Brain PET in the diagnosis of Alzheimer's disease

Nanotechnology: A Promising Targeted Drug Delivery System for Brain Tumours and Alzheimer's Disease

Nanotechnology is the process of modulating shape and size at the nanoscale to design and manufacture structures, devices, and systems. Nanotechnology's prospective breakthroughs are incredible, and some cannot even be comprehended right now. The blood-brain barrier, which is a prominent physiological barrier in the brain, limits the adequate elimination of malignant cells by changing the concentration of therapeutic agents at the target tissue. Nanotechnology has sparked interest in recent years as a way to solve these issues and improve drug delivery. Inorganic and organic nanomaterials have been found to be beneficial for bioimaging approaches and controlled drug delivery systems. Brain cancer (BC) and Alzheimer's disease (AD) are two of the prominent disorders of the brain. Even though the pathophysiology and pathways for both disorders are different, nanotechnology with common features can deliver drugs over the BBB, advancing the treatment of both disorders. This innovative technology could provide a foundation for combining diagnostics, treatments, and delivery of targeted drugs to the tumour site, further supervising the response and designing and delivering materials by employing atomic and molecular elements. There is currently limited treatment for Alzheimer's disease, and reversing further progression is difficult. Recently, various nanocarriers have been investigated to improve the bioavailability and efficacy of many AD treatment drugs. Nanotechnology-assisted drugs can penetrate the BBB and reach the target tissue. However, further research is required in this field to ensure the safety and efficacy of drug-loaded nanoparticles. The application of nanotechnology in the diagnosis and treatment of brain tumours and Alzheimer's disease is briefly discussed in this review.

PET Imaging of Fructose Metabolism in a Rodent Model of Neuroinflammation with 6-[18F]fluoro-6-deoxy-D-fructose

Fluorine-18 labeled 6-fluoro-6-deoxy-D-fructose (6-[¹⁸F]FDF) targets the fructose-preferred facilitative hexose transporter GLUT5, which is expressed predominantly in brain microglia and activated in response to inflammatory stimuli. We hypothesize that 6-[¹⁸F]FDF will specifically image microglia following neuroinflammatory insult. 6-[¹⁸F]FDF and, for comparison, [¹⁸F]FDG were evaluated in unilateral intra-striatal lipopolysaccharide (LPS)-injected male and female rats (50 µg/animal) by longitudinal dynamic PET imaging in vivo. In LPS-injected rats, increased accumulation of 6-[¹⁸F]FDF was observed at 48 h post-LPS injection, with plateaued uptake (60-120 min) that was significantly higher in the ipsilateral vs. contralateral striatum (0.985 ± 0.047 and 0.819 ± 0.033 SUV, respectively; p = 0.002, n = 4M/3F). The ipsilateral-contralateral difference in striatal 6-[¹⁸F]FDF uptake expressed as binding potential (BP_SRTM) peaked at 48 h (0.19 ± 0.11) and was significantly decreased at one and two weeks. In contrast, increased [¹⁸F]FDG uptake in the ipsilateral striatum was highest at one week post-LPS injection (BP_SRTM = 0.25 ± 0.06, n = 4M). Iba-1 and GFAP immunohistochemistry confirmed LPS-induced activation of microglia and astrocytes, respectively, in ipsilateral striatum. This proof-of-concept study revealed an early response of 6-[¹⁸F]FDF to neuroinflammatory stimuli in rat brain. 6-[¹⁸F]FDF represents a potential PET radiotracer for imaging microglial GLUT5 density in brain with applications in neuroinflammatory and neurodegenerative diseases.

Advanced Diagnostic Technique for Alzheimer's Disease using MRI Top-Ranked Volume and Surface-based Features

Background

Alzheimer's disease (AD) is the most dominant type of dementia that has not been treated completely yet. Few Alzheimer's patients are correctly diagnosed on time. Therefore, diagnostic tools are needed for better and more efficient diagnoses.

Objective

This study aimed to develop an efficient automated method to differentiate Alzheimer's patients from normal elderly and present the essential features with accurate Alzheimer's diagnosis.

Material and Methods

In this analytical study, 154 Magnetic Resonance Imaging (MRI) scans were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database, preprocessed, and normalized by the head size for extracting features (volume, cortical thickness, Sulci depth, and Gyrification Index Features (GIF). Relief-F algorithm, t-test, and one way-ANOVA were used for feature ranking to obtain the most effective features representing the AD for the classification process. Finally, in the classification step, four classifiers were used with 10 folds cross-validation as follows: Gaussian Support Vector Machine (GSVM), Linear Support Vector Machine (LSVM), Weighted K-Nearest Neighbors (W-KNN), and Decision Tree algorithm.

Results

The LSVM classifier and W-KNN produce a testing accuracy of 100% with only seven features. Additionally, GSVM and decision tree produce a testing accuracy of 97.83% and 93.48%, respectively.

Conclusion

The proposed system represents an automatic and highly accurate AD detection with a few reliable and effective features and minimum time.

Neuroimaging analyses from a randomized, controlled study to evaluate plasma exchange with albumin replacement in mild-to-moderate Alzheimer's disease: additional results from the AMBAR study

PURPOSE

This study was designed to detect structural and functional brain changes in Alzheimer's disease (AD) patients treated with therapeutic plasma exchange (PE) with albumin replacement, as part of the recent AMBAR phase 2b/3 clinical trial.

METHODS

Mild-to-moderate AD patients were randomized into four arms: three arms receiving PE with albumin (one with low-dose albumin, and two with low/high doses of albumin alternated with IVIG), and a placebo (sham PE) arm. All arms underwent 6 weeks of weekly conventional PE followed by 12 months of monthly low-volume PE. Magnetic resonance imaging (MRI) volumetric analyses and regional and statistical parametric mapping (SPM) analysis on 18F-fluorodeoxyglucose positron emission tomography (18FDG-PET) were performed.

RESULTS

MRI analyses (n = 198 patients) of selected subcortical structures showed fewer volume changes from baseline to final visit in the high albumin + IVIG treatment group (p < 0.05 in 3 structures vs. 4 to 9 in other groups). The high albumin + IVIG group showed no statistically significant reduction of right hippocampus. SPM 18FDG-PET analyses (n = 213 patients) showed a worsening of metabolic activity in the specific areas affected in AD (posterior cingulate, precuneus, and parieto-temporal regions). The high-albumin + IVIG treatment group showed the greatest metabolic stability over the course of the study, i.e., the smallest percent decline in metabolism (MaskAD), and least progression of defect compared to placebo.

CONCLUSIONS

PE with albumin replacement was associated with fewer deleterious changes in subcortical structures and less metabolic decline compared to the typical of the progression of AD. This effect was more marked in the group treated with high albumin + IVIG.

TRIAL REGISTRATION

(AMBAR trial registration: EudraCT#: 2011-001,598-25; ClinicalTrials.gov ID: NCT01561053).

Electrochemical immunosensor based on superwettable microdroplet array for detecting multiple Alzheimer’s disease biomarkers

Alzheimer’s disease (AD) is a neurodegenerative disease caused by neurons damage in the brain, and it poses a serious threat to human life and health. No efficient treatment is available, but early diagnosis, discovery, and intervention are still crucial, effective strategies. In this study, an electrochemical sensing platform based on a superwettable microdroplet array was developed to detect multiple AD biomarkers containing Aβ40, Aβ42, T-tau, and P-tau181 of blood. The platform integrated a superwettable substrate based on nanoAu-modified vertical graphene (VG@Au) into a working electrode, which was mainly used for droplet sample anchoring and electrochemical signal generation. In addition, an electrochemical micro-workstation was used for signals conditioning. This superwettable electrochemical sensing platform showed high sensitivity and a low detection limit due to its excellent characteristics such as large specific surface, remarkable electrical conductivity, and good biocompatibility. The detection limit for Aβ40, Aβ42, T-tau, and P-tau181 were 0.064, 0.012, 0.039, and 0.041 pg/ml, respectively. This study provides a promising method for the early diagnosis of AD.

