Updating the study protocol: Insight 46 - a longitudinal neuroscience sub-study of the MRC National Survey of Health and Development - phases 2 and 3.

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.

Rates of cortical thinning in Alzheimer's disease signature regions associate with vascular burden but not with ß-amyloid status in cognitively normal adults at age 70.

BACKGROUND: Consistent patterns of reduced cortical thickness have been identified in early Alzheimer's disease (AD). However, the pathological factors that influence rates of cortical thinning within these AD signature regions remain unclear. METHODS: Participants were from the Insight 46 substudy of the MRC National Survey of Health and Development (NSHD; 1946 British birth cohort), a prospective longitudinal cohort study. Linear regression was used to examine associations of baseline cerebral ß-amyloid (Aß) deposition, measured using florbetapir positron emission tomography, and baseline white matter hyperintensity volume (WMHV) on MRI, a marker of cerebral small vessel disease, with subsequent longitudinal changes in AD signature cortical thickness quantified from baseline and repeat MRI (mean [SD] interval 2.4 [0.2] years). RESULTS: In a population-based sample of 337 cognitively normal older white adults (mean [SD] age at baseline 70.5 [0.6] years; 48.1% female), higher global WMHV at baseline related to faster subsequent rates of cortical thinning in both AD signature regions (~0.15%/year faster per 10 mL additional WMHV), whereas baseline Aß status did not. Among Aß positive participants (n=56), there was some evidence that greater global Aß standardised uptake value ratio at baseline related to faster cortical thinning in the AD signature Mayo region, but this did not reach statistical significance (p=0.08). CONCLUSIONS: Cortical thinning within AD signature regions may develop via cerebrovascular pathways. Perhaps reflecting the age of the cohort and relatively low prevalence of Aß-positivity, robust Aß-related differences were not detected. Longitudinal follow-up incorporating additional biomarkers will allow assessment of how these relationships evolve closer to expected dementia onset.

Operationalizing the centiloid scale for [18F]florbetapir PET studies on PET/MRI.

INTRODUCTION: The Centiloid scale aims to harmonize amyloid beta (Aß) 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 Aß 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.

Tau accumulation in autosomal dominant Alzheimer's disease: a longitudinal [18F]flortaucipir study.

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.

Cortical tau is associated with microstructural imaging biomarkers of neurite density and dendritic complexity in Alzheimer's disease.

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.

Structural MRI predicts clinical progression in presymptomatic genetic frontotemporal dementia: findings from the GENetic Frontotemporal dementia Initiative cohort.

Biomarkers that can predict disease progression in individuals with genetic frontotemporal dementia are urgently needed. We aimed to identify whether baseline MRI-based grey and white matter abnormalities are associated with different clinical progression profiles in presymptomatic mutation carriers in the GENetic Frontotemporal dementia Initiative. Three hundred eighty-seven mutation carriers were included (160 GRN, 160 C9orf72, 67 MAPT), together with 240 non-carrier cognitively normal controls. Cortical and subcortical grey matter volumes were generated using automated parcellation methods on volumetric 3T T1-weighted MRI scans, while white matter characteristics were estimated using diffusion tensor imaging. Mutation carriers were divided into two disease stages based on their global CDR®+NACC-FTLD score: presymptomatic (0 or 0.5) and fully symptomatic (1 or greater). The w-scores in each grey matter volumes and white matter diffusion measures were computed to quantify the degree of abnormality compared to controls for each presymptomatic carrier, adjusting for their age, sex, total intracranial volume, and scanner type. Presymptomatic carriers were classified as 'normal' or 'abnormal' based on whether their grey matter volume and white matter diffusion measure w-scores were above or below the cut point corresponding to the 10th percentile of the controls. We then compared the change in disease severity between baseline and one year later in both the 'normal' and 'abnormal' groups within each genetic subtype, as measured by the CDR®+NACC-FTLD sum-of-boxes score and revised Cambridge Behavioural Inventory total score. Overall, presymptomatic carriers with normal regional w-scores at baseline did not progress clinically as much as those with abnormal regional w-scores. Having abnormal grey or white matter measures at baseline was associated with a statistically significant increase in the CDR®+NACC-FTLD of up to 4 points in C9orf72 expansion carriers, and 5 points in the GRN group as well as a statistically significant increase in the revised Cambridge Behavioural Inventory of up to 11 points in MAPT, 10 points in GRN, and 8 points in C9orf72 mutation carriers. Baseline regional brain abnormalities on MRI in presymptomatic mutation carriers are associated with different profiles of clinical progression over time. These results may be helpful to inform stratification of participants in future trials.

