18F-FDG PET hypometabolism patterns reflect clinical heterogeneity in sporadic forms of early-onset Alzheimer's disease

Cerebral glucose metabolism in Alzheimer's disease

18F-fluoro-deoxy-glucose positron emission tomography (FDG-PET) is a useful paraclinical exam for the diagnosis of Alzheimer's disease (AD). In this narrative review, we report seminal studies in clinically probable AD that have shown the importance of posterior brain metabolic decrease and the paradoxical variability of the hippocampal metabolism. The FDG-PET pattern was a sensitive indicator of AD in pathologically confirmed cases and it was used for differential diagnosis of dementia conditions. In prodromal AD, the AD FDG-PET pattern was observed in converters and predicted conversion. Automated data analysis techniques provided variable accuracy according to the reported indices and machine learning methods showed variable reliability of results. FDG-PET could confirm AD clinical heterogeneity and image data driven analyses identified hypometabolic subtypes with variable involvement of the hippocampus, reminiscent if the paradoxical FDG uptake. In studies dedicated to clinical and metabolic correlations, episodic memory was related to metabolism in the default mode network (and Papez's circuit) in prodromal and mild AD stages, and specific cognitive processes were associated to precisely distributed brain metabolism. Cerebral metabolic correlates of anosognosia could also be related to current neuropsychological models. AD FDG-PET pattern was reported in preclinical AD stages and related to cognition or to conversion to mild cognitive impairment (MCI). Using other biomarkers, the AD FDG-PET pattern was confirmed in AD participants with positive PET-amyloid. Intriguing observations reported increased metabolism related to brain amyloid and/or tau deposition. Preserved glucose metabolism sometimes appear as a compensation, but it was frequently detrimental and the nature of such a preservation of glucose metabolism remains an open question. Limbic metabolic involvement was frequently related to non-AD biomarkers profile and clinical stability, and it was reported in non-AD pathologies, such as the limbic predominant age-related encephalopathy (LATE). FDG-PET abnormalities observed in the absence of classical AD proteinopathies can be useful to search for pathological mechanisms and differential diagnosis of AD.

Molecular neuroimaging in dominantly inherited versus sporadic early-onset Alzheimer's disease

Approximately 5% of Alzheimer's disease patients develop symptoms before age 65 (early-onset Alzheimer's disease), with either sporadic (sporadic early-onset Alzheimer's disease) or dominantly inherited (dominantly inherited Alzheimer's disease) presentations. Both sporadic early-onset Alzheimer's disease and dominantly inherited Alzheimer's disease are characterized by brain amyloid-β accumulation, tau tangles, hypometabolism and neurodegeneration, but differences in topography and magnitude of these pathological changes are not fully elucidated. In this study, we directly compared patterns of amyloid-β plaque deposition and glucose hypometabolism in sporadic early-onset Alzheimer's disease and dominantly inherited Alzheimer's disease individuals. Our analysis included 134 symptomatic sporadic early-onset Alzheimer's disease amyloid-Positron Emission Tomography (PET)-positive cases from the University of California, San Francisco, Alzheimer's Disease Research Center (mean ± SD age 59.7 ± 5.6 years), 89 symptomatic dominantly inherited Alzheimer's disease cases (age 45.8 ± 9.3 years) and 102 cognitively unimpaired non-mutation carriers from the Dominantly Inherited Alzheimer Network study (age 44.9 ± 9.2). Each group underwent clinical and cognitive examinations, 11C-labelled Pittsburgh Compound B-PET and structural MRI. 18F-Fluorodeoxyglucose-PET was also available for most participants. Positron Emission Tomography scans from both studies were uniformly processed to obtain a standardized uptake value ratio (PIB50-70 cerebellar grey reference and FDG30-60 pons reference) images. Statistical analyses included pairwise global and voxelwise group comparisons and group-independent component analyses. Analyses were performed also adjusting for covariates including age, sex, Mini-Mental State Examination, apolipoprotein ε4 status and average composite cortical of standardized uptake value ratio. Compared with dominantly inherited Alzheimer's disease, sporadic early-onset Alzheimer's disease participants were older at age of onset (mean ± SD, 54.8 ± 8.2 versus 41.9 ± 8.2, Cohen's d = 1.91), with more years of education (16.4 ± 2.8 versus 13.5 ± 3.2, d = 1) and more likely to be apolipoprotein ε4 carriers (54.6% ε4 versus 28.1%, Cramer's V = 0.26), but similar Mini-Mental State Examination (20.6 ± 6.1 versus 21.2 ± 7.4, d = 0.08). Sporadic early-onset Alzheimer's disease had higher global cortical Pittsburgh Compound B-PET binding (mean ± SD standardized uptake value ratio, 1.92 ± 0.29 versus 1.58 ± 0.44, d = 0.96) and greater global cortical 18F-fluorodeoxyglucose-PET hypometabolism (mean ± SD standardized uptake value ratio, 1.32 ± 0.1 versus 1.39 ± 0.19, d = 0.48) compared with dominantly inherited Alzheimer's disease. Fully adjusted comparisons demonstrated relatively higher Pittsburgh Compound B-PET standardized uptake value ratio in the medial occipital, thalami, basal ganglia and medial/dorsal frontal regions in dominantly inherited Alzheimer's disease versus sporadic early-onset Alzheimer's disease. Sporadic early-onset Alzheimer's disease showed relatively greater 18F-fluorodeoxyglucose-PET hypometabolism in Alzheimer's disease signature temporoparietal regions and caudate nuclei, whereas dominantly inherited Alzheimer's disease showed relatively greater hypometabolism in frontal white matter and pericentral regions. Independent component analyses largely replicated these findings by highlighting common and unique Pittsburgh Compound B-PET and 18F-fluorodeoxyglucose-PET binding patterns. In summary, our findings suggest both common and distinct patterns of amyloid and glucose hypometabolism in sporadic and dominantly inherited early-onset Alzheimer's disease.

