In-Vivo Imaging of Grey and White Matter Neuroinflammation

ORIGINAL ARTICLE In-vivo imaging of grey and white matter neuroinﬂammation in Alzheimer’s disease: a positron emission tomography study with a novel radioligand, [18F]-FEPPA

I Suridjan1,2, BG Pollock2,3,4, NPLG Verhoeff2,4,5, AN Voineskos1,2,3,4, T Chow1,2,4,5,6, PM Rusjan1, NJ Lobaugh1,6, S Houle1,4, BH Mulsant3,4 and R Mizrahi1,2,4

Our primary aim was to compare neuroinflammation in cognitively intact control subjects and patients with Alzheimer’s disease (AD) by using positron emission tomography (PET) with translocator protein 18kDa (TSPO)-specific radioligand [18F]-FEPPA. [18F]-FEPPA PET scans were acquired on a high-resolution research tomograph in 21 patients with AD (47– 81 years) and 21 control subjects (49–82 years). They were analyzed by using a 2-tissue compartment model with arterial plasma input function. Differences in neuroinflammation, indexed as [18F]-FEPPA binding were compared, adjusting for differences in binding affinity class as determined by a single polymorphism in the TSPO gene (rs6971). In grey matter areas, [18F]-FEPPA was significantly higher in AD compared with healthy control subjects. Large increases were seen in the hippocampus, prefrontal, temporal, parietal and occipital cortex (average Cohen’s d = 0.89). Voxel-based analyses confirmed significant clusters of neuroinflammation in the frontal, temporal and parietal cortex in patients with AD. In white matter, [18F]-FEPPA binding was elevated in the posterior limb of the internal capsule, and the cingulum bundle. Higher neuroinflammation in the parietal cortex (r = − 0.7, P = 0.005), and posterior limb of the internal capsule (r = − 0.8, P = 0.001) was associated with poorer visuospatial function. In addition, a higher [18F]-FEPPA binding in the posterior limb of the internal capsule was associated with a greater impairment in language ability (r = − 0.7, P = 0.004). Elevated neuroinflammation can be detected in AD patients throughout the brain grey and white matter by using [18F]-FEPPA PET. Our results also suggest that neuroinflammation is associated with some cognitive deficits.

Molecular Psychiatry (2015) 20, 1579–1587; doi:10.1038/mp.2015.1; published online 24 February 2015

INTRODUCTION classifies individuals into high-affinity binders (HAB), mixed- 20,21 Neuroinflammation is an important feature of Alzheimer’s disease affinity binders (MAB) and low-affinity binders (LAB). (AD) pathology.1 Post-mortem examinations of AD brains show A meta-analysis of diffusion tensor imaging (DTI) studies 2–4 reported extensive white matter microstructural abnormalities in clusters of activated microglia surrounding amyloid plaques. 22 Microglia are endogenous key players of the brain’s immune AD. Some of the areas affected include the corpus callosum, fi 5 superior longitudinal fasciculus, cingulum bundle and internal system, providing a rst line of defence against neuronal insults. 22–27 Microglia activation is characterized by an increased expression of capsule. These white matter abnormalities might be related 6 to changes in myelin density, microglia activity levels and translocator protein 18kDa (TSPO). Post-mortem studies of AD – increases in reactive gliosis.28 30 Although an elevated TSPO brains have shown significant elevated TSPO expression in the 7,8 density was detected in the post-mortem white matter tissue of hippocampus, frontal, temporal and parietal cortex. 7 AD patients, TSPO expression in specific white matter regions Neuroinflammation can be quantified in vivo with positron has never been evaluated in vivo. It is also unknown whether emission tomography (PET) by using radioligands that target an increase in neuroinflammation in these white matter regions TSPO.6 Previous TSPO PET studies of AD using the prototypical 11 9–13 might be related to a cognitive impairment in AD. Finally, an TSPO radioligand, [ C]-PK11195, had mixed results. This increase in neuroinflammation in the cortical grey matter regions discrepancy might be because of the known technical limitations 11 has been correlated with a poorer cognitive performance in some of [ C]-PK11195. These limitations have fostered the develop- studies,10,31,32 but not in others.33 ment of second-generation TSPO radioligands with superior In this context, we conducted a study with the primary aim of 14 quality to quantify TSPO expression in vivo. Among these comparing neuroinflammation in cognitively intact control sub- 18 are [ F]-FEPPA, which has a high affinity for TSPO, good metabolic jects and patients with AD by using [18F]-FEPPA PET, adjusting for profile, high brain penetration and good pharmacokinetics.15,16 differences in binding affinity class in both grey and white matter A single polymorphism in the TSPO gene (rs6971) causes variation regions. We hypothesized AD patients would show increased in TSPO binding affinity in all second-generation TSPO radio- neuroinflammation, as indexed by [18F]-FEPPA binding. Finally, we ligands ([11C]PBR28,17 [18F]FEPPA18 and [18F]PBR11119); and explored the relationship between [18F]-FEPPA binding

