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

Factors affecting progression of non-Alzheimer dementia: a retrospective analysis with long-term follow-up

Background

Non-Alzheimer's dementias, including vascular dementia (VaD), frontotemporal dementia (FTD), Lewy body dementia (LBD), and Parkinson's disease dementia (PDD), possess unique characteristics and prognostic factors that remain poorly understood. This study aims to investigate the temporal course of these subtypes and identify the impact of functional, neuropsychiatric, and comorbid medical conditions on prognosis. Additionally, the relationship between hippocampal atrophy, white matter intensities, and disease progression will be examined, along with the identification of key covariates influencing slow or fast progression in non-Alzheimer's dementias.

Methods

A total of 196 patients with non-Alzheimer's dementias who underwent at least three comprehensive evaluations were included, with proportions of VaD, FTD, LBD, and PDD being 50, 19.39, 19.90, and 10.71%, respectively. Patient demographics, comorbidities, neuropsychiatric and neuroimaging parameters, and global evaluation were analyzed using appropriate statistical methods. The study followed patients for a mean duration of 62.57 ± 33.45 months (ranging from 11 to 198 months).

Results

The results from three different visits for each non-AD dementia case demonstrated significant differences in various measures across visits, including functional capacity (BDLAS), cognition (MMSE), and other neuropsychological tests. Notably, certain genotypes and hippocampal atrophy grades were more prevalent in specific subtypes. The results indicate that Fazekas grading and hippocampal atrophy were significant predictors of disease progression, while epilepsy, extrapyramidal symptoms, thyroid dysfunction, coronary artery disease, diabetes mellitus, hypertension, stroke, hyperlipidemia, sleep disorders, smoking, and family history of dementia were not significant predictors. BDLAS and EDLAS scores at the first and second visits showed significant associations with disease progression, while scores at the third visit did not. Group-based trajectory analysis revealed that non-AD cases separated into two reliable subgroups with slow/fast prognosis, showing high reliability (Entropy = 0.790, 51.8 vs. 48.2%).

Conclusion

This study provides valuable insights into the temporal course and prognostic factors of non-Alzheimer's dementias. The findings underscore the importance of considering functional, neuropsychological, and comorbid medical conditions in understanding disease progression. The significant associations between hippocampal atrophy, white matter intensities, and prognosis highlight potential avenues for further research and therapeutic interventions.

Transforming Drug Development for Neurological Disorders: Proceedings from a Multidisease Area Workshop

Neurological disorders represent some of the most challenging therapeutic areas for successful drug approvals. The escalating global burden of death and disability for such diseases represents a significant worldwide public health challenge, and the rate of failure of new therapies for chronic progressive disorders of the nervous system is higher relative to other non-neurological conditions. However, progress is emerging rapidly in advancing the drug development landscape in both rare and common neurodegenerative diseases. In October 2022, the Critical Path Institute (C-Path) and the US Food and Drug Administration (FDA) organized a Neuroscience Annual Workshop convening representatives from the drug development industry, academia, the patient community, government agencies, and regulatory agencies regarding the future development of tools and therapies for neurological disorders. This workshop focused on five chronic progressive diseases: Alzheimer's disease, Parkinson's disease, Huntington's disease, Duchenne muscular dystrophy, and inherited ataxias. This special conference report reviews the key points discussed during the three-day dynamic workshop, including shared learnings, and recommendations that promise to catalyze future advancement of novel therapies and drug development tools.

A Window to the Brain: The Retina to Monitor the Progression and Efficacy of Saffron Repron® Pre-Treatment in an LPS Model of Neuroinflammation and Memory Impairment

A mechanism shared by most neurodegenerative diseases, like Alzheimer's disease (AD) and Parkinson's disease (PD), is neuroinflammation. It has been shown to have a link between cognitive impairment and retinal function under neuroinflammatory conditions, confirming the essential role of the retina as a window to the brain. Here, we characterize a mouse model of LPS-induced neuroinflammation describing the parallel deterioration of both memory and visual function. Then, we demonstrate, using the Novel Object Recognition test (NOR) and electroretinogram (ERG) recordings, that preventive, chronic treatment with saffron Repron® is able to reduce the neuroinflammation process and prevent the impairment of both cognitive and visual function. The improvement in behavioral and visual function is confirmed by the pattern of expression of neuroinflammation-related genes and related proteins where pre-treatment with Repron® saffron presents a positive modulation compared with that obtained in animals treated with LPS alone. These results hold for retinal tissue and partially in the brain, where it appears that the onset of damage was delayed. This trend underlines the critical role of the retina as a most sensitive portion of the central nervous system to LPS-induced damage and could be used as a "sensor" for the early detection of neurodegenerative diseases such as Alzheimer's.

