Imaging Biomarkers of Neurodegeneration in Alzheimer's Disease: Distinct Contributions of Cortical MRI Atrophy and FDG-PET Hypometabolism

Tau Protein Accumulation Trajectory-Based Brain Age Prediction in the Alzheimer's Disease Continuum

Clinical cognitive advancement within the Alzheimer's disease (AD) continuum is intimately connected with sustained accumulation of tau protein pathology. The biological brain age and its gap show great potential for pathological risk and disease severity. In the present study, we applied multivariable linear support vector regression to train a normative brain age prediction model using tau brain images. We further assessed the predicted biological brain age and its gap for patients within the AD continuum. In the AD continuum, evaluated pathologic tau binding was found in the inferior temporal, parietal-temporal junction, precuneus/posterior cingulate, dorsal frontal, occipital, and inferior-medial temporal cortices. The biological brain age gaps of patients within the AD continuum were notably higher than those of the normal controls (p < 0.0001). Significant positive correlations were observed between the brain age gap and global tau protein accumulation levels for mild cognitive impairment (r = 0.726, p < 0.001), AD (r = 0.845, p < 0.001), and AD continuum (r = 0.797, p < 0.001). The pathologic tau-based age gap was significantly linked to neuropsychological scores. The proposed pathologic tau-based biological brain age model could track the tau protein accumulation trajectory of cognitive impairment and further provide a comprehensive quantification index for the tau accumulation risk.

Targeted examination of amyloid beta and tau protein accumulation via positron emission tomography for the differential diagnosis of Alzheimer's disease based on the A/T(N) research framework

Alzheimer's disease (AD) is one of the most common neurodegenerative diseases among the older population. Its main pathological features include the abnormal deposition of extracellular amyloid-β plaques and the intracellular neurofibrillary tangles of tau proteins. Its clinical presentation is complex. This review introduces the pathological processes in AD and other common neurodegenerative diseases. It then discusses the positron emission tomography (PET) probes that target amyloid-β plaques and tau proteins for diagnosing AD. According to the A/T(N) research framework, combined targeted amyloid-β and tau protein detection via PET to further improve the diagnostic accuracy of AD. In particular, the properties of the 18F-flortaucipir and 18F-MK6240 tracers-may be more beneficial in helping to differentiate AD from other common neurodegenerative diseases, such as dementia with Lewy bodies, Parkinson's disease dementia, and frontotemporal dementia. Furthermore, the A/T(N) research framework should be used as the clinical diagnosis model of AD in the future.

Longitudinal Spatial Relationships Between Atrophy and Hypometabolism Across the Alzheimer's Disease Continuum

BACKGROUND

The A/T/N framework allows for the assessment of pathology-specific markers of MRI-derived structural atrophy and hypometabolism on 18FDG-PET. However, how these measures relate to each other locally and distantly across pathology-defined A/T/N groups is currently unclear.

OBJECTIVE

To determine the regions of association between atrophy and hypometabolism in A/T/N groups both within and across time points.

METHODS

We examined multivariate multimodal neuroimaging relationships between MRI and 18FDG-PET among suspected non-Alzheimer's disease pathology (SNAP) (A-T/N+; n = 14), Amyloid Only (A+T-N-; n = 24) and Probable AD (A+T+N+; n = 77) groups. Sparse canonical correlation analyses were employed to model spatially disjointed regions of association between MRI and 18FDG-PET data. These relationships were assessed at three combinations of time points -cross-sectionally, between baseline visits and between month 12 (M-12) follow-up visits, as well as longitudinally between baseline and M-12 follow-up.

RESULTS

In the SNAP group, spatially overlapping relationships between atrophy and hypometabolism were apparent in the bilateral temporal lobes when both modalities were assessed at the M-12 timepoint. Amyloid-Only subjects showed spatially discordant distributed atrophy-hypometabolism relationships at all time points assessed. In Probable AD subjects, local correlations were evident in the bilateral temporal lobes when both modalities were assessed at baseline and at M-12. Across groups, hypometabolism at baseline correlated with non-local, or distant, atrophy at M-12.

