Dissociation of tau pathology and neuronal hypometabolism within the ATN framework of Alzheimer's disease

Genomic stress and impaired DNA repair in Alzheimer disease

Alzheimer disease (AD) is the most prominent form of dementia and has received considerable attention due to its growing burden on economic, healthcare and basic societal infrastructures. The two major neuropathological hallmarks of AD, i.e., extracellular amyloid beta (Aβ) peptide plaques and intracellular hyperphosphorylated Tau neurofibrillary tangles, have been the focus of much research, with an eye on understanding underlying disease mechanisms and identifying novel therapeutic avenues. One often overlooked aspect of AD is how Aβ and Tau may, through indirect and direct mechanisms, affect genome integrity. Herein, we review evidence that Aβ and Tau abnormalities induce excessive genomic stress and impair genome maintenance mechanisms, events that can promote DNA damage-induced neuronal cell loss and associated brain atrophy.

Revised criteria for diagnosis and staging of Alzheimer's disease: Alzheimer's Association Workgroup

The National Institute on Aging and the Alzheimer's Association convened three separate work groups in 2011 and single work groups in 2012 and 2018 to create recommendations for the diagnosis and characterization of Alzheimer's disease (AD). The present document updates the 2018 research framework in response to several recent developments. Defining diseases biologically, rather than based on syndromic presentation, has long been standard in many areas of medicine (e.g., oncology), and is becoming a unifying concept common to all neurodegenerative diseases, not just AD. The present document is consistent with this principle. Our intent is to present objective criteria for diagnosis and staging AD, incorporating recent advances in biomarkers, to serve as a bridge between research and clinical care. These criteria are not intended to provide step-by-step clinical practice guidelines for clinical workflow or specific treatment protocols, but rather serve as general principles to inform diagnosis and staging of AD that reflect current science. HIGHLIGHTS: We define Alzheimer's disease (AD) to be a biological process that begins with the appearance of AD neuropathologic change (ADNPC) while people are asymptomatic. Progression of the neuropathologic burden leads to the later appearance and progression of clinical symptoms. Early-changing Core 1 biomarkers (amyloid positron emission tomography [PET], approved cerebrospinal fluid biomarkers, and accurate plasma biomarkers [especially phosphorylated tau 217]) map onto either the amyloid beta or AD tauopathy pathway; however, these reflect the presence of ADNPC more generally (i.e., both neuritic plaques and tangles). An abnormal Core 1 biomarker result is sufficient to establish a diagnosis of AD and to inform clinical decision making throughout the disease continuum. Later-changing Core 2 biomarkers (biofluid and tau PET) can provide prognostic information, and when abnormal, will increase confidence that AD is contributing to symptoms. An integrated biological and clinical staging scheme is described that accommodates the fact that common copathologies, cognitive reserve, and resistance may modify relationships between clinical and biological AD stages.

Anterior-temporal hippocampal network mechanisms of left angular gyrus-navigated rTMS for memory improvement in aMCI: A sham-controlled study

INTRODUCTION

Neuro-navigated repetitive transcranial magnetic stimulation (rTMS) of the left angular gyrus has been broadly investigated for the treatment of amnestic mild cognitive impairment (aMCI). Although abnormalities in two hippocampal networks, the anterior-temporal (AT) and posterior-medial (PM) networks, are consistent with aMCI and are potential therapeutic targets for rTMS, the underlying mechanisms of the therapeutic effects of rTMS on hippocampal network connections remain unknown. Here, we assessed the impact of left angular gyrus rTMS on activity in these networks and explored whether the treatment response was due to the distance between the clinically applied target (the group average optimal site) and the personalized target in patients with aMCI.

METHODS

Sixty subjects clinically diagnosed with aMCI participated in this study after 20 sessions of sham-controlled rTMS targeting the left angular gyrus. Resting-state functional magnetic resonance imaging and neuropsychological assessments were performed before and after rTMS. Functional connectivity alterations in the PM and AT networks were assessed using seed-based functional connectivity analysis and two-factor repeated measures analysis of variance (ANOVA). We then computed the correlations between the functional connectivity changes and clinical rating scales. Finally, we examined whether the Euclidean distance between the clinically applied and personalized targets predicted the subsequent treatment response.

