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Objective: Alzheimer's disease (AD) and cerebral small vessel disease (cSVD), the two most common causes of dementia, are characterized by white matter (WM) alterations diverging from the physiological changes occurring in healthy aging. Diffusion tensor imaging (DTI) is a valuable tool to quantify WM integrity non‐invasively and identify the determinants of such alterations. Here, we investigated main effects and interactions of AD pathology, APOE‐ε4, cSVD, and cardiovascular risk on spatial patterns of WM alterations in non‐demented older adults. Methods: Within the prospective European Prevention of Alzheimer's Dementia study, we selected 606 participants (64.9 ± 7.2 years, 376 females) with baseline cerebrospinal fluid samples of amyloid β1‐42 and p‐Tau181 and MRI scans, including DTI scans. Longitudinal scans (mean follow‐up time = 1.3 ± 0.5 years) were obtained in a subset (n = 223). WM integrity was assessed by extracting fractional anisotropy and mean diffusivity in relevant tracts. To identify the determinants of WM disruption, we performed a multimodel inference to identify the best linear mixed‐effects model for each tract. Results: AD pathology, APOE‐ε4, cSVD burden, and cardiovascular risk were all associated with WM integrity within several tracts. While limbic tracts were mainly impacted by AD pathology and APOE‐ε4, commissural, associative, and projection tract integrity was more related to cSVD burden and cardiovascular risk. AD pathology and cSVD did not show any significant interaction effect. Interpretation: Our results suggest that AD pathology and cSVD exert independent and spatially different effects on WM microstructure, supporting the role of DTI in disease monitoring and suggesting independent targets for preventive medicine approaches. [ABSTRACT FROM AUTHOR]

INTRODUCTION: To support clinical trial designs focused on early interventions, our study determined reliable early amyloid‐β (Aβ) accumulation based on Centiloids (CL) in pre‐dementia populations. METHODS: A total of 1032 participants from the Amyloid Imaging to Prevent Alzheimer's Disease–Prognostic and Natural History Study (AMYPAD‐PNHS) and Insight46 who underwent [18F]flutemetamol, [18F]florbetaben or [18F]florbetapir amyloid‐PET were included. A normative strategy was used to define reliable accumulation by estimating the 95th percentile of longitudinal measurements in sub‐populations (NPNHS = 101/750, NInsight46 = 35/382) expected to remain stable over time. The baseline CL threshold that optimally predicts future accumulation was investigated using precision‐recall analyses. Accumulation rates were examined using linear mixed‐effect models. RESULTS: Reliable accumulation in the PNHS was estimated to occur at >3.0 CL/year. Baseline CL of 16 [12,19] best predicted future Aβ‐accumulators. Rates of amyloid accumulation were tracer‐independent, lower for APOE ε4 non‐carriers, and for subjects with higher levels of education. DISCUSSION: Our results support a 12–20 CL window for inclusion into early secondary prevention studies. Reliable accumulation definition warrants further investigations. [ABSTRACT FROM AUTHOR]

INTRODUCTION: Alzheimer's disease (AD) trial participants are often screened for eligibility by brain amyloid positron emission tomography/cerebrospinal fluid (PET/CSF), which is inefficient as many are not amyloid positive. Use of blood‐based biomarkers may reduce screen failures. METHODS: We recruited 755 non‐Hispanic White, 115 Hispanic, 112 non‐Hispanic Black, and 19 other minority participants across groups of cognitively normal (n = 417), mild cognitive impairment (n = 312), or mild AD (n = 272) participants. Plasma amyloid beta (Aβ)40, Aβ42, Aβ42/Aβ40, total tau, phosphorylated tau (p‐tau)181, and p‐tau217 were measured; amyloid PET/CSF (n = 956) determined amyloid positivity. Clinical, blood biomarker, and ethnicity/race differences associated with amyloid status were evaluated. RESULTS: Greater impairment, older age, and carrying an apolipoprotein E (apoE) ε4 allele were associated with greater amyloid burden. Areas under the receiver operating characteristic curve for amyloid status of plasma Aβ42/Aβ40, p‐tau181, and p‐tau217 with amyloid positivity were ≥ 0.7117 for all ethnoracial groups (p‐tau217, ≥0.8128). Age and apoE ε4 adjustments and imputation of biomarker values outside limit of quantitation provided small improvement in predictive power. DISCUSSION: Blood‐based biomarkers are highly associated with amyloid PET/CSF results in diverse populations enrolled at clinical trial sites. Highlights: Amyloid beta (Aβ)42/Aβ40, phosphorylated tau (p‐tau)181, and p‐tau 217 blood‐based biomarkers predicted brain amyloid positivity.P‐tau 217 was the strongest predictor of brain amyloid positivity.Biomarkers from diverse ethnic, racial, and clinical cohorts predicted brain amyloid positivity.Community‐based populations have similar Alzheimer's disease (AD) biomarker levels as other populations.A prescreen process with blood‐based assays may reduce the number of AD trial screen failures. [ABSTRACT FROM AUTHOR]

