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.

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.

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.

Pure tone audiometry and cerebral pathology in healthy older adults.

BACKGROUND: Hearing impairment may be a modifiable risk factor for dementia. However, it is unclear how hearing associates with pathologies relevant to dementia in preclinical populations. METHODS: Data from 368 cognitively healthy individuals born during 1 week in 1946 (age range 69.2-71.9 years), who underwent structural MRI, 18F-florbetapir positron emission tomography, pure tone audiometry and cognitive testing as part of a neuroscience substudy the MRC National Survey of Health and Development were analysed. The aim of the analysis was to investigate whether pure tone audiometry performance predicted a range of cognitive and imaging outcomes relevant to dementia in older adults. RESULTS: There was some evidence that poorer pure tone audiometry performance was associated with lower primary auditory cortex thickness, but no evidence that it predicted in vivo ß-amyloid deposition, white matter hyperintensity volume, hippocampal volume or Alzheimer's disease-pattern cortical thickness. A negative association between pure tone audiometry and mini-mental state examination score was observed, but this was no longer evident after excluding a test item assessing repetition of a single phrase. CONCLUSION: Pure tone audiometry performance did not predict concurrent ß-amyloid deposition, small vessel disease or Alzheimer's disease-pattern neurodegeneration, and had limited impact on cognitive function, in healthy adults aged approximately 70 years.

Cortical microstructure in young onset Alzheimer's disease using neurite orientation dispersion and density imaging.

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.

Presymptomatic atrophy in autosomal dominant Alzheimer's disease: A serial magnetic resonance imaging study.

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.

White matter hyperintensities are associated with disproportionate progressive hippocampal atrophy.

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 Aß42 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 Aß42 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.

Study protocol: Insight 46 - a neuroscience sub-study of the MRC National Survey of Health and Development.

BACKGROUND: Increasing age is the biggest risk factor for dementia, of which Alzheimer's disease is the commonest cause. The pathological changes underpinning Alzheimer's disease are thought to develop at least a decade prior to the onset of symptoms. Molecular positron emission tomography and multi-modal magnetic resonance imaging allow key pathological processes underpinning cognitive impairment - including ß-amyloid depostion, vascular disease, network breakdown and atrophy - to be assessed repeatedly and non-invasively. This enables potential determinants of dementia to be delineated earlier, and therefore opens a pre-symptomatic window where intervention may prevent the onset of cognitive symptoms. METHODS/DESIGN: This paper outlines the clinical, cognitive and imaging protocol of "Insight 46", a neuroscience sub-study of the MRC National Survey of Health and Development. This is one of the oldest British birth cohort studies and has followed 5362 individuals since their birth in England, Scotland and Wales during one week in March 1946. These individuals have been tracked in 24 waves of data collection incorporating a wide range of health and functional measures, including repeat measures of cognitive function. Now aged 71 years, a small fraction have overt dementia, but estimates suggest that ~1/3 of individuals in this age group may be in the preclinical stages of Alzheimer's disease. Insight 46 is recruiting 500 study members selected at random from those who attended a clinical visit at 60-64 years and on whom relevant lifecourse data are available. We describe the sub-study design and protocol which involves a prospective two time-point (0, 24 month) data collection covering clinical, neuropsychological, ß-amyloid positron emission tomography and magnetic resonance imaging, biomarker and genetic information. Data collection started in 2015 (age 69) and aims to be completed in 2019 (age 73). DISCUSSION: Through the integration of data on the socioeconomic environment and on physical, psychological and cognitive function from 0 to 69 years, coupled with genetics, structural and molecular imaging, and intensive cognitive and neurological phenotyping, Insight 46 aims to identify lifetime factors which influence brain health and cognitive ageing, with particular focus on Alzheimer's disease and cerebrovascular disease. This will provide an evidence base for the rational design of disease-modifying trials.

Accurate automatic estimation of total intracranial volume: a nuisance variable with less nuisance.

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 (R(2)=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 (R(2)=0.577 CI (0.500, 0.644)) or FreeSurfer (R(2)=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.

Assessing atrophy measurement techniques in dementia: Results from the MIRIAD atrophy challenge.