Investigating associations between blood metabolites, later life brain imaging measures, and genetic risk for 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-ß 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 (pFDR < 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 (pFDR < 0.05) with an imaging outcome (N = 1638). RESULTS: In the fully adjusted model, three lipid modules were associated with a brain volume measure (pFDR < 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 (pFDR < 0.05) with an imaging outcome (whole-brain volume: 22; hippocampal volume: 4). Some nominal associations were reported for amyloid-ß, 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.

Predicting Cognitive Decline in Older Adults Using Baseline Metrics of AD Pathologies, Cerebrovascular Disease, and Neurodegeneration.

BACKGROUND AND OBJECTIVES: Dementia is a growing socioeconomic challenge that requires early intervention. Identifying biomarkers that reliably predict clinical progression early in the disease process would better aid selection of individuals for future trial participation. Here, we compared the ability of baseline, single time-point biomarkers (CSF amyloid 1-42, CSF ptau-181, white matter hyperintensities (WMH), cerebral microbleeds, whole-brain volume, and hippocampal volume) to predict decline in cognitively normal individuals who later converted to mild cognitive impairment (MCI) (CNtoMCI) and those with MCI who later converted to an Alzheimer disease (AD) diagnosis (MCItoAD). METHODS: Standardized baseline biomarker data from AD Neuroimaging Initiative 2 (ADNI2)/GO and longitudinal diagnostic data (including ADNI3) were used. Cox regression models assessed biomarkers in relation to time to change in clinical diagnosis using all follow-up time points available. Models were fit for biomarkers univariately and together in a multivariable model. Hazard ratios (HRs) were compared to evaluate biomarkers. Analyses were performed separately in CNtoMCI and MCItoAD groups. RESULTS: For CNtoMCI (n = 189), there was strong evidence that higher WMH volume (individual model: HR 1.79, p = 0.002; fully adjusted model: HR 1.98, p = 0.003) and lower hippocampal volume (individual: HR 0.54, p = 0.001; fully adjusted: HR 0.40, p < 0.001) were associated with conversion to MCI individually and independently. For MCItoAD (n = 345), lower hippocampal (individual model: HR 0.45, p < 0.001; fully adjusted model: HR 0.55, p < 0.001) and whole-brain volume (individual: HR 0.31, p < 0.001; fully adjusted: HR 0.48, p = 0.02), increased CSF ptau (individual: HR 1.88, p < 0.001; fully adjusted: HR 1.61, p < 0.001), and lower CSF amyloid (individual: HR 0.37, p < 0.001; fully adjusted: HR 0.62, p = 0.008) were most strongly associated with conversion to AD individually and independently. DISCUSSION: Lower hippocampal volume was a consistent predictor of clinical conversion to MCI and AD. CSF and brain volume biomarkers were predictive of conversion to AD from MCI, whereas WMH were predictive of conversion to MCI from cognitively normal. The predictive ability of WMH in the CNtoMCI group may be interpreted as some being on a different pathologic pathway, such as vascular cognitive impairment.

Adulthood cognitive trajectories over 26 years and brain health at 70 years of age: findings from the 1946 British Birth Cohort.