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

Amyloid-β (Aβ) and tau deposition constitute Alzheimer's disease (AD) neuropathology. Cortical tau deposits first in the entorhinal cortex and hippocampus and then propagates to neocortex in an Aβ-dependent manner. Tau also tends to accumulate earlier in higher-order association cortex than in lower-order primary sensory-motor cortex. While previous research has examined the production and spread of tau, little attention has been paid to its clearance. Low-frequency (<0.1 Hz) global brain activity during the resting state is coupled with cerebrospinal fluid (CSF) flow and potentially reflects glymphatic clearance. Here we report that tau deposition in subjects with evaluated Aβ, accompanied by cortical thinning and cognitive decline, is strongly associated with decreased coupling between CSF flow and global brain activity. Substantial modulation of global brain activity is also manifested as propagating waves of brain activation between higher- and lower-order regions, resembling tau spreading. Together, the findings suggest an important role of resting-state global brain activity in AD tau pathology.

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 that is an integral component of 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 Alzheimer's disease between different chronological points.

Methods

We developed a deep-learning framework based on 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 neuron and plays a role in controlling cell growth and differentiation. In addition, MUC7 and PROL1/OPRPNon chromosome 4 were significant at the gene level. The cell-type-specific enrichment analysis and functional enrichment of GWAS signals highlighted the microglia and immune-response pathways. Furthermore, we found that the cognitive decline slope GWAS was positively correlated with previous AD GWAS.

Conclusion

Our deep learning model was demonstrated effective on extracting relevant neuroimaging features and predicting individual cognitive decline. We reported a novel variant (rs144614292) within the NELL1 gene. Our approach has the potential to disentangle accelerated cognitive decline from the normal aging process and to determine its related genetic factors, leveraging opportunities for early intervention.

Predicting the Societal Value of Lecanemab in Early Alzheimer's Disease in Japan: A Patient-Level Simulation

INTRODUCTION

Alzheimer's disease (AD), a neurodegenerative disorder that progresses from mild cognitive impairment (MCI) to dementia, is responsible for significant burden on caregivers and healthcare systems. In this study, data from the large phase III CLARITY AD trial were used to estimate the societal value of lecanemab plus standard of care (SoC) versus SoC alone against a range of willingness-to-pay (WTP) thresholds from a healthcare and societal perspective in Japan.

METHODS

A disease simulation model was used to evaluate the impact of lecanemab on disease progression in early AD based on data from the phase III CLARITY AD trial and published literature. The model used a series of predictive risk equations based on clinical and biomarker data from the Alzheimer's Disease Neuroimaging Initiative and Assessment of Health Economics in Alzheimer's Disease II study. The model predicted key patient outcomes, including life years (LYs), quality-adjusted life years (QALYs), and total healthcare and informal costs of patients and caregivers.

RESULTS

Over a lifetime horizon, patients treated with lecanemab plus SoC gained an additional 0.73 LYs compared with SoC alone (8.50 years vs. 7.77 years). Lecanemab, with an average treatment duration of 3.68 years, was found to be associated with a 0.91 increase in patient QALYs and a total increase of 0.96 when accounting for caregiver utility. The estimated value of lecanemab varied according to the WTP thresholds (JPY 5-15 million per QALY gained) and the perspective employed. From the narrow healthcare payer's perspective, it ranged from JPY 1,331,305 to JPY 3,939,399. From the broader healthcare payer's perspective, it ranged from JPY 1,636,827 to JPY 4,249,702, while from the societal perspective, it ranged from JPY 1,938,740 to JPY 4,675,818.

CONCLUSION

The use of lecanemab plus SoC would improve health and humanistic outcomes with reduced economic burden for patients and caregivers with early AD in Japan.

Impact and Advances in the Role of Bacterial Extracellular Vesicles in Neurodegenerative Disease and Its Therapeutics

Bacterial Extracellular Vesicles (BEVs) possess the capability of intracellular interactions with other cells, and, hence, can be utilized as an efficient cargo for worldwide delivery of therapeutic substances such as monoclonal antibodies, proteins, plasmids, siRNA, and small molecules for the treatment of neurodegenerative diseases (NDs). BEVs additionally possess a remarkable capacity for delivering these therapeutics across the blood-brain barrier to treat Alzheimer's disease (AD). This review summarizes the role and advancement of BEVs for NDs, AD, and their treatment. Additionally, it investigates the critical BEV networks in the microbiome-gut-brain axis, their defensive and offensive roles in NDs, and their interaction with NDs. Furthermore, the part of BEVs in the neuroimmune system and their interference with ND, as well as the risk factors made by BEVs in the autophagy-lysosomal pathway and their potential outcomes on ND, are all discussed. To conclude, this review aims to gain a better understanding of the credentials of BEVs in NDs and possibly discover new therapeutic strategies.

The evolution of Big Data in neuroscience and neurology

Neurological diseases are on the rise worldwide, leading to increased healthcare costs and diminished quality of life in patients. In recent years, Big Data has started to transform the fields of Neuroscience and Neurology. Scientists and clinicians are collaborating in global alliances, combining diverse datasets on a massive scale, and solving complex computational problems that demand the utilization of increasingly powerful computational resources. This Big Data revolution is opening new avenues for developing innovative treatments for neurological diseases. Our paper surveys Big Data's impact on neurological patient care, as exemplified through work done in a comprehensive selection of areas, including Connectomics, Alzheimer's Disease, Stroke, Depression, Parkinson's Disease, Pain, and Addiction (e.g., Opioid Use Disorder). We present an overview of research and the methodologies utilizing Big Data in each area, as well as their current limitations and technical challenges. Despite the potential benefits, the full potential of Big Data in these fields currently remains unrealized. We close with recommendations for future research aimed at optimizing the use of Big Data in Neuroscience and Neurology for improved patient outcomes.

