Using the Alzheimer's Disease Neuroimaging Initiative to improve early detection, diagnosis, and treatment of Alzheimer's disease

The associations of cerebrospinal fluid ApoE and C1q with Alzheimer's disease biomarkers

BackgroundThe roles of complement 1q (C1q) and Apolipoprotein E (ApoE) in driving Alzheimer's disease (AD) progression might be explained by their associations with neuroinflammation and AD pathology which were previously reported.ObjectiveWe examined the associations of cerebrospinal fluid (CSF) C1q and ApoE with CSF neuroinflammatory biomarkers and AD core biomarkers, as well as explored whether C1q mediated the associations of CSF ApoE with these biomarkers.MethodsHere, we analyzed CSF proteomics data from Alzheimer's Disease Neuroimaging Initiative (ADNI) using two different ADNI proteomics datasets-SomaScan (n = 579)and multiple reaction monitoring (MRM[n = 207]). Linear regression analyses were conducted to explore the association of CSF ApoE and C1q. The mediation model and structural equation model (SEM) were conducted to explore the associations of ApoE and C1q with AD biomarkers.ResultsMultiple linear regression showed that CSF ApoE was positively associated with CSF C1q in total participants and Alzheimer's continuum participants. Mediation analyses indicated that C1q mediated the associations of CSF ApoE with CSF T-tau, P-tau, sTREM2 and GFAP (mediation proportions range from 15.06 to 44.64%; all the p values < 0.05) but not with CSF amyloid-β and progranulin (PGRN). The SEM yielded similar results.ConclusionsOur findings suggest that C1q is linked to ApoE, and it mediates the associations of ApoE with T-tau, P-tau, sTREM2, GFAP, indicating C1q association with ApoE might be involved in AD progression.

Advances in neurofilament light chain analysis

This chapter provides a comprehensive summary of clinical laboratory testing for neurofilament light chain (NfL) in neurologic disease. A primer on the NfL structure and function is presented with its potential use as a biomarker. The most widely utilized methods for NfL in biologic samples are highlighted and examined. Limitations of current knowledge are considered, as are outstanding questions related to dissemination and standardization of testing. Herein we focus on methods available today and those in development for clinical use. In the final section, a broad vision is presented of how NfL may be utilized in the future to improve diagnosis and treatment of neurologic diseases as well as for maintaining health.

Development and Validation of an HPLC Method to Determine Chemical and Radiochemical Purity of [18F]Florbetazine Injection

[18F]Florbetazine injection, a radiotracer that could target Aβ plaques and achieve diagnosis of Alzheimer's disease (AD), is a novel positron emission tomography (PET) imaging agent currently in the investigational new drug (IND) application stage. The active ingredient of [18F]Florbetazine injection, [18F]Florbetazine, is a diaryl-azine derivative. Chemical and radiochemical purity is critical quality attributes (CQAs) for [18F]Florbetazine injection, and thus, we have developed and validated a relevant HPLC method. This study describes the specificity, linearity, accuracy, repeatability, and limit of quantification (LOQ) of the HPLC method. The stability of three sample batches was investigated using the established method. The validation results demonstrated the accuracy, precision, and sensitivity of the method, making it suitable for implementation as part of the quality control (QC) process for [18F]Florbetazine injection. The stability of three sample batches revealed a decrease in concentration and radiochemical purity over 10 h. However, all samples maintained a radiochemical purity of over 90% after 10 h. The results provided a foundation for establishing quality standards for [18F]Florbetazine injection. The same methodology employed in this study could be applied and modified for QC protocols of other 18F-labeled radiopharmaceuticals.

Elevated cerebrospinal fluid biomarkers of neuroinflammation and neuronal damage in essential hypertension with secondary insomnia: Implications for Alzheimer's disease risk

Essential hypertension (EH) with secondary insomnia is associated with increased risks of neuroinflammation, neuronal damage, and Alzheimer's disease (AD). However, its relationship with specific cerebrospinal fluid (CSF) biomarkers of neuronal damage and neuroinflammation remains unclear. This case-control study compared CSF biomarker levels across three groups: healthy controls (HC, n = 64), hypertension-controlled (HTN-C, n = 54), and hypertension-uncontrolled (HTN-U, n = 107) groups, all EH participants experiencing secondary insomnia. CSF samples from knee replacement patients were analyzed for key biomarkers, and sleep quality was assessed via the Pittsburgh Sleep Quality Index (PSQI). Our findings showed that the HTN-U group had significantly higher CSF levels of proinflammatory cytokines IL-6, TNF-α, and IL-17 than the HC and HTN-C groups (all p < 0.01). These cytokines correlated positively with secondary insomnia measures, with IL-6 (r = 0.285, p = 0.003), IL-17 (r = 0.324, p = 0.001), and TNF-α (r = 0.274, p = 0.005) linked to PSQI scores. In the HTN-U group, elevated IL-6, TNF-α, and IL-17 levels were also positively associated with neurofilament light (NF-L) and negatively with β-amyloid 42 (Aβ42), both key AD markers (all p < 0.05). Additionally, secondary insomnia was negatively correlated with Aβ42 (r = -0.225, p = 0.021) and positively with NF-L (r = 0.261, p = 0.007). Higher CSF palmitic acid (PA) levels observed in the HTN-U group were linked to poorer sleep quality (r = 0.208, p = 0.033). In conclusion, EH with secondary insomnia is associated with CSF biomarkers of neuronal damage, neuroinflammation, and neurodegeneration, suggesting a potential increase in AD risk among this population.

Association of genetic risk of Alzheimer's disease and cognitive function in two European populations

Although there is some evidence of an association between Alzheimer's disease polygenic risk score (AD PRS) and cognitive function, limited validations have been performed in large populations. We investigated the relationship between AD PRS and cognitive function in the UK Biobank in over 276,000 participants and further validated the association in the Alzheimer's Disease Neuroimaging Initiative (ADNI) sample. We developed the AD PRS (excluded the APOE variants) in the middle age UK Biobank participants (age ranged 39-72, mean age 57 years) of European ancestries by LDpred2. To validate the association of AD PRS and cognitive function internally in the UK Biobank, we linearly regressed standardized cognitive function on continuous standardized AD PRS with age at cognitive test, sex, genotyping array, first 10 principal components of genotyping, smoking, education in years, body mass index, and apolipoprotein E gene ε4 (APOE4) risk allele dosages. To validate the associations externally, we ran the linear mixed effects model in the ADNI sample free of dementia (age ranged 55-91, mean age 73), including similar covariates as fixed effects and participants' IDs as the random effect. Stratification by age, APOE4 carrier status, and cognitive status (cognitively normal or mild cognitive impairment) was also investigated. Our study validated the associations of AD PRS and cognitive function in both midlife and late-life observational cohorts. Although not all of the cognitive measures were significantly associated with AD PRS, non-verbal fluid reasoning (matrix pattern completion, β = - 0.022, P = 0.003), processing speed (such as symbol digit substitution, β = - 0.017, P = 1.08E-05), short-term memory and attention (such as pairs matching, β = - 0.014, P = 1.66E-10), and reaction time (β = - 0.010, P = 1.19E-06) were inversely associated with increasing AD PRS in the UK Biobank. Higher likelihood of cognitive impairment was also associated with higher AD PRS in the ADNI cognitive normal individuals (AD assessment scale β = 0.079, P = 0.02). In summary, we confirmed that poorer cognitive function was associated with a higher polygenic AD risk, and suggested the potential utility of the AD PRS in identifying those who may be at risk for further cognitive decline.

Distinguishing Early from Late Mild Cognitive Impairment Using Magnetic Resonance Free-Water Diffusion Tensor Imaging

Mild Cognitive Impairment (MCI) is a transitional stage between normal aging and Alzheimer's disease. Differentiating early MCI (EMCI) from late MCI (LMCI) is crucial for early diagnosis and intervention. This study used free-water diffusion tensor imaging (fw-DTI) to investigate white matter differences and voxel-based correlations with Mini-Mental State Examination (MMSE) scores. Data from the Alzheimer's Disease Neuroimaging Initiative included 476 healthy controls (CN), 137 EMCI participants, and 62 LMCI participants. Significant MMSE differences were found between the CN and MCI groups, but not between EMCI and LMCI. However, distinct white matter changes were observed: LMCI showed a higher f-index and lower fw-fractional anisotropy (fw-FA) compared to EMCI in several white matter regions. These findings indicate specific white matter tracts involved in MCI progression. Voxel-based correlations between fw-DTI metrics and MMSE scores further supported these results. In conclusion, this study provides crucial insights into white matter changes associated with EMCI and LMCI, offering significant implications for future research and clinical practice.

