Cerebrospinal fluid analysis detects cerebral amyloid-β accumulation earlier than positron emission tomography

Brain, Apr 2016

See Rabinovici (doi: 10.1093/brain/aww025 ) for a scientific commentary on this article.

A PDF file should load here. If you do not see its contents the file may be temporarily unavailable at the journal website or you do not have a PDF plug-in installed and enabled in your browser.

Alternatively, you can download the file locally and open with any standalone PDF reader:

https://academic.oup.com/brain/article-pdf/139/4/1226/24173398/aww015.pdf

Cerebrospinal fluid analysis detects cerebral amyloid-β accumulation earlier than positron emission tomography

Received September Cerebrospinal fluid analysis detects cerebral amyloid-b accumulation earlier than positron emission tomography Sebastian Palmqvist 1 2 3 Niklas Mattsson 0 2 3 Oskar Hansson 0 2 3 0 Memory Clinic, Sk a ̊ne University Hospital , Sweden 1 Department of Neurology, Sk a ̊ne University Hospital , Sweden 2 Clinical Memory Research Unit, Department of Clinical Sciences, Malmo ̈ , Lund University , Sweden 3 Department of Neurology, Ska ̊ ne University Hospital , 221 85 Lund , Sweden The data used in preparation of this article were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database (adni.loni.usc.edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators can be found at: http://adni.loni.usc.edu/wp-content/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf Cerebral accumulation of amyloid-b is thought to be the starting mechanism in Alzheimer's disease. Amyloid-b can be detected by analysis of cerebrospinal fluid amyloid-b42 or amyloid positron emission tomography, but it is unknown if any of the methods can identify an abnormal amyloid accumulation prior to the other. Our aim was to determine whether cerebrospinal fluid amyloid-b42 change before amyloid PET during preclinical stages of Alzheimer's disease. We included 437 non-demented subjects from the prospective, longitudinal Alzheimer's Disease Neuroimaging Initiative (ADNI) study. All underwent 18F-florbetapir positron emission tomography and cerebrospinal fluid amyloid-b42 analysis at baseline and at least one additional positron emission tomography after a mean follow-up of 2.1 years (range 1.1-4.4 years). Group classifications were based on normal and abnormal cerebrospinal fluid and positron emission tomography results at baseline. We found that cases with isolated abnormal cerebrospinal fluid amyloid-b and normal positron emission tomography at baseline accumulated amyloid with a mean rate of 1.2%/year, which was similar to the rate in cases with both abnormal cerebrospinal fluid and positron emission tomography (1.2%/year, P = 0.86). The mean accumulation rate of those with isolated abnormal cerebrospinal fluid was more than three times that of those with both normal cerebrospinal fluid and positron emission tomography (0.35%/year, P = 0.018). The group differences were similar when analysing yearly change in standardized uptake value ratio of florbetapir instead of percentage change. Those with both abnormal cerebrospinal fluid and positron emission tomography deteriorated more in memory and hippocampal volume compared with the other groups (P 5 0.001), indicating that they were closer to Alzheimer's disease dementia. The results were replicated after adjustments of different factors and when using different cut-offs for amyloid-b abnormality including a positron emission tomography classification based on the florbetapir uptake in regions where the initial amyloid-b accumulation occurs in Alzheimer's disease. This is the first study to show that individuals who have abnormal cerebrospinal amyloid-b42 but normal amyloid-b positron emission tomography have an increased cortical amyloid-b accumulation rate similar to those with both abnormal cerebrospinal fluid and positron emission tomography and higher rate than subjects where both modalities are normal. The results indicate that cerebrospinal fluid amyloid-b42 becomes abnormal in the earliest stages of Alzheimer's disease, before amyloid positron emission tomography and before neurodegeneration starts. - Abbreviations: MCI = mild cognitive impairment; SUVR = standardized uptake value ratio Introduction The identification and early treatment of Alzheimer’s disease is a top priority worldwide. A starting event in the pathogenesis of Alzheimer’s disease is the accumulation of amyloid-b in the brain (Sperling et al., 2011) . This has been shown in studies of both sporadic and familial Alzheimer’s disease (Bateman et al., 2012; Vos et al., 2013) . As the disease progresses, affected individuals show neurodegeneration and early clinical symptoms and may be diagnosed with mild cognitive impairment (MCI) (Albert et al., 2011) . Ongoing disease-modifying trials targeting amyloid-b have shown promising results (Biogen, 2015) and secondary prevention trials have been started with anti-amyloid therapies in asymptomatic subjects with signs of amyloid-b pathology (Sperling et al., 2014) . These interventions will most likely have best effect if initiated as early as possible (Sperling et al., 2011; Hardy et al., 2014) . It is therefore important to be able to detect the earliest signs of an abnormal amyloid-b load. Currently, there are two methods for assessing amyloid-b in vivo; PET using ligands that binds to amyloid-b fibrils (amyloid PET) or CSF measurement of the 42amino acid isoform of amyloid-b (amyloid-b42). Many studies have shown that these methods have a high agreement (Fagan et al., 2006, 2009; Grimmer et al., 2009; Jagust et al., 2009; Tolboom et al., 2009; Weigand et al., 2011; Landau et al., 2013; Mattsson et al., 2014; Palmqvist et al., 2014, 2015) . However, roughly 10–20% of examined subjects show discordant results (with a higher proportion in asymptomatic individuals) and most often with abnormal CSF amyloid-b42 levels and normal amyloid PET (CSF+/ PET ) (Cairns et al., 2009; Fagan et al., 2009; Jagust et al., 2009; Landau et al., 2013; Palmqvist et al., 2014) . This may indicate that the abnormal accumulation of amyloid-b in preclinical Alzheimer’s disease can be detected earlier with CSF amyloid-b42 than with amyloid PET (Fagan et al., 2006, 2009; Morris et al., 2010; Mattsson et al., 2014) . However, these arguments are only based on cross-sectional results and there are also contradictory cross-sectional results suggesting that amyloid PET may become abnormal first (Landau