Early astrocytosis in autosomal dominant Alzheimer's disease measured in vivo by multi-tracer positron emission tomography

Abstract

Studying autosomal dominant Alzheimer's disease (ADAD), caused by gene mutations yielding nearly complete penetrance and a distinct age of symptom onset, allows investigation of presymptomatic pathological processes that can identify a therapeutic window for disease-modifying therapies. Astrocyte activation may occur in presymptomatic Alzheimer's disease (AD) because reactive astrocytes surround beta-amyloid (Abeta) plaques in autopsy brain tissue. Positron emission tomography was performed to investigate fibrillar Abeta, astrocytosis and cerebral glucose metabolism with the radiotracers (11)C-Pittsburgh compound-B (PIB), (11)C-deuterium-L-deprenyl (DED) and (18)F-fluorodeoxyglucose (FDG) respectively in presymptomatic and symptomatic ADAD participants (n = 21), patients with mild cognitive impairment (n = 11) and sporadic AD (n = 7). Multivariate analysis using the combined data from all radiotracers clearly separated the different groups along the first and second principal components according to increased PIB retention/decreased FDG uptake (component 1) and increased DED binding (component 2). Presymptomatic ADAD mutation carriers showed significantly higher PIB retention than non-carriers in all brain regions except the hippocampus. DED binding was highest in presymptomatic ADAD mutation carriers. This suggests that non-fibrillar Abeta or early stage plaque depostion might interact with inflammatory responses indicating astrocytosis as an early contributory driving force in AD pathology. The novelty of this finding will be investigated in longitudinal follow-up studies.

Full text

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Scientific RepoRts | 5:16404 | DOI: 10.1038/srep16404
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Early astrocytosis in autosomal
dominant Alzheimer’s disease
measured in vivo by multi-tracer
positron emission tomography
Michael Schöll1,2, Stephen F. Carter1,3, Eric Westman4, Elena Rodriguez-Vieitez1,
Ove Almkvist1,5,6, Steinunn Thordardottir5,7, Anders Wall8, Caroline Gra5,7,
Bengt Långström9 & Agneta Nordberg1,5
Studying autosomal dominant Alzheimer’s disease (ADAD), caused by gene mutations yielding
nearly complete penetrance and a distinct age of symptom onset, allows investigation of
presymptomatic pathological processes that can identify a therapeutic window for disease-modifying
therapies. Astrocyte activation may occur in presymptomatic Alzheimer’s disease (AD) because
reactive astrocytes surround β-amyloid (Aβ) plaques in autopsy brain tissue. Positron emission
tomography was performed to investigate brillar Aβ, astrocytosis and cerebral glucose metabolism
with the radiotracers 11C-Pittsburgh compound-B (PIB), 11C-deuterium-L-deprenyl (DED) and
18F-uorodeoxyglucose (FDG) respectively in presymptomatic and symptomatic ADAD participants
(n = 21), patients with mild cognitive impairment (n = 11) and sporadic AD (n = 7). Multivariate
analysis using the combined data from all radiotracers clearly separated the dierent groups along
the rst and second principal components according to increased PIB retention/decreased FDG
uptake (component 1) and increased DED binding (component 2). Presymptomatic ADAD mutation
carriers showed signicantly higher PIB retention than non-carriers in all brain regions except the
hippocampus. DED binding was highest in presymptomatic ADAD mutation carriers. This suggests
that non-brillar Aβ or early stage plaque depostion might interact with inammatory responses
indicating astrocytosis as an early contributory driving force in AD pathology. The novelty of this
nding will be investigated in longitudinal follow-up studies.
Alzheimer’s disease (AD) is a progressive brain disorder with gradually occurring cognitive decline. e
time course of the underlying pathological changes remains largely veiled. Increasing evidence argues
that these changes start decades before the onset of clinical symptoms. e order and magnitude of these
processes are hitherto not well understood. e typical histopathology of AD includes the presence of
1Department NVS, Center for Alzheimer Research, Division of Translational Alzheimer Neurobiology, Karolinska
Institutet, 141 57 Huddinge, Sweden. 2MedTech West and the Department of Clinical Neuroscience and
Rehabilitation, University of Gothenburg, 413 45 Gothenburg, Sweden. 3Wolfson Molecular Imaging Centre,
University of Manchester, Manchester, M20 3LJ, UK. 4Department NVS, Center for Alzheimer Research, Division of
Clinical Geriatrics, Karolinska Institutet, 141 57 Huddinge, Sweden. 5Department of Geriatric Medicine, Karolinska
University Hospital Huddinge, 141 86 Stockholm, Sweden. 6Department of Psychology, Stockholm University, 106
91 Stockholm, Sweden. 7Department NVS, Center for Alzheimer Research, Division of Neurogeriatrics, Karolinska
Institutet, 141 57 Huddinge, Sweden. 8Department of Surgical Sciences, Section of Nuclear Medicine & PET,
Uppsala University, 751 85 Uppsala, Sweden. 9Department of Chemistry, Uppsala University, 701 05 Uppsala,
Sweden. Correspondence and requests for materials should be addressed to A.N. (email: agneta.k.nordberg@
ki.se)
Received: 01 July 2015
Accepted: 13 October 2015
Published: 10 November 2015
OPEN