The Role of Bioenergetics in Neurodegeneration

Bioenergetic and mitochondrial dysfunction are common hallmarks of neurodegenerative diseases. Decades of research describe how genetic and environmental factors initiate changes in mitochondria and bioenergetics across Alzheimer's disease (AD), Parkinson's disease (PD), and amyotrophic lateral sclerosis (ALS). Mitochondria control many cellular processes, including proteostasis, inflammation, and cell survival/death. These cellular processes and pathologies are common across neurodegenerative diseases. Evidence suggests that mitochondria and bioenergetic disruption may drive pathological changes, placing mitochondria as an upstream causative factor in neurodegenerative disease onset and progression. Here, we discuss evidence of mitochondrial and bioenergetic dysfunction in neurodegenerative diseases and address how mitochondria can drive common pathological features of these diseases.

Alzheimer's disease: a scoping review of biomarker research and development for effective disease diagnosis

INTRODUCTION

Alzheimer's disease (AD) is regarded as the foremost reason for neurodegeneration that prominently affects the geriatric population. Characterized by extracellular accumulation of amyloid-beta (Aβ), intracellular aggregation of hyperphosphorylated tau (p-tau), and neuronal degeneration that causes impairment of memory and cognition. Amyloid/tau/neurodegeneration (ATN) classification is utilized for research purposes and involves amyloid, tau, and neuronal injury staging through MRI, PET scanning, and CSF protein concentration estimations. CSF sampling is invasive, and MRI and PET scanning requires sophisticated radiological facilities which limit its widespread diagnostic use. ATN classification lacks effectiveness in preclinical AD.

AREAS COVERED

This publication intends to collate and review the existing biomarker profile and the current research and development of a new arsenal of biomarkers for AD pathology from different biological samples, microRNA (miRNA), proteomics, metabolomics, artificial intelligence, and machine learning for AD screening, diagnosis, prognosis, and monitoring of AD treatments.

EXPERT OPINION

It is an accepted observation that AD-related pathological changes occur over a long period of time before the first symptoms are observed providing ample opportunity for detection of biological alterations in various biological samples that can aid in early diagnosis and modify treatment outcomes.

Convolution Neural Networks for the Automatic Segmentation of 18F-FDG PET Brain as an Aid to Alzheimer’s Disease Diagnosis

Our work aims to exploit deep learning (DL) models to automatically segment diagnostic regions involved in Alzheimer’s disease (AD) in 18F-fluorodeoxyglucose positron emission tomography (18F-FDG PET) volumetric scans in order to provide a more objective diagnosis of this disease and to reduce the variability induced by manual segmentation. The dataset used in this study consists of 102 volumes (40 controls, 39 with established Alzheimer’s disease (AD), and 23 with established mild cognitive impairment (MCI)). The ground truth was generated by an expert user who identified six regions in original scans, including temporal lobes, parietal lobes, and frontal lobes. The implemented architectures are the U-Net3D and V-Net networks, which were appropriately adapted to our data to optimize performance. All trained segmentation networks were tested on 22 subjects using the Dice similarity coefficient (DSC) and other similarity indices, namely the overlapping area coefficient (AOC) and the extra area coefficient (EAC), to evaluate automatic segmentation. The results of each labeled brain region demonstrate an improvement of 50%, with DSC from about 0.50 for V-Net-based networks to about 0.77 for U-Net3D-based networks. The best performance was achieved by using U-Net3D, with DSC on average equal to 0.76 for frontal lobes, 0.75 for parietal lobes, and 0.76 for temporal lobes. U-Net3D is very promising and is able to segment each region and each class of subjects without being influenced by the presence of hypometabolic regions.

Diagnosis of coexistent neurodegenerative dementias in multiple sclerosis

Among people with multiple sclerosis, cognitive impairment occurs commonly and is a potent predictor of disability. Some multiple sclerosis patients present with severe cognitive impairment, and distinguishing multiple sclerosis-related cognitive impairment from co-existent progressive neurodegenerative diseases such as Alzheimer disease poses a diagnostic challenge. The use of biomarkers such as PET and CSF proteins may facilitate this distinction. The study was a retrospective, descriptive study on convenience samples of separate cohorts, one of cognitively impaired multiple sclerosis patients evaluated on autopsy to demonstrate coincidence of both multiple sclerosis and neurodegenerative cognitive diseases. The second cohort were cognitively impaired multiple sclerosis patients evaluated by biomarker to investigate possible additional neurodegenerative cognitive disorders contributing to the cognitive impairment. We investigated selected biomarkers among 31 severely impaired patients (biomarker cohort) and 12 severely impaired patients assessed at autopsy and selected 24 (23 biomarker cohort, 1 autopsy cohort) had comprehensive neurocognitive testing. Biomarker cohort investigations included 18F-Fluorodeoxyglucose PET and/or CSF amyloid Aβ1-42, phospho-tau and total tau levels. The autopsy cohort was evaluated with comprehensive neuropathological assessment for aetiology of cognitive impairment. The cohorts shared similar sex, age at multiple sclerosis onset and multiple sclerosis clinical course. The autopsy-cohort patients were older at diagnosis (69.5 versus 57 years, P = 0.006), had longer disease duration [median (range) 20 years (3–59) versus 9 (1–32), P = 0.001] and had more impaired bedside mental status scores at last follow-up [Kokmen median (range) 23 (1–38) versus 31 (9–34) P = 0.01]. Autopsy-cohort patients confirmed, or excluded, coexistent neurogenerative disease by neuropathology gold standard. Most biomarker-cohort patients had informative results evaluating coexistent neurogenerative disease. Biomarkers may be useful in indicating a coexistent neurodegenerative disease earlier, and in life, in patients with multiple sclerosis and significant cognitive impairment.

Incremental diagnostic value of 18F-Fluetemetamol PET in differential diagnoses of Alzheimer's Disease-related neurodegenerative diseases from an unselected memory clinic cohort

To evaluate the incremental diagnostic value of 18F-Flutemetamol PET following MRI measurements on an unselected prospective cohort collected from a memory clinic. A total of 84 participants was included in this study. A stepwise study design was performed including initial analysis (based on clinical assessments), interim analysis (revision of initial analysis post-MRI) and final analysis (revision of interim analysis post-18F-Flutemetamol PET). At each time of evaluation, every participant was categorized into SCD, MCI or dementia syndromal group and further into AD-related, non-AD related or non-specific type etiological subgroup. Post 18F-Flutemetamol PET, the significant changes were seen in the syndromal MCI group (57%, p < 0.001) involving the following etiological subgroups: AD-related MCI (57%, p < 0.01) and non-specific MCI (100%, p < 0.0001); and syndromal dementia group (61%, p < 0.0001) consisting of non-specific dementia subgroup (100%, p < 0.0001). In the binary regression model, amyloid status significantly influenced the diagnostic results of interim analysis (p < 0.01). 18F-Flutemetamol PET can have incremental value following MRI measurements, particularly reflected in the change of diagnosis of individuals with unclear etiology and AD-related-suspected patients due to the role in complementing AD-related pathological information.

