Amyloid Imaging Research Articles

Tau as a diagnostic instrument in clinical trials to predict amyloid in Alzheimer's disease.

Alzheimer's disease (AD) is characterized by the presence of both amyloid and tau pathology. In vivo diagnosis can be made with amyloid and tau positron emission tomography (PET) imaging. Emergent evidence supports that amyloid and tau accumulation are associated and that amyloid accumulation may precede that of tau. This report further investigates the relationship between amyloid and tau to assess whether elevated cortical tau can predict elevated amyloid in participants with early symptomatic AD. Florbetapir F18 and flortaucipir F18 uptake were evaluated from baseline PET scans collected in three multi-center studies with cognitively impaired participants, including A05 (N = 306; NCT02016560), TB (N = 310; TRAILBLAZER-ALZ; NCT03367403), and TB2 (N = 1165; TRAILBLAZER-ALZ 2; NCT04437511). Images were assessed using visual and quantitative approaches to establish amyloid (A+) and tau (T+) positivity, as well as a combination method (tauVQ) to establish T+. Associations between global amyloid and tau were evaluated with positive and negative predictive values (PPV, NPV) and likelihood ratios (LR+, LR-). Predictive values within subgroups according to ethnicity, race, cognitive score, age, and sex were also evaluated. The relationship between regional tau (four target and two reference regions were tested) and global amyloid was investigated in A05 participant scans using receiver-operating characteristic (ROC) curves. PPV for amyloid positivity was ≥93% for all three trials using various A+ and T+ definitions, including visual, quantitative, and combination methods. Population characteristics did not have an impact on A+ predictability. Regional analyses (early tau (Eτ) volume of interest (VOI), temporal, parietal, frontal) revealed significant area under the ROC curve in Eτ VOI compared to frontal region, regardless of reference region and consistent among visual and quantitative A+ definitions (p < 0.001). These findings suggest that a positive tau PET scan is associated (≥93%) with amyloid positivity in individuals with early symptomatic AD, with the potential benefits of reducing clinical trial and health care expenses, radiation exposure, and participant time. Positron emission tomography (PET) evaluates candidates for Alzheimer's disease (AD) research. A positive tau PET scan is associated (≥93%) with amyloid positivity.A positive amyloid PET is not necessarily associated with tau positivity.Tau PET could be the sole diagnostic tool to confirm candidates for AD trials.

Safety of Disclosing Amyloid Imaging Results to MCI and AD Patients

Safety of Disclosing Amyloid Imaging Results to MCI and AD Patients

Prognostic value of imaging-based ATN profiles in a memory clinic cohort.

The ATN model represents a research framework used to classify subjects based on the presence or absence of Alzheimer's disease (AD) pathology through biomarkers for amyloid (A), tau (T), and neurodegeneration (N). The aim of this study was to assess the relationship between ATN profiles defined through imaging and cognitive decline in a memory clinic cohort. One hundred-eight patients from the memory clinic of Geneva University Hospitals underwent complete clinical and neuropsychological evaluation at baseline and 23 ± 5months after inclusion, magnetic resonance imaging, amyloid and tau PET scans. ATN profiles were divided into four groups: normal, AD pathological change (AD-PC: A + T-N-, A + T-N +), AD pathology (AD-P: A + T + N-, A + T + N +), and suspected non-AD pathology (SNAP: A-T + N-, A-T-N + , A-T + N +). Mini-Mental State Examination (MMSE) scores were significantly different among groups, both at baseline and follow-up, with the normal group having higher average MMSE scores than the other groups. MMSE scores changed significantly after 2years only in AD-PC and AD-P groups. AD-P profile classification also had the largest number of decliners at follow-up (55%) and the steepest global cognitive decline compared to the normal group. Cox regression showed that participants within the AD-P group had a higher risk of cognitive decline (HR = 6.15, CI = 2.59-14.59), followed by AD-PC (HR = 3.16, CI = 1.17-8.52). Of the different group classifications, AD-P was found to have the most significant effect on cognitive decline over a period of 2years, highlighting the value of both amyloid and tau PET molecular imaging as prognostic imaging biomarkers in clinical practice.