CONCLUSION

These results support the view that local concordance of atrophy and hypometabolism is the result of a tau-mediated process driving neurodegeneration.

Investigating the temporal pattern of neuroimaging-based brain age estimation as a biomarker for Alzheimer's Disease related neurodegeneration

Neuroimaging-based brain-age estimation via machine learning has emerged as an important new approach for studying brain aging. The difference between one's estimated brain age and chronological age, the brain age gap (BAG), has been proposed as an Alzheimer's Disease (AD) biomarker. However, most past studies on the BAG have been cross-sectional. Quantifying longitudinal changes in an individual's BAG temporal pattern would likely improve prediction of AD progression and clinical outcome based on neurophysiological changes. To fill this gap, our study conducted predictive modeling using a large neuroimaging dataset with up to 8 years of follow-up to examine the temporal patterns of the BAG's trajectory and how it varies by subject-level characteristics (sex, APOEɛ4 carriership) and disease status. Specifically, we explored the pattern and rate of change in BAG over time in individuals who remain stable with normal cognition or mild cognitive impairment (MCI), as well as individuals who progress to clinical AD. Combining multimodal imaging data in a support vector regression model to estimate brain age yielded improved performance over single modality. Multilevel modeling results showed the BAG followed a linear increasing trajectory with a significantly faster rate in individuals with MCI who progressed to AD compared to cognitively normal or MCI individuals who did not progress. The dynamic changes in the BAG during AD progression were further moderated by sex and APOEɛ4 carriership. Our findings demonstrate the BAG as a potential biomarker for understanding individual specific temporal patterns related to AD progression.

Multiscale structural mapping of Alzheimer's disease neurodegeneration

The recently described biological framework of Alzheimer's disease (AD) emphasizes three types of pathology to characterize this disorder, referred to as the 'amyloid/tau/neurodegeneration' (A-T-N) status. The 'neurodegenerative' component is typically defined by atrophy measures derived from structural magnetic resonance imaging (MRI) such as hippocampal volume. Neurodegeneration measures from imaging are associated with disease symptoms and prognosis. Thus, sensitive image-based quantification of neurodegeneration in AD has an important role in a range of clinical and research operations. Although hippocampal volume is a sensitive metric of neurodegeneration, this measure is impacted by several clinical conditions other than AD and therefore lacks specificity. In contrast, selective regional cortical atrophy, known as the 'cortical signature of AD' provides greater specificity to AD pathology. Although atrophy is apparent even in the preclinical stages of the disease, it is possible that increased sensitivity to degeneration could be achieved by including tissue microstructural properties in the neurodegeneration measure. However, to facilitate clinical feasibility, such information should be obtainable from a single, short, noninvasive imaging protocol. We propose a multiscale MRI procedure that advances prior work through the quantification of features at both macrostructural (morphometry) and microstructural (tissue properties obtained from multiple layers of cortex and subcortical white matter) scales from a single structural brain image (referred to as 'multi-scale structural mapping'; MSSM). Vertex-wise partial least squares (PLS) regression was used to compress these multi-scale structural features. When contrasting patients with AD to cognitively intact matched older adults, the MSSM procedure showed substantially broader regional group differences including areas that were not statistically significant when using cortical thickness alone. Further, with multiple machine learning algorithms and ensemble procedures, we found that MSSM provides accurate detection of individuals with AD dementia (AUROC = 0.962, AUPRC = 0.976) and individuals with mild cognitive impairment (MCI) that subsequently progressed to AD dementia (AUROC = 0.908, AUPRC = 0.910). The findings demonstrate the critical advancement of neurodegeneration quantification provided through multiscale mapping. Future work will determine the sensitivity of this technique for accurately detecting individuals with earlier impairment and biomarker positivity in the absence of impairment.