Artificial Intelligence on FDG PET Images Identifies Mild Cognitive Impairment Patients with Neurodegenerative Disease

The purpose of this project is to develop and validate a Deep Learning (DL) FDG PET imaging algorithm able to identify patients with any neurodegenerative diseases (Alzheimer's Disease (AD), Frontotemporal Degeneration (FTD) or Dementia with Lewy Bodies (DLB)) among patients with Mild Cognitive Impairment (MCI). A 3D Convolutional neural network was trained using images from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. The ADNI dataset used for the model training and testing consisted of 822 subjects (472 AD and 350 MCI). The validation was performed on an independent dataset from La Fe University and Polytechnic Hospital. This dataset contained 90 subjects with MCI, 71 of them developed a neurodegenerative disease (64 AD, 4 FTD and 3 DLB) while 19 did not associate any neurodegenerative disease. The model had 79% accuracy, 88% sensitivity and 71% specificity in the identification of patients with neurodegenerative diseases tested on the 10% ADNI dataset, achieving an area under the receiver operating characteristic curve (AUC) of 0.90. On the external validation, the model preserved 80% balanced accuracy, 75% sensitivity, 84% specificity and 0.86 AUC. This binary classifier model based on FDG PET images allows the early prediction of neurodegenerative diseases in MCI patients in standard clinical settings with an overall 80% classification balanced accuracy.

Beyond Genetics: The Role of Metabolism in Photoreceptor Survival, Development and Repair

Vision commences in the retina with rod and cone photoreceptors that detect and convert light to electrical signals. The irreversible loss of photoreceptors due to neurodegenerative disease leads to visual impairment and blindness. Interventions now in development include transplanting photoreceptors, committed photoreceptor precursors, or retinal pigment epithelial (RPE) cells, with the latter protecting photoreceptors from dying. However, introducing exogenous human cells in a clinical setting faces both regulatory and supply chain hurdles. Recent work has shown that abnormalities in central cell metabolism pathways are an underlying feature of most neurodegenerative disorders, including those in the retina. Reversal of key metabolic alterations to drive retinal repair thus represents a novel strategy to treat vision loss based on cell regeneration. Here, we review the connection between photoreceptor degeneration and alterations in cell metabolism, along with new insights into how metabolic reprogramming drives both retinal development and repair following damage. The potential impact of metabolic reprogramming on retinal regeneration is also discussed, specifically in the context of how metabolic switches drive both retinal development and the activation of retinal glial cells known as Müller glia. Müller glia display latent regenerative properties in teleost fish, however, their capacity to regenerate new photoreceptors has been lost in mammals. Thus, re-activating the regenerative properties of Müller glia in mammals represents an exciting new area that integrates research into developmental cues, central metabolism, disease mechanisms, and glial cell biology. In addition, we discuss this work in relation to the latest insights gleaned from other tissues (brain, muscle) and regenerative species (zebrafish).

Genomics as a Clinical Decision Support Tool for Identifying and Addressing Modifiable Causes of Cognitive Decline and Improving Outcomes: Proof of Concept Support for This Personalized Medicine Strategy

The landscape of therapeutics for mild cognitive impairment and dementia is quite limited. While many single-agent trials of pharmaceuticals have been conducted, these trials have repeatedly been unable to show improvement in cognition. It is hypothesized that because Alzheimer’s, like many other chronic illnesses, is not a monogenic illness, but is instead caused by the downstream effects of an individual’s genetic variants interacting with each other, the environment, and lifestyle, that improving outcomes will require a personalized, precision medicine approach. This approach requires identifying and then addressing contributing genomic and other factors specific to each individual in a simultaneous fashion. Until recently, the utility of genomics as part of clinical decision-making for Alzheimer’s and cognitive decline has been limited by the lack of availability of a genomic platform designed specifically to evaluate factors contributing to cognitive decline and how to respond to these factors The clinical decision support (CDS) platform used in the cases presented focuses on common variants that relate to topics including, but not limited to brain inflammation, amyloid processing, nutrient carriers, brain ischemia, oxidative stress, and detoxification pathways. Potential interventions based on the scientific literature were included in the CDS, but the final decision on what interventions to apply were chosen by each patient’s physician. Interventions included supplements with “generally regarded as safe (GRAS)” rating, along with targeted diet and lifestyle modifications. We hypothesize that a personalized genomically targeted approach can improve outcomes for individuals with mild cognitive impairment who are at high risk of Alzheimer’s. The cases presented in this report represent a subset of cases from three physicians’ offices and are meant to provide initial proof of concept data demonstrating the efficacy of this method and provide support for this hypothesis. These patients were at elevated risk for Alzheimer’s due to their apolipoprotein E ε4 status. While further prospective and controlled trials need to be done, initial case reports are encouraging and lend support to this hypothesis of the benefit of a genomically targeted personalized medicine approach to improve outcomes in individuals with cognitive decline who are at high risk for Alzheimer’s.

Reactions to Amyloid PET Scan Results and Levels of Anxious and Depressive Symptoms: CARE IDEAS Study

BACKGROUND AND OBJECTIVES

Few studies have examined care partners' reactions to their loved ones receiving amyloid-β positron emission tomography (PET) scan results, which can be indicative of Alzheimer's disease. We explored care partners' reactions qualitatively, and checked the association of scan results and diagnostic category (dementia vs mild cognitive impairment [MCI]) with care partner anxious and depressive symptoms through quantitative analysis.

RESEARCH DESIGN AND METHODS

Using data from 1,761 care partners in the Caregivers' Reactions and Experience, a supplemental study of the Imaging Dementia Evidence for Amyloid Scanning study, we applied an exploratory sequential mixed-methods design and examined the reactions of 196 care partners to receiving amyloid PET scan results through open-ended interview questions. Based on the qualitative content analysis, we hypothesized there would be an association of care partners' depressive (Patient Health Questionnaire-2) and anxious (6-item State-Trait Anxiety Inventory) symptoms with scan results and diagnostic category which we then tested with logistic regression models.

RESULTS

Content analysis of open-ended responses suggests that when scan results follow the care partner's expectations, for example, elevated amyloid in persons with dementia, care partners report relief and gratitude for the information, rather than distress. Adjusted logistic regression models of survey responses support this finding, with significantly higher odds of anxiety, but not depressive symptoms, among care partners of persons with MCI versus dementia and elevated amyloid.

DISCUSSION AND IMPLICATIONS

Care partners of persons with MCI reported distress and had higher odds of anxiety after receiving elevated amyloid PET scan results than care partners of persons with dementia. This has the potential to inform clinical practice through recommendations for mental health screening and referrals.

Chemical-Driven Outflow of Dissociated Amyloid Burden from Brain to Blood

Amyloid-β (Aβ) deposition in the brain is a primary biomarker of Alzheimer's disease (AD) and Aβ measurement for AD diagnosis mostly depends on brain imaging and cerebrospinal fluid analyses. Blood Aβ can become a reliable surrogate biomarker if issues of low concentration for conventional laboratory instruments and uncertain correlation with brain Aβ are solved. Here, brain-to-blood efflux of Aβ is stimulated in AD transgenic mice by orally administrating a chemical that dissociates amyloid plaques and observing the subsequent increase of blood Aβ concentration. 5XFAD transgenic and wild-type mice of varying ages and genders are prepared, and blood samples of each mouse are collected six times for 12 weeks; three weeks of no treatment and additional nine weeks of daily oral administration, ad libitum, of Aβ plaque-dissociating chemical agent. By the dissociation of Aβ aggregates, the altered levels of plasma Aβ distinguish between transgenic and wild-type mice, displaying potential as an amyloid burden marker of AD brains.