RESULTS

Compared with the sham group, the active rTMS group showed rTMS-induced deactivation of functional connectivity within the medial temporal lobe-AT network, with a negative correlation with episodic memory score changes. Moreover, the active rTMS lowers the interdependency of changes in the PM and AT networks. Finally, the Euclidean distance between the clinically applied and personalized target distances could predict subsequent network lever responses in the active rTMS group.

CONCLUSIONS

Neuro-navigated rTMS selectively modulates widespread functional connectivity abnormalities in the PM and AT hippocampal networks in aMCI patients, and the modulation of hippocampal-AT network connectivity can efficiently reverse memory deficits. The results also highlight the necessity of personalized targets for fMRI.

Brain perfusion SPECT in dementia: what radiologists should know

The findings of brain perfusion single-photon emission computed tomography (SPECT), which detects abnormalities often before changes manifest in morphological imaging, mainly reflect neurodegeneration and contribute to dementia evaluation. A major shift is about to occur in dementia practice to the approach of diagnosing based on biomarkers and treating with disease-modifying drugs. Accordingly, brain perfusion SPECT will be required to serve as a biomarker of neurodegeneration. Hypoperfusion in Alzheimer's disease (AD) is typically seen in the posterior cingulate cortex and precuneus early in the disease, followed by the temporoparietal cortices. On the other hand, atypical presentations of AD such as the posterior variant, logopenic variant, frontal variant, and corticobasal syndrome exhibit hypoperfusion in areas related to symptoms. Additionally, hypoperfusion especially in the precuneus and parietal association cortex can serve as a predictor of progression from mild cognitive impairment to AD. In dementia with Lewy bodies (DLB), the differentiating feature is the presence of hypoperfusion in the occipital lobes in addition to that observed in AD. Hypoperfusion of the occipital lobe is not a remarkable finding, as it is assumed to reflect functional loss due to impairment of the cholinergic and dopaminergic systems rather than degeneration per se. Moreover, the cingulate island sign reflects the degree of AD pathology comorbid in DLB. Frontotemporal dementia is characterized by regional hypoperfusion according to the three clinical types, and the background pathology is diverse. Idiopathic normal pressure hydrocephalus shows apparent hypoperfusion around the Sylvian fissure and corpus callosum and apparent hyperperfusion in high-convexity areas. The cortex or striatum with diffusion restriction on magnetic resonance imaging in prion diseases reflects spongiform degeneration and brain perfusion SPECT reveals hypoperfusion in the same areas. Brain perfusion SPECT findings in dementia should be carefully interpreted considering background pathology.

Longitudinal trajectories of cognitive reserve in hypometabolic subtypes of Alzheimer's disease

Previous studies have demonstrated resilience to AD-related neuropathology in a form of cognitive reserve (CR). In this study we investigated a relationship between CR and hypometabolic subtypes of AD, specifically the typical and the limbic-predominant subtypes. We analyzed data from 59 Aβ-positive cognitively normal (CN), 221 prodromal Alzheimer's disease (AD) and 174 AD dementia participants from the Alzheimer's Disease Neuroimaging Initiative (ADNI) from ADNI and ADNIGO/2 phases. For replication, we analyzed data from 5 Aβ-positive CN, 89 prodromal AD and 43 AD dementia participants from ADNI3. CR was estimated as standardized residuals in a model predicting cognition from temporoparietal grey matter volumes and covariates. Higher CR estimates predicted slower cognitive decline. Typical and limbic-predominant hypometabolic subtypes demonstrated similar baseline CR, but the results suggested a faster decline of CR in the typical subtype. These findings support the relationship between subtypes and CR, specifically longitudinal trajectories of CR. Results also underline the importance of longitudinal analyses in research on CR.

Synthesizing images of tau pathology from cross-modal neuroimaging using deep learning