Advanced machine learning approach to increase diagnostic accuracy in atypical Alzheimer’s disease cases

AbstractBackgroundThe early diagnosis of Alzheimer’s disease (AD) and other types of dementia is essential in clinical practice. Up to 50% of patients with any form of dementia may remain undiagnosed during their lifetime and the diagnosis of AD may often be inaccurate. In assessing patients, accurate diagnosis based on only clinical data and structural (MRI or CT) imaging is suboptimal in a subset of patients with atypical presentation. Although amyloid PET imaging and CSF biomarkers can provide incremental benefits, numerous obstacles preclude their wider use in clinical practice. In this work, a parametric study was performed to create a machine learning (ML) model to accurately diagnose hard‐to‐detect cases of AD, to mitigate the need for more expensive or invasive testing such as amyloid imaging or spinal tap.MethodData from 173 participants (MCI and mild dementia), including 105 patients (60%) with AD and 68 (40%) Non‐AD patients, were included from our memory clinic. The dataset for each participant included pertinent history, AD risk factors, scores on a neuropsychological testing battery, functional status, and MRI volumetric studies. Participants were divided into two groups: Group I (easy‐to‐detect) with a high probability of AD diagnosis based on clinical data (subjective and objective amnestic presentation) and volumetric studies (hippocampal and medial temporal lobe atrophy). Group II (hard‐to‐detect), who did not fit into the first group and the diagnosis was confirmed with either amyloid imaging or CSF analysis. The distribution of diagnosis by group: 1‐ Easy to detect (43 AD, 29 Non‐AD),2‐ Hard to detect (62 AD, 39 Non‐AD), a total of three experiments with different combinations of groups were conducted, 1‐ All Groups (Exp.1.), 2‐ Hard to detect (Exp.2.), 3‐ Easy to detect (Exp.3.). In all experiments, Leave‐One‐Out cross‐validation technique was performed.ResultA total number of 132 features utilized, by employing feature selection approach, 13 features were selected. Our model attains the highest accuracy of 87.27% on classification of Exp.1., 83.28% on Exp.2. and 95.80% on Exp.3.ConclusionWe investigated discrimination of AD from Non‐AD on hard‐to‐detect cases using ML. The proposed method obtains competitive performance and achieves improved accuracy results.

Fused multi‐modal similarity network as prior in guiding brain imaging genetic association

AbstractBackgroundBrain imaging genetics aims to explore the genetic architecture underlying brain structure and functions. Recent studies showed that incorporation of prior knowledge, such as subject diagnosis information and brain regional correlation, can help identify significantly stronger imaging genetic associations. However, sometimes such information may be incomplete or even unavailable. In this project, we explore a new data‐driven prior knowledge that captures the subject‐level similarity matrix derived from fusion of multi‐modal similarity networks. It was built into the sparse canonical correlation analysis model, which is expected to identify brain imaging and genetic markers that contribute to the fused similarity matrix.MethodThe Amyloid and Tau imaging data were downloaded from the ADNI database. We have 800 and 291 subjects for amyloid and tau imaging respectively (Table. 1). SNPs with p&lt;5×10−6 (n = 1080) were extracted from the large‐scale GWAS summary statistics from the IGAP. We built similarity networks for imaging and genetic data individually using normalized mutual information and then fused these network following (Fig. 1). Fused network was then applied as a prior knowledge in the discriminative sparse canonical correlation analysis (DSCCA) model. We also tested other prior knowledge in SCCA model for comparison including diagnosis group network and transcriptomic brain network. All methods were evaluated using ADNI genotype, amyloid and tau imaging data, where nested 5‐fold cross validation with the same set of training and test data was applied for fair comparison.ResultSCCA with fused network and diagnosis group showed better performances over others in both training and testing data set (Table. 2,3). In Fig. 2 are top 10 ROIs identified by SCCA with fused network, whose amyloid deposition are strongly associated with APOE SNP (rs429358). For tau, we identified left and right amygdala with tau accumulation strongly and consistently associated with rs429358 (fig.3).ConclusionAs prior knowledge incorporated into SCCA algorithm, fused similarity network can help improve the performance of imaging genetic association over traditional SCCA and that guided by the brain co‐expression network. It showed comparable performance as diagnosis information and thus could be a potential prior knowledge to consider when lack of diagnosis information.

