The diagnostic value of FDG and amyloid PET in Alzheimer’s disease—A systematic review

Frontiers and hotspot evolution in research on Alzheimer's disease and hypertension: a bibliometric analysis from 2004 to 2023

Background

Alzheimer's disease (AD) is a neurodegenerative disease that imposes a heavy burden on patients and their families. Hypertension is an important risk factor for AD, but the specific mechanism of its impact is still unclear. This study thus aimed to analyze the relationship and trend changes between AD and hypertension through bibliometric methods.

Methods

Literature on AD and hypertension was retrieved from the Web of Science Core Collection (WoSCC) database between 2004 and 2023. Data regarding countries, institutions, authors and journals were sourced from WoSCC. CiteSpace and VOSviewer were used for data visualization, including author collaboration, timelines view of references, reference bursts and overlay visualization maps of keywords. Excel 2018 software was used for the statistical analysis.

Results

A total of 1,833 publications were ultimately included. From 2004 to 2023, the number of publications per year basically showed an increasing trend. The United States (United States) not only had the largest output of publications and the highest H-index but also had the seven highest frequencies of publication institutions. Kehoe, Patrick ranked first with the most articles among 9,330 authors. The journal with the most published articles was the Journal of Alzheimer's Disease. Reference analysis revealed a hotspot in the exploration of the pathophysiological association between AD and hypertension. Second, the treatment effects and potential risks of antihypertensive drugs (AHDs) on AD are also the focus of research. Researchers have carried out a series of studies ranging from basic research to clinical research on AHDs for the treatment of AD. Finally, personalized treatment strategies will also become one of the hotspots of future research. Controlling hypertension through lifestyle changes and medication interventions in AD patients is a promising strategy. The analysis of keywords revealed that "amyloid deposition," "preeclampsia," "Corona Virus Disease 2019 (COVID-19)" and "biomarkers" have been research hotspots in recent years.

Conclusion

By analyzing the references and keywords, we summarized the hot topics and research trends in this field. These findings provide useful information for researchers to explore the relationship between hypertension and AD further, with the hope of providing more effective treatments for AD patients to delay disease progression and improve quality of life.

First-in-human healthy volunteer dosimetry studies of the excitatory amino acid transporter 2 (EAAT2) PET imaging tracer methyl N4-(7-[18F]fluoro-9H-fluoren-2-yl)asparaginate, [18F]RP-115

OBJECTIVE AND BACKGROUND

The objective of this first-in-human study was to investigate the radiosynthesis, and the preliminary safety, biodistribution, and organ radiation dosimetry of the positron emission tomography (PET) imaging tracer methyl N4-([18F]7-fluoro-9H-fluoren-2-yl)asparaginate, known as [18F]RP-115, in a small cohort (n=8) of healthy volunteers. The [18F]RP-115 tracer is a methyl ester prodrug and undergoes metabolic saponification in the central nervous system to generate the corresponding carboxylic acid form that selectively binds to the excitatory amino acid transporter 2 (EAAT2) protein.

PROCEDURES AND METHODS

A multi-step high molar activity tracer radiosynthesis was devised to produce doses. Eight healthy human participants (four male and four female), aged 56-75, received a bolus intravenous injection of [18F]RP-115 (administered activity range: 70.3-355 MBq) prior to a total of 94 min of PET-MR scanning performed as three sequential scanning sessions. Regional tissue volumes of interest were defined, time-integrated activity coefficients (TIAC) were derived, and then estimates of organ and tissue activities and effective doses (whole body) were calculated, with two versions of OLINDA software (1.1 and 2.0) that incorporated two tissue weighting factor sets (ICRP-60 and -103), respectively.

MAIN FINDINGS

Tracer was routinely produced in good radiochemical yields and as suitable high molar activity doses for injection. The [18F]RP-115 injections and PET-MR scans were well-tolerated and no adverse events were reported (≤48 h). Radioactivity was widely biodistributed with good organ uptake. TIACs and estimated radiation organ doses were determined, for which a few statistically significant estimated organ dose differences between males and females were noted. The kidneys were identified as the critical target organ.

PRINCIPAL CONCLUSIONS

Injection of [18F]RP-115 was considered safe. The estimated kidney radiation doses relative to administered radioactivity identified a more optimal human [18F]RP-115 tracer injected amount of <211 MBq. This more optimal [18F]RP-115 tracer injected activity definition is similar to the amounts used for other established [18F]labeled clinical PET tracers such as [18F]FDG, and it will be used in future RP-115 clinical PET imaging studies.

Lifespan cardiometabolic exposures are associated with midlife white matter microstructure: The Bogalusa Heart Study

INTRODUCTION

Compartment model analysis of diffusion MRI data provides unique information on the microstructural properties of white matter. However, studies relating compartment model microstructural measures to longitudinal cardiometabolic health data are rare.

METHODS

130 cognitively healthy participants in the Bogalusa Heart Study completed diffusion MRI scans. Compartment model analysis was performed, and summary metrics were measured in organized and diffuse white matter. Multiple linear regression models were used to relate the white matter microstructure metrics to demographics and cardiometabolic risk factors.

RESULTS

In both organized and diffuse white matter, age was associated with worse diffusion metrics, women had better diffusion metrics than men, and African American participants had worse diffusion metrics compared to White participants. Greater blood pressure in pre-adulthood was associated with worse diffusion metrics in midlife.

DISCUSSION

Summary metrics from compartment model analyses of diffusion MRI data were associated cardiometabolic risk factors from youth to midlife as well as demographic factors.

Clinical Applications of PET Imaging in Alzheimer's Disease

Alzheimer's disease (AD) involves a complex pathophysiology of neurodegeneration that leads to severe cognitive deficiencies. Understanding the molecular alterations that underlie this disease is fundamental to clinical management and therapeutic innovation. Functional imaging with positron emission tomography (PET) enables a visualization of these impaired pathways, such as cerebral hypometabolism, amyloid and tau accumulation, and neurotransmitter dysfunction. This review discusses the clinical applications of PET in AD and mild cognitive impairment, focusing on relevant original research studies for disease diagnosis, progression, and treatment response.

Combined Quantitative amyloid-β PET and Structural MRI Features Improve Alzheimer's Disease Classification in Random Forest Model - A Multicenter Study

RATIONALE AND OBJECTIVES

Prior to clinical presentations of Alzheimer's Disease (AD), neuropathological changes, such as amyloid-β and brain atrophy, have accumulated at the earlier stages of the disease. The combination of such biomarkers assessed by multiple modalities commonly improves the likelihood of AD etiology. We aimed to explore the discriminative ability of Aβ PET features and whether combining Aβ PET and structural MRI features can improve the classification performance of the machine learning model in older healthy control (OHC) and mild cognitive impairment (MCI) from AD.

MATERIAL AND METHODS

We collected 94 AD patients, 82 MCI patients, and 85 OHC from three different cohorts. 17 global/regional Aβ features in Centiloid, 122 regional volume, and 68 regional cortical thickness were extracted as imaging features. Single or combined modality features were used to train the random forest model on the testing set. The top 10 features were sorted based on the Gini index in each binary classification.

RESULTS

The results showed that AUC scores were 0.81/0.86 and 0.69/0.68 using sMRI/Aβ PET features on the testing set in differentiating OHC and MCI from AD. The performance was improved while combining two-modality features with an AUC of 0.89 and an AUC of 0.71 in two classifications. Compared to sMRI features, particular Aβ PET features contributed more to differentiating AD from others.

CONCLUSION

Our study demonstrated the discriminative ability of Aβ PET features in differentiating AD from OHC and MCI. A combination of Aβ PET and structural MRI features can improve the RF model performance.

AI for the prediction of early stages of Alzheimer's disease from neuroimaging biomarkers - A narrative review of a growing field

OBJECTIVES

The objectives of this narrative review are to summarize the current state of AI applications in neuroimaging for early Alzheimer's disease (AD) prediction and to highlight the potential of AI techniques in improving early AD diagnosis, prognosis, and management.

METHODS

We conducted a narrative review of studies using AI techniques applied to neuroimaging data for early AD prediction. We examined single-modality studies using structural MRI and PET imaging, as well as multi-modality studies integrating multiple neuroimaging techniques and biomarkers. Furthermore, they reviewed longitudinal studies that model AD progression and identify individuals at risk of rapid decline.

RESULTS

Single-modality studies using structural MRI and PET imaging have demonstrated high accuracy in classifying AD and predicting progression from mild cognitive impairment (MCI) to AD. Multi-modality studies, integrating multiple neuroimaging techniques and biomarkers, have shown improved performance and robustness compared to single-modality approaches. Longitudinal studies have highlighted the value of AI in modeling AD progression and identifying individuals at risk of rapid decline. However, challenges remain in data standardization, model interpretability, generalizability, clinical integration, and ethical considerations.

