Characterizing the Propagation Pattern of Neurodegeneration in Alzheimer's Disease by Longitudinal Network Analysis

Converging evidence shows that Alzheimer's disease (AD) is a neurodegenerative disease that represents a disconnection syndrome, whereby a large-scale brain network is progressively disrupted by one or more neuropathological processes. However, the mechanism by which pathological entities spread across a brain network is largely unknown. Since pathological burden may propagate trans-neuronally, we propose to characterize the propagation pattern of neuropathological events spreading across relevant brain networks that are regulated by the organization of the network. Specifically, we present a novel mixed-effect model to quantify the relationship between longitudinal network alterations and neuropathological events observed at specific brain regions, whereby the topological distance to hub nodes, high-risk AD genetics, and environmental factors (such as education) are considered as predictor variables. Similar to many cross-section studies, we find that AD-related neuropathology preferentially affects hub nodes. Furthermore, our statistical model provides strong evidence that abnormal neuropathological burden diffuses from hub nodes to non-hub nodes in a prion-like manner, whereby the propagation pattern follows the intrinsic organization of the large-scale brain network

The Characterization of Alzheimer’s Disease and the Development of Early Detection Paradigms: Insights from Nosology, Biomarkers and Machine Learning

Alzheimer’s Disease (AD) is the only condition in the top ten leading causes of death for which we do not have an effective treatment that prevents, slows, or stops its progression. Our ability to design useful interventions relies on (a) increasing our understanding of the pathological process of AD and (b) improving our ability for its early detection. These goals are impeded by our current reliance on the clinical symptoms of AD for its diagnosis. This characterizations of AD often falsely assumes a unified, underlying AD-specific pathology for similar presentations of dementia that leads to inconsistent diagnoses. It also hinges on postmortem verification, and so is not a helpful method for identifying patients and research subjects in the beginning phases of the pathophysiological process. Instead, a new biomarker-based approach provides a more biological understanding of the disease and can detect pathological changes up to 20 years before the clinical symptoms emerge. Subjects are assigned a profile according to their biomarker measures of amyloidosis (A), tauopathy (T) and neurodegeneration (N) that reflects their underlying pathology in vivo. AD is confirmed as the underlying pathology when subjects have abnormal values of both amyloid and tauopathy biomarkers, and so have a biomarker profile of A+T+(N)- or A+T+(N)+. This new biomarker based characterization of AD can be combined with machine learning techniques in multimodal classification studies to shed light on the elements of the AD pathological process and develop early detection paradigms. A guiding research framework is proposed for the development of reliable, biologically-valid and interpretable multimodal classification models

Learning Common Harmonic Waves on Stiefel Manifold - A New Mathematical Approach for Brain Network Analyses

Converging evidence shows that disease-relevant brain alterations do not appear in random brain locations, instead, their spatial patterns follow large-scale brain networks. In this context, a powerful network analysis approach with a mathematical foundation is indispensable to understand the mechanisms of neuropathological events as they spread through the brain. Indeed, the topology of each brain network is governed by its native harmonic waves, which are a set of orthogonal bases derived from the Eigen-system of the underlying Laplacian matrix. To that end, we propose a novel connectome harmonic analysis framework that provides enhanced mathematical insights by detecting frequency-based alterations relevant to brain disorders. The backbone of our framework is a novel manifold algebra appropriate for inference across harmonic waves. This algebra overcomes the limitations of using classic Euclidean operations on irregular data structures. The individual harmonic differences are measured by a set of common harmonic waves learned from a population of individual Eigen-systems, where each native Eigen-system is regarded as a sample drawn from the Stiefel manifold. Specifically, a manifold optimization scheme is tailored to find the common harmonic waves, which reside at the center of the Stiefel manifold. To that end, the common harmonic waves constitute a new set of neurobiological bases to understand disease progression. Each harmonic wave exhibits a unique propagation pattern of neuropathological burden spreading across brain networks. The statistical power of our novel connectome harmonic analysis approach is evaluated by identifying frequency-based alterations relevant to Alzheimer's disease, where our learning-based manifold approach discovers more significant and reproducible network dysfunction patterns than Euclidean methods

Left lateralized cerebral glucose metabolism declines in amyloid-β positive persons with mild cognitive impairment