et al., 2013). An alternative explanation for the presence of CSF+/PET in cognitively healthy subjects could be that isolated CSF+ is caused by other conditions than preclinical Alzheimer’s disease or by analytical artefacts, and thereby lack pathological relevance (Fagan et al., 2009; Mattsson et al., 2014) . If this is true, CSF+/PET subjects would not be expected to show signs of amyloidb accumulation over time, as PET + individuals do in preclinical Alzheimer’s disease (Villemagne et al., 2013). To test the hypothesis that CSF amyloid-b42 becomes abnormal before amyloid PET in preclinical Alzheimer’s disease we examined if CSF+/PET subjects accumulate amyloid-b at an abnormal rate, as measured by repeated amyloid PET measurements. We also tested the hypothesis that CSF+/PET subjects lack signs of a neurodegenerative process, which should not be present at the earliest preclinical stage of Alzheimer’s disease according to the dominant model of Alzheimer’s disease biomarker development (Jack et al., 2010, 2013) . Materials and methods Study design Data used in the preparation of this article were obtained from the ADNI database (adni.loni.usc.edu). The ADNI was launched in 2004 by the National Institute on Aging, the Food and Drug Administration, private pharmaceutical companies and nonprofit organizations as a highly innovative public-private partnership. The primary goal of ADNI has been to test whether serial MRI, PET, other biological markers, and clinical and neuropsychological assessment can be combined to measure the progression of MCI and early Alzheimer’s disease. The Principal Investigator of this initiative is Michael W. Weiner, MD, VA Medical Center and University of California, San Francisco. ADNI is the result of efforts of many co-investigators from a broad range of academic institutions and private corporations, and subjects have been recruited from over 50 sites across the USA and Canada. The initial goal of ADNI was to recruit 800 subjects, but ADNI has been followed by ADNI-GO and ADNI-2. To date these three protocols have recruited over 1500 adults. For more information, see www.adni-info.org. Participants Data were downloaded from the ADNI database (adni.loni.usc.edu). We included only non-demented subjects diagnosed as cognitively healthy controls, early MCI or late MCI. Patients with dementia (due to Alzheimer’s disease) were excluded as the aim was to examine detection of the earliest accumulation of amyloid-b, which precedes the dementia stage by many years. Inclusion/exclusion criteria are described in detail at www.adni-info.org. Briefly, all subjects in the present study were included in ADNI-2 and were between the ages of 55 and 90 years, had completed at least 6 years of education, were fluent in Spanish or English, and were free of any significant neurological disease other than Alzheimer’s disease. Controls had Mini-Mental State Examination score (MMSE) 524 (Folstein et al., 1975) , and Clinical Dementia Rating scale (CDR) score 0 (Morris, 1993) . Subjects classified as MCI had MMSE score 524, objective memory loss as shown on scores on delayed recall on the Wechsler Memory Scale Logical Memory II, CDR 0.5, preserved activities of daily living, and absence of dementia. Early and late MCI was differentiated based on the score of Wechsler Memory Scale Logical Memory II (cut-offs ranging from 2 to 8 depending on education level). Only subjects who had at least two 18F-florbetapir PET scans and CSF data from the same visit as the first PET scan were included. This resulted in a population of 437 subjects. The first visit where both CSF and PET data were available was defined as baseline in the present study. Amyloid PET Amyloid-b deposition was visualized using 18F-florbetapir PET. We used data from the latest available dataset (‘UCBERKELEYAV45_01_26_15.csv’). All subjects had a baseline scan and a follow-up scan. If three scans were available, the last one was chosen for the longitudinal analysis. The methods for PET acquisition and analysis have been described in more detail previously (Landau et al., 2012) . The global cortical mean standardized uptake value ratio (SUVR) was calculated relative to a reference region. For the present study we used a composite region made up of the whole cerebellum, brainstem/pons, and subcortical white matter. This composite region has provided more reliable longitudinal florbetapir results in ADNI compared to using only the cerebellum as reference region (Landau et al., 2014; Landau and Jagust, 2015) . The global neocortical uptake was calculated from the weighted mean uptake in the frontal, lateral parietal, lateral temporal and cingulate regions to account for the varying sizes of the regions (Landau and Jagust, 2015) . We also created an additional composite region to assess amyloid-b deposition in brain regions where the deposition is believed to start. The first stage of amyloid-b accumulation has been suggested to occur in the basal medial part of the frontal lobe and the basal part of the temporal lobe (Braak and Braak, 1991; Goedert, 2015) . The latter region was not available in the ADNI dataset (Landau and Jagust, 2015) , but the former was captured using the weighted mean uptake in the left and right medial orbitofrontal cortex, lateral orbitofrontal cortex and the frontal pole (hereafter called the ‘early amyloid-b region’). These early accumulation regions suggested by Braak and Braak (1991) overlap with the results of some amyloid PET studies (Villain et al., 2012; Sepulcre et al., 2013) , but a recent PET study has found that the accumulation might start in the precuneus and the medial prefrontal cortex (Villeneuve et al., 2015). However, we have chosen to create our early amyloid-b region according to the Braak staging but acknowledge that there might be other early regions that are not captured in this staging. Future studies need to clarify this further. CSF biomarkers Amyloid-b42, total (T)-tau and phosphorylated (P)-tau were measured using the multiplex xMAP Luminex platform (Luminex Corp) with the INNOBIA AlzBio3 kit (Innogenetics) (Olsson et al., 2005; Shaw et al., 2009) . For this study, we combined data from the datasets ‘UPENN BIOMK5_10_31_13.csv’, ‘UPENNBIOMK6_07_02_13.csv’, ‘UPENNBIOMK7.csv’, and ‘UPENNBIOMK8.csv’. Composite memory score A composite memory score was used to assess memory function. This is a weighted score based on memory items in Rey Auditory Verbal Learning Test (RAVLT), the ADAS-cog, the