APOE4 and infectious diseases jointly contribute to brain glucose hypometabolism, a biomarker of Alzheimer's pathology: New findings from the ADNI

BACKGROUND

Impaired brain glucose metabolism is a preclinical feature of neurodegenerative diseases such as Alzheimer's disease (AD). Infections may promote AD-related pathology. Therefore, we investigated the interplay between infections and APOE4, a strong genetic risk factor for AD.

METHODS

We analyzed data on 1,509 participants in the Alzheimer's Disease Neuroimaging Initiative (ADNI) database using multivariate linear regression models. The outcomes were rank-normalized hypometabolic convergence index (HCI), statistical regions of interest (SROI) for AD, and mild cognitive impairment (MCI). Marginal mean estimates for infections, stratified by APOE4 carrier status, were then computed.

RESULTS

Prior infections were associated with greater HCI [β = 0.15, 95% CI: 0.03, 0.27, p = 0.01]. The combined effects of infections and APOE4 carriers on HCI levels were significantly greater than either variable alone. Among APOE4 carriers, the estimated marginal mean was 0.62, rising to 0.77, with infections (p<0.001), indicating an interaction effect. Carriers with multiple infections showed greater hypometabolism (higher HCI), with an estimate of 0.44 (p = 0.01) compared to 0.11 (p = 0.08) for those with a single infection, revealing a dose-response relationship. The estimates for the association of infections with SROI AD and SROI MCI were β = -0.01 (p = 0.02) and β = -0.01 (p = 0.04), respectively.

CONCLUSION

Our findings suggest that infections and APOE4 jointly contribute to brain glucose hypometabolism and AD pathology, supporting a "multi-hit" mechanism in AD development.

Resilience mechanisms underlying Alzheimer's disease

Alzheimer's disease (AD) consists of two main pathologies, which are the deposition of amyloid plaque as well as tau protein aggregation. Evidence suggests that not everyone who carries the AD-causing genes displays AD-related symptoms; they might never acquire AD as well. These individuals are referred to as non-demented individuals with AD neuropathology (NDAN). Despite the presence of extensive AD pathology in their brain, it was found that NDAN had better cognitive function than was expected, suggesting that they were more resilient (better at coping) to AD due to differences in their brains compared to other demented or cognitively impaired patients. Thus, identification of the mechanisms underlying resilience is crucial since it represents a promising therapeutic strategy for AD. In this review, we will explore the molecular mechanisms underpinning the role of genetic and molecular resilience factors in improving resilience to AD. These include protective genes and proteins such as APOE2, BDNF, RAB10, actin network proteins, scaffolding proteins, and the basal forebrain cholinergic system. A thorough understanding of these resilience mechanisms is crucial for not just comprehending the development of AD but may also open new treatment possibilities for AD by enhancing the neuroprotective pathway and targeting the pathogenic process.

Overview of Alzheimer's Disease Neuroimaging Initiative and future clinical trials

The overall goal of the Alzheimer's Disease Neuroimaging Initiative (ADNI) is to optimize and validate biomarkers for clinical trials while sharing all data and biofluid samples with the global scientific community. ADNI has been instrumental in standardizing and validating amyloid beta (Aβ) and tau positron emission tomography (PET) imaging. ADNI data were used for the US Food and Drug Administration (FDA) approval of the Fujirebio and Roche Elecsys cerebrospinal fluid diagnostic tests. Additionally, ADNI provided data for the trials of the FDA-approved treatments aducanumab, lecanemab, and donanemab. More than 6000 scientific papers have been published using ADNI data, reflecting ADNI's promotion of open science and data sharing. Despite its enormous success, ADNI has some limitations, particularly in generalizing its data and findings to the entire US/Canadian population. This introduction provides a historical overview of ADNI and highlights its significant accomplishments and future vision to pioneer "the clinical trial of the future" focusing on demographic inclusivity. HIGHLIGHTS: The Alzheimer's Disease Neuroimaging Initiative (ADNI) introduced a novel model for public-private partnerships and data sharing. It successfully validated amyloid and Tau PET imaging, as well as CSF and plasma biomarkers, for diagnosing Alzheimer's disease. ADNI generated and disseminated vital data for designing AD clinical trials.

The prognosis of mild cognitive impairment: A systematic review and meta-analysis

INTRODUCTION

Knowledge gaps remain about the prognosis of mild cognitive impairment (MCI). Conversion rates to dementia vary widely, and reversion to normal cognition has gained attention. This review updates evidence on MCI conversion risk and probability of stability and reversion.

METHODS

We searched databases for studies on MCI prognosis with ≥3 years of follow-up, established criteria for MCI and dementia, and performed a meta-analysis using a random-effects model to assess conversion risk, reversion, and stability probability. Meta-regressions identified sources of heterogeneity and guided subgroup analysis.

RESULTS

From 89 studies (mean follow-up: 5.2 years), conversion risk was 41.5% (38.3%-44.7%) in clinical and 27.0% (22.0%-32.0%) in population-based studies, with Alzheimer's dementia as the most common outcome. Stability rates were 49.3% (clinical) and 49.8% (population). Reversion was 8.7% (clinical) and 28.2% (population).

DISCUSSION

Our findings highlight higher conversion in clinical settings and 30% reversion in population studies, calling for sustainable care pathway development.

Highlights

Prognosis for mild cognitive impairment (MCI) varies by setting; dementia risk is higher and the probability of reversion is lower in clinical-based studies.In both clinical and population settings, cognitive stability is ≈50%.A reorganization of health services could ensure sustainable care for individuals with MCI.Significant heterogeneity in MCI studies impacts data interpretation; follow-up length is crucial.Long-term prognosis studies on MCI in low- and middle-income countries are urgently needed.

ADNI Private Partners Scientific Board (PPSB) Diversity, Equity, and Inclusion (DE&I) Working Group: A new collaboration that crosses boundaries for industry, academia, and under-represented patients

The Alzheimer's Disease Neuroimaging Initiative (ADNI) Private Partners Scientific Board (PPSB) Diversity, Equity, and Inclusion Working Group (DE&I WG) was established to work with the ADNI3 Diversity Task Force to provide an industry perspective on increasing the representation of diverse participants in ADNI3 and to build precompetitive cross-industry knowledge in engagement and recruitment of under-represented participants (URPs). In this article, we review and highlight the role and ongoing activities within the ADNI PPSB DE&I WG and provide a cross-industry perspective on areas where precompetitive collaboration can improve the inclusiveness in clinical trials, drawing on examples from ADNI4. HIGHLIGHTS: New collaboration crosses boundaries to allow PPSB DE&I WG members to work together in a preproprietary way. When faced with the same challenges required by FDA combined with a growing prevalence of AD, the DE&I WG has drafted a range of initiatives that may benefit ADNI, AD patients, care partners, and respective companies involved in this work. In order to address the multifactorial problem of successfully enrolling representative populations in clinical trials, it will "take a village" to bring about sustainable changes.

Global brain activity and its coupling with cerebrospinal fluid flow is related to tau pathology

INTRODUCTION

Factors responsible for the deposition of pathological tau in the brain are incompletely understood. This study links macroscale tau deposition in the human brain to cerebrospinal fluid (CSF) flow dynamics using resting-state functional magnetic resonance imaging (rsfMRI).

METHODS

Low-frequency (< 0.1 Hz) resting-state global brain activity is coupled with CSF flow and potentially reflects CSF dynamics-related clearance. We examined the correlation between rsfMRI measures of CSF inflow and global activity (gBOLD-CSF coupling) as a predictor, interacting with amyloid beta (Aβ), of tau and cortical thickness (dependent variables) across Alzheimer's Disease Neuroimaging Initiative (ADNI) participants from cognitively unimpaired through mild cognitive impairment (MCI) and Alzheimer's disease (AD).

RESULTS

Tau deposition in Aβ+ participants, accompanied by cortical thinning and cognitive decline, is associated with decreased gBOLD-CSF coupling. Tau mediates the relationship between coupling and thickness.

DISCUSSION

Findings suggest that resting-state global brain activity and CSF movements comodulate Alzheimer's tau deposition, presumably related to CSF clearance.

