Japanese and North American Alzheimer's Disease Neuroimaging Initiative studies: Harmonization for international trials

Machine learning models for dementia screening to classify brain amyloid positivity on positron emission tomography using blood markers and demographic characteristics: a retrospective observational study

BACKGROUND

Intracerebral amyloid β (Aβ) accumulation is considered the initial observable event in the pathological process of Alzheimer's disease (AD). Efficient screening for amyloid pathology is critical for identifying patients for early treatment. This study developed machine learning models to classify positron emission tomography (PET) Aβ-positivity in participants with preclinical and prodromal AD using data accessible to primary care physicians.

METHODS

This retrospective observational study assessed the classification performance of combinations of demographic characteristics, routine blood test results, and cognitive test scores to classify PET Aβ-positivity using machine learning. Participants with mild cognitive impairment (MCI) or normal cognitive function who visited Oita University Hospital or had participated in the USUKI study and met the study eligibility criteria were included. The primary endpoint was assessment of the classification performance of the presence or absence of intracerebral Aβ accumulation using five machine learning models (i.e., five combinations of variables), each constructed with three classification algorithms, resulting in a total of 15 patterns. L2-regularized logistic regression, and kernel Support Vector Machine (SVM) and Elastic Net algorithms were used to construct the classification models using 34 pre-selected variables (12 demographic characteristics, 11 blood test results, 11 cognitive test results).

RESULTS

Data from 262 records (260 unique participants) were analyzed. The mean (standard deviation [SD]) participant age was 73.8 (7.8) years. Using L2-regularized logistic regression, the mean receiver operating characteristic (ROC) area under the curve (AUC) (SD) in Model 0 (basic demographic characteristics) was 0.67 (0.01). Classification performance was similar in Model 1 (basic demographic characteristics and Mini Mental State Examination [MMSE] subscores) and Model 2 (demographic characteristics and blood test results) with a cross-validated mean ROC AUC (SD) of 0.70 (0.01) for both. Model 3 (demographic characteristics, blood test results, MMSE subscores) and Model 4 (Model 3 and ApoE4 phenotype) showed improved performance with a mean ROC AUC (SD) of 0.73 (0.01) and 0.76 (0.01), respectively. In models using blood test results, thyroid-stimulating hormone and mean corpuscular volume tended to be the largest contributors to classification. Classification performances were similar using the SVM and Elastic Net algorithms.

CONCLUSIONS

The machine learning models used in this study were useful for classifying PET Aβ-positivity using data from routine physician visits.

TRIAL REGISTRATION

UMIN Clinical Trials Registry (UMIN000051776, registered on 31/08/2023).

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.

Transethnic analysis identifies SORL1 variants and haplotypes protective against Alzheimer's disease

INTRODUCTION

The SORL1 locus exhibits protective effects against Alzheimer's disease (AD) across ancestries, yet systematic studies in diverse populations are sparse.

METHODS

Logistic regression identified AD-associated SORL1 haplotypes in East Asian (N = 5249) and European (N = 8588) populations. Association analysis between SORL1 haplotypes and AD-associated traits or plasma biomarkers was conducted. The effects of non-synonymous mutations were assessed in cell-based systems.

RESULTS

Protective SORL1 variants/haplotypes were identified in the East Asian and European populations. Haplotype Hap_A showed a strong protective effect against AD in East Asians, linked to less severe AD phenotypes, higher SORL1 transcript levels, and plasma proteomic changes. A missense variant within Hap_A, rs2282647-C allele, was linked to a lower risk of AD and decreased expression of a truncated SORL1 protein isoform.

DISCUSSION

Our transethnic analysis revealed key SORL1 haplotypes that exert protective effects against AD, suggesting mechanisms of the protective role of SORL1 in AD.

HIGHLIGHTS

We examined the AD-protective mechanisms of SORL1 in the general population across diverse ancestral backgrounds by jointly analyzing data from three East Asian cohorts (ie, mainland China, Hong Kong, and Japan) and a European cohort. Comparative analysis unveiled key ethnic-specific SORL1 genetic variants and haplotypes. Among these, the SORL1 minor haplotype, Hap_A, emerged as the primary AD-protective factor in East Asians. Hap_A exerts significant AD-protective effects in both APOE ε4 carriers and non-carriers. SORL1 haplotype Hap_A is associated with cognitive function, brain volume, and the activity of specific neuronal and immune-related pathways closely connected to AD risk. Protective variants within Hap_A are linked to increased SORL1 expression in human tissues. We identified an isoform-specific missense variant in Hap_A that modifies the function and levels of a truncated SORL1 protein isoform that is poorly investigated.

Patient-specific brain fluorodeoxyglucose positron emission tomography can detect the first effects of combination antiretroviral therapy in patient with HIV infection

Patient-specific brain fluorodeoxyglucose-positron emission tomography (FDG PET) can detect areas with abnormal FDG uptake in patients with human immunodeficiency virus (HIV) before and after combination antiretroviral therapy (cART). There were few reports about the same patients before and shortly after cART in FDG PET. It is well known that HIV-RNA levels decrease and cognitive impairments in patients with HIV tend to improve on neurocognitive performance tests 6 months after starting cART. We conducted a quantitative imaging analysis (FDG PET and voxel-based morphometry (VBM)) of eight patients at pre- and 6 months post- cART with neurocognitive performance tests. In terms of participant-specific changes between pre- and post-cART imaging, some area showed that the size of area with abnormal FDG uptake shrunk and became a nearly physiological level at 6 months post-cART. No apparent changes in VBM were observed in this short period. FDG PET might detect the first effect of cART.

Ethnic differences in the prevalence of amyloid positivity and cognitive trajectories

INTRODUCTION

We investigated the prevalence of amyloid beta (Aβ) positivity (+) and cognitive trajectories in Koreans and non-Hispanic Whites (NHWs).

METHODS

We included 5121 Koreans from multiple centers across South Korea and 929 NHWs from the Alzheimer's Disease Neuroimaging Initiative (ADNI). Participants underwent Aβ positron emission tomography and were categorized into cognitively unimpaired (CU), mild cognitive impairment (MCI), and dementia stages. Age, sex, education, and apolipoprotein E. genotype were adjusted using multivariable logistic regression and stabilized inverse probability of treatment weights based on the propensity scores to mitigate imbalances in these variables.

RESULTS

The prevalence of Aβ+ was lower in CU Koreans than in CU NHWs (adjusted odds ratio 0.60). Aβ+ Koreans showed a faster cognitive decline than Aβ+ NHWs in the CU (B = -0.314, p = .004) and MCI stages (B = -0.385, p < .001).

DISCUSSION

Ethnic characteristics of Aβ biomarkers should be considered in research and clinical application of Aβ-targeted therapies in diverse populations.

HIGHLIGHTS

Koreans have a lower prevalence of Aβ positivity compared to NHWs in the CU stage. The effects of Alzheimer's risk factors on Aβ positivity differ between Koreans and NHWs. Aβ-positive (Aβ+) Koreans show faster cognitive decline than Aβ+ NHWs in the CU and MCI stages.

Contributions of the ADNI Biostatistics Core

The goal of the Biostatistics Core of the Alzheimer's Disease Neuroimaging Initiative (ADNI) has been to ensure that sound study designs and statistical methods are used to meet the overall goals of ADNI. We have supported the creation of a well-validated and well-curated longitudinal database of clinical and biomarker information on ADNI participants and helped to make this accessible and usable for researchers. We have developed a statistical methodology for characterizing the trajectories of clinical and biomarker change for ADNI participants across the spectrum from cognitively normal to dementia, including multivariate patterns and evidence for heterogeneity in cognitive aging. We have applied these methods and adapted them to improve clinical trial design. ADNI-4 will offer us a chance to help extend these efforts to a more diverse cohort with an even richer panel of biomarker data to support better knowledge of and treatment for Alzheimer's disease and related dementias. HIGHLIGHTS: The Alzheimer's Disease Neuroimaging Initiative (ADNI) Biostatistics Core provides study design and analytic support to ADNI investigators. Core members develop and apply novel statistical methodology to work with ADNI data and support clinical trial design. The Core contributes to the standardization, validation, and harmonization of biomarker data. The Core serves as a resource to the wider research community to address questions related to the data and study as a whole.