The role of structural brain magnetic resonance imaging (MRI) is becoming more and more emphasized in the early diagnostics of Alzheimer's disease (AD). This study aimed to assess the improvement in classification accuracy that can be achieved by combining features from different structural MRI analysis techniques. Automatically estimated MR features used are hippocampal volume, tensor-based morphometry, cortical thickness and a novel technique based on manifold learning. Baseline MRIs acquired from all 834 subjects (231 healthy controls (HC), 238 stable mild cognitive impairment (S-MCI), 167 MCI to AD progressors (P-MCI), 198 AD) from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database were used for evaluation. We compared the classification accuracy achieved with linear discriminant analysis (LDA) and support vector machines (SVM). The best results achieved with individual features are 90% sensitivity and 84% specificity (HC/AD classification), 64%/66% (S-MCI/P-MCI) and 82%/76% (HC/P-MCI) with the LDA classifier. The combination of all features improved these results to 93% sensitivity and 85% specificity (HC/AD), 67%/69% (S-MCI/P-MCI) and 86%/82% (HC/P-MCI). Compared with previously published results in the ADNI database using individual MR-based features, the presented results show that a comprehensive analysis of MRI images combining multiple features improves classification accuracy and predictive power in detecting early AD. The most stable and reliable classification was achieved when combining all available features.

Background: Previous results on amyloid‐PET imaging for predicting functional decline are conflicting. Given that the Centiloid approach (CL) may serve for inclusion in anti‐amyloid trials, we investigated the extent to which CL may predict functional decline in the AMYPAD‐Prognostic and Natural History Study (PNHS), including 1094 participants. Method: We analyzed, cross‐sectionally and longitudinally, the associations between CL and global and sum‐of‐boxes (SOB) Clinical Dementia Rating (CDR), and Instrumental Activity of Daily Living (IADL) scores. Mean follow‐up duration ranged from 2.7‐3.0 years (±1.1‐1.6) depending on the functional score. Longitudinal CDR‐SOB and IADL changes were modeled using linear mixed‐effects models with random intercepts and slopes, covarying age and sex, stratified by CDR global score. Analyses included comparisons between three CL groups (Aβ‐: CL<12; grey‐zone: CL = 12‐50; Aβ+: CL>50) and correlation between the baseline CL and the time for progression on the CDR global score. Result: The baseline CL was significantly higher in CDR = 0 (Mdn = 6.6) than in CDR = 0.5 participants (Mdn = 17.2; p<.001). Baseline CDR‐SOB was significantly higher in Aβ+ (M = 0.48) than in Aβ‐ (M = 0.12) and grey‐zone participants (M = 0.13, p‐values <.001). Baseline IADL was (marginally) significantly lower in Aβ+ (Mdn = 81.3) than in Aβ‐ (Mdn = 82.8, p =.02) and grey‐zone participants (Mdn = 83.5, p =.08, Table 1). Baseline CL significantly predicted subsequent changes in CDR‐SOB and IADL, both in CDR = 0 and CDR = 0.5 participants, but only in the CL>50 groups (βCL12‐50 = 0.03, p =.32,βCL>50 = 0.14, p<.001 for CDR‐SOB in CDR = 0 participants;βCL12‐50 = 0.19, p =.31,βCL>50 = 1.04, p =.01 for CDR‐SOB in CDR = 0.5 participants;βCL12‐50 = ‐0.002, p =.99, βCL>50 = ‐1.00, p =.08 for IADL in CDR = 0 participants; Fig.1). The participants who progressed from global CDR = 0 to CDR = 0.5 (N = 90) had a higher baseline CL (Mdn = 15.16) than stable participants (Mdn = 5.95, p =.002). However, the baseline CL was not associated with a shorter time of clinical progression (rs= ‐.02, p =.8; Fig.2). Conclusion: Cross‐sectionally, amyloid burden expressed in CL units was associated with functional outcomes. A baseline CL>50 predicted subsequent functional decline in CDR‐SOB and IADL scores in clinically normal and mildly clinically impaired participants. At lower CL values, no functional decline was observed during a three‐year follow‐up. Clinically normal individuals with increasing global CDR scores had higher baseline CL values, but these values were not associated with faster clinical progression. [ABSTRACT FROM AUTHOR]

