Surface Feature-Guided Mapping of Cerebral Metabolic Changes in Cognitively Normal and Mildly Impaired Elderly

Purpose: The aim of this study was to investigate the longitudinal positron emission tomography (PET) metabolic changes in the elderly. Procedures: Nineteen nondemented subjects (mean Mini-Mental Status Examination 29.4±0.7 SD) underwent two detailed neuropsychological evaluations and resting 2-deoxy-2-[F-18]fluoro-D-glucose (FDG)-PET scan (interval 21.7±3.7 months), baseline structural 3T magnetic resonance (MR) imaging, and apolipoprotein E4 genotyping. Cortical PET metabolic changes were analyzed in 3-D using the cortical pattern matching technique. Results: Baseline vs. follow-up whole-group comparison revealed significant metabolic decline bilaterally in the posterior temporal, parietal, and occipital lobes and the left lateral frontal cortex. The declining group demonstrated 10–15 % decline in bilateral posterior cingulate/precuneus, posterior temporal, parietal, and occipital cortices. The cognitively stable group showed 2.5–5% similarly distributed decline. ApoE4-positive individuals underwent 5–15 % metabolic decline in the posterior association cortices. Conclusions: Using 3-D surface-based MR-guided FDG-PET mapping, significant metaboli

Neurobiological mechanisms of hallucinations in schizophrenia

All perception is a construct of the brain. Yet occasionally, sensory constructions emerge without origin in the physical world and are experienced as hallucinations. Hallucinations occur transdiagnostically, cross-culturally, and in all sensory modalities. They are common in people with schizophrenia, presenting in 60-80% of patients. Despite over 20 years of active neuroimaging research on hallucinations, the neural systems supporting these anomalous perceptual experiences remain disputed. This dissertation investigates the neurobiology of hallucinations, integrating research across structural and functional magnetic resonance imaging (MRI) to elucidate how hallucinations, chiefly in the context of schizophrenia, are supported by the brain, drawing on MRI indices of neurodevelopment. I introduce the phenomenon of hallucinations and motivate the utility of MRI for studying hallucinations. Considering their prevalence in other medical conditions, I conduct a meta-analysis and systematic review of the structural brain basis of hallucinations across diagnoses, primarily schizophrenia spectrum disorders and Parkinson’s disease. This illustrated distinct neuroanatomical organizations of grey matter associated with hallucinations that occur in neurodevelopmental compared to neurodegenerative disorders, which I hypothesise constitute at least two distinct mechanisms. Focussing on the neurodevelopmental mechanism characterized by fronto-temporal and insular grey matter reductions, I turn to the contribution of cortical sulcation, a product of second and third trimester neurodevelopmental processes, which has been robustly implicated in schizophrenia pathology, and, more recently, in hallucinations. Sulcal patterns derived from structural MRI provide a proxy in adulthood for early brain development. I studied two independent datasets of patients with schizophrenia who underwent clinical assessment and 3T MRI from the United Kingdom and Shanghai, China, stratified into those with and without hallucinations, and healthy controls from Shanghai. I first replicate the finding that left hemisphere paracingulate sulcus (PCS) length is reduced in patients who experience hallucinations, then demonstrate similar associations for superior temporal sulcus depth. Length and depth alterations occurred with focal deviations in sulcal geometry. The interindividual and interhemispheric variability of the PCS necessitated the development of semi-automated methods to characterize its morphology and validation to a manual protocol. I used structural covariance networks of the local gyrification index to investigate how specific sulcal deviations relate to global neurodevelopmental coordination, demonstrating that hallucinations correspond to increased covariance within and between salience and auditory networks. Hypothesizing structure-function relationships, I analyse resting-state functional MRI data from the same datasets described, finding significant interactions between PCS length and hallucinations status, but no main effects. There were no effects of hallucination status on salience and auditory network connectivity or in graph theoretical measures of connectivity, suggesting that resting-state connectivity is not a trait marker for hallucinations. Together, the discovery of neurodevelopmental alterations contributing to hallucinations provides mechanistic insight into the pathological consequences of prenatal origins. The interaction of sulcal alterations and hallucination status are associated with connectivity, which may have a role in the pathophysiology of hallucinations. I provide clear predictions and recommendations for future research.Gates Cambridge Scholarshi

Ventricular dilatation in aging and dementia

The general objective of this thesis was to study the causes and consequences of ventricular dilatation in aging and dementia. For this purpose, we used ventricular shape analysis to study potential new MRI markers of cognitive decline in aging, subjective memory complaints, mild cognitive impairment and Alzheimer's Disease. In addition, we designed a volumetric measure that may objectively quantify the disproportionate ventricular dilatation that is characteristic of Normal Pressure Hydrocephalus (NPH). We investigated the value of this measure for the selection of candidates with NPH for ventricular shunting, studied its association with NPH-like symptoms in the general population and used the measure to explore a possible cardiovascular origin of cerebral ventricular dilatation.UBL - phd migration 201

