PET Neuroimaging of Alzheimer's Disease: Radiotracers and Their Utility in Clinical Research

Depressive Symptoms and Plasma Markers of Alzheimer's Disease and Neurodegeneration: A Coordinated Meta-Analysis of 8 Cohort Studies

BACKGROUND

Depressive symptoms are associated with an increased risk of Alzheimer's disease (AD). There has been a recent emergence in plasma biomarkers for AD pathophysiology, such as amyloid-beta (Aβ) and phosphorylated tau (p-tau), as well as for axonal damage (neurofilament light, NfL) and astrocytic activation (glial fibrillary acidic protein, GFAP). Hypothesizing that depressive symptoms may occur along the AD process, we investigated associations between plasma biomarkers of AD with depressive symptoms in individuals without dementia.

METHODS

A two-stage meta-analysis was performed on 2 clinic-based and 6 population-based cohorts (N = 7210) as part of the Netherlands Consortium of Dementia Cohorts. Plasma markers (Aβ42/40, p-tau181, NfL, and GFAP) were measured using Single Molecular Array (Simoa; Quanterix) assays. Depressive symptoms were measured with validated questionnaires. We estimated the cross-sectional association of each standardized plasma marker (determinants) with standardized depressive symptoms (outcome) using linear regressions, correcting for age, sex, education, and APOE ε4 allele presence, as well as subgrouping by sex and APOE ε4 allele. Effect estimates were entered into a random-effects meta-analysis.

RESULTS

Mean age of participants was 71 years. The prevalence of clinically relevant depressive symptoms ranged from 1% to 22%. None of the plasma markers were associated with depressive symptoms in the meta-analyses. However, NfL was associated with depressive symptoms only in APOE ε4 carriers (β 0.11; 95% CI: 0.05-0.17).

CONCLUSIONS

Late-life depressive symptoms did not show an association to plasma biomarkers of AD pathology. However, in APOE ε4 allele carriers, a more profound role of neurodegeneration was suggested with depressive symptoms.

[18F]2-fluoro-2-deoxy-sorbitol ([18F]FDS) PET imaging repurposed for quantitative estimation of blood-brain barrier permeability in a rat model of Alzheimer's disease

Numerous studies suggest that blood-brain barrier (BBB) dysfunction may contribute to the progression of Alzheimer's disease (AD). Clinically available neuroimaging methods are needed for quantitative "scoring" of BBB permeability in AD patients. [18F]2-fluoro-2-deoxy-sorbitol ([18F]FDS), which can be easily obtained from simple chemical reduction of commercial [18F]2-fluoro-2-deoxy-glucose ([18F]FDG), was investigated as a small-molecule marker of BBB permeability, in a pre-clinical model of AD using in vivo PET imaging. Chemical reduction of [18F]FDG to [18F]FDS was obtained with a 100% conversion yield. Dynamic PET acquisitions were performed in the APP/PS1 rat model of AD (TgF344-AD, n=3) compared with age-matched littermates (WT, n=4). The brain uptake of [18F]FDS was determined in selected brain regions, delineated from a coregistered rat brain template. The brain uptake of [18F]FDS in the brain regions of AD rats versus WT rats was compared using a 2-way ANOVA. The uptake of [18F]FDS was significantly higher in the whole brain of AD rats, as compared with WT rats (P<0.001), suggesting increased BBB permeability. Enhanced brain uptake of [18F]FDS in AD rats was significantly different across brain regions (P<0.001). Minimum difference was observed in the amygdala (+89.0±7.6%, P<0.001) and maximum difference was observed in the midbrain (+177.8±29.2%, P<0.001). [18F]FDS, initially proposed as radio-pharmaceutical to estimate renal filtration using PET imaging, can be repurposed for non-invasive and quantitative determination of BBB permeability in vivo. Making the best with the quantitative properties of PET imaging, it was possible to estimate the extent of enhanced BBB permeability in a rat model of AD.

A radioligand for in vitro autoradiography of CSF1R in post-mortem CNS tissues

BACKGROUND

Reactive microglia and recruited peripheral macrophages contribute to the pathogenesis of Alzheimer's dementia (AD). Monocytes, macrophages and microglia all express the marker colony-stimulating factor 1 receptor (CSF1R). 4-Cyano-N-(4-(4-methylpiperazin-1-yl)-2-(4-methylpiperidin-1-yl)phenyl)-1H-pyrrole-2-carboxamide (1) is a high-affinity antagonist for CSF1R. We report the radiosynthesis of both [3H]1 and [11C]1. The PET imaging properties of [11C]1 in mice and baboon were investigated. [3H]1 was studied in Bmax measurement in post-mortem autoradiography in the frontal cortex, inferior parietal cortex and hippocampus from donors diagnosed with AD and age-matched controls. In vitro binding affinity of 1 was measured commercially. Nor-methyl-1 precursor was radiolabeled with [11C]iodomethane or [3H]iodomethane to produce [11C]1 and [3H]1, respectively. Ex vivo brain biodistribution of [11C]1 was compared in normal mice versus lipopolysaccharide-administered (LPS) murine model of neuroinflammation. Dynamic PET imaging was performed in a healthy male Papio anubis baboon. Post-mortem autoradiography with [3H]1 was performed in frozen sections using a standard saturation binding technique.

RESULTS

Compound 1 exhibits a high in vitro CSF1R binding affinity (0.59 nM). [11C]1 was synthesized with high yield. [3H]1 was synthesized similarly (commercially). Biodistribution of [11C]1 in healthy mice demonstrated moderate brain uptake. In LPS-treated mice the brain uptake of [11C]1 was ~ 50% specific for CSF1R. PET/CT [11C]1 study in baboon revealed low brain uptake (0.36 SUV) of [11C]1. Autoradiography with [3H]1 gave significantly elevated Bmax values in AD frontal cortex versus control (47.78 ± 26.80 fmol/mg vs. 12.80 ± 5.30 fmol/mg, respectively, P = 0.023) and elevated, but not significantly different binding in AD hippocampus grey matter and inferior parietal cortex (IPC) white matter.