MMSE and Logical Memory (Crane et al., 2012) . To examine longitudinal changes, data were extracted from baseline and the 12-, 24-, 36-, 48- and 60-month visits. MRI data Structural MRI brain scans were acquired using 3 T MRI scanners as previously described (Jack et al., 2008) . Automated volume measures were performed with FreeSurfer version 5.1 (http://surfer.nmr.mgh.harvard.edu/fswiki). Hippocampal volumes were extracted from the merged ADNI dataset (‘ADNIMERGE.csv’). Cross-sectional volumes were divided by the total intracranial volume (Table 1). For longitudinal analyses, all available data were used (ADNI baseline and the 3, 6, 12, 18, 24, 36, 48, 60, 72, 84 and 96 month visits). To examine the relationship between the global amyloid-b accumulation and atrophy changes, a composite MRI region of interest was calculated as the average cortical thickness of the regions used in the global neocortical PET volume of interest. The cortical thickness measures from ADNI baseline and the 3, 6, 12, 24, 36, and 48-month visits were extracted from the file ‘UCSFFSX51_05_20_15.csv’. Grouping of subjects The subjects were categorized into four groups depending on the baseline CSF amyloid-b42 and florbetapir PET status; normal CSF and PET (CSF /PET ), abnormal CSF and normal PET (CSF +/PET ), abnormal CSF and PET (CSF+/ PET + ) and normal CSF and abnormal PET (CSF /PET +). A cut-off for abnormal PET status using the composite reference region has been defined previously at 4 0.79 SUVR (Joshi et al., 2012; Landau and Jagust, 2015) . For CSF amyloidb42, a cut-off has previously been defined at CSF amyloidb42 5192 ng/l (Shaw et al., 2009; De Meyer et al., 2010; Weigand et al., 2011) . For this study we excluded borderline cases and used cut-offs that were 5% from the original cutoffs to avoid drawing conclusions based on borderline cases that easily could be misclassified because of variability of the measurements. This approach increases validity of the classification and has also been used in previous publications on CSF/ PET agreement (Landau et al., 2013; Mattsson et al., 2014) . The cut-offs used in the present study were thus: ‘CSF amyloid-b42 + ’ 5182.4 ng/l, ‘CSF amyloid-b42 ’ 4201.6 ng/l, A CSF /PET 160 69/88/24 71.9 (55–94) 45% 28.7 (1.5) 0.73 (0.56) 0.78 (0.76) 16% 16.6 (2.7) 0.025 (0.0003) 2.42 (0.10) 0.70 (0.03) 238 (25) 58 (24) 29 (13) B CSF + /PET Data are given in mean values (standard deviation, SD) if not otherwise specified. Kruskal-Wallis test was not significant for age, sex, education or cortical thickness of the global PET region. Comparisons were made between A-B and B-C using Mann-Whitney analysis. Borderline cases were excluded in this table. There were no CSF /PET+ subjects. aTwo subjects who were cognitively normal at the ADNI baseline converted to MCI at baseline for the present study. These have been categorized as late MCI. bSimilar results were obtained when hippocampal volume was compared between the groups using a linear regression model with intracranial volume as a covariate (A-B, P = 0.44; BC, P = 0.007). cThe average cortical thickness of the brain regions included in the global PET volume of interest. dGlobal neocortical uptake relative a composite reference region. CI = confidence interval; CN = cognitively normal; EMCI = early MCI; LMCI = late MCI; n = number of subjects. ‘amyloid-b PET +’ 40.8295 SUVR and ‘amyloid-b PET ’ 50.7505 SUVR. A second PET classification was done using the ‘early amyloid-b region’ (see above). As no previous cut-off has been established for this region, we used mixture modelling analysis (see ‘Statistical analysis’ section), which produced the cut-off 0.817 SUVR relative the composite reference region. When applying a 5% interval for the cut-off to exclude borderline cases, the final cut-offs were ‘early amyloid-b PET+ ’ 40.8579 SUVR and ‘early amyloid-b PET–’ 50.7762 SUVR. Statistical analysis Group differences were first tested with the Kruskal-Wallis test and if significant they were analysed further with the MannWhitney U-test or 2 test (Tables 1–3). The main dependent variable was the annual SUVR change. We also calculated the relative annual SUVR change in percentage (yearly SUVR change/SUVR at baseline) for easier comparison with other studies that have used other PET tracers, SUVR regions or SUVR reference regions. Full factorial general linear models were used to adjust for sex, age, APOE genotype (presence of APOE e4) and time between PET scans, regarding group differences in SUVR amyloid-b accumulation, memory change and hippocampal volume change. Local regression fits were modelled using the least-squares criterion to fit a line to a set of data points (‘LOESS’) (Jacoby, 2000) . Univariate linear relationships were analysed using Spearman correlations. Longitudinal changes in memory score and hippocampal volume were modelled using linear regression with data from the different visits. Mixture modelling was performed with R version 3.1 (R Foundation for Statistical Computing, Vienna, Austria, 2013) . Mixture modelling provides an unbiased and unsupervised way of determining a cut-off (Benaglia et al., 2009) . It requires a bimodal distribution of the studied condition/biomarker, which for example is the case in amyloid-b pathology but often not in tau. It does not require the knowledge of the underlying biomarker/disease status, as in receiver operating characteristics (ROC) analysis where you also need both a training and validation population. Mixture modelling has successfully been used previously for CSF amyloid-b42 and amyloid PET data in large studies such as ADNI (Toledo et al., 2015) , AiBL (Pietrzak et al., 2015) and the Swedish BioFINDER study (Palmqvist et al., 2014) . Regression coefficients and intercepts were calculated in Microsoft Excel version 14.4.5 for Mac (Pfister et al., 2013) . All other statistical analyses were performed with SPSS for Mac, version 22.0 (SPSS Inc., Chicago, IL). Results Cross-sectional analysis Baseline data are shown in Table 1. The delay between baseline lumbar puncture and PET scan dates was on average 12 days (range: 0–129 days). The classification of subjects resulted in 26 CSF + /PET , 160 CSF /PET , 167 CSF + /PET + , 0 CSF /PET + , and 84 borderline cases ( 5% within the cut-offs). The CSF and PET classification is shown in Fig. 1. The CSF + /PET group consisted of 54% healthy elderly controls, 35% early MCI and 11% late MCI. The distribution was similar in the CSF /PET group (P = 