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

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 comparison of magnetic resonance imaging and neuropsychological examination in the diagnostic distinction of Alzheimer’s disease and behavioral variant frontotemporal dementia

The clinical distinction between Alzheimer's disease (AD) and behavioral variant frontotemporal dementia (bvFTD) remains challenging and largely dependent on the experience of the clinician. This study investigates whether objective machine learning algorithms using supportive neuroimaging and neuropsychological clinical features can aid the distinction between both diseases. Retrospective neuroimaging and neuropsychological data of 166 participants (54 AD; 55 bvFTD; 57 healthy controls) was analyzed via a Naïve Bayes classification model. A subgroup of patients (n = 22) had pathologically-confirmed diagnoses. Results show that a combination of gray matter atrophy and neuropsychological features allowed a correct classification of 61.47% of cases at clinical presentation. More importantly, there was a clear dissociation between imaging and neuropsychological features, with the latter having the greater diagnostic accuracy (respectively 51.38 vs. 62.39%). These findings indicate that, at presentation, machine learning classification of bvFTD and AD is mostly based on cognitive and not imaging features. This clearly highlights the urgent need to develop better biomarkers for both diseases, but also emphasizes the value of machine learning in determining the predictive diagnostic features in neurodegeneration

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

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

Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration: A united approach

Item does not contain fulltextCerebral small vessel disease (SVD) is a common accompaniment of ageing. Features seen on neuroimaging include recent small subcortical infarcts, lacunes, white matter hyperintensities, perivascular spaces, microbleeds, and brain atrophy. SVD can present as a stroke or cognitive decline, or can have few or no symptoms. SVD frequently coexists with neurodegenerative disease, and can exacerbate cognitive deficits, physical disabilities, and other symptoms of neurodegeneration. Terminology and definitions for imaging the features of SVD vary widely, which is also true for protocols for image acquisition and image analysis. This lack of consistency hampers progress in identifying the contribution of SVD to the pathophysiology and clinical features of common neurodegenerative diseases. We are an international working group from the Centres of Excellence in Neurodegeneration. We completed a structured process to develop definitions and imaging standards for markers and consequences of SVD. We aimed to achieve the following: first, to provide a common advisory about terms and definitions for features visible on MRI; second, to suggest minimum standards for image acquisition and analysis; third, to agree on standards for scientific reporting of changes related to SVD on neuroimaging; and fourth, to review emerging imaging methods for detection and quantification of preclinical manifestations of SVD. Our findings and recommendations apply to research studies, and can be used in the clinical setting to standardise image interpretation, acquisition, and reporting. This Position Paper summarises the main outcomes of this international effort to provide the STandards for ReportIng Vascular changes on nEuroimaging (STRIVE)

BrainPrint: A discriminative characterization of brain morphology

We introduce BrainPrint, a compact and discriminative representation of brain morphology. BrainPrint captures shape information of an ensemble of cortical and subcortical structures by solving the eigenvalue problem of the 2D and 3D Laplace–Beltrami operator on triangular (boundary) and tetrahedral (volumetric) meshes. This discriminative characterization enables new ways to study the similarity between brains; the focus can either be on a specific brain structure of interest or on the overall brain similarity. We highlight four applications for BrainPrint in this article: (i) subject identification, (ii) age and sex prediction, (iii) brain asymmetry analysis, and (iv) potential genetic influences on brain morphology. The properties of BrainPrint require the derivation of new algorithms to account for the heterogeneous mix of brain structures with varying discriminative power. We conduct experiments on three datasets, including over 3000 MRI scans from the ADNI database, 436 MRI scans from the OASIS dataset, and 236 MRI scans from the VETSA twin study. All processing steps for obtaining the compact representation are fully automated, making this processing framework particularly attractive for handling large datasets.National Cancer Institute (U.S.) (1K25-CA181632-01)Athinoula A. Martinos Center for Biomedical Imaging (P41-RR014075)Athinoula A. Martinos Center for Biomedical Imaging (P41-EB015896)National Alliance for Medical Image Computing (U.S.) (U54-EB005149)Neuroimaging Analysis Center (U.S.) (P41-EB015902)National Center for Research Resources (U.S.) (U24 RR021382)National Institute of Biomedical Imaging and Bioengineering (U.S.) (5P41EB015896-15)National Institute of Biomedical Imaging and Bioengineering (U.S.) (R01EB006758)National Institute on Aging (AG022381)National Institute on Aging (5R01AG008122-22)National Institute on Aging (AG018344)National Institute on Aging (AG018386)National Center for Complementary and Alternative Medicine (U.S.) (RC1 AT005728-01)National Institute of Neurological Diseases and Stroke (U.S.) (R01 NS052585-01)National Institute of Neurological Diseases and Stroke (U.S.) (1R21NS072652-01)National Institute of Neurological Diseases and Stroke (U.S.) (1R01NS070963)National Institute of Neurological Diseases and Stroke (U.S.) (R01NS083534)National Institutes of Health (U.S.) ((5U01-MH093765