Supplementary Information

The online version contains supplementary material available at 10.1186/s40537-023-00751-2.

Intranasally administered extracellular vesicles from human induced pluripotent stem cell-derived neural stem cells quickly incorporate into neurons and microglia in 5xFAD mice

Introduction

Extracellular vesicles (EVs) released by human-induced pluripotent stem cell (hiPSC)-derived neural stem cells (NSCs) have robust antiinflammatory and neurogenic properties due to therapeutic miRNAs and proteins in their cargo. Hence, hiPSC-NSC-EVs are potentially an excellent biologic for treating neurodegenerative disorders, including Alzheimer's disease (AD).

Methods

This study investigated whether intranasally (IN) administered hiPSC-NSC-EVs would quickly target various neural cell types in the forebrain, midbrain, and hindbrain regions of 3-month-old 5xFAD mice, a model of β-amyloidosis and familial AD. We administered a single dose of 25 × 10⁹ hiPSC-NSC-EVs labeled with PKH26, and different cohorts of naïve and 5xFAD mice receiving EVs were euthanized at 45 min or 6 h post-administration.

Results

At 45 min post-administration, EVs were found in virtually all subregions of the forebrain, midbrain, and hindbrain of naïve and 5xFAD mice, with predominant targeting and internalization into neurons, interneurons, and microglia, including plaque-associated microglia in 5xFAD mice. EVs also came in contact with the plasma membranes of astrocytic processes and the soma of oligodendrocytes in white matter regions. Evaluation of CD63/CD81 expression with the neuronal marker confirmed that PKH26 + particles found within neurons were IN administered hiPSC-NSC-EVs. At 6 h post-administration, EVs persisted in all cell types in both groups, with the distribution mostly matching what was observed at 45 min post-administration. Area fraction (AF) analysis revealed that, in both naïve and 5xFAD mice, higher fractions of EVs incorporate into forebrain regions at both time points. However, at 45 min post-IN administration, AFs of EVs within cell layers in forebrain regions and within microglia in midbrain and hindbrain regions were lower in 5xFAD mice than naïve mice, implying that amyloidosis reduces EV penetrance.

Discussion

Collectively, the results provide novel evidence that IN administration of therapeutic hiPSC-NSC-EVs is an efficient avenue for directing such EVs into neurons and glia in all brain regions in the early stage of amyloidosis. As pathological changes in AD are observed in multiple brain areas, the ability to deliver therapeutic EVs into various neural cells in virtually every brain region in the early stage of amyloidosis is attractive for promoting neuroprotective and antiinflammatory effects.

Sex and gender differences in Alzheimer's disease, Parkinson's disease, and Amyotrophic Lateral Sclerosis: a narrative review

Neurodegenerative diseases (NDs), including Alzheimer's disease (AD), Parkinson's disease (PD), and amyotrophic lateral sclerosis (ALS), exhibit high phenotypic variability and they are very common in the general population. These diseases are associated with poor prognosis and a significant burden on patients and their caregivers. Although increasing evidence suggests that biological sex is an important factor for the development and phenotypical expression of some NDs, the role of sex and gender in the diagnosis and prognosis of NDs has been poorly explored. Current knowledge relating to sex- and gender-related differences in the epidemiology, clinical features, biomarkers, and treatment of AD, PD, and ALS will be summarized in this narrative review. The cumulative evidence hitherto collected suggests that sex and gender are factors to be considered in explaining the heterogeneity of these NDs. Clarifying the role of sex and gender in AD, PD, and ALS is a key topic in precision medicine, which will facilitate sex-specific prevention and treatment strategies to be implemented in the near future.

Machine learning application for classification of Alzheimer's disease stages using 18F-flortaucipir positron emission tomography

BACKGROUND

The progression of Alzheimer's dementia (AD) can be classified into three stages: cognitive unimpairment (CU), mild cognitive impairment (MCI), and AD. The purpose of this study was to implement a machine learning (ML) framework for AD stage classification using the standard uptake value ratio (SUVR) extracted from ¹⁸F-flortaucipir positron emission tomography (PET) images. We demonstrate the utility of tau SUVR for AD stage classification. We used clinical variables (age, sex, education, mini-mental state examination scores) and SUVR extracted from PET images scanned at baseline. Four types of ML frameworks, such as logistic regression, support vector machine (SVM), extreme gradient boosting, and multilayer perceptron (MLP), were used and explained by Shapley Additive Explanations (SHAP) to classify the AD stage.

