Machine Learning for the Prediction of Amyloid Positivity in Amnestic Mild Cognitive Impairment

The association of glucose metabolism measures and diabetes status with Alzheimer's disease biomarkers of amyloid and tau: A systematic review and meta-analysis

Conflicting evidence exists on the relationship between diabetes mellitus (DM) and Alzheimer's disease (AD) biomarkers. Therefore, we conducted a random-effects meta-analysis to evaluate the correlation of glucose metabolism measures (glycated hemoglobin, fasting blood glucose, insulin resistance indices) and DM status with AD biomarkers of amyloid-β and tau measured by positron emission tomography or cerebrospinal fluid. We selected 37 studies from PubMed and Embase, including 11,694 individuals. More impaired glucose metabolism and DM status were associated with higher tau biomarkers (r=0.11[0.03-0.18], p=0.008; I2=68%), but were not associated with amyloid-β biomarkers (r=-0.06[-0.13-0.01], p=0.08; I2=81%). Meta-regression revealed that glucose metabolism and DM were specifically associated with tau biomarkers in population settings (p=0.001). Furthermore, more impaired glucose metabolism and DM status were associated with lower amyloid-β biomarkers in memory clinic settings (p=0.004), and in studies with a higher prevalence of dementia (p<0.001) or lower cognitive scores (p=0.04). These findings indicate that DM is associated with biomarkers of tau but not with amyloid-β. This knowledge is valuable for improving dementia and DM diagnostics and treatment.

Early Diagnosis of Neurodegenerative Diseases: What Has Been Undertaken to Promote the Transition from PET to Fluorescence Tracers

Alzheimer's Disease (AD) and Parkinson's Disease (PD) represent two among the most frequent neurodegenerative diseases worldwide. A common hallmark of these pathologies is the misfolding and consequent aggregation of amyloid proteins into soluble oligomers and insoluble β-sheet-rich fibrils, which ultimately lead to neurotoxicity and cell death. After a hundred years of research on the subject, this is the only reliable histopathological feature in our hands. Since AD and PD are diagnosed only once neuronal death and the first symptoms have appeared, the early detection of these diseases is currently impossible. At present, there is no effective drug available, and patients are left with symptomatic and inconclusive therapies. Several reasons could be associated with the lack of effective therapeutic treatments. One of the most important factors is the lack of selective probes capable of detecting, as early as possible, the most toxic amyloid species involved in the onset of these pathologies. In this regard, chemical probes able to detect and distinguish among different amyloid aggregates are urgently needed. In this article, we will review and put into perspective results from ex vivo and in vivo studies performed on compounds specifically interacting with such early species. Following a general overview on the three different amyloid proteins leading to insoluble β-sheet-rich amyloid deposits (amyloid β1-42 peptide, Tau, and α-synuclein), a list of the advantages and disadvantages of the approaches employed to date is discussed, with particular attention paid to the translation of fluorescence imaging into clinical applications. Furthermore, we also discuss how the progress achieved in detecting the amyloids of one neurodegenerative disease could be leveraged for research into another amyloidosis. As evidenced by a critical analysis of the state of the art, substantial work still needs to be conducted. Indeed, the early diagnosis of neurodegenerative diseases is a priority, and we believe that this review could be a useful tool for better investigating this field.

Free Water MR Imaging of White Matter Microstructural Changes is a Sensitive Marker of Amyloid Positivity in Alzheimer's Disease

BACKGROUND

Extracellular free water (FW) resulting from white matter degeneration limits the sensitivity of diffusion tensor imaging (DTI) in predicting Alzheimer's disease (AD).

PURPOSE

To evaluate the sensitivity of FW-DTI in detecting white matter microstructural changes in AD. To validate the effectiveness of FW-DTI indices to predict amyloid-beta (Aβ) positivity in mild cognitive impairment (MCI) subtypes.

STUDY TYPE

Retrospective.

