Multifactorial causal model of brain (dis)organization and therapeutic intervention: Application to Alzheimer’s disease

Evidence of cortical vascular impairments in early stage of Alzheimer's transgenic mice: Optical imaging

Alzheimer's disease (AD), a neurodegenerative disorder with progressive cognitive decline, remains clinically challenging with limited understanding of etiology and interventions. Clinical studies have reported vascular defects prior to other pathological manifestations of AD, leading to the "Vascular Hypothesis" for the disorder. However, in vivo assessments of cerebral vasculature in AD rodent models have been constrained by limited spatiotemporal resolution or field of view of conventional imaging. We herein employed two in vivo imaging technologies, Dual-Wavelength Imaging and Optical Coherence Doppler Tomography, to evaluate cerebrovascular reactivity (CVR) to vasoconstrictive cocaine and vasodilatory hypercapnia challenges and to detect resting 3D cerebral blood flow (CBF) in living transgenic AD mice at capillary resolution. Results showed that CVR to cocaine and hypercapnia was significantly attenuated in 7-10 months old AD mice vs controls, indicating reduced vascular flexibility and reactivity. Additionally, in the AD mice, arterial CBF velocities were slower and the microvascular density in cortex was decreased compared to controls. These results reveal significant vascular impairments including reduced CVR and resting CBF in early-staged AD mice. Hence, this cutting-edge in vivo optical imaging offers an innovative venue for detecting early neurovascular dysfunction in AD brain with translational potential.

Beyond the usual suspects: multi-factorial computational models in the search for neurodegenerative disease mechanisms

From Alzheimer's disease to amyotrophic lateral sclerosis, the molecular cascades underlying neurodegenerative disorders remain poorly understood. The clinical view of neurodegeneration is confounded by symptomatic heterogeneity and mixed pathology in almost every patient. While the underlying physiological alterations originate, proliferate, and propagate potentially decades before symptomatic onset, the complexity and inaccessibility of the living brain limit direct observation over a patient's lifespan. Consequently, there is a critical need for robust computational methods to support the search for causal mechanisms of neurodegeneration by distinguishing pathogenic processes from consequential alterations, and inter-individual variability from intra-individual progression. Recently, promising advances have been made by data-driven spatiotemporal modeling of the brain, based on in vivo neuroimaging and biospecimen markers. These methods include disease progression models comparing the temporal evolution of various biomarkers, causal models linking interacting biological processes, network propagation models reproducing the spatial spreading of pathology, and biophysical models spanning cellular- to network-scale phenomena. In this review, we discuss various computational approaches for integrating cross-sectional, longitudinal, and multi-modal data, primarily from large observational neuroimaging studies, to understand (i) the temporal ordering of physiological alterations, i(i) their spatial relationships to the brain's molecular and cellular architecture, (iii) mechanistic interactions between biological processes, and (iv) the macroscopic effects of microscopic factors. We consider the extents to which computational models can evaluate mechanistic hypotheses, explore applications such as improving treatment selection, and discuss how model-informed insights can lay the groundwork for a pathobiological redefinition of neurodegenerative disorders.

Understanding the complex interplay between tau, amyloid and the network in the spatiotemporal progression of Alzheimer's Disease

INTRODUCTION

The interaction of amyloid and tau in neurodegenerative diseases is a central feature of AD pathophysiology. While experimental studies point to various interaction mechanisms, their causal direction and mode (local, remote or network-mediated) remain unknown in human subjects. The aim of this study was to compare mathematical reaction-diffusion models encoding distinct cross-species couplings to identify which interactions were key to model success.

METHODS

We tested competing mathematical models of network spread, aggregation, and amyloid-tau interactions on publicly available data from ADNI.

RESULTS

Although network spread models captured the spatiotemporal evolution of tau and amyloid in human subjects, the model including a one-way amyloid-to-tau aggregation interaction performed best.

DISCUSSION

This mathematical exposition of the "pas de deux" of co-evolving proteins provides quantitative, whole-brain support to the concept of amyloid-facilitated-tauopathy rather than the classic amyloid-cascade or pure-tau hypotheses, and helps explain certain known but poorly understood aspects of AD.

Personalized whole-brain neural mass models reveal combined Aβ and tau hyperexcitable influences in Alzheimer's disease

Neuronal dysfunction and cognitive deterioration in Alzheimer's disease (AD) are likely caused by multiple pathophysiological factors. However, mechanistic evidence in humans remains scarce, requiring improved non-invasive techniques and integrative models. We introduce personalized AD computational models built on whole-brain Wilson-Cowan oscillators and incorporating resting-state functional MRI, amyloid-β (Aβ) and tau-PET from 132 individuals in the AD spectrum to evaluate the direct impact of toxic protein deposition on neuronal activity. This subject-specific approach uncovers key patho-mechanistic interactions, including synergistic Aβ and tau effects on cognitive impairment and neuronal excitability increases with disease progression. The data-derived neuronal excitability values strongly predict clinically relevant AD plasma biomarker concentrations (p-tau217, p-tau231, p-tau181, GFAP) and grey matter atrophy obtained through voxel-based morphometry. Furthermore, reconstructed EEG proxy quantities show the hallmark AD electrophysiological alterations (theta band activity enhancement and alpha reductions) which occur with Aβ-positivity and after limbic tau involvement. Microglial activation influences on neuronal activity are less definitive, potentially due to neuroimaging limitations in mapping neuroprotective vs detrimental activation phenotypes. Mechanistic brain activity models can further clarify intricate neurodegenerative processes and accelerate preventive/treatment interventions.

Involvement of mitochondria in Alzheimer's disease pathogenesis and their potential as targets for phytotherapeutics

Alzheimer's disease (AD) is the leading cause of dementia around the globe. The disease's genesis is multifaceted, and its pathophysiology is complicated. Malfunction of mitochondria has been regarded as one of the intracellular events that are substantially damaged in the onset of AD and are likely a common trait of other neurodegenerative illnesses. Several mitochondrial characteristics begin to diminish with age, eventually reaching a state of significant functional failure concurrent with the beginning of neurodegenerative diseases, however, the exact timing of these processes is unknown. Mitochondrial malfunction has a multitude of negative repercussions, including reduced calcium buffering and secondary excitotoxicity contributing to synaptic dysfunction, also free radical production, and activation of the mitochondrial permeability transition. Hence mitochondria are considered a therapeutic target in neurodegenerative disorders such as Alzheimer's. Traditional medicinal systems practiced in different countries employing various medicinal plants postulated to have potential role in the therapy and management of memory impairment including amnesia, dementia as well as AD. Although, the preclinical and clinical studies using these medicinal plants or plant products have demonstrated the therapeutic efficacy for AD, the precise mechanism of action is still obscure. Therefore, this review discusses the contribution of mitochondria towards AD pathogenesis and considering phytotherapeutics as a potential therapeutic strategy.

MVComp toolbox: MultiVariate Comparisons of brain MRI features accounting for common information across metrics

Multivariate approaches have recently gained in popularity to address the physiological unspecificity of neuroimaging metrics and to better characterize the complexity of biological processes underlying behavior. However, commonly used approaches are biased by the intrinsic associations between variables, or they are computationally expensive and may be more complicated to implement than standard univariate approaches. Here, we propose using the Mahalanobis distance (D2), an individual-level measure of deviation relative to a reference distribution that accounts for covariance between metrics. To facilitate its use, we introduce an open-source python-based tool for computing D2 relative to a reference group or within a single individual: the MultiVariate Comparison (MVComp) toolbox. The toolbox allows different levels of analysis (i.e., group- or subject-level), resolutions (e.g., voxel-wise, ROI-wise) and dimensions considered (e.g., combining MRI metrics or WM tracts). Several example cases are presented to showcase the wide range of possible applications of MVComp and to demonstrate the functionality of the toolbox. The D2 framework was applied to the assessment of white matter (WM) microstructure at 1) the group-level, where D2 can be computed between a subject and a reference group to yield an individualized measure of deviation. We observed that clustering applied to D2 in the corpus callosum yields parcellations that highly resemble known topography based on neuroanatomy, suggesting that D2 provides an integrative index that meaningfully reflects the underlying microstructure. 2) At the subject level, D2 was computed between voxels to obtain a measure of (dis)similarity. The loadings of each MRI metric (i.e., its relative contribution to D2) were then extracted in voxels of interest to showcase a useful option of the MVComp toolbox. These relative contributions can provide important insights into the physiological underpinnings of differences observed. Integrative multivariate models are crucial to expand our understanding of the complex brain-behavior relationships and the multiple factors underlying disease development and progression. Our toolbox facilitates the implementation of a useful multivariate method, making it more widely accessible.