Few studies can address how adulthood cognitive trajectories relate to brain health in 70-year-olds. Participants (n = 468, 49% female) from the 1946 British birth cohort underwent 18F-Florbetapir PET/MRI. Cognitive function was measured in childhood (age 8 years) and across adulthood (ages 43, 53, 60-64 and 69 years) and was examined in relation to brain health markers of ß-amyloid (Aß) status, whole brain and hippocampal volume, and white matter hyperintensity volume (WMHV). Taking into account key contributors of adult cognitive decline including childhood cognition, those with greater Aß and WMHV at age 70 years had greater decline in word-list learning memory in the preceding 26 years, particularly after age 60. In contrast, those with smaller whole brain and hippocampal volume at age 70 years had greater decline in processing search speed, subtly manifest from age 50 years. Subtle changes in memory and processing speed spanning 26 years of adulthood were associated with markers of brain health at 70 years of age, consistent with detectable prodromal cognitive effects in early older age.

Validation of the Alzheimer's disease-resemblance atrophy index in classifying and predicting progression in Alzheimer's disease.

Background: Automated tools for characterising 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. Serial T1-weighted images (n = 678) from up to nine time points over a 2-year period, including 179 pairs of back-to-back scans acquired on same participants on the same day and 40 pairs of scans acquired at 2-week intervals were included. All images were automatically processed with AccuBrain® to calculate the AD-RAI. Its same-day repeatability and 2-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 2-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.

Life course, genetic, and neuropathological associations with brain age in the 1946 British Birth Cohort: a population-based study.

BACKGROUND: A neuroimaging-based biomarker termed the brain age is thought to reflect variability in the brain's ageing process and predict longevity. Using Insight 46, a unique narrow-age birth cohort, we aimed to examine potential drivers and correlates of brain age. METHODS: Participants, born in a single week in 1946 in mainland Britain, have had 24 prospective waves of data collection to date, including MRI and amyloid PET imaging at approximately 70 years old. Using MRI data from a previously defined selection of this cohort, we derived brain-predicted age from an established machine-learning model (trained on 2001 healthy adults aged 18-90 years); subtracting this from chronological age (at time of assessment) gave the brain-predicted age difference (brain-PAD). We tested associations with data from early life, midlife, and late life, as well as rates of MRI-derived brain atrophy. FINDINGS: Between May 28, 2015, and Jan 10, 2018, 502 individuals were assessed as part of Insight 46. We included 456 participants (225 female), with a mean chronological age of 70·7 years (SD 0·7; range 69·2 to 71·9). The mean brain-predicted age was 67·9 years (8·2, 46·3 to 94·3). Female sex was associated with a 5·4-year (95% CI 4·1 to 6·8) younger brain-PAD than male sex. An increase in brain-PAD was associated with increased cardiovascular risk at age 36 years (ß=2·3 [95% CI 1·5 to 3·0]) and 69 years (ß=2·6 [1·9 to 3·3]); increased cerebrovascular disease burden (1·9 [1·3 to 2·6]); lower cognitive performance (-1·3 [-2·4 to -0·2]); and increased serum neurofilament light concentration (1·2 [0·6 to 1·9]). Higher brain-PAD was associated with future hippocampal atrophy over the subsequent 2 years (0·003 mL/year [0·000 to 0·006] per 5-year increment in brain-PAD). Early-life factors did not relate to brain-PAD. Combining 12 metrics in a hierarchical partitioning model explained 33% of the variance in brain-PAD. INTERPRETATION: Brain-PAD was associated with cardiovascular risk, and imaging and biochemical markers of neurodegeneration. These findings support brain-PAD as an integrative summary metric of brain health, reflecting multiple contributions to pathological brain ageing, and which might have prognostic utility. FUNDING: Alzheimer's Research UK, Medical Research Council Dementia Platforms UK, Selfridges Group Foundation, Wolfson Foundation, Wellcome Trust, Brain Research UK, Alzheimer's Association.

Associations of ß-Amyloid and Vascular Burden With Rates of Neurodegeneration in Cognitively Normal Members of the 1946 British Birth Cohort.