HIGHLIGHTS

A non-invasive functional magnetic resonance imaging (fMRI) assessment of a CSF clearance-related process is carried out. Global brain activity is coupled with CSF inflow in human fMRI during resting state. Global fMRI-CSF coupling is correlated with tau in Alzheimer's disease (AD). This coupling measure is also associated with cortical thickness, mediated by tau.

The ADNI Administrative Core: Ensuring ADNI's success and informing future AD clinical trials

The Alzheimer's Disease Neuroimaging Initiative (ADNI) Administrative Core oversees and coordinates all ADNI activities, to ensure the success and maximize the impact of ADNI in advancing Alzheimer's disease (AD) research and clinical trials. It manages finances and develops policies for data sharing, publications using ADNI data, and access to ADNI biospecimens. The Core develops and executes pilot projects to guide future ADNI activities and identifies key innovative methods for inclusion in ADNI. For ADNI4, the Administrative Core collaborates with the Engagement, Clinical, and Biomarker Cores to develop and evaluate novel, digital methods and infrastructure for participant recruitment, screening, and assessment of participants. The goal of these efforts is to enroll 500 participants, including > 50% from underrepresented populations, 40% with mild cognitive impairment, and 80% with elevated AD biomarkers. This new approach also provides a unique opportunity to validate novel methods. HIGHLIGHTS: The Alzheimer's Disease Neuroimaging Initiative (ADNI) Administrative Core oversees and coordinates all ADNI activities. The overall goal is to ensure ADNI's success and help design future Alzheimer's disease (AD) clinical trials. A key innovation is data sharing without embargo to maximize scientific impact. For ADNI4, novel, digital methods for recruitment and assessment were developed. New methods are designed to improve the participation of underrepresented populations.

Association of amyloid and cardiovascular risk with cognition: Findings from KBASE

BACKGROUND

Limited research has explored the effect of cardiovascular risk and amyloid interplay on cognitive decline in East Asians.

METHODS

Vascular burden was quantified using Framingham's General Cardiovascular Risk Score (FRS) in 526 Korean Brain Aging Study (KBASE) participants. Cognitive differences in groups stratified by FRS and amyloid positivity were assessed at baseline and longitudinally.

RESULTS

Baseline analyses revealed that amyloid-negative (Aβ-) cognitively normal (CN) individuals with high FRS had lower cognition compared to Aβ- CN individuals with low FRS (p < 0.0001). Longitudinally, amyloid pathology predominantly drove cognitive decline, while FRS alone had negligible effects on cognition in CN and mild cognitive impairment (MCI) groups.

CONCLUSION

Our findings indicate that managing vascular risk may be crucial in preserving cognition in Aβ- individuals early on and before the clinical manifestation of dementia. Within the CN and MCI groups, irrespective of FRS status, amyloid-positive individuals had worse cognitive performance than Aβ- individuals.

HIGHLIGHTS

Vascular risk significantly affects cognition in amyloid-negative older Koreans. Amyloid-negative CN older adults with high vascular risk had lower baseline cognition. Amyloid pathology drives cognitive decline in CN and MCI, regardless of vascular risk. The study underscores the impact of vascular health on the AD disease spectrum.

Enhancing Alzheimer's disease classification through split federated learning and GANs for imbalanced datasets

In the rapidly evolving healthcare sector, using advanced technologies to improve medical classification systems has become crucial for enhancing patient care, diagnosis, and treatment planning. There are two main challenges faced in this domain (i) imbalanced distribution of medical data, leading to biased model performance and (ii) the need to preserve patient privacy and comply with data protection regulations. The primary goal of this project is to develop a medical classification model for Alzheimer's disease detection that can effectively learn from decentralized and imbalanced datasets without compromising on data privacy. The proposed system aims to address these challenges by employing an approach that combines split federated learning (SFL) with conditional generative adversarial networks (cGANs) to enhance medical classification models. SFL enables efficient set of distributed agents that collaboratively train learning models without sharing their data, thus improving data privacy and the integration of conditional GANs aims to improve the model's ability to generalize across imbalanced classes by generating realistic synthetic samples for minority classes. The proposed system provided an accuracy of approximately 83.54 percentage for the Alzheimer's disease classification dataset.

A nonlinear relationship between alcohol consumption and cognitive function in elderly people: a population-based study from NHANES 2011-2014

Objective

This study aims to explore the association between alcohol intake and cognitive function in elderly Americans, including potential nonlinear relationships and interactions across different subgroups.

Methods

The study analyzed data from the National Health and Nutrition Examination Survey (NHANES) from 2011 to 2014. The sample included 2,675 Americans aged 60 or older. Multivariate regression analysis was used to evaluate the relationship between alcohol intake and cognitive function. Smooth curve fitting and threshold effect analysis were employed to explore potential nonlinear relationships. Subgroup analyses were conducted to examine the stability of the results across different subgroups.

Results

The results indicate a significant negative correlation between alcohol intake and cognitive function. In the CERAD total word recall test, for every unit increase in alcohol intake, the score decreased by 0.15 points (-0.15, 95% CI: -0.25, -0.04), and in the CERAD delayed recall test, it decreased by 0.07 points (-0.07, 95% CI: -0.12, -0.01). Compared to Non-Heavy Drinkers, Heavy Drinkers showed a reduction in their CERAD total word recall scores by-0.77 points (-0.77, 95% CI: -1.23, -0.32) and in their CERAD delayed recall scores by-0.28 points (-0.28, 95% CI: -0.52, -0.04). Smooth curve fitting analysis revealed a nonlinear relationship between alcohol intake and cognitive function, with breakpoints at 10.7 for the CERAD total word recall test, 4.7 for the Animal fluency test, and 3.85 for the Digit symbol substitution test. Additionally, subgroup analysis indicated that gender, educational level, and smoking status significantly moderated the relationship between alcohol intake and cognitive function, while marital status, race, hypertension, diabetes, and cancer status showed no significant interactions.

Conclusion

The association between alcohol intake and cognitive function in the elderly is complex, influenced by both the amount of intake and individual subgroup characteristics.

The Alzheimer's Disease Neuroimaging Initiative Clinical Core

The Alzheimer's Disease Neuroimaging Initiative (ADNI) Clinical Core is responsible for coordination of all clinical activities at the ADNI sites, including project management, regulatory oversight, and site management and monitoring, as well as the collection of all clinical data and management of all study data. The Clinical Core is also charged with determining the clinical classifications and criteria for enrollment in evolving AD trials and enabling the ongoing characterization of the cross-sectional features and longitudinal trajectories of the ADNI cohorts with application of these findings to optimal clinical trial designs. More than 2400 individuals have been enrolled in the cohorts since the inception of ADNI, facilitating refinement of our understanding of the AD trajectory and allowing academic and industry investigators to model therapeutic trials across the disease spectrum from the presymptomatic stage through dementia. HIGHLIGHTS: Since 2004, the Alzheimer's Disease Neuroimaging Initiative (ADNI) Clinical Core has overseen the enrollment of > 2400 participants with mild cognitive impairment, mild Alzheimer's disease (AD) dementia, and normal cognition. The longitudinal dataset has elucidated the full cognitive and clinical trajectory of AD from its presymptomatic stage through the onset of dementia. The ADNI data have supported the design of most major trials in the field.

APOE4 and Infectious Diseases Jointly Contribute to Brain Glucose Hypometabolism, a Biomarker of Alzheimer's Pathology: New Findings from the ADNI

Background

Impaired brain glucose metabolism is a preclinical feature of neurodegenerative diseases such as Alzheimer's disease (AD). Infections may promote AD-related pathology. Therefore, we investigated the interplay between infections and APOE4, a strong genetic risk factor for AD.

Methods

We analyzed data on 1,509 participants in the Alzheimer's Disease Neuroimaging Initiative (ADNI) database using multivariate linear regression models. The outcomes were rank-normalized hypometabolic convergence index (HCI), statistical regions of interest (SROI) for AD, and mild cognitive impairment (MCI). Marginal mean estimates for infections, stratified by APOE4 carrier status, were then computed.

Results

Prior infections were associated with greater HCI [β=0.15, 95% CI: 0.03, 0.27, p=0.01]. The combined effects of infections and APOE4 carriers on HCI levels were significantly greater than either variable alone. Among APOE4 carriers, the estimated marginal mean was 0.62, rising to 0.77, with infections (p<0.001), indicating an interaction effect. Carriers with multiple infections showed greater hypometabolism (higher HCI), with an estimate of 0.44 (p=0.01) compared to 0.11 (p=0.08) for those with a single infection, revealing a dose-response relationship. The estimates for the association of infections with SROI AD and SROI MCI were β=-0.01 (p=0.02) and β=-0.01 (p=0.04), respectively.

