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Background: Alzheimer's disease (AD) is a heterogeneous, multifactorial neurodegenerative disorder characterized by three neurobiological factors beta‐amyloid, pathologic tau, and neurodegeneration. There are no effective treatments for AD at a late stage, urging for early detection and prevention. However, existing statistical inference approaches in neuroimaging studies of AD subtype identification do not take into account the pathological domain knowledge, which could lead to ill‐posed results that are sometimes inconsistent with the essential neurological principles. Purpose: Integrating systems biology modeling with machine learning, the study aims to assist clinical AD prognosis by providing a subpopulation classification in accordance with essential biological principles, neurological patterns, and cognitive symptoms. Methods: We propose a novel pathology steered stratification network (PSSN) that incorporates established domain knowledge in AD pathology through a reaction‐diffusion model, where we consider non‐linear interactions between major biomarkers and diffusion along the brain structural network. Trained on longitudinal multimodal neuroimaging data, the biological model predicts long‐term evolution trajectories that capture individual characteristic progression pattern, filling in the gaps between sparse imaging data available. A deep predictive neural network is then built to exploit spatiotemporal dynamics, link neurological examinations with clinical profiles, and generate subtype assignment probability on an individual basis. We further identify an evolutionary disease graph to quantify subtype transition probabilities through extensive simulations. Results: Our stratification achieves superior performance in both inter‐cluster heterogeneity and intra‐cluster homogeneity of various clinical scores. Applying our approach to enriched samples of aging populations, we identify six subtypes spanning AD spectrum, where each subtype exhibits a distinctive biomarker pattern that is consistent with its clinical outcome. Conclusions: The proposed PSSN (i) reduces neuroimage data to low‐dimensional feature vectors, (ii) combines AT[N]‐Net based on real pathological pathways, (iii) predicts long‐term biomarker trajectories, and (iv) stratifies subjects into fine‐grained subtypes with distinct neurological underpinnings. PSSN provides insights into pre‐symptomatic diagnosis and practical guidance on clinical treatments, which may be further generalized to other neurodegenerative diseases. [ABSTRACT FROM AUTHOR]

Background: Despite the striking efforts in investigating neurobiological factors behind the acquisition of amyloid-β (A), protein tau (T), and neurodegeneration ([N]) biomarkers, the mechanistic pathways of how AT[N] biomarkers spreading throughout the brain remain elusive. Objective: To disentangle the massive heterogeneities in Alzheimer's disease (AD) progressions and identify vulnerable/critical brain regions to AD pathology. Methods: In this work, we characterized the interaction of AT[N] biomarkers and their propagation across brain networks using a novel bistable reaction-diffusion model, which allows us to establish a new systems biology underpinning of AD progression. We applied our model to large-scale longitudinal neuroimages from the ADNI database and studied the systematic vulnerability and criticality of brains. Results: Our model yields long term prediction that is statistically significant linear correlated with temporal imaging data, produces clinically consistent risk prediction, and captures the Braak-like spreading pattern of AT[N] biomarkers in AD development. Conclusions: Our major findings include (i) tau is a stronger indicator of regional risk compared to amyloid, (ii) temporal lobe exhibits higher vulnerability to AD-related pathologies, (iii) proposed critical brain regions outperform hub nodes in transmitting disease factors across the brain, and (iv) comparing the spread of neuropathological burdens caused by amyloid-β and tau diffusions, disruption of metabolic balance is the most determinant factor contributing to the initiation and progression of AD. [ABSTRACT FROM AUTHOR]

Background: Mounting evidence shows that the neuropathological burdens manifest preference in affecting brain regions during the dynamic progression of Alzheimer's disease (AD). Since the distinct brain regions are physically wired by white matter fibers, it is reasonable to hypothesize the differential spreading pattern of neuropathological burdens may underlie the wiring topology, which can be characterized using neuroimaging and network science technologies.Objective: To study the dynamic spreading patterns of neuropathological events in AD.Methods: We first examine whether hub nodes with high connectivity in the brain network (assemble of white matter wirings) are susceptible to a higher level of pathological burdens than other regions that are less involved in the process of information exchange in the network. Moreover, we propose a novel linear mixed-effect model to characterize the multi-factorial spreading process of neuropathological burdens from hub nodes to non-hub nodes, where age, sex, and APOE4 indicators are considered as confounders. We apply our statistical model to the longitudinal neuroimaging data of amyloid-PET and tau-PET, respectively.Results: Our meta-data analysis results show that 1) AD differentially affects hub nodes with a significantly higher level of pathology, and 2) the longitudinal increase of neuropathological burdens on non-hub nodes is strongly correlated with the connectome distance to hub nodes rather than the spatial proximity.Conclusion: The spreading pathway of AD neuropathological burdens might start from hub regions and propagate through the white matter fibers in a prion-like manner. [ABSTRACT FROM AUTHOR]