CONCLUSION

AI techniques applied to neuroimaging data have the potential to improve early AD diagnosis, prognosis, and management. Addressing challenges related to data standardization, model interpretability, generalizability, clinical integration, and ethical considerations is crucial for realizing the full potential of AI in AD research and clinical practice. Collaborative efforts among researchers, clinicians, and regulatory agencies are needed to develop reliable, robust, and ethical AI tools that can benefit AD patients and society.

The usefulness of combined analysis using CIScore and VSRAD parameters for differentiating between dementia with Lewy body and Alzheimer's disease

PURPOSE

The Cingulate Island score (CIScore) is useful index for differentiating between dementia with Lewy body (DLB) and Alzheimer's disease (AD) using regional cerebral blood flow (rCBF) SPECT. The Z score standing for medial temporal lobe (MTL) atrophy and the ratio of Z score between dorsal brain stem (DBS) to MTL are useful indices for differentiating between DLB and AD using MRI with VSRAD. The current study investigated the diagnostic ability by the combined use of rCBF SPECT and MRI in the differentiation between AD and DLB.

MATERIALS AND METHODS

In cases with 42 AD and 28 DLB undertaken Tc-99m-ECD SPECT and MRI, we analyzed differential diagnostic ability between AD and DLB among following conditions by single or combined settings. Namely, they were (1) the CIScore as a parameter of rCBF SPECT (DLB ≦ 0.25), (2) Z score value of MTL atrophy (DLB ≦ 2.05), (3) the ratio of Z score of DBS to medial temporal gray matter as a parameter of brain atrophy using VSRAD (DLB ≧ 0.38). Also, we analyzed them both including and omitting the elderly (over 75 years old).

RESULTS

The accuracy of differential diagnosis in this condition was 74% for (1), 69% for (2), and 67% for (3). The accuracy by combination condition was 84% for (1) and (2), 81% for (1) and (3), and 67% for (2) and (3), respectively. The combination method by CIScore and the Z score of MTL showed the best accuracy. When we confined condition to ages younger than 75 years, the accuracy improved to 94% in the combination method.

CONCLUSION

The combined use of CIScore and Z score of MTL was suggested to be useful in the differential diagnosis between DLB and AD particularly in younger than 75 years old.

Evaluation of the relationship between FDG-PET hypometabolism and retinal layer thickness in patients with Alzheimer's disease

We aimed to investigate the diagnostic value of Optical coherence tomography (OCT) in Alzheimer's disease (AD) and to assess the correlation between OCT and fluorodeoxyglucose (FDG)-positron emission tomography (PET) which shows high diagnostic agreement with findings from postmortem histopathology-the gold standard method. Patients who were diagnosed with AD-related dementia were selected for the study. Patients with a mini mental test (MMT) score between 18 and 23 were included in the study (n = 31). Volunteers with MMT ≥ 28 and no cognitive impairment were included in the study as the control group (n = 31). OCT imaging was performed in the patient and control groups after detailed ophthalmological examinations including visual acuity and intraocular pressure measurements. Brain glucose metabolism measurement was performed using 18 F-FDG PET/computed tomography. When adjusted for age and sex, mean retinal nerve fiber layer thickness (RNFL) thickness showed a significant difference between groups and the RNFL thickness in the superior temporal and superior nasal quadrants in AD-related mild dementia group showed a significant difference (p < 0.05). Furthermore, only the RNFL thickness in the inferior nasal quadrant of the right eye showed a significant difference between the groups (p = 0.016). It is thought that OCT is a promising imaging method in the elderly population due to its low-cost, non-invasive and easily applicability, and therefore, it may contribute in the future as a tool in the periodic follow-up of patients diagnosed with AD.

Automated detection of Alzheimer's disease: a multi-modal approach with 3D MRI and amyloid PET

Recent advances in deep learning and imaging technologies have revolutionized automated medical image analysis, especially in diagnosing Alzheimer's disease through neuroimaging. Despite the availability of various imaging modalities for the same patient, the development of multi-modal models leveraging these modalities remains underexplored. This paper addresses this gap by proposing and evaluating classification models using 2D and 3D MRI images and amyloid PET scans in uni-modal and multi-modal frameworks. Our findings demonstrate that models using volumetric data learn more effective representations than those using only 2D images. Furthermore, integrating multiple modalities enhances model performance over single-modality approaches significantly. We achieved state-of-the-art performance on the OASIS-3 cohort. Additionally, explainability analyses with Grad-CAM indicate that our model focuses on crucial AD-related regions for its predictions, underscoring its potential to aid in understanding the disease's causes.

Tracking the progression of Alzheimer's disease: Insights from metabolic patterns of SOMI stages

BACKGROUND

SOMI (Stages of Objective Memory Impairment) is a novel classification that identifies six stages of memory decline in Alzheimer's Disease (AD) using the Free and Cued Selective Reminding Test (FCSRT). However, the relationship between SOMI stages and brain metabolism remains unexplored. This study aims to investigate the metabolic correlates of SOMI stages using FDG-PET in Mild Cognitive Impairment due to AD (MCI-AD) and early AD patients.

METHODS

One hundred twenty-nine-patients (99 aMCI-AD and 30 AD), and 42 healthy controls (HCs) (MMSE = 29.2 ± .8; age:69.1 ± 8.6 years; education:10.7 ± 3.8 years) who underwent an extensive neuropsychological battery including FCSRT and brain FDG-PET were enrolled. According to their clinical relevance and available sample sizes, SOMI-4 (N = 24 subjects; MMSE score:26.6 ± 2.6: age:75.4 ± 3.2; education:9.9 ± 4.5) and SOMI-5 groups (N = 97; MMSE:25.3 ± 2.6; age:73.9 ± 5.8; education:9.4 ± 4.1) were investigated.

RESULTS

Compared to HCs, SOMI-4 showed hypometabolism in the precuneus, medial temporal gyrus bilaterally, right pecuneus and angular gyrus. SOMI-5 exhibited broader hypometabolism, extending to the left posterior cingulate and medial frontal gyrus bilaterally. The conjunction analysis revealed overlapping areas in the precuneus, medial temporal gyrus bilaterally, and in the right angular gyrus and cuneus. The disjunction analysis identified SOMI-5 specific hypometabolism encompassing left inferior temporal gyrus, uncus and parahippocampal gyrus, and medial frontal gyrus bilaterally (p < .001, p-value (FWE) < .05).

DISCUSSION

SOMI-4 relates to posterior hypometabolism, while SOMI-5 to more extensive hypometabolism further encompassing frontal cortices, suggesting SOMI as a biologically relevant classification system of memory decline.

CONCLUSION

Memory decline staged with SOMI is associated with hypometabolism spreading in amnesic MCI-AD/AD, suggesting its usefulness as a clinical marker of increasing neurodegeneration.

Advances in Understanding and Managing Alzheimer's Disease: From Pathophysiology to Innovative Therapeutic Strategies

Alzheimer's disease (AD) is a debilitating neurodegenerative disorder characterized by the presence of amyloid-β (Aβ) plaques and tau-containing neurofibrillary tangles, leading to cognitive and physical decline. Representing the majority of dementia cases, AD poses a significant burden on healthcare systems globally, with onset typically occurring after the age of 65. While most cases are sporadic, about 10% exhibit autosomal forms associated with specific gene mutations. Neurofibrillary tangles and Aβ plaques formed by misfolded tau proteins and Aβ peptides contribute to neuronal damage and cognitive impairment. Currently, approved drugs, such as acetylcholinesterase inhibitors and N-methyl D-aspartate receptor agonists, offer only partial symptomatic relief without altering disease progression. A promising development is using lecanemab, a humanized IgG1 monoclonal antibody, as an immune therapeutic approach. Lecanemab demonstrates selectivity for polymorphic Aβ variants and binds to large soluble Aβ aggregates, providing a potential avenue for targeted treatment. This shift in understanding the role of the adaptive immune response in AD pathogenesis opens new possibilities for therapeutic interventions aiming to address the disease's intricate mechanisms. This review aims to summarize recent advancements in understanding Alzheimer's disease pathophysiology and innovative therapeutic approaches, providing valuable insights for both researchers and clinicians.

Associations Between Metabolomics Findings and Brain Hypometabolism in Mild Cognitive Impairment and Alzheimer's Disease

BACKGROUND

Alzheimer's disease (AD) is a progressive neurodegenerative disease with rising prevalence due to the aging global population. Existing methods for diagnosing AD are struggling to detect the condition in its earliest and most treatable stages. One early indicator of AD is a substantial decrease in the brain's glucose metabolism. Metabolomics can detect disturbances in biofluids, which may be advantageous for early detection of some AD-related changes. The study aims to predict brain hypometabolism in Alzheimer's disease using metabolomics findings and develop a predictive model based on metabolomic data.