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β -amyloid (Aβ ) plaques, neurobrillary tangles, neuronal cell dysfunction and loss, and the activation
of glial cells. It has been hypothesized that the accumulation of Aβ plaques plays a causative role in the
disease development1. However, increasing evidence suggests that neurodegeneration might be triggered
by a combination of processes, including tau deposition and neuroinammation in addition to plaque
accumulation2,3. e rapid development of cerebrospinal uid (CSF) and positron emission tomography
(PET) biomarkers has allowed modelling of the hypothetical sequence of biomarker changes in the pre-
symptomatic, prodromal, and symptomatic stages of AD4,5.
AD is sporadic (sAD) in the great majority of cases, but 1–5% of Alzheimer patients suer from the
autosomal dominant form of the disease (ADAD), which is caused by mutations in the presenilin 1
(PSEN1), presenilin 2 (PSEN2), or amyloid precursor protein (APP) genes6. Within families harbouring
a specic mutation, the age of onset for ADAD or early-onset familial AD (eoFAD) is predictable, which
provides an opportunity for determining the sequence and magnitude of pathological changes that cul-
minate in symptomatic disease7. Given the importance of understanding very early pathological changes
in AD, members of families harbouring these mutations can be studied long before they develop any
symptoms, providing an invaluable tool for research of these processes.
ere are strong indications that factors other than Aβ , such as the activation of astroglia and micro-
glia, and subsequent neuroinammation, contribute to AD genesis and progression8–10. Most of our
current understanding of astrocytosis stems from immunohistochemical studies in postmortem brain
tissue11,12. Reactive astrocytes undergo structural and functional changes regulated by specic signalling
events that occur in a context-dependent manner13. It has been observed that Aβ plaques are surrounded
by activated astrocytes, and that activated astrocytes produce reactive oxygen and nitrogen species, which
may contribute to AD pathogenesis. Nevertheless, much is still unknown regarding the relationship
between reactive astrocytes and Aβ pathology14.
e PET tracer 11C-deuterium-L-deprenyl (DED) binds to monoamine oxidase B (MAO-B) on the
outer mitochondrial membrane in astrocytes; increased DED binding is thought to reect reactive astro-
cytosis15,16. In a 11C-DED PET study of sAD patients, we found evidence for early astrocytosis in 11C-PIB
positive patients with mild cognitive impairment (MCI PIB+ )17. DED binding was increased in MCI
PIB+ patients compared to sAD patients and controls, suggesting that increased astrocytosis occurs in
the earlier prodromal stages of AD17.
is cross-sectional study reports the baseline results from a large, ongoing, longitudinal study aiming
to examine the temporal and regional relationships between astrocytosis, Aβ deposition, and glucose
metabolism as a measure of neurodegeneration in ADAD and sAD. Here we demonstrate for the rst
time the presence of signicant astrocytosis decades before the occurrence of clinical symptoms.
Results
Subjects. e demographic data for the subjects are presented in Table1. ere were signicant dif-
ferences in age, education, and mini-mental state examination (MMSE) scores between the groups. e
presymptomatic mutation carriers in particular were considerably younger than members of the other
groups. Dierences in MMSE were anticipated due to the dierent clinical stages of the groups. e
patients with MCI were subdivided into PIB positive (PIB+ ) and PIB negative (PIB− ) subjects according
to their global-to-cerebellum gray matter PIB retention ratios using a cut-o point of 1.41 derived from
a larger multicenter study of PIB PET18.
Neuropsychology. All raw test scores were transformed into z-scores (Table1). Z-scores < − 1.645
(h percentile) were considered outside the normal range. e sAD patients showed pathological
z-scores in global cognition and episodic memory. Four of the eight MCI PIB+ patients had abnormal
episodic memory compared to the population mean, and episodic memory had declined in the other
four compared to previous assessments or they had abnormal values compared to their estimated pre-
morbid function. One of the MCI PIB− patients showed abnormal episodic memory; the other two had
abnormal performance in non-memory cognitive domains. e episodic memory z-scores were within
the normal range for all non-carriers in ADAD families as well as for the six presymptomatic ADAD
mutation carriers. Two of the three symptomatic ADAD carriers demonstrated results markedly outside
the normal range in global cognition and episodic memory, while the third carrier had abnormal or
close to abnormal results in two episodic memory tests. For detailed individual test scores, please refer
to Table2.
Principal component analysis modeling and model quality. e principal component analysis
PCA model accounted for 67% of the variance of the original data (R2(X)), and its cross-validated pre-
dictability, Q2(X), was 0.61 (considered a valid model)19. Figure1a shows a scatter plot with the distri-
bution of all participants’ data along two components. Figure1b displays a simplied plot showing the
means and standard deviations for each group. Supplementary Fig. S1 shows the inuence of the 25 most
important variables on each component. As demonstrated in Fig.1a, the separation of the groups along
component 1 shows a clear division between symptomatic ADAD mutation carriers, sAD patients, and
MCI PIB+ patients on the one side and presymptomatic ADAD mutation carriers, MCI PIB− patients,
and ADAD mutation non-carriers on the other. According to the loading plot, PIB retention and FDG
uptake on the respective sides accounted for this separation (Fig.1b; for detailed regional information,

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Scientific RepoRts | 5:16404 | DOI: 10.1038/srep16404
please see Supplementary Fig. S1). e order of rankings along the rst component indicated highest
PIB retention and lowest FDG metabolism for symptomatic ADAD mutation carriers, followed in order
by sAD patients, MCI PIB+ patients, presymptomatic ADAD carriers, MCI PIB− patients, and ADAD
non-carriers.
DED slope values were most important for separation along component 2. Here, although the pattern
was not as clear as for component 1, the order of rankings suggested highest slope values for the pre-
symptomatic ADAD mutation carriers, followed by MCI PIB+ patients, symptomatic ADAD carriers,
sAD patients, ADAD non-carriers, and MCI PIB− patients (for detailed regional information, please see
Supplementary Fig. S1).
11C-Pittsburgh compound-B PET region of interest analysis. PIB retention diered signicantly
between the groups (Fig.2, Supplementary Table S1a); Kruskal-Wallis tests in each of 11 ROIs for the four
groups being compared (presymptomatic ADAD carriers, MCI PIB+ , sAD and ADAD non-carriers),
were signicant (p < 0.001) in all ROIs except for the hippocampus. Highest PIB retention values were
seen in the symptomatic mutation carriers in all brain regions, with particularly increased retention in
the putamen (z-score = 17.3) and in the hippocampus (z-score = 9.7) of one carrier.
Following the highest PIB retention values observed in individual symptomatic carriers, the ranked
order for PIB retention derived from Mann-Whitney pair-wise comparisons between groups and the
z-scores for MCI PIB− patients was: sAD patients ≥ MCI PIB+ patients > presymptomatic ADAD
Clinical patients ADAD individuals
sAD MCI PIB+MCI PIB−
Mutation
non-carriers
Presymptomatic
mutation
carriers
Symptomatic
mutation
carriers
n7 8 3 12 6 3
Age (y) 64.1 ± 6.1C,D
(55–73) 61.9 ± 6.8H
(53–75) 64.3 ± 6.7K
(60–72) 54.3 ± 12.9M43.5 ± 9.3 58.7 ± 5.0
Gender (m/f) 5/2 4/4 2/1 8/4 * 2/1
MMSE 24.4 ± 5.7C,D,E
(14–30) 27.7 ± 1.9
(24–30) 27.7 ± 2.3
(25–29) 29.3 ± 1.1
(27–30) 29.6 ± 0.6
(29–30) 16.0 ± 8.7
(11–26)
Education (y) 10.4 ± 1.8A,B
(8–13) 14.0 ± 2.5G
(11–18) 13.7 ± 2.1
(12–16) 11.3 ± 2.3 13.2 ± 2.6 10.0 ± 1.0
Mutations PSEN1 H163Y;
APPswe; APParc
PSEN1 H163Y;
APPswe;
APParc
Time to expected age at
symptom onset (y) − 11.8 ± 8.1 1.9 ± 2.6
APOE ε 3/3: 2; 3/4: 2;
4/4: 3 3/3: 2; 3/4: 6 3/3: 2; 3/4: 1 2/3: 1; 3/3: 7;
3/4: 3; 4/4: 1 3/3: 3; 3/4: 3 3/3: 1; 2/4: 1;
3/4: 1
FSIQ global cognition
composite z-score − 2.3 ± 1.7 − 0.9 ± 1.8 − 0.2 ± 1.4 − 0.2 ± 1.5 0.3 ± 1.3 − 3.3 ± 1.9
Episodic memory
composite z-score#− 2.3 ± 0.8 − 1.3 ± 0.7 − 0.9 ± 0.9 − 0.1 ± 0.5 0.0 ± 0.5 − 2.2 ± 1.1
** sAD MCI PIB+ MCI PIB− Non-carriers Presymptomatic
carriers Symptomatic
carriers
sAD A B C D E
MCI PIB+ A F G H I
MCI PIB− B F J K L
Non-carriers C G J M N
Presymptomatic carriers D H K M O
Symptomatic carriers E I L N O
Table 1. Demographic information. All values are means ± SD (range), unless stated otherwise.
Superscript letters indicate signicant dierences between groups (see legend ** for group comparison codes,
Fisher’s LSD post-hoc test, p < 0.05). *Gender distribution of presymptomatic autosomal dominant AD
mutation carriers is not revealed to preserve condentiality; #sum of Rey auditory verbal learning test, total
learning and delayed retention test, and Rey Osterrieth retention test scores. sAD = sporadic Alzheimer’s
disease; ADAD = autosomal dominant Alzheimer’s disease; APOE = apolipoprotein E; FSIQ = full-
scale intelligence quotient; MCI = mild cognitive impairment; MMSE = mini-mental state examination;
PIB = Pittsburgh compound-B. Z-scores below − 1.645 were considered outside normal range (italics). **
Legend for pairwise comparisons using Fisher’s LSD post hoc test.