Background: Previous publications indicate that Alzheimer\u27s Disease (AD) related cortical atrophy may develop in asymmetric patterns, with accentuation of the left hemisphere. Since fluorodeoxyglucose positron emission tomography (FDG PET) measurements of the regional cerebral metabolic rate of glucose (rCMRgl) provide a sensitive and specific marker of neurodegenerative disease progression, we sought to investigate the longitudinal pattern of rCMRgl in amyloid-positive persons with mild cognitive impairment (MCI) and dementia, hypothesizing asymmetric declines of cerebral glucose metabolism. Methods: Using florbetapir PET and cerebrospinal fluid (CSF) measures to define amyloid-β (Aβ) positivity, 40 Aβ negative (Aβ-) cognitively unimpaired controls (CU; 76 ± 5y), 76 Aβ positive (Aβ+) persons with MCI (76 ± 7y) and 51 Aβ+persons with probable AD dementia (75 ± 7y) from the AD Neuroimaging Initiative (ADNI) were included in this study with baseline and 2-year follow-up FDG PET scans. The degree of lateralization of longitudinal rCMRgl declines in subjects with Aβ+MCI and AD in comparison with Aβ- CU were statistically quantified via bootstrapped lateralization indices [(LI); range−1 (right) to 1 (left)]. Results: Compared to Aβ- CU, Aβ+MCI patients showed marked left hemispheric lateralization (LI: 0.78). In contrast, modest right hemispheric lateralization (LI: −0.33) of rCMRgl declines was found in Aβ+persons with probable AD dementia. Additional comparisons of Aβ+groups (i.e. MCI and probable AD dementia) consequently indicated right hemispheric lateralization (LI: −0.79) of stronger rCMRgl declines in dementia stages of AD. For all comparisons, voxel-based analyses confirmed significant (pFWE\u3c0.05) declines of rCMRgl within AD-typical brain regions. Analyses of cognitive data yielded predominant decline of memory functions in both MCI and dementia stages of AD. Conclusions: These data indicate that in early stages, AD may be characterized by a more lateralized pattern of left hemispheric rCMRgl declines. However, metabolic differences between hemispheres appear to diminish with further progression of the disease

Sleep oscillation-specific associations with Alzheimer’s disease CSF biomarkers : novel roles for sleep spindles and tau

Background: Based on associations between sleep spindles, cognition, and sleep-dependent memory processing, here we evaluated potential relationships between levels of CSF Aβ42, P-tau, and T-tau with sleep spindle density and other biophysical properties of sleep spindles in a sample of cognitively normal elderly individuals. Methods: One-night in-lab nocturnal polysomnography (NPSG) and morning to early afternoon CSF collection were performed to measure CSF Aβ42, P-tau and T-tau. Seven days of actigraphy were collected to assess habitual total sleep time. Results: Spindle density during NREM stage 2 (N2) sleep was negatively correlated with CSF Aβ42, P-tau and T-tau. From the three, CSF T-tau was the most significantly associated with spindle density, after adjusting for age, sex and ApoE4. Spindle duration, count and fast spindle density were also negatively correlated with T-tau levels. Sleep duration and other measures of sleep quality were not correlated with spindle characteristics and did not modify the associations between sleep spindle characteristics and the CSF biomarkers of AD. Conclusions: Reduced spindles during N2 sleep may represent an early dysfunction related to tau, possibly reflecting axonal damage or altered neuronal tau secretion, rendering it a potentially novel biomarker for early neuronal dysfunction. Given their putative role in memory consolidation and neuroplasticity, sleep spindles may represent a mechanism by which tau impairs memory consolidation, as well as a possible target for therapeutic interventions in cognitive decline

Left lateralized cerebral glucose metabolism declines in amyloid-β positive persons with mild cognitive impairment