Effect of Denoising and Deblurring 18F-Fluorodeoxyglucose Positron Emission Tomography Images on a Deep Learning Model’s Classification Performance for Alzheimer’s Disease

Alzheimer’s disease (AD) is the most common progressive neurodegenerative disease. 18F-fluorodeoxyglucose positron emission tomography (18F-FDG PET) is widely used to predict AD using a deep learning model. However, the effects of noise and blurring on 18F-FDG PET images were not considered. The performance of a classification model trained using raw, deblurred (by the fast total variation deblurring method), or denoised (by the median modified Wiener filter) 18F-FDG PET images without or with cropping around the limbic system area using a 3D deep convolutional neural network was investigated. The classification model trained using denoised whole-brain 18F-FDG PET images achieved classification performance (0.75/0.65/0.79/0.39 for sensitivity/specificity/F1-score/Matthews correlation coefficient (MCC), respectively) higher than that with raw and deblurred 18F-FDG PET images. The classification model trained using cropped raw 18F-FDG PET images achieved higher performance (0.78/0.63/0.81/0.40 for sensitivity/specificity/F1-score/MCC) than the whole-brain 18F-FDG PET images (0.72/0.32/0.71/0.10 for sensitivity/specificity/F1-score/MCC, respectively). The 18F-FDG PET image deblurring and cropping (0.89/0.67/0.88/0.57 for sensitivity/specificity/F1-score/MCC) procedures were the most helpful for improving performance. For this model, the right middle frontal, middle temporal, insula, and hippocampus areas were the most predictive of AD using the class activation map. Our findings demonstrate that 18F-FDG PET image preprocessing and cropping improves the explainability and potential clinical applicability of deep learning models.

Impact of TOF on Brain PET With Short-Lived 11C-Labeled Tracers Among Suspected Patients With AD/PD: Using Hybrid PET/MRI

Objective

To explore the impact of the time-of-flight (TOF) reconstruction on brain PET with short-lived 11C-labeled tracers in PET magnetic resonance (PET/MR) brain images among suspected patients with Alzheimer's and Parkinson's disease (AD/PD).

Methods

Patients who underwent 11C-2-ß-carbomethoxy-3-b-(4-fluorophenyl) tropane (11C-CFT) and 2-(4-N-[11C] methylaminophenyl)-6-hydroxybenzothiazole (11C-PiB) PET/MRI were retrospectively included in the study. Each PET LIST mode data were reconstructed with and without the TOF reconstruction algorithm. Standard uptake values (SUVs) of Caudate Nucleus (CN), Putamen (PU), and Whole-brain (WB) were measured. TOF and non-TOF SUVs were assessed by using paired t-test. Standard formulas were applied to measure contrast, signal-to-noise ratio (SNR), and percentage relative average difference of SUVs (%RAD-SUVs).

Results

Total 75 patients were included with the median age (years) and body mass index (BMI-kg/m2) of 60.2 ± 10.9 years and 23.9 ± 3.7 kg/m2 in 11C-CFT (n = 41) and 62.2 ± 6.8 years and 24.7 ± 2.9 kg/m2 in 11C-PiB (n = 34), respectively. Higher average SUVs and positive %RAD-SUVs were observed in CN and PU in TOF compared with non-TOF reconstructions for the two 11C-labeled radiotracers. Differences of SUVmean were significant (p < 0.05) in CN and PU for both 11C-labeled radiotracers. SUVmax was enhanced significantly in CN and PU for 11C-CFT and CN for 11C-PiB, but not in PU. Significant contrast enhancement was observed in PU for both 11C-labeled radiotracers, whereas SNR gain was significant in PU, only for 11C-PiB in TOF reconstruction.

Conclusion

Time-of-flight leads to a better signal vs. noise trade-off than non-TOF in 11C-labeled tracers between CN and PU, improving the SUVs, contrast, and SNR, which were valuable for reducing injected radiation dose. Improved timing resolution aided the rapid decay rate of short-lived 11C-labeled tracers, and it shortened the scan time, increasing the patient comfort, and reducing the motion artifact among patients with AD/PD. However, one should adopt the combined TOF algorithm with caution for the quantitative analysis because it has different effects on the SUVmax, contrast, and SNR of different brain regions.

Portable electrochemical micro-workstation platform for simultaneous detection of multiple Alzheimer's disease biomarkers

Alzheimer's disease, as a most prevalent type of dementia, is quickly becoming one of the most expensive, lethal, and burdening diseases of this century. Though there are still no efficient therapies, early diagnosis and intervention are important directive significance to clinical works. Here, we develop a portable electrochemical micro-workstation platform consisting of an electrochemical micro-workstation and integrated electrochemical microarray for simultaneously detecting multiple AD biomarkers including Aβ40, Aβ42, T-tau, and P-tau181 in serum. The integrated electrochemical microarray is mainly used for droplet sample manipulation and signal generation. The micro-workstation can regulate signals and transfer the signals to a smartphone by Bluetooth embedded inside. This portable electrochemical micro-workstation platform exhibits excellent analysis performance. The LODs for Aβ40, Aβ42, T-tau, and P-tau181 are 0.125 pg/mL, 0.089 pg/mL, 0.142 pg/mL, and 0.176 pg/mL, respectively, which satisfies the needs of detecting AD biomarkers in serum. The combination of portable micro-workstation and integrated electrochemical microarray provides a promising strategy for the early diagnosis of Alzheimer's disease and personal healthcare.

Assessing and disclosing test results for ‘mild cognitive impairment’: the perspective of old age psychiatrists in Scotland

Background

Mild cognitive impairment (MCI) is a condition that exists between normal healthy ageing and dementia with an uncertain aetiology and prognosis. This uncertainty creates a complex dynamic between the clinicians’ conception of MCI, what is communicated to the individual about their condition, and how the individual responds to the information conveyed to them. The aim of this study was to explore clinicians’ views around the assessment and communication of MCI in memory clinics.

Method

As part of a larger longitudinal study looking at patients’ adjustment to MCI disclosure, we interviewed Old Age Psychiatrists at the five participating sites across Scotland. The study obtained ethics approvals and the interviews (carried out between Nov 2020–Jan 2021) followed a semi-structured schedule focusing on [1] how likely clinicians are to use the term MCI with patients; [2] what tests clinicians rely on and how much utility they see in them; and [3] how clinicians communicate risk of progression to dementia. The interviews were voice recorded and were analysed using reflective thematic analysis.

Results

Initial results show that most clinicians interviewed (Total N = 19) considered MCI to have significant limitations as a diagnostic term. Nevertheless, most clinicians reported using the term MCI (n = 15/19). Clinical history was commonly described as the primary aid in the diagnostic process and also to rule out functional impairment (which was sometimes corroborated by Occupational Therapy assessment). All clinicians reported using the Addenbrooke’s Cognitive Examination-III as a primary assessment tool. Neuroimaging was frequently found to have minimal usefulness due to the neuroradiological reports being non-specific.

Conclusion

Our study revealed a mixture of approaches to assessing and disclosing test results for MCI. Some clinicians consider the condition as a separate entity among neurodegenerative disorders whereas others find the term unhelpful due to its uncertain prognosis. Clinicians report a lack of specific and sensitive assessment methods for identifying the aetiology of MCI in clinical practice. Our study demonstrates a broad range of views and therefore variability in MCI risk disclosure in memory assessment services which may impact the management of individuals with MCI.

Pattern of Altered Magnetization Transfer Rate in Alzheimer's Disease

BACKGROUND

Biomarkers for Alzheimer's disease (AD) are crucial for early diagnosis and treatment monitoring once disease modifying therapies become available.

OBJECTIVE

This study aims to quantify the forward magnetization transfer rate (kfor) map from brain tissue water to macromolecular protons and use it to identify the brain regions with abnormal kfor in AD and AD progression.