BACKGROUND AND OBJECTIVES: The goals of this work were to quantify the independent and interactive associations of ß-amyloid (Aß) and white matter hyperintensity volume (WMHV), a marker of presumed cerebrovascular disease (CVD), with rates of neurodegeneration and to examine the contributions of APOE ε4 and vascular risk measured at different stages of adulthood in cognitively normal members of the 1946 British Birth Cohort. METHODS: Participants underwent brain MRI and florbetapir-Aß PET as part of Insight 46, an observational population-based study. Changes in whole-brain, ventricular, and hippocampal volume were directly measured from baseline and repeat volumetric T1 MRI with the boundary shift integral. Linear regression was used to test associations with baseline Aß deposition, baseline WMHV, APOE ε4, and office-based Framingham Heart Study Cardiovascular Risk Score (FHS-CVS) and systolic blood pressure (BP) at ages 36, 53, and 69 years. RESULTS: Three hundred forty-six cognitively normal participants (mean [SD] age at baseline scan 70.5 [0.6] years; 48% female) had high-quality T1 MRI data from both time points (mean [SD] scan interval 2.4 [0.2] years). Being Aß positive at baseline was associated with 0.87-mL/y faster whole-brain atrophy (95% CI 0.03, 1.72), 0.39-mL/y greater ventricular expansion (95% CI 0.16, 0.64), and 0.016-mL/y faster hippocampal atrophy (95% CI 0.004, 0.027), while each 10-mL additional WMHV at baseline was associated with 1.07-mL/y faster whole-brain atrophy (95% CI 0.47, 1.67), 0.31-mL/y greater ventricular expansion (95% CI 0.13, 0.60), and 0.014-mL/y faster hippocampal atrophy (95% CI 0.006, 0.022). These contributions were independent, and there was no evidence that Aß and WMHV interacted in their effects. There were no independent associations of APOE ε4 with rates of neurodegeneration after adjustment for Aß status and WMHV, no clear relationships between FHS-CVS or systolic BP and rates of neurodegeneration when assessed across the whole sample, and no evidence that FHS-CVS or systolic BP acted synergistically with Aß. DISCUSSION: Aß and presumed CVD have distinct and additive effects on rates of neurodegeneration in cognitively normal elderly. These findings have implications for the use of MRI measures as biomarkers of neurodegeneration and emphasize the importance of risk management and early intervention targeting both pathways.

Sex-related differences in whole brain volumes at age 70 in association with hyperglycemia during adult life.

Longitudinal studies of the relationship between hyperglycemia and brain health are rare and there is limited information on sex differences in associations. We investigated whether glycosylated hemoglobin (HbA1c) measured at ages of 53, 60-64 and 69 years, and cumulative glycemic index (CGI), a measure of cumulative glycemic burden, were associated with metrics of brain health in later life. Participants were from Insight 46, a substudy of the Medical Research Council National Survey of Health and Development (NSHD) who undertook volumetric MRI, florbetapir amyloid-PET imaging and cognitive assessments at ages of 69-71. Analyses were performed using linear and logistic regression as appropriate, with adjustment for potential confounders. We observed a sex interaction between HbA1c and whole brain volume (WBV) at all 3 time points. Following stratification of our sample, we observed that HbA1c at all ages, and CGI were positively associated with lower WBV exclusively in females. HbA1c (or CGI) was not associated with amyloid status, white matter hyperintensities (WMHs), hippocampal volumes (HV) or cognitive outcomes in either sex. Higher HbA1c in adulthood is associated with smaller WBV at 69-71 years in females but not in males. This suggests that there may be preferential target organ damage in the brain for females with hyperglycemia.

Loss and dispersion of superficial white matter in Alzheimer's disease: a diffusion MRI study.