Distinct subtypes of spatial brain metabolism patterns in Alzheimer's disease identified by deep learning-based FDG PET clusters

PURPOSE

Alzheimer's disease (AD) is a heterogeneous disease that presents a broad spectrum of clinicopathologic profiles. To date, objective subtyping of AD independent of disease progression using brain imaging has been required. Our study aimed to extract representations of unique brain metabolism patterns different from disease progression to identify objective subtypes of AD.

METHODS

A total of 3620 FDG brain PET images with AD, mild cognitive impairment (MCI), and cognitively normal (CN) were obtained from the ADNI database from 1607 participants at enrollment and follow-up visits. A conditional variational autoencoder model was trained on FDG brain PET images of AD patients with the corresponding condition of AD severity score. The k-means algorithm was applied to generate clusters from the encoded representations. The trained deep learning-based cluster model was also transferred to FDG PET of MCI patients and predicted the prognosis of subtypes for conversion from MCI to AD. Spatial metabolism patterns, clinical and biological characteristics, and conversion rate from MCI to AD were compared across the subtypes.

RESULTS

Four distinct subtypes of spatial metabolism patterns in AD with different brain pathologies and clinical profiles were identified: (i) angular, (ii) occipital, (iii) orbitofrontal, and (iv) minimal hypometabolic patterns. The deep learning model was also successfully transferred for subtyping MCI, and significant differences in frequency (P < 0.001) and risk of conversion (log-rank P < 0.0001) from MCI to AD were observed across the subtypes, highest in S2 (35.7%) followed by S1 (23.4%).

CONCLUSION

We identified distinct subtypes of AD with different clinicopathologic features. The deep learning-based approach to distinguish AD subtypes on FDG PET could have implications for predicting individual outcomes and provide a clue to understanding the heterogeneous pathophysiology of AD.

Apraxia testing to distinguish early Alzheimer's disease from psychiatric causes of cognitive impairment

Objective: Mood- and stress-related disorders commonly cause attentional and memory impairments in middle-aged individuals. In memory testing, these impairments can be mistakenly interpreted as symptoms of dementia; thus, more reliable diagnostic approaches are needed. The present work defines the discriminant accuracy of the Dementia Apraxia Test (DATE) between psychiatric conditions and early-onset Alzheimer's disease (AD) on its own and in combination with memory tests. Method: The consecutive sample included 50-70-year-old patients referred to dementia investigations for recent cognitive and/or affective symptoms. The DATE was administered and scored as a blinded measurement, and a receiver operating curve analysis was used to define the optimal diagnostic cut-off score. Results: A total of 24 patients were diagnosed with probable AD (mean age 61 ± 4) and 23 with a psychiatric condition (mean age 57 ± 4). The AD patients showed remarkable limb apraxia, but the psychiatric patients mainly performed at a healthy level on the DATE. The test showed a total discriminant accuracy of 87% for a total sum cut-off of 47 (sensitivity 79% and specificity 96%). The limb subscale alone reached an accuracy of 91% for a cut-off of 20 (sensitivity 83% and specificity 100%). All memory tests were diagnostically less accurate, while the combination of the limb praxis subscale and a verbal episodic memory test suggested a correct diagnosis in all but one patient. Conclusions: Apraxia testing may improve the accuracy of differentiation between AD and psychiatric aetiologies. Its potential in severe and chronic psychiatric conditions should be examined in the future.

Harnessing acoustic speech parameters to decipher amyloid status in individuals with mild cognitive impairment

Alzheimer's disease (AD) is a neurodegenerative condition characterized by a gradual decline in cognitive functions. Currently, there are no effective treatments for AD, underscoring the importance of identifying individuals in the preclinical stages of mild cognitive impairment (MCI) to enable early interventions. Among the neuropathological events associated with the onset of the disease is the accumulation of amyloid protein in the brain, which correlates with decreased levels of Aβ42 peptide in the cerebrospinal fluid (CSF). Consequently, the development of non-invasive, low-cost, and easy-to-administer proxies for detecting Aβ42 positivity in CSF becomes particularly valuable. A promising approach to achieve this is spontaneous speech analysis, which combined with machine learning (ML) techniques, has proven highly useful in AD. In this study, we examined the relationship between amyloid status in CSF and acoustic features derived from the description of the Cookie Theft picture in MCI patients from a memory clinic. The cohort consisted of fifty-two patients with MCI (mean age 73 years, 65% female, and 57% positive amyloid status). Eighty-eight acoustic parameters were extracted from voice recordings using the extended Geneva Minimalistic Acoustic Parameter Set (eGeMAPS), and several ML models were used to classify the amyloid status. Furthermore, interpretability techniques were employed to examine the influence of input variables on the determination of amyloid-positive status. The best model, based on acoustic variables, achieved an accuracy of 75% with an area under the curve (AUC) of 0.79 in the prediction of amyloid status evaluated by bootstrapping and Leave-One-Out Cross Validation (LOOCV), outperforming conventional neuropsychological tests (AUC = 0.66). Our results showed that the automated analysis of voice recordings derived from spontaneous speech tests offers valuable insights into AD biomarkers during the preclinical stages. These findings introduce novel possibilities for the use of digital biomarkers to identify subjects at high risk of developing AD.

