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INTRODUCTION: Recent technological advances have increased the risk that de‐identified brain images could be re‐identified from face imagery. The Alzheimer's Disease Neuroimaging Initiative (ADNI) is a leading source of publicly available de‐identified brain imaging, who quickly acted to protect participants' privacy. METHODS: An independent expert committee evaluated 11 face‐deidentification ("de‐facing") methods and selected four for formal testing. RESULTS: Effects of de‐facing on brain measurements were comparable across methods and sufficiently small to recommend de‐facing in ADNI. The committee ultimately recommended mri_reface for advantages in reliability, and for some practical considerations. ADNI leadership approved the committee's recommendation, beginning in ADNI4. DISCUSSION: ADNI4 de‐faces all applicable brain images before subsequent pre‐processing, analyses, and public release. Trained analysts inspect de‐faced images to confirm complete face removal and complete non‐modification of brain. This paper details the history of the algorithm selection process and extensive validation, then describes the production workflows for de‐facing in ADNI. Highlights: ADNI is implementing "de‐facing" of MRI and PET beginning in ADNI4."De‐facing" alters face imagery in brain images to help protect privacy.Four algorithms were extensively compared for ADNI and mri_reface was chosen.Validation confirms mri_reface is robust and effective for ADNI sequences.Validation confirms mri_reface negligibly affects ADNI brain measurements. [ABSTRACT FROM AUTHOR]

The magnetic resonance imaging (MRI) Core has been operating since Alzheimer's Disease Neuroimaging Initiative's (ADNI) inception, providing 20 years of data including reliable, multi‐platform standardized protocols, carefully curated image data, and quantitative measures provided by expert investigators. The overarching purposes of the MRI Core include: (1) optimizing and standardizing MRI acquisition methods, which have been adopted by many multicenter studies and trials worldwide and (2) providing curated images and numeric summary values from relevant MRI sequences/contrasts to the scientific community. Over time, ADNI MRI has become increasingly complex. To remain technically current, the ADNI MRI protocol has changed substantially over the past two decades. The ADNI 4 protocol contains nine different imaging types (e.g., three dimensional [3D] T1‐weighted and fluid‐attenuated inversion recovery [FLAIR]). Our view is that the ADNI MRI data are a greatly underutilized resource. The purpose of this paper is to educate the scientific community on ADNI MRI methods and content to promote greater awareness, accessibility, and use. Highlights: The MRI Core provides multi‐platform standardized protocols, carefully curated image data, and quantitative analysis by expert groups.The ADNI MRI protocol has undergone major changes over the past two decades to remain technically current.As of April 25, 2024, the following numbers of image series are available: 17,141 3D T1w; 6877 FLAIR; 3140 T2/PD; 6623 GRE; 3237 dMRI; 2846 ASL; 2968 TF‐fMRI; and 2861 HighResHippo (see Table 1 for abbreviations).As of April 25, 2024, the following numbers of quantitative analyses are available: FreeSurfer 10,997; BSI 6120; tensor based morphometry (TBM) and TBM‐SYN 12,019; WMH 9944; dMRI 1913; ASL 925; TF‐fMRI NFQ 2992; and medial temporal subregion volumes 2726 (see Table 4 for abbreviations).ADNI MRI is an underutilized resource that could be more useful to the research community. [ABSTRACT FROM AUTHOR]

Background: Atrophy of thalamic subregions has been observed across the spectrum of frontotemporal dementia (FTD). To gain better insight into underlying tissue changes, we investigated how thalamic subregional fractional anisotropy (FA) and mean diffusivity (MD) derived from diffusion tensor imaging (DTI) are altered in genetic FTD. Method: We used our newly developed thalamus segmentation tool, which jointly combines structural and diffusion input MRI data, to segment thalami and extract thalamic subregional FA and MD values for 163 genetic mutation carriers and 126 non‐carriers with suitable 3T MRI data from the GENetic FTD Initiative (GENFI). Mutation carriers were divided according to their genetic diagnosis and CDR®+NACC FTLD global scores into presymptomatic/prodromal (≤0.5: 41 C9orf72, 59 GRN, 34 MAPT) and symptomatic (≥1: 8 C9orf72, 11 GRN, 10 MAPT) groups. Mean FA and MD values for thalamic subregions were obtained using diffusion tensors interpolated in the log domain and weighted by segmentation posterior probabilities. Thalamic subregional FA and MD values for presymptomatic and symptomatic mutation carriers within each genetic group were compared with non‐carriers using analysis of covariance with bootstrapping, where age, scanner type, and sex were covariates. We corrected for multiple comparisons and calculated percentage changes in adjusted FA and MD mean values for mutation carriers relative to non‐carriers. Result: The only significant change at the presymptomatic stage was found for C9orf72 expansion carriers, who showed FA reduction in the intralaminar subregion (5%) (Figure 1, Table 1). In symptomatic C9orf72 expansion carriers, FA was reduced in the laterodorsal (21%), lateral posterior (13%), anteroventral (13%) and intralaminar (11%) subregions. Symptomatic MAPT mutation carriers also showed FA reduction in the laterodorsal (15%) and anteroventral (11%) subregions. No significant FA reductions were found for GRN mutation carriers and no significant MD changes were observed for any group after correction for multiple comparisons. Conclusion: We detected FA reductions of thalamic subregions only for C9orf72 expansion carriers at the presymptomatic stage, and for C9orf72 and MAPT mutation carriers at the symptomatic stage. Combined with the lack of robust MD changes, our findings may warrant further assessment of thalamic microstructure with more advanced diffusion models. [ABSTRACT FROM AUTHOR]

Background: Structural brain imaging is essential for the diagnostic workup in dementia and for safety monitoring of disease modifying therapies. MRI has several advantages over other imaging techniques, but its use is limited by cost and availability. Reducing acquisition time holds the potential for its democratisation. We developed a prototype ultra‐fast MRI protocol based on an optimised 3D parallel imaging approach. Here we present the finalised version, developed using a qualitative and quantitative approach to optimisation. Method: In this ongoing real‐world study, participants are sequentially recruited from the cognitive disorders' clinics of a tertiary centre. Patients due to have a standard 3T MRI scan as part of their diagnostic pathway are eligible. The standard‐of‐care protocol includes T1w, T2w, FLAIR and T2*/SWI sequences [scan time ∼18mins]. The prototype rapid protocol utilises Siemens work‐in‐progress 3D Wave‐CAIPI (Controlled Aliasing in Parallel Imaging) sequences, enabling a reduction in scan time of more than 60% (table 1). For qualitative analysis, scans (of 9 individuals) were reviewed by six raters and assessed side‐by‐side against the gold‐standard scan for diagnostic utility. For a quantitative analysis, volumes were calculated from T1w scans using the default cortical reconstruction and volumetric segmentation pipeline in FreeSurfer 7.3.2. Key volumes (e.g., deep grey matter, cortical grey matter, white matter) for each participant were compared between the two protocols. Independent visual QC was performed on T1w datasets by an experienced clinical trials team. Result: Side‐by‐side visual assessment results of 9 participants is shown in figure 1. For all sequences bar FLAIR, raters scored the ultra‐fast sequences of comparable image quality in at least 67% of cases. For quantitative analysis, 10 T1w sequence pairs were reviewed, with 1 clinical scan failing QC due to motion. Bland‐Altman analyses (figure 2.) comparing volumes between the clinical and ultra‐fast sequences show little bias. Conclusion: Our preliminary results demonstrate that for most contrasts the ultra‐fast sequences are diagnostically non‐inferior and quantitatively equivalent to the clinical protocol both for diagnosis and for safety monitoring. [ABSTRACT FROM AUTHOR]

Background: We assess if, and at which ages during 30 years of adulthood, undertaking leisure time physical activity (LTPA) is associated with brain health at age 70, and to what extent brain health metrics explain the positive association between LTPA and later‐life cognition. Method: Participants from the British 1946 birth cohort prospectively reported LTPA five times between ages 36 and 69. Metrics were categorised into: not active (no participation/month); active (participated once or more/month); and summed. Participants underwent 18F‐florbetapir Aβ‐PET and MRI at age 70 (n = 468, 49% female). Regression analyses examined associations between LTPA metrics and later‐life brain health including Aβ‐PET, TIV‐adjusted brain, hippocampal and log‐transformed‐white matter hyperintensity (WMH) volume, adjusting for sex, scan age, childhood cognition, education, and childhood socioeconomic position. Effect modification by sex and APOE‐ε4 were examined. The relationship between cumulative LPTA and later‐life cognition (Addenbrooke's cognitive Examination (ACE‐III)) was assessed adjusting for brain health measures. Result: Participation in LTPA was associated with better brain health at age 70. For brain volume and WMH volume, the strongest associations were with LTPA at age 69 (Figure 1). Being active in one or more period across adulthood was linked to larger hippocampal volume (Figure 1); this relationship was modified by APOE‐ε4 (p<0.01), with a stronger effect shown in ε4 carriers (Figure 2). LTPA at age 43 was also associated with larger hippocampal volumes. There was no evidence of associations with amyloid status. The positive association between cumulative LTPA and better cognition was not attenuated by any of the brain health measures (Figure 3). Conclusion: We provide evidence that LTPA across adulthood is linked to brain health at age 70; being active throughout adulthood was associated with larger hippocampal volume, particularly in APOE ε4 carriers; and being active in later‐life was linked to less WMH and larger brain volume at age 70. However, these brain health metrics did not explain the relationship between LPTA and better cognitive scores, suggesting that these pathways may not underlie the inferred cognitive benefit at this age. Our findings warrant further research to shed light on the mechanisms of physical activity as a potential disease‐modifying intervention of brain health. [ABSTRACT FROM AUTHOR]

