Prediction of amyloid pathology in cognitively unimpaired individuals using voxel-wise analysis of longitudinal structural brain MRI

Retrospective analysis of Braak stage- and APOE4 allele-dependent associations between MR spectroscopy and markers of tau and neurodegeneration in cognitively unimpaired elderly

PURPOSE

The pathological hallmarks of Alzheimer's disease (AD), amyloid, tau, and associated neurodegeneration, are present in the cortical gray matter (GM) years before symptom onset, and at significantly greater levels in carriers of the apolipoprotein E4 (APOE4) allele. Their respective biomarkers, A/T/N, have been found to correlate with aspects of brain biochemistry, measured with magnetic resonance spectroscopy (MRS), indicating a potential for MRS to augment the A/T/N framework for staging and prediction of AD. Unfortunately, the relationships between MRS and A/T/N biomarkers are unclear, largely due to a lack of studies examining them in the context of the spatial and temporal model of T/N progression. Advanced MRS acquisition and post-processing approaches have enabled us to address this knowledge gap and test the hypotheses, that glutamate-plus-glutamine (Glx) and N-acetyl-aspartate (NAA), metabolites reflecting synaptic and neuronal health, respectively, measured from regions on the Braak stage continuum, correlate with: (i) cerebrospinal fluid (CSF) p-tau181 level (T), and (ii) hippocampal volume or cortical thickness of parietal lobe GM (N). We hypothesized that these correlations will be moderated by Braak stage and APOE4 genotype.

METHODS

We conducted a retrospective imaging study of 34 cognitively unimpaired elderly individuals who received APOE4 genotyping and lumbar puncture from pre-existing prospective studies at the NYU Grossman School of Medicine between October 2014 and January 2019. Subjects returned for their imaging exam between April 2018 and February 2020. Metabolites were measured from the left hippocampus (Braak II) using a single-voxel semi-adiabatic localization by adiabatic selective refocusing sequence; and from the bilateral posterior cingulate cortex (PCC; Braak IV), bilateral precuneus (Braak V), and bilateral precentral gyrus (Braak VI) using a multi-voxel echo-planar spectroscopic imaging sequence. Pearson and Spearman correlations were used to examine the relationships between absolute levels of choline, creatine, myo-inositol, Glx, and NAA and CSF p-tau181, and between these metabolites and hippocampal volume or parietal cortical thicknesses. Covariates included age, sex, years of education, Fazekas score, and months between CSF collection and MRI exam.

RESULTS

There was a direct correlation between hippocampal Glx and CSF p-tau181 in APOE4 carriers (Pearson's r = 0.76, p = 0.02), but not after adjusting for covariates. In the entire cohort, there was a direct correlation between hippocampal NAA and hippocampal volume (Spearman's r = 0.55, p = 0.001), even after adjusting for age and Fazekas score (Spearman's r = 0.48, p = 0.006). This relationship was observed only in APOE4 carriers (Pearson's r = 0.66, p = 0.017), and was also retained after adjustment (Pearson's r = 0.76, p = 0.008; metabolite-by-carrier interaction p = 0.03). There were no findings in the PCC, nor in the negative control (late Braak stage) regions of the precuneus and precentral gyrus.

CONCLUSIONS

Our findings are in line with the spatially- and temporally-resolved Braak staging model of pathological severity in which the hippocampus is affected earlier than the PCC. The correlations, between MRS markers of synaptic and neuronal health and, respectively, T and N pathology, were found exclusively within APOE4 carriers, suggesting a connection with AD pathological change, rather than with normal aging. We therefore conclude that MRS has the potential to augment early A/T/N staging, with the hippocampus serving as a more sensitive MRS target compared to the PCC.

Deformation-based morphometry: a sensitive imaging approach to detect radiation-induced brain injury?

BACKGROUND

Radiotherapy is a major therapeutic approach in patients with brain tumors. However, it leads to cognitive impairments. To improve the management of radiation-induced brain sequalae, deformation-based morphometry (DBM) could be relevant. Here, we analyzed the significance of DBM using Jacobian determinants (JD) obtained by non-linear registration of MRI images to detect local vulnerability of healthy cerebral tissue in an animal model of brain irradiation.

METHODS

Rats were exposed to fractionated whole-brain irradiation (WBI, 30 Gy). A multiparametric MRI (anatomical, diffusion and vascular) study was conducted longitudinally from 1 month up to 6 months after WBI. From the registration of MRI images, macroscopic changes were analyzed by DBM and microscopic changes at the cellular and vascular levels were evaluated by quantification of cerebral blood volume (CBV) and diffusion metrics including mean diffusivity (MD). Voxel-wise comparisons were performed on the entire brain and in specific brain areas identified by DBM. Immunohistology analyses were undertaken to visualize the vessels and astrocytes.