Characterizing Early-Onset Alzheimer Disease Using Multiprobe PET/MRI: An AT(N) Framework-Based Study

PURPOSE

Early-onset Alzheimer disease (EOAD) is rare, highly heterogeneous, and associated with poor prognosis. This AT(N) Framework-based study aimed to compare multiprobe PET/MRI findings between EOAD and late-onset Alzheimer disease (LOAD) patients and explore potential imaging biomarkers for characterizing EOAD.

METHODS

Patients with AD who underwent PET/MRI in our PET center were retrospectively reviewed and grouped according to the age at disease onset: EOAD, younger than 60 years; and LOAD, 60 years or older. Clinical characteristics were recorded. All study patients had positive β-amyloid PET imaging; some patients also underwent 18 F-FDG and 18 F-florzolotau PET. Imaging of the EOAD and LOAD groups was compared using region-of-interest and voxel-based analysis. Correlation of onset age and regional SUV ratios were also evaluated.

RESULTS

One hundred thirty-three patients were analyzed (75 EOAD and 58 LOAD patients). Sex ( P = 0.515) and education ( P = 0.412) did not significantly differ between groups. Mini-Mental State Examination score was significantly lower in the EOAD group (14.32 ± 6.74 vs 18.67 ± 7.20, P = 0.004). β-Amyloid deposition did not significantly differ between groups. Glucose metabolism in the frontal, parietal, precuneus, temporal, occipital lobe, and supramarginal and angular gyri was significantly lower in the EOAD group (n = 49) than in the LOAD group (n = 44). In voxel-based morphometry analysis, right posterior cingulate/precuneus atrophy was more obvious in the EOAD ( P < 0.001), although no voxel survived family-wise error correction. Tau deposition in the precuneus, parietal lobe, and angular, supramarginal, and right middle frontal gyri was significantly higher in the EOAD group (n = 18) than in the LOAD group (n = 13).

CONCLUSIONS

Multiprobe PET/MRI showed that tau burden and neuronal damage are more severe in EOAD than in LOAD. Multiprobe PET/MRI may be useful to assess the pathologic characteristics of EOAD.

Anatomically Standardized Detection of MRI Atrophy Patterns in Early-Stage Alzheimer's Disease

MRI studies have consistently identified atrophy patterns in Alzheimer’s disease (AD) through a whole-brain voxel-based analysis, but efforts to investigate morphometric profiles using anatomically standardized and automated whole-brain ROI analyses, performed at the individual subject space, are still lacking. In this study we aimed (i) to utilize atlas-derived measurements of cortical thickness and subcortical volumes, including of the hippocampal subfields, to identify atrophy patterns in early-stage AD, and (ii) to compare cognitive profiles at baseline and during a one-year follow-up of those previously identified morphometric AD subtypes to predict disease progression. Through a prospectively recruited multi-center study, conducted at four Austrian sites, 120 patients were included with probable AD, a disease onset beyond 60 years and a clinical dementia rating of ≤1. Morphometric measures of T1-weighted images were obtained using FreeSurfer. A principal component and subsequent cluster analysis identified four morphometric subtypes, including (i) hippocampal predominant (30.8%), (ii) hippocampal-temporo-parietal (29.2%), (iii) parieto-temporal (hippocampal sparing, 20.8%) and (iv) hippocampal-temporal (19.2%) atrophy patterns that were associated with phenotypes differing predominately in the presentation and progression of verbal memory and visuospatial impairments. These morphologically distinct subtypes are based on standardized brain regions, which are anatomically defined and freely accessible so as to validate its diagnostic accuracy and enhance the prediction of disease progression.

A multimodal, longitudinal study of cognitive heterogeneity in early-onset Alzheimer's disease

BACKGROUND AND PURPOSE

Alzheimer's disease (AD) is a heterogeneous pathology. Young patients with AD are particularly likely to have an atypical presentation. The objectives of the present cluster analysis were to determine whether patients with early-onset AD (EOAD) had several distinct cognitive profiles and to compare the resulting clusters with regard to clinical, neuroimaging, and laboratory characteristics.

METHODS

We collected cognitive, behavioural, functional, neuroimaging, and laboratory data on 72 patients meeting the criteria for probable mild EOAD. The patients were first classified into clinical phenotype groups by a multidisciplinary board of clinicians. The patients' cognitive and functional decline was monitored for 24 months. A k-means clustering analysis was then used to determine clusters on the basis of the patients' neuropsychological test results.

RESULTS

Two distinct clusters were identified: the patients in the first cluster (C1, n = 38) had a predominant memory impairment, whereas patients in the second (C2, n = 34) did not. Dyslipidaemia and the presence of ɛ4 apolipoprotein E allele were more frequent in C1, whereas the cognitive and functional decline was faster in the patients in C2. Moreover, posterior brain abnormalities were more severe in patients in C2 than in patients in C1.