Given the prevalence of dementia and the development of pathology-specific disease-modifying therapies, high-value biomarker strategies to inform medical decision-making are critical. In vivo tau-PET is an ideal target as a biomarker for Alzheimer's disease diagnosis and treatment outcome measure. However, tau-PET is not currently widely accessible to patients compared to other neuroimaging methods. In this study, we present a convolutional neural network (CNN) model that imputes tau-PET images from more widely available cross-modality imaging inputs. Participants (n = 1192) with brain T1-weighted MRI (T1w), fluorodeoxyglucose (FDG)-PET, amyloid-PET and tau-PET were included. We found that a CNN model can impute tau-PET images with high accuracy, the highest being for the FDG-based model followed by amyloid-PET and T1w. In testing implications of artificial intelligence-imputed tau-PET, only the FDG-based model showed a significant improvement of performance in classifying tau positivity and diagnostic groups compared to the original input data, suggesting that application of the model could enhance the utility of the metabolic images. The interpretability experiment revealed that the FDG- and T1w-based models utilized the non-local input from physically remote regions of interest to estimate the tau-PET, but this was not the case for the Pittsburgh compound B-based model. This implies that the model can learn the distinct biological relationship between FDG-PET, T1w and tau-PET from the relationship between amyloid-PET and tau-PET. Our study suggests that extending neuroimaging's use with artificial intelligence to predict protein specific pathologies has great potential to inform emerging care models.

Dimensional Neuroimaging Endophenotypes: Neurobiological Representations of Disease Heterogeneity Through Machine Learning

Machine learning has been increasingly used to obtain individualized neuroimaging signatures for disease diagnosis, prognosis, and response to treatment in neuropsychiatric and neurodegenerative disorders. Therefore, it has contributed to a better understanding of disease heterogeneity by identifying disease subtypes that present significant differences in various brain phenotypic measures. In this review, we first present a systematic literature overview of studies using machine learning and multimodal MRI to unravel disease heterogeneity in various neuropsychiatric and neurodegenerative disorders, including Alzheimer's disease, schizophrenia, major depressive disorder, autism spectrum disorder, multiple sclerosis, as well as their potential in transdiagnostic settings. Subsequently, we summarize relevant machine learning methodologies and discuss an emerging paradigm which we call dimensional neuroimaging endophenotype (DNE). DNE dissects the neurobiological heterogeneity of neuropsychiatric and neurodegenerative disorders into a low-dimensional yet informative, quantitative brain phenotypic representation, serving as a robust intermediate phenotype (i.e., endophenotype) largely reflecting underlying genetics and etiology. Finally, we discuss the potential clinical implications of the current findings and envision future research avenues.

The Alzheimer's Disease Neuroimaging Initiative in the era of Alzheimer's disease treatment: A review of ADNI studies from 2021 to 2022

The Alzheimer's Disease Neuroimaging Initiative (ADNI) aims to improve Alzheimer's disease (AD) clinical trials. Since 2006, ADNI has shared clinical, neuroimaging, and cognitive data, and biofluid samples. We used conventional search methods to identify 1459 publications from 2021 to 2022 using ADNI data/samples and reviewed 291 impactful studies. This review details how ADNI studies improved disease progression understanding and clinical trial efficiency. Advances in subject selection, detection of treatment effects, harmonization, and modeling improved clinical trials and plasma biomarkers like phosphorylated tau showed promise for clinical use. Biomarkers of amyloid beta, tau, neurodegeneration, inflammation, and others were prognostic with individualized prediction algorithms available online. Studies supported the amyloid cascade, emphasized the importance of neuroinflammation, and detailed widespread heterogeneity in disease, linked to genetic and vascular risk, co-pathologies, sex, and resilience. Biological subtypes were consistently observed. Generalizability of ADNI results is limited by lack of cohort diversity, an issue ADNI-4 aims to address by enrolling a diverse cohort.

Emerging diagnostics and therapeutics for Alzheimer disease

Alzheimer disease (AD) is the most common contributor to dementia in the world, but strategies that slow or prevent its clinical progression have largely remained elusive, until recently. This Review highlights the latest advances in biomarker technologies and therapeutic development to improve AD diagnosis and treatment. We review recent results that enable pathological staging of AD with neuroimaging and fluid-based biomarkers, with a particular emphasis on the role of amyloid, tau and neuroinflammation in disease pathogenesis. We discuss the lessons learned from randomized controlled trials, including some supporting the proposal that certain anti-amyloid antibodies slow cognitive decline during the mildly symptomatic phase of AD. In addition, we highlight evidence for newly identified therapeutic targets that may be able to modify AD pathogenesis and progression. Collectively, these recent discoveries-and the research directions that they open-have the potential to move AD clinical care toward disease-modifying treatment strategies with maximal benefits for patients.

When Alzheimer's is LATE: Why Does it Matter?

