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Midlife hypertension confers increased risk for cognitive impairment in late life. The sensitive period for risk exposure and extent that risk is mediated through amyloid or vascular-related mechanisms are poorly understood. We aimed to identify if, and when, blood pressure or change in blood pressure during adulthood were associated with late-life brain structure, pathology, and cognition. Participants were from Insight 46, a neuroscience substudy of the ongoing longitudinal Medical Research Council National Survey of Health and Development, a birth cohort that initially comprised 5362 individuals born throughout mainland Britain in one week in 1946. Participants aged 69–71 years received T1 and FLAIR volumetric MRI, florbetapir amyloid-PET imaging, and cognitive assessment at University College London (London, UK); all participants were dementia-free. Blood pressure measurements had been collected at ages 36, 43, 53, 60–64, and 69 years. We also calculated blood pressure change variables between ages. Primary outcome measures were white matter hyperintensity volume (WMHV) quantified from multimodal MRI using an automated method, amyloid-β positivity or negativity using a standardised uptake value ratio approach, whole-brain and hippocampal volumes quantified from 3D-T1 MRI, and a composite cognitive score—the Preclinical Alzheimer Cognitive Composite (PACC). We investigated associations between blood pressure and blood pressure changes at and between 36, 43, 53, 60–64, and 69 years of age with WMHV using generalised linear models with a gamma distribution and log link function, amyloid-β status using logistic regression, whole-brain volume and hippocampal volumes using linear regression, and PACC score using linear regression, with adjustment for potential confounders. Between May 28, 2015, and Jan 10, 2018, 502 individuals were assessed as part of Insight 46. 465 participants (238 [51%] men; mean age 70·7 years [SD 0·7]; 83 [18%] amyloid-β-positive) were included in imaging analyses. Higher systolic blood pressure (SBP) and diastolic blood pressure (DBP) at age 53 years and greater increases in SBP and DBP between 43 and 53 years were positively associated with WMHV at 69–71 years of age (increase in mean WMHV per 10 mm Hg greater SBP 7%, 95% CI 1–14, p=0·024; increase in mean WMHV per 10 mm Hg greater DBP 15%, 4–27, p=0·0057; increase in mean WMHV per one SD change in SBP 15%, 3–29, p=0·012; increase in mean WMHV per 1 SD change in DBP 15%, 3–30, p=0·017). Higher DBP at 43 years of age was associated with smaller whole-brain volume at 69–71 years of age (−6·9 mL per 10 mm Hg greater DBP, −11·9 to −1·9, p=0·0068), as were greater increases in DBP between 36 and 43 years of age (−6·5 mL per 1 SD change, −11·1 to −1·9, p=0·0054). Greater increases in SBP between 36 and 43 years of age were associated with smaller hippocampal volumes at 69–71 years of age (−0·03 mL per 1 SD change, −0·06 to −0·001, p=0·043). Neither absolute blood pressure nor change in blood pressure predicted amyloid-β status or PACC score at 69–71 years of age. High and increasing blood pressure from early adulthood into midlife seems to be associated with increased WMHV and smaller brain volumes at 69–71 years of age. We found no evidence that blood pressure affected cognition or cerebral amyloid-β load at this age. Blood pressure monitoring and interventions might need to start around 40 years of age to maximise late-life brain health. Alzheimer's Research UK, Medical Research Council, Dementias Platform UK, Wellcome Trust, Brain Research UK, Wolfson Foundation, Weston Brain Institute, Avid Radiopharmaceuticals.