RESULTS

Of a total of 199 participants, 74, 69, and 56 patients were in the CU, MCI, and AD groups, respectively; their mean age was 71.5 years, and 106 (53.3%) were men. In the classification between CU and AD, the effect of clinical and tau SUVR was high in all classification tasks and all models had a mean area under the receiver operating characteristic curve (AUC) > 0.96. In the classification between MCI and AD, the independent effect of tau SUVR in SVM had an AUC of 0.88 (p < 0.05), which was the highest compared to other models. In the classification between MCI and CU, the AUC of each classification model was higher with tau SUVR variables than with clinical variables independently, which yielded an AUC of 0.75(p < 0.05) in MLP, which was the highest. As an explanation by SHAP for the classification between MCI and CU, and AD and CU, the amygdala and entorhinal cortex greatly affected the classification results. In the classification between MCI and AD, the para-hippocampal and temporal cortex affected model performance. Especially entorhinal cortex and amygdala showed a higher effect on model performance than all clinical variables in the classification between MCI and CU.

CONCLUSIONS

The independent effect of tau deposition indicates that it is an effective biomarker in classifying CU and MCI into clinical stages using MLP. It is also very effective in classifying AD stages using SVM with clinical information that can be easily obtained at clinical screening.

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.

A Path to Improved Alzheimer's Care: Simulating Long-Term Health Outcomes of Lecanemab in Early Alzheimer's Disease from the CLARITY AD Trial

INTRODUCTION

Alzheimer's disease (AD), a progressive neurodegenerative disease, is the main cause of dementia and one of the leading causes of death for elderly people in the USA. Lecanemab is a humanized IgG1 monoclonal antibody targeting amyloid protofibrils for the treatment of early AD [i.e., mild cognitive impairment (MCI) or mild AD dementia]. In a recent 18-month phase III trial, using a double-blind, placebo-controlled design, lecanemab treatment led to reduced brain amyloid burden and significant improvements in cognitive and functional abilities in individuals with early AD.

METHODS

An evidence-based patient-level disease simulation model was updated to estimate the long-term health outcomes of lecanemab plus standard of care (SoC) compared to SoC alone in patients with early AD and evidence of brain amyloid burden, using recent phase III trial data and published literature. The disease progression is described by changes in the underlying biomarkers of AD, including measures of amyloid and tau, and their connection to the clinical presentation of the disease assessed through various patient-level scales of cognition and function.

RESULTS

Lecanemab treatment was estimated to slow the progression of AD to moderate and severe stages and reduce the time spent in these more advanced states. In individuals with early AD, lecanemab plus SoC was associated with a gain of 0.71 quality-adjusted life-years (QALYs), a 2.95-year delay in mean time to progression to AD dementia, a reduction of 0.11 years in institutional care, and an additional 1.07 years in community care as shown in the base-case study. Improved health outcomes were demonstrated with lecanemab treatment when initiated earlier based on age, disease severity, or tau pathology, resulting in estimated gains in QALYs ranging from 0.77 to 1.09 years, compared to 0.4 years in the mild AD dementia subset, as shown by the model.

CONCLUSION

The study findings demonstrate the potential clinical value of lecanemab for individuals with early AD by slowing down disease progression and prolonging time in earlier stages of disease, which significantly benefits not only patients and caregivers but also society overall.

TRIAL REGISTRATION

ClinicalTrials.gov identifier, NCT03887455.

Improving generalizability and study design of Alzheimer's disease cohort studies in the United States by including under-represented populations

The poor generalizability of clinical research data due to the enrollment of highly educated, non-Latinx White participants hampers the development of therapies for Alzheimer's disease (AD). Black and Latinx older adults have a greater risk for dementia, yet it is unclear how health-care disparities and sociocultural factors influence potential AD therapies and prognosis. Low enrollment of under-represented populations may be attributable to several factors including greater exclusion due to higher rates of comorbidities, lower access to AD clinics, and the legacy of unethical treatment in medical research. This perspective outlines solutions tested in the Brain Health Registry (BHR) and the Alzheimer's Disease Neuroimaging Initiative (ADNI), including culturally-informed digital research methods, community-engaged research strategies, leadership from under-represented communities, and the reduction of exclusion criteria based on comorbidities. Our successes demonstrate that it is possible to increase the inclusion and engagement of under-represented populations into US-based clinical studies, thereby increasing the generalizability of their results.

Estimated Societal Value of Lecanemab in Patients with Early Alzheimer's Disease Using Simulation Modeling

INTRODUCTION

Alzheimer's disease (AD) is a progressive neurodegenerative disorder associated with memory, cognitive, and behavioral deficits, and brings significant economic burden on caregivers and healthcare systems. This study aims to estimate the long-term societal value of lecanemab plus standard of care (SoC) versus SoC alone, corresponding to a range of willingness-to-pay (WTP) thresholds based on the phase III CLARITY AD trial readouts from both the US payer and societal perspectives.