Data-driven modelling of neurodegenerative disease progression: thinking outside the black box

Data-driven disease progression models are an emerging set of computational tools that reconstruct disease timelines for long-term chronic diseases, providing unique insights into disease processes and their underlying mechanisms. Such methods combine a priori human knowledge and assumptions with large-scale data processing and parameter estimation to infer long-term disease trajectories from short-term data. In contrast to 'black box' machine learning tools, data-driven disease progression models typically require fewer data and are inherently interpretable, thereby aiding disease understanding in addition to enabling classification, prediction and stratification. In this Review, we place the current landscape of data-driven disease progression models in a general framework and discuss their enhanced utility for constructing a disease timeline compared with wider machine learning tools that construct static disease profiles. We review the insights they have enabled across multiple neurodegenerative diseases, notably Alzheimer disease, for applications such as determining temporal trajectories of disease biomarkers, testing hypotheses about disease mechanisms and uncovering disease subtypes. We outline key areas for technological development and translation to a broader range of neuroscience and non-neuroscience applications. Finally, we discuss potential pathways and barriers to integrating disease progression models into clinical practice and trial settings.

Urine biomarkers for Alzheimer's disease: A new opportunity for wastewater-based epidemiology?

While Alzheimer's disease (AD) diagnosis, management, and care have become priorities for healthcare providers and researcher's worldwide due to rapid population aging, epidemiologic surveillance efforts are currently limited by costly, invasive diagnostic procedures, particularly in low to middle income countries (LMIC). In recent years, wastewater-based epidemiology (WBE) has emerged as a promising tool for public health assessment through detection and quantification of specific biomarkers in wastewater, but applications for non-infectious diseases such as AD remain limited. This early review seeks to summarize AD-related biomarkers and urine and other peripheral biofluids and discuss their potential integration to WBE platforms to guide the first prospective efforts in the field. Promising results have been reported in clinical settings, indicating the potential of amyloid β, tau, neural thread protein, long non-coding RNAs, oxidative stress markers and other dysregulated metabolites for AD diagnosis, but questions regarding their concentration and stability in wastewater and the correlation between clinical levels and sewage circulation must be addressed in future studies before comprehensive WBE systems can be developed.

Machine learning analyses identify multi-modal frailty factors that selectively discriminate four cohorts in the Alzheimer's disease spectrum: a COMPASS-ND study

BACKGROUND

Frailty indicators can operate in dynamic amalgamations of disease conditions, clinical symptoms, biomarkers, medical signals, cognitive characteristics, and even health beliefs and practices. This study is the first to evaluate which, among these multiple frailty-related indicators, are important and differential predictors of clinical cohorts that represent progression along an Alzheimer's disease (AD) spectrum. We applied machine-learning technology to such indicators in order to identify the leading predictors of three AD spectrum cohorts; viz., subjective cognitive impairment (SCI), mild cognitive impairment (MCI), and AD. The common benchmark was a cohort of cognitively unimpaired (CU) older adults.

METHODS

The four cohorts were from the cross-sectional Comprehensive Assessment of Neurodegeneration and Dementia dataset. We used random forest analysis (Python 3.7) to simultaneously test the relative importance of 83 multi-modal frailty indicators in discriminating the cohorts. We performed an explainable artificial intelligence method (Tree Shapley Additive exPlanation values) for deep interpretation of prediction effects.

RESULTS

We observed strong concurrent prediction results, with clusters varying across cohorts. The SCI model demonstrated excellent prediction accuracy (AUC = 0.89). Three leading predictors were poorer quality of life ([QoL]; memory), abnormal lymphocyte count, and abnormal neutrophil count. The MCI model demonstrated a similarly high AUC (0.88). Five leading predictors were poorer QoL (memory, leisure), male sex, abnormal lymphocyte count, and poorer self-rated eyesight. The AD model demonstrated outstanding prediction accuracy (AUC = 0.98). Ten leading predictors were poorer QoL (memory), reduced olfaction, male sex, increased dependence in activities of daily living (n = 6), and poorer visual contrast.

CONCLUSIONS

Both convergent and cohort-specific frailty factors discriminated the AD spectrum cohorts. Convergence was observed as all cohorts were marked by lower quality of life (memory), supporting recent research and clinical attention to subjective experiences of memory aging and their potentially broad ramifications. Diversity was displayed in that, of the 14 leading predictors extracted across models, 11 were selectively sensitive to one cohort. A morbidity intensity trend was indicated by an increasing number and diversity of predictors corresponding to clinical severity, especially in AD. Knowledge of differential deficit predictors across AD clinical cohorts may promote precision interventions.

Impairment of cerebral vascular reactivity and resting blood flow in early-staged transgenic AD mice: in vivo optical imaging studies

Background

Alzheimer's disease (AD) is a neurodegenerative disorder with progressive cognitive decline in aging individuals that poses a significant challenge to patients due to an incomplete understanding of its etiology and lack of effective interventions. While "the Amyloid Cascade Hypothesis," the abnormal accumulation of amyloid-β in the brain, has been the most prevalent theory for AD, mounting evidence from clinical and epidemiological studies suggest that defects in cerebral vessels and hypoperfusion appear prior to other pathological manifestations and might contribute to AD, leading to "the Vascular Hypothesis." However, assessment of structural and functional integrity of the cerebral vasculature in vivo in the brain from AD rodent models has been challenging owing to the limited spatiotemporal resolution of conventional imaging technologies.

Methods

We employed two in vivo imaging technologies, i.e., Dual-Wavelength Imaging (DWI) and Optical Coherence Tomography (OCT), to evaluate cerebrovascular reactivity (CVR; responsiveness of blood vessels to vasoconstriction as triggered by cocaine) in a relatively large field of view of the cortex in vivo, and 3D quantitative cerebrovascular blood flow (CBF) imaging in living transgenic AD mice at single vessel resolution.

Results

Our results showed significantly impaired CVR and reduced CBF in basal state in transgenic AD mice compared to non-transgenic littermates in an early stage of AD progression. Changes in total hemoglobin (Δ[HbT]) in response to vasoconstriction were significantly attenuated in AD mice, especially in arteries and tissue, and the recovery time of Δ[HbT] after vasoconstriction was shorter for AD than WT in all types of vessels and cortical tissue, thereby indicating hypoperfusion and reduced vascular flexibility. Additionally, our 3D OCT images revealed that CBF velocities in arteries were slower and that the microvascular network was severely disrupted in the brain of AD mice.

Conclusions

These results suggest significant vascular impairment in basal CBF and dynamic CVR in the neurovascular network in a rodent model of AD at an early stage of the disease. These cutting-edge in vivo optical imaging tools offer an innovative venue for detecting early neurovascular dysfunction in relation to AD pathology and pave the way for clinical translation of early diagnosis and elucidation of AD pathogenesis in the future.

Mitochondrial Complex I and β-Amyloid Peptide Interplay in Alzheimer's Disease: A Critical Review of New and Old Little Regarded Findings

Alzheimer's disease (AD) is the most common neurodegenerative disorder and the main cause of dementia which is characterized by a progressive cognitive decline that severely interferes with daily activities of personal life. At a pathological level, it is characterized by the accumulation of abnormal protein structures in the brain-β-amyloid (Aβ) plaques and Tau tangles-which interfere with communication between neurons and lead to their dysfunction and death. In recent years, research on AD has highlighted the critical involvement of mitochondria-the primary energy suppliers for our cells-in the onset and progression of the disease, since mitochondrial bioenergetic deficits precede the beginning of the disease and mitochondria are very sensitive to Aβ toxicity. On the other hand, if it is true that the accumulation of Aβ in the mitochondria leads to mitochondrial malfunctions, it is otherwise proven that mitochondrial dysfunction, through the generation of reactive oxygen species, causes an increase in Aβ production, by initiating a vicious cycle: there is therefore a bidirectional relationship between Aβ aggregation and mitochondrial dysfunction. Here, we focus on the latest news-but also on neglected evidence from the past-concerning the interplay between dysfunctional mitochondrial complex I, oxidative stress, and Aβ, in order to understand how their interplay is implicated in the pathogenesis of the disease.