Tract-based spatial statistics (TBSS) is a popular method for the analysis of diffusion tensor imaging data. TBSS focuses on differences in white matter voxels with high fractional anisotropy (FA), representing the major fibre tracts, through registering all subjects to a common reference and the creation of a FA skeleton. This work considers the effect of choice of reference in the TBSS pipeline, which can be a standard template, an individual subject from the study, a study-specific template or a group-wise average. While TBSS attempts to overcome registration error by searching the neighbourhood perpendicular to the FA skeleton for the voxel with maximum FA, this projection step may not compensate for large registration errors that might occur in the presence of pathology such as atrophy in neurodegenerative diseases. This makes registration performance and choice of reference an important issue. Substantial work in the field of computational anatomy has shown the use of group-wise averages to reduce biases while avoiding the arbitrary selection of a single individual. Here, we demonstrate the impact of the choice of reference on: (a) specificity (b) sensitivity in a simulation study and (c) a real-world comparison of Alzheimer's disease patients to controls. In (a) and (b), simulated deformations and decreases in FA were applied to control subjects to simulate changes of shape and WM integrity similar to what would be seen in AD patients, in order to provide a \"ground truth\" for evaluating the various methods of TBSS reference. Using a group-wise average atlas as the reference outperformed other references in the TBSS pipeline in all evaluations.

The human hippocampus comprises a number of interconnected histologically and functionally distinct subfields, which may be differentially influenced by cerebral pathology. Automated techniques are now available that estimate hippocampal subfield volumes using in vivo structural MRI data. To date, research investigating the influence of cerebral β-amyloid deposition-one of the earliest hypothesised changes in the pathophysiological continuum of Alzheimer's disease-on hippocampal subfield volumes in cognitively normal older individuals, has been limited. Using cross-sectional data from 408 cognitively normal individuals born in mainland Britain (age range at time of assessment = 69.2-71.9 years) who underwent cognitive assessment, 18F-Florbetapir PET and structural MRI on the same 3 Tesla PET/MR unit (spatial resolution 1.1 x 1.1 x 1.1. mm), we investigated the influences of β-amyloid status, age at scan, and global white matter hyperintensity volume on: CA1, CA2/3, CA4, dentate gyrus, presubiculum and subiculum volumes, adjusting for sex and total intracranial volume. Compared to β-amyloid negative participants (n = 334), β-amyloid positive participants (n = 74) had lower volume of the presubiculum (3.4% smaller, p = 0.012). Despite an age range at scanning of just 2.7 years, older age at time of scanning was associated with lower CA1 (p = 0.007), CA4 (p = 0.004), dentate gyrus (p = 0.002), and subiculum (p = 0.035) volumes. There was no evidence that white matter hyperintensity volume was associated with any subfield volumes. These data provide evidence of differential associations in cognitively normal older adults between hippocampal subfield volumes and β-amyloid deposition and, increasing age at time of scan. The relatively selective effect of lower presubiculum volume in the β-amyloid positive group potentially suggest that the presubiculum may be an area of early and relatively specific volume loss in the pathophysiological continuum of Alzheimer's disease. Future work using higher resolution imaging will be key to exploring these findings further.

Background: Automated tools for characterizing dementia risk have the potential to aid in the diagnosis, prognosis, and treatment of Alzheimer’s Disease (AD). Here, we examined a novel machine learning-based brain atrophy marker, the AD-resemblance atrophy index (AD-RAI), to assess its test-retest reliability and further validate its use in disease classification and prediction. Methods: Age- and sex-matched 44 probable AD (Age: 69.13  7.13; MMSE: 27-30) and 22 non-demented control (Age: 69.38  7.21; MMSE: 27-30) participants were obtained from the Minimal Interval Resonance Imaging in Alzheimer's Disease (MIRIAD) dataset. After data exclusion, we selected serial T1-weighted images (n = 679) from these participants across over a two-year period, including 179 pairs of scans acquired on the same day and 40 pairs of scans acquired at two-week intervals. All images were automatically processed with AccuBrain® to calculate the AD-RAI. Its same-day repeatability and two-week reproducibility were first assessed. The discriminative performance of AD-RAI was evaluated using the receiver operating characteristic curve, where DeLong’s test was used to evaluate its performance against quantitative medial temporal lobe atrophy (QMTA) and hippocampal volume adjusted by intracranial volume (ICV)-proportions and ICV-residuals methods, respectively (HVR and HRV). Linear mixed-effects modelling was used to investigate longitudinal trajectories of AD-RAI and baseline AD-RAI prediction of cognitive decline. Finally, the longitudinal associations between AD-RAI and MMSE scores were assessed. Results: AD-RAI had excellent same-day repeatability and excellent two-week reproducibility. AD-RAI’s AUC (99.8%; 95%CI = [99.3%, 100%]) was equivalent to that of QMTA (96.8%; 95%CI = [92.9%, 100%]), and better than that of HVR (86.8%; 95%CI = [78.2%, 95.4%]) or HRV (90.3%; 95%CI = [83.0%, 97.6%]). While baseline AD-RAI was significantly higher in the AD group, it did not show detectable changes over 2 years. Baseline AD-RAI was negatively associated with MMSE scores and the rate of the change in MMSE scores over time. A negative longitudinal association was also found between AD-RAI values and the MMSE scores among AD patients. Conclusions: The AD-RAI represents a potential biomarker that may support AD diagnosis and be used to predict the rate of future cognitive decline in AD patients.