METHODS

From the Cardiovascular Health Study (CHS) cognition study, magnetization transfer imaging (MTI) was acquired at baseline from 63 participants, including 20 normal controls (NC), 18 with mild cognitive impairment (MCI), and 25 AD subjects. Of those, 53 participants completed a follow-up MRI scan and were divided into four groups: 15 stable NC, 12 NC-to-MCI, 12 stable MCI, and 14 MCI/AD-to-AD subjects. kfor maps were compared across NC, MCI, and AD groups at baseline for the cross-sectional study and across four longitudinal groups for the longitudinal study.

RESULTS

We found a lower kfor in the frontal gray matter (GM), parietal GM, frontal corona radiata (CR) white matter (WM) tracts, frontal and parietal superior longitudinal fasciculus (SLF) WM tracts in AD relative to both NC and MCI. Further, we observed progressive decreases of kfor in the frontal GM, parietal GM, frontal and parietal CR WM tracts, and parietal SLF WM tracts in stable MCI. In the parietal GM, parietal CR WM tracts, and parietal SLF WM tracts, we found trend differences between MCI/AD-to-AD and stable NC.

CONCLUSION

Forward magnetization transfer rate is a promising biomarker for AD diagnosis and progression.

Frontal Cortex Segmentation of Brain PET Imaging Using Deep Neural Networks

¹⁸F-FDG positron emission tomography (PET) imaging of brain glucose use and amyloid accumulation is a research criteria for Alzheimer's disease (AD) diagnosis. Several PET studies have shown widespread metabolic deficits in the frontal cortex for AD patients. Therefore, studying frontal cortex changes is of great importance for AD research. This paper aims to segment frontal cortex from brain PET imaging using deep neural networks. The learning framework called Frontal cortex Segmentation model of brain PET imaging (FSPET) is proposed to tackle this problem. It combines the anatomical prior to frontal cortex into the segmentation model, which is based on conditional generative adversarial network and convolutional auto-encoder. The FSPET method is evaluated on a dataset of 30 brain PET imaging with ground truth annotated by a radiologist. Results that outperform other baselines demonstrate the effectiveness of the FSPET framework.

Quantitative Brain Positron Emission Tomography in Female 5XFAD Alzheimer Mice: Pathological Features and Sex-Specific Alterations

Successful back-translating clinical biomarkers and molecular imaging methods of Alzheimer's disease (AD), including positron emission tomography (PET), are very valuable for the evaluation of new therapeutic strategies and increase the quality of preclinical studies. ¹⁸F-Fluorodeoxyglucose (FDG)–PET and ¹⁸F-Florbetaben–PET are clinically established biomarkers capturing two key pathological features of AD. However, the suitability of ¹⁸F-FDG– and amyloid–PET in the widely used 5XFAD mouse model of AD is still unclear. Furthermore, only data on male 5XFAD mice have been published so far, whereas studies in female mice and possible sex differences in ¹⁸F-FDG and ¹⁸F-Florbetaben uptake are missing. The aim of this study was to evaluate the suitability of ¹⁸F-FDG– and ¹⁸F-Florbetaben–PET in 7-month-old female 5XFAD and to assess possible sex differences between male and female 5XFAD mice. We could demonstrate that female 5XFAD mice showed a significant reduction in brain glucose metabolism and increased cerebral amyloid deposition compared with wild type animals, in accordance with the pathology seen in AD patients. Furthermore, we showed for the first time that the hypometabolism in 5XFAD mice is gender-dependent and more pronounced in female mice. Therefore, these results support the feasibility of small animal PET imaging with ¹⁸F-FDG- and ¹⁸F-Florbetaben in 5XFAD mice in both, male and female animals. Moreover, our findings highlight the need to account for sex differences in studies working with 5XFAD mice.

Bioinspired Synthesis of ZnO@polydopamine/Au for Label-Free Photoelectrochemical Immunoassay of Amyloid-β Protein

Amyloid-β protein (Aβ) is an important biomarker and plays a key role in the early stage of Alzheimer's disease (AD). Here, an ultrasensitive photoelectrochemical (PEC) sensor based on ZnO@polydopamine/Au nanocomposites was constructed for quantitative detection of Aβ. In this sensing system, the ZnO nanorod array decorated with PDA films and gold nanoparticles (Au NPs) have excellent visible-light activity. The PDA film was used as a sensitizer for charge separation, and it also was used for antibody binding. Moreover, Au NPs were loaded on the surface of PDA film by in situ deposition, which further improved the charge transfer efficiency and the PEC activity in visible light due to the localized surface plasmon resonance effect of Au NPs. Therefore, in ZnO@polydopamine/Au nanocomposites, a significantly enhanced photocurrent response was obtained on this photoelectrode, which provides a good and reliable signal for early detection of AD. Under the optimized conditions, the PEC immunosensor displayed a wide linear range from 1 pg/mL to 100 ng/mL and a low detection limit of 0.26 pg/mL. In addition, this PEC immunosensor also presented good selectivity, stability, and reproducibility. This work may provide a promising point-of-care testing method toward advanced PEC immunoassays for AD biomarkers.

Brain transcriptomes of zebrafish and mouse Alzheimer's disease knock-in models imply early disrupted energy metabolism

Energy production is the most fundamentally important cellular activity supporting all other functions, particularly in highly active organs, such as brains. Here, we summarise transcriptome analyses of young adult (pre-disease) brains from a collection of 11 early-onset familial Alzheimer's disease (EOFAD)-like and non-EOFAD-like mutations in three zebrafish genes. The one cellular activity consistently predicted as affected by only the EOFAD-like mutations is oxidative phosphorylation, which produces most of the energy of the brain. All the mutations were predicted to affect protein synthesis. We extended our analysis to knock-in mouse models of APOE alleles and found the same effect for the late onset Alzheimer's disease risk allele ε4. Our results support a common molecular basis for the initiation of the pathological processes leading to both early and late onset forms of Alzheimer's disease, and illustrate the utility of zebrafish and knock-in single EOFAD mutation models for understanding the causes of this disease.

Summary: Young adult zebrafish mutants and a mouse model of a genetic variant promoting early- and late-onset Alzheimer's disease, respectively, share changes in brain gene expression, indicating disturbance of oxidative phosphorylation.

The interplay among oxidative stress, brain insulin resistance and AMPK dysfunction contribute to neurodegeneration in type 2 diabetes and Alzheimer disease

Alzheimer's disease (AD) is the most common form of dementia in the elderly followed by vascular dementia. In addition to clinically diagnosed dementia, cognitive dysfunction has been reported in diabetic patients. Recent studies are now beginning to recognize type 2 diabetes mellitus (T2DM), characterized by chronic hyperglycemia and insulin resistance, as a risk factor for AD and other cognitive disorders. While studies on insulin action have remained traditionally in the domain of peripheral tissues, the detrimental effects of insulin resistance in the central nervous system on cognitive dysfunction are increasingly being reported in recent clinical and preclinical studies. Brain functions require continuous supply of glucose and oxygen and a tight regulation of metabolic processes. Loss of this metabolic regulation has been proposed to be a contributor to memory dysfunction associated with neurodegeneration. Within the above scenario, this review will focus on the interplay among oxidative stress (OS), insulin resistance and AMPK dysfunctions in the brain by highlighting how these neurotoxic events contribute to neurodegeneration. We provide an overview on the detrimental effects of OS on proteins regulating insulin signaling and how these alterations impact cell metabolic dysfunctions through AMPK dysregulation. Such processes, we assert, are critically involved in the molecular pathways that underlie AD.

