Time to Amyloid Positivity and Preclinical Changes in Brain Metabolism, Atrophy, and Cognition: Evidence for Emerging Amyloid Pathology in Alzheimer's Disease

The implications of amyloid-β pathology: only time will tell

This scientific commentary refers to ‘Characterizing brain tau and cognitive decline along the amyloid timeline in Alzheimer’s disease’ by Cody et al. (https://doi.org/10.1093/brain/awae116).

Synthesizing images of tau pathology from cross-modal neuroimaging using deep learning

Given the prevalence of dementia and the development of pathology-specific disease-modifying therapies, high-value biomarker strategies to inform medical decision-making are critical. In vivo tau-PET is an ideal target as a biomarker for Alzheimer's disease diagnosis and treatment outcome measure. However, tau-PET is not currently widely accessible to patients compared to other neuroimaging methods. In this study, we present a convolutional neural network (CNN) model that imputes tau-PET images from more widely available cross-modality imaging inputs. Participants (n = 1192) with brain T1-weighted MRI (T1w), fluorodeoxyglucose (FDG)-PET, amyloid-PET and tau-PET were included. We found that a CNN model can impute tau-PET images with high accuracy, the highest being for the FDG-based model followed by amyloid-PET and T1w. In testing implications of artificial intelligence-imputed tau-PET, only the FDG-based model showed a significant improvement of performance in classifying tau positivity and diagnostic groups compared to the original input data, suggesting that application of the model could enhance the utility of the metabolic images. The interpretability experiment revealed that the FDG- and T1w-based models utilized the non-local input from physically remote regions of interest to estimate the tau-PET, but this was not the case for the Pittsburgh compound B-based model. This implies that the model can learn the distinct biological relationship between FDG-PET, T1w and tau-PET from the relationship between amyloid-PET and tau-PET. Our study suggests that extending neuroimaging's use with artificial intelligence to predict protein specific pathologies has great potential to inform emerging care models.

ADCOMS sensitivity versus baseline diagnosis and progression phenotypes

BACKGROUND

The Alzheimer's Disease COMposite Score (ADCOMS) is more sensitive in clinical trials than conventional measures when assessing pre-dementia. This study compares ADCOMS trajectories using clustered progression characteristics to better understand different patterns of decline.

METHODS

Post-baseline ADCOMS values were analyzed for sensitivity using mean-to-standard deviation ratio (MSDR), partitioned by baseline diagnosis, comparing with the original scales upon which ADCOMS is based. Because baseline diagnosis was not a particularly reliable predictor of progression, individuals were also grouped into similar ADCOMS progression trajectories using clustering methods and the MSDR compared for each progression group.

RESULTS

ADCOMS demonstrated increased sensitivity for clinically important progression groups. ADCOMS did not show statistically significant sensitivity or clinical relevance for the less-severe baseline diagnoses and marginal progression groups.

CONCLUSIONS

This analysis complements and extends previous work validating the sensitivity of ADCOMS. The large data set permitted evaluation-in a novel approach-by the clustered progression group.

Proteomic clusters underlie heterogeneity in preclinical Alzheimer's disease progression

Heterogeneity in progression to Alzheimer's disease (AD) poses challenges for both clinical prognosis and clinical trial implementation. Multiple AD-related subtypes have previously been identified, suggesting differences in receptivity to drug interventions. We identified early differences in preclinical AD biomarkers, assessed patterns for developing preclinical AD across the amyloid-tau-(neurodegeneration) [AT(N)] framework, and considered potential sources of difference by analysing the CSF proteome. Participants (n = 10) enrolled in longitudinal studies at the Knight Alzheimer Disease Research Center completed four or more lumbar punctures. These individuals were cognitively normal at baseline. Cerebrospinal fluid measures of amyloid-β (Aβ)42, phosphorylated tau (pTau181), and neurofilament light chain (NfL) as well as proteomics values were evaluated. Imaging biomarkers, including PET amyloid and tau, and structural MRI, were repeatedly obtained when available. Individuals were staged according to the amyloid-tau-(neurodegeneration) framework. Growth mixture modelling, an unsupervised clustering technique, identified three patterns of biomarker progression as measured by CSF pTau181 and Aβ42. Two groups (AD Biomarker Positive and Intermediate AD Biomarker) showed distinct progression from normal biomarker status to having biomarkers consistent with preclinical AD. A third group (AD Biomarker Negative) did not develop abnormal AD biomarkers over time. Participants grouped by CSF trajectories were re-classified using only proteomic profiles (AUCAD Biomarker Positive versus AD Biomarker Negative = 0.857, AUCAD Biomarker Positive versus Intermediate AD Biomarkers = 0.525, AUCIntermediate AD Biomarkers versus AD Biomarker Negative = 0.952). We highlight heterogeneity in the development of AD biomarkers in cognitively normal individuals. We identified some individuals who became amyloid positive before the age of 50 years. A second group, Intermediate AD Biomarkers, developed elevated CSF ptau181 significantly before becoming amyloid positive. A third group were AD Biomarker Negative over repeated testing. Our results could influence the selection of participants for specific treatments (e.g. amyloid-reducing versus other agents) in clinical trials. CSF proteome analysis highlighted additional non-AT(N) biomarkers for potential therapies, including blood-brain barrier-, vascular-, immune-, and neuroinflammatory-related targets.

Directed Functional Brain Connectivity is Altered in Sub-threshold Amyloid-β Accumulation in Cognitively Normal Individuals

Several studies have shown that amyloid-β (Aβ) deposition below the clinically relevant cut-off levels is associated with subtle changes in cognitive function and increases the risk of developing future Alzheimer's disease (AD). Although functional MRI is sensitive to early alterations occurring during AD, sub-threshold changes in Aβ levels have not been linked to functional connectivity measures. This study aimed to apply directed functional connectivity to identify early changes in network function in cognitively unimpaired participants who, at baseline, exhibit Aβ accumulation below the clinically relevant threshold. To this end, we analyzed baseline functional MRI data from 113 cognitively unimpaired participants of the Alzheimer's Disease Neuroimaging Initiative cohort who underwent at least one 18F-florbetapir-PET after the baseline scan. Using the longitudinal PET data, we classified these participants as Aβ negative (Aβ-) non-accumulators (n = 46) and Aβ- accumulators (n = 31). We also included 36 individuals who were amyloid-positive (Aβ+) at baseline and continued to accumulate Aβ (Aβ+ accumulators). For each participant, we calculated whole-brain directed functional connectivity networks using our own anti-symmetric correlation method and evaluated their global and nodal properties using measures of network segregation (clustering coefficient) and integration (global efficiency). When compared to Aβ- non-accumulators, the Aβ- accumulators showed lower global clustering coefficient. Moreover, the Aβ+ accumulator group exhibited reduced global efficiency and clustering coefficient, which at the nodal level mainly affected the superior frontal gyrus, anterior cingulate cortex, and caudate nucleus. In Aβ- accumulators, global measures were associated with lower baseline regional PET uptake values, as well as higher scores on the Modified Preclinical Alzheimer Cognitive Composite. Our findings indicate that directed connectivity network properties are sensitive to subtle changes occurring in individuals who have not yet reached the threshold for Aβ positivity, which makes them a potentially viable marker to detect negative downstream effects of very early Aβ pathology.

Serotonin Degeneration and Amyloid-β Deposition in Mild Cognitive Impairment: Relationship to Cognitive Deficits

BACKGROUND

Neuropathological and neuroimaging studies have demonstrated degeneration of the serotonin system in Alzheimer's disease (AD). Neuroimaging studies have extended these observations to the preclinical stages of AD, mild cognitive impairment (MCI). Serotonin degeneration has been observed also in transgenic amyloid mouse models, prior to widespread cortical distribution of amyloid-β (Aβ).

OBJECTIVE

The present study evaluated the regional distribution of the serotonin transporter (5-HTT) and of Aβ in individuals with MCI and healthy older controls, as well as the contribution of 5-HTT and Aβ to cognitive deficits.

METHODS

Forty-nine MCI participants and 45 healthy older controls underwent positron emission tomography (PET) imaging of 5-HTT and Aβ, structural magnetic resonance imaging and neuropsychological assessments.