Patient-specific models link neurotransmitter receptor mechanisms with motor and visuospatial axes of Parkinson's disease

Parkinson's disease involves multiple neurotransmitter systems beyond the classical dopaminergic circuit, but their influence on structural and functional alterations is not well understood. Here, we use patient-specific causal brain modeling to identify latent neurotransmitter receptor-mediated mechanisms contributing to Parkinson's disease progression. Combining the spatial distribution of 15 receptors from post-mortem autoradiography with 6 neuroimaging-derived pathological factors, we detect a diverse set of receptors influencing gray matter atrophy, functional activity dysregulation, microstructural degeneration, and dendrite and dopaminergic transporter loss. Inter-individual variability in receptor mechanisms correlates with symptom severity along two distinct axes, representing motor and psychomotor symptoms with large GABAergic and glutamatergic contributions, and cholinergically-dominant visuospatial, psychiatric and memory dysfunction. Our work demonstrates that receptor architecture helps explain multi-factorial brain re-organization, and suggests that distinct, co-existing receptor-mediated processes underlie Parkinson's disease.

Integrated algorithm combining plasma biomarkers and cognitive assessments accurately predicts brain β-amyloid pathology

BACKGROUND

Accurate prediction of cerebral amyloidosis with easily available indicators is urgently needed for diagnosis and treatment of Alzheimer's disease (AD).

METHODS

We examined plasma Aβ42, Aβ40, T-tau, P-tau181, and NfL, with APOE genotypes, cognitive test scores and key demographics in a large Chinese cohort (N = 609, aged 40 to 84 years) covering full AD spectrum. Data-driven integrated computational models were developed to predict brain β-amyloid (Aβ) pathology.

RESULTS

Our computational models accurately predict brain Aβ positivity (area under the ROC curves (AUC) = 0.94). The results are validated in Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort. Particularly, the models have the highest prediction power (AUC = 0.97) in mild cognitive impairment (MCI) participants. Three levels of models are designed with different accuracies and complexities. The model which only consists of plasma biomarkers can predict Aβ positivity in amnestic MCI (aMCI) patients with AUC = 0.89. Generally the models perform better in participants without comorbidities or family histories.

CONCLUSIONS

The innovative integrated models provide opportunity to assess Aβ pathology in a non-invasive and cost-effective way, which might facilitate AD-drug development, early screening, clinical diagnosis and prognosis evaluation.

Magnoflorine-Loaded Chitosan Collagen Nanocapsules Ameliorate Cognitive Deficit in Scopolamine-Induced Alzheimer's Disease-like Conditions in a Rat Model by Downregulating IL-1β, IL-6, TNF-α, and Oxidative Stress and Upregulating Brain-Derived Neurotrophic Factor and DCX Expressions

Dementia or the loss of cognitive functioning is one of the major health issues in elderly people. Alzheimer's disease (AD) is one of the common forms of dementia. Treatment chiefly involves the use of acetylcholinesterase (AChE) inhibitors in AD. However, oxidative stress has also been found to be involved in the proliferation of the disease. Magnoflorine is one of the active compounds of Coptidis Rhizoma and has high anti-oxidative properties. Active principle-loaded nanoparticles have shown increased efficiency for neurodegenerative diseases due to their ability to cross the blood-brain barrier more easily. An in vitro study involving magnoflorine-loaded chitosan collagen nanocapsules (MF-CCNc) has shown them to possess inhibitory effects against oxidative stress and to some extent on AChE as well. In the current study, both nootropic and anti-amnesic effects of magnoflorine and MF-CCNc on scopolamine-induced amnesia in rats were evaluated. The treatment was done intraperitoneally (i.p.) once daily for 17 consecutive days with MF-CCNc (0.25, 0.5, and 1 mg), magnoflorine (1 mg), and donepezil (1 mg). To induce amnesia, hence, cognitive deficit rats were induced with scopolamine (1 mg/kg) daily for the last 9 days. Novel object recognition (NOR) and elevated plus maze (EPM) behavioral analysis were done to assess memory functioning. Hippocampal tissues were extracted to study the effect on biochemicals (AChE, MDA, SOD, and CAT), pro-inflammatory cytokines (IL-1β, IL-6, and TNF-α), and immunohistochemistry (brain-derived neurotrophic factor (BDNF) and DCX). MF-CCNc showed memory-enhancing effects in nootropic as well as chronic scopolamine-treated rats in NOR and an increase in inflexion ratio in EPM. MF-CCNc reduced the levels of AChE and MDA while increasing SOD and CAT levels in the hippocampus. MF-CCNc further lowered the levels of pro-inflammatory cytokines IL-1β, IL-6, and TNF-α. These nanocapsules further increased the expression of BDNF and DCX that are necessary for adult neurogenesis. From the research findings, it can be concluded that MF-CCNc has high anti-amnesic properties and could be a promising candidate for the treatment of AD.

Fornix degeneration in risk factors of Alzheimer's disease, possible trigger of cognitive decline

Risk factors of late-onset Alzheimer's disease (AD) such as aging, type 2 diabetes, obesity, heart failure, and traumatic brain injury can facilitate the appearance of cognitive decline and dementia by triggering cerebrovascular pathology and neuroinflammation. White matter (WM) microstructure and function are especially vulnerable to these conditions. Microstructural WM changes, assessed with diffusion weighted magnetic resonance imaging, can already be detected at preclinical stages of AD, and in the presence of the aforementioned risk factors. Particularly, the limbic system and cortico-cortical association WM tracts, which myelinate late during brain development, degenerate at the earliest stages. The fornix, a C-shaped WM tract that originates from the hippocampus, is one of the limbic tracts that shows early microstructural changes. Fornix integrity is necessary for ensuring an intact executive function and memory performance. Thus, a better understanding of the mechanisms that cause fornix degeneration is critical in the development of therapeutic strategies aiming to prevent cognitive decline in populations at risk. In this literature review, i) we deepen the idea that partial loss of forniceal integrity is an early event in AD, ii) we describe the role that common risk factors of AD can play in the degeneration of the fornix, and iii) we discuss some potential cellular and physiological mechanisms of WM degeneration in the scenario of cerebrovascular disease and inflammation.

The Allosteric Antagonist of the Sigma-2 Receptors-Elayta (CT1812) as a Therapeutic Candidate for Mild to Moderate Alzheimer's Disease: A Scoping Systematic Review

Nearly 35 million people worldwide live with Alzheimer's disease (AD). The prevalence of the disease is expected to rise two-fold by 2050. With only symptomatic treatment options available, it is essential to understand the developments and existing evidence that aims to target brain pathology and dementia outcomes. This scoping systematic review aimed to collate existing evidence of CT1812 for use in patients with AD and summarize the methodologies of ongoing trials. Adhering to PRISMA Statement 2020 guidelines, PubMed/MEDLINE, Embase, Cochrane, and ClinicalTrials.gov were systematically searched through up to 15 November 2022 by applying the following keywords: CT1812, Alzheimer's disease, dementia, and/or sigma-2 receptor. Three completed clinical trials were included along with three ongoing records of clinical trials. The three completed trials were in Phases I and II of testing. The sample size across all three trials was 135. CT1812 reached endpoints across the trials and obtained a maximum concentration in the cerebrospinal fluid with 97-98% receptor occupancy. The findings of this systematic review must be used with caution as the results, while mostly favorable so far, must be replicated in higher-powered, placebo-controlled Phase II-III trials.

Hallmarks of neurodegenerative disease: A systems pharmacology perspective

Age-related central neurodegenerative diseases, such as Alzheimer's and Parkinson's disease, are a rising public health concern and have been plagued by repeated drug development failures. The complex nature and poor mechanistic understanding of the etiology of neurodegenerative diseases has hindered the discovery and development of effective disease-modifying therapeutics. Quantitative systems pharmacology models of neurodegeneration diseases may be useful tools to enhance the understanding of pharmacological intervention strategies and to reduce drug attrition rates. Due to the similarities in pathophysiological mechanisms across neurodegenerative diseases, especially at the cellular and molecular levels, we envision the possibility of structural components that are conserved across models of neurodegenerative diseases. Conserved structural submodels can be viewed as building blocks that are pieced together alongside unique disease components to construct quantitative systems pharmacology (QSP) models of neurodegenerative diseases. Model parameterization would likely be different between the different types of neurodegenerative diseases as well as individual patients. Formulating our mechanistic understanding of neurodegenerative pathophysiology as a mathematical model could aid in the identification and prioritization of drug targets and combinatorial treatment strategies, evaluate the role of patient characteristics on disease progression and therapeutic response, and serve as a central repository of knowledge. Here, we provide a background on neurodegenerative diseases, highlight hallmarks of neurodegeneration, and summarize previous QSP models of neurodegenerative diseases.