Identifying individuals with Alzheimer's disease-like brains based on structural imaging in the Human Connectome Project Aging cohort

Given the difficulty in factoring out typical age effects from subtle Alzheimer's disease (AD) effects on brain structure, identification of very early, as well as younger preclinical “at‐risk” individuals has unique challenges. We examined whether age‐correction procedures could be used to better identify individuals at very early potential risk from adults who did not have any existing cognitive diagnosis. First, we obtained cross‐sectional age effects for each structural feature using data from a selected portion of the Human Connectome Project Aging (HCP‐A) cohort. After age detrending, we weighted AD structural deterioration with patterns quantified from data of the Alzheimer's Disease Neuroimaging Initiative. Support vector machine was then used to classify individuals with brains that most resembled atrophy in AD across the entire HCP‐A sample. Additionally, we iteratively adjusted the pipeline by removing individuals classified as AD‐like from the HCP‐A cohort to minimize atypical brain structural contributions to the age detrending. The classifier had a mean cross‐validation accuracy of 94.0% for AD recognition. It also could identify mild cognitive impairment with more severe AD‐specific biomarkers and worse cognition. In an independent HCP‐A cohort, 8.8% were identified as AD‐like, and they trended toward worse cognition. An “AD risk” score derived from the machine learning models also significantly correlated with cognition. This work provides a proof of concept for the potential to use structural brain imaging to identify asymptomatic individuals at young ages who show structural brain patterns similar to AD and are potentially at risk for a future clinical disorder.

Cortical hypometabolism reflects local atrophy and tau pathology in symptomatic Alzheimer's disease

Posterior cortical hypometabolism measured with [18F]-Fluorodeoxyglucose (FDG)-PET is a well-known marker of Alzheimer's disease-related neurodegeneration, but its associations with underlying neuropathological processes are unclear. We assessed cross-sectionally the relative contributions of three potential mechanisms causing hypometabolism in the retrosplenial and inferior parietal cortices: local molecular (amyloid and tau) pathology and atrophy, distant factors including contributions from the degenerating medial temporal lobe or molecular pathology in functionally connected regions, and the presence of the apolipoprotein E (APOE) ε4 allele. Two hundred and thirty-two amyloid-positive cognitively impaired patients from two cohorts (University of California, San Francisco, UCSF, and Alzheimer's Disease Neuroimaging Initiative, ADNI) underwent MRI and PET with FDG, amyloid-PET using [11C]-Pittsburgh Compound B, [18F]-Florbetapir, or [18F]-Florbetaben, and [18F]-Flortaucipir tau-PET within one year. Standard uptake value ratios (SUVR) were calculated using tracer-specific reference regions. Regression analyses were run within cohorts to identify variables associated with retrosplenial or inferior parietal FDG SUVR. On average, ADNI patients were older and were less impaired than UCSF patients. Regional patterns of hypometabolism were similar between cohorts, though there were cohort differences in regional gray matter atrophy. Local cortical thickness and tau-PET (but not amyloid-PET) were independently associated with both retrosplenial and inferior parietal FDG SUVR (ΔR2 = .09 to .21) across cohorts in models that also included age and disease severity (local model). Including medial temporal lobe volume improved the retrosplenial FDG model in ADNI (ΔR2 = .04, p = .008) but not UCSF (ΔR2 < .01, p = .52), and did not improve the inferior parietal models (ΔR2s < .01, ps > .37). Interaction analyses revealed that medial temporal volume was more strongly associated with retrosplenial FDG SUVR at earlier disease stages (p = .06 in UCSF, p = .046 in ADNI). Exploratory analyses across the cortex confirmed overall associations between hypometabolism and local tau pathology and thickness and revealed associations between medial temporal degeneration and hypometabolism in retrosplenial, orbitofrontal, and anterior cingulate cortices. Finally, our data did not support hypotheses of a detrimental effect of pathology in connected regions or of an effect of the APOE ε4 allele in impaired participants. Overall, in two independent groups of patients at symptomatic stages of Alzheimer's disease, cortical hypometabolism mainly reflected structural neurodegeneration and tau, but not amyloid, pathology.