0.49). The CSF + /PET + group had more MCI subjects compared with the other groups (P 5 0.001). There were no significant differences in prevalence of the APOE e4 allele between the CSF + /PET and CSF + /PET + groups (P = 0.13), but the prevalence was significantly lower in the CSF /PET group (P = 0.005). CSF amyloid-b42 in the CSF + /PET group was significantly higher than in the CSF + /PET + group (mean difference 27 ng/l; 95% CI 18–36) and significantly lower than in the CSF /PET group (difference 76 ng/l; 95% CI 66–86). There were no significant differences in T-tau and P-tau levels between the CSF + /PET and CSF /PET groups. CSF + /PET + had T-tau and P-tau levels that were almost twice that of CSF + /PET (P 50.001) and smaller mean hippocampal volume (P = 0.012). Longitudinal change in amyloid-b as determined by amyloid PET The average time between the first and last florbetapir scan was 2.1 years without differences between the groups (P = 0.73). Yearly amyloid-b accumulation rates are shown in Table 2 and Fig. 2. The CSF+/PET group had a mean amyloid-b accumulation rate (i.e. change in florbetapir SUVR per year) that was more than three times that of the CSF /PET group (P 5 0.01 adjusted for age, APOE genotype, sex and time between PET scans) and a similar accumulation rate compared with CSF+/PET+ (1.2% per year for both; adjusted P = 0.60). Figure 3 shows the yearly amyloid-b accumulation in relation to baseline CSF amyloid-b42 levels for the PET group (Fig. 3A) and the PET + group (Fig. 3B). The CSF /PET subjects showed very modest amyloid-b accumulation rates throughout the Figure 2 Boxplots of the amyloid-b accumulation rate (% SUVR change/year) for the different groups. Group comparisons were analysed with Mann-Whitney. There were no CSF–/PET+ individuals in A and only one in B and C. Therefore, this group is not shown. (A) Amyloidb accumulation rate in the global neocortical region. Group classifications were based on the a priori cut-offs for amyloid-b42 (CSF+5182.4 ng/l, CSF– 4201.6 ng/l) and the global neocortical amyloid-b SUVR relative a composite reference region (PET+40.8295, PET 50.7505). (B) Amyloid-b accumulation rate in the global neocortical region using a PET classification (+/–) based on abnormal/normal amyloid-b in brain regions affected in early amyloid-b deposition (the medial and lateral orbitofrontal cortex and the frontal pole; ‘the early amyloid-b region’). Cut-offs were established with mixture modelling (PET+40.8579 SUVR, PET– 50.7762 SUVR). The CSF classification was the same as in (A). (C) Amyloid-b accumulation rate in the ‘early amyloid-b region’ using the same CSF/PET classification as in (B). Ab = amyloid-b. Data are given in mean values (95% CI) if not otherwise specified. Kruskal-Wallis test was not significant for years between PET scans. Comparisons were made between A-B and B-C using Mann-Whitney analysis. All significant differences were also significant after adjusting for age, sex, prevalence of APOE e4, time between PET scans (for SUVR) and baseline memory score and hippocampal volume (for the memory and hippocampal volume coefficients). Borderline cases were excluded in this table. There were no CSF /PET + subjects. CI = confidence interval. whole range of CSF amyloid-b42 levels, but with a tendency of increased accumulation rates at the lower CSF amyloidb42 values closer to the cut-off (Fig 3A). However, the increased amyloid-b accumulation rate as a function of lower CSF amyloid-b42 levels was much more pronounced for the CSF+/PET group. In this group the cases with clearly reduced CSF amyloid-b42 levels had similar rates of amyloid-b accumulation as the CSF+/PET+ group. In the CSF+/PET+ group the accumulation rate of amyloid-b had already reached a plateau and showed rates unrelated to CSF amyloid-b42 levels (Fig. 3B). To investigate whether the plateau in the amyloid-b accumulation rate was driven by an increased atrophy rate in the CSF+/PET+ group we investigated the correlations between the global PET SUVR change/year with the cortical thickness change/year of the same composite neocortical region of interest. There were no significant correlations between these measures in any of the CSF/PET groups (P = 0.25–0.78). Specifically, there was no significant correlation in the CSF+/PET+ group to support that a plateau in the rate of amyloid-b accumulation was caused mainly by an increased cortical atrophy rate (rs = 0.05; P = 0.53). A CSF−/PET− CSF+/PET− CSF+/PET+ Mean coefficient 95% CI 3 Years 3 Years 1 2 4 5 1 2 4 5 Longitudinal analysis of hippocampal atrophy and memory There was a significant decline in composite memory score over time in CSF + /PET + individuals, but not in CSF + / PET or CSF /PET individuals (Table 2 and Fig. 4A). The change differed significantly between the CSF + /PET + and CSF + /PET groups (P 5 0.013 when adjusting for age, sex, and baseline memory score). There was no significant difference in the change of memory scores between the CSF + /PET and CSF /PET groups (adjusted P = 0.53). The deterioration in hippocampal volume was greater for CSF + /PET + compared with CSF + /PET (P 5 0.001 adjusted for baseline hippocampal volume, sex and age) and there were no differences between CSF + / PET and CSF /PET groups (adjusted P = 0.07; Table 2 and Fig. 4B). PET classification based on abnormal uptake in the early amyloid-b region Table 3 and Fig. 2B and C show the results from the new classification using the previous CSF amyloid-b42 cut-offs and the new PET cut-offs for normal/abnormal SUVR in the ‘early amyloid-b region’ (medial and lateral orbitofrontal cortex and the frontal pole). There was only one subject with CSF–/PET + biomarkers, and it was thus excluded from the analyses. When analysing longitudinal changes both in the global neocortical SUVR and the ‘early amyloid-b region’ we found very similar group differences as in the previous analyses. The amyloid-b accumulation was significantly increased in the CSF + /PET– group compared with the CSF–/PET– group and no differences were found between the CSF + /PET– and CSF + /PET + groups (Table 3 and Fig. 2B and C). Validation of the main results using other cut-offs for CSF amyloid-b42 and global florbetapir PET Three additional group classifications were also made to ensure that the main results were not caused by random classifications: (i) When using a classification with the same CSF amyloidb42 cut-offs, but global PET cut-offs based on SUVR relative to the whole