Background: With the rapid development of neurobiology and neuroimaging technologies, mounting evidence shows that Alzheimer's disease (AD) is caused by the build-up of two abnormal proteins, amyloid-β plaques (A) and neurofibrillary tangles (T). Over time, these AD-related neuropathological burdens begin to spread throughout the brain, which results in the characteristic progression of symptoms in AD.Objective: Although tremendous efforts have been made to link biological indicators to the progression of AD, limited attention has been paid to investigate the multi-factorial role of socioeconomic status (SES) in the prevalence or incidence of AD. There is high demand to explore the synergetic effect of sex and SES factors in moderating the neurodegeneration process caused by the accumulation of A and T biomarkers.Methods: We carry out a meta-data analysis on the longitudinal neuroimaging data, clinical outcomes, genotypes, and demographic data in Alzheimer's Disease Neuroimaging Initiative (ADNI) database (http://adni.loni.usc.edu).Results: Our major findings include 1) education and occupation show resilience effects at the angular gyrus, superior parietal lobule, lateral occipital-temporal sulcus, and posterior transverse collateral sulcus where we found significant slowdown of neurodegeneration due to higher education level or more advanced occupation rank; 2) A and T biomarkers manifest different spatial patterns of brain resilience; 3) BDNF (brain-derived neurotrophic factor) single nucleotide polymorphism (SNP) rs10835211 shows strong association to the identified resilience effect; 4) the identified resilience effect is associated with the clinical manifestation in memory, learning, and organization performance.Conclusion: Several brain regions manifest resilience from SES to A and T biomarkers. BDNF SNPs have a potential association with the resilience effect from SES. In addition, cognitive measures of learning and memory demonstrate the resilience effect. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Background: Although the abnormal depositions of amyloid plaques and neurofibrillary tangles are the hallmark of Alzheimer's disease (AD), converging evidence shows that the individual's neurodegeneration trajectory is regulated by the brain's capability to maintain normal cognition.Objective: The concept of cognitive reserve has been introduced into the field of neuroscience, acting as a moderating factor for explaining the paradoxical relationship between the burden of AD pathology and the clinical outcome. It is of high demand to quantify the degree of conceptual cognitive reserve on an individual basis.Methods: We propose a novel statistical model to quantify an individual's cognitive reserve against neuropathological burdens, where the predictors include demographic data (such as age and gender), socioeconomic factors (such as education and occupation), cerebrospinal fluid biomarkers, and AD-related polygenetic risk score. We conceptualize cognitive reserve as a joint product of AD pathology and socioeconomic factors where their interaction manifests a significant role in counteracting the progression of AD in our statistical model.Results: We apply our statistical models to re-investigate the moderated neurodegeneration trajectory by considering cognitive reserve, where we have discovered that 1) high education individuals have significantly higher reserve against the neuropathology than the low education group; however, 2) the cognitive decline in the high education group is significantly faster than low education individuals after the level of pathological burden increases beyond the tipping point.Conclusion: We propose a computational proxy of cognitive reserve that can be used in clinical routine to assess the progression of AD. [ABSTRACT FROM AUTHOR]

Discriminative methods commonly produce models with relatively good generalization abilities. However, this advantage is challenged in real-world applications (e.g., medical image analysis problems), in which there often exist outlier data points (sample-outliers) and noises in the predictor values (feature-noises). Methods robust to both types of these deviations are somewhat overlooked in the literature. We further argue that denoising can be more effective, if we learn the model using all the available labeled and unlabeled samples, as the intrinsic geometry of the sample manifold can be better constructed using more data points. In this paper, we propose a semi-supervised robust discriminative classification method based on the least-squares formulation of linear discriminant analysis to detect sample-outliers and feature-noises simultaneously, using both labeled training and unlabeled testing data. We conduct several experiments on a synthetic, some benchmark semi-supervised learning, and two brain neurodegenerative disease diagnosis datasets (for Parkinson’s and Alzheimer’s diseases). Specifically for the application of neurodegenerative diseases diagnosis, incorporating robust machine learning methods can be of great benefit, due to the noisy nature of neuroimaging data. Our results show that our method outperforms the baseline and several state-of-the-art methods, in terms of both accuracy and the area under the ROC curve. [ABSTRACT FROM AUTHOR]