Development of disease-modifying therapies against Alzheimer's disease

To successfully develop disease-modifying therapies (DMT) against Alzheimer's disease (AD), it is important to target the mild stage of the disease, before the pathological changes progress and dementia symptoms are fully manifested. To this end, the AD Neuroimaging Initiative (ADNI), a large-scale observational study, was initiated in the U.S. with the goal of development of DMT that are effective in the early stages of mild cognitive impairment (MCI) by utilizing imaging and biomarkers. In Japan, J-ADNI enrolled and followed up 537 patients, mainly with MCI, and established a platform for evaluation including amyloid PET, and demonstrated a high similarity in the clinical course of amyloid-positive MCI (prodromal AD) in Japan and the U.S. In 2023, the anti-Aβ antibody lecanemab successfully completed a Phase III clinical trial for early AD (prodromal AD + mild AD dementia) and was granted regulatory approval and made available both in the US and Japan. Also, phase III trial of donanemab was completed successful. The J-TRC study was initiated in Japan as a "trial ready cohort (TRC)" consisting of participants who met the eligibility criteria for participation in preclinical and prodromal AD trials. Based on such a platform, the development of DMT for AD will progress more rapidly in the future.

How will the emergence of lecanemab change dementia treatment?

The introduction of lecanemab has dramatically changed the field of dementia medicine. Lecanemab, defined as an anti-amyloid-β (Aβ) drug, comprises an antibody against Aβ, a protein structure believed to cause Alzheimer's disease. This drug represents a new direction in dementia treatment. In a phase III study, lecanemab was found to significantly slow cognitive decline, while showing manageable levels of amyloid-related imaging abnormalities, which are side-effects of lecanemab. Furthermore, lecanemab has been shown to effectively reduce Aβ accumulation in patients with early Alzheimer's disease, which might contribute not only to delaying the progression of cognitive decline, but also to improving the quality of life of patients and their families. However, there are conditions for the use of lecanemab, for which the Ministry of Health, Labor and Welfare has issued the Guidelines for Promotion of Optimal Use. These guidelines specify requirements for appropriate patient selection, prescribing physicians and administering medical institutions to ensure safe and effective use. Particular emphasis is placed on the confirmation of amyloid-β accumulation, amyloid-related imaging abnormalities risk management and appropriate handling of side-effects. The clinical use of lecanemab represents an important advancement in the treatment of dementia; however, the understanding and cooperation of healthcare professionals, patients and families are essential to maximize its efficacy and safety. Future issues to be addressed include the sustainability and long-term efficacy of treatment, improvement of clinical symptoms after removal of Aβ and motivation to administer the drug. Although lecanemab offers hope for the treatment of dementia, its use requires careful management. Geriatr Gerontol Int 2024; 24: 841-844.

Efficacy of a non-pharmaceutical multimodal intervention program in a group setting for patients with mild cognitive impairment: A single-arm interventional study with pre-post and external control analyses

Aim

This study aimed to evaluate the efficacy of a non-pharmaceutical multimodal intervention program consisting of physical exercise, cognitive stimulation, and health education in a group setting to slow the progression of mild cognitive impairment (MCI).

Methods

A single-arm interventional study was conducted on 27 patients with MCI. To evaluate the efficacy of the intervention program, a pre-post analysis was performed using EuroQol-5 Dimension (EQ-5D), Mini-Mental State Examination (MMSE), Cognitive Function Instrument (CFI), 5 Cog test, depression, and physical performance before and after the 8-month intervention. Additionally, propensity score and the semi-Bayes analyses were performed to compare the intervention program with standard medical care, using the external control patients' data for MMSE scores.

Results

Twenty-four patients completed the intervention program. During the study period, although EQ-5D and MMSE scores remained unchanged (mean change 0.02 [95 % confidence interval (CI): -0.004, 0.04], 0.5 [-0.2, 1.3]), CFI and the subcategories of 5Cog (attention and reasoning) improved (mean change -1.23 [-2.24, -0.21], 4.3 [0.9, 7.7], 3.0 [0.4, 5.6]). In the additional analysis comparing changes in MMSE scores, patients who underwent the intervention program had less decline than the external control patients (mean change -1.7 [-2.1, -1.3]) with an observed mean difference of 2.25 [1.46, 3.03], and propensity score-adjusted difference of 2.26 [1.46, 3.05]. The semi-Bayesian approach also suggested that the intervention slowed the progression of MCI.

Conclusion

A non-pharmaceutical multimodal intervention program could contribute to slowing cognitive decline in patients with MCI.

A Longitudinal Study of CogEvo's Prediction of Cognitive Decline in Older Adults

The predictive abilities of computer-based screening devices for early cognitive decline (CD) in older adults have rarely been longitudinally examined. Therefore, this study examined the ability of CogEvo, a short-duration, computer-based cognitive screening device requiring little professional involvement, to predict CD among community-dwelling older adults. We determined whether 119 individuals aged ≥ 65 years living in Japanese rural communities who scored ≥ 24 on the Mini-Mental State Examination (MMSE) at baseline developed CD by annually administering the MMSE to them. CD was defined as an MMSE score of ≤23. At baseline, the overall CogEvo judgment grade, with lower grades indicating better cognitive function, was calculated from the results of various cognitive tasks. Over 2 years, 10 participants developed CD. Participants with grades of 4 had a higher percentage of CD cases than those with grades of ≤3 (p < 0.01). This relationship remained significant after controlling for possible confounders, including the MMSE score at baseline. The sensitivity and specificity of the CogEvo grade cutoff of 4 were 50.0% and 93.6%, respectively. In conclusion, CogEvo may be an efficient tool for identifying individuals at a high risk for dementia. The possibility of missing CD cases should be considered when using CogEvo for screening.

Polygenic effects on the risk of Alzheimer's disease in the Japanese population

BACKGROUND

Polygenic effects have been proposed to account for some disease phenotypes; these effects are calculated as a polygenic risk score (PRS). This score is correlated with Alzheimer's disease (AD)-related phenotypes, such as biomarker abnormalities and brain atrophy, and is associated with conversion from mild cognitive impairment (MCI) to AD. However, the AD PRS has been examined mainly in Europeans, and owing to differences in genetic structure and lifestyle, it is unclear whether the same relationships between the PRS and AD-related phenotypes exist in non-European populations. In this study, we calculated and evaluated the AD PRS in Japanese individuals using genome-wide association study (GWAS) statistics from Europeans.

METHODS

In this study, we calculated the AD PRS in 504 Japanese participants (145 cognitively unimpaired (CU) participants, 220 participants with late mild cognitive impairment (MCI), and 139 patients with mild AD dementia) enrolled in the Japanese Alzheimer's Disease Neuroimaging Initiative (J-ADNI) project. In order to evaluate the clinical value of this score, we (1) determined the polygenic effects on AD in the J-ADNI and validated it using two independent cohorts (a Japanese neuropathology (NP) cohort (n = 565) and the North American ADNI (NA-ADNI) cohort (n = 617)), (2) examined the AD-related phenotypes associated with the PRS, and (3) tested whether the PRS helps predict the conversion of MCI to AD.

RESULTS

The PRS using 131 SNPs had an effect independent of APOE. The PRS differentiated between CU participants and AD patients with an area under the curve (AUC) of 0.755 when combined with the APOE variants. Similar AUC was obtained when PRS calculated by the NP and NA-ADNI cohorts was applied. In MCI patients, the PRS was associated with cerebrospinal fluid phosphorylated-tau levels (β estimate = 0.235, p value = 0.026). MCI with a high PRS showed a significantly increased conversion to AD in APOE ε4 noncarriers with a hazard rate of 2.22. In addition, we also developed a PRS model adjusted for LD and observed similar results.

CONCLUSIONS

We showed that the AD PRS is useful in the Japanese population, whose genetic structure is different from that of the European population. These findings suggest that the polygenicity of AD is partially common across ethnic differences.