Background: Previous publications indicate that Alzheimer\u27s Disease (AD) related cortical atrophy may develop in asymmetric patterns, with accentuation of the left hemisphere. Since fluorodeoxyglucose positron emission tomography (FDG PET) measurements of the regional cerebral metabolic rate of glucose (rCMRgl) provide a sensitive and specific marker of neurodegenerative disease progression, we sought to investigate the longitudinal pattern of rCMRgl in amyloid-positive persons with mild cognitive impairment (MCI) and dementia, hypothesizing asymmetric declines of cerebral glucose metabolism. Methods: Using florbetapir PET and cerebrospinal fluid (CSF) measures to define amyloid-β (Aβ) positivity, 40 Aβ negative (Aβ-) cognitively unimpaired controls (CU; 76 ± 5y), 76 Aβ positive (Aβ+) persons with MCI (76 ± 7y) and 51 Aβ+persons with probable AD dementia (75 ± 7y) from the AD Neuroimaging Initiative (ADNI) were included in this study with baseline and 2-year follow-up FDG PET scans. The degree of lateralization of longitudinal rCMRgl declines in subjects with Aβ+MCI and AD in comparison with Aβ- CU were statistically quantified via bootstrapped lateralization indices [(LI); range−1 (right) to 1 (left)]. Results: Compared to Aβ- CU, Aβ+MCI patients showed marked left hemispheric lateralization (LI: 0.78). In contrast, modest right hemispheric lateralization (LI: −0.33) of rCMRgl declines was found in Aβ+persons with probable AD dementia. Additional comparisons of Aβ+groups (i.e. MCI and probable AD dementia) consequently indicated right hemispheric lateralization (LI: −0.79) of stronger rCMRgl declines in dementia stages of AD. For all comparisons, voxel-based analyses confirmed significant (pFWE\u3c0.05) declines of rCMRgl within AD-typical brain regions. Analyses of cognitive data yielded predominant decline of memory functions in both MCI and dementia stages of AD. Conclusions: These data indicate that in early stages, AD may be characterized by a more lateralized pattern of left hemispheric rCMRgl declines. However, metabolic differences between hemispheres appear to diminish with further progression of the disease

Integrative multi-omic network strategies for unraveling complex disease biology and the identification of novel phenotype associated genes

Identifying the genetic risk factors underlying a given disease is an essential step for informing effective drug targets, understanding disease architecture, and predicting at-risk individuals. A commonly applied approach for identifying novel disease-associated genes is the Genome Wide Association Study (GWAS) approach, in which a high number of individuals are sequenced and genetic variants are then tested for an association with disease status. While the GWAS approach has identified countless disease-associated genes, there remain plenty of diseases for which our genetic understanding is still incomplete. One strategy for augmenting the GWAS approach is to incorporate additional omics data in order to prioritize biologically plausible candidate genes. In this thesis work, we integrate network-based strategies with existing genetic analysis pipelines in order to identify novel Alzheimer’s disease (AD) genes. Two types of biological data inform the underlying structure of the networks: a) protein-protein interactions and b) gene expression in the human brain. Genes which interact or are co-expressed across similar conditions have been shown to have a higher probability of being functionally related. Using a set or previously known AD genes, we apply a network propagation strategy to score genes based upon their proximity to the known AD genes within these networks. Then we integrate the network score of each gene with its risk score from GWAS to identify novel candidates. To further affirm the reproducibility of findings, we further incorporate additional information in the form of knockout models in flies, bootstrap aggregation, and external genetic datasets. In addition to predicting novel genes, we are able to utilize regional co-expression networks to further understand how the known AD genes behave within the various sub-divisions of the brain. We find that regions of the brain which are known to have the earliest vulnerability to AD-induced neurodegeneration also tend to be where AD genes are highly correlated

Cerebrospinal fluid biomarkers of neurofibrillary tangles and synaptic dysfunction are associated with longitudinal decline in white matter connectivity: A multi-resolution graph analysis