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mutation carriers > ADAD non-carriers ≥ MCI PIB− ; this pattern is consistent with the results obtained
from the PCA analysis. e ADAD non-carriers and the MCI PIB− subjects had persistently very low
Clinical patients ADAD individuals
sAD MCI PIB+MCI PIB−
Mutation non-
carriers
Presymptomatic
mutation
carriers
Symptomatic
mutation
carriers
WAIS-R Information − 2.00 (2.23) − 0.84 (0.60) − 0.89 (0.90) − 0.40 (1.03) 0.18 (0.53) − 0.75 (1.85)
WAIS-R Similarities − 1.52 (1.54) − 0.64 (1.18) 0.67 (0.79) 0.08 (1.11) − 0.15 (0.79) − 0.94 (0.94)
WAIS-R Block Design − 2.23 (1.33) − 0.85 (1.17) 0.23 (1.75) 0.43 (1.14) 0.73 (0.81) − 1.16 (1.42)
WAIS-R Digit Symbol − 1.17 (1.17) − 0.75 (1.07) − 0.19 (1.44) 0.20 (0.51) − 0.03 (0.36) − 1.56 (1.49)
Rey-Osterrieth copy − 2.16 (3.79) − 0.40 (0.54) − 0.30 (0.88) − 0.15 (0.46) − 0.28 (0.35) − 3.55 (4.95)
Rey-Osterrieth recall − 2.43 (0.84) − 1.37 (0.71) − 0.74 (1.16) − 0.59 (0.85) − 0.22 (0.53) − 1.61 (2.39)
RAVLT trial 1–5 total − 1.89 (0.90) − 1.30 (0.90) 0.62 (0.72) − 0.61 (0.67) − 0.50 (0.74) − 2.13 (0.90)
RAVLT delayed recall − 1.88 (1.54) − 1.74 (0.60) − 1.38 (0.66) − 0.38 (0.83) − 1.25 (1.07) − 1.99 (1.01)
Digit Span forward
UD − 1.23 (0.73) − 0.75 (0.83) − 0.90 (1.29) 0.20 (1.07) − 0.18 (1.66) − 1.02 (1.05)
Corsi Span UD − 2.20 (1.60) − 1.71 (1.70) − 0.49 (2.93) 0.24 (1.20) − 0.93 (1.38) − 1.90 (2.70)
Trail Making A time
(s) − 3.01 (6.37) − 1.14 (1.61) − 0.62 (0.65) 0.85 (0.60) 0.78 (0.47) 0.27 (0.08)
Trail Making B time
(s) − 1.69 (1.66) − 0.80 (1.36) − 0.44 (1.01) 0.38 (0.42) 0.09 (0.36) − 2.22 (2.92)
Table 2. Detailed neuropsychological test results. All values are means (SD) of z-scores, z-scores below
− 1.645 were considered outside normal range (italics). WAIS-R: Wechsler Adult Intelligence Scale–Revised;
RAVLT: Rey Auditory Verbal Learning Test.
Figure 1. (a) Scatter plot displaying results from all examined individuals according to Principal
Component Analysis (PCA). Distribution along the rst two components is shown. (b) e gure displays
a simplied summary of the PCA data for each group; the central shapes represent the mean PCA score for
each group, bars represent standard deviations for each group on each principal component.

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PIB retention scores in all the examined regions. Compared to ADAD non-carriers, the MCI PIB−
patients had signicantly lower PIB retention in the hippocampus (z-score = − 2.1).
e sAD and MCI PIB+ patients had increased PIB retention compared to ADAD non-carriers with
eect sizes r > 0.80 (p < 0.001) in all ROIs, except the hippocampus which did not show a signicant
dierence. Both sAD and MCI PIB+ groups showed signicantly increased PIB retention compared to
presymptomatic ADAD mutation carriers in all cortical regions, most pronounced in parieto-temporal
cortex (eect sizes r ~ 0.60–0.70, p < 0.05), but dierences were not signicant in subcortical regions.
Presymptomatic mutation carriers had higher PIB than ADAD non-carriers in all ROIs (eect sizes
r ~ 0.5–0.7, p < 0.05), except for the hippocampus.
Figure 2. Scatter plots showing all individual PIB retention data in composite cortical (a) and subcortical
(b) bilateral brain regions.

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11C-deuterium-L-deprenyl PET region of interest analysis. Comparisons of four groups (pre-
symptomatic ADAD carriers, MCI PIB+ , sAD and ADAD non-carriers) using Kruskal-Wallis tests in
each of 11 ROIs (except for the cerebellum), showed a trend toward statistical signicance for the ante-
rior cingulate cortex, the thalamus and the hippocampus (p = 0.059–0.069) (Supplementary Table S1b).
e highest DED slope values were seen in the presymptomatic ADAD mutation carriers on average.
e largest dierences among groups were found between presymptomatic ADAD-carriers and sAD,
where Mann-Whitney comparisons were signicant in temporal, anterior/posterior cingulate, thalamus
and hippocampus (r ~ 0.6–0.7, p < 0.05) (Fig.3). e ADAD non-carrier group showed high variance,
whereof two individuals (31 and 43 in Fig.1a) showed very high DED slope values in most brain regions,
Figure 3. Scatter plots showing all individual DED slope data in composite cortical (a) and subcortical (b)
bilateral brain regions. No data are shown for the cerebellum since this was used as the reference region in
the modied Patlak reference tissue model.

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which accounts for the rather high mean values in the non-carriers. Subject 43 showed particularly high
values, especially in the hippocampus and the parahippocampal gyrus (not shown). sAD had lower
DED than MCI PIB+ in the hippocampus (r = 0.51, p < 0.05), consistent with the results from the PCA
analysis. e Mann-Whitney comparisons between sAD and ADAD non-carriers revealed signicantly
lower DED binding in sAD in anterior cingulate, caudate and hippocampus (r = 0.48-0.56, p < 0.05).
18F-uorodeoxyglucose PET region of interest analysis. e pattern of cerebral glucose metab-
olism as measured by FDG uptake was signicantly dierent between the groups (Fig.4, Supplementary
Table S1c); the Kruskal-Wallis tests between the four groups being compared (presymptomatic ADAD
carriers, MCI PIB+ , sAD and ADAD non-carriers) showed signicant results in all ROIs, most pro-
nounced in parieto-temporal, posterior cingulate, and thalamus (p < 0.001). FDG uptake was lowest in
individual symptomatic mutation carriers (most pronounced in parieto-temporal and posterior cingulate
Figure 4. Scatter plots showing all individual FDG uptake data in composite cortical (a) and subcortical (b)
bilateral brain regions.