CONCLUSIONS

Compound 1 exhibits a high in vitro CSF1R binding affinity. [11C]1 specifically labels CSF1R in the mouse neuroinflammation, but lacks the ability to efficiently cross the blood-brain barrier in baboon PET. [3H]1 specifically labels CSF1R in post-mortem human brain. The binding of [3H]1 is significantly higher in the post-mortem frontal cortex of AD versus control subjects.

Exploratory Tau PET/CT with [11C]PBB3 in Patients with Suspected Alzheimer's Disease and Frontotemporal Lobar Degeneration: A Pilot Study on Correlation with PET Imaging and Cerebrospinal Fluid Biomarkers

Accurately diagnosing Alzheimer's disease (AD) and frontotemporal lobar degeneration (FTLD) is challenging due to overlapping symptoms and limitations of current imaging methods. This study investigates the use of [11C]PBB3 PET/CT imaging to visualize tau pathology and improve diagnostic accuracy. Given diagnostic challenges with symptoms and conventional imaging, [11C]PBB3 PET/CT's potential to enhance accuracy was investigated by correlating tau pathology with cerebrospinal fluid (CSF) biomarkers, positron emission tomography (PET), computed tomography (CT), amyloid-beta, and Mini-Mental State Examination (MMSE). We conducted [11C]PBB3 PET/CT imaging on 24 patients with suspected AD or FTLD, alongside [11C]PiB PET/CT (13 patients) and [18F]FDG PET/CT (15 patients). Visual and quantitative assessments of [11C]PBB3 uptake using standardized uptake value ratios (SUV-Rs) and correlation analyses with clinical assessments were performed. The scans revealed distinct tau accumulation patterns; 13 patients had no or faint uptake (PBB3-negative) and 11 had moderate to pronounced uptake (PBB3-positive). Significant inverse correlations were found between [11C]PBB3 SUV-Rs and MMSE scores, but not with CSF-tau or CSF-amyloid-beta levels. Here, we show that [11C]PBB3 PET/CT imaging can reveal distinct tau accumulation patterns and correlate these with cognitive impairment in neurodegenerative diseases. Our study demonstrates the potential of [11C]PBB3-PET imaging for visualizing tau pathology and assessing disease severity, offering a promising tool for enhancing diagnostic accuracy in AD and FTLD. Further research is essential to validate these findings and refine the use of tau-specific PET imaging in clinical practice, ultimately improving patient care and treatment outcomes.

AITeQ: a machine learning framework for Alzheimer's prediction using a distinctive five-gene signature

Neurodegenerative diseases, such as Alzheimer's disease, pose a significant global health challenge with their complex etiology and elusive biomarkers. In this study, we developed the Alzheimer's Identification Tool (AITeQ) using ribonucleic acid-sequencing (RNA-seq), a machine learning (ML) model based on an optimized ensemble algorithm for the identification of Alzheimer's from RNA-seq data. Analysis of RNA-seq data from several studies identified 87 differentially expressed genes. This was followed by a ML protocol involving feature selection, model training, performance evaluation, and hyperparameter tuning. The feature selection process undertaken in this study, employing a combination of four different methodologies, culminated in the identification of a compact yet impactful set of five genes. Twelve diverse ML models were trained and tested using these five genes (CNKSR1, EPHA2, CLSPN, OLFML3, and TARBP1). Performance metrics, including precision, recall, F1 score, accuracy, Matthew's correlation coefficient, and receiver operating characteristic area under the curve were assessed for the finally selected model. Overall, the ensemble model consisting of logistic regression, naive Bayes classifier, and support vector machine with optimized hyperparameters was identified as the best and was used to develop AITeQ. AITeQ is available at: https://github.com/ishtiaque-ahammad/AITeQ.

Non-invasive quantification of 18F-florbetaben with total-body EXPLORER PET

BACKGROUND

Kinetic modeling of 18F-florbetaben provides important quantification of brain amyloid deposition in research and clinical settings but its use is limited by the requirement of arterial blood data for quantitative PET. The total-body EXPLORER PET scanner supports the dynamic acquisition of a full human body simultaneously and permits noninvasive image-derived input functions (IDIFs) as an alternative to arterial blood sampling. This study quantified brain amyloid burden with kinetic modeling, leveraging dynamic 18F-florbetaben PET in aorta IDIFs and the brain in an elderly cohort.

METHODS

18F-florbetaben dynamic PET imaging was performed on the EXPLORER system with tracer injection (300 MBq) in 3 individuals with Alzheimer's disease (AD), 3 with mild cognitive impairment, and 9 healthy controls. Image-derived input functions were extracted from the descending aorta with manual regions of interest based on the first 30 s after injection. Dynamic time-activity curves (TACs) for 110 min were fitted to the two-tissue compartment model (2TCM) using population-based metabolite corrected IDIFs to calculate total and specific distribution volumes (VT, Vs) in key brain regions with early amyloid accumulation. Non-displaceable binding potential ([Formula: see text] was also calculated from the multi-reference tissue model (MRTM).

RESULTS

Amyloid-positive (AD) patients showed the highest VT and VS in anterior cingulate, posterior cingulate, and precuneus, consistent with [Formula: see text] analysis. [Formula: see text]and VT from kinetic models were correlated (r² = 0.46, P < 2[Formula: see text] with a stronger positive correlation observed in amyloid-positive participants, indicating reliable model fits with the IDIFs. VT from 2TCM was highly correlated ([Formula: see text]= 0.65, P < 2[Formula: see text]) with Logan graphical VT estimation.

CONCLUSION

Non-invasive quantification of amyloid binding from total-body 18F-florbetaben PET data is feasible using aorta IDIFs with high agreement between kinetic distribution volume parameters compared to [Formula: see text]in amyloid-positive and amyloid-negative older individuals.