Simplifying Alzheimer's Disease Monitoring: A Novel Machine-Learning Approach to Estimate the Clinical Dementia Rating Sum of Box Changes Using the Mini-Mental State Examination and Functional Activities Questionnaire

Background

Primary outcome measure in the clinical trials of disease modifying therapy (DMT) drugs for Alzheimer's disease (AD) has often been evaluated by Clinical Dementia Rating sum of boxes (CDRSB). However, CDR testing requires specialized training and 30-50 minutes to complete, not being suitable for daily clinical practice.

Objective

Herein, we proposed a machine-learning method to estimate CDRSB changes using simpler cognitive/functional batteries (Mini-Mental State Examination [MMSE] and Functional Activities Questionnaire [FAQ]), to replace CDR testing.

Methods

Baseline data from 944 ADNI and 171 J-ADNI amyloid-positive participants were used to build machine-learning models predicting annualized CDRSB changes between visits, based on MMSE and FAQ scores. Prediction performance was evaluated with mean absolute error (MAE) and R2 comparing predicted to actual rmDeltaCDRSB/rmDeltayear. We further assessed whether decline in cognitive function surpassing particular thresholds could be identified using the predicted rmDeltaCDRSB/rmDeltayear.

RESULTS

The models achieved the minimum required prediction errors (MAE < 1.0) and satisfactory prediction accuracy (R2>0.5) for mild cognitive impairment (MCI) patients for changes in CDRSB over periods of 18 months or longer. Predictions of annualized CDRSB progression>0.5, >1.0, or >1.5 demonstrated a consistent performance (i.e., Matthews correlation coefficient>0.5). These results were largely replicated in the J-ADNI case predictions.

CONCLUSIONS

Our method effectively predicted MCI patient deterioration in the CDRSB based solely on MMSE and FAQ scores. It may aid routine practice for disease-modifying therapy drug efficacy evaluation, without necessitating CDR testing at every visit.

Efficacy and Safety of Cilostazol in Mild Cognitive Impairment: A Randomized Clinical Trial

Importance

Recent evidence indicates the efficacy of β-amyloid immunotherapy for the treatment of Alzheimer disease, highlighting the need to promote β-amyloid removal from the brain. Cilostazol, a selective type 3 phosphodiesterase inhibitor, promotes such clearance by facilitating intramural periarterial drainage.

Objective

To determine the safety and efficacy of cilostazol in mild cognitive impairment.

Design, Setting, and Participants

The COMCID trial (A Trial of Cilostazol for Prevention of Conversion from Mild Cognitive Impairment to Dementia) was an investigator-initiated, double-blind, phase 2 randomized clinical trial. Adult participants were registered between May 25, 2015, and March 31, 2018, and received placebo or cilostazol for up to 96 weeks. Participants were treated in the National Cerebral and Cardiovascular Center and 14 other regional core hospitals in Japan. Patients with mild cognitive impairment with Mini-Mental State Examination (MMSE) scores of 22 to 28 points (on a scale of 0 to 30, with lower scores indicating greater cognitive impairment) and Clinical Dementia Rating scores of 0.5 points (on a scale of 0, 0.5, 1, 2, and 3, with higher scores indicating more severe dementia) were enrolled. The data were analyzed from May 1, 2020, to December 1, 2020.

Interventions

The participants were treated with placebo, 1 tablet twice daily, or cilostazol, 50 mg twice daily, for up to 96 weeks.

Main Outcomes and Measures

The primary end point was the change in the total MMSE score from baseline to the final observation. Safety analyses included all adverse events.

Results

The full analysis set included 159 patients (66 [41.5%] male; mean [SD] age, 75.6 [5.2] years) who received placebo or cilostazol at least once. There was no statistically significant difference between the placebo and cilostazol groups for the primary outcome. The least-squares mean (SE) changes in the MMSE scores among patients receiving placebo were -0.1 (0.3) at the 24-week visit, -0.8 (0.3) at 48 weeks, -1.2 (0.4) at 72 weeks, and -1.3 (0.4) at 96 weeks. Among those receiving cilostazol, the least-squares mean (SE) changes in MMSE scores were -0.6 (0.3) at 24 weeks, -1.0 (0.3) at 48 weeks, -1.1 (0.4) at 72 weeks, and -1.8 (0.4) at 96 weeks. Two patients (2.5%) in the placebo group and 3 patients (3.8%) in the cilostazol group withdrew owing to adverse effects. There was 1 case of subdural hematoma in the cilostazol group, which may have been related to the cilostazol treatment; the patient was successfully treated surgically.

Conclusions and Relevance

In this randomized clinical trial, cilostazol was well tolerated, although it did not prevent cognitive decline. The efficacy of cilostazol should be tested in future trials.

Trial Registration

ClinicalTrials.gov Identifier: NCT02491268.

Artificial intelligence for diagnostic and prognostic neuroimaging in dementia: A systematic review

INTRODUCTION

Artificial intelligence (AI) and neuroimaging offer new opportunities for diagnosis and prognosis of dementia.

METHODS

We systematically reviewed studies reporting AI for neuroimaging in diagnosis and/or prognosis of cognitive neurodegenerative diseases.

RESULTS

A total of 255 studies were identified. Most studies relied on the Alzheimer's Disease Neuroimaging Initiative dataset. Algorithmic classifiers were the most commonly used AI method (48%) and discriminative models performed best for differentiating Alzheimer's disease from controls. The accuracy of algorithms varied with the patient cohort, imaging modalities, and stratifiers used. Few studies performed validation in an independent cohort.

DISCUSSION

The literature has several methodological limitations including lack of sufficient algorithm development descriptions and standard definitions. We make recommendations to improve model validation including addressing key clinical questions, providing sufficient description of AI methods and validating findings in independent datasets. Collaborative approaches between experts in AI and medicine will help achieve the promising potential of AI tools in practice.

HIGHLIGHTS

There has been a rapid expansion in the use of machine learning for diagnosis and prognosis in neurodegenerative disease Most studies (71%) relied on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset with no other individual dataset used more than five times There has been a recent rise in the use of more complex discriminative models (e.g., neural networks) that performed better than other classifiers for classification of AD vs healthy controls We make recommendations to address methodological considerations, addressing key clinical questions, and validation We also make recommendations for the field more broadly to standardize outcome measures, address gaps in the literature, and monitor sources of bias.

Independent effect of Aβ burden on cognitive impairment in patients with small subcortical infarction

BACKGROUND

The effect of amyloid-β (Aβ) on cognitive impairment in patients with small subcortical infarction remains controversial, although a growing body of evidence shows a substantial overlap between Alzheimer's disease (AD) and subcortical ischemic vascular dementia, another form of cerebral small vessel disease (cSVD). Therefore, we investigated the relationships between Aβ positivity and the development of post-stroke cognitive impairment (PSCI) in patients with small subcortical infarction.

METHODS

We prospectively recruited 37 patients aged ≥ 50 years, with first-ever small subcortical infarction, who underwent amyloid positron emission tomography, 3 months after stroke at Korea University Guro Hospital. We also enrolled CU participants matched for age and sex with stroke patients for comparison of Aβ positivity. Patients were followed up at 3 and 12 months after the stroke to assess cognitive decline. Logistic and linear mixed-effect regression analyses were performed to identify the effect of Aβ positivity on PSCI development and long-term cognitive trajectories.

RESULTS

At 3 months after stroke, 12/37 (32.4%) patients developed PSCI, and 11/37 (29.7%) patients had Aβ deposition. Aβ positivity (odds ratio [OR] = 72.2, p = 0.024) was predictive of PSCI development regardless of cSVD burden. Aβ positivity (β = 0.846, p = 0.014) was also associated with poor cognitive trajectory, assessed by the Clinical Dementia Rating-Sum of Box, for 1 year after stroke.

CONCLUSIONS

Our findings highlight that Aβ positivity is an important predictor for PSCI development and cognitive decline over 1 year. Furthermore, our results provide evidence that anti-AD medications may be a strategy for preventing cognitive decline in patients with small subcortical infarctions.

