A Biophysical Model of Shape Changes due to Atrophy in the Brain with Alzheimer's Disease

International audienceThis paper proposes a model of brain deformation triggered by atrophy in Alzheimer's Disease (AD). We introduce a macroscopic biophysical model assuming that the density of the brain remains constant, hence its volume shrinks when neurons die in AD. The deformation in the brain parenchyma minimizes the elastic strain energy with the prescribed local volume loss. The cerebrospinal fluid (CSF) is modelled differently to allow for fluid readjustments occuring at a much faster time-scale. PDEs describing the model is discretized in staggered grid and solved using Finite Difference Method. We illustrate the power of the model by showing different deformation patterns obtained for the same global atrophy but prescribed in gray matter (GM) or white matter (WM) on a generic atlas MRI, and with a realistic AD simulation on a subject MRI. This well-grounded forward model opens a way to study different hypotheses about the distribution of brain atrophy, and to study its impact on the observed changes in MR images

Spectroscopic detection of pathological severity in Alzheimer's disease

Alzheimer’s disease (AD) has emerged as one of the most widespread and devastating forms of dementia. Over the past few decades, AD has consistently increased in prevalence worldwide due to the rising proportion of elderly individuals and lack of effective screening and treatment modalities. To date, few economically viable and widely applicable tools exist to make definitive, early diagnoses of the disease. Therefore, there is a clear need for interventions that facilitate accurate diagnoses, monitoring, and therapeutic treatment of AD. In the course of AD, cognitive impairment is preceded by physiological changes to the central nervous system (CNS). This includes neuronal atrophy, synaptic dysfunction, and the abnormal post-translational modification of the proteins tau and beta-amyloid (A), which contributes to the deposition of intracellular neurofibrillary tangles (NFTs) and extracellular neuritic plaques (NPs). The pathological cellular changes in AD occur long before the clinical course of the disease, and biomarkers for these changes can be detected prior to measurable cognitive decline. Because the biochemical changes associated with AD are irreversible, effective tools for diagnosis must detect the presence and severity of molecular pathology during the preliminary stages of the disease’s insidious onset. Biomarkers of AD can be detected by neuroimaging technologies, including magnetic resonance imaging (MRI), positron emission tomography (PET), and blood or cerebrospinal fluid (CSF) analyses. However, these methods are not currently suited to diagnose and monitor the unique pathogenesis of AD prior to cognitive decline. An ideal instrument for widespread AD screening, diagnosis, and monitoring must be noninvasive, inexpensive, portable, and accommodating to the cognitive sensitivities of patients on a spectrum from mild cognitive impairment (MCI) to full-blown dementia. Recently, several spectroscopic methods of assessing AD pathology have met these criteria and may be better suited for widespread clinical application. The objective of this thesis is to evaluate the use of near-infrared optical spectroscopy (NIRS) to detect pathological severity in human AD. Near-infrared (NIR) light is poorly absorbed by biological tissue, and can safely penetrate bone, skin, vasculature, and neuronal tissue. NIRS has traditionally been used in biomedical contexts to evaluate cerebral oxygenation changes, however the dense protein aggregates NFTs and NPs in AD tissue have recently been shown to characteristically affect several optical parameters of a NIR signal, including fluorescence and particle path (scattering). To date, applications of NIRS have been used to differentiate AD brains from non-AD controls in vitro, and further identify MCI patients in vivo, suggesting the NIR signal can identify molecular changes in AD. Severe AD cases are characterized by increased involvement of NFTs and NPs in the cerebral cortex, which would be expected to further affect the extent of NIR scatter. The current study aims to quantify AD-related pathology for investigation into whether the extent of optical scattering is correlated with the severity of amyloid plaque load and NFT density in the temporal cortex. Quantification of these lesions was accomplished using immunohistochemistry (IHC) and stereological analyses. Preliminary results show that the severity of AD pathology detected via IHC can be correlated with measured parameters of an in vitro near-infrared signal. Future studies aim to further characterize the relationship between scattering intensity and pathological severity, as well as evaluate the in vivo potential of this technology in predicting the clinical outcome and cognitive status of individuals in different stages of AD

Mechanobiology of the brain in ageing and Alzheimer's disease

Just as the epigenome, the proteome and the electrophysiological properties of a cell influence its function, so too do its intrinsic mechanical properties and its extrinsic mechanical environment. This is especially true for neurons of the central nervous system (CNS) as long‐term maintenance of synaptic connections relies on efficient axonal transport machinery and structural stability of the cytoskeleton. Recent reports suggest that profound physical changes occur in the CNS microenvironment with advancing age which, in turn, will impact highly mechanoresponsive neurons and glial cells. Here, we discuss the complex and inhomogeneous mechanical structure of CNS tissue, as revealed by recent mechanical measurements on the brain and spinal cord, using techniques such as magnetic resonance elastography and atomic force microscopy. Moreover, ageing, traumatic brain injury, demyelination and neurodegeneration can perturb the mechanical properties of brain tissue and trigger mechanobiological signalling pathways in neurons, glia and cerebral vasculature. It is, therefore, very likely that significant changes in cell and tissue mechanics contribute to age‐related cognitive decline and deficits in memory formation which are accelerated and magnified in neurodegenerative states, such as Alzheimer's disease. Importantly, we are now beginning to understand how neuronal and glial cell mechanics and brain tissue mechanobiology are intimately linked with neurophysiology and cognition