Conclusion

Our findings suggest that infections and APOE4 jointly contribute to brain glucose hypometabolism and AD pathology, supporting a "multi-hit" mechanism in AD development.

Current state of data stewardship tools in life science

In today's data-centric landscape, effective data stewardship is critical for facilitating scientific research and innovation. This article provides an overview of essential tools and frameworks for modern data stewardship practices. Over 300 tools were analyzed in this study, assessing their utility, relevance to data stewardship, and applicability within the life sciences domain.

Progressive cervical cord atrophy parallels cognitive decline in Alzheimer's disease

Alzheimer's disease (AD) is characterized by progressive episodic memory dysfunction. A prominent hallmark of AD is gradual brain atrophy. Despite extensive research on brain pathology, the understanding of spinal cord pathology in AD and its association with cognitive decline remains understudied. We analyzed serial magnetic resonance imaging (MRI) scans from the ADNI data repository to assess whether progressive cord atrophy is associated with clinical worsening. Cervical cord morphometry was measured in 45 patients and 49 cognitively normal controls (CN) at two time points over 1.5 years. Regression analysis examined associations between cord atrophy rate and cognitive worsening. Cognitive and functional activity performance declined in patients during follow-up. Compared with controls, patients showed a greater rate of decline of the anterior-posterior width of the cross-sectional cord area per month (- 0.12%, p = 0.036). Worsening in the mini-mental state examination (MMSE), clinical dementia rating (CDR), and functional assessment questionnaire (FAQ) was associated with faster rates of cord atrophy (MMSE: r = 0.320, p = 0.037; CDR: r = - 0.361, p = 0.017; FAQ: r = - 0.398, p = 0.029). Progressive cord atrophy occurs in AD patients; its rate over time being associated with cognitive and functional activity decline.

Strategies for measuring concentrations and forms of amyloid-β peptides

Alzheimer's disease (AD) is affecting more and more people worldwide without the effective treatment, while the existed pathological mechanism has been confirmed barely useful in the treatment. Amyloid-β peptide (Aβ), a main component of senile plaque, is regarded as the most promising target in AD treatment. Aβ clearance from AD brain seems to be a reliably therapeutic strategy, as the two exited drugs, GV-971 and aducanumab, are both developed based on it. However, doubt still exists. To exhaustive expound on the pathological mechanism of Aβ, rigorous analyses on the concentrations and aggregation forms are essential. Thus, it is attracting broad attention these years. However, most of the sensors have not been used in pathological studies, as the lack of the bridge between analytical chemist and pathologists. In this review, we made a brief introduce on Aβ-related pathological mechanism included in β-amyloid hypothesis to elucidate the detection conditions of sensor methods. Furthermore, a summary of the sensor methods was made, which were based on Aβ concentrations and form detections that have been developed in the past 10 years. As the greatest number of the sensors were built on fluorescent spectroscopy, electrochemistry, and Roman spectroscopy, detailed elucidation on them was made. Notably, the aggregation process is another important factor in revealing the progress of AD and developing the treatment methods, so the sensors on monitoring Aβ aggregation processes were also summarized.

[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.

Assessing Risk Factors for Cognitive Decline Using Electronic Health Record Data: A Scoping Review

Background

The data and information contained within electronic health records (EHR) provide a rich, diverse, longitudinal view of real-world patient histories, offering valuable opportunities to study antecedent risk factors for cognitive decline. However, the extent to which such records' data have been utilized to elucidate the risk factors of cognitive decline remains unclear.

Methods

A scoping review was conducted following the PRISMA guideline, examining articles published between January 2010 and April 2023, from PubMed, Web of Science, and CINAHL. Inclusion criteria focused on studies using EHR to investigate risk factors for cognitive decline. Each article was screened by at least two reviewers. Data elements were manually extracted based on a predefined schema. The studied risk factors were classified into categories, and a research gap was identified.

Results

From 1,593 articles identified, 80 were selected. The majority (87.5%) were retrospective cohort studies, with 66.3% using datasets of over 10,000 patients, predominantly from the US or UK. Analysis showed that 48.8% of studies addressed medical conditions, 31.3% focused on medical interventions, and 17.5% on lifestyle, socioeconomic status, and environmental factors. Most studies on medical conditions were linked to an increased risk of cognitive decline, whereas medical interventions addressing these conditions often reduced the risk.

Conclusions

EHR data significantly enhanced our understanding of medical conditions, interventions, lifestyle, socioeconomic status, and environmental factors related to the risk of cognitive decline.

Integrating AI in fighting advancing Alzheimer: diagnosis, prevention, treatment, monitoring, mechanisms, and clinical trials

The application of artificial intelligence (AI) in neurology is a growing field offering opportunities to improve accuracy of diagnosis and treatment of complicated neuronal disorders, plus fostering a deeper understanding of the aetiologies of these diseases through AI-based analyses of large omics data. The most common neurodegenerative disease, Alzheimer's disease (AD), is characterized by brain accumulation of specific pathological proteins, accompanied by cognitive impairment. In this review, we summarize the latest progress on the use of AI in different AD-related fields, such as analysis of neuroimaging data enabling early and accurate AD diagnosis; prediction of AD progression, identification of patients at higher risk and evaluation of new treatments; improvement of the evaluation of drug response using AI algorithms to analyze patient clinical and neuroimaging data; the development of personalized AD therapies; and the use of AI-based techniques to improve the quality of daily life of AD patients and their caregivers.

Advances in blood biomarkers for Alzheimer disease (AD): A review

Alzheimer disease (AD) and Alzheimer Disease and Related Dementias (AD/ADRD) are growing public health challenges globally affecting millions of older adults, necessitating concerted efforts to advance our understanding and management of these conditions. AD is a progressive neurodegenerative disorder characterized pathologically by amyloid plaques and tau neurofibrillary tangles that are the primary cause of dementia in older individuals. Early and accurate diagnosis of AD dementia is crucial for effective intervention and treatment but has proven challenging to accomplish. Although testing for AD brain pathology with cerebrospinal fluid (CSF) or positron emission tomography (PET) has been available for over 2 decades, most patients never underwent this testing because of inaccessibility, high out-of-pocket costs, perceived risks, and the lack of AD-specific treatments. However, in recent years, rapid progress has been made in developing blood biomarkers for AD/ADRD. Consequently, blood biomarkers have emerged as promising tools for non-invasive and cost-effective diagnosis, prognosis, and monitoring of AD progression. This review presents the evolving landscape of blood biomarkers in AD/ADRD and explores their potential applications in clinical practice for early detection, prognosis, and therapeutic interventions. It covers recent advances in blood biomarkers, including amyloid beta (Aβ) peptides, tau protein, neurofilament light chain (NfL), and glial fibrillary acidic protein (GFAP). It also discusses their diagnostic and prognostic utility while addressing associated challenges and limitations. Future research directions in this rapidly evolving field are also proposed.

Clinical utility of diffusion MRI-derived measures of cortical microstructure in a real-world memory clinic setting

OBJECTIVE

To investigate cortical microstructural measures from diffusion MRI as "neurodegeneration" markers that could improve prognostic accuracy in mild cognitive impairment (MCI).

METHODS

The prognostic power of Amyloid/Tau/Neurodegeneration (ATN) biomarkers to predict progression from MCI to AD or non-AD dementia was investigated. Ninety patients underwent clinical evaluation (follow-up interval 32 ± 18 months), lumbar puncture, and MRI. Participants were grouped by clinical stage and cerebrospinal fluid Amyloid and Tau status. T1-structural and diffusion MRI scans were analyzed to calculate diffusion metrics related to cortical columnar structure (AngleR, ParlPD, PerpPD+), cortical mean diffusivity, and fractional anisotropy. Statistical tests were corrected for multiple comparisons. Prognostic power was assessed using receiver operating characteristic (ROC) analysis and related indices.

RESULTS

A progressive increase of whole-brain cortical diffusion values was observed along the AD continuum, with all A+ groups showing significantly higher AngleR than A-T-. Investigating clinical progression to dementia, the AT biomarkers together showed good positive predictive value (with 90.91% of MCI A+T+ converting to dementia) but poor negative predictive value (with 40% of MCI A-T- progressing to a mix of AD and non-AD dementias). Adding whole-brain AngleR as an N marker, produced good differentiation between stable and converting MCI A-T- patients (0.8 area under ROC curve) and substantially improved negative predictive value (+21.25%).