Background Although age is the biggest known risk factor for dementia, there remains uncertainty about other factors over the life course that contribute to a person’s risk for cognitive decline later in life. Furthermore, the pathological processes leading to dementia are not fully understood. The main goals of Insight 46—a multi-phase longitudinal observational study—are to collect detailed cognitive, neurological, physical, cardiovascular, and sensory data; to combine those data with genetic and life-course information collected from the MRC National Survey of Health and Development (NSHD; 1946 British birth cohort); and thereby contribute to a better understanding of healthy ageing and dementia. Methods/Design Phase 1 of Insight 46 (2015–2018) involved the recruitment of 502 members of the NSHD (median age = 70.7 years; 49% female) and has been described in detail by Lane and Parker et al . 2017. The present paper describes phase 2 (2018–2021) and phase 3 (2021–ongoing). Of the 502 phase 1 study members who were invited to a phase 2 research visit, 413 were willing to return for a clinic visit in London and 29 participated in a remote research assessment due to COVID-19 restrictions. Phase 3 aims to recruit 250 study members who previously participated in both phases 1 and 2 of Insight 46 (providing a third data time point) and 500 additional members of the NSHD who have not previously participated in Insight 46. Discussion The NSHD is the oldest and longest continuously running British birth cohort. Members of the NSHD are now at a critical point in their lives for us to investigate successful ageing and key age-related brain morbidities. Data collected from Insight 46 have the potential to greatly contribute to and impact the field of healthy ageing and dementia by combining unique life course data with longitudinal multiparametric clinical, imaging, and biomarker measurements. Further protocol enhancements are planned, including in-home sleep measurements and the engagement of participants through remote online cognitive testing. Data collected are and will continue to be made available to the scientific community.

Cortical tau accumulation is a key pathological event that partly defines Alzheimer’s disease (AD) onset and is associated with cognitive decline and future disease progression. However, an improved understanding of the timing and pattern of early tau deposition in AD and how this may be tracked in vivo is needed. Data from 59 participants involved in two longitudinal cohort studies of autosomal dominant AD (ADAD) were used to investigate whether tau PET can detect and track presymptomatic change; seven participants were symptomatic, and 52 were asymptomatic but at a 50% risk of carrying a pathogenic mutation. All had baseline flortaucipir (FTP) PET, MRI and clinical assessments; 26 individuals had more than one FTP PET scan. Standardised uptake value ratios (SUVRs) in prespecified regions of interest (ROIs) were obtained using inferior cerebellar grey matter as the reference region. We compared the changes in FTP SUVRs between presymptomatic carriers, symptomatic carriers and non-carriers, adjusting for age, sex and study site. We also investigated the relationship between regional FTP SUVRs and estimated years to/from symptom onset (EYO). Compared to both non-carriers and presymptomatic carriers, FTP SUVRs were significantly higher in symptomatic carriers in all ROIs tested ( p  < 0.001). There were no significant regional differences between presymptomatic carriers and non-carriers in FTP SUVRs, or their rates of change ( p  > 0.05), although increased FTP signal uptake was seen posteriorly in some individuals around the time of expected symptom onset. When we examined the relationship of FTP SUVR with respect to EYO, the earliest significant regional difference between mutation carriers and non-carriers was detected within the precuneus prior to estimated symptom onset in some cases. This study supports preliminary studies suggesting that presymptomatic tau tracer uptake is rare in ADAD. In cases where early uptake was seen, there was often a predilection for posterior regions (the precuneus and post-cingulate) as opposed to the medial temporal lobe, highlighting the importance of examining in vivo tau uptake beyond the confines of traditional Braak staging.