In addition to the development of beta amyloid plaques and neurofibrillary tangles, Alzheimer's disease (AD) involves the loss of connecting structures including degeneration of myelinated axons and synaptic connections. However, the extent to which white matter tracts change longitudinally, particularly in the asymptomatic, preclinical stage of AD, remains poorly characterized. In this study we used a novel graph wavelet algorithm to determine the extent to which microstructural brain changes evolve in concert with the development of AD neuropathology as observed using CSF biomarkers. A total of 118 participants with at least two diffusion tensor imaging (DTI) scans and one lumbar puncture for CSF were selected from two observational and longitudinally followed cohorts. CSF was assayed for pathology specific to AD (Aβ42 and phosphorylated-tau), neurodegeneration (total-tau), axonal degeneration (neurofilament light chain protein; NFL), and synaptic degeneration (neurogranin). Tractography was performed on DTI scans to obtain structural connectivity networks with 160 nodes where the nodes correspond to specific brain regions of interest (ROIs) and their connections were defined by DTI metrics (i.e., fractional anisotropy (FA) and mean diffusivity (MD)). For the analysis, we adopted a multi-resolution graph wavelet technique called Wavelet Connectivity Signature (WaCS) which derives higher order representations from DTI metrics at each brain connection. Our statistical analysis showed interactions between the CSF measures and the MRI time interval, such that elevated CSF biomarkers and longer time were associated with greater longitudinal changes in white matter microstructure (decreasing FA and increasing MD). Specifically, we detected a total of 17 fiber tracts whose WaCS representations showed an association between longitudinal decline in white matter microstructure and both CSF p-tau and neurogranin. While development of neurofibrillary tangles and synaptic degeneration are cortical phenomena, the results show that they are also associated with degeneration of underlying white matter tracts, a process which may eventually play a role in the development of cognitive decline and dementia

Tau pathology in Alzheimer's disease and other dementias : translational approach from in vitro autoradiography to in vivo PET imaging

Tauopathies, including Alzheimer's disease (AD), corticobasal degeneration (CBD), and progressive supranuclear palsy (PSP), are complex neurodegenerative disorders characterized by the pathological accumulation of tau proteins in the brain. These often overlapping disorders, with intricate pathologies and growing prevalence, lack definitive treatments, highlighting the necessity for advanced research. Positron emission tomography (PET) imaging aids in the diagnosis and monitoring of diseases, by providing in vivo insights into pathological features. This thesis focused on deciphering the binding properties and brain regional distribution of PET tracers for accurate disease differentiation. Spanning four studies, we aimed to bridge in vitro and in vivo PET data to investigate tau pathology and its association with dementia-related markers such as reactive astrogliosis, peripheral inflammation, and dopaminergic dysfunction. The 2nd generation tau PET tracers, 3H-MK6240 and 3H-PI2620, demonstrated high affinity and specificity in AD post-mortem brain tissues, especially in early-onset AD, compared to controls. 3H-PI2620, 3H-MK6240, and 3HRO948 displayed similar binding patterns in AD tissue, with multiple binding sites and equivalent high affinities (Papers I and II). 3H-PI2620 showed specificity in CBD and PSP tissues, in contrast to 3H-MK6240. However, differentiating CBD from PSP brains with 3H-PI2620 remained challenging in multiple brain regions, potentially due to complex tracer-target interactions (Papers II and III). Reactive astrogliosis PET tracers 3H-Deprenyl and 3H-BU99008 bound primarily to stable distinct high-affinity binding sites in AD, CBD and PSP, but also to transient binding sites, differing by brain region and condition. This pattern implied that these tracers may interact with similar or diverse subtypes or populations of astrocytes, expressing varying ratios of transient sites, which may vary depending on the brain location and the disease (Paper III). Using 3H-FEPE2I, we delineated a reduction in dopamine transporter (DAT) levels within the putamen across CBD, PSP and Parkinson's Disease (PD) brains. Concomitantly, elevated 3H-Raclopride binding reflected higher dopamine D2 receptor (D2R) levels in PSP and PD. Nonetheless, our observations underscored the heterogeneity inherent to these neurodegenerative pathologies, emphasizing the criticality of individual variability in neuropathological manifestations (Paper III). Lastly, we investigated late middle-aged cognitively unimpaired Hispanic individuals, in dichotomous groups of in vivo amyloid-β (Aβ) PET (18F-Florbetaben) and plasma neurofilament light (NfL) biomarkers. Our findings suggest that elevated plasma inflammation and tau burden as measured by 18FMK6240, can be detected at early preclinical stages of AD, offering potential for early diagnosis (Paper IV). This thesis underscored the importance of PET imaging in advancing our understanding of tauopathies. The innovative use of multiple PET tracers provided crucial insights into their potential use in clinics to distinguish pathological features of AD, CBD and PSP. The findings emphasized the need for more studies applying a multifaceted approach to studying and managing these complex neurodegenerative disorders, combining advanced imaging techniques with a broad spectrum of biological markers