Recent Advances in Fluorescent Theranostics for Alzheimer's Disease: A Comprehensive Survey on Design, Synthesis, and Properties

Alzheimer's disease (AD) is the most common form of neurodegenerative dementia that is rapidly becoming a major health problem, especially in developed countries because of their increasing life expectancy. Two main problems are often associated with the disease: (i) the absence of a widely accessible "gold-standard" for early diagnosis and (ii) lack of effective therapies with disease-modifying effects. The recent success of the monoclonal antibody lecanemab played an important role not only in clarifying a possible druggable pathway but also in spelling the revival of small molecule drug discovery. Unlike bulky biologics, small molecules are structurally less complex, generally cheaper, and compatible with at-home oral consumption, making it feasible for people to start their drug regimen early and stay on it longer. In this sense, small-molecule near-infrared fluorescent theranostics have been gaining more and more attention from the scientific community, as they have the potential to simultaneously provide diagnostic outputs and deliver therapeutic action, paving the way toward personalized medicine in AD patients. They also have the potential to shift the diagnostic "status-quo" from expensive and limited-access PET radiotracers toward inexpensive and handy imaging tools widely available for primary patient screening and preclinical animal studies. Herein, we review the most recent advances in the field of fluorescent theranostics for Alzheimer's disease, detailing their design strategies, synthetic approaches and imaging and therapeutic properties in vitro and in vivo. With this Review, we intend to provide a milestone in the acquired knowledge in the field of AD theranostics, encouraging the future development of properly designed theranostic compounds with improved chances to reach clinical applications.

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.

A hybrid multimodal machine learning model for Detecting Alzheimer's disease

Alzheimer's disease (AD) diagnosis utilizing single modality neuroimaging data has limitations. Multimodal fusion of complementary biomarkers may improve diagnostic performance. This study proposes a multimodal machine learning framework integrating magnetic resonance imaging (MRI), positron emission tomography (PET) and cerebrospinal fluid (CSF) assays for enhanced AD characterization. The model incorporates a hybrid algorithm combining enhanced Harris Hawks Optimization (HHO) algorithm referred to as ILHHO, with Kernel Extreme Learning Machine (KELM) classifier for simultaneous feature selection and classification. ILHHO enhances HHO's search efficiency by integrating iterative mapping (IM) to improve population diversity and local escaping operator (LEO) to balance exploration-exploitation. Comparative analysis with other improved HHO algorithms, classic meta-heuristic algorithms (MHAs), and state-of-the-art MHAs on IEEE CEC2014 benchmark functions indicates that ILHHO achieves superior optimization performance compared to other comparative algorithms. The synergistic ILHHO-KELM model is evaluated on 202 AD Neuroimaging Initiative (ADNI) subjects. Results demonstrate superior multimodal classification accuracy over single modalities, validating the importance of fusing heterogeneous biomarkers. MRI + PET + CSF achieves 99.2 % accuracy for AD vs. normal control (NC), outperforming conventional and proposed methods. Discriminative feature analysis provides further insights into differential AD-related neurodegeneration patterns detected by MRI and PET. The differential PET and MRI features demonstrate how the two modalities provide complementary biomarkers. The neuroanatomical relevance of selected features supports ILHHO-KELM's potential for extracting sensitive AD imaging signatures. Overall, the study showcases the advantages of capitalizing on complementary multimodal data through advanced feature learning techniques for improving AD diagnosis.

A multi-resolution TOF-DOI detector for human brain dedicated PET scanner

Objective. We propose a single-ended readout, multi-resolution detector design that can achieve high spatial, depth-of-interaction (DOI), and time-of-flight (TOF) resolutions, as well as high sensitivity for human brain-dedicated positron emission tomography (PET) scanners.Approach. The detector comprised two layers of LYSO crystal arrays and a lightguide in between. The top (gamma ray entrance) layer consisted of a 16 × 16 array of 1.53 × 1.53 × 6 mm3LYSO crystals for providing high spatial resolution. The bottom layer consisted of an 8 × 8 array of 3.0 × 3.0 × 15 mm3LYSO crystals that were one-to-one coupled to an 8 × 8 multipixel photon counter (MPPC) array for providing high TOF resolution. The 2 mm thick lightguide introduces inter-crystal light sharing that causes variations of the light distribution patterns for high DOI resolution. The detector was read out by a PETsys TOFPET2 application-specific integrated circuit.Main result. The top and bottom layers were distinguished by a convolutional neural network with 97% accuracy. All crystals in the top and bottom layers were resolved. The inter-crystal scatter (ICS) events in the bottom layer were identified, and the measured average DOI resolution of the bottom layer was 4.1 mm. The coincidence time resolution (CTR) for the top-top, top-bottom, and bottom-bottom coincidences was 476 ps, 405 ps, and 298 ps, respectively. When ICS events were excluded from the bottom layer, the CTR of the bottom-bottom coincidence was 277 ps.Significance. The top layer of the proposed two-layer detector achieved a high spatial resolution and the bottom layer achieved a high TOF resolution. Together with its high DOI resolution and detection efficiency, the proposed detector is well suited for next-generation high-performance brain-dedicated PET scanners.

Dual-Planar Monopole Antenna-Based Remote Sensing System for Microwave Medical Applications

Neurodegenerative diseases (NDs) can be life threatening and have chronic impacts on patients and society. Timely diagnosis and treatment are imperative to prevent deterioration. Conventional imaging modalities, such as Computed Tomography (CT), Magnetic Resonance Imaging (MRI), and Positron Emission Tomography (PET), are expensive and not readily accessible to patients. Microwave sensing and imaging (MSI) systems are promising tools for monitoring pathological changes, namely the lateral ventricle enlargement associated with ND, in a non-invasive and convenient way. This paper presents a dual-planar monopole antenna-based remote sensing system for ND monitoring. First, planar monopole antennas were designed using the simulation software CST Studio Suite. The antenna analysis was carried out regarding the reflection coefficient, gain, radiation pattern, time domain characterization, E-field distribution, and Specific Absorption Rate (SAR). The designed antennas were then integrated with a controlling circuit as a remote sensing system. The system was experimentally validated on brain phantoms using a vector network analyzer and a laptop. The collected reflection coefficient data were processed using a radar-based imaging algorithm to reconstruct images indicating brain abnormality in ND. The results suggest that the system could serve as a low-cost and efficient tool for long-term monitoring of ND, particularly in clinics and care home scenarios.