RESULTS

DBM analysis evidenced time-course of local macrostructural changes; some of which were transient and some were long lasting after WBI. DBM revealed two vulnerable brain areas, namely the corpus callosum and the cortex. DBM changes were spatially associated to microstructural alterations as revealed by both diffusion metrics and CBV changes, and confirmed by immunohistology analyses. Finally, matrix correlations demonstrated correlations between JD/MD in the early phase after WBI and JD/CBV in the late phase both in the corpus callosum and the cortex.

CONCLUSIONS

Brain irradiation induces local macrostructural changes detected by DBM which could be relevant to identify brain structures prone to radiation-induced tissue changes. The translation of these data in patients could represent an added value in imaging studies on brain radiotoxicity.

Comparison of deep learning architectures for predicting amyloid positivity in Alzheimer's disease, mild cognitive impairment, and healthy aging, from T1-weighted brain structural MRI

Abnormal β-amyloid (Aβ) accumulation in the brain is an early indicator of Alzheimer's disease (AD) and is typically assessed through invasive procedures such as PET (positron emission tomography) or CSF (cerebrospinal fluid) assays. As new anti-Alzheimer's treatments can now successfully target amyloid pathology, there is a growing interest in predicting Aβ positivity (Aβ+) from less invasive, more widely available types of brain scans, such as T1-weighted (T1w) MRI. Here we compare multiple approaches to infer Aβ + from standard anatomical MRI: (1) classical machine learning algorithms, including logistic regression, XGBoost, and shallow artificial neural networks, (2) deep learning models based on 2D and 3D convolutional neural networks (CNNs), (3) a hybrid ANN-CNN, combining the strengths of shallow and deep neural networks, (4) transfer learning models based on CNNs, and (5) 3D Vision Transformers. All models were trained on paired MRI/PET data from 1,847 elderly participants (mean age: 75.1 yrs. ± 7.6SD; 863 females/984 males; 661 healthy controls, 889 with mild cognitive impairment (MCI), and 297 with Dementia), scanned as part of the Alzheimer's Disease Neuroimaging Initiative. We evaluated each model's balanced accuracy and F1 scores. While further tests on more diverse data are warranted, deep learning models trained on standard MRI showed promise for estimating Aβ + status, at least in people with MCI. This may offer a potential screening option before resorting to more invasive procedures.

Consensus on rapid screening for prodromal Alzheimer's disease in China

Alzheimer's disease (AD) is a common cause of dementia, characterised by cerebral amyloid-β deposition, pathological tau and neurodegeneration. The prodromal stage of AD (pAD) refers to patients with mild cognitive impairment (MCI) and evidence of AD's pathology. At this stage, disease-modifying interventions should be used to prevent the progression to dementia. Given the inherent heterogeneity of MCI, more specific biomarkers are needed to elucidate the underlying AD's pathology. Although the uses of cerebrospinal fluid and positron emission tomography are widely accepted methods for detecting AD's pathology, their clinical applications are limited by their high costs and invasiveness, particularly in low-income areas in China. Therefore, to improve the early detection of Alzheimer's disease (AD) pathology through cost-effective screening methods, a panel of 45 neurologists, psychiatrists and gerontologists was invited to establish a formal consensus on the screening of pAD in China. The supportive evidence and grades of recommendations are based on a systematic literature review and focus group discussion. National meetings were held to allow participants to review, vote and provide their expert opinions to reach a consensus. A majority (two-thirds) decision was used for questions for which consensus could not be reached. Recommended screening methods are presented in this publication, including neuropsychological assessment, peripheral biomarkers and brain imaging. In addition, a general workflow for screening pAD in China is established, which will help clinicians identify individuals at high risk and determine therapeutic targets.

Combining Blood-Based Biomarkers and Structural MRI Measurements to Distinguish Persons with and without Significant Amyloid Plaques

Background

Amyloid-β (Aβ) plaques play a pivotal role in Alzheimer's disease. The current positron emission tomography (PET) is expensive and limited in availability. In contrast, blood-based biomarkers (BBBMs) show potential for characterizing Aβ plaques more affordably. We have previously proposed an MRI-based hippocampal morphometry measure to be an indicator of Aβ plaques.