CONCLUSIONS

By applying a k-means clustering analysis, we identified two clusters of patients in an EOAD cohort. The clusters differed with regard to certain clinical, imaging, and laboratory characteristics. This clustering procedure might be of value for managing patients with EOAD in general and for identifying those at risk of more rapid decline in particular.

Posterior cortical atrophy phenotypic heterogeneity revealed by decoding 18F-FDG-PET

Posterior cortical atrophy is a neurodegenerative syndrome with a heterogeneous clinical presentation due to variable involvement of the left, right, dorsal and ventral parts of the visual system, as well as inconsistent involvement of other cognitive domains and systems. 18F-fluorodeoxyglucose (FDG)-PET is a sensitive marker for regional brain damage or dysfunction, capable of capturing the pattern of neurodegeneration at the single-participant level. We aimed to leverage these inter-individual differences on FDG-PET imaging to better understand the associations of heterogeneity of posterior cortical atrophy. We identified 91 posterior cortical atrophy participants with FDG-PET data and abstracted demographic, neurologic, neuropsychological and Alzheimer's disease biomarker data. The mean age at reported symptom onset was 59.3 (range: 45-72 years old), with an average disease duration of 4.2 years prior to FDG-PET scan, and a mean education of 15.0 years. Females were more common than males at 1.6:1. After standard preprocessing steps, the FDG-PET scans for the cohort were entered into an unsupervised machine learning algorithm which first creates a high-dimensional space of inter-individual covariance before performing an eigen-decomposition to arrive at a low-dimensional representation. Participant values ('eigenbrains' or latent vectors which represent principle axes of inter-individual variation) were then compared to the clinical and biomarker data. Eight eigenbrains explained over 50% of the inter-individual differences in FDG-PET uptake with left (eigenbrain 1) and right (eigenbrain 2) hemispheric lateralization representing 24% of the variance. Furthermore, eigenbrain-loads mapped onto clinical and neuropsychological data (i.e. aphasia, apraxia and global cognition were associated with the left hemispheric eigenbrain 1 and environmental agnosia and apperceptive prosopagnosia were associated with the right hemispheric eigenbrain 2), suggesting that they captured important axes of normal and abnormal brain function. We used NeuroSynth to characterize the eigenbrains through topic-based decoding, which supported the idea that the eigenbrains map onto a diverse set of cognitive functions. These eigenbrains captured important biological and pathophysiologic data (i.e. limbic predominant eigenbrain 4 patterns being associated with older age of onset compared to frontoparietal eigenbrain 7 patterns being associated with younger age of onset), suggesting that approaches that focus on inter-individual differences may be important to better understand the variability observed within a neurodegenerative syndrome like posterior cortical atrophy.

Differential trajectories of hypometabolism across cognitively-defined Alzheimer's disease subgroups

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Cognitive-subgroups can be identified among individuals with AD dementia.

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Subgroup-specific patterns and longitudinal trajectories of hypometabolism observed.

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Regional hypometabolism matched respective cognitive profiles of subgroups.

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Cognitive-classification yields biologically distinct subgroups.

Disentangling biologically distinct subgroups of Alzheimer’s disease (AD) may facilitate a deeper understanding of the neurobiology underlying clinical heterogeneity. We employed longitudinal [¹⁸F]FDG-PET standardized uptake value ratios (SUVRs) to map hypometabolism across cognitively-defined AD subgroups. Participants were 384 amyloid-positive individuals with an AD dementia diagnosis from ADNI who had a total of 1028 FDG-scans (mean time between first and last scan: 1.6 ± 1.8 years). These participants were categorized into subgroups on the basis of substantial impairment at time of dementia diagnosis in a specific cognitive domain relative to the average across domains. This approach resulted in groups of AD-Memory (n = 135), AD-Executive (n = 8), AD-Language (n = 22), AD-Visuospatial (n = 44), AD-Multiple Domains (n = 15) and AD-No Domains (for whom no domain showed substantial relative impairment; n = 160). Voxelwise contrasts against controls revealed that all AD-subgroups showed progressive hypometabolism compared to controls across temporoparietal regions at time of AD diagnosis. Voxelwise and regions-of-interest (ROI)-based linear mixed model analyses revealed there were also subgroup-specific hypometabolism patterns and trajectories. The AD-Memory group had more pronounced hypometabolism compared to all other groups in the medial temporal lobe and posterior cingulate, and faster decline in metabolism in the medial temporal lobe compared to AD-Visuospatial. The AD-Language group had pronounced lateral temporal hypometabolism compared to all other groups, and the pattern of metabolism was also more asymmetrical (left < right) than all other groups. The AD-Visuospatial group had faster decline in metabolism in parietal regions compared to all other groups, as well as faster decline in the precuneus compared to AD-Memory and AD-No Domains. Taken together, in addition to a common pattern, cognitively-defined subgroups of people with AD dementia show subgroup-specific hypometabolism patterns, as well as differences in trajectories of metabolism over time. These findings provide support to the notion that cognitively-defined subgroups are biologically distinct.

Imaging Techniques in Alzheimer's Disease: A Review of Applications in Early Diagnosis and Longitudinal Monitoring

BACKGROUND

Alzheimer's disease (AD) is a progressive neurodegenerative disorder affecting many individuals worldwide with no effective treatment to date. AD is characterized by the formation of senile plaques and neurofibrillary tangles, followed by neurodegeneration, which leads to cognitive decline and eventually death.