Background: Accelerated Long‐term Forgetting (ALF) is the phenomenon whereby material is retained normally over short intervals (minutes or hours) but forgotten abnormally rapidly over longer periods (days or weeks). ALF may be an early marker of cognitive decline, but little is known about its relationships with preclinical Alzheimer's disease pathology in older adults, and how memory selectivity may influence which material is forgotten. Method: Participants in 'Insight 46', a sub‐study of the MRC National Survey of Health and Development (British 1946 birth cohort), completed cognitive and neuroimaging assessments at two time‐points (baseline at age ∼70; follow‐up ∼2.4 years later). At follow‐up, we assessed Complex Figure Drawing (copy; immediate recall; 30‐minute recall; 7‐day recall). Complex Figure items were categorized as 'outline' or 'detail' (Fig1), to test the hypothesis that forgetting the outline of the structure would be more sensitive to the effect of brain pathologies. ALF scores were calculated as the proportion of material retained after 7 days, relative to 30 minutes. Rates of cerebral atrophy between baseline and follow‐up were quantified from T1‐weighted MRI using the Brain Boundary Shift Integral (BBSI). β‐amyloid status (positive/negative) was determined from 18F‐Florbetapir‐PET. Baseline serum neurofilament light (NfL) was quantified (Quanterix Simoa assay). Multivariable regression models were used to investigate the effects (mutually adjusted) of β‐amyloid status, BBSI and NfL on ALF in n = 316 clinically‐normal individuals (50% female; 22% β‐amyloid positive; 30% APOE‐ε4 carriers), and to explore interactions between these predictors, adjusting for potential confounders including prospectively‐collected childhood cognitive ability and education. Result: 'Outline' items were better retained than 'detail' (Fig1). β‐amyloid‐positive participants had poorer ALF scores for 'outline' (but not 'detail') items (Fig1C; Table 1). Unexpectedly, higher NfL was associated with scores for 'outline' items (Table 1). Greater rate of cerebral atrophy predicted poorer retention among participants with elevated β‐amyloid and higher NfL (Table 1; Fig2). Conclusion: These results provide evidence of associations between biomarkers of brain pathologies and ALF in 73‐year‐olds. Interactions between different biomarkers merit further exploration. ALF may be a sensitive outcome measure for therapeutic trials in preclinical AD. Better retention of 'outline' (vs. 'detail') items illustrates the strategic role of memory selectivity. [ABSTRACT FROM AUTHOR]

Background: Peripheral hearing impairment has been proposed as a risk factor for dementia. However, the relationship between hearing ability, neurodegeneration and cognitive decline, and the extent to which pathological processes associated with increased risk of specific causes of dementia, such as β‐amyloid and small vessel disease, influence these relationships, is unclear. Method: Data were analysed from 287 cognitively normal adults born in the same week of 1946 who underwent pure tone audiometry testing at baseline (mean age = 70.6 years), with cognitive assessment and brain imaging at baseline and at follow‐up on average 2.4 years later. Peripheral hearing impairment was defined as a pure tone average of greater than 25 decibels in the best hearing ear. Rates of change for whole brain, hippocampal and ventricle volume were estimated from structural MRI using the Boundary Shift Integral. Cognition was assessed using the Pre‐clinical Alzheimer's Cognitive Composite. Regression models were performed to evaluate how baseline hearing impairment associated with subsequent brain atrophy and cognitive decline after adjustment for a range of variables including baseline β‐amyloid deposition (assessed using florbetapir‐PET) and baseline small vessel disease burden (estimated using white matter hyperintensity volume). Results: 111 out of 287 participants were defined as having peripheral hearing impairment. Hearing impaired individuals demonstrated faster rates of whole brain atrophy (p = 0.031 – figure/table 1) compared with those with normal hearing. Peripheral hearing impairment did not predict change in PACC performance, but there was evidence of an interaction between hearing impairment and whole brain atrophy rates in terms of effect on change in PACC performance. Specifically, faster rates of whole brain atrophy predicted greater cognitive decline in participants with hearing impairment (p = 0.004), whilst there was no evidence of an association between cognitive change and atrophy in participants with preserved hearing (figure/table 2). There was no evidence that β‐amyloid deposition or white matter hyperintensity volume modified these relationships. Conclusion: We present evidence of associations between peripheral hearing ability at age 70, brain atrophy and cognitive decline independent of β‐amyloid and small vessel disease, suggesting hearing may associate with brain health via mechanisms distinct from those typically implicated in pre‐clinical Alzheimer's disease and vascular cognitive impairment. [ABSTRACT FROM AUTHOR]

Introduction: In Alzheimer's disease (AD), hyperphosphorylated tau is closely associated with focal neurodegeneration, but the mechanism remains uncertain. Methods: We quantified cortical microstructure using neurite orientation dispersion and density imaging in 14 individuals with young onset AD. Diffusion tensor imaging measured mean diffusivity (MD). Amyloid beta and tau positron emission tomography were acquired and associations with microstructural measures were assessed. Results: When regional volume was adjusted for, in the medial temporal lobe there was a significant negative association between neurite density and tau (partial R2 = 0.56, p = 0.008) and between orientation dispersion and tau (partial R2 = 0.66, p = 0.002), but not between MD and tau. In a wider cortical composite, there was an association between orientation dispersion and tau (partial R2 = 0.43, p = 0.030), but not between other measures and tau. Discussion: Our findings are consistent with tau causing first dendritic pruning (reducing dispersion/complexity) followed by neuronal loss. Advanced magnetic resonance imaging (MRI) microstructural measures have the potential to provide information relating to underlying tau deposition. [ABSTRACT FROM AUTHOR]

Background: In older adults, exposure to area disadvantage is associated with poorer cognitive performance, smaller whole brain and hippocampal volumes and cardiovascular risk. However, there is little research on the timing, accumulation and change in exposure to area disadvantage. This study aimed to investigate whether exposure to area disadvantage across the life course is associated with cognition and neurodegenerative pathology in late adulthood. Method: Data from the MRC National Survey of Heath and Development (British 1946 birth cohort; analytical sample: n = 1,762) and a neuroimaging sub‐study (Insight 46; analytical sample: n = 447) were used. Area disadvantage was defined as the percentage of employed individuals in semi‐skilled or unskilled occupations. In the full cohort we assessed the relationship of area disadvantage at 26, 53 and 60‐64 years, cumulative area disadvantage and area disadvantage change score with cognitive performance at 69. In Insight 46 we examined associations between area disadvantage and neuroimaging outcomes (69‐71 years): whole brain, hippocampal, and ventricular volumes, amyloid status, and white matter hyperintensity volume (WMHV). All analyses were adjusted for socio‐economic and lifestyle confounders, with the neuroimaging analyses also adjusted for age at scan and total intracranial volume. Result: Negative associations were found between exposure to area disadvantage at all ages and cognitive performance, with stronger effects at 53 and 60‐64 (Table 1). These effect sizes remained similar when adjusted for sex, father's social class and childhood emotional symptoms, but were attenuated when further adjusted for educational attainment and childhood cognition. A negative association was found between cumulative exposure to area disadvantage and cognitive performance (Table 1), and no effect was found from change in area disadvantage. WMHV was the only neurological outcome found to be associated with area disadvantage variables (Table 2). Conclusion: Living in a disadvantaged area across the life course is associated with poorer cognitive performance at age 69, most of this affect is accounted for by childhood cognition and educational attainment. Living in a disadvantaged area across the life course is also associated with higher WMHV, suggesting presumed cerebrovascular disease, a dementia risk factor. Further research is required to understand the mechanism driving this association. [ABSTRACT FROM AUTHOR]

Background: In Alzheimer's disease (AD), dysfunction and loss of cortical neurons occurs prior to symptom onset. Diffusion tensor imaging, measuring mean diffusivity (MD), provides a metric of microscopic change but lacks specificity for different underlying microstructural processes. We used Neurite Orientation Dispersion and Density Imaging (NODDI) to investigate specific cortical microstructural features across the disease course in autosomal dominant AD (ADAD). Method: Sixty‐three ADAD family members (36 mutation carriers) (Table 1) underwent T1w and multi‐shell diffusion MRI (dMRI). Cortical thickness and cortical MD were estimated, with NODDI providing measures of tissue fraction (TF), neurite density index (NDI) and orientation dispersion index (ODI) (a proxy measure of dendritic complexity). Imaging metrics were sampled across six cortical regions of interest (ROIs) known to be particularly vulnerable to early neurodegeneration. Associations between dMRI measures and 1) disease stage as measured by estimated years to onset (EYO), 2) AD blood biomarkers, and 3) cognitive measures were assessed. Result: Across mutation carriers, ODI (Figure 1) and TF (Figure 2) demonstrated negative associations (p<0.05) with EYO in most ROIs, while MD demonstrated positive correlations. Most associations remained after adjusting for cortical thickness. The association between ODI and EYO in the supramarginal gyrus also remained after adjustment for MD (p = 0.002). In presymptomatic carriers, EYO appeared most prominently associated with ODI (p<0.05 in 3/6 regions). ODI, TF and MD showed widespread associations with MMSE and CDR sum of boxes. TF was negatively, and MD positively, associated with serum NfL across ROIs, but for ODI this was only the case in the supramarginal gyrus. ptau181 demonstrated significant associations with ODI in the supramarginal (p = 0.0089) and inferior parietal (p = 0.033) cortices, and with MD in the entorhinal cortex (p = 0.015). Conclusion: Cortical dendritic complexity (modelled by ODI) and cortical tissue fraction decrease, while overall diffusivity increases as individuals approach symptom onset. ODI appears to be the metric most closely associated with presymptomatic disease stage, possibly reflecting early dendritic pruning, and is associated with ptau – a measure of early pathology – while MD and TF may be more sensitive to later larger scale changes, given their associations with serum NfL and cognitive measures. [ABSTRACT FROM AUTHOR]