cerebellum instead of the composite region ( 5% of 1.11 SUVR) (Joshi et al., 2012) , the CSF+/PET group still had a yearly florbetapir SUVR accumulation rate three times that of the CSF /PET group (P = 0.04 adjusted for age, APOE genotype, sex and time between PET scans) and a similar rate as the CSF+/PET+ group (adjusted P = 0.32). (ii) We also used unbiased cut-offs for both CSF and PET (SUVR relative the composite reference region) derived from mixture modelling analysis on the current population (see ‘Statistical analysis’ section). This resulted in a CSF amyloid-b42 cut-off of 170.5 ng/l and a PET cut-off of 0.797 SUVR where a 5% interval was applied for the final cut-offs. This group classification resulted in a yearly amyloid-b accumulation for CSF + /PET that was four times higher than CSF /PET (adjusted P 5 0.001) and equal to CSF + /PET (adjusted P = 0.32). (iii) Finally, previously proposed cut-offs were used (CSF amyloid-b42 + 5192 ng/l; PET + 40.79 SUVR relative the composite region) without a 5% interval (i.e. no exclusion of borderline cases). This classification also resulted in similar group differences regarding yearly amyloid-b accumulation (higher in CSF + /PET compared with CSF / PET , adjusted P = 0.039 and no differences between CSF + /PET and CSF + /PET + , adjusted P = 0.13). This last classification resulted in a small CSF /PET + group (n = 6), which exhibited no overall increase in yearly SUVR rate (median 0.24%, range –2.3 to 3.9%). BRAIN 2016: 139; 1226–1236 | 1233 aPET group ( + /–) was based on SUVR in a region comprised of the medial and lateral orbitofrontal cortex and the frontal pole, i.e. regions involved in early amyloid-b accumulation (Braak and Braak, 1991; Goedert, 2015) . Discussion Using longitudinal PET data we tested the hypothesis that CSF analysis can detect abnormal amyloid-b accumulation prior to amyloid PET imaging. In accordance with our hypothesis, we found that subjects who were CSF amyloid-b42 positive but PET amyloid-b negative at baseline (CSF+/ PET ) accumulated amyloid-b at a similar rate as those who were positive for both modalities at baseline and more than three times as fast as those that were negative for both modalities. This discordant CSF positive group (CSF+/PET ) had no signs of neurodegeneration or cognitive decline. There were no subjects who were isolated PET amyloid-b positive (CSF–/PET+), which argues against amyloid PET becoming abnormal first. The results were robust using four different types of group classifications for CSF+/ and PET+/ and when classifying PET +/– based on amyloid-b deposition in regions that are affected in the initial phase of amyloid-b accumulation (Braak and Braak, 1991; Goedert, 2015) . Previous cross-sectional studies have put forth the same hypothesis based on higher prevalence of CSF+/PET compared with PET+/CSF cases (Fagan et al., 2009), the presence of CSF+/PET in younger individuals (Morris et al., 2010) and the greater occurrence of CSF+/PET in controls compared with subjects with MCI and Alzheimer’s disease dementia (Mattsson et al., 2014) . To our knowledge, the present study is the first to show that people with abnormally low CSF amyloid-b42 and normal amyloid PET accumulate amyloid-b at an abnormal rate. These longitudinal results indicate that the abnormal amyloid metabolism in Alzheimer’s disease can be detected by CSF biomarkers at a very early stage, before PET imaging becomes abnormal. Pathological amyloid-b accumulation rates have also been calculated in previous studies (Bateman et al., 2012; Toledo et al., 2013; Villemagne et al., 2013) . Using crosssectional Pittsburgh compound B (PiB) PET data from subjects with autosomal dominant Alzheimer’s disease, Bateman et al. (2012) calculated the mean accumulation rate of cortical amyloid-b fibrils to 1.4%/year in the early phases 20–25 years prior to Alzheimer’s disease dementia and prior to neurodegeneration and cognitive decline. This accumulation rate is similar to what we found for the CSF+/PET group (1.2% SUVR increase per year). Individuals who are just starting to accumulate cortical amyloid-b fibrils would not be expected to show signs of tau pathology in CSF, neurodegeneration on MRI or cognitive decline according to the most acknowledged models of Alzheimer’s disease (Braak et al., 2013; Jack et al., 2013; Sperling et al., 2014) . In accordance with this, we found that the CSF + /PET group had similar baseline cognitive function, hippocampal volume, and tau levels as the CSF /PET group, but higher amyloid-b accumulation rate and higher prevalence of APOE e4 carriers. The CSF + /PET group also had similar longitudinal changes in hippocampal volume as the CSF /PET group and showed no deterioration in memory. This fits well with the assumption that CSF + /PET subjects are at the beginning of an Alzheimer’s disease process without overt cognitive decline and hippocampal atrophy. However, it is possible that subtle changes in cognition and brain volume could have been detected with a more sensitive cognitive battery or a larger sample size. In contrast, CSF + /PET + subjects had twice as high tau levels and deteriorated significantly more in hippocampal volume and memory (Table 2 and Fig. 4A and B), indicating that they are closer to Alzheimer’s disease dementia. Another difference was that the CSF + /PET group had slightly higher CSF amyloid-b42 levels than the CSF + /PET + group (Table 1). As shown by the local regression line in Fig. 3A, the rate of amyloid-b accumulation appears to start to increase in CSF /PET cases at CSF amyloid-b42 levels around 220 ng/l and continues to increase at lower CSF amyloid-b42 levels in the CSF + /PET group. This relationship was not seen in CSF + /PET + subjects (Fig. 3B). We cannot rule out that this stagnation of amyloid-b accumulation rate in CSF + / PET + subjects to some extent is caused by an increased atrophy rate in this group, since PET volumes of interest are based on baseline MRI scans in ADNI (Landau et al., 2015) . However, there was no significant negative correlation between global amyloid-b accumulation rate and change in cortical thickness, which would be the case if the accumulation rate was largely explained by an increased rate of atrophy. Further, there were no differences between the CSF/PET groups in terms of baseline cortical thickness of this composite region of interest, indicating that any partial volume effects due to atrophy were similar between the groups (Table 