INTRODUCTION

In AD, pathological changes occur many years before disease onset. Since disease-modifying therapies may be the most beneficial in the early stages of AD, biomarkers for the early diagnosis and longitudinal monitoring of disease progression are essential. Multiple imaging techniques with associated biomarkers are used to identify and monitor AD.

AIM

In this review, we discuss the contemporary early diagnosis and longitudinal monitoring of AD with imaging techniques regarding their diagnostic utility, benefits and limitations. Additionally, novel techniques, applications and biomarkers for AD research are assessed.

FINDINGS

Reduced hippocampal volume is a biomarker for neurodegeneration, but atrophy is not an AD-specific measure. Hypometabolism in temporoparietal regions is seen as a biomarker for AD. However, glucose uptake reflects astrocyte function rather than neuronal function. Amyloid-β (Aβ) is the earliest hallmark of AD and can be measured with positron emission tomography (PET), but Aβ accumulation stagnates as disease progresses. Therefore, Aβ may not be a suitable biomarker for monitoring disease progression. The measurement of tau accumulation with PET radiotracers exhibited promising results in both early diagnosis and longitudinal monitoring, but large-scale validation of these radiotracers is required. The implementation of new processing techniques, applications of other imaging techniques and novel biomarkers can contribute to understanding AD and finding a cure.

CONCLUSIONS

Several biomarkers are proposed for the early diagnosis and longitudinal monitoring of AD with imaging techniques, but all these biomarkers have their limitations regarding specificity, reliability and sensitivity. Future perspectives. Future research should focus on expanding the employment of imaging techniques and identifying novel biomarkers that reflect AD pathology in the earliest stages.

Neuropsychological Similarities and Differences Between Amnestic Alzheimer's Disease and its Non-Amnestic Variants

BACKGROUND

The neuropsychological recognition of early-onset Alzheimer's disease (AD) can be difficult because of non-amnestic variants such as logopenic variant primary progressive aphasia (lvPPA) and posterior cortical atrophy (PCA).

OBJECTIVE

This study evaluated the similarities and differences between typical amnestic AD (tAD) and lvPPA and PCA on a screening neuropsychological battery.

METHODS

We enrolled 51 patients meeting NIA-AA criteria for biomarker-supported AD (amnestic or non-amnestic) and having an age of onset of <65 years of age. Based on additional recommended clinical criteria for lvPPA and PCA, the early-onset AD patients were divided into three groups (28 tAD, 9 lvPPA, 14 PCA) of comparable age and dementia severity. We then analyzed their profiles on a focused, screening neuropsychological battery for early-onset AD.

RESULTS

In addition to greater variance on the Mini-Mental State Examination, the lvPPA and PCA variants had episodic memory impairment that did not significantly differ from the memory impairment in the tAD patients. Despite differences on language and visuospatial tasks, they did not significantly distinguish the lvPPA and PCA from tAD. The lvPPA group, however, was distinguishable by worse performance on measures reflecting working memory (digit span forward, memory registration).

CONCLUSIONS

On neuropsychological screening, all clinical early-onset AD subtypes may have memory impairments. Screening batteries for early-onset AD should also include measures of working memory, which is disproportionately decreased in lvPPA.

The combined effects of microglia activation and brain glucose hypometabolism in early-onset Alzheimer's disease

Background

Early-onset Alzheimer’s disease (EOAD) is characterized by young age of onset (< 65 years), severe neurodegeneration, and rapid disease progression, thus differing significantly from typical late-onset Alzheimer’s disease. Growing evidence suggests a primary role of neuroinflammation in AD pathogenesis. However, the role of microglia activation in EOAD remains a poorly explored field. Investigating microglial activation and its influence on the development of synaptic dysfunction and neuronal loss in EOAD may contribute to the understanding of its pathophysiology and to subject selection in clinical trials. In our study, we aimed to assess the amount of neuroinflammation and neurodegeneration and their relationship in EOAD patients, through positron emission tomography (PET) measures of microglia activation and brain metabolic changes.

Methods

We prospectively enrolled 12 EOAD patients, classified according to standard criteria, who underwent standard neurological and neuropsychological evaluation, CSF analysis, brain MRI, and both [¹⁸F]-FDG PET and [¹¹C]-(R)-PK11195 PET. Healthy controls databases were used for statistical comparison. [¹⁸F]-FDG PET brain metabolism in single subjects and as a group was assessed by an optimized SPM voxel-wise single-subject method. [¹¹C]-PK11195 PET binding potentials were obtained using reference regions selected with an optimized clustering procedure followed by a parametric analysis. We performed a topographic interaction analysis and correlation analysis in AD-signature metabolic dysfunctional regions and regions of microglia activation. A network connectivity analysis was performed using the interaction regions of hypometabolism and [¹¹C]-PK11195 PET BP increases.

Results

EOAD patients showed a significant and extended microglia activation, as [¹¹C]-PK11195 PET binding potential increases, and hypometabolism in typical AD-signature brain regions, i.e., temporo-parietal cortex, with additional variable frontal and occipital hypometabolism in the EOAD variants. There was a spatial concordance in the interaction areas and significant correlations between the two biological changes. The network analysis showed a disruption of frontal connectivity induced by the metabolic/microglia effects.