Background: White matter hyperintensities (WMH) are a common occurrence in ageing populations. Their hypointense appearance on T1‐weighted scans is known to affect automated processing and subsequent extraction of volumetric measurements. While lesion infilling with healthy appearing tissue is a common practice in the context of multiple sclerosis, this is not yet the case in studies of neurodegenerative diseases. We investigated the impact of WMH infilling on the volumetric outputs of two automated parcellation pipelines. Method: T1‐weighted and T2‐FLAIR scans were acquired for 462 individuals in the Insight46 neuroimaging substudy of the MRC National Survey for health and Development (NSHD). WMH were automatically segmented and T1‐weighted scans were subsequently infilled over the lesion masks. Two automated parcellation methods Geodesic Information Flows (GIF) and FastSurfer (FS) were run on the two T1‐weighted scans (original and infilled). Cortex, lateral ventricles, basal ganglia (BG), whole brain and total intracranial volumes (TIV) were derived from the four resulting parcellations. Spearman correlation was used to assess the relationship within and across methods according to infilling status. Correlations were also calculated between the observed volumes or percentage difference (original vs infilled volumes) and the WMH burden expressed as percentage of TIV. Result: The relationship between parcellated volumes and WMH burden varied across methods and tissues/brain areas. While there was little observable relationship for GIF between brain volume or TIV difference and WMH, this relationship was positive for FS. The relationship with cortical volume difference was in opposite directions for GIF (negative relationship) and FS (positive relationship), indicating a tendency to decrease the estimated volume when WMH were visible for GIF and increase it for FS. For lateral ventricles and basal ganglia, volumetric measurements were higher in the presence of visible WMH for GIF and FS (Figure1). Correlations between FS and GIF were slightly stronger after infilling but correlation with WMH burden were attenuated (Table2). Conclusion: The impact of presence of visible WMH on T1‐weighted scan varies according to the chosen technique of analysis and strong correlations were observed between the difference in measured volumes and the WMH burden. Infilling should be considered when T1‐weighted derived volumes are studied. [ABSTRACT FROM AUTHOR]

Background: We investigated imaging biomarkers of Aβ and neurodegeneration in relation to tau‐PET Braak stage in a preclinical birth cohort. Method: Cognitively normal individuals enrolled in Insight 46, the neuroimaging sub‐study of the MRC National Survey of Health and Development (1946 British birth cohort), were scanned on combined PET/MR with [18F]florbetapir Aβ‐PET at age ∼70 years and again at ∼73 years. A sub‐sample enriched for Aβ (Aβ+; Centiloid> = 13, hole cerebellum reference) is currently being assessed with [18F]MK‐6240 tau‐PET at age ∼76 years. For this interim analysis, tau‐PET images (90‐110 minutes post‐injection) were co‐registered with T1‐weighted MRI. Anatomical areas were parcellated on the T1 to form Braak regions. Standard uptake value ratios (SUVRs) were calculated using an inferior cerebellar grey reference without partial volume correction. Tau‐PET positivity (Tau+) was defined using Gaussian mixture modelling in each region. Participants were assigned to Tau‐, Braak I‐II, III‐IV or V‐VI groups based on the most advanced Tau+ region. Group differences in baseline Aβ (Centiloids), Aβ accumulation (Centiloids/year) and hippocampal atrophy rate (%/year) were investigated with Mann‐Whitney U tests. Result: Analysis included 80 individuals with tau‐PET data (Table 1), 45% of the sample were Aβ+ by age 73. Three individuals did not conform to the Braak stage hierarchy (orange triangles, Figure 1). No Aβ‐ individuals were Tau+ beyond Braak I. Figure 2 shows baseline Aβ, Aβ accumulation and hippocampal atrophy rates for Braak stage groups for participants who had data at all timepoints (N = 78). Tau+ individuals (in any Braak region) had significantly higher baseline Aβ than Tau‐ individuals. Rate of annual Aβ accumulation was higher for Braak III‐IV and Braak V‐VI compared to Tau‐ individuals. Hippocampal atrophy rate was elevated for Braak V‐VI compared to Tau‐, and Braak III‐IV was borderline significant. Conclusion: In this preliminary analysis, tau‐PET positivity beyond Braak I was restricted to individuals who were Aβ+ three years prior. Participants with elevated tau aged 76 had increased Aβ at age 70. Increased rates of hippocampal atrophy were occurring at least three years prior to tau scanning in individuals with advanced tau pathology. These findings will be updated as more data is acquired. [ABSTRACT FROM AUTHOR]

Background: White matter hyperintensity volume (WMHV) is a presumed marker of cerebrovascular disease associated with ageing, Alzheimer's disease (AD) and vascular dementia. Higher baseline WMHV is associated with increased brain atrophy rate, with disproportionate effects in the hippocampus – a region affected early in AD. Understanding the relationships between longitudinal change in WMHV and hippocampal atrophy, while accounting for AD and cardiovascular risk factors, will increase our understanding of the multifactorial disease processes occurring during the early stages of AD. Method: Rate of change in WMHV was calculated using Bayesian Model Selection (BaMoS) and total hippocampal atrophy rate was calculated using the Boundary Shift Integral (BSI), measured concurrently between two time‐points. Participants with major neurological disorders were excluded. Linear regression was used to test whether rates of WMHV change predicted rates of hippocampal atrophy while adjusting for total intracranial volume (TIV), sex and age. Further models adjusted for baseline PET amyloid‐β (Aβ) standard uptake value ratio (SUVR), ApoE‐e4 carrier status, and Framingham heart study cardiovascular risk (FHS‐CVS) aged 69, and tested for interactions with rate of WMHV change. Result: 308 individuals with high‐resolution isotropic MRI data at two time‐points were analysed in this study (Table 1). For every 1ml/year increase in WMHV there was an associated 0.013ml/year decrease in total hippocampal volume (Table 2, Model 1). This association remained when adjusting for Aβ SUVR (Table 2, Model 2), ApoE‐e4 status (Table 2, Model 3), and FHS‐CVS (Table 2, Model 4). A separate semi‐partial correlation analysis showed 4.6% of variance in rate of hippocampal atrophy was uniquely explained by rate of WMHV change when accounting for TIV, sex and age. There were no significant interactions between rate of WMHV change and Aβ, ApoE‐e4 or FHS‐CVS in relation to hippocampal atrophy rate. Conclusion: Increases in WMHV were significantly associated with progressive hippocampal atrophy accounting for head size, age and sex. These associations were not materially changed when adjusting for amyloid deposition, ApoE‐e4 or cardiovascular risk factors. These results suggest systemic processes that contribute to WMH development also contribute to hippocampal atrophy. Further work is required to determine the processes that drive this relationship. [ABSTRACT FROM AUTHOR]

INTRODUCTION: The Centiloid scale aims to harmonize amyloid beta (Aβ) positron emission tomography (PET) measures across different analysis methods. As Centiloids were created using PET/computerized tomography (CT) data and are influenced by scanner differences, we investigated the Centiloid transformation with data from Insight 46 acquired with PET/magnetic resonanceimaging (MRI). METHODS: We transformed standardized uptake value ratios (SUVRs) from 432 florbetapir PET/MRI scans processed using whole cerebellum (WC) and white matter (WM) references, with and without partial volume correction. Gaussian‐mixture‐modelling–derived cutpoints for Aβ PET positivity were converted. RESULTS: The Centiloid cutpoint was 14.2 for WC SUVRs. The relationship between WM and WC uptake differed between the calibration and testing datasets, producing implausibly low WM‐based Centiloids. Linear adjustment produced a WM‐based cutpoint of 18.1. DISCUSSION: Transformation of PET/MRI florbetapir data to Centiloids is valid. However, further understanding of the effects of acquisition or biological factors on the transformation using a WM reference is needed. HIGHLIGHTS: Centiloid conversion of amyloid beta positron emission tomography (PET) data aims to standardize results.Centiloid values can be influenced by differences in acquisition.We converted florbetapir PET/magnetic resonance imaging data from a large birth cohort.Whole cerebellum referenced values could be reliably transformed to Centiloids.White matter referenced values may be less generalizable between datasets. [ABSTRACT FROM AUTHOR]

Background: Consistent patterns of reduced cortical thickness (so‐called signature regions) have been identified in early Alzheimer's disease (AD), including in the pre‐dementia stages, but studies investigating the pathological underpinnings and cognitive consequences of longitudinal changes in these regions have been limited. Method: 337 cognitively normal participants (mean [SD] age 70.5 [0.6] years) underwent 18F‐florbetapir amyloid‐ß PET, volumetric MRI, and cognitive assessment as part of Insight 46, a sub‐study of the 1946 British birth cohort (Table 1 for characteristics). Baseline and follow‐up T1‐weighted MRI (mean [SD] interval 2.4 [0.2] years) were processed using Freesurfer's longitudinal stream (v.7.1.0) and cortical thickness was derived in two AD signatures (Table 2 footnote for details). Linear regression was used to test whether rates of change in AD signature cortical thickness were influenced by baseline amyloid‐ß deposition (positive/negative status or continuous SUVR) or white matter hyperintensity volume (WMHV; a marker of presumed cerebrovascular disease), and whether they were related to longitudinal cognitive change as measured using the Preclinical Alzheimer Cognitive Composite (PACC). Covariates included sex, age at baseline scan, childhood cognition, educational attainment, and socioeconomic position. Interaction terms were added to test whether associations with longitudinal cognitive change differed by baseline amyloid‐ß status. Result: Higher baseline WMHV was associated with faster rates of cortical thinning in AD signature regions, but baseline amyloid‐ß status and SUVR were not (Table 2; Figure 1). There were differential effects of rates of change in AD signature cortical thickness by baseline amyloid‐ß status, whereby greater rates of AD signature cortical thinning predicted faster rates of PACC decline in amyloid‐ß positive participants only (Table 3; Figure 2). Conclusion: Cortical thinning in AD signature regions may arise via non‐amyloid‐ß pathways in cognitively normal elderly. However, the presence of amyloid‐ß may make individuals more susceptible to the effects of faster rates of cortical thinning in these regions (or vice versa) since these factors interact to influence rates of cognitive decline. These findings provide insight into processes that might underlie progression to dementia in later life and have implications for the utility of AD signature cortical thickness as a biomarker in the preclinical phase of AD. [ABSTRACT FROM AUTHOR]