INTERPRETATION

Results support the clinical utility of cortical microstructure to aid prognosis, especially in A-T- patients. Further work will investigate other complexities of the real-world clinical setting, including A-T+ groups. Diffusion MRI measures of neurodegeneration may complement fluid AT markers to support clinical decision-making.

Comparing neuropsychological, typical, and ADNI criteria for the diagnosis of mild cognitive impairment in Vietnam-era veterans

OBJECTIVE

Neuropsychological criteria for mild cognitive impairment (MCI) more accurately predict progression to Alzheimer's disease (AD) and are more strongly associated with AD biomarkers and neuroimaging profiles than ADNI criteria. However, research to date has been conducted in relatively healthy samples with few comorbidities. Given that history of traumatic brain injury (TBI) and post-traumatic stress disorder (PTSD) are risk factors for AD and common in Veterans, we compared neuropsychological, typical (Petersen/Winblad), and ADNI criteria for MCI in Vietnam-era Veterans with histories of TBI or PTSD.

METHOD

267 Veterans (mean age = 69.8) from the DOD-ADNI study were evaluated for MCI using neuropsychological, typical, and ADNI criteria. Linear regressions adjusting for age and education assessed associations between MCI status and AD biomarker levels (cerebrospinal fluid [CSF] p-tau181, t-tau, and Aβ42) by diagnostic criteria. Logistic regressions adjusting for age and education assessed the effects of TBI severity and PTSD symptom severity simultaneously on MCI classification by each criteria.

RESULTS

Agreement between criteria was poor. Neuropsychological criteria identified more Veterans with MCI than typical or ADNI criteria, and were associated with higher CSF p-tau181 and t-tau. Typical and ADNI criteria were not associated with CSF biomarkers. PTSD symptom severity predicted MCI diagnosis by neuropsychological and ADNI criteria. History of moderate/severe TBI predicted MCI by typical and ADNI criteria.

CONCLUSIONS

MCI diagnosis using sensitive neuropsychological criteria is more strongly associated with AD biomarkers than conventional diagnostic methods. MCI diagnostics in Veterans would benefit from incorporation of comprehensive neuropsychological methods and consideration of the impact of PTSD.

Machine learning with multimodal neuroimaging data to classify stages of Alzheimer's disease: a systematic review and meta-analysis

In recent years, Alzheimer's disease (AD) has been a serious threat to human health. Researchers and clinicians alike encounter a significant obstacle when trying to accurately identify and classify AD stages. Several studies have shown that multimodal neuroimaging input can assist in providing valuable insights into the structural and functional changes in the brain related to AD. Machine learning (ML) algorithms can accurately categorize AD phases by identifying patterns and linkages in multimodal neuroimaging data using powerful computational methods. This study aims to assess the contribution of ML methods to the accurate classification of the stages of AD using multimodal neuroimaging data. A systematic search is carried out in IEEE Xplore, Science Direct/Elsevier, ACM DigitalLibrary, and PubMed databases with forward snowballing performed on Google Scholar. The quantitative analysis used 47 studies. The explainable analysis was performed on the classification algorithm and fusion methods used in the selected studies. The pooled sensitivity and specificity, including diagnostic efficiency, were evaluated by conducting a meta-analysis based on a bivariate model with the hierarchical summary receiver operating characteristics (ROC) curve of multimodal neuroimaging data and ML methods in the classification of AD stages. Wilcoxon signed-rank test is further used to statistically compare the accuracy scores of the existing models. With a 95% confidence interval of 78.87-87.71%, the combined sensitivity for separating participants with mild cognitive impairment (MCI) from healthy control (NC) participants was 83.77%; for separating participants with AD from NC, it was 94.60% (90.76%, 96.89%); for separating participants with progressive MCI (pMCI) from stable MCI (sMCI), it was 80.41% (74.73%, 85.06%). With a 95% confidence interval (78.87%, 87.71%), the Pooled sensitivity for distinguishing mild cognitive impairment (MCI) from healthy control (NC) participants was 83.77%, with a 95% confidence interval (90.76%, 96.89%), the Pooled sensitivity for distinguishing AD from NC was 94.60%, likewise (MCI) from healthy control (NC) participants was 83.77% progressive MCI (pMCI) from stable MCI (sMCI) was 80.41% (74.73%, 85.06%), and early MCI (EMCI) from NC was 86.63% (82.43%, 89.95%). Pooled specificity for differentiating MCI from NC was 79.16% (70.97%, 87.71%), AD from NC was 93.49% (91.60%, 94.90%), pMCI from sMCI was 81.44% (76.32%, 85.66%), and EMCI from NC was 85.68% (81.62%, 88.96%). The Wilcoxon signed rank test showed a low P-value across all the classification tasks. Multimodal neuroimaging data with ML is a promising future in classifying the stages of AD but more research is required to increase the validity of its application in clinical practice.

Performance of SOBA-AD blood test in discriminating Alzheimer's disease patients from cognitively unimpaired controls in two independent cohorts

Amyloid-beta (Aβ) toxic oligomers are critical early players in the molecular pathology of Alzheimer's disease (AD). We have developed a Soluble Oligomer Binding Assay (SOBA-AD) for detection of these Aβ oligomers that contain α-sheet secondary structure that discriminates plasma samples from patients on the AD continuum from non-AD controls. We tested 265 plasma samples from two independent cohorts to investigate the performance of SOBA-AD. Testing was performed at two different sites, with different personnel, reagents, and instrumentation. Across two cohorts, SOBA-AD discriminated AD patients from cognitively unimpaired (CU) subjects with 100% sensitivity, > 95% specificity, and > 98% area under the curve (AUC) (95% CI 0.95-1.00). A SOBA-AD positive readout, reflecting α-sheet toxic oligomer burden, was found in AD patients, and not in controls, providing separation of the two populations, aside from 5 SOBA-AD positive controls. Based on an earlier SOBA-AD study, the Aβ oligomers detected in these CU subjects may represent preclinical cases of AD. The results presented here support the value of SOBA-AD as a promising blood-based tool for the detection and confirmation of AD.

Data-driven Stochastic Model for Quantifying the Interplay Between Amyloid-beta and Calcium Levels in Alzheimer's Disease

The abnormal aggregation of extracellular amyloid-β ( A β ) in senile plaques resulting in calcium C a + 2 dyshomeostasis is one of the primary symptoms of Alzheimer's disease (AD). Significant research efforts have been devoted in the past to better understand the underlying molecular mechanisms driving A β deposition and C a + 2 dysregulation. Importantly, synaptic impairments, neuronal loss, and cognitive failure in AD patients are all related to the buildup of intraneuronal A β accumulation. Moreover, increasing evidence show a feed-forward loop between A β and C a + 2 levels, i.e. A β disrupts neuronal C a + 2 levels, which in turn affects the formation of A β . To better understand this interaction, we report a novel stochastic model where we analyze the positive feedback loop between A β and C a + 2 using ADNI data. A good therapeutic treatment plan for AD requires precise predictions. Stochastic models offer an appropriate framework for modelling AD since AD studies are observational in nature and involve regular patient visits. The etiology of AD may be described as a multi-state disease process using the approximate Bayesian computation method. So, utilizing ADNI data from 2-year visits for AD patients, we employ this method to investigate the interplay between A β and C a + 2 levels at various disease development phases. Incorporating the ADNI data in our physics-based Bayesian model, we discovered that a sufficiently large disruption in either A β metabolism or intracellular C a + 2 homeostasis causes the relative growth rate in both C a + 2 and A β , which corresponds to the development of AD. The imbalance of C a + 2 ions causes A β disorders by directly or indirectly affecting a variety of cellular and subcellular processes, and the altered homeostasis may worsen the abnormalities of C a + 2 ion transportation and deposition. This suggests that altering the C a + 2 balance or the balance between A β and C a + 2 by chelating them may be able to reduce disorders associated with AD and open up new research possibilities for AD therapy.