Human Brain Lipidomics: Utilities of Chloride Adducts in Flow Injection Analysis

Ceramides have been implicated in a number of disease processes. However, current means of evaluation with flow infusion analysis (FIA) have been limited primarily due to poor sensitivity within our high-resolution mass spectrometry lipidomics analytical platform. To circumvent this deficiency, we investigated the potential of chloride adducts as an alternative method to improve sensitivity with electrospray ionization. Chloride adducts of ceramides and ceramide subfamilies provided 2- to 50-fold increases in sensitivity both with analytical standards and biological samples. Chloride adducts of a number of other lipids with reactive hydroxy groups were also enhanced. For example, monogalactosyl diacylglycerols (MGDGs), extracted from frontal lobe cortical gray and subcortical white matter of cognitively intact subjects, were not detected as ammonium adducts but were readily detected as chloride adducts. Hydroxy lipids demonstrate a high level of specificity in that phosphoglycerols and phosphoinositols do not form chloride adducts. In the case of choline glycerophospholipids, the fatty acid substituents of these lipids could be monitored by MS² of the chloride adducts. Monitoring the chloride adducts of a number of key lipids offers enhanced sensitivity and specificity with FIA. In the case of glycerophosphocholines, the chloride adducts also allow determination of fatty acid substituents. The chloride adducts of lipids possessing electrophilic hydrogens of hydroxyl groups provide significant increases in sensitivity. In the case of glycerophosphocholines, chloride attachment to the quaternary ammonium group generates a dominant anion, which provides the identities of the fatty acid substituents under MS² conditions.

Early atypical signs and insula hypometabolism predict survival in multiple system atrophy

OBJECTIVE

We aim to search for predictors of survival among clinical and brain 18F-FDG positron emission tomography (PET) metabolic features in our cohort of patients with multiple system atrophy (MSA).

METHODS

We included patients with a 'probable' MSA diagnosis for whom a clinical evaluation and a brain PET were performed early in the course of the disease (median 3 years, IQR 2-5). A retrospective analysis was conducted using standardised data collection. Brain PET metabolism was characterised using the Automated Anatomical Labelling Atlas. A Cox model was applied to look for factors influencing survival. Kaplan-Meier method estimated the survival rate. We proposed to develop a predictive 'risk score', categorised into low-risk and high-risk groups, using significant variables entered in multivariate Cox regression analysis.

RESULTS

Eighty-five patients were included. The overall median survival was 8 years (CI 6.64 to 9.36). Poor prognostic factors were orthostatic hypotension (HR=6.04 (CI 1.58 to 23.12), p=0.009), stridor (HR=3.41 (CI 1.31 to 8.87), p=0.012) and glucose PET hypometabolism in the left insula (HR=0.78 (CI 0.66 to 0.92), p=0.004). Good prognostic factors were time to diagnosis (HR=0.68 (CI 0.54 to 0.86), p=0.001) and use of selective serotonin reuptake inhibitor (SSRI) (HR=0.17 (CI 0.06 to 0.46), p<0.001). The risk score revealed a 5-year gap separating the median survival of the two groups obtained (5 years vs 10 years; HR=5.82 (CI 2.94 to 11.49), p<0.001).

CONCLUSION

The clinical prognosis factors we have described support published studies. Here, we also suggest that brain PET is of interest for prognosis assessment and in particular in the search for left insula hypometabolism. Moreover, SSRIs are a potential drug candidate to slow the progression of the disease.

Clinical relevance of brain atrophy subtypes categorization in memory clinics

INTRODUCTION

The clinical relevance of brain atrophy subtypes categorization in non-demented persons without a priori knowledge regarding their amyloid status or clinical presentation is unknown.

METHODS

A total of 2083 outpatients with either subjective cognitive complaint or mild cognitive impairment at study entry were followed during 4 years (MEMENTO cohort). Atrophy subtypes were defined using baseline magnetic resonance imaging (MRI) and previously described algorithms.