Total intracranial volume (TIV/ICV) is an important covariate for volumetric analyses of the brain and brain regions, especially in the study of neurodegenerative diseases, where it can provide a proxy of maximum pre-morbid brain volume. The gold-standard method is manual delineation of brain scans, but this requires careful work by trained operators. We evaluated Statistical Parametric Mapping 12 (SPM12) automated segmentation for TIV measurement in place of manual segmentation and also compared it with SPM8 and FreeSurfer 5.3.0. For T1-weighted MRI acquired from 288 participants in a multi-centre clinical trial in Alzheimer's disease we find a high correlation between SPM12 TIV and manual TIV (R2=0.940, 95% Confidence Interval (0.924, 0.953)), with a small mean difference (SPM12 40.4±35.4ml lower than manual, amounting to 2.8% of the overall mean TIV in the study). The correlation with manual measurements (the key aspect when using TIV as a covariate) for SPM12 was significantly higher (p<0.001) than for either SPM8 (R2=0.577 CI (0.500, 0.644)) or FreeSurfer (R2=0.801 CI (0.744, 0.843)). These results suggest that SPM12 TIV estimates are an acceptable substitute for labour-intensive manual estimates even in the challenging context of multiple centres and the presence of neurodegenerative pathology. We also briefly discuss some aspects of the statistical modelling approaches to adjust for TIV. [Display omitted] •288 T1 MRI from multiple scanners were manually segmented for intracranial volume.•We compare SPM12 with the current methods of estimating intracranial volume.•SPM12 shows a very high correlation with manual measures and little bias.•Newer automated volume measures are more accurate controls for head size variation.

Background In view of reported associations between high adiposity, particularly in midlife and late-life dementia risk, we aimed to determine associations between body mass index (BMI), and BMI changes across adulthood and brain structure and pathology at age 69–71 years. Methods Four hundred sixty-five dementia-free participants from Insight 46, a sub-study of the British 1946 birth cohort, who had cross-sectional T1/FLAIR volumetric MRI, and florbetapir amyloid-PET imaging at age 69–71 years, were included in analyses. We quantified white matter hyperintensity volume (WMHV) using T1 and FLAIR 3D-MRI; β-amyloid (Aβ) positivity/negativity using a SUVR approach; and whole brain (WBV) and hippocampal volumes (HV) using 3D T1-MRI. We investigated the influence of BMI, and BMI changes at and between 36, 43, 53, 60–64, 69 and 71 years, on late-life WMHV, Aβ-status, WBV and mean HV. Analyses were repeated using overweight and obese status. Results At no time-point was BMI, change in BMI or overweight/obese status associated with WMHV or WBV at age 69–71 years. Decreasing BMI in the 1–2 years before imaging was associated with an increased odds of being β-amyloid positive (OR 1.45, 95% confidence interval 1.09, 1.92). There were associations between being overweight and larger mean HV at ages 60–64 ( β  = 0.073 ml, 95% CI 0.009, 0.137), 69 ( β  = 0.076 ml, 95% CI 0.012, 0.140) and 71 years ( β  = 0.101 ml, 95% CI 0.037, 0.165). A similar, albeit weaker, trend was seen with obese status. Conclusions Using WMHV, β-amyloid status and brain volumes as indicators of brain health, we do not find evidence to explain reported associations between midlife obesity and late-life dementia risk. Declining BMI in later life may reflect preclinical Alzheimer’s disease.