Image data harmonization tools for the analysis of post-traumatic epilepsy development in preclinical multisite MRI studies

Preclinical MRI studies have been utilized for the discovery of biomarkers that predict post-traumatic epilepsy (PTE). However, these single site studies often lack statistical power due to limited and homogeneous datasets. Therefore, multisite studies, such as the Epilepsy Bioinformatics Study for Antiepileptogenic Therapy (EpiBioS4Rx), are developed to create large, heterogeneous datasets that can lead to more statistically significant results. EpiBioS4Rx collects preclinical data internationally across sites, including the United States, Finland, and Australia. However, in doing so, there are robust normalization and harmonization processes that are required to obtain statistically significant and generalizable results. This work describes the tools and procedures used to harmonize multisite, multimodal preclinical imaging data acquired by EpiBioS4Rx. There were four main harmonization processes that were utilized, including file format harmonization, naming convention harmonization, image coordinate system harmonization, and diffusion tensor imaging (DTI) metrics harmonization. By using Python tools and bash scripts, the file formats, file names, and image coordinate systems are harmonized across all the sites. To harmonize DTI metrics, values are estimated for each voxel in an image to generate a histogram representing the whole image. Then, the Quantitative Imaging Toolkit (QIT) modules are utilized to scale the mode to a value of one and depict the subsequent harmonized histogram. The standardization of file formats, naming conventions, coordinate systems, and DTI metrics are qualitatively assessed. The histograms of the DTI metrics were generated for all the individual rodents per site. For inter-site analysis, an average of the individual scans was calculated to create a histogram that represents each site. In order to ensure the analysis can be run at the level of individual animals, the sham and TBI cohort were analyzed separately, which depicted the same harmonization factor. The results demonstrate that these processes qualitatively standardize the file formats, naming conventions, coordinate systems, and DTI metrics of the data. This assists in the ability to share data across the study, as well as disseminate tools that can help other researchers to strengthen the statistical power of their studies and analyze data more cohesively.

Higher longitudinal brain white matter atrophy rate in aquaporin-4 IgG-positive NMOSD compared with healthy controls

We aimed to compare longitudinal brain atrophy in patients with neuromyelitis optica spectrum disorder (NMOSD) with healthy controls (HCs). The atrophy rate in patients with anti-aquaporin-4 antibody-positive NMOSD (AQP4 + NMOSD) was compared with age-sex-matched HCs recruited from the Japanese Alzheimer's Disease Neuroimaging Initiative study and another study performed at Chiba University. Twenty-nine patients with AQP4 + NMOSD and 29 HCs were enrolled in the study. The time between magnetic resonance imaging (MRI) scans was longer in the AQP4 + NMOSD group compared with the HCs (median; 3.2 vs. 2.9 years, P = 0.009). The annualized normalized white matter volume (NWV) atrophy rate was higher in the AQP4 + NMOSD group compared with the HCs (median; 0.37 vs. - 0.14, P = 0.018). The maximum spinal cord lesion length negatively correlated with NWV at baseline MRI in patients with AQP4 + NMOSD (Spearman's rho =  - 0.41, P = 0.027). The annualized NWV atrophy rate negatively correlated with the time between initiation of persistent prednisolone usage and baseline MRI in patients with AQP4 + NMOSD (Spearman's rho =  - 0.43, P = 0.019). Patients with AQP4 + NMOSD had a greater annualized NWV atrophy rate than HCs. Suppressing disease activity may prevent brain atrophy in patients with AQP4 + NMOSD.

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.

Forecasting individual progression trajectories in Alzheimer's disease

The anticipation of progression of Alzheimer's disease (AD) is crucial for evaluations of secondary prevention measures thought to modify the disease trajectory. However, it is difficult to forecast the natural progression of AD, notably because several functions decline at different ages and different rates in different patients. We evaluate here AD Course Map, a statistical model predicting the progression of neuropsychological assessments and imaging biomarkers for a patient from current medical and radiological data at early disease stages. We tested the method on more than 96,000 cases, with a pool of more than 4,600 patients from four continents. We measured the accuracy of the method for selecting participants displaying a progression of clinical endpoints during a hypothetical trial. We show that enriching the population with the predicted progressors decreases the required sample size by 38% to 50%, depending on trial duration, outcome, and targeted disease stage, from asymptomatic individuals at risk of AD to subjects with early and mild AD. We show that the method introduces no biases regarding sex or geographic locations and is robust to missing data. It performs best at the earliest stages of disease and is therefore highly suitable for use in prevention trials.

Relationship Between Fasting Blood Glucose Levels in Middle Age and Cognitive Function in Later Life: The Aichi Workers' Cohort Study

BACKGROUND

There is limited evidence regarding the relationship between Diabetes mellitus (DM) in middle age and mild cognitive impairment after a follow-up. Therefore, we investigated the relationship between fasting blood glucose (FBG) levels in middle age and cognitive function assessed using the Japanese version of the Montreal Cognitive Assessment (MoCA-J) in later life, following over 15 years of follow-up in the Aichi Workers' Cohort Study in Japan.

METHODS

Participants were 253 former local government employees aged 60-79 years in 2018 who participated in a baseline survey conducted in 2002. Using baseline FBG levels and self-reported history, participants were classified into the normal, impaired fasting glucose (IFG) and, and DM groups. Total MoCA-J score ranges from 0 to 30, and cognitive impairment was defined as MoCA-J score ≤25 in this study. A general linear model was used to estimate the mean MoCA-J scores in the FBG groups, adjusted for age, sex, educational year, smoking status, alcohol consumption, physical activity, body mass index, systolic blood pressure, total cholesterol, and estimated glomerular filtration rate.

RESULTS

The mean MoCA-J score in the total population was 25.0, and the prevalence of MoCA-J score ≤25 was 49.0%. Multivariable-adjusted total MoCA-J scores were 25.2, 24.8, and 23.4 in the normal, IFG, and DM groups, respectively. The odds ratio of MoCA-J score ≤25 in the DM group was 3.29.

CONCLUSION

FBG level in middle age was negatively associated with total MoCA-J scores assessed later in life, independent of confounding variables.

Decreased circulating branched-chain amino acids are associated with development of Alzheimer's disease in elderly individuals with mild cognitive impairment

Background

Nutritional epidemiology has shown that inadequate dietary protein intake is associated with poor brain function in the elderly population. The plasma free amino acid (PFAA) profile reflects nutritional status and may have the potential to predict future changes in cognitive function. Here, we report the results of a 2-year interim analysis of a 3-year longitudinal study following mild cognitive impairment (MCI) participants.

Method

In a multicenter prospective cohort design, MCI participants were recruited, and fasting plasma samples were collected. Based on clinical assessment of cognitive function up to 2 years after blood collection, MCI participants were divided into two groups: remained with MCI or reverted to cognitively normal ("MCI-stable," N = 87) and converted to Alzheimer's disease (AD) ("AD-convert," N = 68). The baseline PFAA profile was compared between the two groups. Stratified analysis based on apolipoprotein E ε4 (APOE ε4) allele possession was also conducted.

Results

Plasma concentrations of all nine essential amino acids (EAAs) were lower in the AD-convert group. Among EAAs, three branched-chain amino acids (BCAAs), valine, leucine and isoleucine, and histidine (His) exhibited significant differences even in the logistic regression model adjusted for potential confounding factors such as age, sex, body mass index (BMI), and APOE ε4 possession (p < 0.05). In the stratified analysis, differences in plasma concentrations of these four EAAs were more pronounced in the APOE ε4-negative group.

Conclusion

The PFAA profile, especially decreases in BCAAs and His, is associated with development of AD in MCI participants, and the difference was larger in the APOE ε4-negative population, suggesting that the PFAA profile is an independent risk indicator for AD development. Measuring the PFAA profile may have importance in assessing the risk of AD conversion in the MCI population, possibly reflecting nutritional status.

Clinical trial registration

[https://center6.umin.ac.jp/cgi-open-bin/ctr/ctr_view.cgi?recptno=R000025322], identifier [UMIN000021965].