Objective

To develop and validate an integrated model to predict brain amyloid PET positivity combining MRI feature and plasma Aβ42/40 ratio.

Methods

We extracted hippocampal multivariate morphometry statistics from MR images and together with plasma Aβ42/40 trained a random forest classifier to perform a binary classification of participant brain amyloid PET positivity. We evaluated the model performance using two distinct cohorts, one from the Alzheimer's Disease Neuroimaging Initiative (ADNI) and the other from the Banner Alzheimer's Institute (BAI), including prediction accuracy, precision, recall rate, F1 score, and AUC score.

Results

Results from ADNI (mean age 72.6, Aβ+ rate 49.5%) and BAI (mean age 66.2, Aβ+ rate 36.9%) datasets revealed the integrated multimodal (IMM) model's superior performance over unimodal models. The IMM model achieved prediction accuracies of 0.86 in ADNI and 0.92 in BAI, surpassing unimodal models based solely on structural MRI (0.81 and 0.87) or plasma Aβ42/40 (0.73 and 0.81) predictors.

CONCLUSIONS

Our IMM model, combining MRI and BBBM data, offers a highly accurate approach to predict brain amyloid PET positivity. This innovative multiplex biomarker strategy presents an accessible and cost-effective avenue for advancing Alzheimer's disease diagnostics, leveraging diverse pathologic features related to Aβ plaques and structural MRI.

Real-world prediction of preclinical Alzheimer's disease with a deep generative model

Amyloid positivity is an early indicator of Alzheimer's disease and is necessary to determine the disease. In this study, a deep generative model is utilized to predict the amyloid positivity of cognitively normal individuals using proxy measures, such as structural MRI scans, demographic variables, and cognitive scores, instead of invasive direct measurements. Through its remarkable efficacy in handling imperfect datasets caused by missing data or labels, and imbalanced classes, the model outperforms previous studies and widely used machine learning approaches with an AUROC of 0.8609. Furthermore, this study illuminates the model's adaptability to diverse clinical scenarios, even when feature sets or diagnostic criteria differ from the training data. We identify the brain regions and variables that contribute most to classification, including the lateral occipital lobes, posterior temporal lobe, and APOE ϵ4 allele. Taking advantage of deep generative models, our approach can not only provide inexpensive, non-invasive, and accurate diagnostics for preclinical Alzheimer's disease, but also meet real-world requirements for clinical translation of a deep learning model, including transferability and interpretability.

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.

Influence of cerebral blood flow on volumetric loss related to Alzheimer's disease

CBF measured with Arterial Spin Labeling (ASL) obtained by Magnetic Resonance Imaging (MRI) may become an important biomarker by showing changes in early stages of AD, such as in the prodromal stage of Mild Cognitive Impairment (MCI).

Objective

Verify the correlation between atrophy and CBF in patients with MCI and mild phase ADD, to demonstrate whether changes in CBF can be considered as vascular biomarkers in the diagnosis of the DA continuum.

Methods

11 healthy volunteers, 16 MCI and 15 mild ADD were evaluated. Images of the brain were acquired, including CBF measured with Arterial Spin Labeling (ASL).

Results

When comparing MCI with control, a reduction in normalized CBF was observed in left posterior cingulate (estimated difference -0.38; p=0.02), right posterior cingulate (estimated difference -0.45; p=0.02) and right precuneus (estimated difference -0.28; p <0.01); also increase in normalized CBF in right upper temporal pole (estimated difference 0.22; p=0.03). It was also observed that in MCI, the smaller the gray matter volume, the smaller the CBF in the left posterior cingulate; as well as the greater the cerebrospinal fluid volume, consequent to the encephalic volumetric reduction associated with atrophy, the greater the CBF in the right superior temporal pole. When comparing controls, MCI and mild AD, in relation to the other variables, no other correlations were observed between CBF and atrophy.

Conclusion

In patients with MCI, the reduction of CBF in the left posterior cingulate correlated with gray matter atrophy, as well as the increase of CBF in the right upper temporal pole correlated with an increase in cerebrospinal fluid consequent to the encephalic volumetric reduction associated with atrophy, demonstrating the influence of CBF in AD related brain atrophy. These findings position CBF as a possible vascular biomarker for early-stage AD diagnoses.