Assessment of myelin in Alzheimer’s by a R1 and the relationship to amyloid burden

AbstractBackgroundAlzheimer’s disease (AD) is well known as a grey matter disorder, characterized by substantial and progressive neurodegeneration. However, less is known about myelin loss in AD, particularly as the disease progresses in severity. To gain a better insight on the role of myelin over the clinical and biologic progression of AD, we tested for differences in quantitative relaxometry R1, a metric sensitive to brain myelin, among individuals who were cognitively unimpaired (CU), diagnosed with mild cognitive impairment (MCI), and diagnosed with AD, in addition to testing for associations between R1 and amyloid PET. Understanding the course of myelin injury in disease progression is expected to address the gap in knowledge about the trajectory of myelin alterations in AD.MethodsParticipants from the Alzheimer’s Disease Connectome Project underwent T1‐weighted (T1w) MPnRAGE with motion‐correction and amyloid imaging with [11C]PiB PET. Composite T1w images and R1 maps were reconstructed at 1mm isotropic resolution. T1w scans were bias‐field corrected and denoised before undergoing tissue segmentation. R1 was sampled and bilaterally averaged across cortical‐adjacent white matter (WM). An average WM‐R1 was generated for each participant by calculating the mean of cortical regions R1. MANOVA and regression analyses were used to analyze the difference and relationship between each group’s WM‐R1 and PiB‐Index.ResultThe three groups showed an association with PiB‐Index and WM‐R1, Pillai’s Trace = 0.28, F(4, 110) = 4.56, p&lt;0.01. Participants with AD showed higher amyloid and lower WM‐R1. In contrast, UC and MCI participants showed lower amyloid and WM‐R1. A significant correlation was found in individuals with AD wherein amyloid burden was positively correlated with WM‐R1 across the whole brain, R2 = 0.64, F(1, 6) = 10.99, p = 0.016.ConclusionOur findings indicate myelin differences by clinical status, in addition to relationships between amyloid and R1 indices of myelin. While AD was associated with lower myelin as indexed by R1, we unexpectedly found that higher cortical amyloid in AD was associated with higher myelin indexed by R1, suggesting an increase in myelination as AD progresses. Overall, lower myelin in clinical AD, accompanied by the higher myelin with greater amyloid burden suggests possible reparative mechanisms in response to neurodegeneration in AD.

Association of Apolipoprotein E ε4 with Tau and Amyloid‐β PET in a memory clinic cohort

AbstractBackgroundThe Apolipoprotein E ε4 (ApoE4) is the strongest genetic risk factor for Alzheimer’s disease (AD). Previous studies have found a link between ApoE4 genotype and tau (T) deposition in the medial temporal lobe (MTL). However, further studies disentangling the influence of genotype on tau deposition and amyloid (A) are still needed. The aim of this study was to assess tau and amyloid deposition depending on subject genotype in a memory clinic cohort.Method89 subjects from the Geneva Memory Center underwent amyloid and tau PET imaging, and ApoE genotyping (ApoE2 carriers, ApoE3 homozygotes, ApoE4 carriers). The remaining subjects were classified as ApoE3. Amyloid uptake has been calculated as centiloid values and tau uptake as standardized uptake value ratio (SUVR), normalized to cerebellar uptake. Positivity for each biomarker was based on thresholds published by previous studies. Correlation between amyloid centiloid and tau SUVR in general and by genotype was calculated. ANOVA with Tuckey correction for multiple comparisons was used to assess differences between tau or amyloid PET uptake across different genotypes.Result85% of the A+T+ subjects were carriers of the ApoE4 gene. The association between neocortical amyloid and MTL tau stratified by genotype showed that these measures are correlated in ApoE4 carriers (r = 0.52, p = 0.001). The correlation was not significant in non‐carriers. MTL tau SUVR was higher in ApoE4 carriers and significantly different when compared to non‐carriers. Furthermore, tau SUVR from lateral and superior temporal lobes were also significantly different in ApoE4 carriers vs. non‐carriers even when not considering the centiloid value, showing an amyloid‐independent association between tau SUVR and genotype is not limited to the MTL. Centiloid values showed similar differences when comparing ApoE4 carriers with non‐carriers.ConclusionApoE4 gene carriers present a significant increase of both tau and amyloid load with respect to non‐carriers. Furthermore, increased tau deposition independent from amyloid load is not limited to the MTL, with the entire temporal lobe showing higher tau pathology in ApoE4 carriers.