RESULTS

Typical/diffuse atrophy was associated with faster cognitive decline and the highest risk of developing dementia and Alzheimer's disease (AD) over time, both in the whole analytic sample and in amyloid-positive participants. Hippocampal-sparing and limbic-predominant atrophy were also associated with incident dementia, with faster cognitive decline in the limbic predominant atrophy group. Lewy body dementia was more frequent in the hippocampal-sparing and minimal/no atrophy groups.

DISCUSSION

Atrophy subtypes categorization predicted different subsequent patterns of cognitive decline and rates of conversion to distinct etiologies of dementia in persons attending memory clinics.

Awareness of cognitive decline trajectories in asymptomatic individuals at risk for AD

Background

Lack of awareness of cognitive decline (ACD) is common in late-stage Alzheimer’s disease (AD). Recent studies showed that ACD can also be reduced in the early stages.

Methods

We described different trends of evolution of ACD over 3 years in a cohort of memory-complainers and their association to amyloid burden and brain metabolism. We studied the impact of ACD at baseline on cognitive scores’ evolution and the association between longitudinal changes in ACD and in cognitive score.

Results

76.8% of subjects constantly had an accurate ACD (reference class). 18.95% showed a steadily heightened ACD and were comparable to those with accurate ACD in terms of demographic characteristics and AD biomarkers. 4.25% constantly showed low ACD, had significantly higher amyloid burden than the reference class, and were mostly men. We found no overall effect of baseline ACD on cognitive scores’ evolution and no association between longitudinal changes in ACD and in cognitive scores.

Conclusions

ACD begins to decrease during the preclinical phase in a group of individuals, who are of great interest and need to be further characterized.

Trial registration

The present study was conducted as part of the INSIGHT-PreAD study. The identification number of INSIGHT-PreAD study (ID-RCB) is 2012-A01731-42.

Elevation of pS262-Tau and Demethylated PP2A in Retina Occurs Earlier than in Hippocampus During Hyperhomocysteinemia

Hyperhomocysteinemia is an independent risk factor of Alzheimer's disease (AD), which is not diagnosed for many years before onset due to lack of peripherally detectable early biomarkers. Visual dysfunction is prevalent in AD patients and correlates with the severity of cognitive defects. Importantly, alterations in eyes can be non-invasively detected. To search for early biomarkers in eyes from high risk factors of AD, we injected homocysteine (Hcy) into the rats via vena caudalis for 3, 7, and 14 days, respectively, and characterized the chronological order of the AD-like pathologies appearing in retina and the hippocampus during the progression of hyperhomocysteinemia, and their correlations with cognitive impairment. We found that administration of Hcy for 14 days, but not 3 or 7 days, induced hyperhomocysteinemia, although a gradually increased blood Hcy level was detected. In retina and/or the hippocampus, significant loss of retinal ganglion cells and stenosis of retinal arteries with the AD-like tau and amyloid-β (Aβ) pathologies and memory deficit were shown only in the 14-day Hcy group. Interestingly, accumulation of Ser262 hyperphosphorylated tau (pS262-tau) but not Aβ with decreased methylation of protein phosphatase-2A catalytic subunit (M-PP2Ac) and increased de-methylated PP2Ac (DM-PP2Ac) was detected in retina of the 3-day Hcy group, in which the retinal pathologies were preceded by those of the hippocampus. These findings suggest that elevated pS262-tau and DM-PP2Ac and reduced M-PP2Ac in retina may serve as surveillance biomarkers for diagnosis of the hyperhomocysteinemia-induced AD in the early stage.

Clinical Usefulness of 18F-FC119S Positron-Emission Tomography as an Auxiliary Diagnostic Method for Dementia: An Open-Label, Single-Dose, Evaluator-Blind Clinical Trial

BACKGROUND AND PURPOSE

The aim of this study was to determine the diagnostic performance and safety of a new ¹⁸F-labeled amyloid tracer, ¹⁸F-FC119S.