Non-invasive quantification of 18F-florbetaben with total-body EXPLORER PET

Purpose

Kinetic modeling of 18F-florbetaben provides important quantification of brain amyloid deposition in research and clinical settings but its use is limited by the requirement of arterial blood data for quantitative PET. The total-body EXPLORER PET scanner supports the dynamic acquisition of a full human body simultaneously and permits noninvasive image-derived input functions (IDIFs) as an alternative to arterial blood sampling. This study quantified brain amyloid burden with kinetic modeling, leveraging dynamic 18F-florbetaben PET in aorta IDIFs and the brain in an elderly cohort.

Methods

18F-florbetaben dynamic PET imaging was performed on the EXPLORER system with tracer injection (300 MBq) in 3 individuals with Alzheimer's disease (AD), 3 with mild cognitive impairment, and 9 healthy controls. Image-derived input functions were extracted from the descending aorta with manual regions of interest based on the first 30 seconds after injection. Dynamic time-activity curves (TACs) for 110 minutes were fitted to the two-tissue compartment model (2TCM) using population-based metabolite corrected IDIFs to calculate total and specific distribution volumes (VT, Vs) in key brain regions with early amyloid accumulation. Non-displaceable binding potential (BPND) was also calculated from the multi-reference tissue model (MRTM).

Results

Amyloid-positive (AD) patients showed the highest VT and VS in anterior cingulate, posterior cingulate, and precuneus, consistent with BPND analysis. BPND and VT from kinetic models were correlated (r2 = 0.46, P<2e-16) with a stronger positive correlation observed in amyloid-positive participants, indicating reliable model fits with the IDIFs. VT from 2TCM was highly correlated (r2 = 0.65, P< 2e-16) with Logan graphical VT estimation.

Conclusion

Non-invasive quantification of amyloid binding from total-body 18F-florbetaben PET data is feasible using aorta IDIFs with high agreement between kinetic distribution volume parameters compared to BPND in amyloid-positive and negative older individuals.

Development of fluorophores for the detection of oligomeric aggregates of amyloidogenic proteins found in neurodegenerative diseases

Alzheimer's disease and Parkinson's disease are the two most common neurodegenerative diseases globally. These neurodegenerative diseases have characteristic late-stage symptoms allowing for differential diagnosis; however, they both share the presence of misfolded protein aggregates which appear years before clinical manifestation. Historically, research has focused on the detection of higher-ordered aggregates (or amyloids); however, recent evidence has shown that the oligomeric state of these protein aggregates plays a greater role in disease pathology, resulting in increased efforts to detect oligomers to aid in disease diagnosis. In this review, we summarize some of the exciting new developments towards the development of fluorescent probes that can detect oligomeric aggregates of amyloidogenic proteins present in Alzheimer's and Parkinson's disease patients.

Conv-eRVFL: Convolutional Neural Network Based Ensemble RVFL Classifier for Alzheimer's Disease Diagnosis

As per the latest statistics, Alzheimer's disease (AD) has become a global burden over the following decades. Identifying AD at the intermediate stage became challenging, with mild cognitive impairment (MCI) utilizing credible biomarkers and robust learning approaches. Neuroimaging techniques like magnetic resonance imaging (MRI) and positron emission tomography (PET) are practical research approaches that provide structural atrophies and metabolic variations. With the help of MRI and PET scans, metabolic and structural changes in AD patients can be visible even ten years before the disease's onset. This paper proposes a novel wavelet packet transform-based structural and metabolic image fusion approach using MRI and PET scans. An eight-layer trained CNN extracts features from multiple layers and these features are fed to an ensemble of non-iterative random vector functional link (RVFL) models. The RVFL network incorporates the s-membership fuzzy function as an activation function that helps overcome outliers. Lastly, outputs of all the customized RVFL classifiers are averaged and fed to the RVFL classifier to make the final decision. Experiments are performed over Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset, and classification is made over CN vs. AD vs. MCI. The model performance obtained is decent enough to prove the effectiveness of the fusion-based ensemble approach.

Noradrenergic neuromodulation in ageing and disease

The locus coeruleus (LC) is a small brainstem structure located in the lower pons and is the main source of noradrenaline (NA) in the brain. Via its phasic and tonic firing, it modulates cognition and autonomic functions and is involved in the brain's immune response. The extent of degeneration to the LC in healthy ageing remains unclear, however, noradrenergic dysfunction may contribute to the pathogenesis of Alzheimer's (AD) and Parkinson's disease (PD). Despite their differences in progression at later disease stages, the early involvement of the LC may lead to comparable behavioural symptoms such as preclinical sleep problems and neuropsychiatric symptoms as a result of AD and PD pathology. In this review, we draw attention to the mechanisms that underlie LC degeneration in ageing, AD and PD. We aim to motivate future research to investigate how early degeneration of the noradrenergic system may play a pivotal role in the pathogenesis of AD and PD which may also be relevant to other neurodegenerative diseases.

Genetics of Alzheimer's Disease in the African American Population

Black/African American (AA) individuals have a higher risk of Alzheimer's disease (AD) than White non-Hispanic persons of European ancestry (EUR) for reasons that may include economic disparities, cardiovascular health, quality of education, and biases in the methods used to diagnose AD. AD is also heritable, and some of the differences in risk may be due to genetics. Many AD-associated variants have been identified by candidate gene studies, genome-wide association studies (GWAS), and genome-sequencing studies. However, most of these studies have been performed using EUR cohorts. In this paper, we review the genetics of AD and AD-related traits in AA individuals. Importantly, studies of genetic risk factors in AA cohorts can elucidate the molecular mechanisms underlying AD risk in AA and other populations. In fact, such studies are essential to enable reliable precision medicine approaches in persons with considerable African ancestry. Furthermore, genetic studies of AA cohorts allow exploration of the ways the impact of genes can vary by ancestry, culture, and economic and environmental disparities. They have yielded important gains in our knowledge of AD genetics, and increasing AA individual representation within genetic studies should remain a priority for inclusive genetic study design.