Pursuit of precision medicine: Systems biology approaches in Alzheimer's disease mouse models

Alzheimer's disease (AD) is a complex disease that is mediated by numerous factors and manifests in various forms. A systems biology approach to studying AD involves analyses of various body systems, biological scales, environmental elements, and clinical outcomes to understand the genotype to phenotype relationship that potentially drives AD development. Currently, there are many research investigations probing how modifiable and nonmodifiable factors impact AD symptom presentation. This review specifically focuses on how imaging modalities can be integrated into systems biology approaches using model mouse populations to link brain level functional and structural changes to disease onset and progression. Combining imaging and omics data promotes the classification of AD into subtypes and paves the way for precision medicine solutions to prevent and treat AD.

Brain energy failure in dementia syndromes: Opportunities and challenges for glucagon-like peptide-1 receptor agonists

Medications for type 2 diabetes (T2DM) offer a promising path for discovery and development of effective interventions for dementia syndromes. A common feature of dementia syndromes is an energy failure due to reduced energy supply to neurons and is associated with synaptic loss and results in cognitive decline and behavioral changes. Among diabetes medications, glucagon‐like peptide‐1 (GLP‐1) receptor agonists (RAs) promote protective effects on vascular, microglial, and neuronal functions. In this review, we present evidence from animal models, imaging studies, and clinical trials that support developing GLP‐1 RAs for dementia syndromes. The review examines how changes in brain energy metabolism differ in conditions of insulin resistance and T2DM from dementia and underscores the challenges that arise from the heterogeneity of dementia syndromes. The development of GLP‐1 RAs as dementia therapies requires a deeper understanding of the regional changes in brain energy homeostasis guided by novel imaging biomarkers.

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.

A Comprehensive Review of Neuronal Changes in Diabetics

There has been an exponential rise in diabetes mellitus (DM) cases on a global scale. Diabetes affects almost every system of the body, and the nervous system is no exception. Although the brain is dependent on glucose, providing it with the energy required for optimal functionality, glucose also plays a key role in the regulation of oxidative stress, cell death, among others, which furthermore contribute to the pathophysiology of neurological disorders. The variety of biochemical processes engaged in this process is only matched by the multitude of clinical consequences resulting from it. The wide-ranging effects on the central and peripheral nervous system include, but are not limited to axonopathies, neurodegenerative diseases, neurovascular diseases, and general cognitive impairment. All language search was conducted on MEDLINE, COCHRANE, EMBASE, and GOOGLE SCHOLAR till September 2021. The following search strings and Medical Subject Headings (MeSH terms) were used: "Diabetes Mellitus," "CNS," "Diabetic Neuropathy," and "Insulin." We explored the literature on diabetic neuropathy, covering its epidemiology, pathophysiology with the respective molecular pathways, clinical consequences with a special focus on the central nervous system and finally, measures to prevent and treat neuronal changes. Diabetes is slowly becoming an epidemic, rapidly increasing the clinical burden on account of its wide-ranging complications. This review focuses on the neuronal changes occurring in diabetes such as the impact of hyperglycemia on brain function and structure, its association with various neurological disorders, and a few diabetes-induced peripheral neuropathic changes. It is an attempt to summarize the relevant literature about neuronal consequences of DM as treatment options available today are mostly focused on achieving better glycemic control; further research on novel treatment options to prevent or delay the progression of neuronal changes is still needed.

Beam orientation optimization for coherent X-ray scattering from distributed deep targets

Background

Amyloid deposits in the temporal and frontal lobes in patients with Alzheimer’s disease make them potential targets to aid in early diagnosis. Recently, spectral small-angle X-ray scattering techniques have been proposed for interrogating deep targets such as amyloid plaques.

Results

We describe an optimization approach for the orientation of beams for deep target characterization. The model predicts the main features of scattering profiles from targets with varying shape, size and location. We found that increasing target size introduced additional smearing due to location uncertainty, and incidence angle affected the scattering profile by altering the path length or effective target size. For temporal and frontal lobe targets, beam effectiveness varied up to 2 orders of magnitude.

Conclusions

Beam orientation optimization might allow for patient-specific optimal paths for improved signal characterization.

Racial Differences in Alzheimer's Disease Specialist Encounters Are Associated with Usage of Molecular Imaging and Dementia Medications: An Enterprise-Wide Analysis Using i2b2

Background:

African Americans are at increased risk for Alzheimer’s disease (AD) but barriers to optimal clinical care are unclear.

Objective:

To comprehensively evaluate potential racial differences in the diagnosis and treatment of AD in an academic medical center.

Methods:

We used the clinical informatics tool, i2b2, to analyze all patient encounters for AD or mild cognitive impairment (MCI) in the University of Alabama at Birmingham Health System over a three-year period, examining neuroimaging rates and dementia-related medication use by race and clinic site using ratio tests on contingency tables of stratified patient counts.

Results:

Enterprise-wide, African Americans were not underrepresented among outpatients seen for AD/MCI. However, there were differences in the clinic setting where visits occurred, with African Americans overrepresented in Geriatrics and primary care clinics and underrepresented in Memory Disorders specialty clinics. There were no racial differences in the rates at which any clinic ordered PET neuroimaging tests or dementia-related medications. However, unsurprisingly, specialty clinics ordered both PET neuroimaging and dementia-related medications at a higher rate than primary care clinics, and overall across the medical enterprise, African Americans were statistically less likely to have PET neuroimaging or dementia-related medications ordered.

Conclusion:

African Americans with AD/MCI were not underrepresented at this academic medical center but were somewhat less likely to have PET neuroimaging or to be on dementia-related medications, potentially in part from underrepresentation in the specialty clinics where these orders are more likely. The reasons for this underrepresentation in specialty clinics are likely multifactorial and important to better understand.

Generation of synthetic PET images of synaptic density and amyloid from 18 F-FDG images using deep learning

PURPOSE

Positron emission tomography (PET) imaging with various tracers is increasingly used in Alzheimer's disease (AD) studies. However, access to PET scans using new or less-available tracers with sophisticated synthesis and short half-life isotopes may be very limited. Therefore, it is of great significance and interest in AD research to assess the feasibility of generating synthetic PET images of less-available tracers from the PET image of another common tracer, in particular 18 F-FDG.

METHODS

We implemented advanced deep learning methods using the U-Net model to predict 11 C-UCB-J PET images of synaptic vesicle protein 2A (SV2A), a surrogate of synaptic density, from 18 F-FDG PET data. Dynamic 18 F-FDG and 11 C-UCB-J scans were performed in 21 participants with normal cognition (CN) and 33 participants with Alzheimer's disease (AD). Cerebellum was used as the reference region for both tracers. For 11 C-UCB-J image prediction, four network models were trained and tested, which included 1) 18 F-FDG SUV ratio (SUVR) to 11 C-UCB-J SUVR, 2) 18 F-FDG Ki ratio to 11 C-UCB-J SUVR, 3) 18 F-FDG SUVR to 11 C-UCB-J distribution volume ratio (DVR), and 4) 18 F-FDG Ki ratio to 11 C-UCB-J DVR. The normalized root mean square error (NRMSE), structure similarity index (SSIM), and Pearson's correlation coefficient were calculated for evaluating the overall image prediction accuracy. Mean bias of various ROIs in the brain and correlation plots between predicted images and true images were calculated for ROI-based prediction accuracy. Following a similar training and evaluation strategy, 18 F-FDG SUVR to 11 C-PiB SUVR network was also trained and tested for 11 C-PiB static image prediction.