Alzheimer's disease prediction algorithm based on de-correlation constraint and multi-modal feature interaction

Alzheimer's disease (AD) is a neurodegenerative disease characterized by various pathological changes. Utilizing multimodal data from Fluorodeoxyglucose positron emission tomography(FDG-PET) and Magnetic Resonance Imaging(MRI) of the brain can offer comprehensive information about the lesions from different perspectives and improve the accuracy of prediction. However, there are significant differences in the feature space of multimodal data. Commonly, the simple concatenation of multimodal features can cause the model to struggle in distinguishing and utilizing the complementary information between different modalities, thus affecting the accuracy of predictions. Therefore, we propose an AD prediction model based on de-correlation constraint and multi-modal feature interaction. This model consists of the following three parts: (1) The feature extractor employs residual connections and attention mechanisms to capture distinctive lesion features from FDG-PET and MRI data within their respective modalities. (2) The de-correlation constraint function enhances the model's capacity to extract complementary information from different modalities by reducing the feature similarity between them. (3) The mutual attention feature fusion module interacts with the features within and between modalities to enhance the modal-specific features and adaptively adjust the weights of these features based on information from other modalities. The experimental results on ADNI database demonstrate that the proposed model achieves a prediction accuracy of 86.79% for AD, MCI and NC, which is higher than the existing multi-modal AD prediction models.

Advances in nanoprobes for molecular MRI of Alzheimer's disease

Alzheimer's disease is the most common cause of dementia and a leading cause of mortality in the elderly population. Diagnosis of Alzheimer's disease has traditionally relied on evaluation of clinical symptoms for cognitive impairment with a definitive diagnosis requiring post-mortem demonstration of neuropathology. However, advances in disease pathogenesis have revealed that patients exhibit Alzheimer's disease pathology several decades before the manifestation of clinical symptoms. Magnetic resonance imaging (MRI) plays an important role in the management of patients with Alzheimer's disease. The clinical availability of molecular MRI (mMRI) contrast agents can revolutionize the diagnosis of Alzheimer's disease. In this article, we review advances in nanoparticle contrast agents, also referred to as nanoprobes, for mMRI of Alzheimer's disease. This article is categorized under: Diagnostic Tools > In Vivo Nanodiagnostics and Imaging Therapeutic Approaches and Drug Discovery > Nanomedicine for Neurological Disease.

Biomarker Changes during 20 Years Preceding Alzheimer's Disease

BACKGROUND

Biomarker changes that occur in the period between normal cognition and the diagnosis of sporadic Alzheimer's disease have not been extensively investigated in longitudinal studies.

METHODS

We conducted a multicenter, nested case-control study of Alzheimer's disease biomarkers in cognitively normal participants who were enrolled in the China Cognition and Aging Study from January 2000 through December 2020. A subgroup of these participants underwent testing of cerebrospinal fluid (CSF), cognitive assessments, and brain imaging at 2-year-to-3-year intervals. A total of 648 participants in whom Alzheimer's disease developed were matched with 648 participants who had normal cognition, and the temporal trajectories of CSF biochemical marker concentrations, cognitive testing, and imaging were analyzed in the two groups.

RESULTS

The median follow-up was 19.9 years (interquartile range, 19.5 to 20.2). CSF and imaging biomarkers in the Alzheimer's disease group diverged from those in the cognitively normal group at the following estimated number of years before diagnosis: amyloid-beta (Aβ)42, 18 years; the ratio of Aβ42 to Aβ40, 14 years; phosphorylated tau 181, 11 years; total tau, 10 years; neurofilament light chain, 9 years; hippocampal volume, 8 years; and cognitive decline, 6 years. As cognitive impairment progressed, the changes in CSF biomarker levels in the Alzheimer's disease group initially accelerated and then slowed.

CONCLUSIONS

In this study involving Chinese participants during the 20 years preceding clinical diagnosis of sporadic Alzheimer's disease, we observed the time courses of CSF biomarkers, the times before diagnosis at which they diverged from the biomarkers from a matched group of participants who remained cognitively normal, and the temporal order in which the biomarkers became abnormal. (Funded by the Key Project of the National Natural Science Foundation of China and others; ClinicalTrials.gov number, NCT03653156.).

Amyloid-β aggregates activate peripheral monocytes in mild cognitive impairment

The peripheral immune system is important in neurodegenerative diseases, both in protecting and inflaming the brain, but the underlying mechanisms remain elusive. Alzheimer's Disease is commonly preceded by a prodromal period. Here, we report the presence of large Aβ aggregates in plasma from patients with mild cognitive impairment (n = 38). The aggregates are associated with low level Alzheimer's Disease-like brain pathology as observed by 11C-PiB PET and 18F-FTP PET and lowered CD18-rich monocytes. We characterize complement receptor 4 as a strong binder of amyloids and show Aβ aggregates are preferentially phagocytosed and stimulate lysosomal activity through this receptor in stem cell-derived microglia. KIM127 integrin activation in monocytes promotes size selective phagocytosis of Aβ. Hydrodynamic calculations suggest Aβ aggregates associate with vessel walls of the cortical capillaries. In turn, we hypothesize aggregates may provide an adhesion substrate for recruiting CD18-rich monocytes into the cortex. Our results support a role for complement receptor 4 in regulating amyloid homeostasis.

Recent applications of positron emission tomographic (PET) imaging in psychiatric drug discovery

INTRODUCTION

Psychiatry is one of the medical disciplines that suffers most from a lack of innovation in its therapeutic arsenal. Many failures in drug candidate trials can be explained by pharmacological properties that have been poorly assessed upstream, in terms of brain passage, brain target binding and clinical outcomes. Positron emission tomography can provide pharmacokinetic and pharmacodynamic data to help select candidate-molecules for further clinical trials.

AREAS COVERED

This review aims to explain and discuss the various methods using positron-emitting radiolabeled molecules to trace the cerebral distribution of the drug-candidate or indirectly measure binding to its therapeutic target. More than an exhaustive review of PET studies in psychopharmacology, this article highlights the contributions this technology can make in drug discovery applied to psychiatry.

EXPERT OPINION

PET neuroimaging is the only technological approach that can, in vivo in humans, measure cerebral delivery of a drug candidate, percentage and duration of target binding, and even the pharmacological effects. PET studies in a small number of subjects in the early stages of the development of a psychotropic drug can therefore provide the pharmacokinetic/pharmacodynamic data required for subsequent clinical evaluation. While PET technology is demanding in terms of radiochemical, radiopharmacological and nuclear medicine expertise, its integration into the development process of new drugs for psychiatry has great added value.

Early ultrasonic vocalization deficits and related thyroarytenoid muscle pathology in the transgenic TgF344-AD rat model of Alzheimer's disease

Background

Alzheimer's disease (AD) is a progressive neurologic disease and the most common cause of dementia. Classic pathology in AD is characterized by inflammation, abnormal presence of tau protein, and aggregation of β-amyloid that disrupt normal neuronal function and lead to cell death. Deficits in communication also occur during disease progression and significantly reduce health, well-being, and quality of life. Because clinical diagnosis occurs in the mid-stage of the disease, characterizing the prodrome and early stages in humans is currently challenging. To overcome these challenges, we use the validated TgF344-AD (F344-Tg(Prp-APP, Prp-PS1)19/Rrrc) transgenic rat model that manifests cognitive, behavioral, and neuropathological dysfunction akin to AD in humans.

Objectives

The overarching goal of our work is to test the central hypothesis that pathology and related behavioral deficits such as communication dysfunction in part manifest in the peripheral nervous system and corresponding target tissues already in the early stages. The primary aims of this study are to test the hypotheses that: (1) changes in ultrasonic vocalizations (USV) occur in the prodromal stage at 6 months of age and worsen at 9 months of age, (2) inflammation as well as AD-related pathology can be found in the thyroarytenoid muscle (TA) at 12 months of age (experimental endpoint tissue harvest), and to (3) demonstrate that the TgF344-AD rat model is an appropriate model for preclinical investigations of early AD-related vocal deficits.

Methods

USVs were collected from male TgF344-AD (N = 19) and wildtype (WT) Fischer-344 rats (N = 19) at 6 months (N = 38; WT: n = 19; TgF344-AD: n = 19) and 9 months of age (N = 18; WT: n = 10; TgF344-AD: n = 8) and acoustically analyzed for duration, mean power, principal frequency, low frequency, high frequency, peak frequency, and call type. RT-qPCR was used to assay peripheral inflammation and AD-related pathology via gene expressions in the TA muscle of male TgF344-AD rats (n = 6) and WT rats (n = 6) at 12 months of age.