Multimodal multitask learning for predicting MCI to AD conversion using stacked polynomial attention network and adaptive exponential decay

Early identification and treatment of moderate cognitive impairment (MCI) can halt or postpone Alzheimer's disease (AD) and preserve brain function. For prompt diagnosis and AD reversal, precise prediction in the early and late phases of MCI is essential. This research investigates multimodal framework-based multitask learning in the following situations: (1) Differentiating early mild cognitive impairment (eMCI) from late MCI and (2) predicting when an MCI patient would acquire AD. Clinical data and two radiomics features on three brain areas deduced from magnetic resonance imaging were investigated (MRI). We proposed an attention-based module, Stack Polynomial Attention Network (SPAN), to firmly encode clinical and radiomics data input characteristics for successful representation from a small dataset. To improve multimodal data learning, we computed a potent factor using adaptive exponential decay (AED). We used experiments from the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort study, which included 249 eMCI and 427 lMCI participants at baseline visits. The proposed multimodal strategy yielded the best c-index score in time prediction of MCI to AD conversion (0.85) and the best accuracy in MCI-stage categorization ([Formula: see text]). Moreover, our performance was equivalent to that of contemporary research.

The microRNA-485-3p concentration in salivary exosome-enriched extracellular vesicles is related to amyloid β deposition in the brain of patients with Alzheimer's disease

OBJECTIVES

Alzheimer's disease (AD) is an irreversible neurodegenerative disease characterized by progressive long-term memory loss and cognitive dysfunction. Neuroimaging tests for abnormal amyloid-β (Aβ) deposition are considered the most reliable methods for the diagnosis of AD; however, the cost for such testing is very high and generally not covered by national insurance systems. Accordingly, it is only recommended for individuals exhibiting clinical symptoms of AD supported by clinical cognitive assessments. Recently, it was suggested that dysregulated microRNA-485-3p (miRNA-485-3p) in the brain and cerebrospinal fluid is closely related to pathogenesis of AD. However, a relationship between circulating miRNA-485-3p in salivary exosome-enriched extracellular vesicles (EE-EV) and Aβ deposition in the brain has not been observed.

DESIGN & METHODS

Using quantitative real-time polymerase chain reaction, we analyzed miRNA-485-3p concentration in salivary EE-EV. We used receiver operating characteristic (ROC) curve analysis to evaluate its predictive value for Aβ positron emission tomography (Aβ-PET) positivity in patients with AD.

RESULTS

Our results showed that the miRNA-485-3p concentration in salivary EE-EV isolated from patients with AD was significantly increased compared with that in the healthy controls (p<0.0001). In the analysis of all participants, the miRNA-485-3p concentration was significantly increased in Aβ-PET-positive participants compared to Aβ-PET-negative participants (p<0.0001). Further analysis using only AD patients also showed that the miRNA-485-3p concentration was significantly increased in Aβ-PET-positive AD patients vs. Aβ-PET-negative AD patients (p=0.0063). The ROC curve analysis for differentiating Aβ-PET-positive and negative participants showed that the area under the curve for miRNA-485-3p was 0.9217.

CONCLUSION

These findings suggested that the miRNA-485-3p concentration in salivary EE-EV was closely related to Aβ deposition in the brain and had high diagnostic accuracy for predicting Aβ-PET positivity.

Systematic review of 31P-magnetic resonance spectroscopy studies of brain high energy phosphates and membrane phospholipids in aging and Alzheimer's disease

Many lines of evidence suggest that mitochondria have a central role in aging-related neurodegenerative diseases, such as Alzheimer's disease (AD). Mitochondrial dysfunction, cerebral energy dysmetabolism and oxidative damage increase with age, and are early event in AD pathophysiology and may precede amyloid beta (Aβ) plaques. In vivo probes of mitochondrial function and energy metabolism are therefore crucial to characterize the bioenergetic abnormalities underlying AD risk, and their relationship to pathophysiology and cognition. A majority of the research conducted in humans have used 18F-fluoro-deoxygluose (FDG) PET to image cerebral glucose metabolism (CMRglc), but key information regarding oxidative phosphorylation (OXPHOS), the process which generates 90% of the energy for the brain, cannot be assessed with this method. Thus, there is a crucial need for imaging tools to measure mitochondrial processes and OXPHOS in vivo in the human brain. 31Phosphorus-magnetic resonance spectroscopy (31P-MRS) is a non-invasive method which allows for the measurement of OXPHOS-related high-energy phosphates (HEP), including phosphocreatine (PCr), adenosine triphosphate (ATP), and inorganic phosphate (Pi), in addition to potential of hydrogen (pH), as well as components of phospholipid metabolism, such as phosphomonoesters (PMEs) and phosphodiesters (PDEs). Herein, we provide a systematic review of the existing literature utilizing the 31P-MRS methodology during the normal aging process and in patients with mild cognitive impairment (MCI) and AD, with an additional focus on individuals at risk for AD. We discuss the strengths and limitations of the technique, in addition to considering future directions toward validating the use of 31P-MRS measures as biomarkers for the early detection of AD.

Should artificial intelligence be used in conjunction with Neuroimaging in the diagnosis of Alzheimer's disease?

Alzheimer's disease (AD) is a progressive, neurodegenerative disorder that affects memory, thinking, behavior, and other cognitive functions. Although there is no cure, detecting AD early is important for the development of a therapeutic plan and a care plan that may preserve cognitive function and prevent irreversible damage. Neuroimaging, such as magnetic resonance imaging (MRI), computed tomography (CT), and positron emission tomography (PET), has served as a critical tool in establishing diagnostic indicators of AD during the preclinical stage. However, as neuroimaging technology quickly advances, there is a challenge in analyzing and interpreting vast amounts of brain imaging data. Given these limitations, there is great interest in using artificial Intelligence (AI) to assist in this process. AI introduces limitless possibilities in the future diagnosis of AD, yet there is still resistance from the healthcare community to incorporate AI in the clinical setting. The goal of this review is to answer the question of whether AI should be used in conjunction with neuroimaging in the diagnosis of AD. To answer the question, the possible benefits and disadvantages of AI are discussed. The main advantages of AI are its potential to improve diagnostic accuracy, improve the efficiency in analyzing radiographic data, reduce physician burnout, and advance precision medicine. The disadvantages include generalization and data shortage, lack of in vivo gold standard, skepticism in the medical community, potential for physician bias, and concerns over patient information, privacy, and safety. Although the challenges present fundamental concerns and must be addressed when the time comes, it would be unethical not to use AI if it can improve patient health and outcome.