Identification of causal effects of neuroanatomy on cognitive decline requires modeling unobserved confounders

INTRODUCTION

Carrying out a randomized controlled trial to estimate the causal effects of regional brain atrophy due to Alzheimer's disease (AD) is impossible. Instead, we must estimate causal effects from observational data. However, this generally requires knowing and having recorded all confounders, which is often unrealistic.

METHODS

We provide an approach that leverages the dependencies among multiple neuroanatomical measures to estimate causal effects from observational neuroimaging data without the need to know and record all confounders.

RESULTS

Our analyses of N = 732 $N=732$ subjects from the Alzheimer's Disease Neuroimaging Initiative demonstrate that using our approach results in biologically meaningful conclusions, whereas ignoring unobserved confounding yields results that conflict with established knowledge on cognitive decline due to AD.

DISCUSSION

The findings provide evidence that the impact of unobserved confounding can be substantial. To ensure trustworthy scientific insights, future AD research can account for unobserved confounding via the proposed approach.

Self Assessment Memory Scale (SAMS), a new simple method for evaluating memory function

We have developed a new method for easy self-assessment of changes in memory recall impairment, which can be used during the very early stages of dementia. An 8-picture recall and a 16-word regression were assessed, respectively, and the index was calculated by adding up the ratio of correct responses to both tests. A total of 85 subjects including 12 MCI, 8 AD, and 65 older persons with normal cognitive function were evaluated, and the correlation with the WMS-R Logical Memory II score was examined. The results showed that there was a statistically significant correlation between the 8-picture recall (R = 0.872, p &lt; 0.0001) and the index (R = 0.857, p &lt; 0.0001), respectively, with the Logical Memory score. We have named this index as Self Assessment Memory Scale (SAMS), and are now developing a digital tool to enable easy and self-administered evaluation of recall.

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

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

Different AT(N) profiles and clinical progression classified by two different N markers using total tau and neurofilament light chain in cerebrospinal fluid

Background

The AT(N) classification was proposed for categorising individuals according to biomarkers. However, AT(N) profiles may vary depending on the markers chosen and the target population.

Methods

We stratified 177 individuals who participated in the Japanese Alzheimer's Disease Neuroimaging Initiative by AT(N) classification according to cerebrospinal fluid (CSF) biomarkers. We compared the frequency of AT(N) profiles between the classification using total tau and neurofilament light chain (NfL) as N markers (AT(N)_tau and AT(N)_NfL). Baseline characteristics, and longitudinal biological and clinical changes were examined between AT(N) profiles.

Results

We found that 9% of cognitively unimpaired subjects, 49% of subjects with mild cognitive impairment, and 61% of patients with Alzheimer's disease (AD) dementia had the biological AD profile (ie, A+T+) in the cohort. The frequency of AT(N) profiles substantially differed between the AT(N)_tau and AT(N)_NfL classifications. When we used t-tau as the N marker (AT(N)_tau), those who had T- were more frequently assigned to (N)-, whereas those who had T+were more frequently assigned to (N)+ than when we used NfL as the N marker (AT(N)_NfL). During a follow-up, the AD continuum group progressed clinically and biologically compared with the normal biomarker group in both the AT(N)_tau and AT(N)_NfL classifications. More frequent conversion to dementia was observed in the non-AD pathological change group in the AT(N)_tau classification, but not in the AT(N)_NfL classification.

Conclusions

AT(N)_tau and AT(N)_NfL in CSF may capture different aspects of neurodegeneration and provide a different prognostic value. The AT(N) classification aids in understanding the AD continuum biology in various populations.

White matter alterations in the dorsal attention network contribute to a high risk of unsafe driving in healthy older people

Aim

Healthy older drivers may be at high risk of fatal traffic accidents. Our recent study showed that volumetric alterations in gray matter in the brain regions within the dorsal attention network (DAN) were strongly related to the risk of unsafe driving in healthy older people. However, the relationship between white matter (WM) structural connectivity and driving ability in healthy older people is still unclear.

Methods

We used diffusion tensor imaging to examine the association between microstructural alterations in the DAN and the risk of unsafe driving among healthy older people. We enrolled 32 healthy older individuals aged over 65 years and screened unsafe drivers using an on-road driving test. We then determined the pattern of WM aberrations in unsafe drivers using tract-based spatial statistics.

Results

The analysis demonstrated that unsafe drivers had significantly higher axial diffusivity values in nine WM clusters compared with safe drivers. These results were primarily observed bilaterally in the dorsal superior longitudinal fasciculus, which is involved in the DAN. Furthermore, correlation analyses showed that higher axial diffusivity values in the superior longitudinal fasciculus were associated with lower Trail Making Test A scores within unsafe drivers. This result suggests that functionally, WM microstructural alterations in the DAN are associated with attention problems, which may contribute to the risk of unsafe driving among healthy older people.

Conclusion

Our findings may elucidate the neurobiological mechanisms underlying the increased risk of unsafe driving in healthy older people, potentially facilitating the development of new interventions to prevent fatal accidents.

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.

Multi-cohort and longitudinal Bayesian clustering study of stage and subtype in Alzheimer's disease

Understanding Alzheimer's disease (AD) heterogeneity is important for understanding the underlying pathophysiological mechanisms of AD. However, AD atrophy subtypes may reflect different disease stages or biologically distinct subtypes. Here we use longitudinal magnetic resonance imaging data (891 participants with AD dementia, 305 healthy control participants) from four international cohorts, and longitudinal clustering to estimate differential atrophy trajectories from the age of clinical disease onset. Our findings (in amyloid-β positive AD patients) show five distinct longitudinal patterns of atrophy with different demographical and cognitive characteristics. Some previously reported atrophy subtypes may reflect disease stages rather than distinct subtypes. The heterogeneity in atrophy rates and cognitive decline within the five longitudinal atrophy patterns, potentially expresses a complex combination of protective/risk factors and concomitant non-AD pathologies. By alternating between the cross-sectional and longitudinal understanding of AD subtypes these analyses may allow better understanding of disease heterogeneity.

Use of the Tokyo Cognitive Assessment for mild cognitive impairment to characterize elderly people that use day care services in Japan

[Purpose] This study compared the motor skills and cognitive functions of elderly participants who required support with those who did not require support. We aimed to evaluate the characteristics of impairment in sub-items of cognitive function in patients who needed support to predict future clinical issues. [Participants and Methods] We surveyed 31 participants requiring support under the day care service insurance system for which they attended day care service centers in Japan (rehabilitation users) and 10 healthy participants who attended a university for lifelong learning (healthy elders). Data on personal attributes of the participants were collected, and the Cardio-Ankle Vascular Index and motor and cognitive functions were assessed. [Results] Although the participants undergoing rehabilitation were, on average, 6 years older than the healthy elders, we found no significant differences between the two groups in closed-eye, one-legged standing, grip strength, or quadriceps muscle strength. In terms of the Tokyo Cognitive Assessment for mild cognitive impairment, we found no significant differences between those undergoing rehabilitation and healthy elders in clock drawing performance, serial 7 task performance, or orientation; however, there were significant differences in erase character, copy of triangular pyramid, composition, read of digits, go/no-go, word recall, story reproduction, ToCA total score. [Conclusion] We believe that it is imperative for day care service centers to conduct programs that maintain cognitive function in addition to programs for improvement of physical function.

Age-related increase of monoamine oxidase B in amyloid-negative cognitively unimpaired elderly subjects

OBJECTIVE

Monoamine oxidase B (MAO-B) is highly abundant in reactive astrocytes and upregulated in neuroinflammatory processes. However, the age-related change of MAO-B in amyloid-negative cognitively unimpaired elderly subjects has not yet been sufficiently evaluated on positron emission tomography (PET). ¹⁸F-THK5351 is a radiotracer with high affinity to MAO-B, which may potentially serve as an imaging biomarker for detecting neuroinflammation. The purpose of this study was to investigate the age-related topographic change of ¹⁸F-THK5351 PET in amyloid-negative cognitively unimpaired elderly subjects.

METHODS

The age-related change of ¹⁸F-THK5351 retention was evaluated on the visual analysis, voxel and region of interest (ROI)-based analyses using Statistical Parametric Mapping and PETSurfer tool of FreeSurfer in 31 amyloid-negative cognitively unimpaired elderly subjects.