1Research Imaging Centre, Centre for Addiction and Mental Health, Toronto, ON, Canada; 2Institute of Medical Science, University of Toronto, Toronto, ON, Canada; 3Geriatric Psychiatry Division, Centre for Addiction and Mental Health, Toronto, ON, Canada; 4Department of Psychiatry, University of Toronto, Toronto, ON, Canada; 5Centre for Mental Health, Baycrest Health Sciences, Toronto, ON, Canada and 6Department of Medicine (Neurology), University of Toronto, Toronto, ON, Canada. Correspondence: Dr R Mizrahi, Research Imaging Centre, Centre for Addiction and Mental Health (CAMH), 250 College St, Toronto, ON M5T 1R8, Canada. E-mail: [email protected] Received 2 July 2014; revised 16 December 2014; accepted 19 December 2014; published online 24 February 2015 Neuroinflammation in Alzheimer’s disease I Suridjan et al 1580 throughout the grey and white matter regions and specific polymorphism and for obtaining the arterial input function used for the neuropsychological measures to assess the impact of neuro- kinetic analysis of [18F]FEPPA, as previously described.16 inflammation on cognitive function. Regions of Interest (ROI)-based PET image analysis MATERIALS AND METHODS The T1-weighted MRI scans were used for image co-registration with the PET image and for regions of interest (ROI) delineation. The differences in Participants MRI acquisition parameters (1.5 T versus 3 T) did not have a significant Patients with AD were recruited from the memory clinics of the Centre for effect on the [18F]-FEPPA outcome measure (data not shown). Addiction and Mental Health and the Sam and Ida Ross Memory Clinic at The ROIs were automatically delineated as previously described.47 Grey the Baycrest Geriatric Centre, Toronto, ON, Canada. Healthy control matter ROIs known to be affected in AD were examined, including the subjects were recruited from a local advertisement in the same geographic temporal, prefrontal, occipital, parietal cortex and the hippocampus. We area. Twenty-one healthy controls and 21 patients with AD completed all also explored the thalamus and cerebellum, which are typically affected study procedures. All participants underwent a medical and psychiatric only in the late stage of the disease.48 The definitions of these GM-ROIs assessment, including a urine drug screen. All participants with AD had a were based on a neuroanatomical atlas of structural MRI and post-mortem 49 diagnosis of probable AD according to the criteria defined by the tissue, as previously reported. The white matter ROIs (WM-ROIs) were guidelines of the National Institute of Neurological Disorders and Stroke defined according to the Johns Hopkins University DTI atlas in ICBM-152 50 and the AD and Related Disorders Association.34 Diagnoses of probable AD space (ICBM-DTI81). Four relatively large WM-ROIs were selected for were made based on the consensus of neurologists and geriatric examination: the corpus callosum, the cingulum bundle, the superior psychiatrists of the memory clinics (Centre for Addiction and Mental longitudinal fasciculus and the posterior limb of the internal capsule. 18 Health and Baycrest). The exclusion criteria for AD and healthy subjects The kinetics of [ F]-FEPPA were analyzed with a 2-tissue compartment 16,51 included: a current diagnosis of psychiatric illness, including diagnosis of model with total distribution volume (VT) as outcome measure by definitive axis I disorder; the presence of significant medical illness, using PMOD software (PMOD Technologies, Zurich, Switzerland). A partial including cancer, head trauma, stroke and other neurological disorders; volume error correction (PVEC) method was applied to the time activity 52 metal implants; history of claustrophobia; or any other conditions that data of all subjects by using the Mueller–Gartner algorithm as previously 53 would preclude PET or magnetic resonance imaging (MRI) imaging. The implemented. The results of the study are reported by using dynamic use of nonsteroidal anti-inflammatory drugs was not allowed, except for PET images with and without PVEC. 81 mg acetylsalicylic acid. Participants with AD could be treated with stable doses of acetylcholinesterase inhibitors, memantine and/or anti- Voxel-based PET image analysis depressants. Parametric images of [18F]-FEPPA V were generated by using the Logan The protocol was approved by the Research Ethic Boards of Centre for T graphical analysis method by applying a wavelet-based kinetic modeling Addiction and Mental Health and Baycrest Health Sciences. All subjects approach that increases the signal-to-noise ratio without significantly provided a written informed consent after all study procedures were fully 54 affecting the resolution. Differences