Stem cells in the treatment of Alzheimer's disease - Promises and pitfalls

Alzheimer's disease (AD) is the most widespread form of neurodegenerative disorder that causes memory loss and multiple cognitive issues. The underlying mechanisms of AD include the build-up of amyloid-β and phosphorylated tau, synaptic damage, elevated levels of microglia and astrocytes, abnormal microRNAs, mitochondrial dysfunction, hormonal imbalance, and age-related neuronal loss. However, the etiology of AD is complex and involves a multitude of environmental and genetic factors. Currently, available AD medications only alleviate symptoms and do not provide a permanent cure. Therefore, there is a need for therapies that can prevent or reverse cognitive decline, brain tissue loss, and neural instability. Stem cell therapy is a promising treatment for AD because stem cells possess the unique ability to differentiate into any type of cell and maintain their self-renewal. This article provides an overview of the pathophysiology of AD and existing pharmacological treatments. This review article focuses on the role of various types of stem cells in neuroregeneration, the potential challenges, and the future of stem cell-based therapies for AD, including nano delivery and gaps in stem cell technology.

Urinary Biomarkers and Point-of-Care Urinalysis Devices for Early Diagnosis and Management of Disease: A Review

Biosensing and microfluidics technologies are transforming diagnostic medicine by accurately detecting biomolecules in biological samples. Urine is a promising biological fluid for diagnostics due to its noninvasive collection and wide range of diagnostic biomarkers. Point-of-care urinalysis, which integrates biosensing and microfluidics, has the potential to bring affordable and rapid diagnostics into the home to continuing monitoring, but challenges still remain. As such, this review aims to provide an overview of biomarkers that are or could be used to diagnose and monitor diseases, including cancer, cardiovascular diseases, kidney diseases, and neurodegenerative disorders, such as Alzheimer’s disease. Additionally, the different materials and techniques for the fabrication of microfluidic structures along with the biosensing technologies often used to detect and quantify biological molecules and organisms are reviewed. Ultimately, this review discusses the current state of point-of-care urinalysis devices and highlights the potential of these technologies to improve patient outcomes. Traditional point-of-care urinalysis devices require the manual collection of urine, which may be unpleasant, cumbersome, or prone to errors. To overcome this issue, the toilet itself can be used as an alternative specimen collection and urinalysis device. This review then presents several smart toilet systems and incorporated sanitary devices for this purpose.

The Role of Molecular Imaging in Personalized Medicine

The concept of personalized medicine refers to the tailoring of medical treatment to each patient's unique characteristics. Scientific advancements have led to a better understanding of how a person's unique molecular and genetic profile makes them susceptible to certain diseases. It provides individualized medical treatments that will be safe and effective for each patient. Molecular imaging modalities play an essential role in this aspect. They are used widely in screening, detection and diagnosis, treatment, assessing disease heterogeneity and progression planning, molecular characteristics, and long-term follow-up. In contrast to conventional imaging approaches, molecular imaging techniques approach images as the knowledge that can be processed, allowing for the collection of relevant knowledge in addition to the evaluation of enormous patient groups. This review presents the fundamental role of molecular imaging modalities in personalized medicine.

State-of-the-art imaging of neuromodulatory subcortical systems in aging and Alzheimer's disease: Challenges and opportunities

Primary prevention trials have shifted their focus to the earliest stages of Alzheimer's disease (AD). Autopsy data indicates that the neuromodulatory subcortical systems' (NSS) nuclei are specifically vulnerable to initial tau pathology, indicating that these nuclei hold great promise for early detection of AD in the context of the aging brain. The increasing availability of new imaging methods, ultra-high field scanners, new radioligands, and routine deep brain stimulation implants has led to a growing number of NSS neuroimaging studies on aging and neurodegeneration. Here, we review findings of current state-of-the-art imaging studies assessing the structure, function, and molecular changes of these nuclei during aging and AD. Furthermore, we identify the challenges associated with these imaging methods, important pathophysiologic gaps to fill for the AD NSS neuroimaging field, and provide future directions to improve our assessment, understanding, and clinical use of in vivo imaging of the NSS.

Decoding degeneration: the implementation of machine learning for clinical detection of neurodegenerative disorders

Machine learning represents a growing subfield of artificial intelligence with much promise in the diagnosis, treatment, and tracking of complex conditions, including neurodegenerative disorders such as Alzheimer's and Parkinson's diseases. While no definitive methods of diagnosis or treatment exist for either disease, researchers have implemented machine learning algorithms with neuroimaging and motion-tracking technology to analyze pathologically relevant symptoms and biomarkers. Deep learning algorithms such as neural networks and complex combined architectures have proven capable of tracking disease-linked changes in brain structure and physiology as well as patient motor and cognitive symptoms and responses to treatment. However, such techniques require further development aimed at improving transparency, adaptability, and reproducibility. In this review, we provide an overview of existing neuroimaging technologies and supervised and unsupervised machine learning techniques with their current applications in the context of Alzheimer's and Parkinson's diseases.

Poly (ADP-ribose) polymerases as PET imaging targets for central nervous system diseases

Poly (ADP-ribose) polymerases (PARPs) constitute of 17 members that are associated with divergent cellular processes and play a crucial role in DNA repair, chromatin organization, genome integrity, apoptosis, and inflammation. Multiple lines of evidence have shown that activated PARP1 is associated with intense DNA damage and irritating inflammatory responses, which are in turn related to etiologies of various neurological disorders. PARP1/2 as plausible therapeutic targets have attracted considerable interests, and multitudes of PARP1/2 inhibitors have emerged for treating cancer, metabolic, inflammatory, and neurological disorders. Furthermore, PARP1/2 as imaging targets have been shown to detect, delineate, and predict therapeutic responses in many diseases by locating and quantifying the expression levels of PARP1/2. PARP1/2-directed noninvasive positron emission tomography (PET) has potential in diagnosing and prognosing neurological diseases. However, quantitative PARP PET imaging in the central nervous system (CNS) has evaded us due to the challenges of developing blood-brain barrier (BBB) penetrable PARP radioligands. Here, we review PARP1/2's relevance in CNS diseases, summarize the recent progress on PARP PET and discuss the possibilities of developing novel PARP radiotracers for CNS diseases.