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with z-scores between − 3.2 and − 4.2), followed by sAD and MCI PIB+ patients in almost all examined
brain regions (Fig.4). ere were no signicant dierences in FDG uptake between sAD and MCI PIB+
patients. As expected, the highest FDG uptake occurred in the non-carriers, which was signicantly
higher (p < 0.05) than in sAD and in MCI PIB+ groups in all ROIs. e group of presymptomatic
carriers showed to be statistically comparable to MCI PIB+ patients in anterior and posterior cingulate
cortices as well as in the occipital region, indicating incipient hypometabolism in presymptomatic carri-
ers. e pattern of glucose metabolism across groups was consistent with results from the PCA analysis.
Statistical parametric mapping analysis. e statistical parametric mapping (SPM) results gen-
erally demonstrated ongoing progressive pathology in ADAD mutation carriers as the expected age at
symptom onset approached (Fig.5), consistent with the observed regional PET retention values in indi-
vidual presymptomatic mutation carriers (Supplementary Table S2). e data demonstrated that PIB
retention increases, DED binding decreases, and FDG uptake decreases close to and aer the expected
age of symptom onset. Increased DED binding was detectable at the earliest measured time point, nearly
three decades before expected symptom onset. e pattern of changes for each PET tracer was not
homogeneous for each individual.
Discussion
e rapid development of molecular imaging has provided powerful tools for the detection of AD pathol-
ogy at an early stage of the disease. ese tools, in combination with the currently available biomarkers,
provide an unprecedented opportunity to dene a therapeutic window for use in the development of
potential preventive/disease-modifying therapies. For example, high retention of specic Aβ PET tracers
such as Pittsburgh compound-B in patients with mild cognitive impairment seems to predict a high risk
of developing Alzheimer’s disease18,20, at a stage of the disease when cerebral glucose metabolism is less
impaired. e new research diagnostic criteria for Alzheimer’s disease suggested by the International
Working Group and the US National Institute on Aging–Alzheimer’s Association take into account the
recent improved accessibility of CSF and imaging biomarkers21–24. Amyloid PET has been dened as
a diagnostic marker that reects in vivo pathology, while cerebral glucose hypometabolism is a down-
stream marker that monitors the course of neurodegeneration23. Studies in presymptomatic AD patients
are important for further insight into the time course of these pathophysiological processes.
e amyloid cascade hypothesis, the focus of AD research for decades1, has received increasing scru-
tiny due to the recent unsuccessful treatment trials based on this line of thought25, and several other
mechanisms including neuroinammation have been suggested as promising alternative therapeutic tar-
gets26. Astrocyte activation has received increased attention due to its role in neuroinammation8,27,28.
Figure 5. Progression of PET biomarkers in ADAD mutation carriers. Statistical parametric mapping
(SPM) results are displayed for ADAD mutation carriers (n = 7). Each pair of columns represents an
individual mutation carrier compared to ve age-matched non-carriers. Each pair of rows represents a
dierent PET biomarker. e scale from le to right represents the approximate time (in years) to the
expected onset of clinical symptoms. All SPM clusters shown are signicant at p < 0.001 (uncorrected).
e gure demonstrates the progressive and heterogeneous nature of Alzheimer’s disease pathology as
the expected onset of clinical symptoms approaches; neocortical brillar Aβ increases (PIB retention, top
two rows), astrocytosis decreases (DED binding, middle two rows), glucose metabolism decreases (FDG
hypometabolism shown, bottom two rows).

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However, astrocytosis is not completely understood; reactive astrocytes appear to have either neuropro-
tective or neurodegenerative eects at dierent stages of the evolution of the disease8,29.
e chronological order and relative causality of astrocyte activation, brillar Aβ deposition and
glucose hypometabolism, have not been established, yet. Interestingly, it has been shown in cultured rat
astrocytes that Aβ induces MAO-B expression30, which indicates a link between astrocyte activation and
amyloid pathology. Astrocyte activation is a complex, regionally and temporally dependent phenome-
non31, and MAO-B expression in reactive astrocytes diers across brain regions16. In addition, dierent
markers of astrocytosis could detect dierent subpopulations of astrocytes and/or dierent stages in the
disease. Reactive astrocytes have been measured in postmortem AD brain tissue using 3H-DED autoradi-
ography and glial brillary acidic protein (GFAP) immunohistochemistry31–33. A strong regional correla-
tion was observed between postmortem GFAP reactive astrocytes and both in vivo 11C-PIB and in vitro
3H-PIB binding. However, no correlation was found between postmortem 3H-DED and in vivo 11C-PIB33.
ere was no correlation either between 3H-DED reactive astrocytes and brillar Aβ in AD autopsy
brains, using regional and laminar distribution analyses31. Evidence that neurodegenerative processes
might not be solely dependent on Aβ plaque pathology is also based on in vivo PET imaging ndings
such as a lack of correlation between plaque deposition, metabolic dysfunction, atrophy, and clinical
outcome (e.g. cognitive dysfunction) in AD patients34.
While there are several in vitro studies on reactive glia, less is known about their function in vivo.
PET imaging of astrocytosis in mild cognitive impairment and sAD patients using 11C-DED has demon-
strated elevated astrocytosis in MCI PIB+ patients (prodromal AD), suggesting that astrocytosis is an
early event in sAD patients17. We have also recently reported that increased 11C-DED binding correlated
with decreased gray matter density in the parahippocampus of MCI PIB+ patients35. However, no in vivo
studies using 11C-DED in ADAD have been reported to date. Moreover, in vivo studies on astrocytosis
are required to investigate the dierent roles of the reactive astrocytes across dierent disease stages,
especially with regard to neuroprotection vs. neurotoxicity. Furthermore, microglia and astrocytes show
dierential relationships to Aβ pathology and seem to play dierent roles in inammation in AD14,36. In
vivo PET studies using tracers for the translocator protein (TSPO) as a marker for microglial activation,
showed discordant results with both increased tracer uptake in AD patients as compared to healthy con-
trols and no dierence between patients and controls37–40. is discrepancy can partly be explained by a
polymorphism of TSPO41. e time course and interrelationship of microglial and astrocytic involvement
in AD-related inammation are yet to be elucidated.
In this study, the nding of early astrocytosis roughly coinciding temporally but not necessarily spa-
tially with brillar Aβ deposition is consistent with evidence from postmortem and in vivo studies sug-
gesting a general lack of correlation between reactive astrocytes and brillar Aβ . e observed early
astrocytosis might have been caused by Aβ oligomers or other pathological features such as intraneu-
ronal hyperphosphorylated tau protein and adds support to the idea of glial activation as independent
of other pathological substrates in AD.
e nding of early astrocytosis in the AD continuum is strengthened by recent ndings of YKL-40, a
potential astrocyte-derived biomarker measured in CSF, being strongly related to Aβ in AD patients and
with tau pathology as a marker for neurodegeneration, most pronounced at early pre-dementia stages
of AD42–45. Increased levels of CSF YKL-40 were furthermore related to cognitive decline and cortical
thinning43,46.
We also compared cases of ADAD and sAD in this study, since a common neuropathological path-
way has been suggested47, although the onset of symptoms occurs earlier and disease severity is greater
in familial cases48. In recent cross-sectional49 and longitudinal50 ADAD studies, PIB, FDG, and atrophy
were evaluated and compared in order to investigate regional and temporal dierences in biomarker
levels, in relation to the estimated years to symptom onset (EYO). PIB retention was increased in almost
every cortical region, starting earlier than 15 EYO, in ADAD patients. Reduced FDG and cortical thin-
ning were detected especially in the parietal and posterior cingulate cortex (PCC)/precuneus regions
from 10 to 5 EYO. In our study in ADAD, MCI patients and sAD patients, we included assessment of
11C-DED binding in addition to PIB and FDG examinations. e PCA model showed a clear separation
pattern between the dierent groups using the three dierent PET tracers in 24 brain regions allowed.
Interestingly, the direction of the measured signal for the PET biomarkers PIB and FDG (which indicated
worsening of amyloid deposition and glucose hypometabolism with time) diered from that for DED
(which indicated decreased astrocytosis). is observation clearly reveals an opposite direction in time
course between PIB and DED although these PET tracers show similar early presymptomatic presence
but change dierently in disease progression.
e highest level of DED binding was observed in presymptomatic ADAD mutation carriers, while
DED binding was low in symptomatic ADAD mutation carriers. Interestingly, earlier studies in healthy
subjects have reported an age-related increase in MAO-B activity in healthy human brains, as investi-
gated in vitro at autopsy16 and in vivo using PET DED imaging51.
e increase in PIB retention occurred early in presymptomatic ADAD carriers, predominantly in the
anterior and posterior cinguli and the basal ganglia. is pattern of early changes in the subcortical brain
regions is in agreement with recent ADAD studies50, where high PIB retention was detected in regions
such as the caudate nucleus and the pallidum, in the absence of atrophy. An early hypermetabolic phase
25 years before estimated symptom onset was detected in the precuneus and PCC, based on a linear