between diagnostic groups were explained. For participants with AD, a written consent was also obtained ’ tested using the two-sample T-test in statistical parametric mapping from the subject s primary caregiver or substitute decision maker. (SPM8- http://www.fil.ion.ucl.ac.uk/spm/software/spm8). TSPO genotype and age were included as covariates. Significant clusters were thresholded Neuropsychological assessment at Po0.001. The mini-mental status examination was administered to both healthy subjects and participants with AD to evaluate overall cognitive DNA extraction and polymorphism genotyping performance.35 Participants with AD underwent neuropsychological tests fi Genomic DNA was obtained from peripheral leukocytes by using high salt- to assess impairments in more speci c cognitive domains. These include: extraction methods.55 The polymorphism rs6971 was genotyped as the Montreral Cognitive Assessment to evaluate general cognitive 18 36 37 38 39 described previously. Individuals with genotype Ala147/Ala147 were function; trail making test, Stroop test, letter number span and classified as HAB, Ala147/Thr147 as MAB and Thr147/Thr147 as LAB.56 verbal fluency to evaluate executive functions, including processing speed, mental flexibility and cognitive organization; and the repeatable battery for the assessment of neuropsychological status scale to evaluate memory, Statistical analysis visusospatial skills, attention and language ability.40 Some participants with Statistical analyses were performed using SPSS Statistics 17.0 (Chicago, IL, AD were not able to complete all tests and the number of subjects who USA). Demographic variables and [18F]-FEPPA injected parameters were completed each test is indicated in the relevant figures and tables. compared between the AD and healthy control groups using analysis of 18 Dementia severity was assessed by using the clinical dementia rating variance. Regional differences in [ F]-FEPPA VT between diagnostic groups scale.41 Functional disability was evaluated with the Disability Assessment were compared using factorial analysis of variance with TSPO genotype for Dementia.42 Duration of illness for the participants with AD was defined and age added as a covariate. The Cohen’s d for each GM- and WM-ROI as the length of time since the earliest conclusive dementia symptoms was calculated to evaluate the effect sizes. Relationships between [18F]- were noticed by the caregivers. Depressive and other neuropsychiatric FEPPA VT and clinical data were examined by using linear regression symptoms were assessed by using the Neuropsychiatric Inventory (NP1)43 analyses; both genotype and age were added as covariates. As the and the Cornell Scale for Depression in Dementia.44 analyses were performed with a number of cognitive measures, Bonferroni correction was applied (Po0.005). PET and MR image acquisition [18F]-FEPPA was synthesized as previously described.15 All subjects RESULTS 18 underwent one [ F]-FEPPA PET scan and one MRI scan. The MRI scans fi of 14 healthy control subjects were acquired with a General Electric Study participants and TSPO binding af nity classes (Milwaukee, WI, USA) Signa 1.5 Tesla MRI scanner. The other 7 control Twenty-one participants with AD (age range: 47–81 years) and 21 subjects and all participants with AD had the T1 weighted images acquired healthy control subjects (age range: 45–82 years) completed all on a 3-Tesla General Electric MR750 scanner. MRI acquisition parameters study procedures. Demographic and clinical characteristics are 45 for both scanners have been described in detail elsewhere. The PD- presented in Table 1. The participants with AD were on average 7 weighted and T2 FLAIR scans were visually inspected for evidence of focal years older than the healthy control subjects (F (1,40) = 5.54, and vascular lesions including the presence of white matter hyperinten- 46 P = 0.024). There were no significant associations between age and sities, which was determined by following established criteria. 18 45 The PET images were obtained for 125 min following the injection of [ F]-FEPPA VT, as previously reported. Across the whole sample, [18F]-FEPPA using a 3D high-resolution research tomograph brain tomo- anti-hypertensive and/or cholesterol lowering medications for the graph (CS/Siemens, Knoxville, TN, USA) as previously described.16 A dose of management of cardiovascular risk factors were taken by eight 181+15 mBq of intravenous [18F]-FEPPA was administered as a bolus for and six subjects, respectively. None had any history of significant the PET scan. Blood samples were collected for genotyping of TSPO rs6971 medical illness, including hospitalization because of past