Multivariate functional mixed model with MRI data: An application to Alzheimer's disease

Alzheimer's Disease (AD) is the leading cause of dementia and impairment in various domains. Recent AD studies, (ie, Alzheimer's Disease Neuroimaging Initiative (ADNI) study), collect multimodal data, including longitudinal neurological assessments and magnetic resonance imaging (MRI) data, to better study the disease progression. Adopting early interventions is essential to slow AD progression for subjects with mild cognitive impairment (MCI). It is of particular interest to develop an AD predictive model that leverages multimodal data and provides accurate personalized predictions. In this article, we propose a multivariate functional mixed model with MRI data (MFMM-MRI) that simultaneously models longitudinal neurological assessments, baseline MRI data, and the survival outcome (ie, dementia onset) for subjects with MCI at baseline. Two functional forms (the random-effects model and instantaneous model) linking the longitudinal and survival process are investigated. We use Markov Chain Monte Carlo (MCMC) method based on No-U-Turn Sampling (NUTS) algorithm to obtain posterior samples. We develop a dynamic prediction framework that provides accurate personalized predictions of longitudinal trajectories and survival probability. We apply MFMM-MRI to the ADNI study and identify significant associations among longitudinal outcomes, MRI data, and the risk of dementia onset. The instantaneous model with voxels from the whole brain has the best prediction performance among all candidate models. The simulation study supports the validity of the estimation and dynamic prediction method.

Predicting Brain Amyloid Positivity from T1 weighted brain MRI and MRI-derived Gray Matter, White Matter and CSF maps using Transfer Learning on 3D CNNs

Abnormal β-amyloid (Aβ) accumulation in the brain is an early indicator of Alzheimer's disease and practical tests could help identify patients who could respond to treatment, now that promising anti-amyloid drugs are available. Even so, Aβ positivity (Aβ+) is assessed using PET or CSF assays, both highly invasive procedures. Here, we investigate how well Aβ+ can be predicted from T1 weighted brain MRI and gray matter, white matter and cerebrospinal fluid segmentations from T1-weighted brain MRI (T1w), a less invasive alternative. We used 3D convolutional neural networks to predict Aβ+ based on 3D brain MRI data, from 762 elderly subjects (mean age: 75.1 yrs. ± 7.6SD; 394F/368M; 459 healthy controls, 67 with MCI and 236 with dementia) scanned as part of the Alzheimer's Disease Neuroimaging Initiative. We also tested whether the accuracy increases when using transfer learning from the larger UK Biobank dataset. Overall, the 3D CNN predicted Aβ+ with 76% balanced accuracy from T1w scans. The closest performance to this was using white matter maps alone when the model was pre-trained on an age prediction in the UK Biobank. The performance of individual tissue maps was less than the T1w, but transfer learning helped increase the accuracy. Although tests on more diverse data are warranted, deep learned models from standard MRI show initial promise for Aβ+ estimation, before considering more invasive procedures.

Improved Prediction of Amyloid-β and Tau Burden Using Hippocampal Surface Multivariate Morphometry Statistics and Sparse Coding

BACKGROUND

Amyloid-β (Aβ) plaques and tau protein tangles in the brain are the defining 'A' and 'T' hallmarks of Alzheimer's disease (AD), and together with structural atrophy detectable on brain magnetic resonance imaging (MRI) scans as one of the neurodegenerative ('N') biomarkers comprise the "ATN framework" of AD. Current methods to detect Aβ/tau pathology include cerebrospinal fluid (invasive), positron emission tomography (PET; costly and not widely available), and blood-based biomarkers (promising but mainly still in development).

OBJECTIVE

To develop a non-invasive and widely available structural MRI-based framework to quantitatively predict the amyloid and tau measurements.

METHODS

With MRI-based hippocampal multivariate morphometry statistics (MMS) features, we apply our Patch Analysis-based Surface Correntropy-induced Sparse coding and max-pooling (PASCS-MP) method combined with the ridge regression model to individual amyloid/tau measure prediction.

RESULTS

We evaluate our framework on amyloid PET/MRI and tau PET/MRI datasets from the Alzheimer's Disease Neuroimaging Initiative. Each subject has one pair consisting of a PET image and MRI scan, collected at about the same time. Experimental results suggest that amyloid/tau measurements predicted with our PASCP-MP representations are closer to the real values than the measures derived from other approaches, such as hippocampal surface area, volume, and shape morphometry features based on spherical harmonics.

CONCLUSION

The MMS-based PASCP-MP is an efficient tool that can bridge hippocampal atrophy with amyloid and tau pathology and thus help assess disease burden, progression, and treatment effects.