RESULTS

On visual inspection, elderly groups showed the spread of ¹⁸F-THK5351 accumulation from the medial to inferolateral temporal and basal frontal lobes, and cingulate gyrus. Additionally, voxel- and ROI-based analysis demonstrated the correlation between ¹⁸F-THK5351 accumulation and participants' age, especially in the inferior temporal lobes.

CONCLUSIONS

This study demonstrated age-dependent increase of ¹⁸F-THK5351 retention in amyloid-negative cognitively unimpaired subjects, which suggests an increase in MAO-B positive reactive astrocytes with aging.

Estimating the longitudinal trajectory of cognitive function measurement using short-term data with different disease stages: Application in Alzheimer's disease

Alzheimer's disease (AD) is a chronic neurodegenerative disease characterized by a gradual decline in cognitive function over a few decades. The Mini-Mental State Examination (MMSE) is a widely used measure for evaluating global cognitive functioning. Characterizing the longitudinal trajectory of the MMSE in the population of interest is important to detect AD onset for preventive intervention. In this study, we formulate a new class of longitudinal trajectory modeling for MMSE from short-term individual data based on an ordinary differential equation. The proposed method models the relationship between individual decline speed of MMSE and the average MMSE using the fractional polynomial function model and subsequently estimates the longitudinal trajectory of MMSE by solving the ordinary differential equation for the estimated model. The appropriate model for trajectory estimation is selected based on the proposed criterion for quantifying the goodness of trajectory fit. The accuracy of the trajectory estimation of the proposed method was demonstrated via simulation studies. The proposed method was successfully applied to MMSE data from the Japanese Alzheimer's Disease Neuroimaging Initiative study.

A high-generalizability machine learning framework for predicting the progression of Alzheimer's disease using limited data

Alzheimer’s disease is a neurodegenerative disease that imposes a substantial financial burden on society. A number of machine learning studies have been conducted to predict the speed of its progression, which varies widely among different individuals, for recruiting fast progressors in future clinical trials. However, because the data in this field are very limited, two problems have yet to be solved: the first is that models built on limited data tend to induce overfitting and have low generalizability, and the second is that no cross-cohort evaluations have been done. Here, to suppress the overfitting caused by limited data, we propose a hybrid machine learning framework consisting of multiple convolutional neural networks that automatically extract image features from the point of view of brain segments, which are relevant to cognitive decline according to clinical findings, and a linear support vector classifier that uses extracted image features together with non-image information to make robust final predictions. The experimental results indicate that our model achieves superior performance (accuracy: 0.88, area under the curve [AUC]: 0.95) compared with other state-of-the-art methods. Moreover, our framework demonstrates high generalizability as a result of evaluations using a completely different cohort dataset (accuracy: 0.84, AUC: 0.91) collected from a different population than that used for training.

Impact of Early Intervention for Idiopathic Normal Pressure Hydrocephalus on Long-Term Prognosis in Prodromal Phase

Objectives

Because the progression of idiopathic normal pressure hydrocephalus (iNPH) is partially irreversible, we hypothesized that early intervention would markedly improve its prognosis. To test this hypothesis, we retrospectively investigated the long-term prognosis of patients with early intervention in the prodromal phase of iNPH.

Methods

We defined the prodromal phase of iNPH as a 3m Timed Up and Go (TUG) of 13.5 s or less and a Mini-Mental State Examination (MMSE) of 24 or more. Of the 83 iNPH patients who underwent shunt surgery at Osaka Medical and Pharmaceutical University Hospital over 3 years from January 2015, 12 prodromal phase cases (73.3 ± 6.2 years, 10 males and 2 females) were included in the study. The iNPH grading scale (INPHGS), MMSE, Frontal Assessment Battery (FAB), intermittent gait disturbance (IGD), social participation status, and development of comorbidities were evaluated over 4 years.

Results

Preoperative MMSE was 27.2 ± 1.5, FAB was 14.1 ± 1.8, TUG was 10.7 ± 1.4 s, and total iNPHGS was 2.8 ± 1.4. At 1, 2, 3, and 4 years postoperatively, total INPHGS improved to 0.8, 0.9, 1.5, and 1.7, respectively, and remained significantly better than preoperatively except at 4 years postoperatively. The MMSE improved slightly to 27.5 after 1 year and then declined by 0.35 per year. After 4 years, the mean MMSE was 26.1, and only one patient had an MMSE below 23. FAB improved to 15.2 after 1 year and then declined slowly at 0.85/year. Ten patients (83%) maintained a high capacity for social participation postoperatively. The preoperative tendency to fall and IGD in 9 (75%) and 8 (67%) patients, respectively, completely disappeared postoperatively, resulting in improved mobility. Shunt malfunction associated with four weight fluctuations and one catheter rupture caused temporary worsening of symptoms, which were recovered by valve re-setting and catheter revision, respectively.

Conclusion

Early intervention in the prodromal phase of iNPH patients maintained good cognitive and mobility function and social participation ability in the long term. The maintenance of long-term cognitive function suggests its preventive effect on dementia. To realize early intervention for iNPH, it is desirable to establish an early diagnosis system for iNPH.

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

INTRODUCTION

The Alzheimer's Disease Neuroimaging Initiative (ADNI) has accumulated 15 years of clinical, neuroimaging, cognitive, biofluid biomarker and genetic data, and biofluid samples available to researchers, resulting in more than 3500 publications. This review covers studies from 2018 to 2020.

METHODS

We identified 1442 publications using ADNI data by conventional search methods and selected impactful studies for inclusion.

RESULTS

Disease progression studies supported pivotal roles for regional amyloid beta (Aβ) and tau deposition, and identified underlying genetic contributions to Alzheimer's disease (AD). Vascular disease, immune response, inflammation, resilience, and sex modulated disease course. Biologically coherent subgroups were identified at all clinical stages. Practical algorithms and methodological changes improved determination of Aβ status. Plasma Aβ, phosphorylated tau181, and neurofilament light were promising noninvasive biomarkers. Prognostic and diagnostic models were externally validated in ADNI but studies are limited by lack of ethnocultural cohort diversity.

DISCUSSION

ADNI has had a profound impact in improving clinical trials for AD.

Determination of optimal regularization factor in Bayesian penalized likelihood reconstruction of brain PET images using [18 F]FDG and [11 C]PiB

PURPOSE

The Bayesian penalized likelihood (BPL) reconstruction algorithm, Q.Clear, can achieve a higher signal-to-noise ratio on images and more accurate quantitation than ordered subset-expectation maximization (OSEM). The reconstruction parameter (β) in BPL requires optimization according to the radiopharmaceutical tracer. The present study aimed to define the optimal β value in BPL required to diagnose Alzheimer disease from brain PET images acquired using ¹⁸ F-fluoro-2-deoxy-D-glucose ([¹⁸ F]FDG) and ¹¹ C-labeled Pittsburg compound B ([¹¹ C]PiB).

METHODS

Images generated from Hoffman 3D brain and cylindrical phantoms were acquired using a Discovery PET/CT 710 and reconstructed using OSEM + time-of-flight (TOF) under clinical conditions and BPL + TOF (β = 20-1,000). Contrast was calculated from images generated by the Hoffman 3D brain phantom, and noise and uniformity were calculated from those generated by the cylindrical phantom. Five cognitively healthy controls and five patients with Alzheimer disease were assessed using [¹⁸ F]FDG and [¹¹ C]PiB PET to validate the findings from the phantom study. The β values were restricted by the findings of the phantom study, then one certified nuclear medicine physician and two certified nuclear medicine technologists visually determined optimal β values by scoring the quality parameters of image contrast, image noise, cerebellar stability, and overall image quality of PET images from 1 (poor) to 5 (excellent).