Simulating Patient Specific Multiple Time-point MRIs From a Biophysical Model of Brain Deformation in Alzheimer's Disease

International audienceThis paper proposes a framework to simulate patient specific structural Magnetic Resonance Images (MRIs) from the available MRI scans of Alzheimer’s Disease(AD) subjects. We use a biophysical model of brain deformation due to atrophy that can generate biologically plausible deformation for any given desired volume changes at the voxel level of the brain MRI. Large number of brain regions are segmented in 45 AD patients and the atrophy rates per year are estimated in these regions from the available two extremal time-point scans. Assuming linear progression of atrophy, the volume changes in scans closest to the half way time period is computed. These atrophy maps are prescribedto the baseline images to simulate the middle time-point images by using the biophysical model of brain deformation. From the baseline scans,the volume changes in real middle time-point scans are compared to the ones in simulated middle time-point images. This present framework also allows to introduce desired atrophy patterns at different time-points to simulate non-linear progression of atrophy. This opens a way to use abiophysical model of brain deformation to evaluate methods that study the temporal progression and spatial relationships of atrophy of differentregions in the brain with AD

Evaluation of Cerebral Lateral Ventricular Enlargement Derived from Magnetic Resonance Imaging: A Candidate Biomarker of Alzheimer Disease Progression in Vivo

Alzheimer disease (AD) is the most common form of dementia and has grievous mortality rates. Measuring brain volumes from structural magnetic resonance images (MRI) may be useful for illuminating disease progression. The goal of this thesis was to (1) help refine a novel technique used to segment the lateral cerebral ventricles from MRI, (2) validate this tool, and determine group-wise differences between normal elderly controls (NEC) and subjects with mild cognitive impairment (MCI) and AD and (3) determine the number of subjects necessary to detect a 20 percent change from the natural history of ventricular enlargement with respect to genotype. Three dimensional Ti-weighted MRI and cognitive measures were acquired from 504 subjects (NEC n = 152, MCI n = 247 and AD n = 105) participating in the multi-centre Alzheimer\u27s Disease Neuroimaging Initiative. Cerebral ventricular volume was quantified at baseline and after six months. For secondary analyses, all groups were dichotomized for Apolipoprotein E genotype based on the presence of an e4 polymorphism. The AD group had greater ventricular enlargement compared to both subjects with MCI (P = 0.0004) and NEC (P \u3c 0.0001), and subjects with MCI had a greater rate of ventricular enlargement compared to NEC (P =0.0001). MCI subjects that progressed to clinical AD after six months had greater ventricular enlargement than stable MCI subjects (P = 0.0270). Ventricular enlargement was different between apolipoprotein E genotypes within the AD group (P = 0.010). The number of subjects required to demonstrate a 20% change in ventricular enlargement (AD: N=342, MCI: N=1180) was substantially lower than that required to demonstrate a 20% change in cognitive scores (MMSE) (AD: N=7056, MCI: N=7712). Therefore, ventricular enlargement represents a feasible short-term marker of disease progression in subjects with MCI and subjects with AD for multi-centre studie

Doctor of Philosophy

dissertationNeurodegenerative diseases are an increasing health care problem in the United States. Quantitative neuroimaging provides a noninvasive method to illuminate individual variations in brain structure to better understand and diagnose these disorders. The overall objective of this research is to develop novel clinical tools that summarize and quantify changes in brain shape to not only help better understand age-appropriate changes but also, in the future, to dissociate structural changes associated with aging from those caused by dementing neurodegenerative disorders. Because the tools we will develop can be applied for individual assessment, achieving our goals could have a significant clinical impact. An accurate, practical objective summary measure of the brain pathology would augment current subjective visual interpretation of structural magnetic resonance images. Fractal dimension is a novel approach to image analysis that provides a quantitative measure of shape complexity describing the multiscale folding of the human cerebral cortex. Cerebral cortical folding reflects the complex underlying architectural features that evolve during brain development and degeneration including neuronal density, synaptic proliferation and loss, and gliosis. Building upon existing technology, we have developed innovative tools to compute global and local (voxel-wise and regional) cerebral cortical fractal dimensions and voxel-wise cortico-fractal surfaces from high-contrast MR images. Our previous research has shown that fractal dimension correlates with cognitive function and changes during the course of normal aging. We will now apply unbiased diffeomorphic atlasing methodology to dramatically improve the alignment of complex cortical surfaces. Our novel methods will create more accurate, detailed geometrically averaged images to take into account the intragroup differences and make statistical inferences about spatiotemporal changes in shape of the cerebral cortex across the adult human lifespan