Value assessment of artificial intelligence in medical imaging: a scoping review

BACKGROUND

Artificial intelligence (AI) is seen as one of the major disrupting forces in the future healthcare system. However, the assessment of the value of these new technologies is still unclear, and no agreed international health technology assessment-based guideline exists. This study provides an overview of the available literature in the value assessment of AI in the field of medical imaging.

METHODS

We performed a systematic scoping review of published studies between January 2016 and September 2020 using 10 databases (Medline, Scopus, ProQuest, Google Scholar, and six related databases of grey literature). Information about the context (country, clinical area, and type of study) and mentioned domains with specific outcomes and items were extracted. An existing domain classification, from a European assessment framework, was used as a point of departure, and extracted data were grouped into domains and content analysis of data was performed covering predetermined themes.

RESULTS

Seventy-nine studies were included out of 5890 identified articles. An additional seven studies were identified by searching reference lists, and the analysis was performed on 86 included studies. Eleven domains were identified: (1) health problem and current use of technology, (2) technology aspects, (3) safety assessment, (4) clinical effectiveness, (5) economics, (6) ethical analysis, (7) organisational aspects, (8) patients and social aspects, (9) legal aspects, (10) development of AI algorithm, performance metrics and validation, and (11) other aspects. The frequency of mentioning a domain varied from 20 to 78% within the included papers. Only 15/86 studies were actual assessments of AI technologies. The majority of data were statements from reviews or papers voicing future needs or challenges of AI research, i.e. not actual outcomes of evaluations.

CONCLUSIONS

This review regarding value assessment of AI in medical imaging yielded 86 studies including 11 identified domains. The domain classification based on European assessment framework proved useful and current analysis added one new domain. Included studies had a broad range of essential domains about addressing AI technologies highlighting the importance of domains related to legal and ethical aspects.

Brain ventricles, CSF and cognition: a narrative review

The brain ventricles are structures that have been related to cognition since antiquity. They are essential components in the development and maintenance of brain functions. The aging process runs with the enlargement of ventricles and is related to a less selective blood-cerebrospinal fluid barrier and then a more toxic cerebrospinal fluid environment. The study of brain ventricles as a biological marker of aging is promissing because they are structures easily identified in neuroimaging studies, present good inter-rater reliability, and measures of them can identify brain atrophy earlier than cortical structures. The ventricular system also plays roles in the development of dementia, since dysfunction in the clearance of beta-amyloid protein is a key mechanism in sporadic Alzheimer's disease. The morphometric and volumetric studies of the brain ventricles can help to distinguish between healthy elderly and persons with mild cognitive impairment (MCI) and dementia. Brain ventricle data may contribute to the appropriate allocation of individuals in groups at higher risk for MCI-dementia progression in clinical trials and to measuring therapeutic responses in these studies, as well as providing differential diagnosis, such as normal pressure hydrocephalus. Here, we reviewed the pathophysiology of healthy aging and cognitive decline, focusing on the role of the choroid plexus and brain ventricles in this process.

Potential Applications of Artificial Intelligence in Clinical Trials for Alzheimer’s Disease

Clinical trials for Alzheimer’s disease (AD) face multiple challenges, such as the high screen failure rate and the even allocation of heterogeneous participants. Artificial intelligence (AI), which has become a potent tool of modern science with the expansion in the volume, variety, and velocity of biological data, offers promising potential to address these issues in AD clinical trials. In this review, we introduce the current status of AD clinical trials and the topic of machine learning. Then, a comprehensive review is focused on the potential applications of AI in the steps of AD clinical trials, including the prediction of protein and MRI AD biomarkers in the prescreening process during eligibility assessment and the likelihood stratification of AD subjects into rapid and slow progressors in randomization. Finally, this review provides challenges, developments, and the future outlook on the integration of AI into AD clinical trials.

Federated Morphometry Feature Selection for Hippocampal Morphometry Associated Beta-Amyloid and Tau Pathology