METHODS

The data used in this study were acquired from the Alzheimer's Disease Neuroimaging Initiative (ADNI) project. We conducted a longitudinal study with three assessment time points to investigate the predictive power of baseline metabolomics for modeling longitudinal fluorodeoxyglucose- positron emission tomography (FDG-PET) trajectory changes in AD patients. A total of 44 participants with AD were included. The Alzheimer's Disease Assessment Scale (ADAS), the Mini-Mental State Examination (MMSE), and the Clinical Dementia Rating Scale-Sum of Boxes (CDR-SB) were used for cognitive assessments. A single global brain hypo-metabolism index was used as the outcome variable.

RESULTS

Across models, we observed consistent positive relationships between specific cholesterol esters - CE (20:3) (p = 0.005) and CE (18:3) (p = 0.0039) - and FDG-PET metrics, indicating these baseline metabolites may be valuable indicators of future PET score changes. Selected triglycerides like DG-O (16:0-20:4) also showed time-specific positive associations (p = 0.017).

CONCLUSION

This research provides new insights into the disruptions in the metabolic network linked to AD pathology. These findings could pave the way for identifying novel biomarkers and potential treatment targets for AD.

Positron Emission Tomography/Computed Tomography Imaging in Therapeutic Clinical Trials in Alzheimer's Disease: An Overview of the Current State of the Art of Research

The integration of positron emission tomography/computed tomography (PET/CT) has revolutionized the landscape of Alzheimer's disease (AD) research and therapeutic interventions. By combining structural and functional imaging, PET/CT provides a comprehensive understanding of disease pathology and response to treatment assessment. PET/CT, particularly with 2-deoxy-2-[fluorine-18]fluoro-D-glucose (18F-FDG), facilitates the visualization of glucose metabolism in the brain, enabling early diagnosis, staging, and monitoring of neurodegenerative disease progression. The advent of amyloid and tau PET imaging has further propelled the field forward, offering invaluable tools for tracking pathological hallmarks, assessing treatment response, and predicting clinical outcomes. While some therapeutic interventions targeting amyloid plaque load showed promising results with the reduction of cerebral amyloid accumulation over time, others failed to demonstrate a significant impact of anti-amyloid agents for reducing the amyloid plaques burden in AD brains. Tau PET imaging has conversely fueled the advent of disease-modifying therapeutic strategies in AD by supporting the assessment of neurofibrillary tangles of tau pathology deposition over time. Looking ahead, PET imaging holds immense promise for studying additional targets such as neuroinflammation, cholinergic deficit, and synaptic dysfunction. Advances in radiotracer development, dedicated brain PET/CT scanners, and Artificial Intelligence-powered software are poised to enhance the quality, sensitivity, and diagnostic power of molecular neuroimaging. Consequently, PET/CT remains at the forefront of AD research, offering unparalleled opportunities for unravelling the complexities of the disease and advancing therapeutic interventions, although it is not yet enough alone to allow patients' recruitment in therapeutic clinical trials.

Integrated transcriptomics reveals the brain and blood biomarkers in Alzheimer's disease

BACKGROUND

The systematic molecular associations between the peripheral blood cells and brain in Alzheimer's disease (AD) remains unclear, which hinders our understanding of AD pathological mechanisms and the exploration of new diagnostic biomarkers.

METHODS

Here, we performed an integrated analysis of the brain and peripheral blood cells transcriptomics to establish peripheral biomarkers of AD. By employing multiple statistical analyses plus machine learning, we identified and validated multiple regulated central and peripheral network in patients with AD.

RESULTS

By bioinformatics analysis, a total of 243 genes were differentially expressed in the central and peripheral systems, mainly enriched in three modules: immune response, glucose metabolism and lysosome. In addition, lysosome related gene ATP6V1E1 and immune response related genes (IL2RG, OSM, EVI2B TNFRSF1A, CXCR4, STAT5A) were significantly correlated with Aβ or Tau pathology. Finally, receiver operating characteristic (ROC) analysis revealed that ATP6V1E1 showed high-diagnostic potential for AD.

CONCLUSION

Taken together, our data identified the main pathological pathways in AD progression, particularly the systemic dysregulation of the immune response, and provided peripheral biomarkers for AD diagnosis.

The amyloid cascade hypothesis: an updated critical review

Results from recent clinical trials of antibodies that target amyloid-β (Aβ) for Alzheimer's disease have created excitement and have been heralded as corroboration of the amyloid cascade hypothesis. However, while Aβ may contribute to disease, genetic, clinical, imaging and biochemical data suggest a more complex aetiology. Here we review the history and weaknesses of the amyloid cascade hypothesis in view of the new evidence obtained from clinical trials of anti-amyloid antibodies. These trials indicate that the treatments have either no or uncertain clinical effect on cognition. Despite the importance of amyloid in the definition of Alzheimer's disease, we argue that the data point to Aβ playing a minor aetiological role. We also discuss data suggesting that the concerted activity of many pathogenic factors contribute to Alzheimer's disease and propose that evolving multi-factor disease models will better underpin the search for more effective strategies to treat the disease.

Advanced Overview of Biomarkers and Techniques for Early Diagnosis of Alzheimer's Disease

The development of early non-invasive diagnosis methods and identification of novel biomarkers are necessary for managing Alzheimer's disease (AD) and facilitating effective prognosis and treatment. AD has multi-factorial nature and involves complex molecular mechanism, which causes neuronal degeneration. The primary challenges in early AD detection include patient heterogeneity and lack of precise diagnosis at the preclinical stage. Several cerebrospinal fluid (CSF) and blood biomarkers have been proposed to show excellent diagnosis ability by identifying tau pathology and cerebral amyloid beta (Aβ) for AD. Intense research endeavors are being made to develop ultrasensitive detection techniques and find potent biomarkers for early AD diagnosis. To mitigate AD worldwide, understanding various CSF biomarkers, blood biomarkers, and techniques that can be used for early diagnosis is imperative. This review attempts to provide information regarding AD pathophysiology, genetic and non-genetic factors associated with AD, several potential blood and CSF biomarkers, like neurofilament light, neurogranin, Aβ, and tau, along with biomarkers under development for AD detection. Besides, numerous techniques, such as neuroimaging, spectroscopic techniques, biosensors, and neuroproteomics, which are being explored to aid early AD detection, have been discussed. The insights thus gained would help in finding potential biomarkers and suitable techniques for the accurate diagnosis of early AD before cognitive dysfunction.

Neuroimaging modalities in the detection of Alzheimer's disease-associated biomarkers

Alzheimer's disease (AD) is the most common cause of dementia. Neuropathological changes in AD patients occur up to 10-20 years before the emergence of clinical symptoms. Specific diagnosis and appropriate intervention strategies are crucial during the phase of mild cognitive impairment (MCI) and AD. The detection of biomarkers has emerged as a promising tool for tracking the efficacy of potential therapies, making an early disease diagnosis, and prejudging treatment prognosis. Specifically, multiple neuroimaging modalities, including magnetic resonance imaging (MRI), positron emission tomography, optical imaging, and single photon emission-computed tomography, have provided a few potential biomarkers for clinical application. The MRI modalities described in this review include structural MRI, functional MRI, diffusion tensor imaging, magnetic resonance spectroscopy, and arterial spin labelling. These techniques allow the detection of presymptomatic diagnostic biomarkers in the brains of cognitively normal elderly people and might also be used to monitor AD disease progression after the onset of clinical symptoms. This review highlights potential biomarkers, merits, and demerits of different neuroimaging modalities and their clinical value in MCI and AD patients. Further studies are necessary to explore more biomarkers and overcome the limitations of multiple neuroimaging modalities for inclusion in diagnostic criteria for AD.

Ependyma in Neurodegenerative Diseases, Radiation-Induced Brain Injury and as a Therapeutic Target for Neurotrophic Factors

The neuron loss caused by the progressive damage to the nervous system is proposed to be the main pathogenesis of neurodegenerative diseases. Ependyma is a layer of ciliated ependymal cells that participates in the formation of the brain-cerebrospinal fluid barrier (BCB). It functions to promotes the circulation of cerebrospinal fluid (CSF) and the material exchange between CSF and brain interstitial fluid. Radiation-induced brain injury (RIBI) shows obvious impairments of the blood-brain barrier (BBB). In the neuroinflammatory processes after acute brain injury, a large amount of complement proteins and infiltrated immune cells are circulated in the CSF to resist brain damage and promote substance exchange through the BCB. However, as the protective barrier lining the brain ventricles, the ependyma is extremely vulnerable to cytotoxic and cytolytic immune responses. When the ependyma is damaged, the integrity of BCB is destroyed, and the CSF flow and material exchange is affected, leading to brain microenvironment imbalance, which plays a vital role in the pathogenesis of neurodegenerative diseases. Epidermal growth factor (EGF) and other neurotrophic factors promote the differentiation and maturation of ependymal cells to maintain the integrity of the ependyma and the activity of ependymal cilia, and may have therapeutic potential in restoring the homeostasis of the brain microenvironment after RIBI or during the pathogenesis of neurodegenerative diseases.