Background: Determining Aβ‐PET status is crucial for Alzheimer's disease trials. Standard uptake value ratio (SUVR) using a reference region is a common semi‐quantitative technique. Sex differences in regional blood flow and white matter (WM) could impact SUVR differentially depending on the reference region. It is important to understand how methodological factors can influence SUVR derived Aβ status. Method: Individuals from Insight 46 (1946 British birth cohort) underwent PET/MR scanning with [18F]florbetapir (N = 425; mean age (SD) 70.6 (0.7); 49% female). Regions derived from T1‐weighted MRI with geodesic information flows (GIF) were resampled to PET space. SUVR was calculated (50‐60 mins post‐injection) using a cortical composite target normalised to whole cerebellum (WC) or eroded WM, with/without partial volume correction (PVC). Additionally, SUV was calculated normalising to injected dose and weight. Distribution volume ratio (DVR) from Logan graphical analysis (cerebellar grey matter reference) was calculated for N = 391 (51% female). Linear regression was used to investigate differences in Aβ‐PET measures by sex adjusting for region volumes. Aβ status was defined with Gaussian‐mixture modelling. Sex differences in SUVR and DVR were investigated in individuals rated concordantly Aβ‐ with all four SUVR methods. SUVR Aβ status discordance was examined in relation to sex and APOE e4 genotype. Result: Females had significantly higher SUVR with a WC reference; and there were no sex differences with a WM reference or with dynamic DVR (Figure 1). Males had higher SUV in all regions and the difference was greater in the WC compared to WM (Figure 2). PVC decreased the influence of volume on SUV in the WC but not WM. 87% of individuals were classified concordantly Aβ+/‐ on SUVR measures with equal proportion of males/females. More females were Aβ+ only with WC reference with PVC, whereas more males were Aβ‐ only on WC without PVC (Figure 3). [ABSTRACT FROM AUTHOR]

Background: Fixel‐based analysis (FBA) of diffusion MRI allows analysis of brain white matter (WM) tracts with greater specificity than voxel‐based approaches, including measures of microstructural fibre density (FD), macrostructural fibre cross section (FC), and representations of crossing fibres. We use FBA to explore early WM changes associated with amyloid‐β (Aβ) prior to manifestations of Alzheimer’s disease. We additionally explored global WM changes associated with increased cardiovascular risk. Method: We performed FBA on 233 participants in the Insight 46 birth cohort study, all of whom have been followed prospectively since birth in a single week in 1946, including cardiovascular risk assessment using the Framingham Risk Score (FRS). At age 69‐71 participants underwent cognitive assessment and combined 18F‐florbetapir PET‐MRI scans on a single scanner. Aβ positive status was defined using a Gaussian mixed model as a standardised uptake value ratio over 0.6103. Using Aβ positive (n=40) and negative (n=193) participants with no major brain disorders and excellent scan quality, we assessed FD and FC, as well as the combined FD and FC (FDC) metric across all white matter fixels. Subsequently we performed a tract‐of‐interest analysis of associations between Aβ and FDC in 13 selected white matter tracts, 7 defined a priori based on Alzheimer’s disease mechanisms, and 6 based on results of the global analysis. Result: Aβ positivity was associated with changes in microstructural and macrostructural changes (FD, FC and FDC), throughout the right corticospinal tract inferior to the internal capsule, and microstructurally in the right inferior longitudinal fasciculus. Similar left sided corticospinal changes were seen in FC and FDC only. Increased FRS was associated with FD changes in the right superior longitudinal fasciculus. Tract‐of‐interest analyses showed no significant associations between FDC and Aβ after false discovery correction using the Benjamini‐Hochberg method. Conclusion: We show Aβ associated changes in fixel‐based metrics in the corticospinal tracts, predominantly affecting the right hemisphere. These preliminary results raise the possibility of these fibres being predisposed to damage, perhaps in a length dependent manner, though longitudinal analysis based on further phases of Insight 46 may prove more powerful to detect change at this very early stage. [ABSTRACT FROM AUTHOR]

Background: Neurite Orientation Dispersion and Density Imaging (NODDI) provides in‐vivo indices of neurite density (NDI) and orientation dispersion (ODI) within the tissue compartment of each voxel. However, NDI and ODI are treated equally when calculating region of interest (ROI) means, despite tissue fraction (TF) varying within regions undergoing neurodegeneration. Covariation between TF and cortical NODDI measures bias these conventional means and we recommend using tissue‐weighted averages to address this. Method: In this study, we included 22 healthy controls and 33 individuals affected by Young‐Onset Alzheimer's disease (YOAD, see Table 1 for demographics) with suitable diffusion‐weighted and T1‐weighted 3T MR images. Diffusion data were corrected for eddy currents, motion and susceptibility artefacts before fitting the NODDI model to produce NDI, ODI, isotropic volume fraction (ISO) and TF maps (TF=1‐ISO). T1w images were parcellated into cortical ROIs using Geodesic Information Flow (Cardoso et al., IEEE Trans.Med.Im.; 34:1976‐1988, 2015). Five bilateral ROIs expected to undergo neurodegeneration were analysed (Precuneus, Fusiform Gyrus, Superior Parietal, Middle and Inferior Temporal cortex). ROIs were resampled into native diffusion space and two regional measures calculated: 1) conventional, unweighted NDI and ODI averages and 2) tissue‐weighted averages using voxel TF as weights. Within‐participant differences between conventional and tissue‐weighted measures were calculated. Spearman's rank tested correlations and Wilcoxon tests evaluated within‐ and between‐participant differences (Bonferroni adjusted for multiple comparisons). Result: TF positively correlated with GM volume (rs range=0.33‐0.68,p<0.05) in all ROIs except the left fusiform gyrus (rs=0.31,p=0.06) (Figure 1). YOAD individuals had lower TF than healthy controls in all ROIs (Figure 2a), and lower volumes in all ROIs except the right fusiform gyrus (W=475,p=0.27) (Figure 2b). NDI showed small positive/negative biases in six of the ten ROIs (Figure 3a), while ODI showed significant positive biases across all ROIs (Figure 3b). Biases decreased as TF increased towards its maximum of one (Figure 4a‐4b). Conclusion: Lower cortical volumes in YOAD were associated with lower TF and higher bias, suggesting a greater risk for misestimation of cortical region NODDI metrics in studies involving neurodegenerative disease. We recommend tissue‐weighted averages to account for varying intra‐regional TF in NODDI measures. [ABSTRACT FROM AUTHOR]

Background: In Alzheimer's disease, early amyloid‐β and tau deposition may have differential downstream effects on synaptic function and neuronal loss in a stage‐dependent manner. Amyloid‐related synaptic changes are thought to precede tau accumulation, which starts in the medial temporal lobe before propagating and leading to neurodegeneration. We explored whether microstructural MRI measures can detect these different sequential cortical changes. Method: Seventy‐nine healthy, asymptomatic individuals, recruited from the Insight 46 study of the British 1946 birth cohort underwent combined PET/MR with [18F]florbetapir Aβ‐PET at ∼73yrs, and [18F]MK‐6240 tau‐PET at ∼76yrs. Standard uptake value ratios were calculated using a whole cerebellar reference for florbetapir and an inferior cerebellar reference for MK‐6240. Multi‐shell diffusion MRI was acquired; neurite orientation dispersion and density imaging was used to quantify neurite density (NDI) and orientation dispersion (ODI, a proxy measure of dendritic morphology/complexity), with DTI measuring mean diffusivity (MD), a less specific measure of degenerative change. PET and microstructural MRI biomarkers were assessed in a neocortical composite ROI and across Braak stages. Result: Thirty‐six participants were amyloid positive, 18 of whom were tau positive (eight Braak stage 1‐2, four Braak 3‐4, six Braak 5‐6). Across all participants, in the neocortical composite, significant differences between amyloid positive and amyloid negative individuals were found in both tau PET (p = 0.0092) and ODI (p = 0.030) but not for other measures. Within the amyloid positive group, in the cortical composite, significant differences in MD (p = 0.0093) but not ODI were seen when comparing those who were tau positive and negative. In the amyloid positive group, there was an association between tau PET and MD in the MTL (Braak 1‐2) (r = 0.34, p = 0.040) and Braak 3‐4 (r = 0.37, p = 0.025), with an association between tau and ODI (r = 0.48, p = 0.0031) in Braak 3‐4 only. After adjusting for volume, only the association between tau PET and ODI in Braak 3‐4 remained (p = 0.038). Conclusion: Our results are consistent with amyloid deposition causing dendritic/changes (as assessed by ODI) with consequent synaptic dysfunction. Later tau propogation is then associated with frank neuronal breakdown (as measured by MD). dMRI cortical imaging biomarkers have the potential to provide proxy measures of underlying AD stage/pathology prior to symptom onset. [ABSTRACT FROM AUTHOR]