• The statistical model on change point detection offers a new window to stratify the diverse trajectories of cognitive decline in the temporal domain. • The relationship between tau biomarkers and MMSE manifests multiple change points occurring at the ages of 72, 78, and 83. • T biomarker exhibits higher sensitivity in tracking cognitive decline than A and [N] biomarkers. AT[N] biomarkers are more sensitive to the decline of executive function compared to memory performance. • Biological sex moderates the rate of cognitive decline associated with APOE4 genotype. • Higher levels of education, a marker of cognitive reserve, may moderate the effects of APOE4 and play an important role on cognitive decline. Current diagnostic systems for Alzheimer's disease (AD) rely upon clinical signs and symptoms, despite the fact that the multiplicity of clinical symptoms renders various neuropsychological assessments inadequate to reflect the underlying pathophysiological mechanisms. Since putative neuroimaging biomarkers play a crucial role in understanding the etiology of AD, we sought to stratify the diverse relationships between AD biomarkers and cognitive decline in the aging population and uncover risk factors contributing to the diversities in AD. To do so, we capitalized on a large amount of neuroimaging data from the ADNI study to examine the inflection points along the dynamic relationship between cognitive decline trajectories and whole-brain neuroimaging biomarkers, using a state-of-the-art statistical model of change point detection. Our findings indicated that the temporal relationship between AD biomarkers and cognitive decline may differ depending on the synergistic effect of genetic risk and biological sex. Specifically, tauopathy-PET biomarkers exhibit a more dynamic and age-dependent association with Mini-Mental State Examination scores (p < 0.05), with inflection points at 72, 78, and 83 years old, compared with amyloid-PET and neurodegeneration (cortical thickness from MRI) biomarkers. In the landscape of health disparities in AD, our analysis indicated that biological sex moderates the rate of cognitive decline associated with APOE4 genotype. Meanwhile, we found that higher education levels may moderate the effect of APOE4 , acting as a marker of cognitive reserve. [ABSTRACT FROM AUTHOR]

Previous studies have established that neurodegenerative disease such as Alzheimer's disease (AD) is a disconnection syndrome, where the neuropathological burdens often propagate across the brain network to interfere with the structural and functional connections. In this context, identifying the propagation patterns of neuropathological burdens sheds new light on understanding the pathophysiological mechanism of AD progression. However, little attention has been paid to propagation pattern identification by fully considering the intrinsic properties of brain-network organization, which plays an important role in improving the interpretability of the identified propagation pathways. To this end, we propose a novel harmonic wavelet analysis approach to construct a set of region-specific pyramidal multi-scale harmonic wavelets, it allows us to characterize the propagation patterns of neuropathological burdens from multiple hierarchical modules across the brain network. Specifically, we first extract underlying hub nodes through a series of network centrality measurements on the common brain network reference generated from a population of minimum spanning tree (MST) brain networks. Then, we propose a manifold learning method to identify the region-specific pyramidal multi-scale harmonic wavelets corresponding to hub nodes by seamlessly integrating the hierarchically modular property of the brain network. We estimate the statistical power of our proposed harmonic wavelet analysis approach on synthetic data and large-scale neuroimaging data from ADNI. Compared with the other harmonic analysis techniques, our proposed method not only effectively predicts the early stage of AD but also provides a new window to capture the underlying hub nodes and the propagation pathways of neuropathological burdens in AD. • We propose a novel region-specific pyramidal multi-scale harmonic wavelet analysis approach. • We integrate the hierarchically modular property of the brain network into harmonic wavelets. • Such learned harmonic wavelets allow us to characterize the propagation patterns of neuropathological burdens. • A new window to capture the underlying hub nodes and the propagation pathways of neuropathological burdens in AD. [ABSTRACT FROM AUTHOR]