Accelerated hypometabolism with disease progression associated with faster cognitive decline among amyloid positive patients

Objective

To evaluate the progression of brain glucose metabolism among participants with biological signature of Alzheimer's disease (AD) and its relevance to cognitive decline.

Method

We studied 602 amyloid positive individuals who underwent 18F-fluorodeoxyglucose PET (FDG-PET) scan, 18F-AV-45 amyloid PET (AV45-PET) scan, structural MRI scan and neuropsychological examination, including 116 cognitively normal (CN) participants, 314 participants diagnosed as mild cognitive impairment (MCI), and 172 participants diagnosed as AD dementia. The first FDG-PET scan satisfying the inclusion criteria was considered as the baseline scan. Cross-sectional analysis were conducted with the baseline FDG-PET data to compare the regional differences between diagnostic groups after adjusting confounding factors. Among these participants, 229 participants (55 CN, 139 MCI, and 35 AD dementia) had two-year follow-up FDG-PET data available. Regional glucose metabolism was computed and the progression rates of regional glucose metabolism were derived from longitudinal FDG-PET scans. Then the group differences of regional progression rates were examined to assess whether glucose metabolism deficit accelerates or becomes stable with disease progression. The association of cognitive decline rate with baseline regional glucose metabolism, and progression rate in longitudinal data, were evaluated.

Results

Participants with AD dementia showed substantial glucose metabolism deficit than CN and MCI at left hippocampus, in addition to the traditionally reported frontal and parietal-temporal lobe. More substantial metabolic change was observed with the contrast AD - MCI than the contrast MCI - CN, even after adjusting time duration since cognitive symptom onset. With the longitudinal data, glucose metabolism was observed to decline the most rapidly in the AD dementia group and at a slower rate in MCI. Lower regional glucose metabolism was correlated to faster cognitive decline rate with mild-moderate correlations, and the progression rate was correlated to cognitive decline rate with moderate-large correlations.

Discussion and conclusion

Hippocampus was identified to experience hypometabolism in AD pathology. Hypometabolism accelerates with disease progression toward AD dementia. FDG-PET, particularly longitudinal scans, could potentially help predict how fast cognition declines and assess the impact of treatment in interventional trials.

Connectome-based biophysics models of Alzheimer's disease diagnosis and prognosis

With the increasing prevalence of Alzheimer's disease (AD) among aging populations and the limited therapeutic options available to slow or reverse its progression, the need has never been greater for improved diagnostic tools for identifying patients in the preclinical and prodomal phases of AD. Biophysics models of the connectome-based spread of amyloid-beta (Aβ) and microtubule-associated protein tau (τ) have enjoyed recent success as tools for predicting the time course of AD-related pathological changes. However, given the complex etiology of AD, which involves not only connectome-based spread of protein pathology but also the interactions of many molecular and cellular players over multiple spatiotemporal scales, more robust, complete biophysics models are needed to better understand AD pathophysiology and ultimately provide accurate patient-specific diagnoses and prognoses. Here we discuss several areas of active research in AD whose insights can be used to enhance the mathematical modeling of AD pathology as well as recent attempts at developing improved connectome-based biophysics models. These efforts toward a comprehensive yet parsimonious mathematical description of AD hold great promise for improving both the diagnosis of patients at risk for AD and our mechanistic understanding of how AD progresses.

Can T1-Weighted Magnetic Resonance Imaging Significantly Improve Mini-Mental State Examination-Based Distinguishing Between Mild Cognitive Impairment and Early-Stage Alzheimer's Disease?

BACKGROUND

Detecting early-stage Alzheimer's disease (AD) is still problematic in clinical practice. This work aimed to find T1-weighted MRI-based markers for AD and mild cognitive impairment (MCI) to improve the screening process.

OBJECTIVE

Our assumption was to build a screening model that would be accessible and easy to use for physicians in their daily clinical routine.

METHODS

The multinomial logistic regression was used to detect status: AD, MCI, and normal control (NC) combined with the Bayesian information criterion for model selection. Several T1-weighted MRI-based radiomic features were considered explanatory variables in the prediction model.

RESULTS

The best radiomic predictor was the relative brain volume. The proposed method confirmed its quality by achieving a balanced accuracy of 95.18%, AUC of 93.25%, NPV of 97.93%, and PPV of 90.48% for classifying AD versus NC for the European DTI Study on Dementia (EDSD). The comparison of the two models: with the MMSE score only as an independent variable and corrected for the relative brain value and age, shows that the addition of the T1-weighted MRI-based biomarker improves the quality of MCI detection (AUC: 67.04% versus 71.08%) while maintaining quality for AD (AUC: 93.35% versus 93.25%). Additionally, among MCI patients predicted as AD inconsistently with the original diagnosis, 60% from ADNI and 76.47% from EDSD were re-diagnosed as AD within a 48-month follow-up. It shows that our model can detect AD patients a few years earlier than a standard medical diagnosis.

CONCLUSION

The created method is non-invasive, inexpensive, clinically accessible, and efficiently supports AD/MCI screening.

Alterations in Peripheral Metabolites as Key Actors in Alzheimer's Disease

Growing evidence supports that Alzheimer's disease (AD) could be regarded as a metabolic disease, accompanying central and peripheral metabolic disturbance. Nowadays, exploring novel and potentially alternative hallmarks for AD is needed. Peripheral metabolites based on blood and gut may provide new biochemical insights about disease mechanisms. These metabolites can influence brain energy homeostasis, maintain gut mucosal integrity, and regulate the host immune system, which may further play a key role in modulating the cognitive function and behavior of AD. Recently, metabolomics has been used to identify key AD-related metabolic changes and define metabolic changes during AD disease trajectory. This review aims to summarize the key blood- and microbial-derived metabolites that are altered in AD and identify the potential metabolic biomarkers of AD, which will provide future targets for precision therapeutic modulation.

Brain PET and Cerebrovascular Disease

Cerebrovascular disease encompasses a broad spectrum of diseases such as stroke, hemorrhage, and cognitive decline associated with vascular narrowing, obstruction, rupture, and inflammation, among other issues. Recent advances in hardware and software have led to improvements in brain PET. Although still in its infancy, machine learning using convolutional neural networks is gaining traction in this area, often with a focus on providing high-quality images with reduced noise using a shorter acquisition time or less radiation exposure for the patient.

Differences in resting state functional connectivity underlie visuomotor performance declines in older adults with a genetic risk (APOE ε4) for Alzheimer’s disease

Introduction

Non-standard visuomotor integration requires the interaction of large networks in the brain. Previous findings have shown that non-standard visuomotor performance is impaired in individuals with specific dementia risk factors (family history of dementia and presence of the APOE ε4 allele) in advance of any cognitive impairments. These findings suggest that visuomotor impairments are associated with early dementia-related brain changes. The current study examined the underlying resting state functional connectivity (RSFC) associated with impaired non-standard visuomotor performance, as well as the impacts of dementia family history, sex, and APOE status.

Methods

Cognitively healthy older adults (n = 48) were tested on four visuomotor tasks where reach and gaze were increasingly spatially dissociated. Participants who had a family history of dementia or the APOE ε4 allele were considered to be at an increased risk for AD. To quantify RSFC within networks of interest, an EPI sequence sensitive to BOLD contrast was collected. The networks of interest were the default mode network (DMN), somatomotor network (SMN), dorsal attention network (DAN), ventral attention network (VAN), and frontoparietal control network (FPN).

Results

Individuals with the ε4 allele showed abnormalities in RSFC between posterior DMN nodes that predicted poorer non-standard visuomotor performance. Specifically, multiple linear regression analyses revealed lower RSFC between the precuneus/posterior cingulate cortex and the left inferior parietal lobule as well as the left parahippocampal cortex. Presence of the APOE ε4 allele also modified the relationship between mean DAN RSFC and visuomotor control, where lower mean RSFC in the DAN predicted worse non-standard visuomotor performance only in APOE ε4 carriers. There were otherwise no effects of family history, APOE ε4 status, or sex on the relationship between RSFC and visuomotor performance for any of the other resting networks.

Conclusion

The preliminary findings provide insight into the impact of APOE ε4-related genetic risk on neural networks underlying complex visuomotor transformations, and demonstrate that the non-standard visuomotor task paradigm discussed in this study may be used as a non-invasive, easily accessible assessment tool for dementia risk.