Data-driven causal model discovery and personalized prediction in Alzheimer's disease

With the explosive growth of biomarker data in Alzheimer’s disease (AD) clinical trials, numerous mathematical models have been developed to characterize disease-relevant biomarker trajectories over time. While some of these models are purely empiric, others are causal, built upon various hypotheses of AD pathophysiology, a complex and incompletely understood area of research. One of the most challenging problems in computational causal modeling is using a purely data-driven approach to derive the model’s parameters and the mathematical model itself, without any prior hypothesis bias. In this paper, we develop an innovative data-driven modeling approach to build and parameterize a causal model to characterize the trajectories of AD biomarkers. This approach integrates causal model learning, population parameterization, parameter sensitivity analysis, and personalized prediction. By applying this integrated approach to a large multicenter database of AD biomarkers, the Alzheimer’s Disease Neuroimaging Initiative, several causal models for different AD stages are revealed. In addition, personalized models for each subject are calibrated and provide accurate predictions of future cognitive status.

Brain Region and Sex-specific Changes in Mitochondrial Biogenesis Induced by Acute Trimethyltin Exposure

Objective

In this study, we investigated sex- and region-specific effects of acute trimethyltin (TMT) exposure on mitochondrial biogenesis.

Methods

We treated TMT to primary neuronal cultures and 4-week-old male and female mice. We measured the mitochondrial DNA copy numbers using the quantitative polymerase chain reaction method. We also measured mitochondrial biogenesis related genes (sirtuin-1, estrogen-related receptor alpha, cytochrome C oxidase subunit IV) by western blotting.

Results

The mitochondrial DNA copy number increased in the primary hippocampal neuron; however, it decreased in the primary cortical neuron. The mitochondrial copy number increased in the hippocampus and decreased in the cortex in the TMT treated female mice, though the mitochondrial copy number increased in both cortex and hippocampus in the TMT treated male mice. TMT treatment increased sirtuin-1 expression in the male hippocampus but did not in the female brain. In the female brain, estrogen-related receptor alpha expression decreased in the cortex though there is no significant change in the male brain. The protein level of mitochondrial protein, cytochrome C oxidase subunit IV, increased in both cortex and hippocampus after TMT injection in male mice brain, but not in female mice brain.

Conclusion

Our data suggest that acute TMT exposure induces distinct sex-specific metabolic characteristics in the brain before significant sexual maturation.

Shared Risk Factors between Dementia and Atherosclerotic Cardiovascular Disease

Alzheimer's disease is the most common form of dementia, and the prodromal phases of Alzheimer's disease can last for decades. Vascular dementia is the second most common form of dementia and is distinguished from Alzheimer's disease by evidence of previous stroke or hemorrhage and current cerebrovascular disease. A compiled group of vascular-related dementias (vascular dementia and unspecified dementia) is often referred to as non-Alzheimer dementia. Recent evidence indicates that preventing dementia by lifestyle interventions early in life with a focus on reducing cardiovascular risk factors is a promising strategy for reducing future risk. Approximately 40% of dementia cases is estimated to be preventable by targeting modifiable, primarily cardiovascular risk factors. The aim of this review is to describe the association between risk factors for atherosclerotic cardiovascular disease and the risk of Alzheimer's disease and non-Alzheimer dementia by providing an overview of the current evidence and to shed light on possible shared pathogenic pathways between dementia and cardiovascular disease. The included risk factors are body mass index (BMI); plasma triglyceride-, high-density lipoprotein (HDL) cholesterol-, low-density lipoprotein (LDL) cholesterol-, and total cholesterol concentrations; hypertension; diabetes; non-alcoholic fatty liver disease (NAFLD); physical inactivity; smoking; diet; the gut microbiome; and genetics. Furthermore, we aim to disentangle the difference between associations of risk factors in midlife as compared with in late life.

Age-Dependent Behavioral and Metabolic Assessment of App NL-G-F/NL-G-F Knock-in (KI) Mice

Mitochondria play a crucial role in Alzheimer's disease (AD) onset and progression. Traditional transgenic AD mouse models which were widely used in the past decades share a common limitation: The overexpression of APP and overproduction of amyloid-beta (Aβ) are accompanied by other APP peptide fragments, which could introduce artificial and non-clinically relevant phenotypes. Here, we performed an in-depth and time-resolved behavioral and metabolic characterization of a clinically relevant AD mouse model engineered to express normal physiological levels of APP harboring humanized Swedish (K670N/M671L), Beyreuther/Iberian (I716F), and Arctic (E693G) mutations (App ^{NL-G-F/NL-G-F} ), termed APP knock-in (APPKI) mice. Our result showed that APPKI mice exhibited fear learning deficits at 6-m age and contextual memory deficit at 12-m age. Histopathological analysis revealed mild amyloidosis (6E10) accompanied by microgliosis (Iba1) as early as 3 months, which progressed significantly together with significant astrocytosis at 6 and 12 m. We further analyzed hippocampal mitochondrial dysfunction by multiple assays, while 3-m APPKI mice brain mitochondrial function remains a similar level as WT mice. Significant mitochondrial dysfunction characterized by decreased ATP production and higher membrane potential with subsequent overproduction of reactive oxygen species (ROS) was observed in mitochondria isolated from 7-m APPKI mice hippocampal tissue. Morphologically, these mitochondria were larger in volume with a decreased level of mitochondrial fusion protein mitofusin-2 (MFN2). At 12 months, APPKI mice exhibit a significantly decreased total mitochondrial oxygen consumption rate (OCR) in isolated hippocampal mitochondria detected by high-resolution respirometry. These data indicate early mitochondrial dysfunction in the brain at pre-symptomatic age in the App ^NL-G-F/NL-G-mice, which may play a key role in the progression of the disease. Moreover, the identified behavioral and bioenergetic alterations in this clinically relevant AD mouse model provide a valuable tool to optimize the temporal component for therapeutic interventions to treat AD.

Dysfunction of Mitochondria in Alzheimer’s Disease: ANT and VDAC Interact with Toxic Proteins and Aid to Determine the Fate of Brain Cells

Alzheimer’s disease (AD), certainly the most widespread proteinopathy, has as classical neuropathological hallmarks, two groups of protein aggregates: senile plaques and neurofibrillary tangles. However, the research interest is rapidly gaining ground in a better understanding of other pathological features, first, of all the mitochondrial dysfunctions. Several pieces of evidence support the hypothesis that abnormal mitochondrial function may trigger aberrant processing of amyloid progenitor protein or tau and thus neurodegeneration. Here, our aim is to emphasize the role played by two ‘bioenergetic’ proteins inserted in the mitochondrial membranes, inner and outer, respectively, that is, the adenine nucleotide translocator (ANT) and the voltage-dependent anion channel (VDAC), in the progression of AD. To perform this, we will magnify the ANT and VDAC defects, which are measurable hallmarks of mitochondrial dysfunction, and collect all the existing information on their interaction with toxic Alzheimer’s proteins. The pathological convergence of tau and amyloid β-peptide (Aβ) on mitochondria may finally explain why the therapeutic strategies used against the toxic forms of Aβ or tau have not given promising results separately. Furthermore, the crucial role of ANT-1 and VDAC impairment in the onset/progression of AD opens a window for new therapeutic strategies aimed at preserving/improving mitochondrial function, which is suspected to be the driving force leading to plaque and tangle deposition in AD.

Dysregulated mitochondrial and cytosolic tRNA m1A methylation in Alzheimer's disease

RNA modifications affect many aspects of RNA metabolism and are involved in the regulation of many different biological processes. Mono-methylation of adenosine in the N1 position, N1-methyladensoine (m1A), is a reversible modification that is known to target rRNAs and tRNAs. m1A has been shown to increase tRNA structural stability and induce correct tRNA folding. Recent studies have begun to associate the dysregulation of epitranscriptomic control with age-related disorders such as Alzheimer's disease. Here, we applied the newly developed m1A-quant-seq approach to map the brain abundant m1A RNA modification in the cortex of an Alzheimer's disease mouse model, 5XFAD. We observed hypomethylation in both mitochondrial and cytosolic tRNAs in 5XFAD mice compared with wild type. Furthermore, the main enzymes responsible for the addition of m1A in mitochondrial (TRMT10C, HSD17B10) and cytosolic tRNAs (TRMT61A) displayed decreased expression in 5XFAD compared with wild-type mice. Knockdown of these enzymes results in a more severe phenotype in a Drosophila tau model, and differential m1A methylation is correlated with differences in mature mitochondrial tRNA expression. Collectively, this work suggests that hypo m1A modification in tRNAs may play a role in Alzheimer's disease pathogenesis.