Results

This study revealed a significant reduction in mean power of ultrasonic calls from 6 to 9 months of age and increased peak frequency levels over time in TgF344-AD rats compared to WT controls. Additionally, significant downregulation of AD-related genes Uqcrc2, Bace2, Serpina3n, and Igf2, as well as downregulation of pro-inflammatory gene Myd88 was found in the TA muscle of TgF344-AD rats at 12 months of age.

Discussion

Our findings demonstrate early and progressive vocal deficits in the TgF344-AD rat model. We further provide evidence of dysregulation of AD-pathology-related genes as well as inflammatory genes in the TA muscles of TgF344-AD rats in the early stage of the disease, confirming this rat model for early-stage investigations of voice deficits and related pathology.

Controversial Past, Splendid Present, Unpredictable Future: A Brief Review of Alzheimer Disease History

Background: The concept of Alzheimer disease (AD)-since its histological discovery by Alzheimer to the present day-has undergone substantial modifications. Methods: We conducted a classical narrative review of this field with a bibliography selection (giving preference to Medline best match). Results: The following subjects are reviewed and discussed: Alzheimer's discovery, Kraepelin's creation of a new disease that was a rare condition until the 1970's, the growing interest and investment in AD as a major killer in a society with a large elderly population in the second half of the 20th century, the consolidation of the AD clinicopathological model, and the modern AD nosology based on the dominant amyloid hypothesis among many others. In the 21st century, the development of AD biomarkers has supported a novel biological definition of AD, although the proposed therapies have failed to cure this disease. The incidence of dementia/AD has shown a decrease in affluent countries (possibly due to control of risk factors), and mixed dementia has been established as the most frequent etiology in the oldest old. Conclusions: The current concept of AD lacks unanimity. Many hypotheses attempt to explain its complex physiopathology entwined with aging, and the dominant amyloid cascade has yielded poor therapeutic results. The reduction in the incidence of dementia/AD appears promising but it should be confirmed in the future. A reevaluation of the AD concept is also necessary.

Disentangling Accelerated Cognitive Decline from the Normal Aging Process and Unraveling Its Genetic Components: A Neuroimaging-Based Deep Learning Approach

Background

The progressive cognitive decline, an integral component of Alzheimer's disease (AD), unfolds in tandem with the natural aging process. Neuroimaging features have demonstrated the capacity to distinguish cognitive decline changes stemming from typical brain aging and AD between different chronological points.

Objective

To disentangle the normal aging effect from the AD-related accelerated cognitive decline and unravel its genetic components using a neuroimaging-based deep learning approach.

Methods

We developed a deep-learning framework based on a dual-loss Siamese ResNet network to extract fine-grained information from the longitudinal structural magnetic resonance imaging (MRI) data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) study. We then conducted genome-wide association studies (GWAS) and post-GWAS analyses to reveal the genetic basis of AD-related accelerated cognitive decline.

Results

We used our model to process data from 1,313 individuals, training it on 414 cognitively normal people and predicting cognitive assessment for all participants. In our analysis of accelerated cognitive decline GWAS, we identified two genome-wide significant loci: APOE locus (chromosome 19 p13.32) and rs144614292 (chromosome 11 p15.1). Variant rs144614292 (G > T) has not been reported in previous AD GWA studies. It is within the intronic region of NELL1, which is expressed in neurons and plays a role in controlling cell growth and differentiation. The cell-type-specific enrichment analysis and functional enrichment of GWAS signals highlighted the microglia and immune-response pathways.

Conclusions

Our deep learning model effectively extracted relevant neuroimaging features and predicted individual cognitive decline. We reported a novel variant (rs144614292) within the NELL1 gene.

Finding new and better treatments for psychiatric disorders

In contrast to most fields of medicine, progress to discover and develop new and improved psychiatric drugs has been slow and disappointing. The vast majority of currently prescribed drugs to treat schizophrenia, mood and anxiety disorders are arguably no more effective than the first generation of psychiatric drugs introduced well over 50 years ago. With only a few exceptions current psychiatric drugs work via the same fundamental mechanisms of action as first-generation agents. Here we describe the reasons for this slow progress and outline a number of areas of research that involve a greater reliance on experimental therapeutics utilizing recent advances in neuroscience to better understand disease biology. We exemplify the potential impact of these areas of research focus with several recent examples of novel agents that have emerged and which support our optimism that newer, more effective and better tolerated agents, are on the horizon. Together with existing drugs these newer agents and novel mechanisms could offer markedly improved functional outcomes for the millions of people still disabled by psychiatric disorders.

Simple Synthesis of [18F] AV-45 and its Clinical Application in the Diagnosis of Alzheimer's Disease

OBJECTIVE

[18F] AV-45 can be produced in a simple, stable, and repeatable manner on the Tracerlab FXF-N platform using a self-editing synthetic procedure and solid-phase extraction purification method. This technique is applied to positron emission tomography (PET) imaging of Alzheimer's disease (AD) to observe its distribution and characteristics in various brain regions and its diagnostic efficiency for the disease.

METHODS

The precursor was subjected to nucleophilic radiofluorination at 120°C in anhydrous dimethyl sulfoxide, followed by acid hydrolysis of the protecting groups. The neutralized reaction mixture was purified by solid phase extraction to obtain a relatively pure [18F] AV-45 product with a high specific activity. A total of 10 participants who were diagnosed with Alzheimer's disease (AD group) and 10 healthy controls (HC group) were included retrospectively. All of them underwent [18F] AV-45 brain PET/CT imaging. The distribution of [18F] AV-45 in the AD group was analyzed visually and semi-quantitatively.

RESULTS

Six consecutive radiochemical syntheses were performed in this experiment. The average production time of [18F] AV-45 was 52 minutes, the radiochemical yield was 14.2 % ± 2.7% (n = 6), and the radiochemical purity was greater than 95%. When used with PET/CT imaging, the results of the visual analysis indicated increased [18F] AV-45 radioactivity uptake in the frontal, temporal, and parietal lobes in AD patients. Semiquantitative analysis showed the highest diagnostic efficacy in the posterior cingulate gyrus compared with other brain regions (P < 0.001).

CONCLUSION

Intravenous [18F] AV-45 was successfully prepared on the Tracerlab FXF-N platform by solid-phase extraction of crude product and automated radiochemical synthesis. PET/CT imaging can be used to diagnose and evaluate AD patients and provide a more robust basis for clinicians to diagnose AD.

Retina pathology as a target for biomarkers for Alzheimer's disease: Current status, ophthalmopathological background, challenges, and future directions

There is emerging evidence that amyloid beta protein (Aβ) and tau-related lesions in the retina are associated with Alzheimer's disease (AD). Aβ and hyperphosphorylated (p)-tau deposits have been described in the retina and were associated with small amyloid spots visualized by in vivo imaging techniques as well as degeneration of the retina. These changes correlate with brain amyloid deposition as determined by histological quantification, positron emission tomography (PET) or clinical diagnosis of AD. However, the literature is not coherent on these histopathological and in vivo imaging findings. One important reason for this is the variability in the methods and the interpretation of findings across different studies. In this perspective, we indicate the critical methodological deviations among different groups and suggest a roadmap moving forward on how to harmonize (i) histopathologic examination of retinal tissue; (ii) in vivo imaging among different methods, devices, and interpretation algorithms; and (iii) inclusion/exclusion criteria for studies aiming at retinal biomarker validation.

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

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

Biomarker Methodologies: A NIMH Perspective

Biomarkers are critically important in the development of drugs, biologics, medical devices, and psychosocial interventions for psychiatric disorders. As the lead federal agency charged with setting and supporting the national agenda for mental health research, the National Institute of Mental Health (NIMH) funds a broad portfolio of basic, translational, and clinical research focused on identifying, developing, and validating biomarkers for serious mental illnesses and neurodevelopmental conditions. In psychiatric research over the past 10 years, there has been an intensive effort to identify biomarkers as potential tools to improve treatment options for individuals with mental health concerns and increase success in the development of novel interventions. This chapter highlights examples of biomarker technologies that have been utilized to advance understanding of the pathophysiology of psychiatric disorders and the development of novel therapeutics.