Discovering new peripheral plasma biomarkers to identify cognitive decline in type 2 diabetes

Type 2 diabetes mellitus (T2DM) is an independent risk factor of Alzheimer's disease (AD), and thus identifying who among the increasing T2DM populations may develop into AD is important for early intervention. By using TMT-labeling coupled high-throughput mass spectrometry, we conducted a comprehensive plasma proteomic analysis in none-T2DM people (Ctrl, n = 30), and the age-/sex-matched T2DM patients with mild cognitive impairment (T2DM-MCI, n = 30) or T2DM without MCI (T2DM-nMCI, n = 25). The candidate biomarkers identified by proteomics and bioinformatics analyses were verified by ELISA, and their diagnostic capabilities were evaluated with machine learning. A total of 53 differentially expressed proteins (DEPs) were identified in T2DM-MCI compared with T2DM-nMCI patients. These DEPs were significantly enriched in multiple biological processes, such as amyloid neuropathies, CNS disorders, and metabolic acidosis. Among the DEPs, alpha-1-antitrypsin (SERPINA1), major viral protein (PRNP), and valosin-containing protein (VCP) showed strong correlation with AD high-risk genes APP, MAPT, APOE, PSEN1, and PSEN2. Also, the levels of PP2A cancer inhibitor (CIP2A), PRNP, corticotropin-releasing factor-binding protein (CRHBP) were significantly increased, while the level of VCP was decreased in T2DM-MCI patients compared with that of the T2DM-nMCI, and these changes were correlated with the Mini-Mental State Examination (MMSE) score. Further machine learning data showed that increases in PRNP, CRHBP, VCP, and rGSK-3β(T/S9) (ratio of total to serine-9-phosphorylated glycogen synthase kinase-3β) had the greatest power to identify mild cognitive decline in T2DM patients.

Current and Future Trends in Biomarkers for the Early Detection of Alzheimer's Disease in Asia: Expert Opinion

Alzheimer's disease (AD) poses a substantial healthcare burden in the rapidly aging Asian population. Early diagnosis of AD, by means of biomarkers, can lead to interventions that might alter the course of the disease. The amyloid, tau, and neurodegeneration (AT[N]) framework, which classifies biomarkers by their core pathophysiological features, is a biomarker measure of amyloid plaques and neurofibrillary tangles. Our current AD biomarker armamentarium, comprising neuroimaging biomarkers and cerebrospinal fluid biomarkers, while clinically useful, may be invasive and expensive and hence not readily available to patients. Several studies have also investigated the use of blood-based measures of established core markers for detection of AD, such as amyloid-β and phosphorylated tau. Furthermore, novel non-invasive peripheral biomarkers and digital biomarkers could potentially expand access to early AD diagnosis to patients in Asia. Despite the multiplicity of established and potential biomarkers in AD, a regional framework for their optimal use to guide early AD diagnosis remains lacking. A group of experts from five regions in Asia gathered at a meeting in March 2021 to review the current evidence on biomarkers in AD diagnosis and discuss best practice around their use, with the goal of developing practical guidance that can be implemented easily by clinicians in Asia to support the early diagnosis of AD. This article summarizes recent key evidence on AD biomarkers and consolidates the experts' insights into the current and future use of these biomarkers for the screening and early diagnosis of AD in Asia.

Plasma p-tau181 associated with structural changes in mild cognitive impairment

Alzheimer's disease (AD) is a progressive neurodegenerative disease associated with dementia and is a serious concern for the health of individuals and government health care systems worldwide. Gray matter atrophy and white matter damage are major contributors to cognitive deficits in AD patients, as demonstrated by magnetic resonance imaging (MRI). Many of these brain changes associated with AD begin to occur about 15 years before the onset of initial clinical symptoms. Therefore, it is critical to find biomarkers reflective of these brain changes associated with AD to identify this disease and monitor its prognosis and development. The increased plasma level of hyperphosphorylated tau 181 (p-tau181) has been recently considered a novel biomarker for the diagnosis of AD, preclinical AD, and mild cognitive impairment (MCI). In the current study, we examined the association of cerebrospinal fluid (CSF) and plasma levels of p-tau181 with structural brain changes in cortical thickness, cortical volume, surface area, and subcortical volume in MCI patients. In this cross-sectional study, we included the information of 461 MCI patients from the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort. The results of voxel-wise partial correlation analyses showed a significant negative correlation between the increased levels of plasma p-tau181, CSF total tau, and CSF p-tau181 with structural changes in widespread brain regions. These results provide evidence for the use of plasma p-tau181 as a diagnostic marker for structural changes in the brain associated with the early stages of AD and neurodegeneration.

Proteomic Markers and Early Prediction of Alzheimer's Disease

Alzheimer's disease (AD) is the most common socially significant neurodegenerative pathology, which currently affects more than 30 million elderly people worldwide. Since the number of patients grows every year and may exceed 115 million by 2050, and due to the lack of effective therapies, early prediction of AD remains a global challenge, solution of which can contribute to the timely appointment of a preventive therapy in order to avoid irreversible changes in the brain. To date, clinical assays for the markers of amyloidosis in cerebrospinal fluid (CSF) have been developed, which, in conjunction with the brain MRI and PET studies, are used either to confirm the diagnosis based on obligate clinical criteria or to predict the risk of AD developing at the stage of mild cognitive impairment (MCI). However, the problem of predicting AD at the asymptomatic stage remains unresolved. In this regard, the search for new protein markers and studies of proteomic changes in CSF and blood plasma are of particular interest and may consequentially identify particular pathways involved in the pathogenesis of AD. Studies of specific proteomic changes in blood plasma deserve special attention and are of increasing interest due to the much less invasive method of sample collection as compared to CSF, which is important when choosing the object for large-scale screening. This review briefly summarizes the current knowledge on proteomic markers of AD and considers the prospects of developing reliable methods for early identification of AD risk factors based on the proteomic profile.

Positron Emission Tomography-Based Assessment of Cognitive Impairment and Dementias, Critical Role of Fluorodeoxyglucose in such Settings

Positron emission tomography (PET) has been a key component in the diagnostic armamentarium for assessing neurodegenerative diseases such as Alzheimer or Parkinson disease. PET imaging has been useful for diagnosing these disorders, identifying their pathophysiology, and following their treatment. Further, PET imaging has been extensively used for both clinical and research purposes, particularly for helping with potential therapeutic approaches for managing neurodegenerative diseases. This article will review the current literature regarding PET imaging in patients with neurodegenerative disorders. This includes an evaluation of the most commonly used tracer fluorodeoxyglucose that measures cerebral glucose metabolism, tracers that assess neurotransmitter systems, and tracers designed to reveal disease-specific pathophysiological processes. With the continuing development of an expanding variety of radiopharmaceuticals, PET imaging will likely play a prominent role in future research and clinical applications for neurodegenerative diseases.

Deep Learning-Based Diagnosis of Alzheimer's Disease

Alzheimer's disease (AD), the most familiar type of dementia, is a severe concern in modern healthcare. Around 5.5 million people aged 65 and above have AD, and it is the sixth leading cause of mortality in the US. AD is an irreversible, degenerative brain disorder characterized by a loss of cognitive function and has no proven cure. Deep learning techniques have gained popularity in recent years, particularly in the domains of natural language processing and computer vision. Since 2014, these techniques have begun to achieve substantial consideration in AD diagnosis research, and the number of papers published in this arena is rising drastically. Deep learning techniques have been reported to be more accurate for AD diagnosis in comparison to conventional machine learning models. Motivated to explore the potential of deep learning in AD diagnosis, this study reviews the current state-of-the-art in AD diagnosis using deep learning. We summarize the most recent trends and findings using a thorough literature review. The study also explores the different biomarkers and datasets for AD diagnosis. Even though deep learning has shown promise in AD diagnosis, there are still several challenges that need to be addressed.

Shining Damaged Hearts: Immunotherapy-Related Cardiotoxicity in the Spotlight of Nuclear Cardiology

The emerging use of immunotherapies in cancer treatment increases the risk of immunotherapy-related cardiotoxicity. In contrast to conventional chemotherapy, these novel therapies have expanded the forms and presentations of cardiovascular damage to a broad spectrum from asymptomatic changes to fulminant short- and long-term complications in terms of cardiomyopathy, arrythmia, and vascular disease. In cancer patients and, particularly, cancer patients undergoing (immune-)therapy, cardio-oncological monitoring is a complex interplay between pretherapeutic risk assessment, identification of impending cardiotoxicity, and post-therapeutic surveillance. For these purposes, the cardio-oncologist can revert to a broad spectrum of nuclear cardiological diagnostic workup. The most promising commonly used nuclear medicine imaging techniques in relation to immunotherapy will be discussed in this review article with a special focus on the continuous development of highly specific molecular markers and steadily improving methods of image generation. The review closes with an outlook on possible new developments of molecular imaging and advanced image evaluation techniques in this exciting and increasingly growing field of immunotherapy-related cardiotoxicity.