• The paper proposes a manifold-based harmonic network analysis approach to explore a novel imaging biomarker in the form of the AD propagation pattern, which eventually allows us to identify the AD-related spreading pathways of neuropathological events throughout the brain. • The backbone of the proposed framework is to find the region-adaptive common harmonic wavelets on the Stiefel manifold. The wavelets allow us to adaptively characterize the spreading of pathological events localized at each brain region. • Extensive experiments on large-scale neuroimaging data from the ADNI database demonstrate our framework achieve superior performance in separating clinic cohorts of AD than other empirical biomarkers. Furthermore, the harmonic wavelets not only yield a new imaging biomarker to potentially predict the cognitive decline in the early stage but also offer a new window to capture the spreading pathways of neuropathological burden. Empirical imaging biomarkers such as the level of the regional pathological burden are widely used to measure the risk of developing neurodegenerative diseases such as Alzheimer's disease (AD). However, ample evidence shows that the brain network (wirings of white matter fibers) plays a vital role in the progression of AD, where neuropathological burdens often propagate across the brain network in a prion-like manner. In this context, characterizing the spreading pathway of AD-related neuropathological events sheds new light on understanding the heterogeneity of pathophysiological mechanisms in AD. In this work, we propose a manifold-based harmonic network analysis approach to explore a novel imaging biomarker in the form of the AD propagation pattern, which eventually allows us to identify the AD-related spreading pathways of neuropathological events throughout the brain. The backbone of this new imaging biomarker is a set of region-adaptive harmonic wavelets that represent the common network topology across individuals. We conceptualize that the individual's brain network and its associated pathology pattern form a unique system, which vibrates as do all natural objects in the universe. Thus, we can computationally excite such a brain system using selected harmonic wavelets that match the system's resonance frequency, where the resulting oscillatory wave manifests the system-level propagation pattern of neuropathological events across the brain network. We evaluate the statistical power of our harmonic network analysis approach on large-scale neuroimaging data from ADNI. Compared with the other empirical biomarkers, our harmonic wavelets not only yield a new imaging biomarker to potentially predict the cognitive decline in the early stage but also offer a new window to capture the in-vivo spreading pathways of neuropathological burden with a rigorous mathematics insight. [Display omitted] [ABSTRACT FROM AUTHOR]

Abstract: Neuroimage measures from magnetic resonance (MR) imaging, such as cortical thickness, have been playing an increasingly important role in searching for biomarkers of Alzheimer''s disease (AD). Recent studies show that, AD, mild cognitive impairment (MCI) and normal control (NC) can be distinguished with relatively high accuracy using the baseline cortical thickness. With the increasing availability of large longitudinal datasets, it also becomes possible to study the longitudinal changes of cortical thickness and their correlation with the development of pathology in AD. In this study, the longitudinal cortical thickness changes of 152 subjects from 4 clinical groups (AD, NC, Progressive-MCI and Stable-MCI) selected from Alzheimer''s Disease Neuroimaging Initiative (ADNI) are measured by our recently developed 4 D (spatial+temporal) thickness measuring algorithm. It is found that the 4 clinical groups demonstrate very similar spatial distribution of grey matter (GM) loss on cortex. To fully utilize the longitudinal information and better discriminate the subjects from 4 groups, especially between Stable-MCI and Progressive-MCI, 3 different categories of features are extracted for each subject, i.e., (1) static cortical thickness measures computed from the baseline and endline, (2) cortex thinning dynamics, such as the thinning speed (mm/year) and the thinning ratio (endline/baseline), and (3) network features computed from the brain network constructed based on the correlation between the longitudinal thickness changes of different regions of interest (ROIs). By combining the complementary information provided by features from the 3 categories, 2 classifiers are trained to diagnose AD and to predict the conversion to AD in MCI subjects, respectively. In the leave-one-out cross-validation, the proposed method can distinguish AD patients from NC at an accuracy of 96.1%, and can detect 81.7% (AUC = 0.875) of the MCI converters 6 months ahead of their conversions to AD. Also, by analyzing the brain network built via longitudinal cortical thickness changes, a significant decrease (p < 0.02) of the network clustering coefficient (associated with the development of AD pathology) is found in the Progressive-MCI group, which indicates the degenerated wiring efficiency of the brain network due to AD. More interestingly, the decreasing of network clustering coefficient of the olfactory cortex region was also found in the AD patients, which suggests olfactory dysfunction. Although the smell identification test is not performed in ADNI, this finding is consistent with other AD-related olfactory studies. [Copyright &y& Elsevier]

