11C-PIB PET imaging reveals that amyloid deposition in cases with early-onset Alzheimer's disease in the absence of known mutations retains higher levels of PIB in the basal ganglia

Late-onset vs nonmendelian early-onset Alzheimer disease: A distinction without a difference?

There is mounting evidence that only a small fraction of early-onset Alzheimer disease cases (onset <65 years) are explained by known mutations. Even multiplex families with early onset often also have late-onset cases, suggesting that the commonly applied categorization of Alzheimer disease into early- and late-onset forms may not reflect distinct underlying etiology. Nevertheless, this categorization continues to govern today's research and the design of clinical trials. The aim of this review is to evaluate this categorization by providing a comprehensive, critical review of reported clinical, neuropathologic, and genomic characteristics of both onset-based subtypes and explore potential overlap between both categories. The article will lay out the need to comprehensively assess the phenotypic, neuropathologic, and molecular variability in Alzheimer disease and identify factors explaining the observed significant variation in onset age in persons with and without known mutations. The article will critically review ongoing large-scale genomic efforts in Alzheimer disease research (e.g., Alzheimer Disease Sequencing Project, Dominantly Inherited Alzheimer Network, Alzheimer Disease Neuroimaging Initiative) and their shortcomings to disentangle the delineation of unexplained nonmendelian early-onset from late-onset and mendelian forms of Alzheimer disease. In addition, it will outline specific approaches including epigenetic research through which a comprehensive characterization of this delineation can be achieved.

Alzheimer's disease-related dysregulation of mRNA translation causes key pathological features with ageing

Alzheimer's disease (AD) is characterised by Aβ and tau pathology as well as synaptic degeneration, which correlates best with cognitive impairment. Previous work suggested that this pathological complexity may result from changes in mRNA translation. Here, we studied whether mRNA translation and its underlying signalling are altered in an early model of AD, and whether modelling this deficiency in mice causes pathological features with ageing. Using an unbiased screen, we show that exposure of primary neurons to nanomolar amounts of Aβ increases FMRP-regulated protein synthesis. This selective regulation of mRNA translation is dependent on a signalling cascade involving MAPK-interacting kinase 1 (Mnk1) and the eukaryotic initiation factor 4E (eIF4E), and ultimately results in reduction of CYFIP2, an FMRP-binding protein. Modelling this CYFIP2 reduction in mice, we find age-dependent Aβ accumulation in the thalamus, development of tau pathology in entorhinal cortex and hippocampus, as well as gliosis and synapse loss in the hippocampus, together with deficits in memory formation. Therefore, we conclude that early stages of AD involve increased translation of specific CYFIP2/FMRP-regulated transcripts. Since reducing endogenous CYFIP2 expression is sufficient to cause key features of AD with ageing in mice, we suggest that prolonged activation of this pathway is a primary step toward AD pathology, highlighting a novel direction for therapeutic targeting.

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.

Quantifying Neurodegenerative Progression With DeepSymNet, an End-to-End Data-Driven Approach

Alzheimer's disease (AD) is the most common neurodegenerative disorder worldwide and is one of the leading sources of morbidity and mortality in the aging population. There is a long preclinical period followed by mild cognitive impairment (MCI). Clinical diagnosis and the rate of decline is variable. Progression monitoring remains a challenge in AD, and it is imperative to create better tools to quantify this progression. Brain magnetic resonance imaging (MRI) is commonly used for patient assessment. However, current approaches for analysis require strong a priori assumptions about regions of interest used and complex preprocessing pipelines including computationally expensive non-linear registrations and iterative surface deformations. These preprocessing steps are composed of many stacked processing layers. Any error or bias in an upstream layer will be propagated throughout the pipeline. Failures or biases in the non-linear subject registration and the subjective choice of atlases of specific regions are common in medical neuroimaging analysis and may hinder the translation of many approaches to the clinical practice. Here we propose a data-driven method based on an extension of a deep learning architecture, DeepSymNet, that identifies longitudinal changes without relying on prior brain regions of interest, an atlas, or non-linear registration steps. Our approach is trained end-to-end and learns how a patient's brain structure dynamically changes between two-time points directly from the raw voxels. We compare our approach with Freesurfer longitudinal pipelines and voxel-based methods using the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. Our model can identify AD progression with comparable results to existing Freesurfer longitudinal pipelines without the need of predefined regions of interest, non-rigid registration algorithms, or iterative surface deformation at a fraction of the processing time. When compared to other voxel-based methods which share some of the same benefits, our model showed a statistically significant performance improvement. Additionally, we show that our model can differentiate between healthy subjects and patients with MCI. The model's decision was investigated using the epsilon layer-wise propagation algorithm. We found that the predictions were driven by the pallidum, putamen, and the superior temporal gyrus. Our novel longitudinal based, deep learning approach has the potential to diagnose patients earlier and enable new computational tools to monitor neurodegeneration in clinical practice.