Radiological Pathological Correlations in Chronic Traumatic Encephalopathy

Chronic traumatic encephalopathy (CTE) is a progressive neurodegenerative disease that has been increasingly linked to traumatic brain injury. The neuropathology that distinguishes CTE from other tauopathies includes hyperphosphorylated tau (pTau) tangles and tau positive astrocytes irregularly distributed in cortical sulcal depths and clustered around perivascular foci. These features are clearly identified using immunohistochemistry, but are undetectable to current clinical imaging methods. Diffusion imaging has been proposed as a noninvasive method to detect the pathognomonic lesion of CTE in vivo because of its high sensitivity to microstructural alterations in tissue structure. While several diffusion imaging approaches, ranging from diffusion tensor imaging (DTI) to more advanced schemes such as generalized q-sampling imaging (GQI) and diffusion kurtosis imaging (DKI) may prove useful, the relationship between changes in diffusion-derived metrics and the underlying pathology remains unknown. We have developed and implemented a method of perform radiological-pathological correlations in tissues with diagnoses of CTE, aimed to determine whether high spatial resolution diffusion imaging is capable of sensitively detecting pTau pathology. Human ex vivo cortical tissues diagnoses with Stage III/IV CTE, Alzheimer’s disease (AD) or frontotemporal lobar dementia (FTLD) were scanned in an 11.74T Agilent MRI scanner using DTI, GQI and DKI acquisition schemes with isotropic in-plane spatial resolution of 250µm and 500µm slice thickness. Following image acquisition, tissues were sectioned and stained for histopathological markers including AT8 (pTau), GFAP (astrocytes) and Myelin Black Gold II (myelinated white matter). A custom script was used to co-register histological to MRI images, allowing for the ability to perform high spatial resolution correlations of histological with diffusion metrics. Using this approach, we found no relationship between pTau in sulcal depths and any of our DTI, GQI and DKI based measures. Interestingly, we found that white matter underlying sulcal depths in CTE tissues showed signs of disruption, a finding that we did not observe in AD or FTLD tissues. Furthermore, white matter integrity in these regions was correlated with fractional anisotropy. These findings demonstrate that high spatial resolution diffusion imaging is capable of detecting white matter disorganization closely related to pTau pathology in CTE, and may provide a more sensitive and specific means of diagnosing CTE

Assessment of the potentials and limitations of cortical-based analysis for the integration of structure and function in normal and pathological brains using MRI

The software package Brainvisa (www.brainvisa.tnfo) offers a wide range of possibilities for cortical analysis using its automatic sulci recognition feature. Automated sulci identification is an attractive feature as the manual labelling of the cortical sulci is often challenging even for the experienced neuro-radiologists. This can also be of interest in fMRI studies of individual subjects where activated regions of the cortex can simply be identified using sulcal labels without the need for normalization to an atlas. As it will be explained later in this thesis, normalization to atlas can especially be problematic for pathologic brains. In addition, Brainvisa allows for sulcal morphometry from structural MR images by estimating a wide range of sulcal properties such as size, coordinates, direction, and pattern. Morphometry of abnormal brains has gained huge interest and has been widely used in finding the biomarkers of several neurological diseases or psychiatric disorders. However mainly because of its complexity, only a limited use of sulcal morphometry has been reported so far. With a wide range of possibilities for sulcal morphometry offered by Brainvisa, it is possible to thoroughly investigate the sulcal changes due to the abnormality. However, as any other automated method, Brainvisa can be susceptible to limitations associated with image quality. Factors such as noise, spatial resolution, and so on, can have an impact on the detection of the cortical folds and estimation of their attributes. Hence the robustness of Brainvisa needs to be assessed. This can be done by estimating the reliability and reproducibility of results as well as exploring the changes in results caused by other factors. This thesis is an attempt to investigate the possible benefits of sulci identification and sulcal morphometry for functional and structural MRI studies as well as the limitations of Brainvisa. In addition, the possibility of improvement of activation localization with functional MRI studies is further investigated. This investigation was motivated by a review of other cortical-based analysis methods, namely the cortical surface-based methods, which are discussed in the literature review chapter of this thesis. The application of these approaches in functional MRI data analysis and their potential benefits is used in this investigation

Computerized Analysis of Magnetic Resonance Images to Study Cerebral Anatomy in Developing Neonates