RESULTS

Lower cortical, striatal, and limbic 5-HTT and higher cortical Aβ was observed in MCIs relative to healthy controls. Lower 5-HTT, mainly in limbic regions, was correlated with greater deficits in auditory-verbal and visual-spatial memory and semantic, not phonemic fluency. Higher cortical A β was associated with greater deficits in auditory-verbal and visual-spatial memory and in semantic, not phonemic fluency. When modeling the association between cognition, gray matter volumes and Aβ, inclusion of 5-HTT in limbic and in select cortical regions significantly improved model fit for auditory-verbal and visual-spatial memory and semantic, but not phonemic fluency.

CONCLUSIONS

These results support the role of serotonin degeneration in the memory and semantic fluency deficits observed in MCI.

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.

Molecular imaging of the association between serotonin degeneration and beta-amyloid deposition in mild cognitive impairment

BACKGROUND

Degeneration of the serotonin system has been observed in Alzheimer's disease (AD) and in mild cognitive impairment (MCI). In transgenic amyloid mouse models, serotonin degeneration is detected prior to widespread cortical beta-amyloid (Aβ) deposition, also suggesting that serotonin degeneration may be observed in preclinical AD.

METHODS

The differences in the distribution of serotonin degeneration (reflected by the loss of the serotonin transporter, 5-HTT) relative to Aβ deposition was measured with positron emission tomography in a group of individuals with MCI and a group of healthy older adults. A multi-modal partial least squares (mmPLS) algorithm was applied to identify the spatial covariance pattern between 5-HTT availability and Aβ deposition.

RESULTS

Forty-five individuals with MCI and 35 healthy older adults were studied, 22 and 27 of whom were included in the analyses who were "amyloid positive" and "amyloid negative", respectively. A pattern of lower cortical, subcortical and limbic 5-HTT availability and higher cortical Aβ deposition distinguished the MCI from the healthy older control participants. Greater expression of this pattern was correlated with greater deficits in memory and executive function in the MCI group, not in the control group.

CONCLUSION

A spatial covariance pattern of lower 5-HTT availability and Aβ deposition was observed to a greater extent in an MCI group relative to a control group and was associated with cognitive impairment in the MCI group. The results support the application of mmPLS to understand the neurochemical changes associated with Aβ deposition in the course of preclinical AD.

Increasing empathic concern relates to salience network hyperconnectivity in cognitively healthy older adults with elevated amyloid-β burden

Enhanced emotional empathy, the ability to share others' affective experiences, can be a feature of Alzheimer's disease (AD), but whether emotional empathy increases in the preclinical phase of the disease is unknown. We measured emotional empathy over time (range = 0 - 7.3 years, mean = 2.4 years) in 86 older adults during a period in which they were cognitively healthy, functionally normal, and free of dementia symptoms. For each participant, we computed longitudinal trajectories for empathic concern (i.e., an other-oriented form of emotional empathy that promotes prosocial actions) and emotional contagion (i.e., a self-focused form of emotional empathy often accompanied by feelings of distress) from informant ratings of participants' empathy on the Interpersonal Reactivity Index. Amyloid-β (Aβ) positron emission tomography (PET) scans were used to classify participants as either Aβ positive (Aβ+, n = 23) or negative (Aβ-, n = 63) based on Aβ-PET cortical binding. Participants also underwent structural and task-free functional magnetic resonance imaging approximately two years on average after their last empathy assessment, at which time most participants remained cognitively healthy. Results indicated that empathic concern, but not emotional contagion, increased more over time in Aβ+ participants than in Aβ- participants despite no initial group difference at the first measurement. Higher connectivity between certain salience network node-pairs (i.e., pregenual anterior cingulate cortex and periaqueductal gray) predicted longitudinal increases in empathic concern in the Aβ+ group but not in the Aβ- group. The Aβ+ participants also had higher overall salience network connectivity than Aβ- participants despite no differences in gray matter volume. These results suggest gains in empathic concern may be a very early feature of AD pathophysiology that relates to hyperconnectivity in the salience network, a system that supports emotion generation and interoception. A better understanding of emotional empathy trajectories in the early stages of AD pathophysiology will broaden the lens on preclinical AD changes and help clinicians to identify older adults who should be screened for AD biomarkers.

Aberrant Energy Metabolism in Alzheimer's Disease

To maintain energy supply to the brain, a direct energy source called adenosine triphosphate (ATP) is produced by oxidative phosphorylation and aerobic glycolysis of glucose in the mitochondria and cytoplasm. Brain glucose metabolism is reduced in many neurodegenerative diseases, including Alzheimer's disease (AD), where it appears presymptomatically in a progressive and region-specific manner. Following dysregulation of energy metabolism in AD, many cellular repair/regenerative processes are activated to conserve the energy required for cell viability. Glucose metabolism plays an important role in the pathology of AD and is closely associated with the tricarboxylic acid cycle, type 2 diabetes mellitus, and insulin resistance. The glucose intake in neurons is from endothelial cells, astrocytes, and microglia. Damage to neurocentric glucose also damages the energy transport systems in AD. Gut microbiota is necessary to modulate bidirectional communication between the gastrointestinal tract and brain. Gut microbiota may influence the process of AD by regulating the immune system and maintaining the integrity of the intestinal barrier. Furthermore, some therapeutic strategies have shown promising therapeutic effects in the treatment of AD at different stages, including the use of antidiabetic drugs, rescuing mitochondrial dysfunction, and epigenetic and dietary intervention. This review discusses the underlying mechanisms of alterations in energy metabolism in AD and provides potential therapeutic strategies in the treatment of AD.

A Computational Monte Carlo Simulation Strategy to Determine the Temporal Ordering of Abnormal Age Onset Among Biomarkers of Alzheimer's Disease

To quantitatively determining the temporal ordering of abnormal age onsets (AAO) among various biomarkers for Alzheimer's disease (AD), we introduced a computational Monte-Carlo simulation (CMCS) to statistically examine such ordering of an AAO pair or over all AAOs. The CMCS 1) simulates longitudinal data, estimates AAO for each iteration, and finally assesses the type-I error of an AAO pair or all AAO ordering. Using hippocampus volume (VHC), cerebral glucose hypometabolic convergence index (HCI), plasma neurofilament light (NfL), mini-mental state exam (MMSE), the auditory verbal learning test-long term memory (AVLT-LTM), short term memory (AVLT-STM) and clinical-dementia rating sum of box scale (CDR-SOB) from 382 mild cognitive impairment converters and non-converters, the CMCS estimated type-I error for the earlier AAO of VHC, AVLT_STM and AVLT_LTM each than MMSE was significant (p<0.002). The type-I error for the overall AAO temporal ordering of VHC ≤ AVLT_STM ≤ AVLT_LTM < HCI ≤ MMSE ≤ CDR-SOB ≤ NfL was p = 0.012. These findings showed that our CMCS is capable of providing statistical inferences for quantifying AAO ordering which has important implications in advancing our understanding of AD.

Associations Between Sub-Threshold Amyloid-β Deposition, Cortical Volume, and Cognitive Function Modulated by APOE ɛ4 Carrier Status in Cognitively Normal Older Adults

Background:

There has been renewed interest in the deteriorating effects of sub-threshold amyloid-β (Aβ) accumulation in Alzheimer’s disease (AD). Despite evidence suggesting a synergistic interaction between the APOE ɛ4 allele and Aβ deposition in neurodegeneration, few studies have investigated the modulatory role of this allele in sub-threshold Aβ deposition during the preclinical phase.

Objective:

We aimed to explore the differential effect of the APOE ɛ4 carrier status on the association between sub-threshold Aβ deposition, cortical volume, and cognitive performance in cognitively normal older adults (CN).

Methods:

A total of 112 CN with sub-threshold Aβ deposition was included in the study. Participants underwent structural magnetic resonance imaging, [¹⁸F] flutemetamol PET-CT, and a neuropsychological battery. Potential interactions between APOE ɛ4 carrier status, Aβ accumulation, and cognitive function for cortical volume were assessed with whole-brain voxel-wise analysis.

Results:

We found that greater cortical volume was observed with higher regional Aβ deposition in the APOE ɛ4 carriers, which could be attributed to an interaction between the APOE ɛ4 carrier status and regional Aβ deposition in the posterior cingulate cortex/precuneus. Finally, the APOE ɛ4 carrier status-neuropsychological test score interaction demonstrated a significant effect on the gray matter volume of the left middle occipital gyrus.