RESULTS

The results showed that all four network models obtained satisfactory 11 C-UCB-J static and parametric images. For 11 C-UCB-J SUVR prediction, the mean ROI bias was -0.3% ± 7.4% for the AD group and -0.5% ± 7.3% for the CN group with 18 F-FDG SUVR as the input, -0.7% ± 8.1% for the AD group, and -1.3% ± 7.0% for the CN group with 18 F-FDG Ki ratio as the input. For 11 C-UCB-J DVR prediction, the mean ROI bias was -1.3% ± 7.5% for the AD group and -2.0% ± 6.9% for the CN group with 18 F-FDG SUVR as the input, -0.7% ± 9.0% for the AD group, and -1.7% ± 7.8% for the CN group with 18 F-FDG Ki ratio as the input. For 11 C-PiB SUVR image prediction, which appears to be a more challenging task, the incorporation of additional diagnostic information into the network is needed to control the bias below 5% for most ROIs.

CONCLUSIONS

It is feasible to use 3D U-Net-based methods to generate synthetic 11 C-UCB-J PET images from 18 F-FDG images with reasonable prediction accuracy. It is also possible to predict 11 C-PiB SUVR images from 18 F-FDG images, though the incorporation of additional non-imaging information is needed.

Positron emission tomographic imaging in drug discovery

Positron emission tomography (PET) is an extensively used nuclear functional imaging technique, especially for central nervous system (CNS) and oncological disorders. Currently, drug development is a lengthy and costly pursuit. Imaging with PET radiotracers could be an effective way to hasten drug discovery and advancement, because it facilitates the monitoring of key facets, such as receptor occupancy quantification, drug biodistribution, pharmacokinetic (PK) analyses, validation of target engagement, treatment monitoring, and measurement of neurotransmitter concentrations. These parameters demand careful analyses for the robust appraisal of newly formulated drugs during preclinical and clinical trials. In this review, we discuss the usage of PET imaging in radiopharmaceutical development; drug development approaches with PET imaging; and PET developments in oncological and cardiac drug discovery.

Synthesis and evaluation of curcumin-based near-infrared fluorescent probes for the in vivo optical imaging of amyloid-β plaques

The abnormal self-assembly of amyloid-beta (Aβ) peptides into oligomers, as well as insoluble fibrils, has been identified as a key factor for monitoring the progression of Alzheimer's disease (AD). The noninvasive imaging of Aβ aggregates utilizing chemical probes can be a powerful and practical technique for accurately diagnosing and monitoring the progress of AD, as well as evaluating the effectiveness of therapeutic drug candidates in treating or managing it. Particularly, the near-infrared (NIR) fluorescence imaging of Aβ plaques is a potentially promising approach toward the efficient detection of the biomarker. In this study, we describe a new NIR fluorophore, which was based on curcumin derivatives. The fluorophore is equipped with desirable optical properties for in vivo brain imaging. The emission wavelength of the probe, 8b, is 667 nm, and its fluorescent intensity is significantly increased through binding with the Aβ aggregates. The probe allows the clear visualization of the Aβ plaques 10 min post administration, and the intensity of the fluorescent signal in the brain of a 5XFAD transgenic mouse model is more than three times higher than that of the normal control group. These results demonstrate that the designed probe can be an effective tool for visualizing Aβ plaques, as well as investigating the pathological progress of AD.

A Concise Overview of Biosensing Technologies for the Detection of Alzheimer's Disease Biomarkers

Alzheimer&#039;s disease (AD) is a brain-linked pathophysiological condition with neuronal degeneration, cognition dysfunctions, and other debilitations. Due to the growing prevalence of AD, there is a highly commended tendency to accelerate and develop analytical technologies for easy, cost-effective, and sensitive detection of AD biomarkers. In the last decade, remarkable advancements have been achieved on the gate to the progression of biosensors, predominantly optical and electrochemical, to detect AD biomarkers. Biosensors are commanding analytical devices that can conduct biological responses on transducers into measurable signals. These analytical devices can assist the case finding and management of AD. This review focuses on up-to-date developments, contests, and tendencies regarding AD biosensing principally, emphasizing the exclusive possessions of nanomaterials.

MRI-Visible Perivascular Spaces in the Centrum Semiovale Are Associated with Brain Amyloid Deposition in Patients with Alzheimer Disease-Related Cognitive Impairment

BACKGROUND AND PURPOSE

The association of perivascular spaces in the centrum semiovale with amyloid accumulation among patients with Alzheimer disease-related cognitive impairment is unknown. We evaluated this association in patients with Alzheimer disease-related cognitive impairment and β-amyloid deposition, assessed with [¹⁸F] florbetaben PET/CT.

MATERIALS AND METHODS

MR imaging and [¹⁸F] florbetaben PET/CT images of 144 patients with Alzheimer disease-related cognitive impairment were retrospectively evaluated. MR imaging-visible perivascular spaces were rated on a 4-point visual scale: a score of ≥3 or <3 indicated a high or low degree of MR imaging-visible perivascular spaces, respectively. Amyloid deposition was evaluated using the brain β-amyloid plaque load scoring system.

RESULTS

Compared with patients negative for β-amyloid, those positive for it were older and more likely to have lower cognitive function, a diagnosis of Alzheimer disease, white matter hyperintensity, the Apolipoprotein E ε4 allele, and a high degree of MR imaging-visible perivascular spaces in the centrum semiovale. Multivariable analysis, adjusted for age and Apolipoprotein E status, revealed that a high degree of MR imaging-visible perivascular spaces in the centrum semiovale was independently associated with β-amyloid positivity (odds ratio, 2.307; 95% CI, 1.036-5.136; P = .041).

CONCLUSIONS

A high degree of MR imaging-visible perivascular spaces in the centrum semiovale independently predicted β-amyloid positivity in patients with Alzheimer disease-related cognitive impairment. Thus, MR imaging-visible perivascular spaces in the centrum semiovale are associated with amyloid pathology of the brain and could be an indirect imaging marker of amyloid burden in patients with Alzheimer disease-related cognitive impairment.

Differences in in vitro microglial accumulation of the energy metabolism tracers [18F]FDG and [18F]BCPP-EF during LPS- and IL4 stimulation

The positron emission tomography probes 2-deoxy-2-[18F]fluoro-D-glucose ([18F]FDG) and 2-tert-butyl-4-chloro-5-{6-[2-(2-[18F]fluoroethoxy)-ethoxy]-pyridin-3-ylmethoxy}-2H-pyridazin-3-one ([18F]BCPP-EF) are designed to evaluate glycolysis and oxidative phosphorylation, respectively, and are both used to estimate neuronal activity. However, previous studies have shown a discrepancy in these probes' accumulation in the compromised region, possibly due to the presence of activated microglia acting like deleterious or neuroprotective phenotypes. Hence, we evaluated lipopolysaccharide (LPS)- and interleukin 4 (IL4)-stimulated microglial uptake of [14C]2DG and [18F]BCPP-EF to give a new insight into the hypothesis that different uptake of [18F]FDG and [18F]BCPP-EF can be ascribed to the different metabolic pathways activated during microglial activation. LPS or IL4 stimulation increased the proinflammatory or anti-inflammatory marker gene expression in microglial cells. In LPS-stimulated cells, [14C]2DG uptake and glycolysis related gene expression were elevated, and [18F]BCPP-EF uptake was reduced. In IL4-stimulated cells, [18F]BCPP-EF uptake was increased, and [14C]2DG uptake was decreased. The expression of genes involved in glycolysis and mitochondrial complex I subunits was not changed by IL4 stimulation. The uptake of [14C]2DG and [18F]BCPP-EF differs in LPS- and IL4-stimulated polarized microglial cells. The present results suggest that the in vivo accumulation of metabolic tracers [18F]FDG and [18F]BCPP-EF can be influenced by the different aspects of neuroinflammation.