METHODS

This study prospectively recruited 105 participants, comprising 53 with Alzheimer's disease (AD) patients, 16 patients with dementia other than AD (non-AD), and 36 healthy controls (HCs). In the first screening visit, the Seoul Neuropsychological Screening Battery cognitive function test was given to the dementia group, while HC subjects completed the Korean version of the Mini Mental State Examination. Individuals underwent ¹⁸F-FC119S PET, ¹⁸F-fluorodeoxyglucose (FDG) PET, and brain MRI. The diagnostic performance of ¹⁸F-FC119S PET for AD was compared to a historical control (comprising previously reported and currently used amyloid-beta PET agents), ¹⁸F-FDG PET, and MRI. The standardized uptake value (SUV) ratio (ratio of the cerebral cortical SUV to the cerebellar SUV) was measured for each PET data set to provide semiquantitative analysis. All adverse effects during the clinical trial periods were monitored.

RESULTS

Visual assessments of the ¹⁸F-FC119S PET data revealed a sensitivity of 92% and a specificity of 84% in detecting AD. ¹⁸F-FC119S PET demonstrated equivalent or better diagnostic performance for AD detection than the historical control, ¹⁸F-FDG PET (sensitivity of 80.0% and specificity of 76.0%), and MRI (sensitivity of 98.0% and specificity of 50.0%). The SUV ratios differed significantly between AD patients and the other groups, at 1.44±0.17 (mean±SD) for AD, 1.24±0.09 for non-AD, and 1.21±0.08 for HC. No clinically significant adverse effects occurred during the trial periods.

CONCLUSIONS

¹⁸F-FC119S PET provides high sensitivity and specificity in detecting AD and therefore may be considered a useful diagnostic tool for AD.

Brain Connectivity Based Prediction of Alzheimer's Disease in Patients With Mild Cognitive Impairment Based on Multi-Modal Images

Structural and metabolic connectivity are advanced features that facilitate the diagnosis of patients with Alzheimer’s disease (AD) and mild cognitive impairment (MCI). Connectivity from a single imaging modality, however, did not show evident discriminative value in predicting MCI-to-AD conversion, possibly because the inter-modal information was not considered when quantifying the relationship between brain regions. Here we introduce a novel approach that extracts connectivity based on both structural and metabolic information to improve AD early diagnosis. Principal component analysis was performed on each imaging modality to extract the key discriminative patterns of each brain region in an independent auxiliary domain composed of AD and normal control (NC) subjects, which were then used to project the two subtypes of MCI to the low-dimensional space. The connectivity between each target brain region and all other regions was quantified via a multi-task regression model using the projected data. The prediction performance was evaluated in 75 stable MCI (sMCI) patients and 51 progressive MCI (pMCI) patients who converted to AD within 3 years. We achieved 79.37% accuracy, with 74.51% sensitivity and 82.67% specificity, in predicting MCI-to-AD progression, superior to other existing algorithms using either structural and metabolic connectivities alone or a combination thereof. Our results demonstrate the effectiveness of multi-modal connectivity, serving as robust biomarker for early AD diagnosis.

Neuroimaging Biomarkers for Alzheimer's Disease

Currently, over five million Americans suffer with Alzheimer's disease (AD). In the absence of a cure, this number could increase to 13.8 million by 2050. A critical goal of biomedical research is to establish indicators of AD during the preclinical stage (i.e. biomarkers) allowing for early diagnosis and intervention. Numerous advances have been made in developing biomarkers for AD using neuroimaging approaches. These approaches offer tremendous versatility in terms of targeting distinct age-related and pathophysiological mechanisms such as structural decline (e.g. volumetry, cortical thinning), functional decline (e.g. fMRI activity, network correlations), connectivity decline (e.g. diffusion anisotropy), and pathological aggregates (e.g. amyloid and tau PET). In this review, we survey the state of the literature on neuroimaging approaches to developing novel biomarkers for the amnestic form of AD, with an emphasis on combining approaches into multimodal biomarkers. We also discuss emerging methods including imaging epigenetics, neuroinflammation, and synaptic integrity using PET tracers. Finally, we review the complementary information that neuroimaging biomarkers provide, which highlights the potential utility of composite biomarkers as suitable outcome measures for proof-of-concept clinical trials with experimental therapeutics.