Conclusion

The severe microglia activation characterizing EOAD and contributing to neurodegeneration may be a marker of rapid disease progression. The coupling between brain glucose hypometabolism and local immune response in AD-signature regions supports their biological interaction.

Early-onset Alzheimer Disease and Its Variants

PURPOSE OF REVIEW

Early-onset Alzheimer disease (AD) is defined as having an age of onset younger than 65 years. While early-onset AD is often overshadowed by the more common late-onset AD, recognition of the differences between early- and late-onset AD is important for clinicians.

RECENT FINDINGS

Early-onset AD comprises about 5% to 6% of cases of AD and includes a substantial percentage of phenotypic variants that differ from the usual amnestic presentation of typical AD. Characteristics of early-onset AD in comparison to late-onset AD include a larger genetic predisposition (familial mutations and summed polygenic risk), more aggressive course, more frequent delay in diagnosis, higher prevalence of traumatic brain injury, less memory impairment and greater involvement of other cognitive domains on presentation, and greater psychosocial difficulties. Neuroimaging features of early-onset AD in comparison to late-onset AD include greater frequency of hippocampal sparing and posterior neocortical atrophy, increased tau burden, and greater connectomic changes affecting frontoparietal networks rather than the default mode network.

SUMMARY

Early-onset AD differs substantially from late-onset AD, with different phenotypic presentations, greater genetic predisposition, and differences in neuropathologic burden and topography. Early-onset AD more often presents with nonamnestic phenotypic variants that spare the hippocampi and with greater tau burden in posterior neocortices. The early-onset AD phenotypic variants involve different neural networks than typical AD. The management of early-onset AD is similar to that of late-onset AD but with special emphasis on targeting specific cognitive areas and more age-appropriate psychosocial support and education.

Three-year changes of cortical 18F-FDG in amnestic vs. non-amnestic sporadic early-onset Alzheimer's disease

PURPOSE

To examine and compare longitudinal changes of cortical glucose metabolism in amnestic and non-amnestic sporadic forms of early-onset Alzheimer's disease and assess potential associations with neuropsychological performance over a 3-year period time.

METHODS

Eighty-two participants meeting criteria for early-onset (< 65 years) sporadic form of probable Alzheimer's disease and presenting with a variety of clinical phenotypes (47 amnestic and 35 non-amnestic forms) were included at baseline and followed up for 1.44 ± 1.23 years. All of the participants underwent a work-up at baseline and every year during the follow-up period, which includes clinical examination, neuropsychological testing, genotyping, cerebrospinal fluid biomarker assays, and structural MRI and ¹⁸F-FDG PET. Vertex-wise partial volume-corrected glucose metabolic maps across the entire cortical surface were generated and longitudinally assessed together with the neuropsychological scores using linear mixed-effects modeling as a function of amnestic and non-amnestic sporadic forms of early-onset Alzheimer's disease.

RESULTS

Similar evolution patterns of glucose metabolic decline between amnestic and non-amnestic forms were observed in widespread neocortical cortices. However, only non-amnestic forms appeared to have a greater reduction of glucose metabolism in lateral orbitofrontal and bilateral medial temporal cortices associated with more severe declines of neuropsychological performance compared with amnestic forms. Furthermore, results suggest that glucose metabolic decline in amnestic forms would progress along an anterior-to-posterior axis, whereas glucose metabolic decline in non-amnestic forms would progress along a posterior-to-anterior axis.

CONCLUSIONS

We found differences in spatial distribution and temporal trajectory of glucose metabolic decline between amnestic and non-amnestic early-onset Alzheimer's disease groups, suggesting that one might want to consider treating the two forms of the disease as two separate entities.

Using EQ·PET to reduce reconstruction-dependent variations in [18F]FDG-PET brain imaging

This study aims at assessing whether EANM harmonisation strategy combined with EQ·PET methodology could be successfully applied to harmonize brain 2-deoxy-2[¹⁸F]fluoro-D-glucose ([¹⁸F]FDG) positron emission tomography (PET) images. The NEMA NU 2 body phantom was prepared according to the EANM guidelines with an [¹⁸F]FDG solution. Raw PET phantom data were reconstructed with three different reconstruction protocols frequently used in clinical PET brain imaging: ([Formula: see text]) Ordered subset expectation maximization (OSEM) 3D with time of flight (TOF), 2 iterations and 21 subsets; ([Formula: see text]) OSEM 3D with TOF, 6 iterations and 21 subsets; and ([Formula: see text]) OSEM 3D with TOF, point spread function (PSF), and 8 iterations and 21 subsets. EQ·PET filters were computed as the Gaussian smoothing that best independently aligned the recovery coefficients (RCs) of reconstructions [Formula: see text] and [Formula: see text] with the RCs of the reference reconstruction, [Formula: see text]. The performance of the EQ·PET filter to reduce variations in quantification due to differences in reconstruction was investigated using clinical PET brain images of 35 early-onset Alzheimer's disease (EOAD) patients. Qualitative assessments and multiple quantitative metrics on the cortical surface at different scale levels with or without partial volume effect correction were evaluated on the [¹⁸F]FDG brain data before and after application of the EQ·PET filter. The EQ·PET methodology succeeded in finding the optimal smoothing that minimised root-mean-square error (RMSE) calculated using human brain [¹⁸F]FDG-PET datasets of EOAD patients, providing harmonized comparisons in the neurological context. Performance was superior for TOF than for TOF  +  PSF reconstructions. Results showed the capability of the EQ·PET methodology to minimize reconstruction-induced variabilities between brain [¹⁸F]FDG-PET images. However, moderate variabilities remained after harmonizing PSF reconstructions with standard non-PSF OSEM reconstructions, suggesting that precautions should be taken when using PSF modelling.