Conclusion:

There might be a compensatory response to initiating Aβ in APOE ɛ4 carriers during the earliest AD stage. Despite its exploratory nature, this study offers some insight into recent interests concerning probabilistic AD modeling, focusing on the modulating role of the APOE ɛ4 carrier status during the preclinical period.

Using the Montreal cognitive assessment to identify individuals with subtle cognitive decline

OBJECTIVE

Dementia is a devastating neurological disease that may be better managed if diagnosed earlier when subclinical neurodegenerative changes are already present, including subtle cognitive decline and mild cognitive impairment. In this study, we used item-level performance on the Montreal Cognitive Assessment (MoCA) to identify individuals with subtle cognitive decline.

METHOD

Individual MoCA item data from the Alzheimer's Disease Neuroimaging Initiative was grouped using k-modes cluster analysis. These clusters were validated and examined for association with convergent neuropsychological tests. The clusters were then compared and characterized using multinomial logistic regression.

RESULTS

A three-cluster solution had 77.3% precision, with Cluster 1 (high performing) displaying no deficits in performance, Cluster 2 (memory deficits) displaying lower memory performance, and Cluster 3 (compound deficits) displaying lower performance on memory and executive function. Age at MoCA (older in compound deficits), gender (more females in memory deficits), and marital status (fewer married in compound deficits) were significantly different among clusters. Age was not associated with increased odds of membership in the high-performing cluster compared to the others.

CONCLUSIONS

We identified three clusters of individuals classified as cognitively unimpaired using cluster analysis. Individuals in the compound deficits cluster performed lower on the MoCA and were older and less often married than individuals in other clusters. Demographic analyses suggest that cluster identity was due to a combination of both cognitive and clinical factors. Identifying individuals at risk for future cognitive decline using the MoCA could help them receive earlier evidence-based interventions to slow further cognitive decline. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Impact of APOE ε4 Carrier Status on Associations Between Subthreshold, Positive Amyloid-β Deposition, Brain Function, and Cognitive Performance in Cognitively Normal Older Adults: A Prospective Study

Background

A growing body of evidence suggests a deteriorating effect of subthreshold amyloid-beta (Aβ) accumulation on cognition before the onset of clinical symptoms of Alzheimer's disease (AD). Despite the association between the Aβ-dependent pathway and the APOE ε4 allele, the impact of this allele on the progression from the subthreshold Aβ deposits to cognitive function impairment is unclear. Furthermore, the comparative analysis of positive Aβ accumulation in the preclinical phase is lacking.

Objective

This study aimed to explore the differential effect of the APOE ε4 carrier status on the association between Aβ deposition, resting-state brain function, and cognitive performance in cognitively normal (CN) older adults, depending on the Aβ burden status.

Methods

One hundred and eighty-two older CN adults underwent resting-state functional magnetic resonance imaging, [18F] flutemetamol (FMM) positron emission tomography, a neuropsychological battery, and APOE genotyping. We evaluated the resting-state brain function by measuring the local and remote functional connectivity (FC) and measured the remote FC in the default-mode network (DMN), central-executive network (CEN), and salience network (SN). In addition, the subjects were dichotomized into those with subthreshold and positive Aβ deposits using a neocortical standardized uptake value ratio with the cut-off value of 0.62, which was calculated with respect to the pons.

Results

The present result showed that APOE ε4 carrier status moderated the relationship between Aβ deposition, local and remote resting-state brain function, and cognitive performance in each CN subthreshold and positive Aβ group. We observed the following: (i) the APOE ε4 carrier status-Aβ deposition and APOE ε4 carrier status-local FC interaction for the executive and memory function; (ii) the APOE ε4 carrier status-regional Aβ accumulation interaction for the local FC; and (iv) the APOE ε4 carrier status-local FC interaction for the remote inter-network FC between the DMN and CEN, contributing higher cognitive performance in the APOE ε4 carrier with higher inter-network FC. Finally, these results were modulated according to Aβ positivity.

Conclusion

This study is the first attempt to thoroughly examine the influence of the APOE ε4 carrier status from the subthreshold to positive Aβ accumulation during the preclinical phase.

Predicting Brain Amyloid-β PET Grades with Graph Convolutional Networks Based on Functional MRI and Multi-Level Functional Connectivity

BACKGROUND

The detection of amyloid-β (Aβ) deposition in the brain provides crucial evidence in the clinical diagnosis of Alzheimer's disease (AD). However, the current positron emission tomography (PET)-based brain Aβ examination suffers from the problems of coarse visual inspection (in many cases, with 2-class stratification) and high scanning cost.

OBJECTIVE

1) To characterize the non-binary Aβ deposition levels in the AD continuum based on clustering of PET data, and 2) to explore the feasibility of predicting individual Aβ deposition grades with non-invasive functional magnetic resonance imaging (fMRI).

METHODS

1) Individual whole-brain Aβ-PET images from the OASIS-3 dataset (N = 258) were grouped into three clusters (grades) with t-SNE and k-means. The demographical data as well as global and regional standard uptake value ratios (SUVRs) were compared among the three clusters with Chi-square tests or ANOVA tests. 2) From resting-state fMRI, both conventional functional connectivity (FC) and high-order FC networks were constructed and the topological architectures of the two networks were jointly learned with graph convolutional networks (GCNs) to predict the Aβ-PET grades for each individual.

RESULTS

We found three clearly separated clusters, indicating three Aβ-PET grades. There were significant differences in gender, age, cognitive ability, APOE type, as well as global and regional SUVRs among the three grades we found. The prediction of Aβ-PET grades with GCNs on FC for the 258 participants in the AD continuum reached a satisfactory averaged accuracy (78.8%) in the two-class classification tasks.

CONCLUSION

The results demonstrated the feasibility of using deep learning on a non-invasive brain functional imaging technique to approximate PET-based Aβ deposition grading.

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.

Predicting Tau Accumulation in Cerebral Cortex with Multivariate MRI Morphometry Measurements, Sparse Coding, and Correntropy

Biomarker-assisted diagnosis and intervention in Alzheimer's disease (AD) may be the key to prevention breakthroughs. One of the hallmarks of AD is the accumulation of tau plaques in the human brain. However, current methods to detect tau pathology are either invasive (lumbar puncture) or quite costly and not widely available (Tau PET). In our previous work, structural MRI-based hippocampal multivariate morphometry statistics (MMS) showed superior performance as an effective neurodegenerative biomarker for preclinical AD and Patch Analysis-based Surface Correntropy-induced Sparse coding and max-pooling (PASCS-MP) has excellent ability to generate low-dimensional representations with strong statistical power for brain amyloid prediction. In this work, we apply this framework together with ridge regression models to predict Tau deposition in Braak12 and Braak34 brain regions separately. We evaluate our framework on 925 subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI). Each subject has one pair consisting of a PET image and MRI scan which were collected at about the same times. Experimental results suggest that the representations from our MMS and PASCS-MP have stronger predictive power and their predicted Braak12 and Braak34 are closer to the real values compared to the measures derived from other approaches such as hippocampal surface area and volume, and shape morphometry features based on spherical harmonics (SPHARM).

Genetic effects on longitudinal cognitive decline during the early stages of Alzheimer's disease

Cognitive decline in early-stage Alzheimer's disease (AD) may depend on genetic variability. In the Swedish BioFINDER study, we used polygenic scores (PGS) (for AD, intelligence, and educational attainment) to predict longitudinal cognitive change (measured by mini-mental state examination (MMSE) [primary outcome] and other cognitive tests) over a mean of 4.2 years. We included 260 β-amyloid (Aβ) negative cognitively unimpaired (CU) individuals, 121 Aβ-positive CU (preclinical AD), 50 Aβ-negative mild cognitive impairment (MCI) patients, and 127 Aβ-positive MCI patients (prodromal AD). Statistical significance was determined at Bonferroni corrected p value < 0.05. The PGS for intelligence (beta = 0.1, p = 2.9e-02) was protective against decline in MMSE in CU and MCI participants regardless of Aβ status. The polygenic risk score for AD (beta =  - 0.12, p = 9.4e-03) was correlated with the rate of change in MMSE and was partially mediated by Aβ-pathology (mediation effect 20%). There was no effect of education PGS on cognitive measures. Genetic variants associated with intelligence mitigate cognitive decline independent of Aβ-pathology, while effects of genetic variants associated with AD are partly mediated by Aβ-pathology.