RESULTS

The contrast in BPL satisfied the Japanese Society of Nuclear Medicine (JSNM) criterion of ≥ 55% and exceeded that of OSEM at ranges of β = 20-450 and 20-600 for [¹⁸ F]FDG and [¹¹ C]PiB, respectively. The image noise in BPL satisfied the JSNM criterion of ≤ 15% and was below that in OSEM when β = 150-1000 and 400-1,000 for [¹⁸ F]FDG and [¹¹ C]PiB, respectively. The phantom study restricted the ranges of β values to 100-300 and 300-500 for [¹⁸ F]FDG and [¹¹ C]PiB, respectively. The BPL scores for grey-white matter contrast and image noise, exceeded those of OSEM in [¹⁸ F]FDG and [¹¹ C]PiB images regardless of β values. Visual evaluation confirmed that the optimal β values were 200 and 450 for [¹⁸ F]FDG and [¹¹ C]PiB, respectively.

CONCLUSIONS

The BPL achieved better image contrast and less image noise than OSEM, while maintaining quantitative SUVR due to full convergence, more rigorous noise control and edge preservation. The optimal β values for [¹⁸ F]FDG and [¹¹ C]PiB brain PET were apparently 200 and 450, respectively. The present study provides useful information about how to determine optimal β values in BPL for brain PET imaging. This article is protected by copyright. All rights reserved.

Dissection of the polygenic architecture of neuronal Aβ production using a large sample of individual iPSC lines derived from Alzheimer's disease patients

Genome-wide association studies have demonstrated that polygenic risks shape Alzheimer's disease (AD). To elucidate the polygenic architecture of AD phenotypes at a cellular level, we established induced pluripotent stem cells from 102 patients with AD, differentiated them into cortical neurons and conducted a genome-wide analysis of the neuronal production of amyloid β (Aβ). Using such a cellular dissection of polygenicity (CDiP) approach, we identified 24 significant genome-wide loci associated with alterations in Aβ production, including some loci not previously associated with AD, and confirmed the influence of some of the corresponding genes on Aβ levels by the use of small interfering RNA. CDiP genotype sets improved the predictions of amyloid positivity in the brains and cerebrospinal fluid of patients in the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort. Secondary analyses of exome sequencing data from the Japanese ADNI and the ADNI cohorts focused on the 24 CDiP-derived loci associated with alterations in Aβ led to the identification of rare AD variants in KCNMA1.

Development of a Novel Nutrition-Related Multivariate Biomarker for Mild Cognitive Impairment Based on the Plasma Free Amino Acid Profile

Nutritional epidemiology has shown the importance of protein intake for maintaining brain function in the elderly population. Mild cognitive impairment (MCI) may be associated with malnutrition, especially protein intake. We explored blood-based biomarkers linking protein nutritional status with MCI in a multicenter study. In total, 219 individuals with MCI (79.5 ± 5.7 year) from 10 institutions and 220 individuals who were cognitively normal (CN, 76.3 ± 6.6 year) in four different cities in Japan were recruited. They were divided into the training (120 MCI and 120 CN) and validation (99 MCI and 100 CN) groups. A model involving concentrations of PFAAs and albumin to discriminate MCI from CN individuals was constructed by multivariate logistic regression analysis in the training dataset, and the performance was evaluated in the validation dataset. The concentrations of some essential amino acids and albumin were significantly lower in MCI group than CN group. An index incorporating albumin and PFAA discriminated MCI from CN participants with the AUC of 0.705 (95% CI: 0.632–0.778), and the sensitivities at specificities of 90% and 60% were 25.3% and 76.8%, respectively. No significant association with BMI or APOE status was observed. This cross-sectional study suggests that the biomarker changes in MCI group may be associated with protein nutrition.

Unraveling the heterogeneity in Alzheimer's disease progression across multiple cohorts and the implications for data-driven disease modeling

INTRODUCTION

Given study-specific inclusion and exclusion criteria, Alzheimer's disease (AD) cohort studies effectively sample from different statistical distributions. This heterogeneity can propagate into cohort-specific signals and subsequently bias data-driven investigations of disease progression patterns.

METHODS

We built multi-state models for six independent AD cohort datasets to statistically compare disease progression patterns across them. Additionally, we propose a novel method for clustering cohorts with regard to their progression signals.

RESULTS

We identified significant differences in progression patterns across cohorts. Models trained on cohort data learned cohort-specific effects that bias their estimations. We demonstrated how six cohorts relate to each other regarding their disease progression.

DISCUSSION

Heterogeneity in cohort datasets impedes the reproducibility of data-driven results and validation of progression models generated on single cohorts. To ensure robust scientific insights, it is advisable to externally validate results in independent cohort datasets. The proposed clustering assesses the comparability of cohorts in an unbiased, data-driven manner.

The Worldwide Alzheimer's Disease Neuroimaging Initiative: ADNI-3 updates and global perspectives

The Worldwide Alzheimer's Disease Neuroimaging Initiative (WW-ADNI) is a collaborative effort to investigate imaging and biofluid markers that can inform Alzheimer's disease treatment trials. It is a public-private partnership that spans North America, Argentina, Australia, Canada, China, Japan, Korea, Mexico, and Taiwan. In 2004, ADNI researchers began a naturalistic, longitudinal study that continues today around the globe. Through several successive phases (ADNI-1, ADNI-GO, ADNI-2, and ADNI-3), the study has fueled amyloid and tau phenotyping and refined neuroimaging methodologies. WW-ADNI researchers have successfully standardized analyses and openly share data without embargo, providing a rich data set for other investigators. On August 26, 2020, the Alzheimer's Association convened WW-ADNI researchers who shared updates from ADNI-3 and their vision for ADNI-4.

Machine learning of brain structural biomarkers for Alzheimer's disease (AD) diagnosis, prediction of disease progression, and amyloid beta deposition in the Japanese population

INTRODUCTION

We developed machine learning (ML) designed to analyze structural brain magnetic resonance imaging (MRI), and trained it on the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. In this study, we verified its utility in the Japanese population.

METHODS

A total of 535 participants were enrolled from the Japanese ADNI database, including 148 AD, 152 normal, and 235 mild cognitive impairment (MCI). Probability of AD was expressed as AD likelihood scores (ADLS).

RESULTS

The accuracy of AD diagnosis was 88.0% to 91.2%. The accuracy of predicting the disease progression in non-dementia participants over a 3-year observation was 76.0% to 79.3%. More than 90% of the participants with low ADLS did not progress to AD within 3 years. In the amyloid positron emission tomography (PET)-positive MCI, the hazard ratio of progression was 2.39 with low ADLS, and 5.77 with high ADLS. When high ADLS was defined as N+ and Pittsburgh compound B (PiB) PET positivity was defined as A+, the time to disease progression for 50% of MCI participants was 23.7 months in A+N+, whereas it was 52.3 months in A+N-.

CONCLUSION

These results support the feasibility of our ML for the diagnosis of AD and prediction of the disease progression.

Age-Related Cognitive Decline and Prevalence of Mild Cognitive Impairment in the Iwaki Health Promotion Project

BACKGROUND

The Iwaki Health Promotion Project (IHPP) is a community-based study for the prevention of lifestyle-related diseases and improvement of quality of life.

OBJECTIVE

Between 2014 and 2017, a total of 4,442 Iwaki town residents from 19 to 93 years of age participated in annual surveys to clarify the natural course of age-related cognitive decline and mild cognitive impairment (MCI).

METHODS

Modified OLD and SED-11Q questionnaires, MMSE, Logical Memory II, educational history, and APOE genotypes were examined at the first screening. MCI and dementia were diagnosed at the second examination by detailed neurological examination, CDR, and MRI, and followed for 3 years. Spline regression analyses based on a linear mixed model was adopted for statistical analysis.

RESULTS

MMSE scores declined with age from 55 to 64 years. There was also interaction between levels of education and ages. At the second examination, 56 MCI and 5 dementia patients were identified. None of the MCI cases progressed to dementia during the 3 years. During follow-up examinations, 13 cases showed improved MMSE scores (0.95 point/year), 5 remained stable, and 7 deteriorated (-0.83 point/year). Five cases showed improved CDR-SOB scores (-0.28 point/year), 9 remained stable, and 6 deteriorated (0.3 point/year).