Tissue Damage Quantification in Alzheimer\u27s Disease Brain via Magnetic Resonance Gradient Echo Plural Contrast Imaging (GEPCI)

Alzheimer’s disease (AD) affected approximately 48 million people worldwide in 2015. Its devastating consequences have stimulated an intense search for AD prevention and treatment. Clinically, AD is characterized by memory deficits and progressive cognitive impairment, leading to dementia. Over the past two to three decades, researchers have found that amyloidbeta (Aβ) plaques and neurofibrillary tau tangles occur during a long pre-symptomatic period (preclinical stage) before the onset of clinical symptoms. As a result, identification of the preclinical stage is essential for the initiation of prevention trials in asymptomatic individuals. Currently, Positron Emission Tomography (PET) imaging with injected 11C or 18F containing radiotracers (e.g., Pittsburgh compound B, PiB or florbetapir-fluorine-18, 18F-AV-45) is widely used to detect amyloid deposition in vivo and to identify this preclinical stage. However, PET scans are time consuming (about 1 hour), require injection of a radiotracer, thus, exposing the patient to ionizing radiation. After the preclinical stage, AD patients begin to show clinical symptoms, referred as a very mild or mild AD group. Post-mortem studies show that neuronal damage is the most proximate pathological substrate of cognitive impairment in AD compared with amyloid and tau deposition. Thus, a diagnostic tool is needed for detection of neuronal loss in vivo. As a faster, non-invasive, and radiation free imaging technique, Magnetic Resonance Imaging (MRI) plays an important role in the diagnosis of cognitive diseases. Conventional MRI yields superb definition of brain anatomy and structure and provide important volumetric information (e.g., brain atrophy). However, conventional MRI cannot provide microstructural and functional insight into the pathology of AD. The approach developed in Yablonskiy’s lab is based on the Gradient Echo Plural Contrast Imaging (GEPCI) protocol, which provides quantitative in vivo measurements of transverse relaxation properties of the tissue water 1H spins as determined from the gradient echo MRI signal. The measurements are corrected for macroscopic magnetic field inhomogeneity effects and physiologic-motion-driven fluctuations in magnetic field as these are the major artifacts present with the gradient echo technique. The principal relaxation property used in this dissertation is the tissue-specific transverse relaxation rate constant, R2*. The R2* value reflects the microscopic and mesoscopic magnetic field inhomogeneities rising from the complex tissuewater-environment within the human brain. In turn, changes in R2* reflect changes in the tissue’s microscopic and mesoscopic tissue structure. However, because of the presence of the cerebral blood vessel network, the magneticsusceptibility-driven blood-oxygen-level dependent (BOLD) effect also makes a significant contribution to R2*. A previously developed approach, quantitative BOLD (qBOLD), allows the separation of R2* into a tissue specific R2t* without blood vessel effects and the BOLD component. Quantifying the BOLD component allows the calculation of cerebral hemodynamics parameters, such as oxygen extraction fraction (OEF) and deoxygenated cerebral blood volume (dCBV). These parameters (R2*, R2t*, OEF, dCBV) describe structural and functional properties of tissue at the microstructural level in the human brain. In the study of normal aging, quantitative GEPCI measurements showed that R2t* increases with age while hemodynamic parameters, i.e., relative OEF and dCBV remain constant in most cerebral cortical regions. The comparison between quantitative GEPCI measurements and literature information suggest that (a) age-related increases in the cortical R2t* mostly reflect the age-related increases in the cellular packing density (or neuronal density); (b) regions in a brain characterized by higher R2t* contain a higher concentration of neurons with less developed cellular processes (dendrites, spines, etc.); and (c) brain regions characterized by lower R2t* represent regions with lower concentration of neurons but more developed cellular processes. In the Alzheimer study, R2* and R2t* together demonstrated significant differences among the normal, preclinical and mild AD groups. First, the results uncovered strong correlations between R2* and Aβ deposition measured by the PiB PET-tracer in several cortical regions (e.g., medial temporal lobe and precuneus). This finding indicates that R2* may be a potential surrogate marker for Aβ deposition. The strongest correlation was found in the medial temporal lobe (MTL), particularly in the parahippocampal cortex, which can be used to distinguish the normal and preclinical groups. Second, R2t* in the hippocampus, which characterized the hippocampal cellular integrity demonstrated much stronger correlations with psychometric tests than volume quantification of hippocampal atrophy. Importantly, decreased R2t* characterizing cellular damage was detected even in the hippocampal areas not affected by atrophy. In addition, R2t* significantly decreased in the mild AD group but was preserved in the preclinical group compared with the normal group. These results indicate a significant cellular density decrease in the mild group but not in the preclinical group, which is consistent with previous histological studies. In summary, GEPCI provides a new approach for evaluation of AD-related tissue pathology in vivo in the preclinical and early symptomatic stages of AD. Since MRI is widely available worldwide and does not require radiation exposure, it provides the opportunity to obtain new information on the pathogenesis of AD and for pre-screening cohorts (stratification) for clinical drug trials