Table 1. Participants’ demographic characteristics and [18F]-FEPPA injection parameters (mean±SD).

Descriptives Healthy control subjects Alzheimer's disease

n 21 21 Age* 61.3 ± 9.9 68.3 ± 9.4 Sex 9M, 12 F 11M, 10 F Education (years) 15.4 ± 1.5 15.7 ± 3.9 Duration of illness (years) N/A 4.3 ± 3.0 MMSE 29.4 ± 0.9 17.5 ± 7.0 CDR Sum of Boxes N/A 7.6 ± 3.7 TSPO Genotype, HH: HL: LL 14:7:0 10:8:3 Concurrent medication Donepezil 0 12 Memantine 0 6 Rivastigmine 0 5 Galantamine 0 1 Anti-hypertensives 2 8 Statins 1 6 Antidepressants 0 9 Baby aspirin 1 3 [18F]-FEPPA injected parameters Amount injected (MBq)* 176.98 ± 11.76 185.99 ± 16.69 Speciﬁc activity (GBq μmol−1) 106.50 ± 87.72 75.41 ± 46.42 Mass injected (μg) 1.15 ± 0.89 1.51 ± 1.17 Abbreviations: AD, Alzheimer's disease; CDR, Clinical Dementia Rating; MMSE, Mini-Mental State Examination Score; TSPO, translocator protein 18 kDa. *Po0.05 for comparison between healthy controls and AD.