Background: Earlier menopause age associates with poorer cognitive performance post‐menopause, but links with dementia risk are mixed and the potential mechanisms underlying such associations are unclear. Few studies have examined the relationship between menopause age and later‐life brain health. Method: Our study included 126 women from Insight 46, the neuroscience sub‐study of the 1946 British birth cohort, which included combined amyloid PET and MRI scanning at mean age 70.7 years (standard deviation = 0.66). Age at natural menopause was self‐reported, during midlife, as years since birth at the time of period cessation; women who reported surgical menopause were excluded (n = 63). We tested for interactions of menopause age with APOE‐ε4 status and used multivariable regression analyses to test associations of menopause age with later‐life total brain volume (TBV) and hippocampal volume. A generalised linear model with log‐linked gamma distribution tested associations with white matter hyperintensity volume (WMHV). Models were cumulatively adjusted for total intracranial volume and age at scan (model 0/M0), hormone therapy (HT) use (M1:M0+HT) and for prospective early‐life and sociodemographic (M2:M1+childhood cognition, education, adult socioeconomic position), reproductive (M3:M2+menarche age, parity), and health‐related (M4:M3+smoking, BMI and blood pressure at age 36 years, APOE‐ε4 status) covariables. Result: The association of menopause age with WMHV was modified by APOE‐ε4 status (p = 0.024); each 1‐year increase in menopause age associated with a 7.4% decrease in WMHV for ε4 carriers (n = 35) and a 2.4% WMHV increase for non‐carriers (n = 90). Menopause age did not associate with hippocampal volume, but there was a positive association with TBV (Table 1) which remained significant until we accounted for health‐related factors in model 4 (Figure 1). In post‐hoc analysis, adjustment for blood pressure at age 36 attenuated the association by 11.24%, explaining more of the attenuation than other model 4 covariables. Conclusion: We find evidence for links between age at natural menopause and later‐life neuroimaging measures. Later menopause associated with greater brain volume, partly explained by midlife blood pressure. Interactive effects of APOE‐ε4 status on associations with WMHV, an indicator of brain pathology, warrant further investigation. [ABSTRACT FROM AUTHOR]

Background: Whole‐brain volume loss using T1‐weighted imaging is an established measurement of neurodegeneration. The Boundary Shift Integral (BSI) produces reliable metrics of longitudinal volume loss, used in research studies and clinical trials. With the increased acquisition of volumetric T2‐FLAIR imaging in studies, there is opportunity to produce atrophy metrics using a composite atrophy measure (T1‐weighted and T2‐FLAIR) that is more precise than either modality alone. Method: Data from Alzheimer's Disease Neuroimaging Initiative (ADNI3) were used. Volumetric T1 and T2‐FLAIR weighted imaging were obtained (n = 269), for baseline and follow‐up; Table 1. T1‐weighted images were bias corrected and underwent Geodesic Information Flow (GIF), producing probabilistic brain masks. Masks were resampled to T2‐FLAIR space. The BSI was performed on serial T1‐weighted (gBSI), and T2‐FLAIR (fBSI) separately. We tested differences by diagnosis (control (CN), mild cognitive impairment (MCI) and Alzheimer's disease (AD)) between indirect volume change, gBSI, fBSI, and composite BSI, using linear regression and effect size. We further compared metric differences within diagnosis, using pairwise correlation, paired samples t‐test, and Pitman's test of variance. Result: Demographic information is reported in Table 1. fBSI showed good Pearson's correlation (r) with both gBSI (CN 0.7, p < 0.001; MCI 0.8, p < 0.001; AD 0.9, p < 0.001), and indirect change (CN 0.6, p < 0.001; MCI 0.6, < 0.001; AD 0.7, < 0.001). The composite BSI further showed good correlation with indirect change (CN 0.8, p < 0.001; MCI 0.8, p < 0.001; AD 0.8, 0.004). Mean values by diagnosis are shown in figure 1, along with paired samples t and variance tests. Pairwise comparisons reported in table 2, highlight good diagnostic group separation in all BSI metrics, with overall strong effect sizes for fBSI, composite BSI, and gBSI respectively. Conclusion: Calculating atrophy using T2‐weighted imaging is possible using fBSI. This can be used in a composite atrophy measure using fBSI and gBSI. Both fBSI and composite BSI perform well with group separation, and with good correlations with classical atrophy metrics. Future work will explore the influence of White Matter Hyperintensities on BSI. [ABSTRACT FROM AUTHOR]

Background: Frontotemporal dementia (FTD) is characterized by abnormal white matter (WM) integrity measured with conventional diffusion tensor imaging techniques. No study has yet investigated the microstructural WM changes across different clinical FTD forms using the novel neurite orientation dispersion and density imaging (NODDI). Here, we focused the analyses on symptomatic individuals, investigating the difference between the sporadic and genetic origin within the same diagnostic group. Method: Neurite density index (NDI) and orientation dispersion index (ODI) were extracted from NODDI sequences, processed and then corrected for tissue‐weighted means. Images were acquired on a 3T MRI Siemens Prisma scanner from a cohort of participants seen in clinic at the National Hospital for Neurology and Neurosurgery, University College London, UK. Data were available for 27 individuals with behavioural variant FTD (bvFTD) and 27 with primary progressive aphasias (11 svPPA, 8 nfvPPA, 4 lvPPA, 4 PPA‐NOS). 18 of these individuals (16 bvFTD and 2 PPA‐NOS) carried an FTD‐linked genetic mutation (6 C9orf72, 7 MAPT, 5 GRN). W‐scores for NDI and ODI were computed from a regression model on 62 non‐carrier healthy individuals, adjusting for their age, sex, and total intracranial volumes. Result: The most abnormal values (<2.5th percentile of controls) were found in bvFTD (NDI: anterior corona radiata and left cingulum), lvPPA (NDI and ODI: left superior corona radiata; ODI: splenium of the corpus callosum) and PPA‐NOS (NDI: left uncinate fasciculus) (Figure). Sporadic bvFTD and PPA‐NOS showed more abnormal and widespread WM abnormalities compared to symptomatic mutation carriers. Conclusion: Changes in WM structure in this cohort of patients with symptomatic FTD appear to be related primarily to a reduction in axonal density. Despite similar clinical features, sporadic forms tend to show more severe and widespread WM involvement than genetic cases. [ABSTRACT FROM AUTHOR]

Objective: To assess associations between head injury (HI) with loss of consciousness (LOC), ageing and markers of later‐life cerebral pathology; and to explore whether those effects may help explain subtle cognitive deficits in dementia‐free individuals. Methods: Participants (n = 502, age = 69–71) from the 1946 British Birth Cohort underwent cognitive testing (subtests of Preclinical Alzheimer Cognitive Composite), 18F‐florbetapir Aβ‐PET and MR imaging. Measures include Aβ‐PET status, brain, hippocampal and white matter hyperintensity (WMH) volumes, normal appearing white matter (NAWM) microstructure, Alzheimer's disease (AD)‐related cortical thickness, and serum neurofilament light chain (NFL). LOC HI metrics include HI occurring: (i) >15 years prior to the scan (ii) anytime up to age 71. Results: Compared to those with no evidence of an LOC HI, only those reporting an LOC HI>15 years prior (16%, n = 80) performed worse on cognitive tests at age 69–71, taking into account premorbid cognition, particularly on the digit‐symbol substitution test (DSST). Smaller brain volume (BV) and adverse NAWM microstructural integrity explained 30% and 16% of the relationship between HI and DSST, respectively. We found no evidence that LOC HI was associated with Aβ load, hippocampal volume, WMH volume, AD‐related cortical thickness or NFL (all p > 0.01). Interpretation: Having a LOC HI aged 50's and younger was linked with lower later‐life cognitive function at age ~70 than expected. This may reflect a damaging but small impact of HI; explained in part by smaller BV and different microstructure pathways but not via pathology related to AD (amyloid, hippocampal volume, AD cortical thickness) or ongoing neurodegeneration (serum NFL). [ABSTRACT FROM AUTHOR]

Background: Accelerated Forgetting (AF) is the phenomenon whereby material is retained normally over short intervals (minutes or hours) but forgotten abnormally rapidly over longer periods (days or weeks). AF has been observed in presymptomatic carriers of mutations causing familial Alzheimer's disease (AD) (doi:10.1016/S1474‐4422(17)30434‐9). To our knowledge, no studies have investigated whether AF is sensitive to preclinical AD pathology in cognitively‐normal older adults. Method: Participants in the Insight 46 study, a sub‐study of the British 1946 birth cohort, completed baseline cognitive and neuroimaging assessments at age 69‐71. For the follow‐up visits (∼29 months later), we complemented the clinic visit assessments of Complex Figure Drawing and the Face‐Name test (FNAME‐12) with a 7‐day delay version administered by telephone (Figure 1). AF scores were calculated as the percentage of material retained after 7 days, relative to retention after 30 minutes. Cerebral atrophy between baseline and follow‐up was quantified from T1‐weighted MRI using the Brain Boundary Shift Integral (BBSI). β‐amyloid status at baseline (positive / negative) was determined from 18F‐Florbetapir‐PET. As follow‐up assessments are still underway, preliminary interim analyses have been conducted based on 195 cognitively‐normal individuals with complete neuroimaging data (see Table 1 for characteristics). Multivariable regression models were used to investigate the effects of β‐amyloid status and BBSI on AF, and to explore interactions between these two predictors, adjusting for potential confounders including prospectively‐collected measures of childhood cognitive ability and education. Result: Despite no statistically‐significant differences after a 30‐minute delay, β‐amyloid‐positive participants retained a lower percentage of Complex Figure material over 7 days (71.8% vs. 80.7%, p=0.010) and a trend to a lower percentage of FNAME‐12 material (69.4% vs. 77.2%, p = 0.083) (Table 2, Figure 2). Higher education predicted better retention of the Complex Figure. Among β‐amyloid‐positive participants only, greater cerebral atrophy predicted poorer retention of the Complex Figure (Table 2, Figure 3). Conclusion: These results provide novel evidence of AF in cognitively‐normal β‐amyloid‐positive 72‐year‐olds. AF may be a sensitive outcome measure for therapeutic trials in preclinical AD, as it may reveal subtle memory decline at an earlier stage than traditional assessments. The interaction between β‐amyloid pathology and cerebral atrophy merits longitudinal investigation. [ABSTRACT FROM AUTHOR]

