Distinct tau PET imaging patterns in typical and atypical Alzheimer's disease

A machine learning-based prediction of tau load and distribution in Alzheimer's disease using plasma, MRI and clinical variables

Tau positron emission tomography (PET) is a reliable neuroimaging technique for assessing regional load of tau pathology in the brain, commonly used in Alzheimer's disease (AD) research and clinical trials. However, its routine clinical use is limited by cost and accessibility barriers. Here we explore using machine learning (ML) models to predict clinically useful tau-PET composites from low-cost and non-invasive features, e.g., basic clinical variables, plasma biomarkers, and structural magnetic resonance imaging (MRI). Results demonstrated that models including plasma biomarkers yielded the most accurate predictions of tau-PET burden (best model: R-squared=0.66-0.68), with especially high contribution from plasma P-tau217. In contrast, MRI variables stood out as best predictors (best model: R-squared=0.28-0.42) of asymmetric tau load between the two hemispheres (an example of clinically relevant spatial information). The models showed high generalizability to external test cohorts with data collected at multiple sites. Based on these results, we also propose a proof-of-concept two-step classification workflow, demonstrating how the ML models can be translated to a clinical setting. This study uncovers current potential in predicting tau-PET information from scalable cost-effective variables, which could improve diagnosis and prognosis of AD.

Insulin Resistance and Cognitive Impairment: Evidence From Neuroimaging

Insulin is a peptide well known for its role in regulating glucose metabolism in peripheral tissues. Emerging evidence from human and animal studies indicate the multifactorial role of insulin in the brain, such as neuronal and glial metabolism, glucose regulation, and cognitive processes. Insulin resistance (IR), defined as reduced sensitivity to the action of insulin, has been consistently proposed as an important risk factor for developing neurodegeneration and cognitive impairment. Although the exact mechanism of IR-related cognitive impairment still awaits further elucidation, neuroimaging offers a versatile set of novel contrasts to reveal the subtle cerebral abnormalities in IR. These imaging contrasts, including but not limited to brain volume, white matter (WM) microstructure, neural function and brain metabolism, are expected to unravel the nature of the link between IR, cognitive decline, and brain abnormalities, and their changes over time. This review summarizes the current neuroimaging studies with multiparametric techniques, focusing on the cerebral abnormalities related to IR and therapeutic effects of IR-targeting treatments. According to the results, brain regions associated with IR pathophysiology include the medial temporal lobe, hippocampus, prefrontal lobe, cingulate cortex, precuneus, occipital lobe, and the WM tracts across the globe. Of these, alterations in the temporal lobe are highly reproducible across different imaging modalities. These structures have been known to be vulnerable to Alzheimer's disease (AD) pathology and are critical in cognitive processes such as memory and executive functioning. Comparing to asymptomatic subjects, results are more mixed in patients with metabolic disorders such as type 2 diabetes and obesity, which might be attributed to a multifactorial mechanism. Taken together, neuroimaging, especially MRI, is beneficial to reveal early abnormalities in cerebral structure and function in insulin-resistant brain, providing important evidence to unravel the underlying neuronal substrate that reflects the cognitive decline in IR. EVIDENCE LEVEL: 5 TECHNICAL EFFICACY: Stage 2.

Image-level trajectory inference of tau pathology using variational autoencoder for Flortaucipir PET

Purpose

Alzheimer’s disease (AD) studies revealed that abnormal deposition of tau spreads in a specific spatial pattern, namely Braak stage. However, Braak staging is based on post mortem brains, each of which represents the cross section of the tau trajectory in disease progression, and numerous studies were reported that do not conform to that model. This study thus aimed to identify the tau trajectory and quantify the tau progression in a data-driven approach with the continuous latent space learned by variational autoencoder (VAE).

Methods

A total of 1080 [¹⁸F]Flortaucipir brain positron emission tomography (PET) images were collected from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. VAE was built to compress the hidden features from tau images in latent space. Hierarchical agglomerative clustering and minimum spanning tree (MST) were applied to organize the features and calibrate them to the tau progression, thus deriving pseudo-time. The image-level tau trajectory was inferred by continuously sampling across the calibrated latent features. We assessed the pseudo-time with regard to tau standardized uptake value ratio (SUVr) in AD-vulnerable regions, amyloid deposit, glucose metabolism, cognitive scores, and clinical diagnosis.