POPULATION

Thirty-eight Aβ-negative cognitively healthy (CH) controls (68.74 ± 8.28 years old, 55% female), 15 Aβ-negative MCI patients (MCI-n) (68.87 ± 8.83 years old, 60% female), 29 Aβ-positive MCI patients (MCI-p) (73.03 ± 7.05 years old, 52% female), and 29 Aβ-positive AD patients (72.93 ± 9.11 years old, 55% female).

FIELD STRENGTH/SEQUENCE

3.0T; DTI, T1 -weighted, T2 -weighted, T2 star-weighted angiography, and Aβ PET (18 F-florbetaben or 11 C-PIB).

ASSESSMENT

FW-corrected and standard diffusion indices were analyzed using trace-based spatial statistics. Area under the curve (AUC) in distinguishing MCI subtypes were compared using support vector machine (SVM).

STATISTICAL TESTS

Chi-squared test, one-way analysis of covariance, general linear regression analyses, nonparametric permutation tests, partial Pearson's correlation, receiver operating characteristic curve analysis, and linear SVM. A P value <0.05 was considered statistically significant.

RESULTS

Compared with CH/MCI-n/MCI-p, AD showed significant change in tissue compartment indices of FW-DTI. No difference was found in the FW index among pair-wise group comparisons (the minimum FWE-corrected P = 0.114). There was a significant association between FW-DTI indices and memory and visuospatial function. The SVM classifier with tissue radial diffusivity as an input feature had the best classification performance of MCI subtypes (AUC = 0.91), and the classifying accuracy of FW-DTI was all over 89.89%.

DATA CONCLUSION

FW-DTI indices prove to be potential biomarkers of AD. The classification of MCI subtypes based on SVM and FW-DTI indices has good accuracy and could help early diagnosis.

EVIDENCE LEVEL

4 TECHNICAL EFFICACY: Stage 2.

MRI-based Deep Learning Assessment of Amyloid, Tau, and Neurodegeneration Biomarker Status across the Alzheimer Disease Spectrum

Background PET can be used for amyloid-tau-neurodegeneration (ATN) classification in Alzheimer disease, but incurs considerable cost and exposure to ionizing radiation. MRI currently has limited use in characterizing ATN status. Deep learning techniques can detect complex patterns in MRI data and have potential for noninvasive characterization of ATN status. Purpose To use deep learning to predict PET-determined ATN biomarker status using MRI and readily available diagnostic data. Materials and Methods MRI and PET data were retrospectively collected from the Alzheimer's Disease Imaging Initiative. PET scans were paired with MRI scans acquired within 30 days, from August 2005 to September 2020. Pairs were randomly split into subsets as follows: 70% for training, 10% for validation, and 20% for final testing. A bimodal Gaussian mixture model was used to threshold PET scans into positive and negative labels. MRI data were fed into a convolutional neural network to generate imaging features. These features were combined in a logistic regression model with patient demographics, APOE gene status, cognitive scores, hippocampal volumes, and clinical diagnoses to classify each ATN biomarker component as positive or negative. Area under the receiver operating characteristic curve (AUC) analysis was used for model evaluation. Feature importance was derived from model coefficients and gradients. Results There were 2099 amyloid (mean patient age, 75 years ± 10 [SD]; 1110 male), 557 tau (mean patient age, 75 years ± 7; 280 male), and 2768 FDG PET (mean patient age, 75 years ± 7; 1645 male) and MRI pairs. Model AUCs for the test set were as follows: amyloid, 0.79 (95% CI: 0.74, 0.83); tau, 0.73 (95% CI: 0.58, 0.86); and neurodegeneration, 0.86 (95% CI: 0.83, 0.89). Within the networks, high gradients were present in key temporal, parietal, frontal, and occipital cortical regions. Model coefficients for cognitive scores, hippocampal volumes, and APOE status were highest. Conclusion A deep learning algorithm predicted each component of PET-determined ATN status with acceptable to excellent efficacy using MRI and other available diagnostic data. © RSNA, 2023 Supplemental material is available for this article.