Background: Age is the biggest risk factor for dementia, yet human brains do not age uniformly. The British 1946 birth cohort, the world's longest continuously running birth cohort, provides a unique opportunity to assess these variations in biological ageing. So‐called 'brain age' is a biomarker of brain ageing, derived from machine‐learning analysis trained on a large sample of healthy brains (N=2001). Brain age has previously been related to cognitive ageing, physiological ageing and mortality risk (DOI: 10.1038/mp.2017.62), supporting the validity of this approach for assessing biological ageing. Method: 502 participants in the Insight 46 study, all born during one week in 1946, completed baseline cognitive and neuroimaging assessments at age 69‐71. 468 underwent combined 18florbetapir PET‐MRI scans, from which amyloid status (positive/negative), whole brain volume (WBV), total intracranial volume (TIV) and hippocampal volumes (HV) were derived. The T1‐weighted sequence was passed through the Brain‐age algorithm (https://github.com/james‐cole/brainageR), deriving brain predicted‐age (BPA) and brain‐predicted age difference (brain‐PAD; BPA minus chronological age). Serum neurofilament light (NFL) concentration was measured via Simoa immunoassay. A Preclinical Alzheimer's Cognitive Composite Score (PACC) was calculated as a mean of z‐scores of the Mini‐mental state exam (MMSE), logical memory delayed recall, digit symbol substitution score and the Face‐Name test. Life course metrics (childhood cognitive scores, education level and Framingham Risk scores) were obtained from previous cohort assessments. Multivariate regression models were used to investigate whether life course metrics predict BPA, as well as whether NFL levels, brain volumes, or cognitive scores correlated with BPA, adjusting for chronological age. Result: There was a significant difference between the 229 females assessed (mean BPA 65.2 years) compared with the 239 males assessed (mean BPA 70.7). BPA was independently associated with serum NFL concentration (p = 0.071) and inversely with whole brain volume (p < 0.001). Life course factors did not predict brain age. Conclusion: The results showed a significant association of BPA, a cross‐sectional imaging metric, with a biochemical marker of neuronal damage (NFL) and sex. BPA has utility as an imaging metric that can integrate multiple modalities contributing to biological age, with potential as a predictive biomarker of cognitive decline. [ABSTRACT FROM AUTHOR]

Background: Amyloid‐PET has high sensitivity for cerebral amyloid, a hallmark of Alzheimer's disease (AD). Typically, a global measure of amyloid burden in used to classify individuals as amyloid positive or negative. Measures like the Standarised Uptake Value ratio (SUVr) rely on a reference region to normalise amyloid measures, but are non‐specific, sensitive to delineation errors, and reference region choice is debated. This study evaluates a summary measure based on the multivariate pattern of amyloid deposition without a reference region, named Amyloid Pattern Similarity Score (AMPSS). Method: Alzheimer's Disease Neuroimaging initiative (ADNI) individual and volumetric T1 MRI data were co‐registered. MRI data was then spatially normalised using DARTEL. Combined, these two transforms then map PET data into common space. A support vector machine (SVM) was trained using a) age‐matched normal controls with higher CSF Aβ42, and b) individuals with clinical diagnosis of AD and lower CSF Aβ42(Table 1). Normalised grey matter voxels were used as the SVM features. Rather than conventional binary classification, the SVM generated probabilistic scores based on logistic regression (i.e. AMPSS). Three evaluations were performed: (1) a validation set (Table 1), (2) comparison with CSF determined amyloid status, and (3) initial longitudinal trajectory evaluation (Table 2). Result: The validation set AMPSS perfectly classified AD and controls. AMPSS results did not differ depending on specific reference region or global signal normalisation (between subjects ANOVA F(2,26) = 0.65, p = 0.526, r2.99). AMPSS showed high concordance with CSF defined amyloid status. Figure 1 highlights the strong AMPSS and CSF relationship. There was a nominal, non‐significant improvement in accuracy for this method compared to SUVr (p=.176), Figure 2. Longitudinal data highlighted that individuals with baseline AMPSS >50% tended to show increased AMPSS at follow‐up, while most individuals <50% baseline AMPSS remained stable. However, there is evidence of some individuals with subthreshold amyloid accumulation. (Table 3, Figure 3.). Conclusion: The Amyloid Pattern Similarity Score is a reference‐free summary metric of amyloid deposition that performs comparably with conventional SUVr measures. Initial validation shows high agreement with CSF and good sensitivity to increasing amyloid accumulation over time, even in early disease. [ABSTRACT FROM AUTHOR]

Background: We examined cross‐sectional associations between plasma phospho‐tau181 (p‐tau181) and amyloid PET, MRI and cognitive outcomes in Insight 46, a sub‐study of the British 1946 birth cohort. Method: At age 69‐71, participants underwent blood sampling, neurocognitive assessment, and 3T‐MRI with simultaneous 18F‐florbetapir‐PET (yielding amyloid status binarized at grey matter:eroded white matter SUVR 0.6104). Plasma p‐tau181 was measured by a homebrew Simoa immunoassay. ROC analyses were performed for amyloid status incorporating p‐tau181 into a model combining age, sex and APOE ε4 carrier status (APOE ε4). Linear regression examined associations between p‐tau181 (predictor) and (A): cognitive measures (preclinical AD cognitive composite (PACC), digit‐symbol substitution (DSS), delayed logical memory (LMD), and 12‐item‐face‐name association memory (FNAME‐12)), adjusting for age, sex, APOE ε4, childhood cognition, socioeconomic position and education; and (B): imaging biomarkers of neurodegeneration (whole brain, ventricular and hippocampal volumes (WBV,VV,HV) and AD‐signature cortical thickness (CTh)) and vascular disease (white matter hyperintensity volume (WMHV)), adjusting for age, sex, APOE ε4, amyloid status and total intracranial volume as appropriate. Result: After excluding those with prior neurological diagnoses, mild cognitive impairment or dementia, 444 individuals had complete plasma p‐tau181 data (Table 1); 410 had high‐quality amyloid PET data. An amyloid status ROC model using plasma p‐tau181 alone had an AUC of 0.720,95%CI[0.657,0.783]. This was not significantly different from the prediction of a base model incorporating age, sex and APOE ε4 (0.692[0.622,0.761]), but adding plasma p‐tau181 to the base model improved it significantly (0.787[0.737,0.837],2p<0.001: figure 1). The numbers needed to pre‐screen for a pre‐symptomatic Alzheimer's trial could be reduced by about 27% by using the latter compared to the base model (table 2). Higher p‐tau181 was associated with lower FNAME‐12 (z‐score‐change for 10% p‐tau181 rise: ‐0.020,95%CI[‐0.033,‐0.007],p=0.003) and lower PACC (‐0.013[‐0.022,‐0.005],p=0.002). Only the latter association was significantly attenuated by further adjustment for amyloid status. Higher p‐tau181 was associated with higher VV (ratio‐change for 10% p‐tau181 rise: 1.010, 95%CI[1.003,1.017],p=0.007) and lower CTh (0.999[0.998,1.000],p=0.007). DSS, LMD, HV, WBV and WMHV had no significant associations with p‐tau181. Conclusion: In cognitively normal individuals, plasma p‐tau181 may contribute toward pre‐screening for amyloid PET positivity, and is associated with cognitive performance and imaging biomarkers of neurodegeneration. [ABSTRACT FROM AUTHOR]

Background: Research studies of Alzheimer's disease (AD) are increasingly using biomarker‐based criteria to define disease. One useful definition is amyloid positivity since it identifies those on a pathological pathway that likely results in AD. However, within amyloid positive individuals, there is substantial heterogeneity in terms of when biomarkers that measure different aspects of the disease become abnormal. Here we examine this heterogeneity amongst biomarkers that reflect various aspects of the disease process. Method: CSF Amyloid positive individuals' data from Alzheimer's Disease Neuroimaging Initiative (ADNIGO/2/3) were used (n = 376). Demographics were compared across diagnostic group using linear regression and Fisher's exact tests where appropriate, table 1. CSF phosphorylated tau (ptau), whole‐brain and hippocampal volume, Logical memory (LM) and Trails A and B, and white matter hyperintensity (WMH) volumes were used to derive data‐driven subtypes using the z‐score version of the SuStaIn algorithm. The algorithm produced subtypes with differing patterns of biomarker orderings. SuStaIn calculated z‐scores using 86 CSF amyloid negative, APOE e4 non‐carrier healthy controls, selected from ADNIGO/2/3. Proportions of APOE e4 carriers, and clinical diagnosis were compared between subtypes using Fisher's exact test. Result: 359 individuals were grouped into three subtypes (figure 1); determined using cross‐validation. Subtype one (n = 136) had an initial event of WMH, followed by LM, trails A and B, and whole‐brain volume (hippocampal sparing). Subtype two (n = 115) had initial events of ptau, followed by LM and hippocampal volume (tau‐led). Subtype three (n = 108) had initial events of LM total, followed by ptau and hippocampal volume (tau‐late). 17 individuals were not subtyped, as their biomarker data showed no significant abnormalities and would suggest that they are not on an AD pathway. There was a trend towards a higher proportion APOE e4 carriers in subtype one and three (p =.06). There was a significant difference in clinical diagnosis between subtypes (p <.001), with subtype one having a lower proportion of CNs and a higher proportion of MCIs than subtypes two and three (figure 1). Conclusion: We found considerable heterogeneity in biomarker progression sequences in individuals with elevated amyloid. [ABSTRACT FROM AUTHOR]