Amyloid-β (Aβ) plaques and tau protein tangles in the brain are now widely recognized as the defining hallmarks of Alzheimer's disease (AD), followed by structural atrophy detectable on brain magnetic resonance imaging (MRI) scans. One of the particular neurodegenerative regions is the hippocampus to which the influence of Aβ/tau on has been one of the research focuses in the AD pathophysiological progress. This work proposes a novel framework, Federated Morphometry Feature Selection (FMFS) model, to examine subtle aspects of hippocampal morphometry that are associated with Aβ/tau burden in the brain, measured using positron emission tomography (PET). FMFS is comprised of hippocampal surface-based feature calculation, patch-based feature selection, federated group LASSO regression, federated screening rule-based stability selection, and region of interest (ROI) identification. FMFS was tested on two Alzheimer's Disease Neuroimaging Initiative (ADNI) cohorts to understand hippocampal alterations that relate to Aβ/tau depositions. Each cohort included pairs of MRI and PET for AD, mild cognitive impairment (MCI), and cognitively unimpaired (CU) subjects. Experimental results demonstrated that FMFS achieves an 89× speedup compared to other published state-of-the-art methods under five independent hypothetical institutions. In addition, the subiculum and cornu ammonis 1 (CA1 subfield) were identified as hippocampal subregions where atrophy is strongly associated with abnormal Aβ/tau. As potential biomarkers for Aβ/tau pathology, the features from the identified ROIs had greater power for predicting cognitive assessment and for survival analysis than five other imaging biomarkers. All the results indicate that FMFS is an efficient and effective tool to reveal associations between Aβ/tau burden and hippocampal morphometry.

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.

Intravoxel incoherent motion diffusion-weighted imaging in the characterization of Alzheimer's disease

OBJECTIVES

Alzheimer's disease (AD) is the most common type of dementia, and characterizing brain changes in AD is important for clinical diagnosis and prognosis. This study was designed to evaluate the classification performance of intravoxel incoherent motion (IVIM) diffusion-weighted imaging in differentiating between AD patients and normal control (NC) subjects and to explore its potential effectiveness as a neuroimaging biomarker.

METHODS

Thirty-one patients with probable AD and twenty NC subjects were included in the prospective study. IVIM data were subjected to postprocessing, and parameters including the apparent diffusion coefficient (ADC), slow diffusion coefficient (D_s), fast diffusion coefficient (D_f), perfusion fraction (fp) and D_f*fp were calculated. The classification model was developed and confirmed with cross-validation (group A/B) using Support Vector Machine (SVM). Correlations between IVIM parameters and Mini-Mental State Examination (MMSE) scores in AD patients were investigated using partial correlation analysis.

RESULTS

Diffusion MRI revealed significant region-specific differences that aided in differentiating AD patients from controls. Among the analyzed regions and parameters, the D_f of the right precuneus (PreR) (ρ = 0.515; P = 0.006) and the left cerebellum (CL) (ρ = 0.429; P = 0.026) demonstrated significant associations with the cognitive function of AD patients. An area under the receiver operating characteristics curve (AUC) of 0.84 (95% CI: 0.66, 0.99) was calculated for the validation in dataset B after the prediction model was trained on dataset A. When the datasets were reversed, an AUC of 0.90 (95% CI: 0.75, 1.00) was calculated for the validation in dataset A, after the prediction model trained in dataset B.

CONCLUSION

IVIM imaging is a promising method for the classification of AD and NC subjects, and IVIM parameters of precuneus and cerebellum might be effective biomarker for the diagnosis of AD.

Deep learning-based amyloid PET positivity classification model in the Alzheimer's disease continuum by using 2-[18F]FDG PET

BACKGROUND

Considering the limited accessibility of amyloid position emission tomography (PET) in patients with dementia, we proposed a deep learning (DL)-based amyloid PET positivity classification model from PET images with 2-deoxy-2-[fluorine-18]fluoro-D-glucose (2-[18F]FDG).

METHODS

We used 2-[18F]FDG PET datasets from the Alzheimer's Disease Neuroimaging Initiative and Korean Brain Aging Study for the Early diagnosis and prediction of Alzheimer's disease for model development. Moreover, we used an independent dataset from another hospital. A 2.5-D deep learning architecture was constructed using 291 submodules and three axes images as the input. We conducted the voxel-wise analysis to assess the regions with substantial differences in glucose metabolism between the amyloid PET-positive and PET-negative participants. This facilitated an understanding of the deep model classification. In addition, we compared these regions with the classification probability from the submodules.

RESULTS

There were 686 out of 1433 (47.9%) and 50 out of 100 (50%) amyloid PET-positive participants in the training and internal validation datasets and the external validation datasets, respectively. With 50 times iterations of model training and validation, the model achieved an AUC of 0.811 (95% confidence interval (CI) of 0.803-0.819) and 0.798 (95% CI, 0.789-0.807) on the internal and external validation datasets, respectively. The area under the curve (AUC) was 0.860 when tested with the model with the highest value (0.864) on the external validation dataset. Moreover, it had 75.0% accuracy, 76.0% sensitivity, 74.0% specificity, and 75.0% F1-score. We found an overlap between the regions within the default mode network, thus generating high classification values.