18 cardiovascular events. Two participants with AD had severe white Relationships between [ F]-FEPPA VT and disease severity, matter hyperintensities.46 Genetic analysis revealed 10 HAB, length of illness, neuropsychological scores and functional 8 MAB and 3 LAB in the AD group, and 14 HAB, 7 MAB and no disability LAB in the healthy control group. The three LABs were excluded The correlations between neuroinflammation in both GM and WM 18 from the analyses as their [ F]-FEPPA data were not quanti- and severity or length of illness were not statistically significant. 18 fiable. The frequency of HAB and MAB was not significantly Across the grey matter ROIs, there were strong Bonferroni 2 different between AD and healthy control group (X = 0.51, corrected significant negative associations between [18F]-FEPPA P = 0.48), even when the LAB were included in the analyses V in the parietal cortex and repeatable battery for the assessment 2 T (X = 3.73, P = 0.15). of neuropsychological status visuospatial scores (non-PVEC partial coefficient r (df = 12) = − 0.701, P = 0.005). However, the correlation 18 Differences in [ F]-FEPPA VT between the AD and healthy control did not survive Bonferroni correction in the PVEC images (partial subjects in the grey matter ROIs coefficient r (df = 12) = − 0.682, P = 0.007; Figure 3a; Supplemen- Participants with AD had a significantly higher neuroinflammation tary Material Supplementary Table S3). In addition, a significant than the healthy control subjects in the hippocampus, temporal, negative Bonferroni corrected association was observed between 18 prefrontal, parietal and occipital cortex, after controlling for the prefrontal cortex PVEC [ F]-FEPPA VT and visuospatial score the effect of TSPO genotype and age (Table 2, Figure 1). The (partial coefficient r (df = 12) = − 0.7, P = 0.005; Figure 3b; Supple- 18 differences in [ F]-FEPPA VT were smaller and did not reach mentary Material Supplementary Table S3). statistical significance in the cerebellum and thalamus, two In the WM-ROI, there were significant correlations (Bonferroni 18 regions that are typically affected in late phases of the disease corrected) between [ F]-FEPPA VT in the posterior limb of the (Table 2). In both groups, the effect of the rs6971 polymorphism internal capsule, and both repeatable battery for the assessment was also significant in all grey matter ROIs, where HAB had a of neuropsychological status language scores (partial coeffi- 18 − higher [ F]-FEPPA VT compared with MAB (Table 2, Supplemen- cient r (df = 12) = 0.718, P = 0.004; Figure 3c), and repeatable tary Material Supplementary Table S1). battery for the assessment of neuropsychological status visuospatial scores (partial coefficient r (df = 12) = − 0.766, P = 0.001; 18 Figure 3d). With the non-partial volume corrected images, Differences in [ F]-FEPPA VT between the AD and healthy control 18 subjects in the WM-ROI there was a correlation between [ F]-FEPPA VT in the temporal cortex and the planning subset of the Disability Assessment Because of the lower uptake and slower washout of [18F]-FEPPA for Dementia scale (r (df = 12) = 0.523, P = 0.045). However, no kinetics in the white matter regions, the TAC data from one significant correlations were seen between Disability Assessment healthy and two AD subjects could not be analyzed with the for Dementia scores and [18F]-FEPPA V in the WM-ROI 2-tissue compartment model, and these subjects were excluded T (Supplementary Table S3). from these analyses.51 After controlling for the effect of both TSPO genotype and age, participants with AD had a significantly higher neuroinflammation Voxel-based analyses than the healthy control subjects in the cingulum bundle and in Congruent with the ROI analyses, the voxel-based analysis the posterior limb of the internal capsule, but not in the superior revealed large clusters of increased neuroinflammation in AD longitudinal fasciculus, or the corpus callosum (Table 2, Figure 2). in the temporal, prefrontal, occipital and parietal cortex. In both group, the effect of the rs6971 polymorphism was also A summary of all significant clusters and the corresponding MNI 18 significant, where HAB had higher [ F]-FEPPA VT compared with coordinates regions, t-scores, z-scores and family wise corrected MAB in all the WM-ROI, except in the corpus callosum (Table 2, P-values from the t-statistical map is reported in the Supple- Supplementary Material Table S1). mentary Material.

18 Table 2. Regional [ F]-FEPPA VT for AD and healthy control groups. Factorial ANOVA were performed for each ROI to compare differences between diagnostic groups with genotype and age added 18 18 as covariates. % Difference was calculated as the difference in [ F]-FEPPA VT between the groups divided by [ F]-FEPPA VT of the healthy control group. Neuroin – 57©21 amla ulsesLimited Publishers Macmillan 2015 © 1587 Grey matter ROI HV (n = 21) AD (n = 18) Model Diagnostic effect Genetic effect Age effect ﬂ maini Alzheimer in ammation

VT (partial volume error corrected) Adjusted mean Std err Adjusted mean Std err % Difference Cohen’sD F (3,35) PF(1,35) PF(1,35) PF(1,35) P

Temporal cortex 12.61 1.23 18.45 1.33 46.34% 0.96 5.837 0.002 9.771 0.004 7.94 0.008 1.678 0.204 Prefrontal cortex 14.54 1.17 21.91 1.27 50.64% 1.22 9.005 o0.001 17.157 o0.001 10.675 0.002 2.139 0.153 Parietal cortex 14.54 1.22 20.99 1.33 44.32% 1.04 7.043 0.001 12.005 0.001 9.668 0.004 1.597 0.215 Occipital cortex 12.26 1.45 18.51 1.57 51.02% 0.92 3.769 0.019 8.034 0.008 3.433 0.072 1.366 0.250