Public-Private Partnerships for Neuropsychiatric Drug Development: A Perspective

There is a long-standing interest in developing biomarkers for neuropsychiatric disorders that might assist in drug development or clinical management. To date, however, progress has been limited in part because of the limited nature of the studies and the absence of the types of large-scale networks that would be required for clinical validation. The first public-private partnership (PPP) for biomarker validation-the Alzheimer's Disease Neuroimaging Initiative (ADNI)-was formed in 2004 to focus on the development of amyloid deposition in the brain as a potential biomarker of Alzheimer's disease pathology. Over the past 20 years, ADNI has achieved many of its initial deliverables, while others remain a work in progress. ADNI also serves as a point of reference for other more recently established PPPs. Key components for PPP development include processes for identifying stakeholders and deliverables, governance and management, funding, and data access/sharing. These issues are discussed in relationship to more recently developed PPPs. Given the extensive investment needed for biomarker development within the pre-competitive space, PPPs provide a critical path for the development of next-generation tools for central nervous system (CNS) drug development.

Large-Scale Neuroimaging of Mental Illness

Neuroimaging has provided important insights into the brain variations related to mental illness. Inconsistencies in prior studies, however, call for methods that lead to more replicable and generalizable brain markers that can reliably predict illness severity, treatment course, and prognosis. A paradigm shift is underway with large-scale international research teams actively pooling data and resources to drive consensus findings and test emerging methods aimed at achieving the goals of precision psychiatry. In parallel with large-scale psychiatric genomics studies, international consortia combining neuroimaging data are mapping the transdiagnostic brain signatures of mental illness on an unprecedented scale. This chapter discusses the major challenges, recent findings, and a roadmap for developing better neuroimaging-based tools and markers for mental illness.

A Pragmatic, Investigator-Driven Process for Disclosure of Amyloid PET Scan Results to ADNI-4 Research Participants

BACKGROUND

Prior studies of Alzheimer's disease (AD) biomarker disclosure have answered important questions about individuals' safety after learning and comprehending their amyloid PET results; however, these studies have typically employed highly structured disclosure protocols and focused on the psychological impact of disclosure (e.g., anxiety, depression, and suicidality) in homogeneous populations. More work is needed to develop flexible disclosure protocols and study outcomes in ethnoculturally representative samples.

METHODS

The Alzheimer's Disease Neuroimaging Initiative (ADNI) is formally incorporating amyloid PET disclosure into the newest protocol (ADNI-4). Participants across the cognitive spectrum who wish to know their amyloid PET results may learn them. The pragmatic disclosure process spans four timepoints: (1) a pre-disclosure visit, (2) the PET scan and its read, (3) a disclosure visit, and (4) a post-disclosure check-in. This process applies to all participants, with slight modifications to account for their cognitive status. In designing this process, special emphasis was placed on utilizing investigator discretion. Participant measures include perceived risk of dementia, purpose in life, and disclosure satisfaction. Investigator assessment of the disclosure visit (e.g., challenges encountered, topics discussed, etc.) is also included.

RESULTS

Data collection is ongoing. Results will allow for more robust characterization of the impact of learning amyloid PET results on individuals and describe the perspectives of investigators.

CONCLUSION

The pragmatic design of the disclosure process in ADNI-4 coupled with the novel participant and investigator data will inform future disclosure practices. This is especially important as disclosure of biomarker results expands in research and care.

Immunotherapy in Alzheimer's Disease: Current Status and Future Directions

Alzheimer's disease (AD) is a progressive neurological disorder characterized by memory loss, cognitive decline, and behavioral changes. Immunotherapy aims to harness the immune system to target the underlying pathology of AD and has shown promise as a disease-modifying treatment for AD. By focusing on the underlying disease pathogenesis and encouraging the removal of abnormal protein aggregates in the brain, immunotherapy shows promise as a potential treatment for AD. The development of immunotherapy for AD began with early attempts to use antibodies to target beta-amyloid. The amyloid hypothesis which suggests that the accumulation of beta-amyloid in the brain triggers the pathological cascade that leads to AD has been a driving force behind the development of immunotherapy for AD. However, recent clinical trials of monoclonal antibodies targeting amyloid-β have shown mixed results, highlighting the need for further research into alternative immunotherapy approaches. Additionally, the safety and efficacy of immunotherapy for AD remain an area of active investigation. Some immunotherapeutic approaches have shown promise, while others have been associated with significant side effects, including inflammation of the brain. Sleep has a significant impact on various physiological processes, including the immune system, and has been linked to the pathogenesis of AD. Thus, improving sleep quality and duration may benefit the immune system and potentially enhance the effectiveness of immunotherapeutic approaches for AD. In this review, we discussed the promises of immunotherapy as a disease-modifying treatment for AD as well as possible methods to improve the efficacy and safety of immunotherapy to achieve better therapeutic outcomes.

Markov model combined with MR diffusion tensor imaging for predicting the onset of Alzheimer's disease

Alzheimer's disease (AD) affects cognition, behavior, and memory of brain. It causes 60-80% of dementia cases. Cross-sectional imaging investigations of AD show that magnetic resonance (MR) with diffusion tensor image (DTI)-detected lesion locations in AD patients are heterogeneous and distributed across the imaging area. This study suggested that Markov model (MM) combined with MR-DTI (MM + MR-DTI) was offered as a method for predicting the onset of AD. In 120 subjects (normal controls [NCs], amnestic mild cognitive impairment [aMCI] patients, and AD patients) from a discovery dataset and 122 subjects (NCs, aMCI, and AD) from a replicated dataset, we used them to evaluate the white matter (WM) integrity and abnormalities. We did this by using automated fiber quantification, which allowed us to identify 20 central WM tracts. Point-wise alterations in WM tracts were shown using discovery and replication datasets. The statistical analysis revealed a substantial correlation between microstructural WM alterations and output in the patient groups and cognitive performance, suggesting that this may be a potential biomarker for AD. The MR-based classifier demonstrated the following performance levels for the basis classifiers, with DTI achieving the lowest performance. The following outcomes were seen in MM + MR-DTI using multimodal techniques when combining two modalities. Finally, a combination of every imaging method produced results with an accuracy of 98%, a specificity of 97%, and a sensitivity of 99%. In summary, DTI performs better when paired with structural MR, despite its relatively weak performance when used alone. These findings support the idea that WM modifications play a significant role in AD.

Shifting the trajectory of therapeutic development for neurological and psychiatric disorders

Clinical trials for central nervous system disorders often enroll patients with unrecognized heterogeneous diseases, leading to costly trials that have high failure rates. Here, we discuss the potential of emerging technologies and datasets to elucidate disease mechanisms and identify biomarkers to improve patient stratification and monitoring of disease progression in clinical trials for neuropsychiatric disorders. Greater efforts must be centered on rigorously standardizing data collection and sharing of methods, datasets, and analytical tools across sectors. To address health care disparities in clinical trials, diversity of genetic ancestries and environmental exposures of research participants and associated biological samples must be prioritized.

Association between cerebrospinal fluid clusterin and biomarkers of Alzheimer's disease pathology in mild cognitive impairment: a longitudinal cohort study

Background

Clusterin, a glycoprotein implicated in Alzheimer's disease (AD), remains unclear. The objective of this study was to analyze the effect of cerebrospinal fluid (CSF) clusterin in relation to AD biomarkers using a longitudinal cohort of non-demented individuals.

Methods

We gathered a sample comprising 86 individuals under cognition normal (CN) and 134 patients diagnosed with MCI via the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. To investigate the correlation of CSF clusterin with cognitive function and markers of key physiological changes, we employed multiple linear regression and mixed-effect models. We undertook a causal mediation analysis to inspect the mediating influence of CSF clusterin on cognitive abilities.

Results

Pathological characteristics associated with baseline Aβ42, Tau, brain volume, exhibited a correlation with initial CSF clusterin in the general population, Specifically, these correlations were especially prominent in the MCI population; CSF Aβ42 (PCN = 0.001; PMCI = 0.007), T-tau (PCN < 0.001; PMCI < 0.001), and Mid temporal (PCN = 0.033; PMCI = 0.005). Baseline CSF clusterin level was predictive of measurable cognitive shifts in the MCI population, as indicated by MMSE (β = 0.202, p = 0.029), MEM (β = 0.186, p = 0.036), RAVLT immediate recall (β = 0.182, p = 0.038), and EF scores (β = 0.221, p = 0.013). In MCI population, the alterations in brain regions (17.87% of the total effect) mediated the effect of clusterin on cognition. It was found that variables such as age, gender, and presence of APOE ε4 carrier status, influenced some of these connections.

Conclusion

Our investigation underscored a correlation between CSF clusterin concentrations and pivotal AD indicators, while also highlighting clusterin's potential role as a protective factor for cognitive abilities in MCI patients.