METHODS

An evidence-based model was developed to simulate the effects of lecanemab on disease progression in early AD using interconnected predictive equations based on longitudinal clinical and biomarker data derived from the Alzheimer's Disease Neuroimaging Initiative (ADNI). The model was informed with the results of the phase III CLARITY AD trial and published literature. Key model outcomes included patient life-years (LYs), quality-adjusted life-years (QALYs), and total costs of both the direct and indirect costs of patients and caregivers over a lifetime horizon.

RESULTS

Patients treated with lecanemab plus SoC gained an additional 0.62 years of life versus SoC alone (6.23 years vs. 5.61 years). The mean time on lecanemab was 3.91 years, and the treatment was associated with an increase in patient QALYs of 0.61 and an increase in total QALYs of 0.64 when both patient and caregiver utilities were considered. The model estimated that the annual value of lecanemab for the US payer perspective was US$18,709-35,678 ($19,710-37,351 for societal perspective) at the WTP threshold of $100,000-200,000 per QALY gained, respectively. Scenario analyses of patient subgroups, time horizon, input sources, treatment stopping rules, and treatment dosing were conducted to explore the impact of alternative assumptions on the model results.

CONCLUSION

The economic study suggested that lecanemab plus SoC would improve health and humanistic (quality of life) outcomes and reduce economic burden for patients and caregivers in early AD.

New Pathways Identify Novel Drug Targets for the Prevention and Treatment of Alzheimer's Disease

Alzheimer's disease (AD) is an incurable, progressive neurodegenerative disorder. AD is a complex and multifactorial disease that is responsible for 60-80% of dementia cases. Aging, genetic factors, and epigenetic changes are the main risk factors for AD. Two aggregation-prone proteins play a decisive role in AD pathogenesis: β-amyloid (Aβ) and hyperphosphorylated tau (pTau). Both of them form deposits and diffusible toxic aggregates in the brain. These proteins are the biomarkers of AD. Different hypotheses have tried to explain AD pathogenesis and served as platforms for AD drug research. Experiments demonstrated that both Aβ and pTau might start neurodegenerative processes and are necessary for cognitive decline. The two pathologies act in synergy. Inhibition of the formation of toxic Aβ and pTau aggregates has been an old drug target. Recently, successful Aβ clearance by monoclonal antibodies has raised new hopes for AD treatments if the disease is detected at early stages. More recently, novel targets, e.g., improvements in amyloid clearance from the brain, application of small heat shock proteins (Hsps), modulation of chronic neuroinflammation by different receptor ligands, modulation of microglial phagocytosis, and increase in myelination have been revealed in AD research.

Status of Metabolomic Measurement for Insights in Alzheimer's Disease Progression-What Is Missing?

Alzheimer's disease (AD) is an aging-related neurodegenerative disease, leading to the progressive loss of memory and other cognitive functions. As there is still no cure for AD, the growth in the number of susceptible individuals represents a major emerging threat to public health. Currently, the pathogenesis and etiology of AD remain poorly understood, while no efficient treatments are available to slow down the degenerative effects of AD. Metabolomics allows the study of biochemical alterations in pathological processes which may be involved in AD progression and to discover new therapeutic targets. In this review, we summarized and analyzed the results from studies on metabolomics analysis performed in biological samples of AD subjects and AD animal models. Then this information was analyzed by using MetaboAnalyst to find the disturbed pathways among different sample types in human and animal models at different disease stages. We discuss the underlying biochemical mechanisms involved, and the extent to which they could impact the specific hallmarks of AD. Then we identify gaps and challenges and provide recommendations for future metabolomics approaches to better understand AD pathogenesis.

A Visual Interpretation Algorithm for Assessing Brain Tauopathy with 18F-MK-6240 PET

In vivo characterization of pathologic deposition of tau protein in the human brain by PET imaging is a promising tool in drug development trials of Alzheimer disease (AD). 6-(fluoro-18F)-3-(1H-pyrrolo[2,3-c]pyridin-1-yl)isoquinolin-5-amine (18F-MK-6240) is a radiotracer with high selectivity and subnanomolar affinity for neurofibrillary tangles that shows favorable nonspecific brain penetration and excellent kinetic properties. The purpose of the present investigation was to develop a visual assessment method that provides both an overall assessment of brain tauopathy and regional characterization of abnormal tau deposition. Methods: 18F-MK-6240 scans from 102 participants (including cognitively normal volunteers and patients with AD or other neurodegenerative disorders) were reviewed by an expert nuclear medicine physician masked to each participant's diagnosis to identify common patterns of brain uptake. This initial visual read method was field-tested in a separate, nonoverlapping cohort of 102 participants, with 2 additional naïve readers trained on the method. Visual read outcomes were compared with semiquantitative assessments using volume-of-interest SUV ratio. Results: For the visual read, the readers assessed 8 gray-matter regions per hemisphere as negative (no abnormal uptake) or positive (1%-25% of the region involved, 25%-75% involvement, or >75% involvement) and then characterized the tau binding pattern as positive or negative for evidence of tau and, if positive, whether brain uptake was in an AD pattern. The readers demonstrated agreement 94% of the time for overall positivity or negativity. Concordance on the determination of regional binary outcomes (negative or positive) showed agreement of 74.3% and a Fleiss κ of 0.912. Using clinical diagnosis as the ground truth, the readers demonstrated a sensitivity of 73%-79% and specificity of 91%-93%, with a combined reader-concordance sensitivity of 80% and specificity of 93%. The average SUV ratio in cortical regions showed a robust correlation with visually derived ratings of regional involvement (r = 0.73, P < 0.0001). Conclusion: We developed a visual read algorithm for 18F-MK-6240 PET offering determination of both scan positivity and the regional degree of cortical involvement. These cross-sectional results show strong interreader concordance on both binary and regional assessments of tau deposition, as well as good sensitivity and excellent specificity supporting use as a tool for clinical trials.