Long‐term memory over 1‐week and markers of brain pathology in cognitively unimpaired individuals from a Colombian cohort with autosomal dominant Alzheimer’s disease

AbstractBackgroundLong‐term forgetting (LTF) over days or weeks may be a sensitive early marker of Alzheimer’s disease (AD), useful for preclinical AD trials. We examined associations between LTF and markers of brain pathology in individuals from the world’s largest kindred with autosomal dominant AD due to the Presenilin‐1 (PSEN1) E280A mutation.MethodsA total of 14 cognitively unimpaired PSEN1 mutation carriers (mean age: 35.9 years) and 16 matched non‐carriers (mean age: 32.8 years) from the Colombia‐Boston Biomarker Study of autosomal dominant AD were included. Participants underwent amyloid (11‐C Pittsburgh compound B) and tau (Flortaucipir) PET imaging, and memory tasks (NEUROPSI word list, stories and semi‐complex figure). We examined performance on learning and delayed recall after a 20‐min, 1‐day and 7‐day interval. LTF was calculated for the three tasks as the proportion of material retained at 20 min that was recalled 7 days later. Mann‐Whitney tests were conducted to compare memory performance and LTF between PSEN1 mutation carriers and non‐carriers. Spearman’s correlations were used to examine the association between LTF and brain pathology markers of amyloid burden and regional tau (entorhinal and inferior temporal cortices). Fisher’s exact tests were used to compare correlation coefficients between groups.ResultsLearning did not differ between carriers and non‐carriers. Compared to non‐carriers, carriers had lower 20‐min recall in all three tasks (p&lt;0.05), and lower 1‐day and 7‐day recall of word list and stories. There were no group differences in word list, stories or figure LTF. Further, no group differences were observed in the association of amyloid burden and LTF on any of the tasks. Lower word list LTF was associated with greater entorhinal burden among carriers, and the strength of this association was significantly different between carriers and non‐carriers (p = 0.046; carriers: r = 0.73, p = 0.003; non‐carriers: r = 0.43, p = 0.094).ConclusionsOur preliminary findings show that, while rates of LTF over 7 days did not differ between PSEN1 carriers and non‐carriers, carriers had lower verbal memory recall over 1 and 7 days. Further, LTF was associated with early tau accumulation, suggesting that longer recall intervals may be more sensitive to detect subtle memory changes in cognitively unimpaired individuals at risk for AD.

Analysis of amyloid deposits in the retina and implications for use as a biomarker of Alzheimer’s Disease

AbstractBackgroundIn humans and the naturally occurring canine model of Alzheimer’s disease (AD), we and others observe amyloid deposits in the retina en face. We have previously shown that detecting the number of amyloid deposits with polarised light, using wide field, dye free retinal imaging, can predict the degree of severity of AD pathology in the brain. But some who take thin, transverse retinal sections find amyloid while others do not. Here, we report the surface area of retinal amyloid deposits in human and the canine model of AD.MethodFormalin‐fixed retinas from 29 individuals with a moderate to high likelihood of AD (with some comorbidities excluded) and 20 canines were flat‐mounted and imaged in florescence and polarized light (Fig 1). The full retina was imaged for 24 human and 16 canine retinas. For 4 canines, in‐vivo blue light fluorescent retinal images were collected before and after injection of an amyloid dye, CRANAD‐28. Deposits visible only after injection were counted. Fluorescence and polarization images were compared post‐mortem in a canine after an injection of CRANAD‐28. Amyloid deposits were segmented using custom methods. The total area of segmented deposits was then divided by the total retinal area imaged.ResultIn retinas from individuals with AD brain pathology, the area of the retinal covered by amyloid deposits was on average 0.01% and correlated with the number of deposits (Fig 2). In vivo (Fig 3), the area of the canine retina covered by amyloid deposits was on average 0.29% (Fig 4). In one ex vivo retina, fluorescent and polarization positive deposits had a concurrence of 93% and the average area of fluorescent deposits (0.14%) was larger than the same deposits imaged in polarimetry (0.08%) (Fig. 5).ConclusionThese findings and our previous report of an almost constant density of amyloid deposits across the retina, should guide in vivo imaging of amyloid in the retina as a biomarker of AD. Across all human and canine retinas, amyloid deposits covered less than 0.6% of the retinal surface. Careful, extensive sampling is needed. The surface area of amyloid deposits is an additional potential biomarker of disease severity.