Background: While APOE‐ε4 carriers are at higher risk of Alzheimer's disease (AD), there is evidence that APOE‐ε4 may have some beneficial effects across the life‐span, including on cognition. It is unclear how such effects may relate to subtle memory decline during the preclinical phase of AD. Two previous studies reported that APOE‐ε4 carriers recalled object locations more accurately than non‐carriers on the "What was where?" visual short‐term memory binding test (10.1016/j.cortex.2016.12.016; 10.1016/j.neurobiolaging.2018.09.017), but these studies did not account for preclinical AD pathology. Method: The "What was where?" task (Figure 1) was administered to participants in Insight 46 – a sub‐study of the British 1946 birth cohort – who were all born during the same week (aged 69‐71 at assessment (Table 1)). Outcomes included object identification and a sensitive analogue measure of localisation error (the distance between the location reported by the participant and the true location). Two‐dimensional mixture models (10.31234/osf.io/q57fm) were used to isolate three sources of localisation error: imprecision, guessing, and misbinding (swapping an object's location with that of a different object). β‐Amyloid status (positive / negative) was determined from 18F‐Florbetapir‐PET. Multivariable regression models were used to investigate differential effects of APOE genotype (ε4‐carrier / non‐carrier) and β‐amyloid status on performance in 398 cognitively‐normal participants, adjusting for confounders including a prospectively‐collected measure of childhood cognitive ability. Result: APOE‐ε4 and β‐amyloid had opposing effects on object identification, with APOE‐ε4 predicting better recall and β‐amyloid‐positivity predicting poorer recall. APOE‐ε4 carriers also recalled object locations more precisely, but a subtle detrimental effect of β‐amyloid on localisation was seen only among non‐carriers (Table 2, Figure 2). Childhood cognitive ability also predicted performance over 60 years later (Table 2). Conclusion: In this large population‐based sample of cognitively‐normal ∼70‐year‐olds, a positive association between APOE‐ε4 and short‐term visual memory was observed. For the localization measure, this appeared to be protective against a subtle deficit associated with β‐amyloid pathology. This is consistent with the antagonistic pleiotropy hypothesis – whereby a gene controls both beneficial and detrimental traits – and provides novel evidence that these effects persist into older age, even among individuals who may be in the preclinical stages of AD. [ABSTRACT FROM AUTHOR]

Background: Alzheimer's (AD) and cerebrovascular disease are common causes of cognitive impairment in later life and often co‐exist. Understanding how AD and vascular pathologies act independently or together to influence neurodegeneration in later life is important for the development of effective treatments and clinical trial design. Method: 219 cognitively normal participants underwent cognitive testing, structural MRI and 18F‐florbetapir amyloid‐PET scans at two visits approximately two years apart. Changes in whole brain, ventricular and hippocampal volumes between time‐points were determined using the Boundary Shift Integral (BSI) (doi:10.1016/j.neuroimage.2009.12.059). Baseline white matter hyperintensity volume (WMHV) was generated using BaMoS (doi:10.1109/TMI.2015.2419072). Baseline amyloid SUVRs were derived with eroded subcortical white matter as the reference region and a composite grey matter target region. A cut‐point of 0.6104 was used to define amyloid positivity. Linear regression was used to investigate relationships of amyloid and WMHV with atrophy rates. Specifically, models were fitted with BSI as the outcome, scan interval as the explanatory variable, and interactions between scan interval and i) the explanatory variable of interest and ii) each of the covariates (age at baseline scan, sex and total intracranial volume). Amyloid and WMHV were assessed separately and then together within the same model. An interaction between amyloid, WMHV and scan interval was also tested in a further model. Result: 199 cognitively normal participants (mean baseline age 70.1±0.4 years; 47% female) had high‐quality imaging data (Table 1). Positive amyloid status was associated with greater rates of brain and hippocampal atrophy and ventricular expansion, with a positive relationship between SUVR and ventricular expansion and hippocampal atrophy (Table 2). Larger WMHV was associated with higher rates of brain and hippocampal atrophy and ventricular expansion (Table 2). None of these associations were meaningfully altered by including amyloid and WMHV within the same model (Table 3). There was no evidence of an interaction between amyloid and WMHV for any BSI measure (interaction p>0.13, all tests). Conclusion: Markers of amyloid and presumed small‐vessel disease were independently associated with atrophy rates, and there was no evidence that either pathological process modified the effect of the other. [ABSTRACT FROM AUTHOR]

Abstract: Alzheimer's disease (AD) is associated with extensive alterations in grey matter microstructure, but our ability to quantify this in vivo is limited. Neurite orientation dispersion and density imaging (NODDI) is a multi‐shell diffusion MRI technique that estimates neuritic microstructure in the form of orientation dispersion and neurite density indices (ODI/NDI). Mean values for cortical thickness, ODI, and NDI were extracted from predefined regions of interest in the cortical grey matter of 38 patients with young onset AD and 22 healthy controls. Five cortical regions associated with early atrophy in AD (entorhinal cortex, inferior temporal gyrus, middle temporal gyrus, fusiform gyrus, and precuneus) and one region relatively spared from atrophy in AD (precentral gyrus) were investigated. ODI, NDI, and cortical thickness values were compared between controls and patients for each region, and their associations with MMSE score were assessed. NDI values of all regions were significantly lower in patients. Cortical thickness measurements were significantly lower in patients in regions associated with early atrophy in AD, but not in the precentral gyrus. Decreased ODI was evident in patients in the inferior and middle temporal gyri, fusiform gyrus, and precuneus. The majority of AD‐related decreases in cortical ODI and NDI persisted following adjustment for cortical thickness, as well as each other. There was evidence in the patient group that cortical NDI was associated with MMSE performance. These data suggest distinct differences in cortical NDI and ODI occur in AD and these metrics provide pathologically relevant information beyond that of cortical thinning. [ABSTRACT FROM AUTHOR]

Introduction: Identifying at what point atrophy rates first change in Alzheimer's disease is important for informing design of presymptomatic trials. Methods: Serial T1‐weighted magnetic resonance imaging scans of 94 participants (28 noncarriers, 66 carriers) from the Dominantly Inherited Alzheimer Network were used to measure brain, ventricular, and hippocampal atrophy rates. For each structure, nonlinear mixed‐effects models estimated the change‐points when atrophy rates deviate from normal and the rates of change before and after this point. Results: Atrophy increased after the change‐point, which occurred 1–1.5 years (assuming a single step change in atrophy rate) or 3–8 years (assuming gradual acceleration of atrophy) before expected symptom onset. At expected symptom onset, estimated atrophy rates were at least 3.6 times than those before the change‐point. Discussion: Atrophy rates are pathologically increased up to seven years before "expected onset". During this period, atrophy rates may be useful for inclusion and tracking of disease progression. [ABSTRACT FROM AUTHOR]

ABSTRACT This study investigates relationships between white matter hyperintensity (WMH) volume, cerebrospinal fluid (CSF) Alzheimer's disease (AD) pathology markers, and brain and hippocampal volume loss. Subjects included 198 controls, 345 mild cognitive impairment (MCI), and 154 AD subjects with serial volumetric 1.5-T MRI. CSF Aβ42 and total tau were measured ( n = 353). Brain and hippocampal loss were quantified from serial MRI using the boundary shift integral (BSI). Multiple linear regression models assessed the relationships between WMHs and hippocampal and brain atrophy rates. Models were refitted adjusting for (a) concurrent brain/hippocampal atrophy rates and (b) CSF Aβ42 and tau in subjects with CSF data. WMH burden was positively associated with hippocampal atrophy rate in controls ( P = 0.002) and MCI subjects ( P = 0.03), and with brain atrophy rate in controls ( P = 0.03). The associations with hippocampal atrophy rate remained following adjustment for concurrent brain atrophy rate in controls and MCIs, and for CSF biomarkers in controls ( P = 0.007). These novel results suggest that vascular damage alongside AD pathology is associated with disproportionately greater hippocampal atrophy in nondemented older adults. © 2016 The Authors Hippocampus Published by Wiley Periodicals, Inc. [ABSTRACT FROM AUTHOR]

Background: Alzheimer’s disease is currently one of our greatest socioeconomic challenges. Understanding the ability of biomarkers to predict clinical progression, prior to significant cognitive decline, is critical when considering secondary prevention strategies. Here we consider the association between a range of single time‐point baseline biomarkers in a cognitively‐normal group and the subsequent risk of developing Mild Cognitive Impairment (MCI). Method: Data from Alzheimer’s Disease Neuroimaging Initiative (ADNIGO/2/3) were used. Baseline markers (CSF amyloid 1‐42, CSF ptau, white matter hyperintensities [WMH], microbleeds, whole‐brain volume, and hippocampal volume) were obtained for 192 cognitively‐normal (CN) individuals (controls and with significant memory concern; see table). Longitudinal diagnostic data to calculate conversion time was also collected. Cox regression models were fitted for time to diagnosis of MCI. Models were fitted for each marker separately, and in a fully‐adjusted model (containing all markers); adjustments for total intracranial volume (TIV) were performed when needed. Models were refitted adjusting for baseline age. Result: There was strong evidence that increased WMH volume and lower hippocampal volume were associated with conversion to MCI. This was found in both individual models, and in the fully‐adjusted model (see figure). Decreased whole brain volume, CSF amyloid, and increased microbleeds were found to be associated with conversion to MCI when considered individually but not in a fully‐adjusted model. CSF ptau was not associated with conversion, either individually or in the fully‐adjusted model. Adjustments for baseline age did not materially change results. Conclusion: Single time‐point baseline measurements of increased WMH and decreased hippocampal volume were independently predictive of subsequent progression to MCI. Hippocampal volume and WMH may represent different processes; lower hippocampal volumes representing greater neurodegeneration and WMH as a surrogate marker of numerous pathologies, including cerebrovascular disease. While decreased whole brain volume, decreased CSF amyloid, and increased microbleeds were predictive of conversion to MCI when assessed separately, their non‐significant association in the fully‐adjusted model suggests that these are not as strongly predictive of conversion. [ABSTRACT FROM AUTHOR]