Recent therapeutic advances provide heightened motivation for accurate diagnosis of the underlying biologic causes of dementia. This review focuses on the importance of clinical recognition of limbic-predominant age-related TDP-43 encephalopathy (LATE). LATE affects approximately one-quarter of older adults and produces an amnestic syndrome that is commonly mistaken for Alzheimer's disease (AD). Although AD and LATE often co-occur in the same patients, these diseases differ in the protein aggregates driving neuropathology (Aβ amyloid/tau vs TDP-43). This review discusses signs and symptoms, relevant diagnostic testing, and potential treatment implications for LATE that may be helpful for physicians, patients, and families. ANN NEUROL 2023;94:211-222.

Tau-Neurodegeneration mismatch reveals vulnerability and resilience to comorbidities in Alzheimer's continuum

Variability in the relationship of tau-based neurofibrillary tangles (T) and degree of neurodegeneration (N) in Alzheimer's Disease (AD) is likely attributable to the non-specific nature of N, which is also modulated by such factors as other co-pathologies, age-related changes, and developmental differences. We studied this variability by partitioning patients within the Alzheimer's continuum into data-driven groups based on their regional T-N dissociation, which reflects the residuals after the effect of tau pathology is "removed". We found six groups displaying distinct spatial T-N mismatch and thickness patterns despite similar tau burden. Their T-N patterns resembled the neurodegeneration patterns of non-AD groups partitioned on the basis of z-scores of cortical thickness alone and were similarly associated with surrogates of non-AD factors. In an additional sample of individuals with antemortem imaging and autopsy, T-N mismatch was associated with TDP-43 co-pathology. Finally, T-N mismatch training was then applied to a separate cohort to determine the ability to classify individual patients within these groups. These findings suggest that T-N mismatch may provide a personalized approach for determining non-AD factors associated with resilience/vulnerability to Alzheimer's disease.

A Surface-Based Federated Chow Test Model for Integrating APOE Status, Tau Deposition Measure, and Hippocampal Surface Morphometry

BACKGROUND

Alzheimer's disease (AD) is the most common type of age-related dementia, affecting 6.2 million people aged 65 or older according to CDC data. It is commonly agreed that discovering an effective AD diagnosis biomarker could have enormous public health benefits, potentially preventing or delaying up to 40% of dementia cases. Tau neurofibrillary tangles are the primary driver of downstream neurodegeneration and subsequent cognitive impairment in AD, resulting in structural deformations such as hippocampal atrophy that can be observed in magnetic resonance imaging (MRI) scans.

OBJECTIVE

To build a surface-based model to 1) detect differences between APOE subgroups in patterns of tau deposition and hippocampal atrophy, and 2) use the extracted surface-based features to predict cognitive decline.

METHODS

Using data obtained from different institutions, we develop a surface-based federated Chow test model to study the synergistic effects of APOE, a previously reported significant risk factor of AD, and tau on hippocampal surface morphometry.

RESULTS

We illustrate that the APOE-specific morphometry features correlate with AD progression and better predict future AD conversion than other MRI biomarkers. For example, a strong association between atrophy and abnormal tau was identified in hippocampal subregion cornu ammonis 1 (CA1 subfield) and subiculum in e4 homozygote cohort.

CONCLUSION

Our model allows for identifying MRI biomarkers for AD and cognitive decline prediction and may uncover a corner of the neural mechanism of the influence of APOE and tau deposition on hippocampal morphology.

CSF Biomarkers of Alzheimer Disease in Patients With Concomitant α-Synuclein Pathology

BACKGROUND AND OBJECTIVES

CSF biomarkers β-amyloid 1-42 (Aβ42), phosphorylated tau 181 (p-tau181), total tau (t-tau), and neurogranin (Ng) can diagnose Alzheimer disease (AD) in life. However, it is unknown whether CSF concentrations, and thus their accuracies, are affected by concomitant pathologies common in AD, such as α-synuclein (αSyn). Our primary goal was to test whether biomarkers in patients with AD are altered by concomitant αSyn. We compared CSF Aβ42, p-tau181, t-tau, and Ng levels across autopsy-confirmed AD and concomitant AD and αSyn (AD + αSyn). Antemortem CSF levels were related to postmortem accumulations of αSyn. Finally, we tested how concommitant AD + αSyn affected the diagnostic accuracy of 2 CSF-based strategies: the amyloid/tau/neurodegeneration (ATN) framework and the t-tau/Aβ42 ratio.