Hippocampus 9.99 1.19 15.59 1.29 56.00% 0.97 5.394 0.004 9.535 0.004 7.423 0.01 0.571 0.455 Suridjan I Cerebellum 11.35 0.87 13.66 0.94 20.35% 0.54 4.315 0.011 3.079 0.088 9.893 0.003 0.461 0.502 ’ Thalamus 13.57 1.31 17.22 1.41 26.89% 0.58 3.269 0.033 3.361 0.075 6.276 0.017 0.791 0.380 disease s tal et

VT Adjusted mean Std err Adjusted mean Std err % Difference Cohen’s D F (3,35) P F (1,35) P F (1,35) P F (1,35) P

Temporal Cortex 10.01 0.81 12.69 0.88 25.66% 0.62 5.607 0.003 4.439 0.042 12.333 0.001 0.777 0.348 Prefrontal Cortex 10.29 0.75 13.16 0.81 27.90% 0.72 7.360 0.001 6.383 0.016 16.390 o0.001 0.390 0.563 Parietal Cortex 10.66 0.78 13.76 0.85 28.99% 0.75 7.338 0.001 6.784 0.013 15.866 o0.001 0.519 0.476 Occipital Cortex 9.87 0.96 12.87 1.04 30.41% 0.65 3.429 0.027 4.218 0.048 6.262 0.017 0.567 0.456 Hippocampus 9.29 0.97 12.14 1.06 30.69% 0.60 3.969 0.016 3.705 0.062 8.741 0.006 0.000 0.986 Cerebellum 10.13 0.77 11.56 0.83 14.03% 0.37 4.409 0.01 1.488 0.231 11.979 0.001 0.023 0.881 Thalamus 11.98 1.17 14.37 1.27 19.88% 0.42 3.085 0.04 1.792 0.189 7.426 0.01 0.283 0.598 White matter ROI HV (n = 20) AD (n = 18) See footnote Model Diagnostic effect Genetic effect Age effect

VT Adjusted mean Std err Adjusted mean Std err % Difference Cohen’sD F (3,35) PF(1,35) PF(1,35) PF(1,35) P

Superior longitudinal fasciculus 7.65 0.84 9.39 0.91 22.67% 0.23 3.845 0.018 1.772 0.192 8.209 0.007 1.404 0.244 Posterior limb internal capsule 7.18 0.72 9.64 0.79 34.33% 0.33 3.086 0.041 4.772 0.036 4.569 0.04 0.021 0.885 Corpus callosuma 7.04 0.88 8.07 1.02 14.59% 0.15 1.423 0.255 0.538 0.469 3.392 0.075 0.519 0.477 Cingulum bundleb 7.49 0.68 10.28 0.76 37.18% 0.35 3.74 0.021 6.841 0.013 4.851 0.035 0.337 0.566 a b Abbreviations: AD, Alzheimer's disease; ANOVA, analysis of variance; ROI, regions of interest. n = 16; two AD patients had poor VT identifiability, and were excluded from the corpus callosum analysis. n = 17; one AD patient (one of the two that was excluded from the corpus callosum) had poor VT identifiability, and was excluded from the cingulum bundle analysis. Neuroinflammation in Alzheimer’s disease I Suridjan et al 1583

18 Figure 1. [ F]-FEPPA total distribution volume (VT) with partial volume error correction in participants with Alzheimer’s disease (n = 18) and cognitively intact healthy subjects (n = 21) in grey matter regions of interest, shown separately for high-affinity binders and mixed-affinity 18 binders. (a) Across regions of interest [ F]-FEPPA VT was 38% higher in high-affinity binders participants with Alzheimer’s disease than high- 18 affinity binders healthy control subjects. (b) Similarly, [ F]-FEPPA VT was 33% higher in mixed-affinity binders participants with Alzheimer’s disease than mixed-affinity binders controls. The effect sizes were large for both high-affinity binders and mixed-affinity binders (the average Cohen’s d across regions was 0.96 for high-affinity binders and 0.69 for mixed-affinity binders, Supplementary Material, Supplementary Table S1). *P ⩽ 0.05, **P ⩽ 0.01 ***P ⩽ 0.001.