Pathological cerebral white matter changes in Alzheimer's disease have been shown using diffusion tensor imaging. Superficial white matter changes are relatively understudied despite their importance in cortico-cortical connections. Measuring superficial white matter degeneration using diffusion tensor imaging is challenging due to its complex organizational structure and proximity to the cortex. To overcome this, we investigated diffusion MRI changes in young-onset Alzheimer's disease using standard diffusion tensor imaging and Neurite Orientation Dispersion and Density Imaging to distinguish between disease-related changes that are degenerative (e.g. loss of myelinated fibres) and organizational (e.g. increased fibre dispersion). Twenty-nine young-onset Alzheimer's disease patients and 22 healthy controls had both single-shell and multi-shell diffusion MRI. We calculated fractional anisotropy, mean diffusivity, neurite density index, orientation dispersion index and tissue fraction (1-free water fraction). Diffusion metrics were sampled in 15 a priori regions of interest at four points along the cortical profile: cortical grey matter, grey/white boundary, superficial white matter (1 mm below grey/white boundary) and superficial/deeper white matter (2 mm below grey/white boundary). To estimate cross-sectional group differences, we used average marginal effects from linear mixed effect models of participants' diffusion metrics along the cortical profile. The superficial white matter of young-onset Alzheimer's disease individuals had lower neurite density index compared to controls in five regions (superior and inferior parietal, precuneus, entorhinal and parahippocampus) (all P < 0.05), and higher orientation dispersion index in three regions (fusiform, entorhinal and parahippocampus) (all P < 0.05). Young-onset Alzheimer's disease individuals had lower fractional anisotropy in the entorhinal and parahippocampus regions (both P < 0.05) and higher fractional anisotropy within the postcentral region (P < 0.05). Mean diffusivity was higher in the young-onset Alzheimer's disease group in the parahippocampal region (P < 0.05) and lower in the postcentral, precentral and superior temporal regions (all P < 0.05). In the overlying grey matter, disease-related changes were largely consistent with superficial white matter findings when using neurite density index and fractional anisotropy, but appeared at odds with orientation dispersion and mean diffusivity. Tissue fraction was significantly lower across all grey matter regions in young-onset Alzheimer's disease individuals (all P < 0.001) but group differences reduced in magnitude and coverage when moving towards the superficial white matter. These results show that microstructural changes occur within superficial white matter and along the cortical profile in individuals with young-onset Alzheimer's disease. Lower neurite density and higher orientation dispersion suggests underlying fibres undergo neurodegeneration and organizational changes, two effects previously indiscernible using standard diffusion tensor metrics in superficial white matter.

Dissociable effects of APOE-ε4 and ß-amyloid pathology on visual working memory.

Although APOE-ε4 carriers are at significantly higher risk of developing Alzheimer's disease than non-carriers1, controversial evidence suggests that APOE-ε4 might confer some advantages, explaining the survival of this gene (antagonistic pleiotropy)2,3. In a population-based cohort born in one week in 1946 (assessed aged 69-71), we assessed differential effects of APOE-ε4 and ß-amyloid pathology (quantified using 18F-Florbetapir-PET) on visual working memory (object-location binding). In 398 cognitively normal participants, APOE-ε4 and ß-amyloid had opposing effects on object identification, predicting better and poorer recall respectively. ε4-carriers also recalled locations more precisely, with a greater advantage at higher ß-amyloid burden. These results provide evidence of superior visual working memory in ε4-carriers, showing that some benefits of this genotype are demonstrable in older age, even in the preclinical stages of Alzheimer's disease.

Presumed small vessel disease, imaging and cognition markers in the Alzheimer's Disease Neuroimaging Initiative.