Multimodal neuroimage data fusion based on multikernel learning in personalized medicine

Neuroimaging has been widely used as a diagnostic technique for brain diseases. With the development of artificial intelligence, neuroimaging analysis using intelligent algorithms can capture more image feature patterns than artificial experience-based diagnosis. However, using only single neuroimaging techniques, e.g., magnetic resonance imaging, may omit some significant patterns that may have high relevance to the clinical target. Therefore, so far, combining different types of neuroimaging techniques that provide multimodal data for joint diagnosis has received extensive attention and research in the area of personalized medicine. In this study, based on the regularized label relaxation linear regression model, we propose a multikernel version for multimodal data fusion. The proposed method inherits the merits of the regularized label relaxation linear regression model and also has its own superiority. It can explore complementary patterns across different modal data and pay more attention to the modal data that have more significant patterns. In the experimental study, the proposed method is evaluated in the scenario of Alzheimer’s disease diagnosis. The promising performance indicates that the performance of multimodality fusion via multikernel learning is better than that of single modality. Moreover, the decreased square difference between training and testing performance indicates that overfitting is reduced and hence the generalization ability is improved.

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.

Amyloid-β protein and MicroRNA-384 in NCAM-Labeled exosomes from peripheral blood are potential diagnostic markers for Alzheimer's disease

Objective

We aimed to establish a method to determine whether amyloid‐β (Aβ) protein and miR‐384 in peripheral blood neural cell adhesion molecule (NCAM)/ATP‐binding cassette transporter A1 (ABCA1) dual‐labeled exosomes may serve as diagnostic markers for the diagnosis of Alzheimer's disease (AD).

Methods

This was a multicenter study using a two‐stage design. The subjects included 45 subjective cognitive decline (SCD) patients, 50 amnesic mild cognitive impairment (aMCI) patients, 40 AD patients, and 30 controls in the discovery stage. The results were validated in the verification stage in 47 SCD patients, 45 aMCI patients, 45 AD patients, and 30 controls. NCAM single‐labeled and NCAM/ABCA1 double‐labeled exosomes in the peripheral blood were captured and detected by immunoassay.

Results

The Aβ42, Aβ_42/40, Tau, P‐T181‐tau, and miR‐384 levels in NCAM single‐labeled and NCAM/ABCA1 double‐labeled exosomes of the aMCI and AD groups were significantly higher than those of the SCD, control, and vascular dementia (VaD) groups (all p < 0.05). The Aβ42 and miR‐384 levels in NCAM/ABCA1 dual‐labeled exosomes of the aMCI and AD groups were higher than those of the control and VaD groups (all p < 0.05). The exosomal Aβ42, Aβ_42/40, Tau, P‐T181‐tau, and miR‐384 levels in peripheral blood were correlated with those in cerebrospinal fluid (all p < 0.05).

Conclusion

This study, for the first time, established a method that sorts specific surface marker exosomes using a two‐step immune capture technology. The plasma NCAM/ABCA1 dual‐labeled exosomal Aβ_42/40 and miR‐384 had potential advantages in the diagnosis of SCD.

MicroRNA-29c-3p in dual-labeled exosome is a potential diagnostic marker of subjective cognitive decline

OBJECTIVE

The present study aimed to determine whether peripheral blood neural cell adhesion molecule (NCAM)/amphiphysin 1 dual-labeled exosomal proteins and microRNAs (miRs) might serve as a marker for the early diagnosis of Alzheimer's disease (AD).

METHODS

This observational, retrospective, multicenter study used a two-stage design conducted in Beijing and Shanghai, China. The subjects included 76 patients with subjective cognitive decline (SCD), 80 with amnestic mild cognitive impairment (aMCI), 76 with dementia of Alzheimer's type (AD), 40 with vascular dementia (VaD), and 40 controls in the discovery stage. These results were confirmed in the verification stage. The levels of Aβ42, Aβ_42/40, T-Tau, P-T181-tau, neurofilament light chain (NfL), and miR-29c-3p in peripheral blood amphiphysin 1 single-labeled and NCAM/amphiphysin 1 dual-labeled exosomes were captured and detected by immunoassay.

RESULTS

In the discovery stage, the levels of Aβ42 and miR-29c-3p in peripheral blood NCAM/amphiphysin 1 dual-labeled exosome of the SCD group were significantly higher than those in control and VaD groups (all P < 0.05). The verification stage further confirmed the results of the discovery stage. Plasma NCAM/amphiphysin 1 dual-labeled exosomal miR-29c-3p showed a good diagnostic performance. The NCAM/amphiphysin 1 dual-labeled exosomal miR-29c-3p had the highest AUC for diagnosis of SCD. The levels of Aβ42, Aβ_42/40, Tau, P-T181-tau, and miR-29c-3p in peripheral blood exosomes were correlated to those in CSF (all P < 0.05). The combination of exosomal biomarkers had slightly higher diagnostic efficiency than the individual biomarkers and that the exosomal biomarkers had the same diagnostic power as the CSF biomarkers.

CONCLUSION

The plasma NCAM/amphiphysin 1 dual-labeled exosomal miR-29c-3p had potential advantages in the diagnosis of SCD.