CONCLUSION

The proposed model based on the 2-[18F]FDG PET imaging data and a DL framework might successfully classify amyloid PET positivity in clinical practice, without performing amyloid PET, which have limited accessibility.

The search for a convenient procedure to detect one of the earliest signs of Alzheimer's disease: A systematic review of the prediction of brain amyloid status

INTRODUCTION

Convenient, cost-effective tests for amyloid beta (Aβ) are needed to identify those at higher risk for developing Alzheimer's disease (AD). This systematic review evaluates recent models that predict dichotomous Aβ. (PROSPERO: CRD42020144734).

METHODS

We searched Embase and identified 73 studies from 29,581 for review. We assessed study quality using established tools, extracted information, and reported results narratively.

RESULTS

We identified few high-quality studies due to concerns about Aβ determination and analytical issues. The most promising convenient, inexpensive classifiers consist of age, apolipoprotein E genotype, cognitive measures, and/or plasma Aβ. Plasma Aβ may be sufficient if pre-analytical variables are standardized and scalable assays developed. Some models lowered costs associated with clinical trial recruitment or clinical screening.

DISCUSSION

Conclusions about models are difficult due to study heterogeneity and quality. Promising prediction models used demographic, cognitive/neuropsychological, imaging, and plasma Aβ measures. Further studies using standardized Aβ determination, and improved model validation are required.

Detection of β-amyloid positivity in Alzheimer's Disease Neuroimaging Initiative participants with demographics, cognition, MRI and plasma biomarkers

In vivo gold standard for the ante-mortem assessment of brain β-amyloid pathology is currently β-amyloid positron emission tomography or cerebrospinal fluid measures of β-amyloid42 or the β-amyloid42/β-amyloid40 ratio. The widespread acceptance of a biomarker classification scheme for the Alzheimer's disease continuum has ignited interest in more affordable and accessible approaches to detect Alzheimer's disease β-amyloid pathology, a process that often slows down the recruitment into, and adds to the cost of, clinical trials. Recently, there has been considerable excitement concerning the value of blood biomarkers. Leveraging multidisciplinary data from cognitively unimpaired participants and participants with mild cognitive impairment recruited by the multisite biomarker study of Alzheimer's Disease Neuroimaging Initiative, here we assessed to what extent plasma β-amyloid42/β-amyloid40, neurofilament light and phosphorylated-tau at threonine-181 biomarkers detect the presence of β-amyloid pathology, and to what extent the addition of clinical information such as demographic data, APOE genotype, cognitive assessments and MRI can assist plasma biomarkers in detecting β-amyloid-positivity. Our results confirm plasma β-amyloid42/β-amyloid40 as a robust biomarker of brain β-amyloid-positivity (area under curve, 0.80-0.87). Plasma phosphorylated-tau at threonine-181 detected β-amyloid-positivity only in the cognitively impaired with a moderate area under curve of 0.67, whereas plasma neurofilament light did not detect β-amyloid-positivity in either group of participants. Clinical information as well as MRI-score independently detected positron emission tomography β-amyloid-positivity in both cognitively unimpaired and impaired (area under curve, 0.69-0.81). Clinical information, particularly APOE ε4 status, enhanced the performance of plasma biomarkers in the detection of positron emission tomography β-amyloid-positivity by 0.06-0.14 units of area under curve for cognitively unimpaired, and by 0.21-0.25 units for cognitively impaired; and further enhancement of these models with an MRI-score of β-amyloid-positivity yielded an additional improvement of 0.04-0.11 units of area under curve for cognitively unimpaired and 0.05-0.09 units for cognitively impaired. Taken together, these multi-disciplinary results suggest that when combined with clinical information, plasma phosphorylated-tau at threonine-181 and neurofilament light biomarkers, and an MRI-score could effectively identify β-amyloid+ cognitively unimpaired and impaired (area under curve, 0.80-0.90). Yet, when the MRI-score is considered in combination with clinical information, plasma phosphorylated-tau at threonine-181 and plasma neurofilament light have minimal added value for detecting β-amyloid-positivity. Our systematic comparison of β-amyloid-positivity detection models identified effective combinations of demographics, APOE, global cognition, MRI and plasma biomarkers. Promising minimally invasive and low-cost predictors such as plasma biomarkers of β-amyloid42/β-amyloid40 may be improved by age and APOE genotype.