Mitochondrial Dysfunction in Alzheimer's Disease: Opportunities for Drug Development

Alzheimer's disease (AD) is one of the major reasons for 60-80% cases of senile dementia occurring as a result of the accumulation of plaques and tangles in the hippocampal and cortical neurons of the brain leading to neurodegeneration and cell death. The other pathological features of AD comprise abnormal microvasculature, network abnormalities, interneuronal dysfunction, increased β-amyloid production and reduced clearance, increased inflammatory response, elevated production of reactive oxygen species, impaired brain metabolism, hyperphosphorylation of tau, and disruption of acetylcholine signaling. Among all these pathologies, Mitochondrial Dysfunction (MD), regardless of it being an inciting insult or a consequence of the alterations, is related to all the associated AD pathologies. Observed altered mitochondrial morphology, distribution and movement, increased oxidative stress, dysregulation of enzymes involved in mitochondrial functioning, impaired brain metabolism, and impaired mitochondrial biogenesis in AD subjects suggest the involvement of mitochondrial malfunction in the progression of AD. Here, various pre-clinical and clinical evidence establishing MD as a key mediator in the progression of neurodegeneration in AD are reviewed and discussed with an aim to foster future MD based drug development research for the management of AD.

Alzheimer's Disease: Key Insights from Two Decades of Clinical Trial Failures

Given the acknowledged lack of success in Alzheimer’s disease (AD) drug development over the past two decades, the objective of this review was to derive key insights from the myriad failures to inform future drug development. A systematic and exhaustive review was performed on all failed AD compounds for dementia (interventional phase II and III clinical trials from ClinicalTrials.gov) from 2004 to the present. Starting with the initial ∼2,700 AD clinical trials, ∼550 trials met our initial criteria, from which 98 unique phase II and III compounds with various mechanisms of action met our criteria of a failed compound. The two recent reported phase III successes of aducanumab and oligomannate are very encouraging; however, we are awaiting real-world validation of their effectiveness. These two successes against the 98 failures gives a 2.0% phase II and III success rate since 2003, when the previous novel compound was approved. Potential contributing methodological factors for the clinical trial failures were categorized into 1) insufficient evidence to initiate the pivotal trials, and 2) pivotal trial design shortcomings. Our evaluation found that rational drug development principles were not always followed for AD therapeutics development, and the question remains whether some of the failed compounds may have shown efficacy if the principles were better adhered to. Several recommendations are made for future AD therapeutic development. The whole database of the 98 failed compounds is presented in the Supplementary Material.

Pathological changes within the cerebral vasculature in Alzheimer's disease: New perspectives

Cerebrovascular disease underpins vascular dementia (VaD), but structural and functional changes to the cerebral vasculature contribute to disease pathology and cognitive decline in Alzheimer's disease (AD). In this review, we discuss the contribution of cerebral amyloid angiopathy and non‐amyloid small vessel disease in AD, and the accompanying changes to the density, maintenance and remodelling of vessels (including alterations to the composition and function of the cerebrovascular basement membrane). We consider how abnormalities of the constituent cells of the neurovascular unit – particularly of endothelial cells and pericytes – and impairment of the blood‐brain barrier (BBB) impact on the pathogenesis of AD. We also discuss how changes to the cerebral vasculature are likely to impair Aβ clearance – both intra‐periarteriolar drainage (IPAD) and transport of Aβ peptides across the BBB, and how impaired neurovascular coupling and reduced blood flow in relation to metabolic demand increase amyloidogenic processing of APP and the production of Aβ. We review the vasoactive properties of Aβ peptides themselves, and the probable bi‐directional relationship between vascular dysfunction and Aβ accumulation in AD. Lastly, we discuss recent methodological advances in transcriptomics and imaging that have provided novel insights into vascular changes in AD, and recent advances in assessment of the retina that allow in vivo detection of vascular changes in the early stages of AD.

Network structure and transcriptomic vulnerability shape atrophy in frontotemporal dementia

Connections among brain regions allow pathological perturbations to spread from a single source region to multiple regions. Patterns of neurodegeneration in multiple diseases, including behavioural variant of frontotemporal dementia (bvFTD), resemble the large-scale functional systems, but how bvFTD-related atrophy patterns relate to structural network organization remains unknown. Here we investigate whether neurodegeneration patterns in sporadic and genetic bvFTD are conditioned by connectome architecture. Regional atrophy patterns were estimated in both genetic bvFTD (75 patients, 247 controls) and sporadic bvFTD (70 patients, 123 controls). First, we identified distributed atrophy patterns in bvFTD, mainly targeting areas associated with the limbic intrinsic network and insular cytoarchitectonic class. Regional atrophy was significantly correlated with atrophy of structurally- and functionally-connected neighbours, demonstrating that network structure shapes atrophy patterns. The anterior insula was identified as the predominant group epicentre of brain atrophy using data-driven and simulation-based methods, with some secondary regions in frontal ventromedial and antero-medial temporal areas. We found that FTD-related genes, namely C9orf72 and TARDBP, confer local transcriptomic vulnerability to the disease, modulating the propagation of pathology through the connectome. Collectively, our results demonstrate that atrophy patterns in sporadic and genetic bvFTD are jointly shaped by global connectome architecture and local transcriptomic vulnerability, providing an explanation as to how heterogenous pathological entities can lead to the same clinical syndrome.

Data-driven staging of genetic frontotemporal dementia using multi-modal MRI

Frontotemporal dementia in genetic forms is highly heterogeneous and begins many years to prior symptom onset, complicating disease understanding and treatment development. Unifying methods to stage the disease during both the presymptomatic and symptomatic phases are needed for the development of clinical trials outcomes. Here we used the contrastive trajectory inference (cTI), an unsupervised machine learning algorithm that analyzes temporal patterns in high‐dimensional large‐scale population datasets to obtain individual scores of disease stage. We used cross‐sectional MRI data (gray matter density, T1/T2 ratio as a proxy for myelin content, resting‐state functional amplitude, gray matter fractional anisotropy, and mean diffusivity) from 383 gene carriers (269 presymptomatic and 115 symptomatic) and a control group of 253 noncarriers in the Genetic Frontotemporal Dementia Initiative. We compared the cTI‐obtained disease scores to the estimated years to onset (age—mean age of onset in relatives), clinical, and neuropsychological test scores. The cTI based disease scores were correlated with all clinical and neuropsychological tests (measuring behavioral symptoms, attention, memory, language, and executive functions), with the highest contribution coming from mean diffusivity. Mean cTI scores were higher in the presymptomatic carriers than controls, indicating that the method may capture subtle pre‐dementia cerebral changes, although this change was not replicated in a subset of subjects with complete data. This study provides a proof of concept that cTI can identify data‐driven disease stages in a heterogeneous sample combining different mutations and disease stages of genetic FTD using only MRI metrics.

Neurovascular coupling unit dysfunction and dementia: Retinal measurements as tools to move towards population-based evidence

Dysfunction of the neurovascular coupling unit may be an important contributor to dementia. The neurovascular coupling unit comprises neuronal structures (e.g. astrocytes) and vascular structures (e.g. endothelial cells) that functionally interact both at the level of the arterioles as well as at the capillary level (blood-brain barrier) to regulate optimal metabolic conditions in the brain. However, it remains unclear how and to what extent dysfunction of the neurovascular coupling unit contributes to the early-stage pathobiology of dementia. Currently, limited data are available on the association between neurovascular coupling unit dysfunction, as quantified by cerebral imaging techniques, and cognitive performance. In particular, there is a lack of population-based human data (defined as studies with a sample size ~n>500). This is an important limitation because population-based studies, in comparison with smaller clinical studies, provide data which is better representative of the general population; are less susceptible to selection bias; and have a larger statistical power to detect small associations. To acquire population-based data, however, alternative imaging techniques than cerebral imaging techniques may be required. Disadvantages of cerebral imaging techniques, which limit use in population-based studies, are that these techniques are relatively expensive, time-consuming, and/or invasive. In this review, we propose that retinal imaging techniques can be used for population-based studies: on the one hand the retina and brain have many anatomical and physiological similarities; and on the other hand retinal imaging techniques are non-invasive, highly accurate, relatively inexpensive, and require relatively short measurement time. To provide support for this concept, we provide an overview on the human (population-based) evidence on the associations of retinal indices of neurodegeneration, microvascular dysfunction, and dysfunction of the neurovascular coupling unit with magnetic resonance imaging (MRI) features of structural brain abnormalities and cognitive performance.