Appropriateness of applying CSF biomarker cutoffs from Alzheimer’s disease to Parkinson’s disease

AbstractBackgroundWhile cutoffs for abnormal levels of the cerebrospinal fluid (CSF) biomarkers amyloid β 1‐42 (Aβ 1‐42), total tau (t‐tau), phosphorylated tau (p‐tau), and the ratios of t‐tau/Aβ 1‐42 and p‐tau/Aβ 1‐42, have been established in Alzheimer’s disease (AD), biologically relevant cutoffs have not been studied extensively in Parkinson’s disease (PD). We aim to assess the suitability and diagnostic accuracy of established AD‐derived CSF biomarker cutoffs in the PD population.MethodBaseline and longitudinal data on CSF biomarkers, cognitive diagnoses, and PET amyloid imaging in 423 newly diagnosed patients with PD from the Parkinson’s Progression Markers Initiative (PPMI) cohort were used to evaluate established AD biomarker cutoffs compared with optimal cutoffs derived from the PPMI cohort.ResultUsing PET amyloid imaging as the gold standard for AD pathology, the optimal cutoff of Aβ 1‐42 was higher than the AD cutoff, the optimal cutoffs of t‐tau/Aβ 1‐42 and p‐tau/Aβ 1‐42 were lower than the AD cutoffs, and their confidence intervals (CIs) did not overlap with the AD cutoffs. Optimal cutoffs for t‐tau and p‐tau to predict cognitive impairment were significantly lower than the AD cutoffs, and their CIs did not overlap with the AD cutoffs.ConclusionOptimal cutoffs for the PPMI cohort for Aβ 1‐42, t‐tau/Aβ 1‐42, and p‐tau/Aβ 1‐42 to predict amyloid‐PET positivity and for t‐tau and p‐tau to predict cognitive impairment differ significantly from cutoffs derived from AD populations. The presence of additional pathologies such as alpha‐synuclein in PD may lead to disease‐specific CSF biomarker characteristics.

Using amyloid PET as a biomarker to detect progression of early Alzheimer disease

AbstractBackgroundAlzheimer’s disease (AD), the most common form of dementia, is a complex polygenic disease with genetic, cellular, pathologic, and clinical heterogeneity. Recently, significant attempts have been made for identifying AD biomarkers for reliably tracking disease progression in its early asymptomatic stages. To this end, amyloid PET imaging has provided useful information tracking accumulation of parenchymal amyloid beta (Aβ) deposits in the brain. Previous investigations have conducted genetic association studies of CSF Aβ42, both continuously as well as using a threshold for amyloid positivity, and identified novel genes and loci potentially contributing to the preclinical stage in AD. In a recent case‐control based study using amyloid PET as a quantitative trait, Raghavan et al., identified a novel locus for amyloidosis within RBFOX1 gene in 4,314 participants, however, further investigations are needed to replicate and expand on these findings.MethodIn order to investigate the underlying genetic basis for brain amyloidosis in AD, we systematically analyzed the largest collection of amyloid imaging data (N = 6,320), across multiple ethnicities from multicenter cohorts (ADRC, A4, DIAN, ADNI, ADNIDOD, UPitt, and HABS) as a quantitative trait to identify the functional variants and genes driving the association of AD. Furthermore, we have conducted diagnosis‐, gender‐, and APOE‐stratified analyses to investigate the effect of these variables on the brain amyloidosis.ResultPreliminary results found a strong APOE signal in chr19 (min P = 1e‐185) and a genome‐wide significant hit (P = 2e‐08) in the chromosome 7 (potentially, associated to TMEM106B gene) warranting further investigation (Fig. 1). Moreover, some suggestive signals were detected in chr10 (P = 1e‐07) and chr12 (P = 5e‐07). Consistent with previous finding, we found RBFOX1 to be associated with increased amyloid burden (β = 0.07), however, the signal was only nominally significant (P = 0.03). Additional analyses including multi‐ancestry meta‐analysis using FHS, AIBL, and EISAI cohorts and post‐GWAS analyses are ongoing.ConclusionThe identification of pre‐clinical AD‐specific molecular signatures and pathways will enable the characterization of appropriate therapeutic targets for the prevention and/or treatment of AD. Results generated from our investigation will further serve to generate prediction models for amyloid positivity and Mendelian Randomization (MR) analyses.