A multimodal fusion method for Alzheimer's disease based on DCT convolutional sparse representation

Introduction

The medical information contained in magnetic resonance imaging (MRI) and positron emission tomography (PET) has driven the development of intelligent diagnosis of Alzheimer's disease (AD) and multimodal medical imaging. To solve the problems of severe energy loss, low contrast of fused images and spatial inconsistency in the traditional multimodal medical image fusion methods based on sparse representation. A multimodal fusion algorithm for Alzheimer' s disease based on the discrete cosine transform (DCT) convolutional sparse representation is proposed.

Methods

The algorithm first performs a multi-scale DCT decomposition of the source medical images and uses the sub-images of different scales as training images, respectively. Different sparse coefficients are obtained by optimally solving the sub-dictionaries at different scales using alternating directional multiplication method (ADMM). Secondly, the coefficients of high-frequency and low-frequency subimages are inverse DCTed using an improved L1 parametric rule combined with improved spatial frequency novel sum-modified SF (NMSF) to obtain the final fused images.

Results and discussion

Through extensive experimental results, we show that our proposed method has good performance in contrast enhancement, texture and contour information retention.

Associations of MRI-visible perivascular spaces with longitudinal cognitive decline across the Alzheimer's disease spectrum

OBJECTIVE

To investigate the characteristics and associations of MRI-visible perivascular spaces (PVS) with clinical progression and longitudinal cognitive decline across the Alzheimer's disease spectrum.

METHODS

We included 1429 participants (641 [44.86%] female) from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. PVS number and grade in the centrum semiovale (CSO-PVS), basal ganglia (BG-PVS), and hippocampus (HP-PVS) were compared among the control (CN), mild cognitive impairment (MCI), and Alzheimer's disease (AD) groups. PVS were tested as predictors of diagnostic progression (i.e., CN to MCI/AD or MCI to AD) and longitudinal changes in the 13-item Alzheimer's Disease Assessment Scale-cognitive subscale (ADAS-Cog 13), Mini-Mental State Examination (MMSE), memory (ADNI-MEM), and executive function (ADNI-EF) using multiple linear regression, linear mixed-effects, and Cox proportional hazards modeling.

RESULTS

Compared with CN subjects, MCI and AD subjects had more CSO-PVS, both in number (p < 0.001) and grade (p < 0.001). However, there was no significant difference in BG-PVS and HP-PVS across the AD spectrum (p > 0.05). Individuals with moderate and frequent/severe CSO-PVS had a higher diagnostic conversion risk than individuals with no/mild CSO-PVS (log-rank p < 0.001 for all) in the combined CN and MCI group. Further Cox regression analyses revealed that moderate and frequent/severe CSO-PVS were associated with a higher risk of diagnostic conversion (HR = 2.007, 95% CI = 1.382-2.914, p < 0.001; HR = 2.676, 95% CI = 1.830-3.911, p < 0.001, respectively). A higher CSO-PVS number was associated with baseline cognitive performance and longitudinal cognitive decline in all cognitive tests (p < 0.05 for all).

CONCLUSIONS

CSO-PVS were more common in MCI and AD and were associated with cognitive decline across the AD spectrum.

Treatment of Alzheimer's disease by combination of acupuncture and Chinese medicine based on pathophysiological mechanism: A review

Alzheimer's disease (AD) is a neurodegenerative disorder characterized by neurodegeneration, nerve loss, neurofibrillary tangles, and Aβ plaques. In modern medical science, there has been a serious obstacle to the effective treatment of AD. At present, there is no clinically proven and effective western medicine treatment for AD. The reason is that the etiology of AD is not yet fully understood. In 2018, the international community put forward a purely biological definition of AD, but soon this view of biomarkers was widely questioned, because the so-called AD biomarkers are shared with other neurological diseases, the diagnostic accuracy is low, and they face various challenges in the process of clinical diagnosis and treatment. Nowadays, scholars increasingly regard AD as the result of multimechanism and multicenter interaction. Because there is no exact Western medicine treatment for AD, the times call for the comprehensive treatment of AD in traditional Chinese medicine (TCM). AD belongs to the category of "dull disease" in TCM. For thousands of years, TCM has accumulated a lot of relevant treatment experience in the process of diagnosis and treatment. TCM, acupuncture, and the combination of acupuncture and medicine all play an important role in the treatment of AD. Based on the research progress of modern medicine on the pathophysiology of AD, this paper discusses the treatment of this disease with the combination of acupuncture and medicine.