The role of amyloid PET in patient selection for extra-ventricular shunt insertion for the treatment of idiopathic normal pressure hydrocephalus: A pooled analysis

BACKGROUND

Idiopathic Normal Pressure Hydrocephalus (iNPH) can be effectively treated through shunt insertion. However, most shunted patients experience little or no clinical benefit, which suggests suboptimal patient selection. While contentious, multiple studies have reported poorer shunt outcomes associated with concomitant Alzheimer's disease. Prompted by this observation, multiple studies have assessed the role of amyloid PET, a specific test for Alzheimer's disease, in patient selection for shunting.

METHODS

A comprehensive literature search was performed to identify studies that assessed the association between amyloid PET result and the clinical response to shunting in patients with suspected iNPH. Pooled diagnostic statistics were calculated.

RESULTS

Across three relevant studies, a total of 38 patients with suspected iNPH underwent amyloid PET imaging and shunt insertion. Twenty-three patients had a positive clinical response to shunting. 18/28 (64.3%) of patients with a negative amyloid PET and 5/10 (50%) with a positive amyloid PET had a positive response to shunting. The pooled sensitivity, specificity and accuracy was 33.3%, 76.2% and 58.3%. None of these statistics reached statistical significance.

CONCLUSION

The results of this pooled analysis do not support the selection of patients with suspected iNPH for shunting on the basis of amyloid PET alone. However, due to small cohort sizes and weakness in study design, further high-quality studies are required to properly determine the role of amyloid PET in assessing this complex patient group.

Amyloid-PET imaging offers small improvements in predictions of future cognitive trajectories

BACKGROUND

Amyloid β (Aβ) is thought to initiate a cascade of pathology culminating in Alzheimer's disease-related cognitive decline. Aβ accumulation in brain tissues may begin one to two decades prior to clinical diagnosis of Alzheimer's disease. Prior studies have demonstrated that Aβ detected in vivo with positron emission tomography with amyloid ligands (amyloid-PET) predicts contemporaneously measured cognition and future cognitive trajectories. Prior studies have not evaluated the added value of Aβ measures in predicting future cognition when repeated past cognitive measures are available. We evaluated the extent to which amyloid-PET improves prediction of future cognitive changes over and above predictions based only on sociodemographics and past cognitive measures.

METHODS

We used data from participants in the University of California Davis Alzheimer's Disease Research cohort who were cognitively normal at baseline, participated in amyloid-PET imaging, and completed at least three cognitive assessments prior to amyloid-PET imaging (N = 132 for memory andN = 135 for executive function). We used sociodemographic and cognitive measures taken prior to amyloid-PET imaging to predict cognitive trajectory after amyloid-PET imaging and assessed whether measures of amyloid burden improved predictions of subsequent cognitive change. Improvements in prediction were characterized as percent reduction in the mean squared error (MSE) in predicted cognition post amyloid-PET and increase in percent variance explained.

RESULTS

The base model using only sociodemographics and past cognitive performance explained the majority of variance in both predicted memory measures (55.6%) and executive function measures (74.5%) following amyloid-PET. Adding amyloid positivity to the model reduced the MSE for memory by 0.2%, 95% CI: (0%, 2.6%), p = 0.48 and for executive function by 3.4%, 95% CI: (0.6%, 10.2%), p = 0.002. This corresponded to an increase in the percent variance explained of 0.1%, 95% CI: (0%, 1.2%) for memory and 0.9%, 95% CI: (0.1%, 2.8%) for executive function. Similar results were obtained using a continuous measure of amyloid burden.

CONCLUSION

In this cohort, the addition of amyloid burden slightly improved predictions of executive function compared to models based only on past cognitive assessments and sociodemographics. When repeated cognitive assessments are available, the additional utility of amyloid-PET in predicting future cognitive impairment may be limited.

Detecting retinal cell stress and apoptosis with DARC: Progression from lab to clinic

DARC (Detection of Apoptosing Retinal Cells) is a retinal imaging technology that has been developed within the last 2 decades from basic laboratory science to Phase 2 clinical trials. It uses ANX776 (fluorescently labelled Annexin A5) to identify stressed and apoptotic cells in the living eye. During its development, DARC has undergone biochemistry optimisation, scale-up and GMP manufacture and extensive preclinical evaluation. Initially tested in preclinical glaucoma and optic neuropathy models, it has also been investigated in Alzheimer, Parkinson's and Diabetic models, and used to assess efficacy of therapies. Progression to clinical trials has not been speedy. Intravenous ANX776 has to date been found to be safe and well-tolerated in 129 patients, including 16 from Phase 1 and 113 from Phase 2. Results on glaucoma and AMD patients have been recently published, and suggest DARC with an AI-aided algorithm can be used to predict disease activity. New analyses of DARC in GA prediction are reported here. Although further studies are needed to validate these findings, it appears there is potential of the technology to be used as a biomarker. Much larger clinical studies will be needed before it can be considered as a diagnostic, although the relatively non-invasive nature of the nasal as opposed to intravenous administration would widen its acceptability in the future as a screening tool. This review describes DARC development and its progression into Phase 2 clinical trials from lab-based research. It discusses hypotheses, potential challenges, and regulatory hurdles in translating technology.

Modeling autosomal dominant Alzheimer's disease with machine learning

INTRODUCTION

Machine learning models were used to discover novel disease trajectories for autosomal dominant Alzheimer's disease.

METHODS

Longitudinal structural magnetic resonance imaging, amyloid positron emission tomography (PET), and fluorodeoxyglucose PET were acquired in 131 mutation carriers and 74 non-carriers from the Dominantly Inherited Alzheimer Network; the groups were matched for age, education, sex, and apolipoprotein ε4 (APOE ε4). A deep neural network was trained to predict disease progression for each modality. Relief algorithms identified the strongest predictors of mutation status.

RESULTS

The Relief algorithm identified the caudate, cingulate, and precuneus as the strongest predictors among all modalities. The model yielded accurate results for predicting future Pittsburgh compound B (R2  = 0.95), fluorodeoxyglucose (R2  = 0.93), and atrophy (R2  = 0.95) in mutation carriers compared to non-carriers.

DISCUSSION

Results suggest a sigmoidal trajectory for amyloid, a biphasic response for metabolism, and a gradual decrease in volume, with disease progression primarily in subcortical, middle frontal, and posterior parietal regions.

mTOR in Alzheimer disease and its earlier stages: Links to oxidative damage in the progression of this dementing disorder

Alzheimer's disease (AD) is the most prevalent form of dementia in the elderly population and has worldwide impact. The etiology of the disease is complex and results from the confluence of multiple mechanisms ultimately leading to neuronal loss and cognitive decline. Among risk factors, aging is the most relevant and accounts for several pathogenic events that contribute to disease-specific toxic mechanisms. Accumulating evidence linked the alterations of the mammalian target of rapamycin (mTOR), a serine/threonine protein kinase playing a key role in the regulation of protein synthesis and degradation, to age-dependent cognitive decline and pathogenesis of AD. To date, growing studies demonstrated that aberrant mTOR signaling in the brain affects several pathways involved in energy metabolism, cell growth, mitochondrial function and proteostasis. Recent advances associated alterations of the mTOR pathway with the increased oxidative stress. Disruption of all these events strongly contribute to age-related cognitive decline including AD. The current review discusses the main regulatory roles of mTOR signaling network in the brain, focusing on its role in autophagy, oxidative stress and energy metabolism. Collectively, experimental data suggest that targeting mTOR in the CNS can be a valuable strategy to prevent/slow the progression of AD.