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model of FDG vs. EYO50. Furthermore, previous studies in ADAD patients have shown thalamic hypo-
metabolism 20 years before estimated symptom onset7 and atrophy on average 6 years before symptom
onset52 in asymptomatic PSEN1 mutation carriers, suggesting that the thalamus may also be involved in
early disease mechanisms in ADAD patients. Subtle cognitive dysfunction in ADAD patients has been
detected relatively close to the onset of symptoms (from about ve years EYO)53. However, our study
observed that performance can be quite variable in subjects from ve years before symptom onset, with
some subjects showing greater impairment and others remaining cognitively normal.
To conclude, we have demonstrated that measures of astrocytosis in ADAD mutation carriers can be
observed decades before symptom onset, possibly coinciding with early brillar Aβ plaque deposition,
both of which are followed later on by impaired glucose metabolism. Multivariate analysis of the PET
results clearly separated the subject groups in a dierent order for the PIB/FDG tracers compared to the
DED tracer. e analysis also suggested that DED might not yield equally high levels of sensitivity or
specicity as PIB/FDG. It appears, however, that astrocytosis is initiated very early on, possibly before
or at a similarly early stage as brillar Aβ deposition; this supports the notion that non-brillar forms
of amyloid might cause inammatory responses but also supports the possibility that astrocytosis is an
important early contributory driving force in AD pathology. e novelty and importance of these nd-
ings will be conrmed in ongoing longitudinal studies.
Methods
Participants. Forty-four participants were recruited from the Department of Geriatric Medicine,
Karolinska University Hospital Huddinge, Stockholm, Sweden; four were excluded for missing data and
one PSEN1 carrier was excluded for incomplete penetrance of the mutation. Eleven MCI patients, seven
sAD patients, and 21 ADAD family members were eligible for inclusion (demographic details are pro-
vided in Table1).
e ADAD families are part of an ongoing longitudinal clinical study at the Karolinska Institute and
were recruited without reference to their mutation status. e family members are regularly followed up
clinically and examined using neuropsychological assessment, MRI, collection of CSF, and blood tests54.
In this study, we included mutation carriers and non-carriers from families harbouring the Arctic APP
(APParc), Swedish APP (APPswe), and presenilin 1 (PSEN1) (p.H163Y) mutations. Among the 21 family
members, nine were carrying an ADAD mutation: two carried the APParc, two the APPswe, and ve the
PSEN1 mutations. e presence of the mutations in the subjects was conrmed by sequencing.
All participants underwent complete clinical examination, DED- , PIB- , and FDG-PET, MRI, and
neuropsychological testing. e PIB PET data from the two APParc mutation carriers were not included
in the analyses because, according to our previously published results, symptomatic carriers of this muta-
tion do not show PIB PET retention, while exhibiting other clinical and biomarker features comparable
with other mutation carriers55. e sAD subjects and one mutation carrier fullled the criteria for AD
as outlined by the NINCDS-ADRDA56. e MCI patients and one symptomatic mutation carrier ful-
lled the criteria for MCI as outlined by Petersen57. All subjects provided written informed consent to
participate in the study, which was conducted according to the Declaration of Helsinki and subsequent
revisions and was approved by the Regional Human Ethics Committee of Stockholm and the Isotope
Committee of Uppsala University, Sweden.
Neuropsychological evaluation. All participants underwent routine clinical neuropsychological
testing, which involved tests for global cognitive function, language, visuospatial function, episodic
memory, attention, and executive ability, typically within six months of the PET examinations. Test
scores were converted into z-scores in comparison with a reference group of healthy elderly people from
the Karolinska University Hospital Huddinge58, while controlling for demographic conditions. Table1
presents the composite z-scores for global cognition (full-scale intelligence quotient; FSIQ) and epi-
sodic memory performance (average of three scores: Rey auditory verbal learning test, total learning
and delayed retention test, and Rey Osterrieth retention test), while Table2 provides detailed individual
test results.
PET image acquisition and processing. e PET investigations were performed at Uppsala PET
center on ECAT EXACT HR+ (Siemens/CTI) and GE discovery ST PET/CT (GE Healthcare) scanners.
e PET scans for all three radiotracers were commonly performed in the order PIB, DED, and FDG in
each subject on the same day with 2–3 hours between tracer injections. For a few subjects, and due to
tracer synthesis failures, two scans were within four weeks of the others. e orbito-meatal line was used
to center the heads of the participants. e emission scans for the DED investigation consisted of 19 time
frames (4 × 30 s, 8 × 60 s, 4 × 300 s and 3 × 600 s) with a total duration of 60 min; the emission scans for
the PIB investigations consisted of 24 frames (4 × 30, 9 × 60, 3 × 180 and 8 × 300 s) over 60 min. A late
40–60 min PIB sum image was created and used for subsequent image analysis. For each FDG emission
scan, seven frames (1 × 60 s, 1 × 1140 s, 5 × 300 s) were acquired over 45 min. A late 30–45 min FDG
sum image was created and used for subsequent analysis. Patients were required to fast for 4 h preceding
the FDG scan, which was performed in a quiet room with dimmed light and eyes closed. e mean
injected doses for each tracer were DED: 209 ± 57 MBq, PIB: 217 ± 74 MBq, and FDG: 232 ± 45 MBq.
All emission data were acquired in 3D mode and reconstructed with ltered back-projection using a