Hearing impairment is associated with cognitive decline, brain atrophy and tau pathology

BACKGROUND

Hearing impairment was recently identified as the most prominent risk factor for dementia. However, the mechanisms underlying the link between hearing impairment and dementia are still unclear.

METHODS

We investigated the association of hearing performance with cognitive function, brain structure and cerebrospinal fluid (CSF) proteins in cross-sectional, longitudinal, mediation and genetic association analyses across the UK Biobank (N = 165,550), the Chinese Alzheimer's Biomarker and Lifestyle (CABLE, N = 863) study, and the Alzheimer's Disease Neuroimaging Initiative (ADNI, N = 1770) database.

FINDINGS

Poor hearing performance was associated with worse cognitive function in the UK Biobank and in the CABLE study. Hearing impairment was significantly related to lower volume of temporal cortex, hippocampus, inferior parietal lobe, precuneus, etc., and to lower integrity of white matter (WM) tracts. Furthermore, a higher polygenic risk score (PRS) for hearing impairment was strongly associated with lower cognitive function, lower volume of gray matter, and lower integrity of WM tracts. Moreover, hearing impairment was correlated with a high level of CSF tau protein in the CABLE study and in the ADNI database. Finally, mediation analyses showed that brain atrophy and tau pathology partly mediated the association between hearing impairment and cognitive decline.

INTERPRETATION

Hearing impairment is associated with cognitive decline, brain atrophy and tau pathology, and hearing impairment may reflect the risk for cognitive decline and dementia as it is related to bran atrophy and tau accumulation in brain. However, it is necessary to assess the mechanism in future animal studies.

FUNDING

A full list of funding bodies that supported this study can be found in the Acknowledgements section.

Predicting progression to dementia with “comprehensive visual rating scale” and machine learning algorithms

Background and Objective

Identifying biomarkers for predicting progression to dementia in patients with mild cognitive impairment (MCI) is crucial. To this end, the comprehensive visual rating scale (CVRS), which is based on magnetic resonance imaging (MRI), was developed for the assessment of structural changes in the brains of patients with MCI. This study aimed to investigate the use of the CVRS score for predicting dementia in patients with MCI over a 2-year follow-up period using various machine learning (ML) algorithms.

Methods

We included 197 patients with MCI who were followed up more than once. The data used for this study were obtained from the Japanese-Alzheimer's Disease Neuroimaging Initiative study. We assessed all the patients using their CVRS scores, cortical thickness data, and clinical data to determine their progression to dementia during a follow-up period of over 2 years. ML algorithms, such as logistic regression, random forest (RF), XGBoost, and LightGBM, were applied to the combination of the dataset. Further, feature importance that contributed to the progression from MCI to dementia was analyzed to confirm the risk predictors among the various variables evaluated.

Results

Of the 197 patients, 108 (54.8%) showed progression from MCI to dementia. Tree-based classifiers, such as XGBoost, LightGBM, and RF, achieved relatively high performance. In addition, the prediction models showed better performance when clinical data and CVRS score (accuracy 0.701–0.711) were used than when clinical data and cortical thickness (accuracy 0.650–0.685) were used. The features related to CVRS helped predict progression to dementia using the tree-based models compared to logistic regression.

Conclusions

Tree-based ML algorithms can predict progression from MCI to dementia using baseline CVRS scores combined with clinical data.

Application of positron emission tomography in psychiatry-methodological developments and future directions

Mental disorders represent an increasing source of disability and high costs for societies globally. Molecular imaging techniques such as positron emission tomography (PET) represent powerful tools with the potential to advance knowledge regarding disease mechanisms, allowing the development of new treatment approaches. Thus far, most PET research on pathophysiology in psychiatric disorders has focused on the monoaminergic neurotransmission systems, and although a series of discoveries have been made, the results have not led to any material changes in clinical practice. We outline areas of methodological development that can address some of the important obstacles to fruitful progress. First, we point towards new radioligands and targets that can lead to the identification of processes upstream, or parallel to disturbances in monoaminergic systems. Second, we describe the development of new methods of PET data quantification and PET systems that may facilitate research in psychiatric populations. Third, we review the application of multimodal imaging that can link molecular imaging data to other aspects of brain function, thus deepening our understanding of disease processes. Fourth, we highlight the need to develop imaging study protocols to include longitudinal and interventional paradigms, as well as frameworks to assess dimensional symptoms such that the field can move beyond cross-sectional studies within current diagnostic boundaries. Particular effort should be paid to include also the most severely ill patients. Finally, we discuss the importance of harmonizing data collection and promoting data sharing to reach the desired sample sizes needed to fully capture the phenotype of psychiatric conditions.