Exploratory imaging outcomes of a phase 1b/2a clinical trial of allopregnanolone as a regenerative therapeutic for Alzheimer's disease: Structural effects and functional connectivity outcomes

Introduction

Allopregnanolone (ALLO), an endogenous neurosteroid, promoted neurogenesis and oligogenesis and restored cognitive function in animal models of Alzheimer's disease (AD). Based on these discovery research findings, we conducted a randomized-controlled phase 1b/2a multiple ascending dose trial of ALLO in persons with early AD (NCT02221622) to assess safety, tolerability, and pharmacokinetics. Exploratory imaging outcomes to determine whether ALLO impacted hippocampal structure, white matter integrity, and functional connectivity are reported.

Methods

Twenty-four individuals participated in the trial (n = 6 placebo; n = 18 ALLO) and underwent brain magnetic resonance imaging (MRI) before and after 12 weeks of treatment. Hippocampal atrophy rate was determined from volumetric MRI, computed as rate of change, and qualitatively assessed between ALLO and placebo sex, apolipoprotein E (APOE) ε4 allele, and ALLO dose subgroups. White matter microstructural integrity was compared between placebo and ALLO using fractional and quantitative anisotropy (QA). Changes in local, inter-regional, and network-level functional connectivity were also compared between groups using resting-state functional MRI.

Results

Rate of decline in hippocampal volume was slowed, and in some cases reversed, in the ALLO group compared to placebo. Gain of hippocampal volume was evident in APOE ε4 carriers (range: 0.6% to 7.8% increased hippocampal volume). Multiple measures of white matter integrity indicated evidence of preserved or improved integrity. ALLO significantly increased fractional anisotropy (FA) in 690 of 690 and QA in 1416 of 1888 fiber tracts, located primarily in the corpus callosum, bilateral thalamic radiations, and bilateral corticospinal tracts. Consistent with structural changes, ALLO strengthened local, inter-regional, and network level functional connectivity in AD-vulnerable regions, including the precuneus and posterior cingulate, and network connections between the default mode network and limbic system.

Discussion

Indicators of regeneration from previous preclinical studies and these exploratory MRI-based outcomes from this phase 1b/2a clinical cohort support advancement to a phase 2 proof-of-concept efficacy clinical trial of ALLO as a regenerative therapeutic for mild AD (REGEN-BRAIN study; NCT04838301).

Exosomes in Age-Related Cognitive Decline: Mechanistic Insights and Improving Outcomes

Aging is the most prominent risk factor for cognitive decline, yet behavioral symptomology and underlying neurobiology can vary between individuals. Certain individuals exhibit significant age-related cognitive impairments, while others maintain intact cognitive functioning with only minimal decline. Recent developments in genomic, proteomic, and functional imaging approaches have provided insights into the molecular and cellular substrates of cognitive decline in age-related neuropathologies. Despite the emergence of novel tools, accurately and reliably predicting longitudinal cognitive trajectories and improving functional outcomes for the elderly remains a major challenge. One promising approach has been the use of exosomes, a subgroup of extracellular vesicles that regulate intercellular communication and are easily accessible compared to other approaches. In the current review, we highlight recent findings which illustrate how the analysis of exosomes can improve our understanding of the underlying neurobiological mechanisms that contribute to cognitive variation in aging. Specifically, we focus on exosome-mediated regulation of miRNAs, neuroinflammation, and aggregate-prone proteins. In addition, we discuss how exosomes might be used to enhance individual patient outcomes by serving as reliable biomarkers of cognitive decline and as nanocarriers to deliver therapeutic agents to the brain in neurodegenerative conditions.

An optimized MRI and PET based clinical protocol for improving the differential diagnosis of geriatric depression and Alzheimer's disease

Amyloid positron emission tomography (PET) and hippocampal volume derived from magnetic resonance imaging may be useful clinical biomarkers for differentiating between geriatric depression and Alzheimer's disease (AD). Here we investigated the incremental value of using hippocampal volume and 18F-flutemetmol amyloid PET measures in tandem and sequentially to improve discrimination in unclassified participants. Two approaches were compared in 41 participants with geriatric depression and 27 participants with probable AD: (1) amyloid and hippocampal volume combined in one model and (2) classification based on hippocampal volume first and then subsequent stratification using standardized uptake value ratio (SUVR)-determined amyloid positivity. Hippocampal volume and amyloid SUVR were significant diagnostic predictors of depression (sensitivity: 95%, specificity: 89%). 51% of participants were correctly classified according to clinical diagnosis based on hippocampal volume alone, increasing to 87% when adding amyloid data (sensitivity: 94%, specificity: 78%). Our results suggest that hippocampal volume may be a useful gatekeeper for identifying depressed individuals at risk for AD who would benefit from additional amyloid biomarkers when available.

Monitoring Alzheimer's Disease Progression in Mild Cognitive Impairment Stage Using Machine Learning-Based FDG-PET Classification Methods

BACKGROUND

We previously introduced a machine learning-based Alzheimer's Disease Designation (MAD) framework for identifying AD-related metabolic patterns among neurodegenerative subjects.

OBJECTIVE

We sought to assess the efficiency of our MAD framework for tracing the longitudinal brain metabolic changes in the prodromal stage of AD.

METHODS

MAD produces subject scores using five different machine-learning algorithms, which include a general linear model (GLM), two different approaches of scaled subprofile modeling, and two different approaches of a support vector machine. We used our pre-trained MAD framework, which was trained based on metabolic brain features of 94 patients with AD and 111 age-matched cognitively healthy (CH) individuals. The MAD framework was applied on longitudinal independent test sets including 54 CHs, 51 stable mild cognitive impairment (sMCI), and 39 prodromal AD (pAD) patients at the time of the clinical diagnosis of AD, and two years prior.

RESULTS

The GLM showed excellent performance with area under curve (AUC) of 0.96 in distinguishing sMCI from pAD patients at two years prior to the time of the clinical diagnosis of AD while other methods showed moderate performance (AUC: 0.7-0.8). Significant annual increment of MAD scores were identified using all five algorithms in pAD especially when it got closer to the time of diagnosis (p < 0.001), but not in sMCI. The increased MAD scores were also significantly associated with cognitive decline measured by Mini-Mental State Examination in pAD (q < 0.01).

CONCLUSION

These results suggest that MAD may be a relevant tool for monitoring disease progression in the prodromal stage of AD.

Cognitive Impairment in Acute Heart Failure: Narrative Review

Cognitive impairment (CI) represents a common but often veiled comorbidity in patients with acute heart failure (AHF) that deserves more clinical attention. In the AHF setting, it manifests as varying degrees of deficits in one or more cognitive domains across a wide spectrum ranging from mild CI to severe global neurocognitive disorder. On the basis of the significant negative implications of CI on quality of life and its overwhelming association with poor outcomes, there is a compelling need for establishment of detailed consensus guidelines on cognitive screening methods to be systematically implemented in the population of patients with heart failure (HF). Since limited attention has been drawn exclusively on the field of CI in AHF thus far, the present narrative review aims to shed further light on the topic. The underlying pathophysiological mechanisms of CI in AHF remain poorly understood and seem to be multifactorial. Different pathophysiological pathways may come into play, depending on the clinical phenotype of AHF. There is some evidence that cognitive decline closely follows the perturbations incurred across the long-term disease trajectory of HF, both along the time course of stable chronic HF as well as during episodes of HF exacerbation. CI in AHF remains a rather under recognized scientific field that poses many challenges, since there are still many unresolved issues regarding cognitive changes in patients hospitalized with AHF that need to be thoroughly addressed.

Secondary metabolites of Galactomyces geotrichum from Laminaria japonica ameliorate cognitive deficits and brain oxidative stress in D-galactose induced Alzheimer's disease mouse model

Accumulating evidences have shown the beneficial effects of natural products for Alzheimer's disease (AD) treatment. The present study was designed to investigate the neuroprotective effects of secondary metabolites of Galactomyces geotrichum (SMGG) on D-galactose induced AD mice. SMGG was extracted and its toxicological evaluation was conducted. To explore the neuroprotective mechanism responsible for anti-AD activity of SMGG, spatial learning and memory behavioral, oxidative stress levels, acetylcholinesterase and choline acetyltransferase activity assays were employed. The AD mice received SMGG treatment exhibited significant improvement in cognitive performance, enhanced antioxidant capacity, decreased acetylcholinesterase activity and increased choline acetyltransferase activity. Meanwhile, SMGG had no toxicity and seven compounds were separated from it: 7,8-dimethyl-iso-alloxazine, 1-methyl-3-benzyl-6-(4-hydroxybenzyl)-2,5-piperzainedione, cyclo-(Phe-Pro), cyclo-(Leu-Pro), cyclo-(Pro-Gly), cyclo-(Gly-Leu) and uracil, respectively. Overall, these data suggested that SMGG protects the brain against D-galactose induced cognitive impairment, oxidative damages and acetylcholine content decrease in AD mice.