Computational Approaches for Supporting Combination Therapy in the Post-Aducanumab Era in Alzheimer’s Disease

With the approval of aducanumab on the “Accelerated Approval Pathway” and the recognition of amyloid load as a surrogate marker, new successful therapeutic approaches will be driven by combination therapy as was the case in oncology after the launch of immune checkpoint inhibitors. However, the sheer number of therapeutic combinations substantially complicates the search for optimal combinations. Data-driven approaches based on large databases or electronic health records can identify optimal combinations and often using artificial intelligence or machine learning to crunch through many possible combinations but are limited to the pharmacology of existing marketed drugs and are highly dependent upon the quality of the training sets. Knowledge-driven in silico modeling approaches use multi-scale biophysically realistic models of neuroanatomy, physiology, and pathology and can be personalized with individual patient comedications, disease state, and genotypes to create ‘virtual twin patients’. Such models simulate effects on action potential dynamics of anatomically informed neuronal circuits driving functional clinical readouts. Informed by data-driven approaches this knowledge-driven modeling could systematically and quantitatively simulate all possible target combinations for a maximal synergistic effect on a clinically relevant functional outcome. This approach seamlessly integrates pharmacokinetic modeling of different therapeutic modalities. A crucial requirement to constrain the parameters is the access to preferably anonymized individual patient data from completed clinical trials with various selective compounds. We believe that the combination of data- and knowledge driven modeling could be a game changer to find a cure for this devastating disease that affects the most complex organ of the universe.

Disentangling Mitochondria in Alzheimer's Disease

Alzheimer's disease (AD) is a major cause of dementia in older adults and is fast becoming a major societal and economic burden due to an increase in life expectancy. Age seems to be the major factor driving AD, and currently, only symptomatic treatments are available. AD has a complex etiology, although mitochondrial dysfunction, oxidative stress, inflammation, and metabolic abnormalities have been widely and deeply investigated as plausible mechanisms for its neuropathology. Aβ plaques and hyperphosphorylated tau aggregates, along with cognitive deficits and behavioral problems, are the hallmarks of the disease. Restoration of mitochondrial bioenergetics, prevention of oxidative stress, and diet and exercise seem to be effective in reducing Aβ and in ameliorating learning and memory problems. Many mitochondria-targeted antioxidants have been tested in AD and are currently in development. However, larger streamlined clinical studies are needed to provide hard evidence of benefits in AD. This review discusses the causative factors, as well as potential therapeutics employed in the treatment of AD.

Personalized brain models identify neurotransmitter receptor changes in Alzheimer's disease

Alzheimer's disease (AD) involves many neurobiological alterations from molecular to macroscopic spatial scales, but we currently lack integrative, mechanistic brain models characterizing how factors across different biological scales interact to cause clinical deterioration in a way that is subject-specific or personalized. Neurotransmitter receptors, as important signaling molecules and potential drug targets, are key mediators of interactions between many neurobiological processes altered in AD. We present a neurotransmitter receptor-enriched multifactorial brain model, which integrates spatial distribution patterns of 15 neurotransmitter receptors from post-mortem autoradiography with multiple in-vivo neuroimaging modalities (tau, amyloid-β and glucose PET, and structural, functional and arterial spin labeling MRI) in a personalized, generative, whole-brain formulation. Applying this data-driven model to a heterogeneous aged population (N = 423, ADNI data), we observed that personalized receptor-neuroimaging interactions explained about 70% (± 20%) of the across-population variance in longitudinal changes to the six neuroimaging modalities, and up to 39.7% (P < 0.003, FWE-corrected) of inter-individual variability in AD cognitive deterioration via an axis primarily affecting executive function. Notably, based on their contribution to the clinical severity in AD, we found significant functional alterations to glutamatergic interactions affecting tau accumulation and neural activity dysfunction, and GABAergic interactions concurrently affecting neural activity dysfunction, amyloid and tau distributions, as well as significant cholinergic receptor effects on tau accumulation. Overall, GABAergic alterations had the largest effect on cognitive impairment (particularly executive function) in our AD cohort (N = 25). Furthermore, we demonstrate the clinical applicability of this approach by characterizing subjects based on individualized 'fingerprints' of receptor alterations. This study introduces the first robust, data-driven framework for integrating several neurotransmitter receptors, multi-modal neuroimaging and clinical data in a flexible and interpretable brain model. It enables further understanding of the mechanistic neuropathological basis of neurodegenerative progression and heterogeneity, and constitutes a promising step towards implementing personalized, neurotransmitter-based treatments.

The Beneficial Role of Natural Endocrine Disruptors: Phytoestrogens in Alzheimer's Disease

Alzheimer's disease (AD) is the most common form of dementia with a growing incidence rate primarily among the elderly. It is a neurodegenerative, progressive disorder leading to significant cognitive loss. Despite numerous pieces of research, no cure for halting the disease has been discovered yet. Phytoestrogens are nonestradiol compounds classified as one of the endocrine-disrupting chemicals (EDCs), meaning that they can potentially disrupt hormonal balance and result in developmental and reproductive abnormalities. Importantly, phytoestrogens are structurally, chemically, and functionally akin to estrogens, which undoubtedly has the potential to be detrimental to the organism. What is intriguing, although classified as EDCs, phytoestrogens seem to have a beneficial influence on Alzheimer's disease symptoms and neuropathologies. They have been observed to act as antioxidants, improve visual-spatial memory, lower amyloid-beta production, and increase the growth, survival, and plasticity of brain cells. This review article is aimed at contributing to the collective understanding of the role of phytoestrogens in the prevention and treatment of Alzheimer's disease. Importantly, it underlines the fact that despite being EDCs, phytoestrogens and their use can be beneficial in the prevention of Alzheimer's disease.

Integrative Neuroinformatics for Precision Prognostication and Personalized Therapeutics in Moderate and Severe Traumatic Brain Injury

Despite changes in guideline-based management of moderate/severe traumatic brain injury (TBI) over the preceding decades, little impact on mortality and morbidity have been seen. This argues against the "one-treatment fits all" approach to such management strategies. With this, some preliminary advances in the area of personalized medicine in TBI care have displayed promising results. However, to continue transitioning toward individually-tailored care, we require integration of complex "-omics" data sets. The past few decades have seen dramatic increases in the volume of complex multi-modal data in moderate and severe TBI care. Such data includes serial high-fidelity multi-modal characterization of the cerebral physiome, serum/cerebrospinal fluid proteomics, admission genetic profiles, and serial advanced neuroimaging modalities. Integrating these complex and serially obtained data sets, with patient baseline demographics, treatment information and clinical outcomes over time, can be a daunting task for the treating clinician. Within this review, we highlight the current status of such multi-modal omics data sets in moderate/severe TBI, current limitations to the utilization of such data, and a potential path forward through employing integrative neuroinformatic approaches, which are applied in other neuropathologies. Such advances are positioned to facilitate the transition to precision prognostication and inform a top-down approach to the development of personalized therapeutics in moderate/severe TBI.

Graph Models of Pathology Spread in Alzheimer's Disease: An Alternative to Conventional Graph Theoretic Analysis

Background: Graph theory and connectomics are new techniques for uncovering disease-induced changes in the brain's structural network. Most prior studied have focused on network statistics as biomarkers of disease. However, an emerging body of work involves exploring how the network serves as a conduit for the propagation of disease factors in the brain and has successfully mapped the functional and pathological consequences of disease propagation. In Alzheimer's disease (AD), progressive deposition of misfolded proteins amyloid and tau is well-known to follow fiber projections, under a "prion-like" trans-neuronal transmission mechanism, through which misfolded proteins cascade along neuronal pathways, giving rise to network spread. Methods: In this review, we survey the state of the art in mathematical modeling of connectome-mediated pathology spread in AD. Then we address several open questions that are amenable to mathematically precise parsimonious modeling of pathophysiological processes, extrapolated to the whole brain. We specifically identify current formal models of how misfolded proteins are produced, aggregate, and disseminate in brain circuits, and attempt to understand how this process leads to stereotyped progression in Alzheimer's and other related diseases. Conclusion: This review serves to unify current efforts in modeling of AD progression that together have the potential to explain observed phenomena and serve as a test-bed for future hypothesis generation and testing in silico. Impact statement Graph theory is a powerful new approach that is transforming the study of brain processes. There do not exist many focused reviews of the subfield of graph modeling of how Alzheimer's and other dementias propagate within the brain network, and how these processes can be mapped mathematically. By providing timely and topical review of this subfield, we fill a critical gap in the community and present a unified view that can serve as an in silico test-bed for future hypothesis generation and testing. We also point to several open and unaddressed questions and controversies that future practitioners can tackle.