1). Our interpretation of Fig. 3 is therefore that CSF amyloid-b42 might continue to drop and the amyloid-b accumulation rate continue to increase in many of the CSF + /PET individuals before a plateau is seen for both the level of CSF amyloid-b42 and the yearly rate of change in florbetapir SUVR, corresponding to the findings in the CSF + /PET + group. This assumption fits well with a previous publication showing that healthy elderly with normal CSF amyloid-b42 (4192 ng/l) but with values below 225 ng/l are at increased risk to develop abnormal CSF amyloid-b42 levels if followed longitudinally for 3 years (Mattsson et al., 2015b) . The CSF amyloid-b42 levels associated with increased amyloid-b accumulation rate in the present study also coincide with a range of CSF amyloid-b42 levels associated with increased atrophy rates in Alzheimer’s disease-related brain regions (Insel et al., 2015) . The lack of progression of cognitive impairment in CSF + /PET and CSF /PET indicates that the MCI classification of some of these subjects is probably not related to Alzheimer’s disease but could represent benign conditions or misclassification of the MCI diagnosis. The amyloid-b pathology identified by CSF in these subjects thereby likely represents preclinical Alzheimer’s disease. CSF amyloid-b42 and amyloid PET measures partly different aspects of amyloid-b pathology, which explains the possibility of individuals being CSF+/PET despite the high overall agreement between the methods. Previous cross-sectional studies have proposed that the amyloid-b process starts with the formation of non-fibrillar amyloidb species that results in lowered CSF amyloid-b42 but are non-detectable with amyloid PET (Fagan et al., 2009; Morris et al., 2010) . These amyloid-b species later become fibrillar (neuritic plaques) and are consequently detectable with amyloid PET (Mathis et al., 2012). This idea was supported by an autopsy study where a case that was CSF+/PET had numerous diffuse neocortical amyloid-b plaques but only sparse neuritic plaques (Cairns et al., 2009) . A CSF+/PET status is also present in a rare variant of familial Alzheimer’s disease (the Arctic APP mutation), which only develops diffuse, not neuritic, plaques (Scholl et al., 2012) . These studies thus provide a rationale for CSF becoming abnormal before PET in preclinical Alzheimer’s disease. As a limitation of the study, we acknowledge that the sample size of the CSF+/PET group was relatively small (n = 26) and the results need to be replicated in a larger cohort. To our knowledge there is currently no larger cohort available with the repeated measurements needed for this analysis. Using one of the alternative classification systems (without excluding borderline cases) the CSF+/ PET group contained 48 subjects and the results were robust. One previous article has examined longitudinal florbetapir PET in relation to baseline CSF amyloid-b42 in subjects from the ADNI study (Toledo et al., 2015) . That study did not report increased rates of florbetapir PET accumulation in CSF+/PET subjects, but there are several important differences between that study and the present study. Toledo et al. (2015) included data from already demented patients and used an older dataset with shorter follow-up times and fewer subjects than what was available for our study (they examined 150–304 subjects depending on type of analysis including demented patients compared with 353–437 non-demented subjects in the present study). This likely increased our power to detect early effects of CSF amyloid-b42 on longitudinal florbetapir PET. We also included analyses of PET+ /– classifications based on regional amyloid-b deposition in the brain areas where the amyloid-b accumulation is believed to start (Braak and Braak, 1991; Goedert, 2015) . With this alternative PET classification the CSF+/PET– group still accumulated both regional and global amyloid-b faster than CSF–/PET– and at a similar rate as CSF+/PET+ (Table 3 and Fig. 2B and C). Nevertheless, a longer follow-up using repeated MRI, amyloid PET, cognitive tests and CSF samples is needed to verify that CSF+/PET individuals indeed have preclinical Alzheimer’s disease and later become PET+ and develop signs of neurodegeneration and cognitive dysfunction. Especially, a longer follow-up with repeated PET scans is important given the test-retest reliability of florbetapir (Joshi et al., 2012) and that the mean time between scans only was 2.1 years. In summary, we found evidence indicating that CSF amyloid-b42 can detect amyloid-b pathology earlier than amyloid PET. A discordant profile with isolated CSF positivity could be the first measureable sign of amyloid pathology in preclinical Alzheimer’s disease. Consequently, a CSF +/PET status may indicate a suitable stage for starting disease-modifying treatment targeting amyloid-b pathology (Hardy et al., 2014) or interventions of modifiable risk factors (Ngandu et al., 2015) . However, we find it likely that only a subpopulation of CSF +/PET– individuals would benefit from therapeutic interventions since it is not clear if cognitive decline is an inevitable result of abnormal amyloid-b accumulation and even if it is, the time between the initial pathology and cognitive impairment can be more than one to two decades (Sperling et al., 2014) . Therefore, further studies are needed to better determine other factors that can timely predict future cognitive impairment at this very early stage of amyloid-b accumulation. Acknowledgements The authors would like to thank Michael Scho¨ ll, PhD (Gothenburg University and Lund University), Martin Schain, PhD (Columbia University) and Olof Lindberg, PhD (Karolinska Institutet) for advice on analyses regarding the relationship between cortical atrophy rates and amyloid-b accumulation rates. Funding Work at the authors’ research centre was supported by the Strategic Research Area MultiPark (Multidisciplinary Research in Parkinson’s disease) at Lund University, the European Research Council, the Swedish Research Council, the Crafoord Foundation, the Swedish Brain Foundation, the Ska˚ ne University Hospital Foundation, the Swedish Alzheimer Association, and the Swedish federal government under the ALF agreement. The ADNI data collection and sharing for this project was funded by the Alzheimer’s Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904) and DOD ADNI (Department of Defense award number W81XWH-12-2-0012). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: Alzheimer’s Association; Alzheimer’s Drug Discovery Foundation; Araclon Biotech; BioClinica, Inc.; Biogen Idec Inc.; Bristol-Myers Squibb Company; Eisai Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; EuroImmun; F. Hoffmann-La Roche Ltd and its affiliated company Genentech, Inc.; Fujirebio; GE Healthcare; IXICO Ltd.; Janssen Alzheimer Immunotherapy Research & Development, LLC.; Johnson & Johnson Pharmaceutical Research & Development LLC.; Medpace, Inc.; Merck & Co., Inc.; Meso Scale Diagnostics, LLC.; NeuroRx Research; Neurotrack Technologies; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Piramal Imaging; Servier; Synarc Inc.; and Takeda Pharmaceutical Company. The Canadian Institutes of Health Research is providing funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health (www.fnih.org). The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer’s Disease Cooperative Study at the University of California, San Diego. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California. Supplementary material Supplementary material is available at Brain online. Albert MS , DeKosky ST , Dickson D , Dubois B , Feldman HH , Fox NC , et al. The diagnosis of mild cognitive impairment due to Alzheimer's disease: recommendations from the National Institute on Aging-Alzheimer's Association workgroups on diagnostic guidelines for Alzheimer's disease . Alzheimers Dement 2011 ; 7 : 270 - 9 . Bateman RJ , Xiong C , Benzinger TL , Fagan AM , Goate A , Fox NC , et al. Clinical and biomarker changes in dominantly inherited Alzheimer's disease . N Engl J Med 2012 ; 367 : 795 - 804 . Benaglia T , Chauveau D , Hunter DR , Young D. mixtools: an R package for analyzing finite mixture models . J Stat Softw 2009 ; 32 : 1 - 29 . Biogen . A Randomized, Blinded, Placebo-Controlled Single Ascending Dose Study of the Safety, Tolerability, and Pharmacokinetics of BIIB037 in Subjects with Mild to Moderate Alzheimer's Disease . In: AD/PD International Conference, Nice, France, 2015 . Braak H , Braak E. Neuropathological stageing of Alzheimer-related changes . Acta Neuropathol 1991 ; 82 : 239 - 59 . Braak H , Zetterberg H , Del Tredici K , Blennow K. Intraneuronal tau aggregation precedes diffuse plaque deposition, but amyloid-beta changes occur before increases of tau in cerebrospinal fluid . Acta Neuropathol 2013 ; 126 : 631 - 41 . Cairns NJ , Ikonomovic MD , Benzinger T , Storandt M , Fagan AM , Shah AR , et al. Absence of Pittsburgh compound B detection of cerebral amyloid beta in a patient with clinical, cognitive, and cerebrospinal fluid markers of Alzheimer disease: a case report . Arch Neurol 2009 ; 66 : 1557 - 62 . Crane PK , Carle A , Gibbons LE , Insel P , Mackin RS , Gross A , et al. Development and assessment of a composite score for memory in the Alzheimer's Disease Neuroimaging Initiative (ADNI) . Brain Imaging Behav 2012 ; 6 : 502 - 16 . De Meyer G , Shapiro F , Vanderstichele H , Vanmechelen E , Engelborghs S , De Deyn PP , et al. Diagnosis-independent Alzheimer disease biomarker signature in cognitively normal elderly people . Arch Neurol 2010 ; 67 : 949 - 56 . Fagan AM , Mintun MA , Mach RH , Lee SY , Dence CS , Shah AR , et al. Inverse relation between in vivo amyloid imaging load and cerebrospinal fluid Abeta42 in humans . Ann Neurol 2006 ; 59 : 512 - 19 . Fagan AM , Mintun MA , Shah AR , Aldea P , Roe CM , Mach RH , et al. Cerebrospinal fluid tau and ptau(181) increase with cortical amyloid deposition in cognitively normal individuals: implications for future clinical trials of Alzheimer's disease . EMBO Mol Med 2009 ; 1 : 371 - 80 . Folstein MF , Folstein SE , McHugh PR . “ Mini-mental state”. A practical method for grading the cognitive state of patients for the clinician . J Psychiatr Res 1975 ; 12 : 189 - 98 . Goedert M. NEURODEGENERATION . Alzheimer's and Parkinson's diseases: the prion concept in relation to assembled Abeta, tau, and alpha-synuclein . Science 2015 ; 349 : 1255555 Grimmer T , Riemenschneider M , Forstl H , Henriksen G , Klunk WE , Mathis CA , et al. Beta amyloid in Alzheimer's disease: increased deposition in brain is reflected in reduced concentration in cerebrospinal fluid . Biol Psychiatry 2009 ; 65 : 927 - 34 . Hardy J , Bogdanovic N , Winblad B , Portelius E , Andreasen N , Cedazo-Minguez A , et al. Pathways to Alzheimer's disease . J Intern Med 2014 ; 275 : 296 - 303 . Insel PS , Mattsson N , Donohue MC , Mackin RS , Aisen PS , Jack CR Jr, et al. The transitional association between beta-amyloid pathology and regional brain atrophy . Alzheimers Dement 2015 ; 11 : 1171 - 9 . Jack CR Jr, Bernstein MA , Fox NC , Thompson P , Alexander G , Harvey D , et al. The Alzheimer's Disease Neuroimaging Initiative (ADNI): MRI methods . J Magn Reson Imaging 2008 ; 27 : 685 - 91 . Jack CR Jr, Knopman DS , Jagust WJ , Petersen RC , Weiner MW , Aisen PS , et al. Tracking pathophysiological processes in Alzheimer's disease: an updated hypothetical model of dynamic biomarkers . Lancet Neurol 2013 ; 12 : 207 - 16 . Jack CR Jr, Knopman DS , Jagust WJ , Shaw LM , Aisen PS , Weiner MW , et al. Hypothetical model of dynamic biomarkers of the Alzheimer's pathological cascade . Lancet Neurol 2010 ; 9 : 119 - 28 . Jacoby W. Loess : a nonparametric, graphical tool for depicting relationships between variables . Electoral Studies 2000 ; 19 : 577 - 613 . Jagust WJ , Landau SM , Shaw LM , Trojanowski JQ , Koeppe RA , Reiman EM , et al. Relationships between biomarkers in aging and dementia . Neurology 2009 ; 73 : 1193 - 9 . Joshi AD , Pontecorvo MJ , Clark CM , Carpenter AP , Jennings DL , Sadowsky CH , et al. Performance characteristics of amyloid PET with florbetapir F 18 in patients with alzheimer's disease and cognitively normal subjects . J Nucl Med 2012 ; 53 : 378 - 84 . Landau S , Jagust W. Florbetapir processing methods . http://ida.loni.usc.edu Helen Wills Neuroscience Institute, UC Berkeley and Lawrence Berkeley National Laboratory California; 2015 . Landau SM , Baker SL , Jagust WJ . Amyloid change early in disease is related to increased glucose metabolism and episodic memory decline . In: Human Amyloid Imaging Meeting , Miami, FL; 2014 . Landau SM , Fero A , Baker SL , Koeppe R , Mintun M , Chen K , et al. Measurement of longitudinal beta-amyloid change with 18F-florbetapir PET and standardized uptake value ratios . J Nucl Med 2015 ; 56 : 567 - 74 . Landau SM , Lu M , Joshi AD , Pontecorvo M , Mintun MA , Trojanowski JQ , et al. Comparing positron emission tomography imaging and