Prognostic value of Alzheimer's biomarkers in mild cognitive impairment: the effect of age at onset

OBJECTIVE

The aim of this study is to assess the impact of age at onset on the prognostic value of Alzheimer's biomarkers in a large sample of patients with mild cognitive impairment (MCI).

METHODS

We measured Aβ42, t-tau, hippocampal volume on magnetic resonance imaging (MRI) and cortical metabolism on fluorodeoxyglucose-positron emission tomography (FDG-PET) in 188 MCI patients followed for at least 1 year. We categorised patients into earlier and later onset (EO/LO). Receiver operating characteristic curves and corresponding areas under the curve (AUCs) were performed to assess and compar the biomarker prognostic performances in EO and LO groups. Linear Model was adopted for estimating the time-to-progression in relation with earlier/later onset MCI groups and biomarkers.

RESULTS

In earlier onset patients, all the assessed biomarkers were able to predict cognitive decline (p < 0.05), with FDG-PET showing the best performance. In later onset patients, all biomarkers but t-tau predicted cognitive decline (p < 0.05). Moreover, FDG-PET alone in earlier onset patients showed a higher prognostic value than the one resulting from the combination of all the biomarkers in later onset patients (earlier onset AUC 0.935 vs later onset AUC 0.753, p < 0.001). Finally, FDG-PET showed a different prognostic value between earlier and later onset patients (p = 0.040) in time-to-progression allowing an estimate of the time free from disease.

DISCUSSION

FDG-PET may represent the most universal tool for the establishment of a prognosis in MCI patients and may be used for obtaining an onset-related estimate of the time free from disease.

Characterization of a temporoparietal junction subtype of Alzheimer's disease

Alzheimer's disease (AD) subtypes have been described according to genetics, neuropsychology, neuropathology, and neuroimaging. Thirty-one patients with clinically probable AD were selected based on perisylvian metabolic decrease on FDG-PET. They were compared to 25 patients with a typical pattern of decreased posterior metabolism. Tree-based machine learning was used on those 56 images to create a classifier that was subsequently applied to 207 Alzheimer's Disease Neuroimaging Initiative (ADNI) patients with AD. Machine learning was also used to discriminate between the two ADNI groups based on neuropsychological scores. Compared to AD patients with a typical precuneus metabolic decrease, the new subtype showed stronger hypometabolism in the temporoparietal junction. The classifier was able to distinguish the two groups in the ADNI population. Both groups could only be distinguished cognitively by Trail Making Test-A scores. This study further confirms that there is more than a typical metabolic pattern in probable AD with amnestic presentation.

Differences in cortical perfusion detected by arterial spin labeling in nonamnestic and amnestic subtypes of early-onset Alzheimer's disease

OBJECTIVES

Early-onset Alzheimer's disease (EOAD) begins before the age of 65 and is characterized by a faster clinical course and the frequency of nonamnestic symptoms compared to late onset Alzheimer disease (LOAD). However, the pathophysiological process of EOAD remains unclear. We expected that ASL may show widespread cortical hypoperfusion in EOAD compared to LOAD and in nonamnestic EOAD compared to amnestic EOAD.

METHODS

In this study, 26 EOAD patients (16 amnestic and 10 nonamnestic patients), 29 LOAD patients and 12 healthy controls underwent pseudo-continuous ASL and 3D FFE T1 sequences. Statistical comparisons between EOAD, LOAD and control groups were made after surface-based analysis of CBF maps in regressing out the cortical thickness.

RESULTS

ASL showed a more severe hypoperfusion in nonamnestic EOAD patients compared to amnestic EOAD ones, with mean CBF values (± std) of 26.9 (± 3.8) and 46.6 (± 24.1) mL/100 g/min respectively (P = 0.014), located in the bilateral temporo-parietal neocortex, the precuneus, the posterior cingulate cortices (PCC) and frontal lobes. Comparison between EOAD and LOAD patients showed a trend to hypoperfusion in the left parietal lobe, PCC and precuneus in EOAD (P < 0.001 uncorrected).

CONCLUSIONS

Different patterns of hypoperfusion between nonamnestic and amnestic EOAD subtypes were identified, with a more severe and extensive hypoperfusion in nonamnestic patients. A trend towards more severe hypoperfusion was detected in EOAD compared to LOAD. Further studies are needed to validate ASL as a potential tool for the distinction of EOAD subtypes and the prediction of the time course of the disease.

