until the 1990s. In the last two decades, medical imaging has investigated cerebral blood flow changes in the motor and non­ motor cortex to correlate with motor dysfunction and clinical dementia, respectively. The aim of this thesis is to describe early cerebral hemodynamic disturbances with the goal to determine a marker for cognitive decline in MND. Chapter 2 describes the relationship between changes in cerebral hemodynamics and cognition in primary lateral sclerosis (PLS) patients compared to normal controls. Neuropsychological testing revealed subtle frontotemporal changes characterized by executive dysfunction that were associated with global increases in mean transit time (MTT) in grey and white matter, and increased cerebral blood volume (CBV) in the frontotemporal grey matter. Chapter 3 présents a longitudinal clinical study of early cerebral hemodynamic changes in amyotrophie lateral sclerosis (ALS) patients without evidence of cognitive impairment at study onset. This Chapter characterized the relationship between duration ofdiseaseandMTTinthecorticalgreymatter. MTTwasfoundtobethemostsensitive indicator of early cerebral hemodynamic change accompanying disease progression in ALS. Furthermore,thesefindingscorroboratethetrendofincreasedMTTintheabsence of cognitive impairment found in PLS patients in Chapter 2, and may further indicate that hemodynamic changes may occur before the onset of cognitive impairment. iii The aim of Chapter 4 was to elucidate a biological mechanism for increased MTT described in the previous Chapters 2 and 3. A rabbit model of global hypotension was used to demonstrate that MTT is an indicator of cerebral perfusion pressure (CPP). A spectrum of cognitive dysfunction has now been described in MND. The use of sensitive neuropsychological testing has enabled us to identify patients with mild changes in cognitive function from those who are cognitively intact. With the help of this stratification, we were able to show that changes in MTT was associated with disease progression and cognitive impairment. The experimental data presented in this thesis suggest that vascular factors may contribute to cognitive dysfunction in MND

Cerebral Hemodynamic Disturbances in Motor Neuron Disease

An association between motor neuron disease (MND) and dementia was first realized in the late 1800s, yet substantiating research and a description of dementia as part of the clinical syndrome would not appear until the 1990s. In the last two decades, medical imaging has investigated cerebral blood flow changes in the motor and nonmotor cortex to correlate with motor dysfunction and clinical dementia, respectively. The aim of this thesis is to describe early cerebral hemodynamic disturbances with the goal to determine a marker for cognitive decline in MND. Chapter 2 describes the relationship between changes in cerebral hemodynamics and cognition in primary lateral sclerosis (PLS) patients compared to normal controls. Neuropsychological testing revealed subtle frontotemporal changes characterized by executive dysfunction that were associated with global increases in mean transit time (MTT) in grey and white matter, and increased cerebral blood volume (CBV) in the frontotemporal grey matter. Chapter 3 presents a longitudinal clinical study of early cerebral hemodynamic changes in amyotrophic lateral sclerosis (ALS) patients without evidence of cognitive impairment at study onset. This Chapter characterized the relationship between duration of disease and MTT in the cortical grey matter. MTT was found to be the most sensitive indicator of early cerebral hemodynamic change accompanying disease progression in ALS. Furthermore, these findings corroborate the trend of increased MTT in the absence of cognitive impairment found in PLS patients in Chapter 2, and may further indicate that hemodynamic changes may occur before the onset of cognitive impairment. in The aim of Chapter 4 was to elucidate a biological mechanism for increased MTT described in the previous Chapters 2 and 3. A rabbit model of global hypotension was used to demonstrate that MTT is an indicator of cerebral perfusion pressure (CPP). A spectrum of cognitive dysfunction has now been described in MND. The use of sensitive neuropsychological testing has enabled us to identify patients with mild changes in cognitive function from those who are cognitively intact. With the help of this stratification, we were able to show that changes in MTT was associated with disease progression and cognitive impairment. The experimental data presented in this thesis suggest that vascular factors may contribute to cognitive dysfunction in MND

Unconventional markers of Alzheimer Disease

Although typically conceptualized as a cortical disease, recent neuropathological and neuroimaging investigations on Alzheimer Disease suggest that other brain structures play an important role in the pathogenesis and progression of this devastating condition. In this thesis, we explored novel markers of Alzheimer Disease beyond the classical cortical pathology measures of amyloid, tau, and neurodegeneration. We focused on the role of white matter abnormalities, assessed with magnetic resonance imaging but also with amyloid positron emission tomography, in predicting early pathologic changes and disease progression, as well as on the added value of cognition to amyloid, tau, and neurodegeneration biomarkers. Overall, we found that these unconventional markers provide useful information to detect the earliest pathological changes of the disease, providing a better understanding of the mechanisms that lead to amyloid deposition and cognitive decline

The Neural Correlates of Visual Hallucinations in Parkinson's Disease

Visual hallucinations are common in Parkinson’s disease (PD) and linked to worse outcomes: increased mortality, higher carer burden, cognitive decline, and worse quality of life. Recent functional studies have aided our understanding, showing large-scale brain network imbalance in PD hallucinations. Imbalance of different influences on visual perception also occurs, with impaired accumulation of feedforward signals from the eyes and visual parts of the brain. Whether feedback signals from higher brain control centres are also affected is unknown and the mechanisms driving network imbalance in PD hallucinations remain unclear. In this thesis I will clarify the computational and structural changes underlying PD hallucinations and link these to functional abnormalities and regional changes at the cellular level. To achieve this, I will employ behavioural testing, diffusion weighted imaging, structural and functional MRI in PD patients with and without hallucinations. I will quantify the use of prior knowledge during a visual learning task and show that PD with hallucinations over-rely on feedback signals. I will examine underlying structural connectivity changes at baseline and longitudinally and will show that posterior thalamic connections are affected early, with frontal connections remaining relatively preserved. I will show that PD hallucinations are associated with a subnetwork of reduced structural connectivity strength, affecting areas crucial for switching the brain between functional states. I will assess the role of the thalamus as a potential driver of network-level changes and show preferential medial thalamus involvement. I will utilise data from the Allen Institute transcription atlas and show how differences in regional gene expression in health contributes to the selective vulnerability of specific white matter connections in PD hallucinations. These findings reveal the structural correlates of visual hallucinations in PD, link these to functional and behavioural changes and provide insights into the cellular mechanisms that drive regional vulnerability, ultimately leading to hallucinations