Melatonin protects against methamphetamine-induced Alzheimer's disease-like pathological changes in rat hippocampus

Methamphetamine (METH) is a psychostimulant drug of abuse. METH use is associated with cognitive impairments and neurochemical abnormalities comparable to pathological changes observed in Alzheimer's disease (AD). These observations have stimulated the idea that METH abusers might be prone to develop AD-like signs and symptoms. Melatonin, the pineal hormone, is considered as a potential therapeutic intervention against AD. We thus conducted the present study to explore potential protective roles of melatonin against METH-induced deficits in learning and memory as well as in the appearance of AD-like pathological changes in METH-treated male Wistar rats. We found that melatonin ameliorated METH-induced cognitive impairments in those rats. Melatonin prevented METH-induced decrease in dopamine transporter (DAT) expression in rat hippocampus. Melatonin reversed METH-induced activation of β-arrestin2, reduction of phosphorylation of protein kinase B (Akt) and METH-induced excessive activity of glycogen synthase kinase-3β (GSK3β). Importantly, melatonin inhibited METH-induced changes in the expression of β-site APP cleaving enzyme (BACE1), disintegrin and metalloproteinase 10 (ADAM10), and presenilin 1 (PS1), as well as the reduction of amyloid beta (Aβ)42 production. Immunofluorescence double-labeling demonstrated that melatonin not only prevented the METH-induced loss of DAT but also prevented METH-induced Aβ42 overexpression in the dentate gyrus, CA1, and CA3. Furthermore, melatonin also suppressed METH-induced increase in phosphorylated tau. Significantly, melatonin attenuated METH-induced increase in N-methyl-D-aspartate receptor subtype 2 B (NR2B) protein expression and restored METH-induced reduction of Ca²⁺/calmodulin-dependent protein kinase II (CaMKII). This suggested that melatonin attenuated the toxic effect of METH on the hippocampus involving the amyloidogenic pathway. Taken together, our data suggest that METH abuse may be a predisposing risk factor for AD and that melatonin could serve as a potential therapeutic agent to prevent METH-induced AD like pathology.

The Use, Standardization, and Interpretation of Brain Imaging Data in Clinical Trials of Neurodegenerative Disorders

Imaging biomarkers play a wide-ranging role in clinical trials for neurological disorders. This includes selecting the appropriate trial participants, establishing target engagement and mechanism-related pharmacodynamic effect, monitoring safety, and providing evidence of disease modification. In the early stages of clinical drug development, evidence of target engagement and/or downstream pharmacodynamic effect-especially with a clear relationship to dose-can provide confidence that the therapeutic candidate should be advanced to larger and more expensive trials, and can inform the selection of the dose(s) to be further tested, i.e., to "de-risk" the drug development program. In these later-phase trials, evidence that the therapeutic candidate is altering disease-related biomarkers can provide important evidence that the clinical benefit of the compound (if observed) is grounded in meaningful biological changes. The interpretation of disease-related imaging markers, and comparability across different trials and imaging tools, is greatly improved when standardized outcome measures are defined. This standardization should not impinge on scientific advances in the imaging tools per se but provides a common language in which the results generated by these tools are expressed. PET markers of pathological protein aggregates and structural imaging of brain atrophy are common disease-related elements across many neurological disorders. However, PET tracers for pathologies beyond amyloid β and tau are needed, and the interpretability of structural imaging can be enhanced by some simple considerations to guard against the possible confound of pseudo-atrophy. Learnings from much-studied conditions such as Alzheimer's disease and multiple sclerosis will be beneficial as the field embraces rarer diseases.

Time between milestone events in the Alzheimer's disease amyloid cascade

OBJECTIVE

Estimate the time-course of the spread of key pathological markers and the onset of cognitive dysfunction in Alzheimer's disease.

METHODS

In a cohort of 335 older adults, ranging in cognitive functioning, we estimated the time of initial changes of Aβ, tau, and decreases in cognition with respect to the time of Aβ-positivity.

RESULTS

Small effect sizes of change in CSF Aβ42 and regional Aβ PET were estimated to occur several decades before Aβ-positivity. Increases in CSF tau occurred 7-8 years before Aβ-positivity. Temporoparietal tau PET showed increases 4-5 years before Aβ-positivity. Subtle cognitive dysfunction was observed 4-6 years before Aβ-positivity.

CONCLUSIONS

Increases in tau and cognitive dysfunction occur years before commonly used thresholds for Aβ-positivity. Explicit estimates of the time for these events provide a clearer picture of the time-course of the amyloid cascade and identify potential windows for specific treatments.

Association of amyloid-β CSF/PET discordance and tau load 5 years later

OBJECTIVE

To investigate the association between discordant β-amyloid (Aβ) PET and CSF biomarkers at baseline and the emergence of tau pathology 5 years later.

METHODS

We included 730 Alzheimer's Disease Neuroimaging Initiative (ADNI) participants without dementia (282 cognitively normal, 448 mild cognitive impairment) with baseline [18F]florbetapir PET and CSF Aβ42 available. Aβ CSF/PET status was determined at baseline using established cutoffs. Longitudinal data were available for [18F]florbetapir (Aβ) PET (baseline to 4.3 ± 1.9 years), CSF (p)tau (baseline to 2.0 ± 0.1 years), cognition (baseline to 4.3 ± 2.0 years), and [18F]flortaucipir (tau) PET (measured 5.2 ± 1.2 years after baseline to 1.6 ± 0.7 years later). We used linear mixed modeling to study the association between Aβ CSF/PET status and tau pathology measured in CSF or using PET. We calculated the proportion of CSF+/PET- participants who during follow-up (1) progressed to Aβ CSF+/PET+ or (2) became tau-positive based on [18F]flortaucipir PET.

RESULTS

Aβ CSF+/PET+ (n = 318) participants had elevated CSF (p)tau levels and worse cognitive performance at baseline, while CSF+/PET- (n = 80) participants were overall similar to the CSF-/PET- (N = 306) group. Five years after baseline, [18F]flortaucipir PET uptake in the CSF+/PET- group (1.20 ± 0.13) did not differ from CSF-/PET- (1.18 ± 0.08, p = 0.69), but was substantially lower than CSF+/PET+ (1.48 ± 0.44, p < 0.001). Of the CSF+/PET- participants, 21/64 (33%) progressed to Aβ CSF+/PET+, whereas only one (3%, difference p < 0.05) became tau-positive based on [18F]flortaucipir PET.

CONCLUSIONS

Aβ load detectable by both CSF and PET seems to precede substantial tau deposition. Compared to participants with abnormal Aβ levels on both PET and CSF, the CSF+/PET- group has a distinctly better prognosis.

Protective Factors Modulate the Risk of Beta Amyloid in Alzheimer's Disease

Aim

To identify the factors protecting Abeta-positive subjects with normal cognition (NC) or mild cognitive impairment (MCI) from conversion to Alzheimer's disease (AD).

Methods

Subjects with MCI in the Alzheimer's Disease Neuroimaging Initiative (ADNI) database, with baseline data for neuropsychological tests, brain beta amyloid (Abeta), magnetic resonance imaging (MRI), APOE genotyping, and 18F-FDG-PET (FDG), were included for analysis.

Results

Elevated brain amyloid was associated with a higher risk of conversion from MCI to AD (41.5%) relative to Abeta levels of <1.231 (5.5%) but was not associated with conversion from NC to AD (0.0 vs. 1.4%). In the multivariate Cox regression analyses, elevated Abeta increased the risk of AD, while higher whole-brain cerebral glucose metabolism (CGM) assessed by FDG decreased the risk of AD in subjects with the same amount of Abeta. Even in the patients with heavily elevated brain amyloid, those with FDG > 5.946 had a lower risk of AD. ApoE4 carrier status did not influence the protective effect.

Conclusion

Higher average CGM based on FDG modified the progression to AD, indicating a protective function. The results suggest that the inclusion of this CGM measured by FDG would enrich clinical trial design and that increasing CGM along with the use of anti-Abeta agents might be a potential prevention strategy for AD.

Grey zone amyloid burden affects memory function: the SCIENCe project

Purpose

To determine thresholds for amyloid beta pathology and evaluate associations with longitudinal memory performance with the aim to identify a grey zone of early amyloid beta accumulation and investigate its clinical relevance.