Structural MRI is widely used for investigating brain atrophy in many neurodegenerative disorders, with several research groups developing and publishing techniques to provide quantitative assessments of this longitudinal change. Often techniques are compared through computation of required sample size estimates for future clinical trials. However interpretation of such comparisons is rendered complex because, despite using the same publicly available cohorts, the various techniques have been assessed with different data exclusions and different statistical analysis models. We created the MIRIAD atrophy challenge in order to test various capabilities of atrophy measurement techniques. The data consisted of 69 subjects (46 Alzheimer's disease, 23 control) who were scanned multiple (up to twelve) times at nine visits over a follow-up period of one to two years, resulting in 708 total image sets. Nine participating groups from 6 countries completed the challenge by providing volumetric measurements of key structures (whole brain, lateral ventricle, left and right hippocampi) for each dataset and atrophy measurements of these structures for each time point pair (both forward and backward) of a given subject. From these results, we formally compared techniques using exactly the same dataset. First, we assessed the repeatability of each technique using rates obtained from short intervals where no measurable atrophy is expected. For those measures that provided direct measures of atrophy between pairs of images, we also assessed symmetry and transitivity. Then, we performed a statistical analysis in a consistent manner using linear mixed effect models. The models, one for repeated measures of volume made at multiple time-points and a second for repeated "direct" measures of change in brain volume, appropriately allowed for the correlation between measures made on the same subject and were shown to fit the data well. From these models, we obtained estimates of the distribution of atrophy rates in the Alzheimer's disease (AD) and control groups and of required sample sizes to detect a 25% treatment effect, in relation to healthy ageing, with 95% significance and 80% power over follow-up periods of 6, 12, and 24months. Uncertainty in these estimates, and head-to-head comparisons between techniques, were carried out using the bootstrap. The lateral ventricles provided the most stable measurements, followed by the brain. The hippocampi had much more variability across participants, likely because of differences in segmentation protocol and less distinct boundaries. Most methods showed no indication of bias based on the short-term interval results, and direct measures provided good consistency in terms of symmetry and transitivity. The resulting annualized rates of change derived from the model ranged from, for whole brain: -1.4% to -2.2% (AD) and -0.35% to -0.67% (control), for ventricles: 4.6% to 10.2% (AD) and 1.2% to 3.4% (control), and for hippocampi: -1.5% to -7.0% (AD) and -0.4% to -1.4% (control). There were large and statistically significant differences in the sample size requirements between many of the techniques. The lowest sample sizes for each of these structures, for a trial with a 12month follow-up period, were 242 (95% CI: 154 to 422) for whole brain, 168 (95% CI: 112 to 282) for ventricles, 190 (95% CI: 146 to 268) for left hippocampi, and 158 (95% CI: 116 to 228) for right hippocampi. This analysis represents one of the most extensive statistical comparisons of a large number of different atrophy measurement techniques from around the globe. The challenge data will remain online and publicly available so that other groups can assess their methods.

Short-interval observational data to inform clinical trial design in Huntington's disease.

OBJECTIVES: To evaluate candidate outcomes for disease-modifying trials in Huntington's disease (HD) over 6-month, 9-month and 15-month intervals, across multiple domains. To present guidelines on rapid efficacy readouts for disease-modifying trials. METHODS: 40 controls and 61 patients with HD, recruited from four EU sites, underwent 3 T MRI and standard clinical and cognitive assessments at baseline, 6 and 15 months. Neuroimaging analysis included global and regional change in macrostructure (atrophy and cortical thinning), and microstructure (diffusion metrics). The main outcome was longitudinal effect size (ES) for each outcome. Such ESs can be used to calculate sample-size requirements for clinical trials for hypothesised treatment efficacies. RESULTS: Longitudinal changes in macrostructural neuroimaging measures such as caudate atrophy and ventricular expansion were significantly larger in HD than controls, giving rise to consistently large ES over the 6-month, 9-month and 15-month intervals. Analogous ESs for cortical metrics were smaller with wide CIs. Microstructural (diffusion) neuroimaging metrics ESs were also typically smaller over the shorter intervals, although caudate diffusivity metrics performed strongly over 9 and 15 months. Clinical and cognitive outcomes exhibited small longitudinal ESs, particularly over 6-month and 9-month intervals, with wide CIs, indicating a lack of precision. CONCLUSIONS: To exploit the potential power of specific neuroimaging measures such as caudate atrophy in disease-modifying trials, we propose their use as (1) initial short-term readouts in early phase/proof-of-concept studies over 6 or 9 months, and (2) secondary end points in efficacy studies over longer periods such as 15 months.

Magnetic resonance imaging in Alzheimer's Disease Neuroimaging Initiative 2.

INTRODUCTION: Alzheimer's Disease Neuroimaging Initiative (ADNI) is now in its 10th year. The primary objective of the magnetic resonance imaging (MRI) core of ADNI has been to improve methods for clinical trials in Alzheimer's disease (AD) and related disorders. METHODS: We review the contributions of the MRI core from present and past cycles of ADNI (ADNI-1, -Grand Opportunity and -2). We also review plans for the future-ADNI-3. RESULTS: Contributions of the MRI core include creating standardized acquisition protocols and quality control methods; examining the effect of technical features of image acquisition and analysis on outcome metrics; deriving sample size estimates for future trials based on those outcomes; and piloting the potential utility of MR perfusion, diffusion, and functional connectivity measures in multicenter clinical trials. DISCUSSION: Over the past decade the MRI core of ADNI has fulfilled its mandate of improving methods for clinical trials in AD and will continue to do so in the future.