MRI-derived features of presumed cerebral small vessel disease are frequently found in Alzheimer's disease. Influences of such markers on disease-progression measures are poorly understood. We measured markers of presumed small vessel disease (white matter hyperintensity volumes; cerebral microbleeds) on baseline images of newly enrolled individuals in the Alzheimer's Disease Neuroimaging Initiative cohort (GO and 2) and used linear mixed models to relate these to subsequent atrophy and neuropsychological score change. We also assessed heterogeneity in white matter hyperintensity positioning within biomarker abnormality sequences, driven by the data, using the Subtype and Stage Inference algorithm. This study recruited both sexes and included: controls: [n = 159, mean(SD) age = 74(6) years]; early and late mild cognitive impairment [ns = 265 and 139, respectively, mean(SD) ages =71(7) and 72(8) years, respectively]; Alzheimer's disease [n = 103, mean(SD) age = 75(8)] and significant memory concern [n = 72, mean(SD) age = 72(6) years]. Baseline demographic and vascular risk-factor data, and longitudinal cognitive scores (Mini-Mental State Examination; logical memory; and Trails A and B) were collected. Whole-brain and hippocampal volume change metrics were calculated. White matter hyperintensity volumes were associated with greater whole-brain and hippocampal volume changes independently of cerebral microbleeds (a doubling of baseline white matter hyperintensity was associated with an increase in atrophy rate of 0.3 ml/year for brain and 0.013 ml/year for hippocampus). Cerebral microbleeds were found in 15% of individuals and the presence of a microbleed, as opposed to none, was associated with increases in atrophy rate of 1.4 ml/year for whole brain and 0.021 ml/year for hippocampus. White matter hyperintensities were predictive of greater decline in all neuropsychological scores, while cerebral microbleeds were predictive of decline in logical memory (immediate recall) and Mini-Mental State Examination scores. We identified distinct groups with specific sequences of biomarker abnormality using continuous baseline measures and brain volume change. Four clusters were found; Group 1 showed early Alzheimer's pathology; Group 2 showed early neurodegeneration; Group 3 had early mixed Alzheimer's and cerebrovascular pathology; Group 4 had early neuropsychological score abnormalities. White matter hyperintensity volumes becoming abnormal was a late event for Groups 1 and 4 and an early event for 2 and 3. In summary, white matter hyperintensities and microbleeds were independently associated with progressive neurodegeneration (brain atrophy rates) and cognitive decline (change in neuropsychological scores). Mechanisms involving white matter hyperintensities and progression and microbleeds and progression may be partially separate. Distinct sequences of biomarker progression were found. White matter hyperintensity development was an early event in two sequences.

Investigating the relationship between BMI across adulthood and late life brain pathologies.

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; ß-amyloid (Aß) 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, Aß-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 ß-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 (ß = 0.073 ml, 95% CI 0.009, 0.137), 69 (ß = 0.076 ml, 95% CI 0.012, 0.140) and 71 years (ß = 0.101 ml, 95% CI 0.037, 0.165). A similar, albeit weaker, trend was seen with obese status. CONCLUSIONS: Using WMHV, ß-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.

A population-based study of head injury, cognitive function and pathological markers.

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), 18 F-florbetapir Aß-PET and MR imaging. Measures include Aß-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 Aß 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).

Visuomotor integration deficits are common to familial and sporadic preclinical Alzheimer's disease.

We investigated whether subtle visuomotor deficits were detectable in familial and sporadic preclinical Alzheimer's disease. A circle-tracing task-with direct and indirect visual feedback, and dual-task subtraction-was completed by 31 individuals at 50% risk of familial Alzheimer's disease (19 presymptomatic mutation carriers; 12 non-carriers) and 390 cognitively normal older adults (members of the British 1946 Birth Cohort, all born during the same week; age range at assessment = 69-71 years), who also underwent ß-amyloid-PET/MRI to derive amyloid status (positive/negative), whole-brain volume and white matter hyperintensity volume. We compared preclinical Alzheimer's groups against controls cross-sectionally (mutation carriers versus non-carriers; amyloid-positive versus amyloid-negative) on speed and accuracy of circle-tracing and subtraction. Mutation carriers (mean 7 years before expected onset) and amyloid-positive older adults traced disproportionately less accurately than controls when visual feedback was indirect, and were slower at dual-task subtraction. In the older adults, the same pattern of associations was found when considering amyloid burden as a continuous variable (Standardized Uptake Value Ratio). The effect of amyloid was independent of white matter hyperintensity and brain volumes, which themselves were associated with different aspects of performance: greater white matter hyperintensity volume was also associated with disproportionately poorer tracing accuracy when visual feedback was indirect, whereas larger brain volume was associated with faster tracing and faster subtraction. Mutation carriers also showed evidence of poorer tracing accuracy when visual feedback was direct. This study provides the first evidence of visuomotor integration deficits common to familial and sporadic preclinical Alzheimer's disease, which may precede the onset of clinical symptoms by several years.