Methods

We included 162 cognitively normal participants with subjective cognitive decline from the SCIENCe cohort (64 ± 8 years, 38% F, MMSE 29 ± 1). Each underwent a dynamic [¹⁸F] florbetapir PET scan, a T1-weighted MRI scan and longitudinal memory assessments (RAVLT delayed recall, n = 655 examinations). PET scans were visually assessed as amyloid positive/negative. Additionally, we calculated the mean binding potential (BP_ND) and standardized uptake value ratio (SUVr_50–70) for an a priori defined composite region of interest. We determined six amyloid positivity thresholds using various data-driven methods (resulting thresholds: BP_ND 0.19/0.23/0.29; SUVr 1.28/1.34/1.43). We used Cohen’s kappa to analyse concordance between thresholds and visual assessment. Next, we used quantiles to divide the sample into two to five subgroups of equal numbers (median, tertiles, quartiles, quintiles), and operationalized a grey zone as the range between the thresholds (0.19–0.29 BP_ND/1.28–1.43 SUVr). We used linear mixed models to determine associations between thresholds and memory slope.

Results

As determined by visual assessment, 24% of 162 individuals were amyloid positive. Concordance with visual assessment was comparable but slightly higher for BP_ND thresholds (range kappa 0.65–0.70 versus 0.60–0.63). All thresholds predicted memory decline (range beta − 0.29 to − 0.21, all p < 0.05). Analyses in subgroups showed memory slopes gradually became steeper with higher amyloid load (all p for trend < 0.05). Participants with a low amyloid burden benefited from a practice effect (i.e. increase in memory), whilst high amyloid burden was associated with memory decline. Memory slopes of individuals in the grey zone were intermediate.

Conclusion

We provide evidence that not only high but also grey zone amyloid burden subtly impacts memory function. Therefore, in case a binary classification is required, we suggest using a relatively low threshold which includes grey zone amyloid pathology.

Brain energy rescue: an emerging therapeutic concept for neurodegenerative disorders of ageing

The brain requires a continuous supply of energy in the form of ATP, most of which is produced from glucose by oxidative phosphorylation in mitochondria, complemented by aerobic glycolysis in the cytoplasm. When glucose levels are limited, ketone bodies generated in the liver and lactate derived from exercising skeletal muscle can also become important energy substrates for the brain. In neurodegenerative disorders of ageing, brain glucose metabolism deteriorates in a progressive, region-specific and disease-specific manner - a problem that is best characterized in Alzheimer disease, where it begins presymptomatically. This Review discusses the status and prospects of therapeutic strategies for countering neurodegenerative disorders of ageing by improving, preserving or rescuing brain energetics. The approaches described include restoring oxidative phosphorylation and glycolysis, increasing insulin sensitivity, correcting mitochondrial dysfunction, ketone-based interventions, acting via hormones that modulate cerebral energetics, RNA therapeutics and complementary multimodal lifestyle changes.

Grey matter network trajectories across the Alzheimer's disease continuum and relation to cognition

Biomarkers are needed to monitor disease progression in Alzheimer's disease. Grey matter network measures have such potential, as they are related to amyloid aggregation in cognitively unimpaired individuals and to future cognitive decline in predementia Alzheimer's disease. Here, we investigated how grey matter network measures evolve over time within individuals across the entire Alzheimer's disease cognitive continuum and whether such changes relate to concurrent decline in cognition. We included 190 cognitively unimpaired, amyloid normal (controls) and 523 individuals with abnormal amyloid across the cognitive continuum (preclinical, prodromal, Alzheimer's disease dementia) from the Alzheimer's Disease Neuroimaging Initiative and calculated single-subject grey matter network measures (median of five networks per individual over 2 years). We fitted linear mixed models to investigate how network measures changed over time and whether such changes were associated with concurrent changes in memory, language, attention/executive functioning and on the Mini-Mental State Examination. We further assessed whether associations were modified by baseline disease stage. We found that both cognitive functioning and network measures declined over time, with steeper rates of decline in more advanced disease stages. In all cognitive stages, decline in network measures was associated with concurrent decline on the Mini-Mental State Examination, with stronger effects for individuals closer to Alzheimer's disease dementia. Decline in network measures was associated with concurrent cognitive decline in different cognitive domains depending on disease stage: In controls, decline in networks was associated with decline in memory and language functioning; preclinical Alzheimer's disease showed associations of decline in networks with memory and attention/executive functioning; prodromal Alzheimer's disease showed associations of decline in networks with cognitive decline in all domains; Alzheimer's disease dementia showed associations of decline in networks with attention/executive functioning. Decline in grey matter network measures over time accelerated for more advanced disease stages and was related to concurrent cognitive decline across the entire Alzheimer's disease cognitive continuum. These associations were disease stage dependent for the different cognitive domains, which reflected the respective cognitive stage. Our findings therefore suggest that grey matter measures are helpful to track disease progression in Alzheimer's disease.

Natural Medicines and Their Underlying Mechanisms of Prevention and Recovery from Amyloid Β-Induced Axonal Degeneration in Alzheimer's Disease

In Alzheimer’s disease (AD), amyloid β (Aβ) induces axonal degeneration, neuronal network disruption, and memory impairment. Although many candidate drugs to reduce Aβ have been clinically investigated, they failed to recover the memory function in AD patients. Reportedly, Aβ deposition occurred before the onset of AD. Once neuronal networks were disrupted by Aβ, they could hardly be recovered. Therefore, we speculated that only removal of Aβ was not enough for AD therapy, and prevention and recovery from neuronal network disruption were also needed. This review describes the challenges related to the condition of axons for AD therapy. We established novel in vitro models of Aβ-induced axonal degeneration. Using these models, we found that several traditional medicines and their constituents prevented or helped recover from Aβ-induced axonal degeneration. These drugs also prevented or helped recover from memory impairment in in vivo models of AD. One of these drugs ameliorated memory decline in AD patients in a clinical study. These results indicate that prevention and recovery from axonal degeneration are possible strategies for AD therapy.

The dynamics of biomarkers across the clinical spectrum of Alzheimer's disease

Background

Quantifying changes in the levels of biological and cognitive markers prior to the clinical presentation of Alzheimer’s disease (AD) will provide a template for understanding the underlying aetiology of the clinical syndrome and, concomitantly, for improving early diagnosis, clinical trial recruitment and treatment assessment. This study aims to characterise continuous changes of such markers and determine their rate of change and temporal order throughout the AD continuum.

Methods

The methodology is founded on the development of stochastic models to estimate the expected time to reach different clinical disease states, for different risk groups, and synchronise short-term individual biomarker data onto a disease progression timeline. Twenty-seven markers are considered, including a range of cognitive scores, cerebrospinal (CSF) and plasma fluid proteins, and brain structural and molecular imaging measures. Data from 2014 participants in the Alzheimer’s Disease Neuroimaging Initiative database is utilised.

Results

The model suggests that detectable memory dysfunction could occur up to three decades prior to the onset of dementia due to AD (ADem). This is closely followed by changes in amyloid-β CSF levels and the first cognitive decline, as assessed by sensitive measures. Hippocampal atrophy could be observed as early as the initial amyloid-β accumulation. Brain hypometabolism starts later, about 14 years before onset, along with changes in the levels of total and phosphorylated tau proteins. Loss of functional abilities occurs rapidly around ADem onset. Neurofilament light is the only protein with notable early changes in plasma levels. The rate of change varies, with CSF, memory, amyloid PET and brain structural measures exhibiting the highest rate before the onset of ADem, followed by a decline. The probability of progressing to a more severe clinical state increases almost exponentially with age. In accordance with previous studies, the presence of apolipoprotein E4 alleles and amyloid-β accumulation can be associated with an increased risk of developing the disease, but their influence depends on age and clinical state.

Conclusions

Despite the limited longitudinal data at the individual level and the high variability observed in such data, the study elucidates the link between the long asynchronous pathophysiological processes and the preclinical and clinical stages of AD.