Profiles of white matter tract pathology in frontotemporal dementia.

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.

Magnetic resonance imaging evidence for presymptomatic change in thalamus and caudate in familial Alzheimer's disease.

Amyloid imaging studies of presymptomatic familial Alzheimer's disease have revealed the striatum and thalamus to be the earliest sites of amyloid deposition. This study aimed to investigate whether there are associated volume and diffusivity changes in these subcortical structures during the presymptomatic and symptomatic stages of familial Alzheimer's disease. As the thalamus and striatum are involved in neural networks subserving complex cognitive and behavioural functions, we also examined the diffusion characteristics in connecting white matter tracts. A cohort of 20 presenilin 1 mutation carriers underwent volumetric and diffusion tensor magnetic resonance imaging, neuropsychological and clinical assessments; 10 were symptomatic, 10 were presymptomatic and on average 5.6 years younger than their expected age at onset; 20 healthy control subjects were also studied. We conducted region of interest analyses of volume and diffusivity changes in the thalamus, caudate, putamen and hippocampus and examined diffusion behaviour in the white matter tracts of interest (fornix, cingulum and corpus callosum). Voxel-based morphometry and tract-based spatial statistics were also used to provide unbiased whole-brain analyses of group differences in volume and diffusion indices, respectively. We found that reduced volumes of the left thalamus and bilateral caudate were evident at a presymptomatic stage, together with increased fractional anisotropy of bilateral thalamus and left caudate. Although no significant hippocampal volume loss was evident presymptomatically, reduced mean diffusivity was observed in the right hippocampus and reduced mean and axial diffusivity in the right cingulum. In contrast, symptomatic mutation carriers showed increased mean, axial and in particular radial diffusivity, with reduced fractional anisotropy, in all of the white matter tracts of interest. The symptomatic group also showed atrophy and increased mean diffusivity in all of the subcortical grey matter regions of interest, with increased fractional anisotropy in bilateral putamen. We propose that axonal injury may be an early event in presymptomatic Alzheimer's disease, causing an initial fall in axial and mean diffusivity, which then increases with loss of axonal density. The selective degeneration of long-coursing white matter tracts, with relative preservation of short interneurons, may account for the increase in fractional anisotropy that is seen in the thalamus and caudate presymptomatically. It may be owing to their dense connectivity that imaging changes are seen first in the thalamus and striatum, which then progress to involve other regions in a vulnerable neuronal network.

An unbiased longitudinal analysis framework for tracking white matter changes using diffusion tensor imaging with application to Alzheimer's disease.

We introduce a novel image-processing framework for tracking longitudinal changes in white matter microstructure using diffusion tensor imaging (DTI). Charting the trajectory of such temporal changes offers new insight into disease progression but to do so accurately faces a number of challenges. Recent developments have highlighted the importance of processing each subject's data at multiple time points in an unbiased way. In this paper, we aim to highlight a different challenge critical to the processing of longitudinal DTI data, namely the approach to image alignment. Standard approaches in the literature align DTI data by registering the corresponding scalar-valued fractional anisotropy (FA) maps. We propose instead a DTI registration algorithm that leverages full tensor information to drive improved alignment. This proposed pipeline is evaluated against the standard FA-based approach using a DTI dataset from an ongoing study of Alzheimer's disease (AD). The dataset consists of subjects scanned at two time points and at each time point the DTI acquisition consists of two back-to-back repeats in the same scanning session. The repeated scans allow us to evaluate the specificity of each pipeline, using a test-retest design, and assess precision, using bootstrap-based method. The results show that the tensor-based pipeline achieves both higher specificity and precision than the standard FA-based approach. Tensor-based registration for longitudinal processing of DTI data in clinical studies may be of particular value in studies assessing disease progression.

MIRIAD--Public release of a multiple time point Alzheimer's MR imaging dataset.

The Minimal Interval Resonance Imaging in Alzheimer's Disease (MIRIAD) dataset is a series of longitudinal volumetric T1 MRI scans of 46 mild-moderate Alzheimer's subjects and 23 controls. It consists of 708 scans conducted by the same radiographer with the same scanner and sequences at intervals of 2, 6, 14, 26, 38 and 52 weeks, 18 and 24 months from baseline, with accompanying information on gender, age and Mini Mental State Examination (MMSE) scores. Details of the cohort and imaging results have been described in peer-reviewed publications, and the data are here made publicly available as a common resource for researchers to develop, validate and compare techniques, particularly for measurement of longitudinal volume change in serially acquired MR.

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.

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.

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.

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.