Friend or Foe? Defining the Role of Glutamate in Aging and Alzheimer’s Disease

Aging is a naturally occurring decline of physiological processes and biological pathways that affects both the structural and functional integrity of the body and brain. These physiological changes reduce motor skills, executive function, memory recall, and processing speeds. Aging is also a major risk factor for multiple neurodegenerative disorders including Alzheimer’s disease (AD). Identifying a biomarker, or biomarkers, that signals the transition from physiological to pathological aging would aid in earlier therapeutic options or interventional strategies. Considering the importance of glutamate signaling in synaptic plasticity, motor movement, and cognition, this neurotransmitter serves as a juncture between cognitive health and disease. This article discusses glutamatergic signaling during physiological aging and the pathological changes observed in AD patients. Findings from studies in mouse models of successful aging and AD are reviewed and provide a biological context for this transition. Finally, current techniques to monitor brain glutamate are highlighted. These techniques may aid in elucidating time-point specific therapeutic windows to modify disease outcome.

PET Imaging in Animal Models of Alzheimer’s Disease

The successful development and translation of PET imaging agents targeting β-amyloid plaques and hyperphosphorylated tau tangles have allowed for in vivo detection of these hallmarks of Alzheimer’s disease (AD) antemortem. Amyloid and tau PET have been incorporated into the A/T/N scheme for AD characterization and have become an integral part of ongoing clinical trials to screen patients for enrollment, prove drug action mechanisms, and monitor therapeutic effects. Meanwhile, preclinical PET imaging in animal models of AD can provide supportive information for mechanistic studies. With the recent advancement of gene editing technologies and AD animal model development, preclinical PET imaging in AD models will further facilitate our understanding of AD pathogenesis/progression and the development of novel treatments. In this study, we review the current state-of-the-art in preclinical PET imaging using animal models of AD and suggest future research directions.

Neuroproteomics Chip-Based Mass Spectrometry and Other Techniques for Alzheimer´S Disease Biomarkers – Update

Background:

Alzheimer's disease (AD) is a progressive neurodegenerative disease of growing interest given that there is cognitive damage and symptom onset acceleration. Therefore, it is important to find AD biomarkers for early diagnosis, disease progression, and discrimination of AD and other diseases.

Objective:

To update the relevance of mass spectrometry for the identification of peptides and proteins involved in AD useful as discriminating biomarkers.

Methods:

Proteomics and peptidomics technologies that show the highest possible specificity and selectivity for AD biomarkers are analyzed, together with the biological fluids used. In addition to positron emission tomography and magnetic resonance imaging, MALDI-TOF mass spectrometry is widely used to identify proteins and peptides involved in AD. The use of protein chips in SELDI technology and electroblotting chips for peptides makes feasible small amounts (L) of samples for analysis.

Results:

Suitable biomarkers are related to AD pathology, such as intracellular neurofibrillary tangles; extraneuronal senile plaques; neuronal and axonal degeneration; inflammation and oxidative stress. Recently, peptides were added to the candidate list, which are not amyloid-b or tau fragments, but are related to coagulation, brain plasticity, and complement/neuroinflammation systems involving the neurovascular unit.

Conclusion:

The progress made in the application of mass spectrometry and recent chip techniques is promising for discriminating between AD, mild cognitive impairment, and matched healthy controls. The application of this technique to blood samples from patients with AD has shown to be less invasive and fast enough to determine the diagnosis, stage of the disease, prognosis, and follow-up of the therapeutic response.

Cognitive Decline Assessment: A Review From Medical Imaging Perspective

Aging is a complex process that involves changes at both molecular and morphological levels. However, our understanding of how aging affects brain anatomy and function is still poor. In addition, numerous biomarkers and imaging markers, usually associated with neurodegenerative diseases such as Alzheimer's disease (AD), have been clinically used to study cognitive decline. However, the path of cognitive decline from healthy aging to a mild cognitive impairment (MCI) stage has been studied only marginally. This review presents aspects of cognitive decline assessment based on the imaging differences between individuals cognitively unimpaired and in the decline spectrum. Furthermore, we discuss the relationship between imaging markers and the change in their patterns with aging by using neuropsychological tests. Our goal is to delineate how aging has been studied by using medical imaging tools and further explore the aging brain and cognitive decline. We find no consensus among the biomarkers to assess the cognitive decline and its relationship with the cognitive decline trajectory. Brain glucose hypometabolism was found to be directly related to aging and indirectly to cognitive decline. We still need to understand how to quantify an expected hypometabolism during cognitive decline during aging. The Aβ burden should be longitudinally studied to achieve a better consensus on its association with changes in the brain and cognition decline with aging. There exists a lack of standardization of imaging markers that highlight the need for their further improvement. In conclusion, we argue that there is a lot to investigate and understand cognitive decline better and seek a window for a suitable and effective treatment strategy.

Brain-Derived Exosomal Proteins as Effective Biomarkers for Alzheimer's Disease: A Systematic Review and Meta-Analysis

Alzheimer's disease (AD), a progressive neurodegenerative disease, affects approximately 50 million people worldwide, which warrants the search for reliable new biomarkers for early diagnosis of AD. Brain-derived exosomal (BDE) proteins, which are extracellular nanovesicles released by all cell lineages of the central nervous system, have been focused as biomarkers for diagnosis, screening, prognosis prediction, and monitoring in AD. This review focused on the possibility of BDE proteins as AD biomarkers. The articles published prior to 26 January 2021 were searched in PubMed, EMBASE, Web of Science, and Cochrane Library to identify all relevant studies that reported exosome biomarkers in blood samples of patients with AD. From 342 articles, 20 studies were selected for analysis. We conducted a meta-analysis of six BDE proteins and found that levels of amyloid-β42 (standardized mean difference (SMD) = 1.534, 95% confidence interval [CI]: 0.595-2.474), total-tau (SMD = 1.224, 95% CI: 0.534-1.915), tau phosphorylated at threonine 181 (SMD = 4.038, 95% CI: 2.312-5.764), and tau phosphorylated at serine 396 (SMD = 2.511, 95% CI: 0.795-4.227) were significantly different in patients with AD compared to those in control. Whereas, those of p-tyrosine-insulin receptor substrate-1 and heat shock protein 70 did not show significant differences. This review suggested that Aβ42, t-tau, p-T181-tau, and p-S396-tau could be effective in diagnosing AD as blood biomarkers, despite the limitation in the meta-analysis based on the availability of data. Therefore, certain BDE proteins could be used as effective biomarkers for AD.