• A novel multivariate hub identification method to jointly find a set of critical connector hub nodes in the network. • An extension of population-wise hub identification was also proposed to identify a set of common hubs from a group of networks across different populations or longitudinal time points. • Experiments based on both structural and functional data demonstrated the population-wise hub identification has a more power on distinguishing network alterations related to disorders such as Alzheimer's disease and obsessive-compulsive disorder. Recent developments in neuroimaging allow us to investigate the structural and functional connectivity between brain regions in vivo. Mounting evidence suggests that hub nodes play a central role in brain communication and neural integration. Such high centrality, however, makes hub nodes particularly susceptible to pathological network alterations and the identification of hub nodes from brain networks has attracted much attention in neuroimaging. Current popular hub identification methods often work in a univariate manner, i.e., selecting the hub nodes one after another based on either heuristic of the connectivity profile at each node or predefined settings of network modules. Since the topological information of the entire network (such as network modules) is not fully utilized, current methods have limited power to identify hubs that link multiple modules (connector hubs) and are biased toward identifying hubs having many connections within the same module (provincial hubs). To address this challenge, we propose a novel multivariate hub identification method. Our method identifies connector hubs as those that partition the network into disconnected components when they are removed from the network. Furthermore, we extend our hub identification method to find the population-based hub nodes from a group of network data. We have compared our hub identification method with existing methods on both simulated and human brain network data. Our proposed method achieves more accurate and replicable discovery of hub nodes and exhibits enhanced statistical power in identifying network alterations related to neurological disorders such as Alzheimer's disease and obsessive-compulsive disorder. [Display omitted] [ABSTRACT FROM AUTHOR]

• The paper proposes a novel multi-graph fusion method to fuse FCNs and automatically learn the connections of brain regions. • The proposed framework employs L1SVM to integrate the disease diagnosis and related brain regions selection in a unified framework. It is noteworthy that previous methods focused on separately conducting brain regions selection and disease diagnosis. [Display omitted] In this paper, we propose a framework for functional connectivity network (FCN) analysis, which conducts the brain disease diagnosis on the resting state functional magnetic resonance imaging (rs-fMRI) data, aiming at reducing the influence of the noise, the inter-subject variability, and the heterogeneity across subjects. To this end, our proposed framework investigates a multi-graph fusion method to explore both the common and the complementary information between two FCNs, i.e., a fully-connected FCN and a 1 nearest neighbor (1NN) FCN, whereas previous methods only focus on conducting FCN analysis from a single FCN. Specifically, our framework first conducts the graph fusion to produce the representation of the rs-fMRI data with high discriminative ability, and then employs the L1SVM to jointly conduct brain region selection and disease diagnosis. We further evaluate the effectiveness of the proposed framework on various data sets of the neuro-diseases, i.e., Fronto-Temporal Dementia (FTD), Obsessive-Compulsive Disorder (OCD), and Alzheimers Disease (AD). The experimental results demonstrate that the proposed framework achieves the best diagnosis performance via selecting reasonable brain regions for the classification tasks, compared to state-of-the-art FCN analysis methods. [ABSTRACT FROM AUTHOR]

• We trained a model to sequentially recognize different length partial MR image sequences from different stages of AD. • Our method is leveraged by the inexorably progressive nature of AD. To that end, a Temporally Structured Support Vector Machine (TS-SVM) model is proposed to constrain the partial MR image sequence's detection score to increase monotonically with AD progression. • Furthermore, in order to select the best morphological features for enabling classifiers, we propose a joint feature selection and classification framework. The enormous social and economic cost of Alzheimer's disease (AD) has driven a number of neuroimaging investigations for early detection and diagnosis. Towards this end, various computational approaches have been applied to longitudinal imaging data in subjects with Mild Cognitive Impairment (MCI), as serial brain imaging could increase sensitivity for detecting changes from baseline, and potentially serve as a diagnostic biomarker for AD. However, current state-of-the-art brain imaging diagnostic methods have limited utility in clinical practice due to the lack of robust predictive power. To address this limitation, we propose a flexible spatial-temporal solution to predict the risk of MCI conversion to AD prior to the onset of clinical symptoms by sequentially recognizing abnormal structural changes from longitudinal magnetic resonance (MR) image sequences. Firstly, our model is trained to sequentially recognize different length partial MR image sequences from different stages of AD. Secondly, our method is leveraged by the inexorably progressive nature of AD. To that end, a Temporally Structured Support Vector Machine (TS-SVM) model is proposed to constrain the partial MR image sequence's detection score to increase monotonically with AD progression. Furthermore, in order to select the best morphological features for enabling classifiers, we propose a joint feature selection and classification framework. We demonstrate that our early diagnosis method using only two follow-up MR scans is able to predict conversion to AD 12 months ahead of an AD clinical diagnosis with 81.75% accuracy. Image, graphical abstract [ABSTRACT FROM AUTHOR]