Kernel flow and the eye‐ brain connectome: Towards a more robust technique to identify high‐risk individuals before cognitive decline

AbstractBackgroundOptical brain imaging allows an understanding of the structure and function of neurons. Researchers can help patients who suffer from nervous system diseases, such as Alzheimer’s disease and Lewy’s Body Dementia, by exploring brain structure and function. Despite the advent of new PET diagnostics to image amyloid deposits in living patients' brains, there remains a need for more readily available and affordable diagnostic testing.MethodA time‐domain functional near‐infrared spectroscopy (TD‐fNIRS) system (Kernel Flow, Kernel Inc.) is presented. The system has been designed as a small modular device that can provide real‐time monitoring of tissue oxygenation in the brain as subjects take tests, perform cognitive tasks, and/or receive stimulation.ResultWe show the performance of the wearable TD‐fNIRS device in multiple settings and aim to use this technology to develop a protocol for identifying and tracking brain changes in aging and dementia from the preclinical stage of the disease.ConclusionLaboratory tests with study subjects and preliminary data recorded demonstrate the potential of the system as a clinical tool to be employed in multiple settings to not only monitor brain activity but with the potential for additional human neuroscience applications, making neuroimaging more accessible across health and science platforms.

Improving evaluation for TTR amyloidosis by interactive filtering of Tc-99m PYP SPECT images. The role for "clean blood pool" imaging.

Myocardial imaging with bone agents such as Tc-99m PYP and HMDP has assumed a central role in the evaluation of patients with suspected transthyretin (TTR) amyloidosis. Visual scoring (VS) (0-3 +) and the heart to contralateral lung ratio (HCL) classify many patients as equivocal when mediastinal uptake is apparent but cannot be further differentiated into myocardial uptake versus blood pool. SPECT imaging has been recommended but current reconstruction protocols frequently produce amorphous mediastinal activity that also fails to discriminate between myocardial activity and blood pool. We hypothesized that interactive filtering interactively using a deconvolving filter would improve this. We identified 176 sequential patients referred for TTR amyloid imaging. All patients had planar imaging, 101 had planar imaging with a large field of view camera that allowed HCL measurements. SPECT imaging was performed on a 3-headed digital camera with lead fluorescence attenuation correction. One study was excluded for technical reasons. We created software to allow interactive filtering while reconstructing the images then overlay them on attenuation mu maps to assist localization of myocardial/mediastinal uptake. Conventional Butterworth and an interactive inverse Gaussian filters were employed to differentiate myocardial uptake from residual blood pool. We defined "clean blood pool" (CBP) as recognizable blood pool with no activity in the surrounding myocardium. A scan was determined diagnostic if it showed CBP, positive uptake or no identifiable mediastinal uptake. 76/175 (43%) were equivocal (1 +) by visual uptake. Of these 22 (29%) were diagnostic by Butterworth but 71 (93%) were by inverse gaussian (p < .0001). 71/101 (70%) were equivocal by HCL (1-1.5). Of these, 25 (35%) were diagnostic by Butterworth but 68 (96%) were diagnostic by inverse gaussian (p < .0001). This was driven by a greater than threefold increase in the identification of CBP by inverse gaussian filtering. CBP can be identified in the vast majority of patients with equivocal PYP scans using optimized reconstruction and can greatly reduce the number of equivocal scans.

A Case Report of Early-Onset Alzheimer's Disease Using 18F-FDG PET and 18F-FBB PET.

We describe a 40-year-old female patient who presented with sleep disturbance, intermittent headache, and gradual subjective cognitive decline. 18F-fluorodeoxyglucose (FDG) positron emission tomography (PET) showed mild FDG hypometabolism in bilateral parietal and temporal lobes. However, 18F-florbetaben (FBB) amyloid PET revealed diffuse amyloid retention in the lateral temporal cortex, frontal cortex, posterior cingulate cortex/precuneus, parietal cortex, and cerebellum. This finding supports the clinical significance of amyloid imaging in diagnostic work-up of early-onset Alzheimer's disease (EOAD).

Fused multi-modal similarity network as prior in guiding brain imaging genetic association.