Mosaic Somatic Gene Recombination as a Potentially Unifying Hypothesis for Alzheimer's Disease

The recent identification of somatic gene recombination(SGR) in human neurons affecting the well-known Alzheimer's disease (AD) pathogenic gene, amyloid precursor protein (APP), has implications for the normal and the diseased human brain. The amyloid hypothesis has been the prevailing theory for sporadic AD (SAD) pathogenesis since the discovery of APP gene involvement in familial AD and Down syndrome. Yet, despite enormous scientific and clinical effort, no disease-modifying therapy has emerged. SGR offers a novel mechanism to explain AD pathogenesis and the failures of amyloid-related clinical trials, while maintaining consistency with most aspects of the amyloid hypothesis and additionally supporting possible roles for tau, oxidative stress, inflammation, infection, and prions. SGR retro-inserts novel "genomic complementary DNAs" (gencDNAs) into neuronal genomes and becomes dysregulated in SAD, producing numerous mosaic APP variants, including DNA mutations observed in familial AD. Notably, SGR requires gene transcription, DNA strand-breaks, and reverse transcriptase (RT) activity, all of which may be promoted by well-known AD risk factors and provide a framework for the pursuit of new SGR-based therapeutics. In this perspective, we review evidence for APP SGR in AD pathogenesis and discuss its possible relevance to other AD-related dementias. Further, SGR's requirement for RT activity and the relative absence of AD in aged HIV -infected patients exposed to RT inhibitors suggest that these Food and Drug Administration (FDA)-approved drugs may represent a near-term disease-modifying therapy for AD.

Amyloid-β Positivity Predicts Cognitive Decline but Cognition Predicts Progression to Amyloid-β Positivity

BACKGROUND

Stage 1 of the National Institute on Aging-Alzheimer's Association's proposed Alzheimer's disease continuum is defined as amyloid-β (Aβ) positive but cognitively normal. Identifying at-risk individuals before Aβ reaches pathological levels could have great benefits for early intervention. Although Aβ levels become abnormal long before severe cognitive impairments appear, increasing evidence suggests that subtle cognitive changes may begin early, potentially before Aβ surpasses the threshold for abnormality. We examined whether baseline cognitive performance would predict progression from normal to abnormal levels of Aβ.

METHODS

We examined the association of baseline cognitive composites (Preclinical Alzheimer Cognitive Composite, Alzheimer's Disease Neuroimaging Initiative (ADNI) memory factor composite) with progression to Aβ positivity in 292 nondemented, Aβ-negative ADNI participants. Additional analyses included continuous cerebrospinal fluid biomarker levels to examine the effects of subthreshold pathology.

RESULTS

Forty participants progressed to Aβ positivity during follow-up. Poorer baseline performance on both cognitive measures was significantly associated with increased odds of progression. More abnormal levels of baseline cerebrospinal fluid phosphorylated tau and subthreshold Aβ were associated with increased odds of progression to Aβ positivity. Nevertheless, baseline ADNI memory factor composite performance predicted progression even after controlling for baseline biomarker levels and APOE genotype (Preclinical Alzheimer Cognitive Composite was trend level). Survival analyses were largely consistent: controlling for baseline biomarker levels, baseline Preclinical Alzheimer Cognitive Composite still significantly predicted progression time to Aβ positivity (ADNI memory factor composite was trend level).

CONCLUSIONS

The possibility of intervening before Aβ reaches pathological levels is of obvious benefit. Low-cost, noninvasive cognitive measures can be informative for determining who is likely to progress to Aβ positivity, even after accounting for baseline subthreshold biomarker levels.

The A4 study: β-amyloid and cognition in 4432 cognitively unimpaired adults

Objective

To clarify the preclinical stage of Alzheimer’s disease by estimating when β‐amyloid accumulation first becomes associated with changes in cognition.

Methods

Here we studied a large group (N = 4432) of cognitively unimpaired individuals who were screened for inclusion in the A4 trial (age 65–85) to assess the effect of subthreshold levels of β‐amyloid on cognition and to identify which cognitive domains first become affected.

Results

β‐amyloid accumulation was linked to significant cognitive dysfunction in cognitively unimpaired participants with subthreshold levels of β‐amyloid in multiple measures of memory (Logical Memory Delayed Recall, P = 0.03; Free and Cued Selective Reminding Test, P < 0.001), the Preclinical Alzheimer’s Cognitive Composite (P = 0.01), and was marginally associated with decreased executive function (Digit Symbol Substitution, P = 0.07). Significantly, decreased cognitive scores were associated with suprathreshold levels of β‐amyloid, across all measures (P < 0.05). The Free and Cued Selective Reminding Test, a list recall memory test, appeared most sensitive to β‐amyloid ‐related decreases in average cognitive scores, outperforming all other cognitive domains, including the narrative recall memory test, Logical Memory.

Interpretation

Clinical trials for cognitively unimpaired β‐amyloid‐positive individuals will include a large number of individuals where mechanisms downstream from β‐amyloid pathology are already activated. These findings have implications for primary and secondary prevention of Alzheimer’s disease.

NeAT: a Nonlinear Analysis Toolbox for Neuroimaging

NeAT is a modular, flexible and user-friendly neuroimaging analysis toolbox for modeling linear and nonlinear effects overcoming the limitations of the standard neuroimaging methods which are solely based on linear models. NeAT provides a wide range of statistical and machine learning non-linear methods for model estimation, several metrics based on curve fitting and complexity for model inference and a graphical user interface (GUI) for visualization of results. We illustrate its usefulness on two study cases where non-linear effects have been previously established. Firstly, we study the nonlinear effects of Alzheimer’s disease on brain morphology (volume and cortical thickness). Secondly, we analyze the effect of the apolipoprotein APOE-ε4 genotype on brain aging and its interaction with age. NeAT is fully documented and publicly distributed at https://imatge-upc.github.io/neat-tool/.

Prediction of rapid amyloid and phosphorylated‐Tau accumulation in cognitively healthy individuals

Objective

To test the hypothesis that among cognitively healthy individuals, distinct groups exist in terms of amyloid and phosphorylated-tau accumulation rates; that if rapid accumulator groups exist, their membership can be predicted by Alzheimer's disease (AD) risk factors, and that time points of significant increase in AD protein accumulation will be evident.

Methods

The analysis reports data from 263 individuals from the BIOCARD and 184 individuals from the Baltimore Longitudinal Study of Aging with repeated cerebrospinal fluid (CSF) and positron emission tomography (PET) sampling, respectively. We used latent class mixed-effect models to identify distinct classes of amyloid (CSF and PET) and p-Tau (CSF) accumulation rates and generalized additive modeling to investigate non-linear changes to AD biomarkers.

Results

For both amyloid and p-Tau latent class models we confirmed the existence of two separate classes: accumulators and non-accumulators. The accumulator and non-accumulator groups differed significantly in terms of baseline AD protein levels and slope of change. APOE ε4 carrier status and episodic memory predicted amyloid class membership. Non-linear models revealed time points of significant increase in the rate of amyloid and p-Tau accumulation whereby APOE ε4 carrier status associated with earlier age at onset of rapid accumulation.

Conclusions

The current analysis demonstrates the existence of distinct classes of amyloid and p-Tau accumulators. Predictors of class membership were identified but the overall accuracy of the models was modest, highlighting the need for additional biomarkers that are sensitive to early disease phenotypes.

Amyloid duration is associated with preclinical cognitive decline and tau PET

INTRODUCTION

This study applies a novel algorithm to longitudinal amyloid positron emission tomography (PET) imaging to identify age-heterogeneous amyloid trajectory groups, estimate the age and duration (chronicity) of amyloid positivity, and investigate chronicity in relation to cognitive decline and tau burden.

METHODS

Cognitively unimpaired participants (n = 257) underwent one to four amyloid PET scans (Pittsburgh Compound B, PiB). Group-based trajectory modeling was applied to participants with longitudinal scans (n = 171) to identify and model amyloid trajectory groups, which were combined with Bayes theorem to estimate age and chronicity of amyloid positivity. Relationships between chronicity, cognition, clinical progression, and tau PET (MK-6240) were investigated using regression models.

RESULTS

Chronicity explained more heterogeneity in amyloid burden than age and binary amyloid status. Chronicity was associated with faster cognitive decline, increased risk of abnormal cognition, and higher entorhinal tau.

DISCUSSION

Amyloid chronicity provides unique information about cognitive decline and neurofibrillary tangle development and may be useful to investigate preclinical Alzheimer's disease.