Results

We identified four clusters that plausibly capture certain stages of AD and organized the clusters in the latent space. The inferred tau trajectory agreed with the Braak staging. According to the derived pseudo-time, tau first deposits in the parahippocampal and amygdala, and then spreads to the fusiform, inferior temporal lobe, and posterior cingulate. Prior to the regional tau deposition, amyloid accumulates first.

Conclusion

The spatiotemporal trajectory of tau progression inferred in this study was consistent with Braak staging. The profile of other biomarkers in disease progression agreed well with previous findings. We addressed that this approach additionally has the potential to quantify tau progression as a continuous variable by taking a whole-brain tau image into account.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00259-021-05662-z.

Tau Seeding Mouse Models with Patient Brain-Derived Aggregates

Tauopathies are a heterogeneous class of neurodegenerative diseases characterized by intracellular inclusions of aggregated tau proteins. Tau aggregates in different tauopathies have distinct structural features and can be found in different cell types. Transgenic animal models overexpressing human tau have been used for over two decades in the research of tau pathology. However, these models poorly recapitulate the heterogeneity of tauopathies found in human brains. Recent findings demonstrate that injection of purified tau aggregates from the brains of human tauopathy patients recapitulates both the structural features and cell-type specificity of the tau pathology of the donor tauopathy. These models may therefore have unique translational value in the study of functional consequences of tau pathology, tau-based diagnostics, and tau targeting therapeutics. This review provides an update of the literature relating to seeding-based tauopathy and their potential applications.

Amyloid, tau, pathogen infection and antimicrobial protection in Alzheimer's disease -conformist, nonconformist, and realistic prospects for AD pathogenesis

BACKGROUND

Alzheimer's disease (AD) is a fatal disease that threatens the quality of life of an aging population at a global scale. Various hypotheses on the etiology of AD have been developed over the years to guide efforts in search of therapeutic strategies.

MAIN BODY

In this review, we focus on four AD hypotheses currently relevant to AD onset: the prevailing amyloid cascade hypothesis, the well-recognized tau hypothesis, the increasingly popular pathogen (viral infection) hypothesis, and the infection-related antimicrobial protection hypothesis. In briefly reviewing the main evidence supporting each hypothesis and discussing the questions that need to be addressed, we hope to gain a better understanding of the complicated multi-layered interactions in potential causal and/or risk factors in AD pathogenesis. As a defining feature of AD, the existence of amyloid deposits is likely fundamental to AD onset but is insufficient to wholly reproduce many complexities of the disorder. A similar belief is currently also applied to hyperphosphorylated tau aggregates within neurons, where tau has been postulated to drive neurodegeneration in the presence of pre-existing Aβ plaques in the brain. Although infection of the central nerve system by pathogens such as viruses may increase AD risk, it is yet to be determined whether this phenomenon is applicable to all cases of sporadic AD and whether it is a primary trigger for AD onset. Lastly, the antimicrobial protection hypothesis provides insight into a potential physiological role for Aβ peptides, but how Aβ/microbial interactions affect AD pathogenesis during aging awaits further validation. Nevertheless, this hypothesis cautions potential adverse effects in Aβ-targeting therapies by hindering potential roles for Aβ in anti-viral protection.

CONCLUSION

AD is a multi-factor complex disorder, which likely requires a combinatorial therapeutic approach to successfully slow or reduce symptomatic memory decline. A better understanding of how various causal and/or risk factors affecting disease onset and progression will enhance the likelihood of conceiving effective treatment paradigms, which may involve personalized treatment strategies for individual patients at varying stages of disease progression.