Brain imaging genetics aims to explore the genetic architecture underlying brain structure and functions. Recent studies showed that the incorporation of prior knowledge, such as subject diagnosis information and brain regional correlation, can help identify significantly stronger imaging genetic associations. However, sometimes such information may be incomplete or even unavailable. In this study, we explore a new data-driven prior knowledge that captures the subject-level similarity by fusing multi-modal similarity networks. It was incorporated into the sparse canonical correlation analysis (SCCA) model, which is aimed to identify a small set of brain imaging and genetic markers that explain the similarity matrix supported by both modalities. It was applied to amyloid and tau imaging data of the ADNI cohort, respectively. Fused similarity matrix across imaging and genetic data was found to improve the association performance better or similarly well as diagnosis information, and therefore would be a potential substitute prior when the diagnosis information is not available (i.e., studies focused on healthy controls). Our result confirmed the value of all types of prior knowledge in improving association identification. In addition, the fused network representing the subject relationship supported by multi-modal data showed consistently the best or equally best performance compared to the diagnosis network and the co-expression network.

Large multi-ethnic genetic analyses of amyloid imaging identify new genes for Alzheimer disease

Amyloid PET imaging has been crucial for detecting the accumulation of amyloid beta (Aβ) deposits in the brain and to study Alzheimer’s disease (AD). We performed a genome-wide association study on the largest collection of amyloid imaging data (N = 13,409) to date, across multiple ethnicities from multicenter cohorts to identify variants associated with brain amyloidosis and AD risk. We found a strong APOE signal on chr19q.13.32 (top SNP: APOE ɛ4; rs429358; β = 0.35, SE = 0.01, P = 6.2 × 10–311, MAF = 0.19), driven by APOE ɛ4, and five additional novel associations (APOE ε2/rs7412; rs73052335/rs5117, rs1081105, rs438811, and rs4420638) independent of APOE ɛ4. APOE ɛ4 and ε2 showed race specific effect with stronger association in Non-Hispanic Whites, with the lowest association in Asians. Besides the APOE, we also identified three other genome-wide loci: ABCA7 (rs12151021/chr19p.13.3; β = 0.07, SE = 0.01, P = 9.2 × 10–09, MAF = 0.32), CR1 (rs6656401/chr1q.32.2; β = 0.1, SE = 0.02, P = 2.4 × 10–10, MAF = 0.18) and FERMT2 locus (rs117834516/chr14q.22.1; β = 0.16, SE = 0.03, P = 1.1 × 10–09, MAF = 0.06) that all colocalized with AD risk. Sex-stratified analyses identified two novel female-specific signals on chr5p.14.1 (rs529007143, β = 0.79, SE = 0.14, P = 1.4 × 10–08, MAF = 0.006, sex-interaction P = 9.8 × 10–07) and chr11p.15.2 (rs192346166, β = 0.94, SE = 0.17, P = 3.7 × 10–08, MAF = 0.004, sex-interaction P = 1.3 × 10–03). We also demonstrated that the overall genetic architecture of brain amyloidosis overlaps with that of AD, Frontotemporal Dementia, stroke, and brain structure-related complex human traits. Overall, our results have important implications when estimating the individual risk to a population level, as race and sex will needed to be taken into account. This may affect participant selection for future clinical trials and therapies.

Utility of Amyloid Positron Emission Tomography Imaging in Older Adults With Epilepsy and Cognitive Decline.

In older adults with cognitive decline and epilepsy, diagnosing the etiology of cognitive decline is challenging. We identified 6 subjects enrolled in the Imaging Dementia-Evidence of Amyloid Imaging Scanning (IDEAS) study and nonlesional epilepsy. Three cognitive neurologists reviewed each case to determine the likelihood of underlying Alzheimer's disease (AD) pathology. Their impressions were compared to amyloid PET findings. In 3 cases the impression was concordant with PET findings. In 2 cases "possibly suggestive," the PET reduced diagnostic uncertainty, with 1 having a PET without elevated amyloid and the other PET with intermediate amyloid. In the remaining case with lack of reviewer concordance, the significance of PET with elevated amyloid remains uncertain. This case series highlights that in individuals with a history of epilepsy and cognitive decline, amyloid PET can be a useful tool in evaluating the etiology of cognitive decline when used in an appropriate context.