CONCLUSION

IHPP revealed that age- and education-related cognitive decline began and advanced from 55 years of age. The prevalence of MCI and dementia was estimated to be 5.9%in the Iwaki town cohort over 60 yeas of age. About 30%of MCI cases showed progression of cognitive decline.

A Novel Heterozygous Missense Variant in the CIAO1 Gene in a Family with Alzheimer's Disease: The Val67Ile Variant Promotes the Interaction of CIAO1 and Amyloid-β Protein Precursor

Familial dementia is a rare inherited disease involving progressive impairment of memory, thinking, and behavior. We report a novel heterozygous pathogenic variant (c.199G > A, p.Val67Ile) in the CIAO1 gene that appears to be co-segregated with Alzheimer’s disease in a Japanese family. Biochemical analysis of CIAO1 protein revealed that the variant increases the interaction of CIAO1 with immature amyloid-β protein precursor (AβPP), but not mature or soluble AβPP, indicating plausible CIAO1 involvement in AβPP processing. Our study indicates that a heterozygous variant in the CIAO1 gene may be closely related to autosomal dominant familial dementia.

Effect of a Combined Exercise and Cognitive Activity Intervention on Cognitive Function in Community-dwelling Older Adults: A Pilot Randomized Controlled Trial

OBJECTIVE

The purpose of this study is to investigate the effect of an intervention combining exercise and cognitive activity on cognitive function in healthy older adults.

METHODS

This pilot randomized controlled trial recruited 33 eligible, healthy communitydwelling older adults (mean age, 77.1 years old; women, 51.5%), who were divided into intervention and waitlist control groups. The intervention group was engaged weekly in a group activity comprising exercise and discussions of homework, which included reading aloud, simple arithmetic, and simple activities, like spotting differences, for cognitive stimulation. They were also required to complete cognitive activity homework twice a week. The waitlist control group received no intervention. The main outcomes were cognitive function assessed using the Mini-Mental State Examination, delayed recall score on the Logical Memory IIA of the Wechsler Memory Scale Revised, Trail Making Test, and digit symbol substitution test.

RESULTS

According to the results, Mini-Mental State Examination scores were maintained in the intervention group but declined in the control group [Mean change in outcomes in control group (95% confidence interval): -1.68 (-2.89 to -0.48)]. Additional mean change in outcomes in intervention group were found [1.68 (0.02 to 3.35)].

CONCLUSIONS

Interventions combining exercise and cognitive activity can be helpful for preserving cognitive function in healthy older adults.

CERMEP-IDB-MRXFDG: a database of 37 normal adult human brain [18F]FDG PET, T1 and FLAIR MRI, and CT images available for research

We present a database of cerebral PET FDG and anatomical MRI for 37 normal adult human subjects (CERMEP-IDB-MRXFDG). Thirty-nine participants underwent static [18F]FDG PET/CT and MRI, resulting in [18F]FDG PET, T1 MPRAGE MRI, FLAIR MRI, and CT images. Two participants were excluded after visual quality control. We describe the acquisition parameters, the image processing pipeline and provide participants' individual demographics (mean age 38 ± 11.5 years, range 23-65, 20 women). Volumetric analysis of the 37 T1 MRIs showed results in line with the literature. A leave-one-out assessment of the 37 FDG images using Statistical Parametric Mapping (SPM) yielded a low number of false positives after exclusion of artefacts. The database is stored in three different formats, following the BIDS common specification: (1) DICOM (data not processed), (2) NIFTI (multimodal images coregistered to PET subject space), (3) NIFTI normalized (images normalized to MNI space). Bona fide researchers can request access to the database via a short form.

Comparing Pathological Risk Factors for Dementia between Cognitively Normal Japanese and Americans

The Alzheimer's Disease Neuroimaging Initiative showed that Japanese had significantly lower brain Aβ burden than Americans among a cognitively normal population. This cross-sectional study aimed to compare vascular disease burden, Aβ burden, and neurodegeneration between cognitively normal elderly Japanese and Americans. Japanese and American participants were matched for age (±4-year-old), sex, and Apolipoprotein E (APOE) genotype. Brain vascular disease burden and brain Aβ burden were measured using white matter lesions (WMLs) and 11C-labeled Pittsburgh Compound B (PiB) retention, respectively. Neurodegeneration was measured using hippocampal volumes and cortical thickness. A total of 95 Japanese and 95 Americans were recruited (50.5% men, mean age = 82). Compared to Americans, Japanese participants had larger WMLs, and a similar global Aβ standardized uptake value ratio (SUVR), cortical thickness and hippocampal volumes. Japanese had significantly lower regional Aβ SUVR in the anterior ventral striatum, posterior cingulate cortex, and precuneus. Cognitively normal elderly Japanese and Americans had different profiles regarding vascular disease and Aβ burden. This suggests that multiple risk factors are likely to be involved in the development of dementia. Additionally, Japanese might have a lower risk of dementia due to lower Aβ burden than Americans. Longitudinal follow-up of these cohorts is warranted to ascertain the predictive accuracy of these findings.

Effect of different examination conditions on image quality and quantitative value of amyloid positron emission tomography using 18F-flutemetamol

OBJECTIVE

The recommended start time for 18F-flutemetamol amyloid positron emission tomography (PET) examination is 60-120 min after 18F-flutemetamol injection, while an acquisition time of 10-30 min is generally recommended. We aimed to elucidate the effects of different examination conditions on image quality, diagnostic ability, and quantitative value of amyloid PET using 18F-flutemetamol.

METHODS

We acquired data on a Discovery PET/computed tomography 710 scanner using Hoffman brain and pillar phantoms with 20 MBq of 18F for 30 min. The images were reconstructed into 10-, 20-, and 30-min periods. The ordered subset-expectation maximization algorithm was used for image reconstruction, which uses a 2- or 4-mm Gaussian filter and a combination of iteration and subset numbers. The percentage contrast and coefficient of variation (CV; as the image noise) were used as physical evaluation indices for reconstructed images, and images with superior contrast and low image noise were selected for clinical evaluation. The imaging data of 15 symptomatic patients (n = 7 and n = 8 for positive and negative diagnoses of Alzheimer's disease, respectively) were reconstructed under the phantom study conditions. Radiographers visually evaluated and ranked the clinical images based on the overall contrast and image noise, and nuclear medicine specialists diagnosed Alzheimer's disease. We compared the standardized uptake value ratio (SUVR) obtained with different acquisition conditions.

RESULTS

The basic study using the phantom revealed high convergence of contrast and image noise in five patterns of acquisition time and filter strengths. Regarding visual evaluation, the use of a 2-mm Gaussian filter caused difficulties in diagnosis because the brain parenchymal accumulation was mottled with high image noise. Differences in image quality and diagnostic ability due to different examination times were not significant. Differences in the SUVR were not significant in patients with a negative Alzheimer's disease diagnosis; in patients with a positive diagnosis, the SUVR showed significant fluctuation depending on the acquisition conditions.

CONCLUSION

The differences in image quality and diagnostic performance due to the differences in 10-min acquisition time were not significant; however, of note, SUVR showed significant fluctuation depending on the acquisition conditions in patients diagnosed with Alzheimer's disease.

State-of-the-art of lumbar puncture and its place in the journey of patients with Alzheimer's disease

Recent advances in developing disease‐modifying therapies (DMT) for Alzheimer's disease (AD), and the recognition that AD pathophysiology emerges decades before clinical symptoms, necessitate a paradigm shift of health‐care systems toward biomarker‐guided early detection, diagnosis, and therapeutic decision‐making. Appropriate incorporation of cerebrospinal fluid biomarker analysis in clinical practice is an essential step toward system readiness for accommodating the demand of AD diagnosis and proper use of DMTs—once they become available. However, the use of lumbar puncture (LP) in individuals with suspected neurodegenerative diseases such as AD is inconsistent, and the perception of its utility and safety differs considerably among medical specialties as well as among regions and countries. This review describes the state‐of‐the‐art evidence concerning the safety profile of LP in older adults, discusses the risk factors for LP‐associated adverse events, and provides recommendations and an outlook for optimized use and global implementation of LP in individuals with suspected AD.