Simvastatin inhibits the apoptosis of hippocampal cells in a mouse model of Alzheimer's disease

Alzheimer's disease is associated with cognitive impairments that affect memory and executive functions. Simvastatin is a cholesterol-lowering statin drug that is used to control levels of cholesterol in the blood, particularly in cases of hypercholesterolemia, and may be used in the treatment of aneurysmal subarachnoid hemorrhage. Previous results have indicated that the apoptosis of hippocampal cells may serve a critical role in the progression of Alzheimer's disease. In the present study, it was determined whether Simvastatin inhibited the apoptosis of hippocampal cells in vitro and in vivo. The therapeutic effects of Simvastatin were evaluated in 24-month-old triple-transgenic Alzheimer's disease (3×Tg-AD) mice, and the efficacy of Simvastatin in attenuating memory and cognitive impairment was investigated. Levels of apoptosis-related gene expression in the hippocampus and hippocampal cells of experimental mice were also detected. In addition, neuron excitability was assessed in the functionally relevant brain regions in the hippocampus. The data indicated that Simvastatin significantly suppressed the apoptosis of hippocampal cells in 3×Tg-AD model mice compared with controls (P<0.01). Furthermore, treatment with Simvastatin improved the dementia status of 3×Tg-AD mice, as determined by a learning task in which mice exhibited significantly reduced attention impairment, impulsivity and compulsivity (P<0.01). In addition, results demonstrated that Simvastatin significantly inhibited hippocampal damage and significantly improved neuronal loss in hippocampal structures classically associated with attentional performance when compared with untreated mice (P<0.01). Thus, Simvastatin prevented cognitive impairment by decreasing hippocampal cell apoptosis and improving learning-memory ability. Simvastatin treatment also increased the expression of anti-apoptotic genes and decreased the expression pro-apoptotic genes (P<0.01), which may have been associated with improved motor attention and cognitive competence in 3×Tg-AD mice. Collectively, these preclinical data indicated that Simvastatin was efficient in attenuating memory lapse and hippocampal cell apoptosis in a 3×Tg-AD mouse model. Thus, Simvastatin may be useful in improving the clinical outcome of patients with Alzheimer's disease.

Insulin Resistance as a Link between Amyloid-Beta and Tau Pathologies in Alzheimer's Disease

Current hypotheses and theories regarding the pathogenesis of Alzheimer’s disease (AD) heavily implicate brain insulin resistance (IR) as a key factor. Despite the many well-validated metrics for systemic IR, the absence of biomarkers for brain-specific IR represents a translational gap that has hindered its study in living humans. In our lab, we have been working to develop biomarkers that reflect the common mechanisms of brain IR and AD that may be used to follow their engagement by experimental treatments. We present two promising biomarkers for brain IR in AD: insulin cascade mediators probed in extracellular vesicles (EVs) enriched for neuronal origin, and two-dimensional magnetic resonance spectroscopy (MRS) measures of brain glucose. As further evidence for a fundamental link between brain IR and AD, we provide a novel analysis demonstrating the close spatial correlation between brain expression of genes implicated in IR (using Allen Human Brain Atlas data) and tau and beta-amyloid pathologies. We proceed to propose the bold hypotheses that baseline differences in the metabolic reliance on glycolysis, and the expression of glucose transporters (GLUT) and insulin signaling genes determine the vulnerability of different brain regions to Tau and/or Amyloid beta (Aβ) pathology, and that IR is a critical link between these two pathologies that define AD. Lastly, we provide an overview of ongoing clinical trials that target IR as an angle to treat AD, and suggest how biomarkers may be used to evaluate treatment efficacy and target engagement.

Insulin resistance in Alzheimer's disease

The links between systemic insulin resistance (IR), brain-specific IR, and Alzheimer's disease (AD) have been an extremely productive area of current research. This review will cover the fundamentals and pathways leading to IR, its connection to AD via cellular mechanisms, the most prominent methods and models used to examine it, an introduction to the role of extracellular vesicles (EVs) as a source of biomarkers for IR and AD, and an overview of modern clinical studies on the subject. To provide additional context, we also present a novel analysis of the spatial correlation of gene expression in the brain with the aid of Allen Human Brain Atlas data. Ultimately, examining the relation between IR and AD can be seen as a means of advancing the understanding of both disease states, with IR being a promising target for therapeutic strategies in AD treatment. In conclusion, we highlight the therapeutic potential of targeting brain IR in AD and the main strategies to pursue this goal.