Background Identifying blood-based signatures of brain health and preclinical pathology may offer insights into early disease mechanisms and highlight avenues for intervention. Here, we systematically profiled associations between blood metabolites and whole-brain volume, hippocampal volume, and amyloid-β status among participants of Insight 46—the neuroscience sub-study of the National Survey of Health and Development (NSHD). We additionally explored whether key metabolites were associated with polygenic risk for Alzheimer’s disease (AD). Methods Following quality control, levels of 1019 metabolites—detected with liquid chromatography-mass spectrometry—were available for 1740 participants at age 60–64. Metabolite data were subsequently clustered into modules of co-expressed metabolites using weighted coexpression network analysis. Accompanying MRI and amyloid-PET imaging data were present for 437 participants (age 69–71). Regression analyses tested relationships between metabolite measures—modules and hub metabolites—and imaging outcomes. Hub metabolites were defined as metabolites that were highly connected within significant ( p FDR  < 0.05) modules or were identified as a hub in a previous analysis on cognitive function in the same cohort. Regression models included adjustments for age, sex, APOE genotype, lipid medication use, childhood cognitive ability, and social factors. Finally, associations were tested between AD polygenic risk scores (PRS), including and excluding the APOE region, and metabolites and modules that significantly associated ( p FDR  < 0.05) with an imaging outcome ( N  = 1638). Results In the fully adjusted model, three lipid modules were associated with a brain volume measure ( p FDR  < 0.05): one enriched in sphingolipids (hippocampal volume: ß  = 0.14, 95% CI = [0.055,0.23]), one in several fatty acid pathways (whole-brain volume: ß  =  − 0.072, 95%CI = [− 0.12, − 0.026]), and another in diacylglycerols and phosphatidylethanolamines (whole-brain volume: ß  =  − 0.066, 95% CI = [− 0.11, − 0.020]). Twenty-two hub metabolites were associated ( p FDR  < 0.05) with an imaging outcome (whole-brain volume: 22; hippocampal volume: 4). Some nominal associations were reported for amyloid-β, and with an AD PRS in our genetic analysis, but none survived multiple testing correction. Conclusions Our findings highlight key metabolites, with functions in membrane integrity and cell signalling, that associated with structural brain measures in later life. Future research should focus on replicating this work and interrogating causality.

ObjectiveTo summarise the incidental findings detected on brain imaging and blood tests during the first wave of data collection for the Insight 46 study.DesignProspective observational sub-study of a birth cohort.SettingSingle-day assessment at a research centre in London, UK.Participants502 individuals were recruited from the MRC National Survey of Health and Development (NSHD), the 1946 British birth cohort, based on pre-specified eligibility criteria; mean age was 70.7 (SD: 0.7) and 49% were female.Outcome measuresData regarding the number and types of incidental findings were summarised as counts and percentages, and 95% confidence intervals were calculated.Results93.8% of participants completed a brain scan (n=471); 4.5% of scanned participants had a pre-defined reportable abnormality on brain MRI (n=21); suspected vascular malformations and suspected intracranial mass lesions were present in 1.9% (n=9) and 1.5% (n=7) respectively; suspected cerebral aneurysms were the single most common vascular abnormality, affecting 1.1% of participants (n=5), and suspected meningiomas were the most common intracranial lesion, affecting 0.6% of participants (n=3); 34.6% of participants had at least one abnormality on clinical blood tests (n=169), but few reached the prespecified threshold for urgent action (n=11).ConclusionsIn older adults, aged 69-71 years, potentially serious brain MRI findings were detected in around 5% of participants, and clinical blood test abnormalities were present in around one third of participants. Knowledge of the expected prevalence of incidental findings in the general population at this age is useful in both research and clinical settings.