cerebrospinal fluid measurements of b-amyloid . Ann Neurol 2013 ; 74 : 826 - 36 . Landau SM , Mintun MA , Joshi AD , Koeppe RA , Petersen RC , Aisen PS , et al. Amyloid deposition, hypometabolism, and longitudinal cognitive decline . Ann Neurol 2012 ; 72 : 578 - 86 . Mathis CA , Mason NS , Lopresti BJ , Klunk WE . Development of positron emission tomography beta-amyloid plaque imaging agents . Semin Nucl Med 2012 ; 42 : 423 - 32 . Mattsson N , Insel P , Donohue M , Landau S , Jagust W , Shaw L , et al. Independent information from cerebrospinal fluid b-amyloid and florbetapir imaging in Alzheimer's disease . Brain 2015a; 138 (Pt 3 ): 772 - 83 . Mattsson N , Insel P , Landau S , Jagust W , Donohue MC , Shaw LM , et al. Diagnostic accuracy of CSF Ab42 and florbetapir PET for Alzheimer's disease . Ann Clin Transl Neurol 2014 ; 1 : 534 - 43 . Mattsson N , Insel PS , Donohue M , Jagust W , Sperling R , Aisen P , et al. Predicting reduction of cerebrospinal fluid beta-amyloid 42 in cognitively healthy controls . JAMA Neurol 2015b; 72 : 554 - 60 . Morris JC . The Clinical Dementia Rating (CDR): current version and scoring rules . Neurology 1993 ; 43 : 2412 - 14 . Morris JC , Roe CM , Xiong C , Fagan AM , Goate AM , Holtzman DM , et al. APOE predicts amyloid-beta but not tau Alzheimer pathology in cognitively normal aging . Ann Neurol 2010 ; 67 : 122 - 31 . Ngandu T , Lehtisalo J , Solomon A , Levalahti E , Ahtiluoto S , Antikainen R , et al. A 2 year multidomain intervention of diet, exercise, cognitive training, and vascular risk monitoring versus control to prevent cognitive decline in at-risk elderly people (FINGER): a randomised controlled trial . Lancet 2015 ; 385 : 2255 - 63 . Olsson A , Vanderstichele H , Andreasen N , De Meyer G, Wallin A , Holmberg B , et al. Simultaneous measurement of beta-amyloid(1- 42), total tau, and phosphorylated tau (Thr181) in cerebrospinal fluid by the xMAP technology . Clin Chem 2005 ; 51 : 336 - 45 . Palmqvist S , Zetterberg H , Blennow K , Vestberg S , Andreasson U , Brooks DJ , et al. Accuracy of brain amyloid detection in clinical practice using cerebrospinal fluid beta-amyloid 42: a cross-validation study against amyloid positron emission tomography . JAMA Neurol 2014 ; 71 : 1282 - 9 . Palmqvist S , Zetterberg H , Mattsson N , Johansson P , Minthon L , Blennow K ; Alzheimer's Disease Neuroimaging Initiative , et al. Detailed comparison of amyloid PET and CSF biomarkers for identifying early Alzheimer's disease . Neurology 2015 ; 85 : 1240 - 9 . Pfister R , Schwarz K , Carson R , Jancyzk M. Easy methods for extracting individual regression slopes: comparing SPSS, R, and Excel. Tutor. Quant. Methods Psychol . 2013 ; 9 : 72 - 8 . Pietrzak RH , Lim YY , Ames D , Harrington K , Restrepo C , Martins RN , et al. Trajectories of memory decline in preclinical Alzheimer's disease: results from the Australian Imaging, Biomarkers and Lifestyle Flagship Study of Ageing . Neurobiol Aging 2015 ; 36 : 1231 - 8 . Scholl M , Wall A , Thordardottir S , Ferreira D , Bogdanovic N , Langstrom B , et al. Low PiB PET retention in presence of pathologic CSF biomarkers in Arctic APP mutation carriers . Neurology 2012 ; 79 : 229 - 36 . Sepulcre J , Sabuncu MR , Becker A , Sperling R , Johnson KA . In vivo characterization of the early states of the amyloid-beta network . Brain 2013 ; 136 (Pt 7): 2239 - 52 . Shaw LM , Vanderstichele H , Knapik-Czajka M , Clark CM , Aisen PS , Petersen RC , et al. Cerebrospinal fluid biomarker signature in Alzheimer's disease neuroimaging initiative subjects . Ann Neurol 2009 ; 65 : 403 - 13 . Sperling R , Mormino E , Johnson K. The evolution of preclinical Alzheimer's disease: implications for prevention trials . Neuron 2014 ; 84 : 608 - 22 . Sperling RA , Aisen PS , Beckett LA , Bennett DA , Craft S , Fagan AM , et al. Toward defining the preclinical stages of Alzheimer's disease: recommendations from the National Institute on Aging-Alzheimer's Association workgroups on diagnostic guidelines for Alzheimer's disease . Alzheimers Dement 2011 ; 7 : 280 - 92 . Sperling RA , Jack CR Jr, Aisen PS. Testing the right target and right drug at the right stage . Sci Transl Med 2011 ; 3 : 111cm33 Sperling RA , Rentz DM , Johnson KA , Karlawish J , Donohue M , Salmon DP , et al. The A4 study: stopping AD before symptoms begin? Sci Transl Med 2014 ; 6 : 228fs13 Tolboom N , van der Flier WM , Yaqub M , Boellaard R , Verwey NA , Blankenstein MA , et al. Relationship of cerebrospinal fluid markers to 11C-PiB and 18F-FDDNP binding . J Nucl Med 2009 ; 50 : 1464 - 70 . Toledo JB , Bjerke M , Da X , Landau SM , Foster NL , Jagust W , et al. Nonlinear association between cerebrospinal fluid and Florbetapir F18 beta-amyloid measures across the spectrum of Alzheimer disease . JAMA Neurol 2015 ; 72 : 571 - 81 . Toledo JB , Xie SX , Trojanowski JQ , Shaw LM . Longitudinal change in CSF Tau and Abeta biomarkers for up to 48 months in ADNI . Acta Neuropathol 2013 ; 126 : 659 - 70 . Villemagne VL , Burnham S , Bourgeat P , Brown B , Ellis KA , Salvado O , et al. Amyloid beta deposition, neurodegeneration, and cognitive decline in sporadic Alzheimer's disease: a prospective cohort study . Lancet Neurol 2013 ; 12 : 357 - 67 . Villeneuve S , Rabinovici GD , Cohn-Sheehy BI , Madison C , Ayakta N , Ghosh PM , et al. Existing Pittsburgh Compound-B positron emission tomography thresholds are too high: statistical and pathological evaluation . Brain 2015 ; 138 (Pt 7): 2020 - 33 . Villain N , Chetelat G , Grassiot B , Bourgeat P , Jones G , Ellis KA , et al. Regional dynamics of amyloid-beta deposition in healthy elderly, mild cognitive impairment and Alzheimer's disease: a voxelwise PiB-PET longitudinal study . Brain 2012 ; 135 (Pt 7): 2126 - 39 . Vos SJ , Xiong C , Visser PJ , Jasielec MS , Hassenstab J , Grant EA , et al. Preclinical Alzheimer's disease and its outcome: a longitudinal cohort study . Lancet Neurol 2013 ; 12 : 957 - 65 . Weigand SD , Vemuri P , Wiste HJ , Senjem ML , Pankratz VS , Aisen PS , et al. Transforming cerebrospinal fluid Abeta42 measures into calculated Pittsburgh Compound B units of brain Abeta amyloid . Alzheimers Dement 2011 ; 7 : 133 - 41 .


This is a preview of a remote PDF: https://academic.oup.com/brain/article-pdf/139/4/1226/24173398/aww015.pdf

Palmqvist, Sebastian, Mattsson, Niklas, Hansson, Oskar. Cerebrospinal fluid analysis detects cerebral amyloid-β accumulation earlier than positron emission tomography, Brain, 2016, 1226-1236, DOI: 10.1093/brain/aww015