Psychedelics as Novel Therapeutics in Alzheimer's Disease: Rationale and Potential Mechanisms

Serotonin 2A receptor (5-HT_2AR) agonist "classic psychedelics" are drawing increasing interest as potential mental health treatments. Recent work suggests psychedelics can exert persisting anxiolytic and antidepressant effects lasting up to several months after a single administration. Data indicate acute subjective drug effects as important psychological factors involved in observed therapeutic benefits. Additionally, animal models have shown an important role for 5-HT_2AR agonists in modulating learning and memory function with relevance for Alzheimer's Disease (AD) and related dementias. A number of biological mechanisms of action are under investigation to elucidate 5-HT_2AR agonists' therapeutic potential, including enhanced neuroplasticity, anti-inflammatory effects, and alterations in brain functional connectivity. These diverse lines of research are reviewed here along with a discussion of AD pathophysiology and neuropsychiatric symptoms to highlight classic psychedelics as potential novel pharmacotherapies for patients with AD. Human clinical research suggests a possible role for high-dose psychedelic administration in symptomatic treatment of depressed mood and anxiety in early-stage AD. Preclinical data indicate a potential for low- or high-dose psychedelic treatment regimens to slow or reverse brain atrophy, enhance cognitive function, and slow progression of AD. In conclusion, rationale and potential approaches for preliminary research with psychedelics in patients with AD are presented, and ramifications of this line of investigation for development of novel AD treatments are discussed.

Vascular endothelial growth factor associated dissimilar cerebrovascular phenotypes in two different mouse models of Alzheimer's Disease

Vascular perturbations and cerebral hypometabolism are emerging as important components of Alzheimer's disease (AD). While various in vivo imaging modalities have been designed to detect changes of cerebral perfusion and metabolism in AD patients and animal models, study results were often heterogenous with respect to imaging techniques and animal models. We therefore evaluated cerebral perfusion and glucose metabolism of two popular transgenic AD mouse strains, TgCRND8 and 5xFAD, at 7 and 12 months-of-age under identical conditions and analyzed possible molecular mechanisms underlying heterogeneous cerebrovascular phenotypes. Results revealed disparate findings in these two strains, displaying important aspects of AD progression. TgCRND8 mice showed significantly decreased cerebral blood flow and glucose metabolism with unchanged cerebral blood volume (CBV) at 12 months-of-age whereas 5xFAD mice showed unaltered glucose metabolism with significant increase in CBV at 12 months-of-age and a biphasic pattern of early hypoperfusion followed by a rebound to normal cerebral blood flow in late disease. Finally, immunoblotting assays suggested that VEGF dependent vascular tone change may restore normoperfusion and increase CBV in 5xFAD.

Bias-generating factors in biofluid amyloid-β measurements for Alzheimer's disease diagnosis

Alzheimer's disease (AD) is the most prevalent cause of dementia worldwide, yet the dearth of readily accessible diagnostic biomarkers is a substantial hindrance towards progressing to effective preventive and therapeutic approaches. Due to a long delay between cerebral amyloid-β (Aβ) accumulation and the onset of cognitive impairments, biomarkers that reflect Aβ pathology and enable routine screening for disease progression are of urgent need for application in the clinical diagnosis of AD. According to accumulating evidences, cerebrospinal fluid (CSF) and plasma offer windows to the brain as they allow monitoring of biochemical changes in the brain. Considering the high availability and accuracy in depicting Aβ deposition in the brain, Aβ levels in CSF and plasma are regarded as promising fluid biomarkers for the diagnosis of AD patients at an early stage. However, clinical data with intra- and interindividual variations in the concentrations of CSF and plasma Aβ implicate the need to reevaluate current Aβ detection methods and establish a standardized operating procedure. Therefore, this review introduces three bias-generating factors in biofluid Aβ measurement that may hamper the accurate Aβ quantification and how such complications can be overcome for the widespread implementation of fluid Aβ detection in clinical practice.

The effects of cerebral amyloidopathy on regional glucose metabolism in older adults with depression and mild cognitive impairment while performing memory tasks

Co-occurring depression and mild cognitive impairment (MCI) in older adults are important because they have a high risk of conversion to dementia. In the present study, task-related F-18 fluorodeoxyglucose positron emission tomography (FDG-PET) was used to analyse older adults with concomitant depression and MCI. We recruited 20 older adults with simultaneous depression and MCI and 10 older adults with normal cognition (NC). The Verbal Paired Associates test and digit span test were used for the task-related FDG-PET. The 20 older adults with depression and MCI were classified into two groups based on the F-18 florbetaben PET results: depressed MCI patients with (LLD-MCI-A[+]; n = 11) and without amyloid accumulation (LLD-MCI-A[-]; n = 9). Reduced regional cerebral glucose metabolism (rCMglc) in the left superior frontal region was observed in the LLD-MCI-A(-) group compared with the NC group. Analyses of the NC and LLD-MCI-A(+) groups showed significantly decreased rCMglc in the right inferior parietal and left middle frontal regions in the LLD-MCI-A(+) group. rCMglc in the left precuneus was lower in the LLD-MCI-A(+) group than in the LLD-MCI-A(-) group. Significant correlations between the rCMglc in the right inferior parietal/left precuneus regions and memory task scores were observed based on correlation analyses of NC and LLD-MCI-A(+) groups. The findings in the present study indicate the presence of amyloid accumulation influences glucose metabolism in depressed elderly subjects with MCI while performing cognitive tasks. Task-related FDG-PET examinations may help differentiate MCI associated with depression from comorbid depression in patients with prodromal Alzheimer's disease.

Polygenic Hazard Score Associated Multimodal Brain Networks Along the Alzheimer's Disease Continuum

BACKGROUND

Late-onset Alzheimer's disease (AD) is a polygenic neurodegenerative disease. Identifying the neuroimaging phenotypes behind the genetic predisposition of AD is critical to the understanding of AD pathogenesis. Two major questions which previous studies have led to are: (1) should the general "polygenic hazard score" (PHS) be a good choice to identify the individual genetic risk for AD; and (2) should researchers also include inter-modality relationships in the analyses considering these may provide complementary information about the AD etiology.

METHODS

We collected 88 healthy controls, 77 patients with mild cognitive impairment (MCI), and 22 AD patients to simulate the AD continuum included from the ADNI database. PHS-guided multimodal fusion was used to investigate the impact of PHS on multimodal brain networks in AD-continuum by maximizing both inter-modality association and reference-modality correlation. Fractional amplitude of low frequency fluctuations, gray matter (GM) volume, and amyloid standard uptake value ratios were included as neuroimaging features. Eventually, the changes in neuroimaging features along AD continuum were investigated, and relationships between cognitive performance and identified PHS associated multimodal components were established.

RESULTS

We found that PHS was associated with multimodal brain networks, which showed different functional and structural impairments under increased amyloid deposits. Notably, along with AD progression, functional impairment occurred before GM atrophy, amyloid deposition started from the MCI stage and progressively increased throughout the disease continuum.

CONCLUSION

PHS is associated with multi-facets of brain impairments along the AD continuum, including cognitive dysfunction, pathological deposition, which might underpin the AD pathogenesis.

Cost-effectiveness of using amyloid positron emission tomography in individuals with mild cognitive impairment

Background

Amyloid positron emission tomography (PET) makes it possible to diagnose Alzheimer’s disease (AD) in its prodromal phase including mild cognitive impairment (MCI). This study evaluated the cost-effectiveness of including amyloid-PET for assessing individuals with MCI.

Methods

The target population was 60-year-old patients who were diagnosed with MCI. We constructed a Markov model for the natural history of AD with the amyloid positivity (AP). Because amyloid-PET can detect the AP MCI state, AD detection can be made faster by reducing the follow-up interval for a high-risk group. The health outcomes were evaluated in quality-adjusted life years (QALYs) and the final results of cost-effectiveness analysis were presented in the form of the Incremental Cost-Effectiveness Ratio (ICER). To handle parameter uncertainties, one-way sensitivity analyses for various variables were performed.

Results

Our model showed that amyloid-PET increased QALYs by 0.003 in individuals with MCI. The estimated additional costs for adopting amyloid-PET amounted to a total of 1250 USD per patient when compared with the cost when amyloid-PET is not adopted. The ICER was 3,71,545 USD per QALY. According to the sensitivity analyses, treatment effect of Donepezil and virtual intervention effect in MCI state were the most influential factors.

Conclusions

In our model, using amyloid-PET at the MCI stage was not cost-effective. Future advances in management of cognitive impairment would enhance QALYs, and consequently improve cost-effectiveness.

Differential trajectories of hypometabolism across cognitively-defined Alzheimer's disease subgroups

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Cognitive-subgroups can be identified among individuals with AD dementia.