Cerebrospinal fluid and plasma biomarker trajectories with increasing amyloid deposition in Alzheimer's disease

Failures in Alzheimer's disease (AD) drug trials highlight the need to further explore disease mechanisms and alterations of biomarkers during the development of AD. Using cross‐sectional data from 377 participants in the BioFINDER study, we examined seven cerebrospinal fluid (CSF) and six plasma biomarkers in relation to β‐amyloid (Aβ) PET uptake to understand their evolution during AD. In CSF, Aβ42 changed first, closely followed by Aβ42/Aβ40, phosphorylated‐tau (P‐tau), and total‐tau (T‐tau). CSF neurogranin, YKL‐40, and neurofilament light increased after the point of Aβ PET positivity. The findings were replicated using Aβ42, Aβ40, P‐tau, and T‐tau assays from five different manufacturers. Changes were seen approximately simultaneously for CSF and plasma biomarkers. Overall, plasma biomarkers had smaller dynamic ranges, except for CSF and plasma P‐tau which were similar. In conclusion, using state‐of‐the‐art biomarkers, we identified the first changes in Aβ, closely followed by soluble tau. Only after Aβ PET became abnormal, biomarkers of neuroinflammation, synaptic dysfunction, and neurodegeneration were altered. These findings lend in vivo support of the amyloid cascade hypotheses in humans.

Grant Report on PREDICT-ADFTD: Multimodal Imaging Prediction of AD/FTD and Differential Diagnosis

We report on the ongoing project "PREDICT-ADFTD: Multimodal Imaging Prediction of AD/FTD and Differential Diagnosis" describing completed and future work supported by this grant. This project is a multi-site, multi-study collaboration effort with research spanning seven sites across the US and Canada. The overall goal of the project is to study neurodegeneration within Alzheimer's Disease, Frontotemporal Dementia, and related neurodegenerative disorders, using a variety of brain imaging and computational techniques to develop methods for the early and accurate prediction of disease and its course. The overarching goal of the project is to develop the earliest and most accurate biomarker that can differentiate clinical diagnoses to inform clinical trials and patient care. In its third year, this project has already completed several projects to achieve this goal, focusing on (1) structural MRI (2) machine learning and (3) FDG-PET and multimodal imaging. Studies utilizing structural MRI have identified key features of underlying pathology by studying hippocampal deformation that is unique to clinical diagnosis and also post-mortem confirmed neuropathology. Several machine learning experiments have shown high classification accuracy in the prediction of disease based on Convolutional Neural Networks utilizing MRI images as input. In addition, we have also achieved high accuracy in predicting conversion to DAT up to five years in the future. Further, we evaluated multimodal models that combine structural and FDG-PET imaging, in order to compare the predictive power of multimodal to unimodal models. Studies utilizing FDG-PET have shown significant predictive ability in the prediction and progression of disease.

Circulating Plasma microRNAs are Altered with Amyloidosis in a Mouse Model of Alzheimer's Disease

Pathological changes underlying Alzheimer's disease (AD) begin decades before the classical symptoms of memory loss become evident. As microRNAs are released from neurons and enter the bloodstream, circulating microRNAs may be reflective of AD progression and are ideal candidates as biomarkers for early-stage disease detection. Here, we provide a novel, in-depth analysis of how plasma microRNAs alter with aging, the most prominent risk factor for AD, and with development of amyloid-β (Aβ) plaque deposition. We assessed the circulating microRNAs in APPswe/PSEN1dE9 transgenic mice and wild-type controls at 4, 8 and 15 m (n = 8-10) using custom designed Taqman arrays representing 185 neuropathology-related microRNAs. We performed a linear mixed-effects model to investigate the effects of age and genotype on plasma microRNAs expression. Following this analysis, we found 8 microRNAs were significantly affected by age alone in wild-type animals and 12 microRNAs altered in APPswe/PSEN1dE9 mice, either prior to Aβ plaque deposition (4 m) or during the development of AD-like pathogenesis (8 m or 15 m). Importantly, we found that differing sets of microRNAs were identified at each time point. Functional analysis of these data revealed that while common biological pathways, such as Inflammatory Response, were enriched throughout the disease process, Free Radical Scavenging, Immunological Disease, and Apoptosis Signaling were specifically enriched later in the disease process. Overall, this study reinforces that distinct biological processes underpin the early versus late stages of AD-like pathogenesis and highlights potential pre-symptomatic microRNAs biomarkers of neurodegeneration.

Incident cognitive impairment: longitudinal changes in molecular, structural and cognitive biomarkers

Longer periods are needed to examine how biomarker changes occur relative to incident sporadic cognitive impairment. We evaluated molecular (CSF and imaging), structural, and cognitive biomarkers to predict incident cognitive impairment and examined longitudinal biomarker changes before and after symptomatic onset. Data from participants who were cognitively normal, underwent amyloid imaging using Pittsburgh compound B and/or CSF studies, and at least two clinical assessments were used. Stepwise Cox proportional hazards models tested associations of molecular (Pittsburgh compound B; CSF amyloid-β42, tau, ptau181, tau/amyloid-β42, ptau181/amyloid-β42), structural (normalized hippocampal volume, normalized whole brain volume), and cognitive (Animal Naming, Trail Making A, Trail Making B, Selective Reminding Test - Free Recall) biomarkers with time to Clinical Dementia Rating (CDR) > 0. Cognitively normal participants (n = 664), aged 42 to 90 years (mean ± standard deviation = 71.4 ± 9.2) were followed for up to 16.9 years (mean ± standard deviation = 6.2 ± 3.5 years). Of these, 145 (21.8%) participants developed a CDR > 0. At time of incident cognitive impairment, molecular, structural, and cognitive markers were abnormal for CDR > 0 compared to CDR = 0. Linear mixed models indicated rates of change in molecular biomarkers were similar for CDR = 0 and CDR > 0, suggesting that the separation in values between CDR = 0 and CDR > 0 must have occurred prior to the observation period. Rate of decline for structural and cognitive biomarkers was faster for CDR > 0 compared to CDR = 0 (P < 0.0001). Structural and cognitive biomarkers for CDR > 0 diverged from CDR 0 at 9 and 12 years before incident cognitive impairment, respectively. Within those who developed CDR > 0, a natural separation occurred for Pittsburgh compound B values. In particular, CDR > 0 who had at least one APOE ɛ4 allele had higher, and more rapid increase in Pittsburgh compound B, while APOE ɛ2 was observed to have slower increases in Pittsburgh compound B. Of molecular biomarker-positive participants followed for at least 10 years (n = 16-23), ∼70% remained CDR = 0 over the follow-up period. In conclusion, conversion from cognitively normal to CDR > 0 is characterized by not only the magnitude of molecular biomarkers but also rate of change in cognitive and structural biomarkers. Findings support theoretical models of biomarker changes seen during transition to cognitive impairment using longitudinal data and provide a potential time for changes seen during this transition. These findings support the use of molecular biomarkers for trial inclusion and cognitive/structural biomarkers for evaluating trial outcomes. Finally, results support a potential role for APOE ɛ in modulating amyloid accumulation in CDR > 0 with APOE ɛ4 being deleterious and APOE ɛ2 protective.

Three-dimensional pseudocontinuous arterial spin labeling and susceptibility-weighted imaging associated with clinical progression in amnestic mild cognitive impairment and Alzheimer's disease

BACKGROUND

This study aimed to evaluate the value of 3-dimensional pseudocontinuous arterial spin labeling (3D-pcASL) and susceptibility-weighted imaging (SWI) for the early disease-sensitive markers of conversion from amnestic MCI (aMCI) to Alzheimer disease (AD) in this process.

METHODS

Forty patients with aMCI and AD respectively were recruited in the study, and 40 healthy subjects were taken as controls. Data were recorded using 3T MR scanner. We assessed the cerebral blood flow (CBF) in 11 different regions of interest, and counted number of microhemorrhages (MB) in 3 regions of brain lobes, bilateral basal ganglia/thalamus, and brain stem/cerebellum, and then investigated correlations between Montreal Cognitive Assessment (MoCA) scores, CBF, and susceptibility-weighted imaging (SWI) features in these 3 groups.