Synthesis of benzylidene-benzofuranone derivatives as probes for detection of amyloid fibrils in cells

Aggregated protein is the common cause of a wide variety of neurodegenerative diseases, such as Alzheimer’s disease (AD), Parkinson’s disease, etc. It is proven that protein aggregation like amyloid β (Aβ) is one of the critical factors causing AD and, its diagnosis in the early stages of the disease is important for the treatment or prevention of AD. To have a better understanding of protein aggregation and its pathologies, there is a huge need to design and develop new and more trustworthy probe molecules for in vitro amyloid quantification and in vivo amyloid imaging. In this study, 17 new biomarker compounds, have been synthesized from benzofuranone derivatives, to detect and identify amyloid in vitro (dye-binding assay) as well as in the cell by staining method. According to the results, some of these synthetic derivatives can be considered suitable identifiers and quantifiers to detect amyloid fibrils in vitro. Compared to thioflavin T, 4 probes out of 17 probes have shown good results in selectivity and detectability of Aβ depositions, and their binding properties were also confirmed with in silico analysis. The drug-likeness prediction results for selected compounds by the Swiss ADME server show a satisfactory percentage of blood-brain barrier (BBB) permeability and gastrointestinal (GI) absorption. Among all of them, compound 10 was able to show better binding properties than others, and in vivo study showed that this compound was capable of detecting intracellular amyloid. Communicated by Ramaswamy H. Sarma

Vigorous, regular physical exercise may slow disease progression in Alzheimer's disease.

Mild to moderate exercise may decrease Alzheimer's disease (AD) risk, but the effects of vigorous, regular physical exercise remain unclear. Two patients with initial diagnoses of amnestic mild cognitive impairment (MCI) demonstrated positive AD biomarkers throughout 16 and 8 years of follow-up, with final diagnoses of mild AD and amnestic MCI, respectively. Patient 1 was diagnosed with amnestic MCI at age 64. Neuropsychological testing, magnetic resonance imaging (MRI), fluorodeoxyglucose-positron emission tomography (FDG-PET), amyloid imaging PET, and cerebrospinal fluid (CSF) biomarkers during follow-ups remained consistent with AD. By age 80, progression was minimal with Montreal Cognitive Assessment (MoCA) 26 of 30. Patient 2 was diagnosed with amnestic MCI at age 72. Neuropsychological testing, MRI, FDG-PET, and amyloid imaging PET during follow-ups remained consistent with AD. At age 80, MoCA was 27 of 30 with no clinical progression. Both patients regularly performed vigorous, regular exercise that increased after retirement/work reduction. Vigorous, regular exercise may slow disease progression in biomarker-positive amnestic MCI and mild AD.

The amyloid imaging for the prevention of Alzheimer's disease consortium: A European collaboration with global impact.

Amyloid-β (Aβ) accumulation is considered the earliest pathological change in Alzheimer's disease (AD). The Amyloid Imaging to Prevent Alzheimer's Disease (AMYPAD) consortium is a collaborative European framework across European Federation of Pharmaceutical Industries Associations (EFPIA), academic, and 'Small and Medium-sized enterprises' (SME) partners aiming to provide evidence on the clinical utility and cost-effectiveness of Positron Emission Tomography (PET) imaging in diagnostic work-up of AD and to support clinical trial design by developing optimal quantitative methodology in an early AD population. In the Diagnostic and Patient Management Study (DPMS), 844 participants from eight centres across three clinical subgroups (245 subjective cognitive decline, 342 mild cognitive impairment, and 258 dementia) were included. The Prognostic and Natural History Study (PNHS) recruited pre-dementia subjects across 11 European parent cohorts (PCs). Approximately 1600 unique subjects with historical and prospective data were collected within this study. PET acquisition with [18F]flutemetamol or [18F]florbetaben radiotracers was performed and quantified using the Centiloid (CL) method. AMYPAD has significantly contributed to the AD field by furthering our understanding of amyloid deposition in the brain and the optimal methodology to measure this process. Main contributions so far include the validation of the dual-time window acquisition protocol to derive the fully quantitative non-displaceable binding potential (BP ND ), assess the value of this metric in the context of clinical trials, improve PET-sensitivity to emerging Aβ burden and utilize its available regional information, establish the quantitative accuracy of the Centiloid method across tracers and support implementation of quantitative amyloid-PET measures in the clinical routine. The AMYPAD consortium has succeeded in recruiting and following a large number of prospective subjects and setting up a collaborative framework to integrate data across European PCs. Efforts are currently ongoing in collaboration with ARIDHIA and ADDI to harmonize, integrate, and curate all available clinical data from the PNHS PCs, which will become openly accessible to the wider scientific community.