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Subgroup-specific patterns and longitudinal trajectories of hypometabolism observed.

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Regional hypometabolism matched respective cognitive profiles of subgroups.

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Cognitive-classification yields biologically distinct subgroups.

Disentangling biologically distinct subgroups of Alzheimer’s disease (AD) may facilitate a deeper understanding of the neurobiology underlying clinical heterogeneity. We employed longitudinal [¹⁸F]FDG-PET standardized uptake value ratios (SUVRs) to map hypometabolism across cognitively-defined AD subgroups. Participants were 384 amyloid-positive individuals with an AD dementia diagnosis from ADNI who had a total of 1028 FDG-scans (mean time between first and last scan: 1.6 ± 1.8 years). These participants were categorized into subgroups on the basis of substantial impairment at time of dementia diagnosis in a specific cognitive domain relative to the average across domains. This approach resulted in groups of AD-Memory (n = 135), AD-Executive (n = 8), AD-Language (n = 22), AD-Visuospatial (n = 44), AD-Multiple Domains (n = 15) and AD-No Domains (for whom no domain showed substantial relative impairment; n = 160). Voxelwise contrasts against controls revealed that all AD-subgroups showed progressive hypometabolism compared to controls across temporoparietal regions at time of AD diagnosis. Voxelwise and regions-of-interest (ROI)-based linear mixed model analyses revealed there were also subgroup-specific hypometabolism patterns and trajectories. The AD-Memory group had more pronounced hypometabolism compared to all other groups in the medial temporal lobe and posterior cingulate, and faster decline in metabolism in the medial temporal lobe compared to AD-Visuospatial. The AD-Language group had pronounced lateral temporal hypometabolism compared to all other groups, and the pattern of metabolism was also more asymmetrical (left < right) than all other groups. The AD-Visuospatial group had faster decline in metabolism in parietal regions compared to all other groups, as well as faster decline in the precuneus compared to AD-Memory and AD-No Domains. Taken together, in addition to a common pattern, cognitively-defined subgroups of people with AD dementia show subgroup-specific hypometabolism patterns, as well as differences in trajectories of metabolism over time. These findings provide support to the notion that cognitively-defined subgroups are biologically distinct.

Neuronal Hyperexcitability in APPSWE/PS1dE9 Mouse Models of Alzheimer's Disease

Transgenic mouse models serve a better understanding of Alzheimer's disease (AD) pathogenesis and its consequences on neuronal function. Well-known and broadly used AD models are APPswe/PS1dE9 mice, which are able to reproduce features of amyloid-β (Aβ) plaque formations as well as neuronal dysfunction as reflected in electrophysiological recordings of neuronal hyperexcitability. The most prominent findings include abnormal synaptic function and synaptic reorganization as well as changes in membrane threshold and spontaneous neuronal firing activities leading to generalized excitation-inhibition imbalances in larger neuronal circuits and networks. Importantly, these findings in APPswe/PS1dE9 mice are at least partly consistent with results of electrophysiological studies in humans with sporadic AD. This underscores the potential to transfer mechanistic insights into amyloid related neuronal dysfunction from animal models to humans. This is of high relevance for targeted downstream interventions into neuronal hyperexcitability, for example based on repurposing of existing antiepileptic drugs, as well as the use of combinations of imaging and electrophysiological readouts to monitor effects of upstream interventions into amyloid build-up and processing on neuronal function in animal models and human studies. This article gives an overview on the pathogenic and methodological basis for recording of neuronal hyperexcitability in AD mouse models and on key findings in APPswe/PS1dE9 mice. We point at several instances to the translational perspective into clinical intervention and observation studies in humans. We particularly focus on bi-directional relations between hyperexcitability and cerebral amyloidosis, including build-up as well as clearance of amyloid, possibly related to sleep and so called glymphatic system function.

18 F-FDG PET/CT use in functional assessment of the testes: A systematic review

INTRODUCTION

Our study analysed previous studies employing positron emission tomography with co-registered computer tomography (PET/CT) in andrological patient evaluation and assessed the differences in 2-[¹⁸ F]F-fluoro-2'-deoxyglucose (FDG) uptake between three groups: healthy testes, benign and malignant testicular pathology.

METHODS

Medline and Embase were systematically searched for studies involving FDG-PET/CT imaging of testes with results expressed as mean standardised uptake value (SUV_mean ). A one-way ANOVA was used to compare SUV_mean between three groups. All papers assessing andrological parameters were pooled to compare fertility data.

RESULTS

Seventeen studies, including three relating to fertility diagnosis, with a total of 830 patients, were included in the review. One-way ANOVA showed a statistical difference between mean values of tracer SUV_mean in healthy and malignant testes (Dif. = -2.77, 95% CI = -4.32 to 1.21, p < 0.01) as well as benign and malignant (Dif. = -2.95, 95% CI = -4.33 to -1.21, p < 0.01) but no difference between healthy and benign (Dif. = 0.19, 95% CI = -0.96 to 1.33, p = 0.90). There is some evidence to suggest that FDG uptake and testicular volume are positively correlated to total sperm count, sperm concentration and sperm motility and that germ cells are likely to account for the majority of testicular FDG accumulation.

CONCLUSION

Our findings indicate that malignant testicular lesions demonstrate a significantly higher FDG uptake than benign testicular lesions or healthy testes. Some evidence also suggests that FDG-PET could visualise metabolic activity and thus spermatogenesis; however more studies are required to determine whether FDG-PET could also be used to diagnose infertility. Further studies should focus on correlating both sex hormone-serum levels and semen analysis results with imaging data.

Platelet biomarkers for a descending cognitive function: A proteomic approach

Memory loss is the most common clinical sign in Alzheimer's disease (AD); thus, searching for peripheral biomarkers to predict cognitive decline is promising for early diagnosis of AD. As platelets share similarities to neuron biology, it may serve as a peripheral matrix for biomarkers of neurological disorders. Here, we conducted a comprehensive and in-depth platelet proteomic analysis using TMT-LC-MS/MS in the populations with mild cognitive impairment (MCI, MMSE = 18-23), severe cognitive impairments (AD, MMSE = 2-17), and the age-/sex-matched normal cognition controls (MMSE = 29-30). A total of 360 differential proteins were detected in MCI and AD patients compared with the controls. These differential proteins were involved in multiple KEGG pathways, including AD, AMP-activated protein kinase (AMPK) pathway, telomerase RNA localization, platelet activation, and complement activation. By correlation analysis with MMSE score, three positively correlated pathways and two negatively correlated pathways were identified to be closely related to cognitive decline in MCI and AD patients. Partial least squares discriminant analysis (PLS-DA) showed that changes of nine proteins, including PHB, UQCRH, CD63, GP1BA, FINC, RAP1A, ITPR1/2, and ADAM10 could effectively distinguish the cognitively impaired patients from the controls. Further machine learning analysis revealed that a combination of four decreased platelet proteins, that is, PHB, UQCRH, GP1BA, and FINC, was most promising for predicting cognitive decline in MCI and AD patients. Taken together, our data provide a set of platelet biomarkers for predicting cognitive decline which may be applied for the early screening of AD.

Visual versus semiquantitative analysis of 18F- fluorodeoxyglucose-positron emission tomography brain images in patients with dementia

Various studies have reported to the superiority of semiquantitative (SQ) analysis over visual analysis in detecting metabolic changes in the brain. In this study, we aimed to determine the limitations of SQ analysis programs and the current status of ¹⁸F- fluorodeoxyglucose (FDG)-positron emission tomography (PET) scan in dementia. ¹⁸F- FDG-PET/computed tomography (CT) brain images of 39 patients with a history of dementia were analyzed both visually and semiquantitatively. Using the visually markedly abnormal ¹⁸F- FDG-PET images as standard, we wanted to test the accuracy of two commercially available SQ analysis programs. SQ analysis results were classified as matching, partially matching and nonmatching with visually markedly abnormal studies. On visual analysis, ¹⁸F- FDG-PET showed marked regional hypometabolism in 19 patients, mild abnormalities in 8 and was normal in 12 patients. SQ analysis-1 results matched with visual analysis in 8 patients (42.1%) and partially matched in 11. SQ analysis-2 findings matched with visual analysis in 11 patients (57.8%) and partially matched in 7 and did not match in 1. Marked regional hypometabolism was either on the left side of the brain or was more significant on the left than the right in 63% of patients. Preservation of metabolism in sensorimotor cortex was seen in various dementia subtypes. Reviewing images in color scale and maximum intensity projection (MIP) image was helpful in demonstrating and displaying regional abnormalities, respectively. SQ analysis provides less accurate results as compared to visual analysis by experts. Due to suboptimal image registration and selection of brain areas, SQ analysis provides inaccurate results, particularly in small areas and areas in close proximity. Image registration and selection of areas with SQ programs should be checked carefully before reporting the results.