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

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

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

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

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

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

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

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

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. [ABSTRACT FROM AUTHOR]

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

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

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

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

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

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

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

Background: 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. [ABSTRACT FROM AUTHOR]

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

MR imaging shows features of cerebral amyloid angiopathy and diffuse white matter disease. Although rare, FBD should be considered in patients presenting with cognitive decline and imaging features of cerebral amyloid angiopathy, particularly if a family history is present. The superficial location of intracerebral haemorrhage in this case is, however, typical of those associated with cerebral amyloid angiopathy due to preferential involvement of the cortical and meningeal vasculature [[9]]. We show that the pattern of amyloid PET accumulation mirrors the amyloid angiopathy present and is distinct from that seen in AD, and that despite the presence of hippocampal atrophy and known neurofibrillary tau pathology in FBD, tau PET (flortaucipir) binding was absent. [Extracted from the article]

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

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