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4 mm Hanning lter, resulting in a transaxial spatial resolution of 5 mm in the eld of view. e matrix
included 128 × 128 pixels, and a 2.5 zoom factor was used. e reconstructed frames were re-aligned to
correct for patient motion during each PET scan. e PET protocol followed the protocol established in
our previous publication17.
MRI image acquisition. All patients and ADAD family members underwent structural T1 MPRAGE
MRI scanning using a 3T (Siemens Trio) scanner at the Karolinska University Hospital Huddinge,
Stockholm, on average within ve months of the PET examinations.
Region of interest PET image analysis. All images were processed and analysed using a probabilis-
tic atlas approach, as described previously17. In short, all image analyses were performed in the space of
the DED PET images to preserve the delity of these PET data. First, all DED data from 10–60 min for
each participant was summed to create a DED PET sum image in native space. e individual T1 MR
images were co-registered and re-sliced to their corresponding DED sum image (using SPM8; Functional
Imaging Laboratory, Wellcome Department of Imaging Neuroscience, University College London). is
step created a T1-weighted MR reference image for each participant in DED PET space. Subsequently,
each patient’s PIB and FDG images were co-registered and re-sliced to their individual T1 MR reference
image.
All T1 MR reference images were then segmented into gray and white matter tissue classes using
SPM8 59. e resultant probabilistic gray matter map was thresholded at 0.5 to create a binary gray matter
mask. An inverse non-linear transform parameter le was generated as part of the segmentation algo-
rithm. e inverse parameter le allowed data in the MNI (Montreal Neurological Institute) space to be
transformed back into a native DED PET image space. e inverse parameter le from each participant
was used to transform a simplied digital probabilistic atlas60, consisting of 24 cortical and subcortical
regions, into native DED PET space. ese atlases were multiplied by the corresponding binary gray
matter mask, which generated a specic gray matter digital atlas for each participant. is step resulted
in a single digital atlas for each participant that could be easily applied to analyse all three sets of PET
data without additional manipulation.
Raw co-registered and re-sliced FDG (Bq/ml) and PIB (Bq/ml) PET data for each patient were sam-
pled using the same individual digital atlases. Using this method, mean FDG uptake and PIB retention
values were measured for each atlas region, as described previously17. Regional gray matter ratio values
were created for FDG and PIB by dividing by the respective mean uptake in the pons.
11C-deuterium-L-deprenyl PET data modelling. e PET data for each participant were analysed
using the individual brain atlases generated in the steps described above. Regional parametric data were
generated from dynamic DED data from 20 to 60 minutes. No arterial blood samples were available as
an input function for the DED modelling because of the clinical character of the study. Instead, a mod-
ied Patlak reference tissue model was used for kinetic analysis, according to earlier study methods17,61.
Cerebellar gray matter from the individual atlases was used as the reference region in this model. Because
net tracer accumulation also occurred in the cerebellum, the model was modied by correcting k3 in the
reference region for irreversible binding with a xed correction factor of 0.01, which was the minimum
value for correction still leading to linearization in the model. is graphical reference Patlak model
resulted in two measurements: the intercept (initial tracer distribution volume) and the slope (kI = net
DED binding to MAO-B). Since our main interest was to evaluate DED binding we used the slope value
in subsequent analyses.
Principal component analysis. Principal component analysis (PCA)62 is a multivariate method
implemented in the soware package SIMCA-P+ ; Umetrics AB, Umea, Sweden19. It is an unsupervised
method meaning it does not use a priori information about groups for the analysis. Statistically, PCA
reduces the dimensionality and complexity of the data by nding lines and planes in the n-dimensional
space (n = number of variables in the model) that approximates the data in the best way possible in
the least squares sense. is gives us the opportunity to get an overview of the data to observe group
belonging, trends and outliers. It is also possible to view relationships between the observations and
the variables. One of the advantages of multivariate methods like PCA is that it can handle many more
variables than observations19.
e PCA model was created to include all individuals: sAD, MCI PIB+ patients, MCI PIB− patients,
ADAD mutation non-carriers, presymptomatic ADAD mutation carriers, and symptomatic ADAD
mutation carriers. In total 72 variables (24 regions for PIB retention, FDG uptake, and DED binding
slope) were included for each subject in the PCA analysis. e pre-processing steps mean-centering and
unit variance scaling were performed. Mean-centering improves the interpretability of the data by sub-
tracting the variable average, which repositions the data set around the origin. Large variance variables
are more likely to be expressed in modelling than low variance variables. Consequently, unit variance
scaling was selected to scale the data appropriately. is scaling method calculates the standard deviation
of each variable. e inverse standard deviation is used as a scaling weight for each PET measurement.
e results from the PCA were visualized by plotting the rst two components in a scatter plot. Each
point in the scatter plot represents one individual subject. Loading plots were also created to illustrate