METHODS

Inclusion criteria were neuropathologic diagnoses of AD, mixed AD + αSyn, and αSyn. A convenience sample of nonimpaired controls was selected with available CSF and a Mini-Mental State Examination (MMSE) ≥ 27. αSyn without AD and controls were included as reference groups. Analyses of covariance (ANCOVAs) tested planned comparisons were CSF Aβ42, p-tau181, t-tau, and Ng differences across AD and AD + αSyn. Linear models tested how biomarkers were altered by αSyn accumulation in AD, accounting for pathologic β-amyloid and tau. Receiver operating characteristic and area under the curve (AUC), including 95% CI, evaluated diagnostic accuracy.

RESULTS

Participants were 61 patients with AD, 39 patients with mixed AD + αSyn, 20 patients with αSyn, and 61 controls. AD had similar median age (73 [interquartile range {IQR} = 12] years), MMSE (23 [IQR = 9]), and sex distribution (male = 49%) compared with AD + αSyn age (70 [IQR = 13] years; p = 0.3), MMSE (25 [IQR = 9.5]; p = 0.19), and sex distribution (male = 69%; p = 0.077). ANCOVAs showed that AD + αSyn had lower p-tau181 (F(1,94) = 17, p < 2.6e-16), t-tau (F(1,93) = 11, p = 0.0004), and Ng levels (F(1,50) = 12, p = 0.0004) than AD; there was no difference in Aβ42 (p = 0.44). Models showed increasing αSyn related to lower p-tau181 (β = -0.26, SE = 0.092, p = 0.0065), t-tau (β = -0.19, SE = 0.092, p = 0.041), and Ng levels (β = -0.2, SE = 0.066, p = 0.0046); αSyn was not a significant factor for Aβ42 (p = 1). T-tau/Aβ42 had the highest accuracy when detecting AD, including mixed AD + αSyn cases (AUC = 0.95; CI 0.92-0.98).

DISCUSSION

Findings demonstrate that concomitant αSyn pathology in AD is associated with lower CSF p-tau181, t-tau, and Ng levels and can affect diagnostic accuracy in patients with AD.

Limbic-Predominant Age-Related TDP-43 Encephalopathy: LATE-Breaking Updates in Clinicopathologic Features and Biomarkers

PURPOSE OF REVIEW

Limbic-predominant age-related TDP-43 encephalopathy (LATE) is a recently defined neurodegenerative disease characterized by amnestic phenotype and pathological inclusions of TAR DNA-binding protein 43 (TDP-43). LATE is distinct from rarer forms of TDP-43 diseases such as frontotemporal lobar degeneration with TDP-43 but is also a common copathology with Alzheimer's disease (AD) and cerebrovascular disease and accelerates cognitive decline. LATE contributes to clinicopathologic heterogeneity in neurodegenerative diseases, so it is imperative to distinguish LATE from other etiologies.

RECENT FINDINGS

Novel biomarkers for LATE are being developed with magnetic resonance imaging (MRI) and positron emission tomography (PET). When cooccurring with AD, LATE exhibits identifiable patterns of limbic-predominant atrophy on MRI and hypometabolism on 18F-fluorodeoxyglucose PET that are greater than expected relative to levels of local AD pathology. Efforts are being made to develop TDP-43-specific radiotracers, molecularly specific biofluid measures, and genomic predictors of TDP-43. LATE is a highly prevalent neurodegenerative disease distinct from previously characterized cognitive disorders.

Roles of traditional chinese medicine regulating neuroendocrinology on AD treatment

The incidence of sporadic Alzheimer's disease (AD) is increasing in recent years. Studies have shown that in addition to some genetic abnormalities, the majority of AD patients has a history of long-term exposure to risk factors. Neuroendocrine related risk factors have been proved to be strongly associated with AD. Long-term hormone disorder can have a direct detrimental effect on the brain by producing an AD-like pathology and result in cognitive decline by impairing neuronal metabolism, plasticity and survival. Traditional Chinese Medicine(TCM) may regulate the complex process of endocrine disorders, and improve metabolic abnormalities, as well as the resulting neuroinflammation and oxidative damage through a variety of pathways. TCM has unique therapeutic advantages in treating early intervention of AD-related neuroendocrine disorders and preventing cognitive decline. This paper reviewed the relationship between neuroendocrine and AD as well as the related TCM treatment and its mechanism. The advantages of TCM intervention on endocrine disorders and some pending problems was also discussed, and new insights for TCM treatment of dementia in the future was provided.