Despite considerable interest in improving clinical and neurobiological characterisation of frontotemporal dementia and in defining the role of brain network disintegration in its pathogenesis, information about white matter pathway alterations in frontotemporal dementia remains limited. Here we investigated white matter tract damage using an unbiased, template-based diffusion tensor imaging (DTI) protocol in a cohort of 27 patients with the behavioral variant of frontotemporal dementia (bvFTD) representing both major genetic and sporadic forms, in relation both to healthy individuals and to patients with Alzheimer's disease. Widespread white matter tract pathology was identified in the bvFTD group compared with both healthy controls and Alzheimer's disease group, with prominent involvement of uncinate fasciculus, cingulum bundle and corpus callosum. Relatively discrete and distinctive white matter profiles were associated with genetic subgroups of bvFTD associated with MAPT and C9ORF72 mutations. Comparing diffusivity metrics, optimal overall separation of the bvFTD group from the healthy control group was signalled using radial diffusivity, whereas optimal overall separation of the bvFTD group from the Alzheimer's disease group was signalled using fractional anisotropy. Comparing white matter changes with regional grey matter atrophy (delineated using voxel based morphometry) in the bvFTD cohort revealed co-localisation between modalities particularly in the anterior temporal lobe, however white matter changes extended widely beyond the zones of grey matter atrophy. Our findings demonstrate a distributed signature of white matter alterations that is likely to be core to the pathophysiology of bvFTD and further suggest that this signature is modulated by underlying molecular pathologies. Hum Brain Mapp 35:4163-4179, 2014. © 2014 The Authors. Human Brain Mapping Published by Wiley Periodicals, Inc. [ABSTRACT FROM AUTHOR]

Background: Mid‐life hypertension is an established risk factor for late‐life cognitive impairment. Whilst previous studies demonstrate mid‐life hypertension is associated with larger white matter (WM) hyperintensity volumes, Differences in normal appearing white matter (NAWM) microstructure may provide an earlier indication of WM injury. In a population‐based life‐course study of cognitively healthy individuals, we explored the relationship between blood pressure (BP) over 30 years and NAWM microstructural metrics in later life. Method: Participants from Insight 46, a sub‐study of the 1946 British birth cohort, underwent multi‐modal MR imaging including T1, T2, FLAIR and multi‐shell diffusion‐weighted sequences at age 69‐71. Diffusion‐weighted images were processed by automated pipelines, NAWM masks were derived by subtracting the BaMoS‐derived white matter hyperintensity mask from GIF pipeline generated WM mask (eroded by 1 voxel) using FSL. Mean values of microstructural integrity metrics (fractional anisotropy (FA), mean diffusivity (MD), neurite density index (NDI), orientation dispersion index (ODI)) were extracted from T1‐registered diffusion maps using FSL and NODDI toolbox. Individuals with a major brain or neurodegenerative disorder such as dementia, neuroinflammatory condition or stroke were excluded. Linear regression analyses examined relationships between systolic blood pressure (SBP) and diastolic blood pressure (DBP) at ages 36, 43, 53, 60‐64 and 69 and microstructural metrics at age 69‐71 adjusting for sex, age, socioeconomic class, educational attainment, childhood cognition and antihypertensive medication. Result: 379 participants were included (mean age at imaging 70.7 years, 50% female). Higher SBP at ages 53 and 69 was associated with lower FA and NDI; and higher MD, and SBP at 69 was associated with higher ODI. Similarly, higher DBP at ages 53, 60‐64 and 69 were associated with lower FA and NDI; and higher MD. There was no evidence of associations between BP at age 36 or 43 and NAWM diffusion metrics. Conclusion: Higher systolic and diastolic blood pressure from age 53 onwards are shown to be associated with differences in diffusion‐based measures of white matter microstructural integrity later in life, suggesting that systolic or diastolic hypertension in over 50's may contribute to cognitive impairment risk via alterations in NAWM microstructure differences in later life. [ABSTRACT FROM AUTHOR]

Background: Cigarette smoking is implicated as a risk factor for dementia, but the underlying mechanisms are poorly understood. In a population‐based sample free of dementia, we examine associations between smoking patterns over the life course and imaging markers associated with dementia. Method: Dementia‐free participants from Insight 46 (n=458, 49% female, age 69‐71), a sub‐study of the 1946 British Birth Cohort, underwent 18F‐florbetapir Aβ‐PET and multi‐modal MR imaging including T1, T2, FLAIR and multi‐shell diffusion‐weighted sequences. Information on smoking frequency and cessation (current/former/never) were obtained at multiple timepoints, spanning ages 15‐69 years. Pack‐years were calculated as number of cigarettes smoked/day divided by 20, multiplied by years of smoking. Age and sex adjusted regression analyses examined relationships between smoking metrics and later‐life imaging measures; including Aβ‐PET status, brain, hippocampal and white matter hyperintensity (WMH) volumes, normal appearing white matter (NAWM) fractional anisotropy (FA), mean diffusivity (MD), neurite density index (NDI) and orientation dispersion index (ODI), and Alzheimer's disease (AD)‐related cortical thickness. Result: Increased smoking pack‐years was associated with alterations in NAWM microstructure metrics (lower FA and NDI; higher MD and ODI) and smaller brain and hippocampal volume (Figure 1). There was no significant relationship with Aβ‐PET status (OR=0.99 [95% CI 0.97,1.01]), WMH volume or AD‐related cortical thickness (Figure 1). Unlike current smokers (n=16, 3%), former smokers (n=285, 61%) had comparable NAWM microstructure metrics to those who had never smoked (n=163, 35%). Conclusion: In a population‐based sample without dementia or other major neurological problems, increased smoking frequency and duration over 50 years was associated with altered white matter microstructural metrics, and smaller brain and hippocampal volumes. However, there was no evidence that smoking was associated with markers of AD pathology (amyloid‐PET, AD‐related cortical thickness) or cerebral small vessel disease (WMH). Former smokers were comparable to non‐smokers on measures of microstructural metrics, suggesting that smoking‐related microstructural changes may at least partly be reversible. Stopping or reducing smoking may help reduce risks to brain health via microstructural pathways. [ABSTRACT FROM AUTHOR]

Background: Superficial white matter (SWM), subjacent to the cortex, has unique vulnerabilities to Alzheimer's disease (AD) but is understudied. The organisational complexity of SWM means conventional diffusion tensor imaging (DTI) has difficulty characterising both the degeneration and dispersion of fibres. We used both DTI and Neurite Orientation Dispersion and Density Imaging (NODDI) to assess AD‐related SWM changes while accounting for fibre density and dispersion. Method: 51 participants were included: 22 healthy controls (mean age 61 (+/‐6) years) and 29 individuals with young onset AD (YOAD: mean age 62 (+/‐5) years; 18 with typical AD, 11 posterior cortical atrophy). We used single‐shell (b=1000) and multi‐shell (b=300; b=700; b=2000) diffusion MRI (dMRI) data. Preprocessing involved correcting for eddy currents, motion and susceptibility artefacts followed by fitting DTI and NODDI models. DTI and NODDI metrics were sampled across the GM/WM FreeSurfer surface in 15 cortical regions of interest (ROI) (Figure 1). Linear mixed effect models of fractional anisotropy (FA), mean diffusivity (MD), neurite density index (NDI) and orientation dispersion index (ODI) were fit for each ROI while controlling for cortical thickness (correcting for multiple comparisons using False Discovery Rate of 0.05). Average Marginal Effects (AMEs) representing the change in YOAD versus controls, while holding other fixed effects constant, were calculated. Result: In the GM, widespread significant differences were observed in both DTI and NODDI metrics (pFDR < 0.05) (Figure 2). In the SWM, YOAD patients had lower FA and/or changes in MD in five ROIs (pFDR < 0.05) (including parahippocampal gyrus and superior temporal cortex). NODDI metrics showed more widespread differences in the SWM, as the YOAD group had lower NDI and/or higher ODI across ten ROIs (pFDR < 0.05) (including parahippocampal gyrus and inferior parietal cortex). Conclusion: Diffusion imaging can detect microstructural changes occurring within the SWM of YOAD individuals in regions associated with AD‐pathology. These changes in SWM not only persist, but are more prominent, when independently accounting for the density and dispersion of fibres using NODDI. These novel NODDI SWM measures may uncover previously under‐recognised degeneration and organisational WM alterations in AD. [ABSTRACT FROM AUTHOR]

Background: The two commonest contributors to late‐life cognitive impairment are Alzheimer's (AD) and cerebrovascular disease; these two conditions almost invariably overlap. Understanding the determinants of the pathologies that underpin these conditions and how they interact to influence late‐life brain health is vital for rational risk prevention and for clinical trials. Method: Data from the Insight 46 cohort will be presented, comprising individuals from the MRC 1946 British Birth Cohort all born in mainland Britain in one week in 1946. These individuals have been followed prospectively including serial measures of cognition from age 8, cardiovascular risk since the mid‐30s, and a range of cardiac and vascular outcomes in their 60s. At age 69‐71 they had detailed cognitive testing, 3T‐MRI including determination of white matter hyperintensity volume (WMHV) and 18F‐Florbetapir (β‐amyloid) PET. All individuals are being seen for a second visit, two‐years after the first. Results: A total of 502 participants were assessed cross‐sectionally; data from up to 465 participants (51.0% male, mean age=70.7±0.7, 18.2% β‐amyloid positive) are available. Results of cross‐sectional analyses investigating the relationships between life course cardiovascular risk factors genetics, β‐amyloid and WMHV will be presented, alongside analyses of the associations of these pathologies with cross‐sectional cognitive function and MRI metrics. Investigations exploring mechanistic relationships e.g. between cardiac and vascular outcomes including pulse wave velocity and echocardiography, β‐amyloid and WMHV and cognition will be presented. Interim results of analyses exploring relationships between baseline β‐amyloid and WMHV and rates of cognitive decline and brain atrophy will be described in n=250 (47.6% female, age=72.5±0.33, 18.1% β‐amyloid positive) of the cohort. These results will be compared and contrasted with those from other cohort studies including the Atherosclerosis Risk in Communities (ARIC) study, and Mayo Clinic Study of Ageing. Conclusion: Combining life course data, contemporaneous measurement of PET‐amyloid status, WMHV and cognition, vascular metrics and longitudinal measures of brain atrophy and cognitive change provides a powerful opportunity to explore how and when vascular and β‐amyloid pathology influence brain health in later‐life. Emerging evidence from several studies suggest that vascular risk influences the development of cognitive impairment and dementia principally via non‐amyloidogenic pathways. [ABSTRACT FROM AUTHOR]