RESULTS

The results revealed that for AD patients, the MoCA scores and CBF values in frontal gray matter (FGM), occipital gray matter (OGM), temporal gray matter (TGM), parietal gray matter (PGM), hippocampus, anterior cingulate cortex (ACC), precuneus, posterior cingulate cortex (PCC), precuneus, basal ganglia and thalamus decreased compared with aMCI patients and control group, and significant difference was revealed among the 3 groups. While in cerebellum, statistical significance was only found between AD patients and control group. On SWI, the average numbers of hemorrhage in regions of lobes for AD patients were significantly higher than aMCI patients and control group. The same results occurred in the bilateral basal ganglia/thalamus. We further found the MoCA score was positively correlated with CBF, but negatively correlated with hypointense signal on SWI.

CONCLUSION

3D-pCASL and SWI have promising potential to be biomarkers for conversion from aMCI to AD in this process.

Association between white matter lesions and the cerebral glucose metabolism in patients with cognitive impairment

AIM

White matter lesions (WMLs), detected as hyperintensities on T2-weighted MRI, represent small vessel disease in the brain and are considered a potential risk factor for memory and cognitive impairment. It has not been sufficiently evident that cognitive impairment in patients with Alzheimer's disease is caused by WMLs as well as β-amyloid (Aβ) pathology. The aim of this study was to evaluate relationship between WMLs and cerebral glucose metabolism in patients with cognitive impairment after adjustment of cerebral Aβ burden.

MATERIALS AND METHODS

Eighty-three subjects with cognitive performance ranging from normal to dementia, who underwent brain MRI and ¹⁸F-florbetaben positron emission tomography (PET) and ¹⁸F-fluorodeoxyglucose PET, were included in this cross-sectional study. The Fazekas scale was used to quantify WMLs on brain T2-weighted MRI. The cerebral Aβ burden and cerebral glucose metabolism were quantitatively estimated using volume-of-interest analysis. Differences in the regional cerebral glucose metabolism were evaluated between low-WML (Fazekas scale<2) and high-WML (Fazekas scale≥2) groups. Multiple linear regression analysis adjusted for age, sex and cerebral Aβ burden was performed to evaluate the relationship between the Fazekas scale score and cerebral glucose metabolism.

RESULTS

The regional cerebral glucose metabolism for the bilateral frontal, temporal, and parietal cortices, and limbic lobes in the high-WML group were significantly lower than those in the low-WML group. There were significant negative correlations between the Fazekas scale score and regional cerebral glucose metabolism in the bilateral frontal, bilateral temporal and left parietal cortices, and bilateral limbic lobes. Multiple linear regression analysis revealed that the Fazekas scale score was an independent determinant of the glucose metabolism in the bilateral frontal and temporal cortices and limbic lobes.

CONCLUSIONS

WMLs are associated with decreased cerebral glucose metabolism. Our findings suggest that small vessel disease, as well as Aβ pathology, may contribute to cognitive impairment in patients with Alzheimer's disease.

Prevalence and risk of progression of preclinical Alzheimer's disease stages: a systematic review and meta-analysis

Background

Alzheimer’s disease (AD) pathology begins several years before the clinical onset. The long preclinical phase is composed of three stages according to the 2011National Institute on Aging and Alzheimer’s Association (NIA-AA) criteria, followed by mild cognitive impairment (MCI), a featured clinical entity defined as “due to AD”, or “prodromal AD”, when pathophysiological biomarkers (i.e., cerebrospinal fluid or positron emission tomography with amyloid tracer) are positive. In the clinical setting, there is a clear need to detect the earliest symptoms not yet fulfilling MCI criteria, in order to proceed to biomarker assessment for diagnostic definition, thus offering treatment with disease-modifying drugs to patients as early as possible. According to the available evidence, we thus estimated the prevalence and risk of progression at each preclinical AD stage, with special interest in Stage 3.

Methods

Cross-sectional and longitudinal studies published from April 2008 to May 2018 were obtained through MEDLINE-PubMed, screened, and systematically reviewed by four independent reviewers. Data from included studies were meta-analyzed using random-effects models. Heterogeneity was assessed by I² statistics.

Results

Estimated overall prevalence of preclinical AD was 22% (95% CI = 18–26%). Rate of biomarker positivity overlapped in cognitively normal individuals and people with subjective cognitive decline. The risk of progression increases across preclinical AD stages, with individuals classified as NIA-AA Stage 3 showing the highest risk (73%, 95% CI = 40–92%) compared to those in Stage 2 (38%, 95% CI = 21–59%) and Stage 1 (20%, 95% CI = 10–34%).

Conclusion

Available data consistently show that risk of progression increases across the preclinical AD stages, where Stage 3 shows a risk of progression comparable to MCI due to AD. Accordingly, an effort should be made to also operationalize the diagnostic work-up in subjects with subtle cognitive deficits not yet fulfilling MCI criteria. The possibility to define, in the clinical routine, a patient as “pre-MCI due to AD” could offer these subjects the opportunity to use disease-modifying drugs at best.

Electronic supplementary material

The online version of this article (10.1186/s13195-018-0459-7) contains supplementary material, which is available to authorized users.

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.

Imaging correlations of tau, amyloid, metabolism, and atrophy in typical and atypical Alzheimer's disease

INTRODUCTION

Neuroimaging modalities can measure different aspects of the disease process in Alzheimer's disease, although the relationship between these modalities is unclear.

METHODS

We assessed subject-level regional correlations between tau on [¹⁸F]AV-1451 positron emission tomography (PET), β amyloid on Pittsburgh compound B PET, hypometabolism on [¹⁸F] fluorodeoxyglucose PET, and cortical thickness on magnetic resonance imaging in 96 participants with typical and atypical Alzheimer's disease presentations. We also assessed how correlations between modalities varied according to age, presenting syndrome, tau-PET severity, and asymmetry.

RESULTS

[¹⁸F]AV-1451 uptake showed the strongest regional correlation with hypometabolism. Correlations between [¹⁸F]AV-1451 uptake and both hypometabolism and cortical thickness were stronger in participants with greater cortical tau severity. In addition, age, tau asymmetry, and clinical diagnosis influenced the strength of the correlation between [¹⁸F]AV-1451 uptake and cortical thickness.

DISCUSSION

These findings support a close relationship between tau and hypometabolism in Alzheimer's disease but show that correlations between neuroimaging modalities vary across participants.

Imaging Aβ and tau in early stage Alzheimer's disease with [18F]AV45 and [18F]AV1451

BACKGROUND

AD is a progressive neurodegenerative disorder that is associated with the accumulation of two different insoluble protein aggregates, Aβ plaques and hyperphosphorylated tau. This study aimed to investigate the optimal acquisition and quantification of [18F]AV45 and [18F]AV1451 to image Aβ and tau, respectively, in subjects with AD. Fifteen subjects with early stage AD underwent a T1-weighted structural MRI and two dynamic PET scans to image Aβ (60 min, [18F]AV45) and tau (120 min, [18F]AV1451). Both dynamic BPND and static SUVR outcome measures were calculated and compared for 12 out of 15 subjects who completed 60 min of the Aβ PET scan and at least 110 min of the tau PET scan. The SRTM and reference Logan graphical analysis were applied to the dynamic data to estimate regional BPND values and SUVR ratios from the static data. Optimal acquisition windows were explored for both the dynamic and static acquisitions. In addition, the spatial correlation between regional Aβ and tau signals was explored.

RESULTS

Both the SRTM and graphical analysis methods showed a good fit to the dynamic data for both Aβ and tau dynamic PET scans. Mean regional BPND estimates became stable 30 min p.i. for [18F]AV45 and 80 min p.i. for [18F]AV1451. Time stability analysis of static SUVR data showed that the outcome measure starts to become stable for scan windows of 30-50 min p.i. for [18F]AV45 and 80-100 min p.i. for [18F]AV1451. The results from these time windows correlated well with the results from the full dynamic analysis for both tracers (R2 = 0.74 for [18F]AV45 and R2 = 0.88 for [18F]AV1451). There was a high correlation between amyloid uptake estimate using both dynamic analysis methods in thalamus and tau uptake in thalamus, hippocampus and amygdala.

CONCLUSIONS

Short static PET scans at appropriate time windows provided SUVR values which were in reasonable agreement with BPND values calculated from dynamic scans using SRTM and reference Logan. These simplified methods may be appropriate for classification and intervention studies, although caution should be employed when considering interventional studies where blood flow and extraction could change.