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how the PET variables included in the model inuenced the observed pattern in the scatter plot along
each component (only the 25 most important variables are shown).
Region of interest statistical analyses. For each PET tracer, regional mean uptake values were
compared between groups using two-tailed non-parametric Kruskal-Wallis tests, followed by post-hoc
Mann-Whitney U tests in SPSS soware. Non-parametric tests were applied due to the small sample sizes.
e groups with n ≥ 5 being compared were: presymptomatic ADAD mutation carriers, MCI PIB+ , sp o-
radic AD (sAD) and ADAD mutation non-carriers. e comparisons were performed for 11 bilateral
ROIs: frontal, parietal, temporal, occipital, anterior and posterior cingulate cortices, caudate nucleus,
putamen, thalamus, hippocampus and cerebellum. Signicance level was set at p < 0.05. Size eects (r)
of Mann-Whitney comparisons were calculated using r = z/(√N), where z is the Mann-Whitney z and N
the sum of individuals from the two groups being compared. Due to the small sample sizes of the MCI
PIB− and symptomatic ADAD mutation carrier groups, individual z-score values were obtained instead
with reference to the ADAD non-carrier group; z-score values were considered abnormal at |z| > 1.645.
Statistical parametric modeling analysis. e PET data (PIB, FDG, DED) from each participant,
which had been realigned to the DED image in native space, were spatially normalized using non-linear
transformation from the segmentation of the T1 MRI data. is resulted in PIB and FDG ratio (/pons)
images and DED slope (binding; min−1) images in standard MNI space for each participant. Aer spatial
normalization, each individual PET image from each ADAD mutation carrier was compared separately,
using a two-sample t-test (SPM), with images from a group of ve non-carriers (controls) who were the
most proximal in age to the mutation carrier investigated. An explicit binary gray matter mask was used
so that only voxels within the mask were compared in the SPM analysis. All SPM results for each tracer
were analysed at a p value of < 0.001 (uncorrected for multiple comparisons).
References
1. Hardy, J. A. & Higgins, G. A. Alzheimer’s disease: the amyloid cascade hypothesis. Science 256, 184–185 (1992).
2. Nordberg, A. Molecular imaging in Alzheimer’s disease: new perspectives on biomarers for early diagnosis and drug
development. Alzheimers es er 3, 34 (2011).
3. Chetelat, G. Alzheimer disease: Abeta-independent processes-rethining preclinical AD. Nat ev Neurol 9, 123–124 (2013).
4. Jac, C. . Jr. et al. Tracing pathophysiological processes in Alzheimer’s disease: an updated hypothetical model of dynamic
biomarers. Lancet Neurol. 12, 207–216 (2013).
5. Jac, C. . Jr. et al. Hypothetical model of dynamic biomarers of the Alzheimer’s pathological cascade. Lancet Neurol. 9, 119–128
(2010).
6. Beris, L. M., Yu, C. E., Bird, T. D. & Tsuang, D. W. Genetics of Alzheimer disease. J Geriatr Psychiatry Neurol 23, 213–227
(2010).
7. Schöll, M. et al. Glucose metabolism and PIB binding in carriers of a His163Tyr presenilin 1 mutation. Neurobiol Aging 32,
1388–1399 (2011).
8. Verhratsy, A., Olabarria, M., Noristani, H. N., Yeh, C. Y. & odriguez, J. J. Astrocytes in Alzheimer’s disease. Neurotherapeutics
7, 399–412 (2010).
9. Heppner, F. L., ansoho, . M. & Becher, B. Immune attac: the role of inammation in Alzheimer disease. Nat ev Neurosci
16, 358–372 (2015).
10. Henea, M. T., Golenboc, D. T. & Latz, E. Innate immunity in Alzheimer’s disease. Nat Immunol 16, 229–236 (2015).
11. McGeer, P. L. & McGeer, E. G. e inammatory response system of brain: implications for therapy of Alzheimer and other
neurodegenerative diseases. Brain es Brain es ev 21, 195–218 (1995).
12. McGeer, E. G. & McGeer, P. L. Neuroinammation in Alzheimer’s disease and mild cognitive impairment: a eld in its infancy.
J Alzheimers Dis 19, 355–361 (2010).
13. Burda, J. E. & Sofroniew, M. V. eactive gliosis and the multicellular response to CNS damage and disease. Neuron 81, 229–248
(2014).
14. Serrano-Pozo, A. et al. Dierential relationships of reactive astrocytes and microglia to brillar amyloid deposits in Alzheimer
disease. J Neuropath Exp Neur 72, 462–471 (2013).
15. Fowler, J. S., Logan, J., Volow, N. D. & Wang, G. J. Translational neuroimaging: positron emission tomography studies of
monoamine oxidase. Mol Imaging Biol 7, 377–387 (2005).
16. Tong, J. et al. Distribution of monoamine oxidase proteins in human brain: implications for brain imaging studies. J Cereb Blood
Flow Me tab 33 (2013).
17. Carter, S. F. et al. Evidence for astrocytosis in prodromal Alzheimer disease provided by 11C-deuterium-L-deprenyl: a multitracer
PET paradigm combining 11C-Pittsburgh compound B and 18F-FDG. J Nucl Med 53, 37–46 (2012).
18. Nordberg, A. et al. A European multicentre PET study of brillar amyloid in Alzheimer’s disease. Eur J Nucl Med Mol I 40 (2013).
19. Erisson, L. et al. Multi- and Megavariate Data Analysis (Part I -Basics and Principals and Applications). 2nd edn, 95–98 (Umetrics
AB, 2006).
20. Forsberg, A. et al. PET imaging of amyloid deposition in patients with mild cognitive impairment. Neurobiol Aging 29, 1456–1465
(2008).
21. Dubois, B. et al. evising the denition of Alzheimer’s disease: a new lexicon. Lancet Neurol. 9, 1118–1127 (2010).
22. Dubois, B. et al. esearch criteria for the diagnosis of Alzheimer’s disease: revising the NINCDS-ADDA criteria. Lancet Neurol
6, 734–746 (2007).
23. Dubois, B. et al. Advancing research diagnostic criteria for Alzheimer’s disease: the IWG-2 criteria. Lancet Neurol 13, 614–629
(2014).
24. Mchann, G. M. et al. e diagnosis of dementia due to Alzheimer’s disease: recommendations from the National Institute on
Aging-Alzheimer’s Association worgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement 7 (2011).
25. Marchesi, V. T. Alzheimer’s disease 2012: the great amyloid gamble. Am J Pathol 180, 1762–1767 (2012).
26. Pimpliar, S. W. Neuroinammation in Alzheimer’s disease: from pathogenesis to a therapeutic target. J Clin Immunol 34 Suppl
1, S64–69 (2014).