Integrating molecular, histopathological, neuroimaging and clinical neuroscience data with NeuroPM-box

Understanding and treating heterogeneous brain disorders requires specialized techniques spanning genetics, proteomics, and neuroimaging. Designed to meet this need, NeuroPM-box is a user-friendly, open-access, multi-tool cross-platform software capable of characterizing multiscale and multifactorial neuropathological mechanisms. Using advanced analytical modeling for molecular, histopathological, brain-imaging and/or clinical evaluations, this framework has multiple applications, validated here with synthetic (N > 2900), in-vivo (N = 911) and post-mortem (N = 736) neurodegenerative data, and including the ability to characterize: (i) the series of sequential states (genetic, histopathological, imaging or clinical alterations) covering decades of disease progression, (ii) concurrent intra-brain spreading of pathological factors (e.g., amyloid, tau and alpha-synuclein proteins), (iii) synergistic interactions between multiple biological factors (e.g., toxic tau effects on brain atrophy), and (iv) biologically-defined patient stratification based on disease heterogeneity and/or therapeutic needs. This freely available toolbox ( neuropm-lab.com/neuropm-box.html ) could contribute significantly to a better understanding of complex brain processes and accelerating the implementation of Precision Medicine in Neurology.

Integrated transcriptomic and neuroimaging brain model decodes biological mechanisms in aging and Alzheimer's disease

Both healthy aging and Alzheimer's disease (AD) are characterized by concurrent alterations in several biological factors. However, generative brain models of aging and AD are limited in incorporating the measures of these biological factors at different spatial resolutions. Here, we propose a personalized bottom-up spatiotemporal brain model that accounts for the direct interplay between hundreds of RNA transcripts and multiple macroscopic neuroimaging modalities (PET, MRI). In normal elderly and AD participants, the model identifies top genes modulating tau and amyloid-β burdens, vascular flow, glucose metabolism, functional activity, and atrophy to drive cognitive decline. The results also revealed that AD and healthy aging share specific biological mechanisms, even though AD is a separate entity with considerably more altered pathways. Overall, this personalized model offers novel insights into the multiscale alterations in the elderly brain, with important implications for identifying effective genetic targets for extending healthy aging and treating AD progression.

Simulating the outcome of amyloid treatments in Alzheimer's disease from imaging and clinical data

In this study, we investigate SimulAD, a novel quantitative instrument for the development of intervention strategies for disease-modifying drugs in Alzheimer's disease. SimulAD is based on the modeling of the spatio-temporal dynamics governing the joint evolution of imaging and clinical biomarkers along the history of the disease, and allows the simulation of the effect of intervention time and drug dosage on the biomarkers' progression. When applied to multi-modal imaging and clinical data from the Alzheimer's Disease Neuroimaging Initiative the method enables to generate hypothetical scenarios of amyloid lowering interventions. The results quantify the crucial role of intervention time, and provide a theoretical justification for testing amyloid modifying drugs in the pre-clinical stage. Our experimental simulations are compatible with the outcomes observed in past clinical trials, and suggest that anti-amyloid treatments should be administered at least 7 years earlier than what is currently being done in order to obtain statistically powered improvement of clinical endpoints.

Investigating hypotheses of neurodegeneration by learning dynamical systems of protein propagation in the brain

We introduce a theoretical framework for estimating, comparing and interpreting mechanistic hypotheses on long term protein propagation across brain networks in neurodegenerative disorders (ND). The model is expressed within a Bayesian non-parametric regression setting, where mechanisms of protein dynamics are inferred by means of gradient matching on dynamical systems (DS). The Bayesian formalism, combined with stochastic variational inference, naturally allows for model comparison via assessment of model evidence, while providing uncertainty quantification of causal relationship underlying protein progressions. When applied to in-vivo AV45-PET brain imaging data measuring topographic amyloid deposition in Alzheimer's disease (AD), our model identified the mechanisms of accumulation, clearance and propagation as the best suited DS for bio-mechanical description of amyloid dynamics in AD, enabling realistic and accurate personalized simulation of amyloidosis.

Alzheimer's disease: An evolving understanding of noradrenergic involvement and the promising future of electroceutical therapies

Alzheimer's disease (AD) poses a significant global health concern over the next several decades. Multiple hypotheses have been put forth that attempt to explain the underlying pathophysiology of AD. Many of these are briefly reviewed here, but to-date no disease-altering therapy has been achieved. Despite this, recent work expanding on the role of noradrenergic system dysfunction in both the pathogenesis and symptomatic exacerbation of AD has shown promise. The role norepinephrine (NE) plays in AD remains complicated but pre-tangle tau has consistently been shown to arise in the locus coeruleus (LC) of patients with AD decades before symptom onset. The current research reviewed here indicates NE can facilitate neuroprotective and memory-enhancing effects through β adrenergic receptors, while α2A adrenergic receptors may exacerbate amyloid toxicity through a contribution to tau hyperphosphorylation. AD appears to involve a disruption in the balance between these two receptors and their various subtypes. There is also a poorly characterized interplay between the noradrenergic and cholinergic systems. LC deterioration leads to maladaptation in the remaining LC-NE system and subsequently inhibits cholinergic neuron function, eventually leading to the classic cholinergic disruption seen in AD. Understanding AD as a dysfunctional noradrenergic system, provides new avenues for the use of advanced neural stimulation techniques to both study and therapeutically target the earliest stages of neuropathology. Direct LC stimulation and non-invasive vagus nerve stimulation (VNS) have both demonstrated potential use as AD therapeutics. Significant work remains, though, to better understand the role of the noradrenergic system in AD and how electroceuticals can provide disease-altering treatments.

A prospective cohort study of prodromal Alzheimer's disease: Prospective Imaging Study of Ageing: Genes, Brain and Behaviour (PISA)

This prospective cohort study, "Prospective Imaging Study of Ageing: Genes, Brain and Behaviour" (PISA) seeks to characterise the phenotype and natural history of healthy adult Australians at high future risk of Alzheimer's disease (AD). In particular, we are recruiting midlife and older Australians with high and low genetic risk of dementia to discover biological markers of early neuropathology, identify modifiable risk factors, and establish the very earliest phenotypic and neuronal signs of disease onset. PISA utilises genetic prediction to recruit and enrich a prospective cohort and follow them longitudinally. Online surveys and cognitive testing are used to characterise an Australia-wide sample currently totalling over 3800 participants. Participants from a defined at-risk cohort and positive controls (clinical cohort of patients with mild cognitive impairment or early AD) are invited for onsite visits for detailed functional, structural and molecular neuroimaging, lifestyle monitoring, detailed neurocognitive testing, plus blood sample donation. This paper describes recruitment of the PISA cohort, study methodology and baseline demographics.

Biomarkers: Our Path Towards a Cure for Alzheimer Disease

Over the last decade, biomarkers have significantly improved our understanding of the pathophysiology of Alzheimer disease (AD) and provided valuable tools to examine different disease mechanisms and their progression over time. While several markers of amyloid, tau, neuronal, synaptic, and axonal injury, inflammation, and immune dysregulation in AD have been identified, there is a relative paucity of biomarkers which reflect other disease mechanisms such as oxidative stress, mitochondrial injury, vascular or endothelial injury, and calcium-mediated excitotoxicity. Importantly, there is an urgent need to standardize methods for biomarker assessments across different centers, and to identify dynamic biomarkers which can monitor disease progression over time and/or response to potential disease-modifying treatments. The updated research framework for AD, proposed by the National Institute of Aging- Alzheimer’s Association (NIA-AA) Work Group, emphasizes the importance of incorporating biomarkers in AD research and defines AD as a biological construct consisting of amyloid, tau, and neurodegeneration which spans pre-symptomatic and symptomatic stages. As results of clinical trials of AD therapeutics have been disappointing, it has become increasingly clear that the success of future AD trials will require the incorporation of biomarkers in participant selection, prognostication, monitoring disease progression, and assessing response to treatments. We here review the current state of fluid AD biomarkers, and discuss the advantages and limitations of the updated NIA-AA research framework. Importantly, the integration of biomarker data with clinical, cognitive, and imaging domains through a systems biology approach will be essential to adequately capture the molecular, genetic, and pathological heterogeneity of AD and its spatiotemporal evolution over time.

Vascular contributions to cognitive impairment and dementia (VCID): A report from the 2018 National Heart, Lung, and Blood Institute and National Institute of Neurological Disorders and Stroke Workshop

Vascular contributions to cognitive impairment and dementia (VCID) are characterized by the aging neurovascular unit being confronted with and failing to cope with biological insults due to systemic and cerebral vascular disease, proteinopathy including Alzheimer's biology, metabolic disease, or immune response, resulting in cognitive decline. This report summarizes the discussion and recommendations from a working group convened by the National Heart, Lung, and Blood Institute and the National Institute of Neurological Disorders and Stroke to evaluate the state of the field in VCID research, identify research priorities, and foster collaborations. As discussed in this report, advances in understanding the biological mechanisms of VCID across the wide spectrum of pathologies, chronic systemic comorbidities, and other risk factors may lead to potential prevention and new treatment strategies to decrease the burden of dementia. Better understanding of the social determinants of health that affect risks for both vascular disease and VCID could provide insight into strategies to reduce racial and ethnic disparities in VCID.