PRedicting the EVolution of SubjectIvE Cognitive Decline to Alzheimer's Disease With machine learning: the PREVIEW study protocol

BACKGROUND

As disease-modifying therapies (DMTs) for Alzheimer's disease (AD) are becoming a reality, there is an urgent need to select cost-effective tools that can accurately identify patients in the earliest stages of the disease. Subjective Cognitive Decline (SCD) is a condition in which individuals complain of cognitive decline with normal performances on neuropsychological evaluation. Many studies demonstrated a higher prevalence of Alzheimer's pathology in patients diagnosed with SCD as compared to the general population. Consequently, SCD was suggested as an early symptomatic phase of AD. We will describe the study protocol of a prospective cohort study (PREVIEW) that aim to identify features derived from easily accessible, cost-effective and non-invasive assessment to accurately detect SCD patients who will progress to AD dementia.

METHODS

We will include patients who self-referred to our memory clinic and are diagnosed with SCD. Participants will undergo: clinical, neurologic and neuropsychological examination, estimation of cognitive reserve and depression, evaluation of personality traits, APOE and BDNF genotyping, electroencephalography and event-related potential recording, lumbar puncture for measurement of Aβ42, t-tau, and p-tau concentration and Aβ42/Aβ40 ratio. Recruited patients will have follow-up neuropsychological examinations every two years. Collected data will be used to train a machine learning algorithm to define the risk of being carriers of AD and progress to dementia in patients with SCD.

DISCUSSION

This is the first study to investigate the application of machine learning to predict AD in patients with SCD. Since all the features we will consider can be derived from non-invasive and easily accessible assessments, our expected results may provide evidence for defining cost-effective and globally scalable tools to estimate the risk of AD and address the needs of patients with memory complaints. In the era of DMTs, this will have crucial implications for the early identification of patients suitable for treatment in the initial stages of AD.

TRIAL REGISTRATION NUMBER (TRN)

NCT05569083.

Do radiomics or diffusion-tensor images provide additional information to predict brain amyloid-beta positivity?

The aim of the present study was to predict amyloid-beta positivity using a conventional T1-weighted image, radiomics, and a diffusion-tensor image obtained by magnetic resonance imaging (MRI). We included 186 patients with mild cognitive impairment (MCI) who underwent Florbetaben positron emission tomography (PET), MRI (three-dimensional T1-weighted and diffusion-tensor images), and neuropsychological tests at the Asan Medical Center. We developed a stepwise machine learning algorithm using demographics, T1 MRI features (volume, cortical thickness and radiomics), and diffusion-tensor image to distinguish amyloid-beta positivity on Florbetaben PET. We compared the performance of each algorithm based on the MRI features used. The study population included 72 patients with MCI in the amyloid-beta-negative group and 114 patients with MCI in the amyloid-beta-positive group. The machine learning algorithm using T1 volume performed better than that using only clinical information (mean area under the curve [AUC]: 0.73 vs. 0.69, p < 0.001). The machine learning algorithm using T1 volume showed better performance than that using cortical thickness (mean AUC: 0.73 vs. 0.68, p < 0.001) or texture (mean AUC: 0.73 vs. 0.71, p = 0.002). The performance of the machine learning algorithm using fractional anisotropy in addition to T1 volume was not better than that using T1 volume alone (mean AUC: 0.73 vs. 0.73, p = 0.60). Among MRI features, T1 volume was the best predictor of amyloid PET positivity. Radiomics or diffusion-tensor images did not provide additional benefits.

Prediction of Amyloid β-Positivity with both MRI Parameters and Cognitive Function Using Machine Learning

Purpose

To investigate the MRI markers for the prediction of amyloid β (Aβ)-positivity in mild cognitive impairment (MCI) and Alzheimer's disease (AD), and to evaluate the differences in MRI markers between Aβ-positive (Aβ [+]) and -negative groups using the machine learning (ML) method.

Materials and Methods

This study included 139 patients with MCI and AD who underwent amyloid PET-CT and brain MRI. Patients were divided into Aβ (+) (n = 84) and Aβ-negative (n = 55) groups. Visual analysis was performed with the Fazekas scale of white matter hyperintensity (WMH) and cerebral microbleeds (CMB) scores. The WMH volume and regional brain volume were quantitatively measured. The multivariable logistic regression and ML using support vector machine, and logistic regression were used to identify the best MRI predictors of Aβ-positivity.