The study of cerebral anatomy in developing neonates is of great importance for the understanding of brain development during the early period of life. This dissertation therefore focuses on three challenges in the modelling of cerebral anatomy in neonates during brain development. The methods that have been developed all use Magnetic Resonance Images (MRI) as source data. To facilitate study of vascular development in the neonatal period, a set of image analysis algorithms are developed to automatically extract and model cerebral vessel trees. The whole process consists of cerebral vessel tracking from automatically placed seed points, vessel tree generation, and vasculature registration and matching. These algorithms have been tested on clinical Time-of- Flight (TOF) MR angiographic datasets. To facilitate study of the neonatal cortex a complete cerebral cortex segmentation and reconstruction pipeline has been developed. Segmentation of the neonatal cortex is not effectively done by existing algorithms designed for the adult brain because the contrast between grey and white matter is reversed. This causes pixels containing tissue mixtures to be incorrectly labelled by conventional methods. The neonatal cortical segmentation method that has been developed is based on a novel expectation-maximization (EM) method with explicit correction for mislabelled partial volume voxels. Based on the resulting cortical segmentation, an implicit surface evolution technique is adopted for the reconstruction of the cortex in neonates. The performance of the method is investigated by performing a detailed landmark study. To facilitate study of cortical development, a cortical surface registration algorithm for aligning the cortical surface is developed. The method first inflates extracted cortical surfaces and then performs a non-rigid surface registration using free-form deformations (FFDs) to remove residual alignment. Validation experiments using data labelled by an expert observer demonstrate that the method can capture local changes and follow the growth of specific sulcus

Clinical diagnosis and risk factors for chronic traumatic encephalopathy

Chronic traumatic encephalopathy (CTE) is a neurodegenerative disease characterized by a pathognomonic distribution of hyperphosphorylated tau accumulations in neurons, astrocytes, and cell processes, situated around vessels at the depths of cortical sulci. Case reports of CTE pathology exhibit a common exposure to repetitive head impacts (RHI), suggesting that RHI are a necessary factor in the disease’s etiology. Currently, it is only possible to definitively diagnose CTE after death using histopathological techniques and consensus-based neuropathological diagnostic criteria recently established by the National Institute of Health and National Institute of Neurological Disorders and Stroke. Though considerable progress has been made in characterizing the neuropathology of CTE, less is known about the clinical aspects of the disease. Specifically, additional research is needed to identify disease-specific clinical manifestations, clinicopathological correlations, and a means of diagnosis during life, all of which are critical to developing future epidemiological studies, preventative measures, and treatment trials. Moreover, it is not yet known which specific aspects of RHI exposure (type, frequency, duration) are causally linked to developing clinically meaningful neurological impairments or CTE neuropathology, nor have studies identified risk-modifying factors, such as genotype (e.g. APOE). The objective of this dissertation’s published works was to systematically address these gaps in knowledge. First, to define a common clinical presentation of CTE, we conducted a retrospective analysis of medical records and semi-structured next-of-kin reports of 36 former athletes with autopsy-confirmed CTE without comorbid neurodegenerative disease. We then published clinical diagnostic criteria for CTE based on a systematic review of clinical features exhibited in 202 former athlete cases and a pooled analysis of 83 neuropathologically confirmed CTE cases. In subsequent analyses, we operationalized clinical criteria to investigate specific clinicopathological associations between tau, amyloid beta, age, APOE genotype, and clinical outcomes and utilized the clinical criteria to explore potential risk-factors related to RHI from boxing and football. Lastly, we developed a metric to quantify cumulative RHI exposure in retired, living, football players. Using this metric, our study was the first to indicate a causal relationship between cumulative RHI exposure and risk of later life cognitive, mood, and behavioral impairment. These studies are preliminary, and our results require replication and validation in larger, longitudinal prospective studies

A Machine-Learning-Based Investigation of Schizophrenia Using Structural MRI

openSchizophrenia is a serious mental health concerns that affects 1% of the population (Jones et al., 2005). This study aimed to create objective tools that can correctly classify people with schizophrenia according to their diagnosis, predominant symptoms, illness duration, and illness severity based on their structural brain imaging variables. 1087 brain images (700=healthy controls, 387=people with schizophrenia) included in the analysis. Support Vector Machines, random forests, logistic regression, and XGBoost were used for diagnostic classification and reached 71% of maximum accuracy. Sulcal width was found to be the most important brain imaging variable that differed between groups. Support vector machines and random forests were used to classify patients according to their predominant symptoms and these classifications reached a maximum accuracy of 66%. Support vector machines could correctly classify people with schizophrenia according to their illness duration with a 75% accuracy and according to their illness severity with 69%. The result of the study shows that using machine learning methods, it is possible to create objective tools for schizophrenia that can be later used in clinics. Keywords: Schizophrenia, Structural MRI, Machine Learning ClassificationSchizophrenia is a serious mental health concerns that affects 1% of the population (Jones et al., 2005). This study aimed to create objective tools that can correctly classify people with schizophrenia according to their diagnosis, predominant symptoms, illness duration, and illness severity based on their structural brain imaging variables. 1087 brain images (700=healthy controls, 387=people with schizophrenia) included in the analysis. Support Vector Machines, random forests, logistic regression, and XGBoost were used for diagnostic classification and reached 71% of maximum accuracy. Sulcal width was found to be the most important brain imaging variable that differed between groups. Support vector machines and random forests were used to classify patients according to their predominant symptoms and these classifications reached a maximum accuracy of 66%. Support vector machines could correctly classify people with schizophrenia according to their illness duration with a 75% accuracy and according to their illness severity with 69%. The result of the study shows that using machine learning methods, it is possible to create objective tools for schizophrenia that can be later used in clinics. Keywords: Schizophrenia, Structural MRI, Machine Learning Classificatio