Hemispheric Cortical, Cerebellar and Caudate Atrophy Associated to Cognitive Impairment in Metropolitan Mexico City Young Adults Exposed to Fine Particulate Matter Air Pollution

Exposures to fine particulate matter PM_2.5 are associated with Alzheimer's, Parkinson's (AD, PD) and TDP-43 pathology in young Metropolitan Mexico City (MMC) residents. High-resolution structural T1-weighted brain MRI and/or Montreal Cognitive Assessment (MoCA) data were examined in 302 volunteers age 32.7 ± 6.0 years old. We used multivariate linear regressions to examine cortical surface area and thickness, subcortical and cerebellar volumes and MoCA in ≤30 vs. ≥31 years old. MMC residents were exposed to PM_2.5 ~ 30.9 µg/m³. Robust hemispheric differences in frontal and temporal lobes, caudate and cerebellar gray and white matter and strong associations between MoCA total and index scores and caudate bilateral volumes, frontotemporal and cerebellar volumetric changes were documented. MoCA LIS scores are affected early and low pollution controls ≥ 31 years old have higher MoCA vs. MMC counterparts (p ≤ 0.0001). Residency in MMC is associated with cognitive impairment and overlapping targeted patterns of brain atrophy described for AD, PD and Fronto-Temporal Dementia (FTD). MMC children and young adult longitudinal studies are urgently needed to define brain development impact, cognitive impairment and brain atrophy related to air pollution. Identification of early AD, PD and FTD biomarkers and reductions on PM2.5 emissions, including poorly regulated heavy-duty diesel vehicles, should be prioritized to protect 21.8 million highly exposed MMC urbanites.

Alzheimer's Disease Seen through the Eye: Ocular Alterations and Neurodegeneration

Alzheimer’s Disease (AD) is one of the main neurodegenerative diseases worldwide. Unfortunately, AD shares many similarities with other dementias at early stages, which impedes an accurate premortem diagnosis. Therefore, it is urgent to find biomarkers to allow for early diagnosis of the disease. There is increasing scientific evidence highlighting the similarities between the eye and other structures of the CNS, suggesting that knowledge acquired in eye research could be useful for research and diagnosis of AD. For example, the retina and optic nerve are considered part of the central nervous system, and their damage can result in retrograde and anterograde axon degeneration, as well as abnormal protein aggregation. In the anterior eye segment, the aqueous humor and tear film may be comparable to the cerebrospinal fluid. Both fluids are enriched with molecules that can be potential neurodegenerative biomarkers. Indeed, the pathophysiology of AD, characterized by cerebral deposits of amyloid-beta (Aβ) and tau protein, is also present in the eyes of AD patients, besides numerous structural and functional changes observed in the structure of the eyes. Therefore, all this evidence suggests that ocular changes have the potential to be used as either predictive values for AD assessment or as diagnostic tools.

Intrinsic Brain Activity Alterations in Patients With Mild Cognitive Impairment-to-Normal Reversion: A Resting-State Functional Magnetic Resonance Imaging Study From Voxel to Whole-Brain Level

Mild cognitive impairment (MCI) reversion refers to patients with MCI who revert from MCI to a normal cognitive state. Exploring the underlying neuromechanism of MCI reverters may contribute to providing new insights into the pathogenesis of Alzheimer's disease and developing therapeutic interventions. Information on patients with MCI and healthy controls (HCs) was collected from the Alzheimer's Disease Neuroimaging Initiative database. We redefined MCI reverters as patients with MCI whose logical memory scores changed from MCI to normal levels using the logical memory criteria. We explored intrinsic brain activity alterations in MCI reverters from voxel, regional, and whole-brain levels by comparing resting-state functional magnetic resonance imaging metrics of the amplitude of low-frequency of fluctuation (ALFF), the fractional amplitude of low-frequency fluctuation (fALFF), percent amplitude of fluctuation (PerAF), regional homogeneity (ReHo), and degree centrality (DC) between MCI reverters and HCs. Finally, partial correlation analyses were conducted between cognitive scale scores and resting-state functional magnetic resonance imaging metrics of brain regions, revealing significant group differences. Thirty-two patients with MCI from the Alzheimer's Disease Neuroimaging Initiative database were identified as reverters. Thirty-seven age-, sex-, and education-matched healthy individuals were also enrolled. At the voxel level, compared with the HCs, MCI reverters had increased ALFF, fALFF, and PerAF in the frontal gyrus (including the bilateral orbital inferior frontal gyrus and left middle frontal gyrus), increased PerAF in the left fusiform gyrus, and decreased ALFF and fALFF in the right inferior cerebellum. Regarding regional and whole-brain levels, MCI reverters showed increased ReHo in the left fusiform gyrus and right median cingulate and paracingulate gyri; increased DC in the left inferior temporal gyrus and left medial superior frontal; decreased DC in the right inferior cerebellum and bilateral insular gyrus relative to HCs. Furthermore, significant correlations were found between cognitive performance and neuroimaging changes. These findings suggest that MCI reverters show significant intrinsic brain activity changes compared with HCs, potentially related to the cognitive reversion of patients with MCI. These results enhance our understanding of the underlying neuromechanism of MCI reverters and may contribute to further exploration of Alzheimer's disease.