An Automated Pipeline for the Analysis of PET Data on the Cortical Surface

We present a fully automatic pipeline for the analysis of PET data on the cortical surface. Our pipeline combines tools from FreeSurfer and PETPVC, and consists of (i) co-registration of PET and T1-w MRI (T1) images, (ii) intensity normalization, (iii) partial volume correction, (iv) robust projection of the PET signal onto the subject's cortical surface, (v) spatial normalization to a template, and (vi) atlas statistics. We evaluated the performance of the proposed workflow by performing group comparisons and showed that the approach was able to identify the areas of hypometabolism characteristic of different dementia syndromes: Alzheimer's disease (AD) and both the semantic and logopenic variants of primary progressive aphasia. We also showed that these results were comparable to those obtained with a standard volume-based approach. We then performed individual classifications and showed that vertices can be used as features to differentiate cognitively normal and AD subjects. This pipeline is integrated into Clinica, an open-source software platform for neuroscience studies available at www.clinica.run.

Possible Clues for Brain Energy Translation via Endolysosomal Trafficking of APP-CTFs in Alzheimer's Disease

Vascular dysfunctions, hypometabolism, and insulin resistance are high and early risk factors for Alzheimer's disease (AD), a leading neurological disease associated with memory decline and cognitive dysfunctions. Early defects in glucose transporters and glycolysis occur during the course of AD progression. Hypometabolism begins well before the onset of early AD symptoms; this timing implicates the vulnerability of hypometabolic brain regions to beta-secretase 1 (BACE-1) upregulation, oxidative stress, inflammation, synaptic failure, and cell death. Despite the fact that ketone bodies, astrocyte-neuron lactate shuttle, pentose phosphate pathway (PPP), and glycogenolysis compensate to provide energy to the starving AD brain, a considerable energy crisis still persists and increases during disease progression. Studies that track brain energy metabolism in humans, animal models of AD, and in vitro studies reveal striking upregulation of beta-amyloid precursor protein (β-APP) and carboxy-terminal fragments (CTFs). Currently, the precise role of CTFs is unclear, but evidence supports increased endosomal-lysosomal trafficking of β-APP and CTFs through autophagy through a vague mechanism. While intracellular accumulation of Aβ is attributed as both the cause and consequence of a defective endolysosomal-autophagic system, much remains to be explored about the other β-APP cleavage products. Many recent works report altered amino acid catabolism and expression of several urea cycle enzymes in AD brains, but the precise cause for this dysregulation is not fully explained. In this paper, we try to connect the role of CTFs in the energy translation process in AD brain based on recent findings.

The relationship between CSF biomarkers and cerebral metabolism in early-onset Alzheimer's disease

PURPOSE

One can reasonably suppose that cerebrospinal spinal fluid (CSF) biomarkers can identify distinct subgroups of Alzheimer's disease (AD) patients. In order to better understand differences in CSF biomarker patterns, we used FDG PET to assess cerebral metabolism in CSF-based subgroups of AD patients.

METHODS

Eighty-five patients fulfilling the criteria for probable early-onset AD (EOAD) underwent lumbar puncture, brain ¹⁸F-FDG PET and MRI. A cluster analysis was performed, with the CSF biomarkers for AD as variables. Vertex-wise, partial-volume-corrected metabolic maps were computed for the patients and compared between the clusters of patients. Linear correlations between each CSF biomarker and the metabolic maps were assessed.

RESULTS

Three clusters emerged. The "Aβ42" cluster contained 32 patients with low levels of Aβ42, while tau and p-tau remained within the normal range. The "Aβ42 + tau" cluster contained 41 patients with low levels of Aβ42 and high levels of tau and p-tau. Lastly, the "tau" cluster contained 12 patients with very high levels of tau and p-tau and low-normal levels of Aβ42. There were no inter-cluster differences in age, sex ratio, educational level, APOE genotype, disease duration or disease severity. The "Aβ42 + tau" and "tau" clusters displayed more marked frontal hypometabolism than the "Aβ42" cluster did, and frontal metabolism was significantly negatively correlated with the CSF tau level. The "Aβ42" and "Aβ42 + tau" clusters displayed more marked hypometabolism in the left occipitotemporal region than the "tau" cluster did, and metabolism in this region was significantly and positively correlated with the CSF Aβ42 level.

CONCLUSION

The CSF biomarkers can be used to identify metabolically distinct subgroups of patients with EOAD. Future research should seek to establish whether these biochemical differences have clinical consequences.

Effects of APOE ε4 on neuroimaging, cerebrospinal fluid biomarkers, and cognition in prodromal Alzheimer's disease

Apolipoprotein (APOE) ε4 is a major genetic risk factor for Alzheimer's disease (AD), but its importance for the clinical and biological heterogeneity in AD is unclear, particularly at the prodromal stage. We analyzed 151 prodromal AD patients (44 APOE ε4-negative and 107 APOE ε4-positive) from the BioFINDER study. We tested cognition, 18F-flutemetamol β-amyloid (Aβ) positron emission tomography, cerebrospinal fluid biomarkers of Aβ, tau and neurodegeneration, and magnetic resonance imaging of white matter pathology and brain structure. Despite having similar cortical Aβ-load and baseline global cognition (mini mental state examination), APOE ε4-negative prodromal AD had more nonamnestic cognitive impairment, higher cerebrospinal fluid levels of Aβ-peptides and neuronal injury biomarkers, more white matter pathology, more cortical atrophy, and faster decline of mini mental state examination, compared to APOE ε4-positive prodromal AD. The absence of APOE ε4 is associated with an atypical phenotype of prodromal AD. This suggests that APOE ε4 may impact both the diagnostics of AD in early stages and potentially also effects of disease-modifying treatments.