Results

The Fazekas scale of WMH (p = 0.02) and CMB scores (p = 0.04) were higher in Aβ (+). The volumes of hippocampus, entorhinal cortex, and precuneus were smaller in Aβ (+) (p < 0.05). The third ventricle volume was larger in Aβ (+) (p = 0.002). The logistic regression of ML showed a good accuracy (81.1%) with mini-mental state examination (MMSE) and regional brain volumes.

Conclusion

The application of ML using the MMSE, third ventricle, and hippocampal volume is helpful in predicting Aβ-positivity with a good accuracy.

A risk prediction model based on machine learning for early cognitive impairment in hypertension: Development and validation study

Background

Clinical practice guidelines recommend early identification of cognitive impairment in individuals with hypertension with the help of risk prediction tools based on risk factors.

Objective

The aim of this study was to develop a superior machine learning model based on easily collected variables to predict the risk of early cognitive impairment in hypertensive individuals, which could be used to optimize early cognitive impairment risk assessment strategies.

Methods

For this cross-sectional study, 733 patients with hypertension (aged 30-85, 48.98% male) enrolled in multi-center hospitals in China were divided into a training group (70%) and a validation group (30%). After least absolute shrinkage and selection operator (LASSO) regression analysis with 5-fold cross-validation determined the modeling variables, three machine learning classifiers, logistic regression (LR), XGBoost (XGB), and gaussian naive bayes (GNB), were developed. The area under the ROC curve (AUC), accuracy, sensitivity, specificity, and F1 score were used to evaluate the model performance. Shape Additive explanation (SHAP) analysis was performed to rank feature importance. Further decision curve analysis (DCA) assessed the clinical performance of the established model and visualized it by nomogram.

Results

Hip circumference, age, education levels, and physical activity were considered significant predictors of early cognitive impairment in hypertension. The AUC (0.88), F1 score (0.59), accuracy (0.81), sensitivity (0.84), and specificity (0.80) of the XGB model were superior to LR and GNB classifiers.

Conclusion

The XGB model based on hip circumference, age, educational level, and physical activity has superior predictive performance and it shows promise in predicting the risk of cognitive impairment in hypertensive clinical settings.

Clinical Characteristic in Primary Progressive Aphasia in Relation to Alzheimer's Disease Biomarkers

BACKGROUND

Primary progressive aphasia (PPA) is associated with amyloid-β (Aβ) pathology. However, clinical feature of PPA based on Aβ positivity remains unclear.

OBJECTIVE

We aimed to assess the prevalence of Aβ positivity in patients with PPA and compare the clinical characteristics of patients with Aβ-positive (A+) and Aβ-negative (A-) PPA. Further, we applied Aβ and tau classification system (AT system) in patients with PPA for whom additional information of in vivo tau biomarker was available.

METHODS

We recruited 110 patients with PPA (41 semantic [svPPA], 27 non-fluent [nfvPPA], 32 logopenic [lvPPA], and 10 unclassified [ucPPA]) who underwent Aβ-PET imaging at multi centers. The extent of language impairment and cortical atrophy were compared between the A+ and A-PPA subgroups using general linear models.

RESULTS

The prevalence of Aβ positivity was highest in patients with lvPPA (81.3%), followed by ucPPA (60.0%), nfvPPA (18.5%), and svPPA (9.8%). The A+ PPA subgroup manifested cortical atrophy mainly in the left superior temporal/inferior parietal regions and had lower repetition scores compared to the A-PPA subgroup. Further, we observed that more than 90% (13/14) of the patients with A+ PPA had tau deposition.

CONCLUSION

Our findings will help clinicians understand the patterns of language impairment and cortical atrophy in patients with PPA based on Aβ deposition. Considering that most of the A+ PPA patents are tau positive, understanding the influence of Alzheimer's disease biomarkers on PPA might provide an opportunity for these patients to participate in clinical trials aimed for treating atypical Alzheimer's disease.