A 5-year longitudinal evaluation in patients with mild cognitive impairment by 11C-PIB PET/CT: a visual analysis

OBJECTIVE

The aim of this study was to evaluate the cerebral amyloid distribution in patients with mild cognitive impairment (MCI), assessed by carbon-11-Pittsburgh compound B (C-PIB) PET/CT, after 5 years of follow-up.

PATIENTS AND METHODS

Ten amnestic MCI (A-MCI) and four nonamnestic (NA-MCI) patients were studied by C-PIB PET/CT and re-evaluated 5 years later by a new C-PIB PET/CT. PET/CT scans were acquired 60-90 min after the administration of 555 MBq C-PIB and analyzed visually, to obtain a score of the cerebral cortical C-PIB retention in the frontal, basal ganglia (BG), temporoparietal (TP), occipital, posterior cingulate, and cerebellum areas. Initial and 5-year follow-up C-PIB retentions were compared.

RESULTS

Initially, 9/10 A-MCI patients were C-PIB positive and one was C-PIB negative. All four NA-MCI patients were C-PIB negative. Of the C-PIB-positive A-MCI patients, seven progressed to Alzheimer's disease dementia (AD-D), one to mixed dementia and one remained as A-MCI. The C-PIB-negative A-MCI patient remained as A-MCI. Of the four C-PIB-negative NA-MCI, one progressed to semantic dementia. All changes in C-PIB retention were of low intensity. The A-MCI patients who progressed to AD-D (n=7) showed an increase in C-PIB retention in the frontal (5/7), BG (3/7), TP (3/7), occipital (1/7), and posterior cingulate (1/7) regions. The A-MCI patient who progressed to mix dementia showed an increase in C-PIB retention in the frontal region. The C-PIB-positive A-MCI patient who remained as A-MCI showed an increase in C-PIB retention in the frontal, BG, and TP areas. The amyloid deposition in the anterior part of the brain (frontal, TP, and BG) increased more than that in the posterior part (occipital and precuneus) (7/9 vs. 2/9; P<0.05).

CONCLUSION

PIB retention increased predominantly in the frontal, BG, and TP areas. C-PIB-positive A-MCI patients mostly progressed to AD-D, showing similar topographic changes in their cerebral C-PIB pattern than the patient who remained as A-MCI.

Fluid and PET biomarkers for amyloid pathology in Alzheimer's disease

Alzheimer's disease (AD) is characterized by amyloid plaques and tau pathology (neurofibrillary tangles and neuropil threads). Amyloid plaques are primarily composed of aggregated and oligomeric β-amyloid (Aβ) peptides ending at position 42 (Aβ42). The development of fluid and PET biomarkers for Alzheimer's disease (AD), has allowed for detection of Aβ pathology in vivo and marks a major advancement in understanding the role of Aβ in Alzheimer's disease (AD). In the recent National Institute on Aging and Alzheimer's Association (NIA-AA) Research Framework, AD is defined by the underlying pathology as measured in patients during life by biomarkers (Jack et al., 2018), while clinical symptoms are used for staging of the disease. Therefore, sensitive, specific and robust biomarkers to identify brain amyloidosis are central in AD research. Here, we discuss fluid and PET biomarkers for Aβ and their application.

Atypical Localization and Dissociation between Glucose Uptake and Amyloid Deposition in Cognitively Normal APOE*E4 Homozygotic Elders Compared with Patients with Late-Onset Alzheimer's Disease

Alzheimer’s disease (AD) progresses insidiously over decades. Therefore, study of preclinical AD is critical to identify early pathophysiological changes as potential targets for prevention or treatment. The brain processes at the preclinical stage remain minimally understood. Aside from age, the E4 allele of APOE flags a group at particularly high risk of late-onset AD (LOAD). Studies of these individuals could provide insights about the ontogenesis of AD offering clues for novel treatment strategies. To this end, cognitively normal, APOE*E4 homozygotes from the Alzheimer’s Diseases Neuroimaging Research Initiative database (ADNI-LONI) provided fluorodeoxyglucose and amyloid (florbetapir) PET scans (n = 8 and 7, respectively; mean age 76 years). Their scans were compared to those of matched cognitively normal elders who were not E4 carriers. There was dissociation in the distribution between glucose uptake and amyloid deposition in the homozygotes. Peak hypometabolism localized bilaterally along the medial temporal cortex. In contrast, peak amyloid deposition localized principally to the putamen, a finding also seen in preclinical carriers of autosomal dominant AD mutations and preclinical AD associated with Down syndrome. Additional regions of amyloid deposition in homozygotes were medial prefrontal cortices including the anterior cingulate, middle and inferior frontal cortices, and middle and inferior occipital cortices. These findings contrast with those reported for LOAD. These data begin to characterize elders with normal cognition despite high AD risk in comparison to the known phenotypes of patients with LOAD.