Neurological update: neuroimaging in dementia

Neuroimaging for dementia has made remarkable progress in recent years, shedding light on diagnostic subtypes of dementia, predicting prognosis and monitoring pathology. This review covers some updates in the understanding of dementia using structural imaging, positron emission tomography (PET), structural and functional connectivity, and using big data and artificial intelligence. Progress with neuroimaging methods allows neuropathology to be examined in vivo, providing a suite of biomarkers for understanding neurodegeneration and for application in clinical trials. In addition, we highlight quantitative susceptibility imaging as an exciting new technique that may prove to be a sensitive biomarker for a range of neurodegenerative diseases. There are challenges in translating novel imaging techniques to clinical practice, particularly in developing standard methodologies and overcoming regulatory issues. It is likely that clinicians will need to lead the way if these obstacles are to be overcome. Continued efforts applying neuroimaging to understand mechanisms of neurodegeneration and translating them to clinical practice will complete a revolution in neuroimaging.

Blood and brain gene expression trajectories mirror neuropathology and clinical deterioration in neurodegeneration

Most prevalent neurodegenerative disorders take decades to develop and their early detection is challenged by confounding non-pathological ageing processes. For all neurodegenerative conditions, we continue to lack longitudinal gene expression data covering their large temporal evolution, which hinders the understanding of the underlying dynamic molecular mechanisms. Here, we overcome this key limitation by introducing a novel gene expression contrastive trajectory inference (GE-cTI) method that reveals enriched temporal patterns in a diseased population. Evaluated on 1969 subjects in the spectrum of late-onset Alzheimer's and Huntington's diseases (from ROSMAP, HBTRC and ADNI datasets), this unsupervised machine learning algorithm strongly predicts neuropathological severity (e.g. Braak, amyloid and Vonsattel stages). Furthermore, when applied to in vivo blood samples at baseline (ADNI), it significantly predicts clinical deterioration and conversion to advanced disease stages, supporting the identification of a minimally invasive (blood-based) tool for early clinical screening. This technique also allows the discovery of genes and molecular pathways, in both peripheral and brain tissues, that are highly predictive of disease evolution. Eighty-five to ninety per cent of the most predictive molecular pathways identified in the brain are also top predictors in the blood. These pathways support the importance of studying the peripheral-brain axis, providing further evidence for a key role of vascular structure/functioning and immune system response. The GE-cTI is a promising tool for revealing complex neuropathological mechanisms, with direct implications for implementing personalized dynamic treatments in neurology.

Spread of pathological tau proteins through communicating neurons in human Alzheimer's disease

Tau is a hallmark pathology of Alzheimer's disease, and animal models have suggested that tau spreads from cell to cell through neuronal connections, facilitated by β-amyloid (Aβ). We test this hypothesis in humans using an epidemic spreading model (ESM) to simulate tau spread, and compare these simulations to observed patterns measured using tau-PET in 312 individuals along Alzheimer's disease continuum. Up to 70% of the variance in the overall spatial pattern of tau can be explained by our model. Surprisingly, the ESM predicts the spatial patterns of tau irrespective of whether brain Aβ is present, but regions with greater Aβ burden show greater tau than predicted by connectivity patterns, suggesting a role of Aβ in accelerating tau spread. Altogether, our results provide evidence in humans that tau spreads through neuronal communication pathways even in normal aging, and that this process is accelerated by the presence of brain Aβ.

Mitochondrial Dysfunctions: A Red Thread across Neurodegenerative Diseases

Mitochondria play a central role in a plethora of processes related to the maintenance of cellular homeostasis and genomic integrity. They contribute to preserving the optimal functioning of cells and protecting them from potential DNA damage which could result in mutations and disease. However, perturbations of the system due to senescence or environmental factors induce alterations of the physiological balance and lead to the impairment of mitochondrial functions. After the description of the crucial roles of mitochondria for cell survival and activity, the core of this review focuses on the "mitochondrial switch" which occurs at the onset of neuronal degeneration. We dissect the pathways related to mitochondrial dysfunctions which are shared among the most frequent or disabling neurodegenerative diseases such as Alzheimer's, Parkinson's, and Huntington's, Amyotrophic Lateral Sclerosis, and Spinal Muscular Atrophy. Can mitochondrial dysfunctions (affecting their morphology and activities) represent the early event eliciting the shift towards pathological neurobiological processes? Can mitochondria represent a common target against neurodegeneration? We also review here the drugs that target mitochondria in neurodegenerative diseases.

Study of Tissue Variation and Analysis of MR Brain Images using Optimized Multilevel Threshold and Deep CNN Features in Neurodegenerative Disorders

Dementia is a degenerative irreversible disorder that globally causes a high socio-economic burden. The pathology progression of mild cognitive impairment (MCI) and Alzheimer diseases (AD) are correlated with each other. There is a need to examine the pathology variation to discriminate the disorder to provide appropriate treatment strategies. This study investigates about the brain tissue variations to identify the subtle change in progression. The considered normal, MCI and AD magnetic resonance (MR) images are obtained from Alzheimer's disease Neuroimaging Initiative (ADNI). In this work, multilevel Tsallis based grey wolf optimization (GWO) is used to segment the brain tissues. Then the feature is extracted from segmented white matter (WM), grey matter (GM) and cerebro spinal fluid (CSF) using convolution neural network (CNN). The obtained deep features are given to principal component analysis (PCA) to obtain a prominent feature set for normal, MCI and AD. Further the tissue variation of optimized deep features is analyzed using support vector machine (SVM). The results shows that Tsallis based GWO perform reliable tissue segmentation for normal, MCI and AD. The deep features are able to observe discrimination than the fully considered feature set. Finally, the classifier result shows distinct tissue variation among normal, MCI and AD subjects. Further the prominent features give a classification accuracy of 77%, 80.22% and 78.7% for WM, GM and CSF respectively. This concludes that GM variation is a close biological substrate of dementia progressive condition than the effects of time or aging. Thus, the proposed framework can be used as an effective system for diagnosis of progression in neurodegenerative disorders.

Gaussian Graphical Models Reveal Inter-Modal and Inter-Regional Conditional Dependencies of Brain Alterations in Alzheimer's Disease

Alzheimer's disease (AD) is characterized by a sequence of pathological changes, which are commonly assessed in vivo using various brain imaging modalities such as magnetic resonance imaging (MRI) and positron emission tomography (PET). Currently, the most approaches to analyze statistical associations between regions and imaging modalities rely on Pearson correlation or linear regression models. However, these models are prone to spurious correlations arising from uninformative shared variance and multicollinearity. Notably, there are no appropriate multivariate statistical models available that can easily integrate dozens of multicollinear variables derived from such data, being able to utilize the additional information provided from the combination of data sources. Gaussian graphical models (GGMs) can estimate the conditional dependency from given data, which is conceptually expected to closely reflect the underlying causal relationships between various variables. Hence, we applied GGMs to assess multimodal regional brain alterations in AD. We obtained data from N = 972 subjects from the Alzheimer's Disease Neuroimaging Initiative. The mean amyloid load (AV45-PET), glucose metabolism (FDG-PET), and gray matter volume (MRI) were calculated for each of the 108 cortical and subcortical brain regions. GGMs were estimated using a Bayesian framework for the combined multimodal data and the resulted conditional dependency networks were compared to classical covariance networks based on Pearson correlation. Additionally, graph-theoretical network statistics were calculated to determine network alterations associated with disease status. The resulting conditional dependency matrices were much sparser (≈10% density) than Pearson correlation matrices (≈50% density). Within imaging modalities, conditional dependency networks yielded clusters connecting anatomically adjacent regions. For the associations between different modalities, only few region-specific connections were detected. Network measures such as small-world coefficient were significantly altered across diagnostic groups, with a biphasic u-shape trajectory, i.e., increased small-world coefficient in early mild cognitive impairment (MCI), similar values in late MCI, and decreased values in AD dementia patients compared to cognitively normal controls. In conclusion, GGMs removed commonly shared variance among multimodal measures of regional brain alterations in MCI and AD, and yielded sparser matrices compared to correlation networks based on the Pearson coefficient. Therefore, GGMs may be used as alternative to thresholding-approaches typically applied to correlation networks to obtain the most informative relations between variables.