The Infratentorial Localization of Brain Metastases May Correlate with Specific Clinical Characteristics and Portend Worse Outcomes Based on Voxel-Wise Mapping

The infratentorial regions are vulnerable to develop brain metastases (BMs). However, the associations between the infratentorial localization of BMs and clinical characteristics remained unclear. We retrospectively studied 1102 patients with 4365 BM lesions. Voxel-wise mapping of MRI was applied to construct the tumor frequency heatmaps after normalization and segmentation. The analysis of differential involvement (ADIFFI) was further used to obtain statistically significant clusters. Kaplan-Meier method and Cox regression were used to analyze the prognosis. The parietal, insular and left occipital lobes, and cerebellum were vulnerable to BMs with high relative metastatic risks. Infratentorial areas were site-specifically affected by the lung, breast, and colorectal cancer BMs, but inversely avoided by melanoma BMs. Significant infratentorial clusters were associated with young age, male sex, lung neuroendocrine and squamous cell carcinomas, high expression of Ki-67 of primaries and BMs, and patients with poorer prognosis. Inferior OS was observed in patients with ≥3 BMs and those who received whole-brain radiotherapy alone. Infratentorial involvement of BMs was an independent risk factor of poor prognosis for patients who received surgery (p = 0.023, hazard ratio = 1.473, 95% confidence interval = 1.055-2.058). The current study may add valuable clinical recognition of BMs and provide references for BMs diagnosis, treatment evaluation, and prognostic prediction.

Biomarker testing in MCI patients-deciding who to test

BACKGROUND

We aimed to derive an algorithm to define the optimal proportion of patients with mild cognitive impairment (MCI) in whom cerebrospinal fluid (CSF) testing is of added prognostic value.

METHODS

MCI patients were selected from the Amsterdam Dementia Cohort (n = 402). Three-year progression probabilities to dementia were predicted using previously published models with and without CSF data (amyloid-beta1-42 (Abeta), phosphorylated tau (p-tau)). We incrementally augmented the proportion of patients undergoing CSF, starting with the 10% patients with prognostic probabilities based on clinical data around the median (percentile 45-55), until all patients received CSF. The optimal proportion was defined as the proportion where the stepwise algorithm showed similar prognostic discrimination (Harrell's C) and accuracy (three-year Brier scores) compared to CSF testing of all patients. We used the BioFINDER study (n = 221) for validation.

RESULTS

The optimal proportion of MCI patients to receive CSF testing selected by the stepwise approach was 50%. CSF testing in only this proportion improved the performance of the model with clinical data only from Harrell's C = 0.60, Brier = 0.198 (Harrell's C = 0.61, Brier = 0.197 if the information on magnetic resonance imaging was available) to Harrell's C = 0.67 and Brier = 0.190, and performed similarly to a model in which all patients received CSF testing. Applying the stepwise approach in the BioFINDER study would again select half of the MCI patients and yielded robust results with respect to prognostic performance.

INTERPRETATION

CSF biomarker testing adds prognostic value in half of the MCI patients. As such, we achieve a CSF saving recommendation while simultaneously retaining optimal prognostic accuracy.

Projection to Latent Spaces Disentangles Pathological Effects on Brain Morphology in the Asymptomatic Phase of Alzheimer's Disease

Alzheimer's disease (AD) continuum is defined as a cascade of several neuropathological processes that can be measured using biomarkers, such as cerebrospinal fluid (CSF) levels of Aβ, p-tau, and t-tau. In parallel, brain anatomy can be characterized through imaging techniques, such as magnetic resonance imaging (MRI). In this work we relate both sets of measurements and seek associations between biomarkers and the brain structure that can be indicative of AD progression. The goal is to uncover underlying multivariate effects of AD pathology on regional brain morphological information. For this purpose, we used the projection to latent structures (PLS) method. Using PLS, we found a low dimensional latent space that best describes the covariance between both sets of measurements on the same subjects. Possible confounder effects (age and sex) on brain morphology are included in the model and regressed out using an orthogonal PLS model. We looked for statistically significant correlations between brain morphology and CSF biomarkers that explain part of the volumetric variance at each region-of-interest (ROI). Furthermore, we used a clustering technique to discover a small set of CSF-related patterns describing the AD continuum. We applied this technique to the study of subjects in the whole AD continuum, from the pre-clinical asymptomatic stages all the way through to the symptomatic groups. Subsequent analyses involved splitting the course of the disease into diagnostic categories: cognitively unimpaired subjects (CU), mild cognitively impaired subjects (MCI), and subjects with dementia (AD-dementia), where all symptoms were due to AD.