www.nature.com/scientificreports/
13
Scientific RepoRts | 5:16404 | DOI: 10.1038/srep16404
27. Eielenboom, P. et al. Neuroinammation - an early event in both the history and pathogenesis of Alzheimer’s disease.
Neurodegener Dis 7, 38–41 (2010).
28. Steele, M. L. & obinson, S. . eactive astrocytes give neurons less support: implications for Alzheimer’s disease. Neurobiol
Aging 33, 423 e421-413 (2012).
29. Verhratsy, A., Marutle, A., odriguez-Arellano, J. J. & Nordberg, A. Glial Asthenia and Functional Paralysis: A New Perspective
on Neurodegeneration and Alzheimer’s Disease. Neuroscientist (2014).
30. Song, W., Zhou, L. J., Zheng, S. X. & Zhu, X. Z. Amyloid-beta 25-35 peptide induces expression of monoamine oxidase B in
cultured rat astrocytes. Acta Pharmacol Sin 21, 557–563 (2000).
31. Marutle, A. et al. 3H-Deprenyl and 3H-PIB autoradiography show dierent laminar distributions of astroglia and brillar beta-
amyloid in Alzheimer brain. J Neuroinammation 10, 90 (2013).
32. Jossan, S. S. et al. Quantitative localization of human brain monoamine oxidase B by large section autoradiography using L-[3H]
deprenyl. Brain es 547, 69–76 (1991).
33. adir, A. et al. Positron emission tomography imaging and clinical progression in relation to molecular pathology in the rst
Pittsburgh Compound B positron emission tomography patient with Alzheimer’s disease. Brain 134, 301–317 (2011).
34. La Joie, . et al. egion-specic hierarchy between atrophy, hypometabolism, and beta-amyloid (Abeta) load in Alzheimer’s
disease dementia. J Neurosci 32, 16265–16273 (2012).
35. Choo, I. H., Carter, S. F., Schöll, M. L. & Nordberg, A. Astrocytosis measured by (11)C-deprenyl PET correlates with decrease
in gray matter density in the parahippocampus of prodromal Alzheimer’s patients. Eur J Nucl Med Mol I 41, 2120–2126 (2014).
36. von Bernhardi, ., Eugenin-von Bernhardi, L. & Eugenin, J. Microglial cell dysregulation in brain aging and neurodegeneration.
Front Aging Neurosci 7, 124 (2015).
37. reisl, W. C. et al. In v ivo radioligand binding to translocator protein correlates with severity of Alzheimer’s disease. Brain 136,
2228–2238 (2013).
38. Varrone, A. et al. In vivo imaging of the 18-Da translocator protein (TSPO) with [18F]FEDAA1106 and PET does not show
increased binding in Alzheimer’s disease patients. Eur J Nucl Med Mol I 40, 921–931 (2013).
39. Varley, J., Broos, D. J. & Edison, P. Imaging neuroinammation in Alzheimer’s and other dementias: ecent advances and future
directions. Alzheimers Dement 11, 1110–20 (2014).
40. Golla, S. S. et al. Quantication of [18F]DPA-714 binding in the human brain: initial studies in healthy controls and Alzheimer’s
disease patients. J Cereb Blood Flow Metab 35, 766–772 (2015).
41. Owen, D. . et al. An 18-Da translocator protein (TSPO) polymorphism explains dierences in binding anity of the PET
radioligand PB28. J Cereb Blood Flow Metab 32 (2012).
42. ousseau, A. et al. Expression of oligodendroglial and astrocytic lineage marers in diuse gliomas: use of YL-40, ApoE,
ASCL1, and NX2-2. J Neuropath Exp Neur 65, 1149–1156 (2006).
43. Craig-Schapiro, . et al. YL-40: a novel prognostic uid biomarer for preclinical Alzheimer’s disease. Biol Psychiatry 68,
903–912 (2010).
44. Antonell, A. et al. Cerebrospinal uid level of YL-40 protein in preclinical and prodromal Alzheimer’s disease. J Alzheimers Dis
42, 901–908 (2014).
45. osen, C. et al. Increased Levels of Chitotriosidase and YL-40 in Cerebrospinal Fluid from Patients with Alzheimer’s Disease.
Dement Geriatr Cogn Dis Extra 4, 297–304 (2014).
46. Alcolea, D. et al. elationship between cortical thicness and cerebrospinal uid YL-40 in predementia stages of Alzheimer’s
disease. Neurobiol Aging 36, 2018–2023 (2015).
47. Bateman, . J. et al. Autosomal-dominant Alzheimer’s disease: a review and proposal for the prevention of Alzheimer’s disease.
Alzheimers es er 3, 1 (2011).
48. Lippa, C. F. et al. Familial and sporadic Alzheimer’s disease: neuropathology cannot exclude a nal common pathway. Neurology
46, 406–412 (1996).
49. Bateman, . J. et al. Clinical and biomarer changes in dominantly inherited Alzheimer’s disease. N Engl J Med 367, 795–804
(2012).
50. Benzinger, T. L. et al. egional variability of imaging biomarers in autosomal dominant Alzheimer’s disease. P Natl Acad Sci
USA 110, E4502–4509 (2013).
51. Fowler, J. S. et al. Age-related increases in brain monoamine oxidase B in living healthy human subjects. Neurobiol Aging 18,
431–435 (1997).
52. yan, N. S. & Fox, N. C. eply: Implications of presymptomatic change in thalamus and caudate in Alzheimer’s disease. Brain
136, e259 (2013).
53. Storandt, M., Balota, D. A., Aschenbrenner, A. J. & Morris, J. C. Clinical and psychological characteristics of the initial cohort
of the Dominantly Inherited Alzheimer Networ (DIAN). Neuropsychology 28, 19–29 (2014).
54. ordardottir, S. et al. Preclinical Cerebrospinal Fluid and Volumetric Magnetic esonance Imaging Biomarers in Swedish
Familial Alzheimer’s Disease. J Alzheimers Dis 43, 1393–402 (2014).
55. Schöll, M. et al. Low PiB PET retention in presence of pathologic CSF biomarers in Arctic APP mutation carriers. Neurology
79, 229–236 (2012).
56. Mchann, G. et al. Clinical diagnosis of Alzheimer’s disease: report of the NINCDS-ADDA Wor Group under the auspices
of Department of Health and Human Services Tas Force on Alzheimer’s Disease. Neurology 34, 939–944 (1984).
57. Petersen, . C. Mild cognitive impairment as a diagnostic entity. J Intern Med 256, 183–194 (2004).
58. Bergman, I., Blomberg, M. & Almvist, O. e importance of impaired physical health and age in normal cognitive aging. Scand
J Psychol 48, 115–125 (2007).
59. Ashburner, J. & Friston, . J. Unied segmentation. Neuroimage 26, 839–851 (2005).
60. Hammers, A. et al. ree-dimensional maximum probability atlas of the human brain, with particular reference to the temporal
lobe. Hum Brain Mapp 19, 224–247 (2003).
61. Johansson, A. et al. Evidence for astrocytosis in ALS demonstrated by [11C](L)-deprenyl-D2 PET. J Neurol Sci 255, 17–22 (2007).
62. Pearson, . On Lines and Planes of Closest Fit to Systems of Points in Space. Philos Mag A 2, 559–572 (1901).
Acknowledgements
We would like to express our gratitude to all participants who have made this study possible. Dr. Anne
Kinhult Ståhlbom is acknowledged for professional help and Mr. Johan Lilja is acknowledged for support
related to the imaging soware VOIager. Funding: is work was supported by grants from the Knut
and Alice Wallenberg foundation, GE Healthcare (unrestricted grant), the Swedish Research Council
(projects 05817, 521-2010-3134), the Regional Agreement on Medical Training and Clinical Research
(ALF) between Stockholm County Council and Karolinska Institutet, the Strategic Research Program in
Neuroscience at Karolinska Institutet, Karolinska Institutet’s Doctoral Funding, the Swedish Foundation

www.nature.com/scientificreports/
14
Scientific RepoRts | 5:16404 | DOI: 10.1038/srep16404
for Strategic Research (SFF), the Swedish Brain Power network, the Old Servants foundation, Gun and
Bertil Stohne’s foundation, the Alzheimer Foundation in Sweden, the King Gustaf V and Queen Victoria’s
Foundation of Freemasons, and e Brain Foundation.
Author Contributions
A.N., M.S., B.L. and C.G. designed and planned, and A.N. coordinated the study. S.T., C.G. and A.N.
conducted clinical investigations. A.W. performed the PET scans. O.A. distributed neuropsychological
testing. M.S., S.C., E.W., A.W. and E.R.-V. collected and analysed all data. First manuscript was draed
by M.S. and A.N., M.S., S.C., E.W., E.R.-V., O.A., S.T., A.W., C.G., B.L. and A.N. revised the manuscript.
Additional Information
Supplementary information accompanies this paper at http://www.nature.com/srep
Competing nancial interests: e authors declare no competing nancial interests.
How to cite this article: Schöll, M. et al. Early astrocytosis in autosomal dominant Alzheimer's disease
measured in vivo by multi-tracer positron emission tomography. Sci. Rep. 5, 16404; doi: 10.1038/
srep16404 (2015).
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