Background: While the association between tau deposition on PET and brain atrophy on MRI is well documented in the literature, the relation between amyloid deposition and neurodegeneration in the earliest stages of Alzheimer's disease (AD) is less studied. We investigated this relationship in a large cohort of non‐demented individuals. Method: We included 1355 participants from the AMYPAD Prognostic & Natural History study (PNHS) with available MRI and amyloid‐PET. Among those, 753 had longitudinal MRI, with a mean follow‐up time of 3.47 years (SD = 1.55). The FreeSurfer 7.0.1 longitudinal pipeline was used to measure gray matter (GM) thickness and volumes in 40 regions (Reuter et al., 2012; Desikan et al., 2006). From PET scans, cortical amyloid burden was assessed globally using the Centiloid (CL) method. MRI‐derived regional volumes and thickness were harmonized across sites with neuroCombat (Fortin et al., 2018); all variables were Z‐scored. Linear mixed‐effect models were used to investigate the effect of amyloid burden at baseline and its interaction with time on regional volume and thickness measurements. Models were corrected for age, sex, and CDR. P‐values were FDR‐adjusted. Result: Cohort characteristics are shown in Table 1. Decreasing cortical thickness and volumes were observed over time in most regions. At baseline, higher amyloid burden was related to smaller volumes of the hippocampus, amygdala, putamen, and lateral ventricles; and thinner entorhinal cortex, precuneus and posterior cingulate cortex. At follow‐up, subjects with higher baseline amyloid burden showed greater loss of volume and thickness in mainly temporal and parietal regions, as well as amygdala and hippocampal volume (Figure 1). Conclusion: In the largely asymptomatic AMYPAD PNHS cohort, we show that amyloid burden at baseline is predictive of future neurodegeneration, especially affecting temporal volume and parietal thickness. Cortical thickness was slightly more sensitive towards amyloid burden at baseline, while loss of cortical volume was greater than loss of thickness. [ABSTRACT FROM AUTHOR]

Background: In clinical practice, amyloid‐PET images are visually assessed to determine the presence or absence of amyloid pathology. The expected arrival of disease‐modifying treatments warrants the need for objective quantification of amyloid burden to further support treatment decisions. Here, we investigated whether quantification increases reader confidence and influences visual read (VR) classification in patients with challenging or ambiguous amyloid information sampled across the clinical continuum. Methods: From the AMYPAD‐DPMS trial (N = 840), we selected a subsample of 85 amyloid‐PET scans ([18F]flutemetamol, N = 48/[18F]florbetaben, N = 53), for which 1) visual assessment was discordant with amyloid status based on Centiloid (CL) (cut‐off = 21) and/or 2) were assessed with a low confidence (i.e. ≤3 on a 5‐point scale) by the local reader. In addition, concordant negative and positive (N = 8/8) scans across tracers were included to balance the dataset (N = 101, Table‐1). PET images were processed using GEHCs AMYPYPE PET‐only pipeline, providing global CL and regional z‐scores for 7 cortical ROIs. VR classification and corresponding confidence was assessed for 3 independent certified assessors before and after disclosure of the quantification results. In addition, readers stated whether CL quantification and/or the regional z‐scores supported their assessment. Reader agreement was assessed using Fleiss Kappa. Changes in reader confidence after disclosure was assessed using paired t‐test. Results: Overall, readers reported global CL and regional z‐score quantification to be VR‐supportive in 76.2% and 49.7% of assessments, respectively. After disclosure of quantitative results, concordance between readers (Kbaseline= 0.60; Kpost‐disclosure= 0.79), concordance between majority‐VR and CL‐status (agreementbaseline = 75.2%; agreementpost‐disclosure = 81.2%), and reader confidence (Mbaseline= 4.0 vs. Mpost‐disclosure= 4.37, t = ‐5.8, p<0.001) significantly improved. Classification changed for 26 (8.6%) assessments, with 8 (30.8%) changing to VR‐ and 18 (69.2%) to VR+. In these cases, CL was considered supportive in 16 (61.5%) cases, regional z‐scores were considered supportive for 8/10 remaining cases, and most often for the posterior cingulate/precuneus region. The 2 remaining cases had a low confidence at both time‐points. Example cases are shown in Figure 1. Conclusion: In a dataset enriched with challenging amyloid‐PET scans for visual assessment, quantification resulted in a significantly increased agreement and confidence across readers. We further aim to determine in which cases quantification was considered most supportive. [ABSTRACT FROM AUTHOR]