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Background: Early amyloid deposition results in functional and structural brain alterations in predementia stages of Alzheimer's disease (AD). However, how early functional and structural brain changes are related to each other remains unclear. Investigating the simultaneous disruptions of functional‐structural brain features within individuals in relation to amyloid deposition may increase our understanding of the neuropathological processes underlying AD. Method: We included 648 non‐demented participants from the European Prevention for Alzheimer's Dementia (EPAD) cohort vIMI baseline data release. The cut‐off for cerebrospinal fluid (CSF) amyloid‐β positivity (A+) was defined at <1000pg/mL (Elecsys assay). Functional connectivity was estimated with functional eigenvector centrality (EC) from the resting state functional MRI. White matter (WM) integrity was estimated with fractional anisotropy (FA), computed on diffusion MRI. Both measures were computed at the voxel level within the gray and white matter, respectively. First, we used linear regression models to investigate differences between A+ and A‐ participants within each modality separately, adjusting for age, sex, and site. In the whole group, we performed sparse canonical correlation analysis (sCCA) on functional and structural measures, identifying shared components between the two modalities. Individual sCCA scores were compared between amyloid groups using a generalized linear model (GLM). To evaluate this association in A+ individuals, the same sCCA analysis was performed normalizing the structural and functional matrices using the mean and SD from A‐ participants. Results: Sample characteristics are provided in Table 1. Differences in voxelwise EC and FA between amyloid groups are shown in Figure 1. In the whole group, sCCA analysis showed that EC in the default mode, executive and cerebellar networks covary with FA in parietal, temporal and cerebellar areas (Figure 2). sCCA projections were differentially expressed according to amyloid status (Table 2). In A+ participants, stronger association of EC in the posterior DMN and cerebellum with FA in the splenium and brainstem were found (Figure 3). Conclusion: Using a data‐driven multivariate analysis, we identified concerted patterns of functional and structural changes that occur with amyloid deposition in the predementia stage. These results highlight the importance of multimodal assessment for early diagnosis. [ABSTRACT FROM AUTHOR]

Background: We classified non‐demented European Prevention of Alzheimer's Dementia (EPAD) participants through the amyloid/tau/neurodegeneration (ATN) scheme and assessed their neuropsychological and imaging profiles. Materials and methods: From 1500 EPAD participants, 312 were excluded. Cerebrospinal fluid cut‐offs of 1000 pg/mL for amyloid beta (Aß)1‐42 and 27 pg/mL for p‐tau181 were validated using Gaussian mixture models. Given strong correlation of p‐tau and t‐tau (R2 = 0.98, P < 0.001), neurodegeneration was defined by age‐adjusted hippocampal volume. Multinomial regressions were used to test whether neuropsychological tests and regional brain volumes could distinguish ATN stages. Results: Age was 65 ± 7 years, with 58% females and 38% apolipoprotein E (APOE) ε4 carriers; 57.1% were A–T–N–, 32.5% were in the Alzheimer's disease (AD) continuum, and 10.4% suspected non‐Alzheimer's pathology. Age and cerebrovascular burden progressed with biomarker positivity (P < 0.001). Cognitive dysfunction appeared with T+. Paradoxically higher regional gray matter volumes were observed in A+T–N– compared to A–T–N– (P < 0.001). Discussion: In non‐demented individuals along the AD continuum, p‐tau drives cognitive dysfunction. Memory and language domains are affected in the earliest stages. [ABSTRACT FROM AUTHOR]

Background: Magnetic Resonance Imaging (MRI) of the brain is prone to artefacts that may worsen image quality and subsequent analyses. Despite the growing number of large‐scale multi‐institutional imaging studies, standardized approaches for defining inclusion and exclusion criteria on the basis of image data quality are still lacking and quality assessment is often based on visual inspection. We introduce ExploreQC, a MATLAB‐based toolbox which implements a semi‐automatic pipeline to assess QC metrics, select the most relevant parameters, and to derive informed inclusion thresholds. Methods: The efficacy of ExploreQC was demonstrated on scans from 436 participants of the multi‐centre European Prevention of Alzheimer's Dementia (EPAD) longitudinal cohort study. Five different MRI sequences from five sites of the EPAD study were used: structural (T1w, FLAIR, diffusion MRI), resting state functional (rs‐fMRI) and ASL perfusion MRI. For each sequence, QC features were selected following a literature review of recently published studies on multi‐parameter QC (Alfaro‐Almagro, F. et al. 2018; Bastiani, M. et al. 2019; Esteban, O. et al. 2017; Fallatah, S. M. 2018; Shehzad, Z. 2015). Similar parameters were grouped in image feature domains (IFDs). Results: Table 1 shows the 38 QC features within 19 IFDs computed in the EPAD cohort covering each MRI scan‐type. ExploreQC generated within‐ and between‐site distribution plots of each QC parameter to identify outliers for subsequent visual inspection in order to establish the relevance of the parameter (Fig 1). Conclusions: ExploreQC facilitates the computation and visualization of informative QC parameters, within and between sites and modalities. Application of ExploreQC to the EPAD data showed that the investigation of distributions of different quantitative QC features can provide an overview of the general quality of the data, identify outliers and derive informed reference thresholds for exclusion criteria. [ABSTRACT FROM AUTHOR]

Background: Structural Grey Matter Networks (GMN) show progressive disruptions in Alzheimer Disease (AD), starting from early, preclinical stages, and are related to amyloid aggregation. However, GMN associations with tau biomarkers remain unclear. Here, we investigated the relationship between GMN and phospho‐tau (p‐Tau) levels in CSF and whether this relationship was dependent on amyloid beta (Aβ), in a non‐demented population. Method: We selected 423 individuals from the first data release of the European Prevention of Alzheimer Disease (EPAD, v500.0) that had MRI and CSF markers available; 84% were cognitively unimpaired (CDR=0) and 16% had CDR=0.5 (Table 1). Abnormal Aβ was defined by levels of <1025 pg/mL, and abnormal p‐Tau by levels of >24 pg/mL in the CSF. Single‐subject networks were extracted from 3D‐T1w structural MRI using a previously described method (Tijms BM et al., 2012), and for each individual we computed whole brain betweenness centrality, degree, density, clustering, modularity, path length and small world properties. Association of network metrics with Aβ and p‐Tau was investigated using linear models including Aβ, p‐Tau+ and Aβ‐p‐Tau interaction, both dichotomously (model 1) and continuously (model 2), adjusted for age, sex and Clinical Dementia Rating (CDR) scores. Results: In total 154 (36%) of individuals had Aβ+ (Table 1). Abnormal Aβ (both dichotomously and countinuously) was associated with lower degree, density, clustering and small‐world network properties, independently of tau (p<0.001). Higher p‐Tau levels were related to a reduction in clustering, modularity and small‐worldness values in the Aβ+ group only (p<0.05). Conclusion: In cognitively unimpaired subjects, grey matter networks show alterations in amyloid‐positive subjects, independently of tau, and within these individuals stronger GMN disruptions are related to higher p‐Tau levels. Our results indicate a relationship between molecular markers of AD and atrophy patterns at the single‐subject level, specifically going towards a more random and less clustered network topology, and suggest a possible role of GMN measures in identifying early stages of the pathology. [ABSTRACT FROM AUTHOR]