18 Figure 2. [ F]-FEPPA total distribution volume (VT) in participants with Alzheimer’s disease (n = 17) and cognitively intact healthy subjects (n = 20) in the white matter regions, shown separately for high-affinity binders and mixed-affinity binders. (a) Across regions of interest, [18F]- FEPPA VT was 37% higher in high-affinity binders participants with Alzheimer’s disease than high-affinity binders healthy control subjects. 18 (b)[ F]-FEPPA VT was 17% higher in mixed-affinity binders participants with Alzheimer’s disease than in mixed-affinity binders control subjects. The effect sizes were moderate to large for high-affinity binders (average Cohen’s d: 0.72), and lower for mixed-affinity binders (average Cohen’s d: 0.4) (Supplementary Material, Supplementary Table S1).

DISCUSSION Higher neuroinflammation in the parietal cortex was associated Neuroinflammation, as indexed by [18F]-FEPPA binding to TSPO, with greater impairment in visuospatial ability. Greater neuroin- was significantly higher in participants with AD than in healthy flammation in the posterior limb of the internal capsule was control subjects. In the grey matter, significant increases were significantly associated with greater impairment in visuospatial, observed in the hippocampus, prefrontal, temporal, parietal and language, and memory function. occipital cortex. In the white matter, significant increases were Our results are consistent with published histopathology seen in the cingulum bundle and posterior limb of the internal studies, which found a elevated TSPO expression in the hippo- 7,57–59 capsule. Voxel-wise analyses confirmed the ROI-based results. campus, frontal and temporal cortex. The results are also

18 Figure 3. Relationships between [ F]-FEPPA total distribution volume (VT) and neuropsychological scores in participants with Alzheimer’s disease, shown separately for high-afﬁnity binders and mixed-afﬁnity binders. In the grey matter regions of interest, strong negative 18 associations were seen between [ F]-FEPPA VT in the parietal cortex and repeatable battery for the assessment of neuropsychological status visuospatial score (a), prefrontal cortex and repeatable battery for the assessment of neuropsychological status visusopatial score (b). In 18 the white matter regions of interest, strong negative associations were seen between [ F]-FEPPA VT in the posterior limb of internal capsule and repeatable battery for the assessment of neuropsychological status language scores (c), posterior limb of internal capsule and repeatable battery for the assessment of neuropsychological status visuospatial score (d). Repeatable battery for the assessment of neuropsychological status scores were standardized for age and gender. Data were partial volume error corrected.

consistent with findings from two previous PET studies using transgenic mouse model of AD indicated that elevated microglia other second-generation TSPO radioligands,32,33 although there activation in the white matter might be associated with the are notable differences in the methodology and demographic absence of myelin basic protein.61 Although DTI studies have characteristic among the studies. In the study that used unequivocally showed that patients with AD have an extensive [11C]-DAA1106, the majority of patients with AD were not taking white matter microstructural damage,22 it is yet to be determined acetylcholinesterase inhibitors or memantine, and the results were whether these white matter changes are related to increases in not corrected for partial volume errors or adjusted for differences neuroinflammation. Consistent with our results, several studies in binding affinity class.33 We do not have information on the have reported associations between the loss of integrity of white biomarker evidence of AD pathology, such as the positive [11C]PIB matter microstructure as measured by DTI and cognitive brain uptake. However, another published TSPO PET study that impairment in patients with AD.62–65 In some of these studies, a had information about [11C]PIB binding reported a consistent reduction in microstructural integrity of the superior longitudinal magnitude of increase in [11C]PBR28 binding throughout the grey fasciulus and cingulum bundle were significantly correlated with matter cortex in ‘PIB positive’ participants with AD as compared lower performance on general cognitive tests, as well as with ‘PIB negative’ healthy control subjects.32 performance on more specific cognitive domains, such as memory To the best of our knowledge, this is the first in vivo investi- recall, verbal recognition and visuospatial function.62–66 gation of neuroinflammation using TSPO PET imaging in specific The frequency and severity of white matter hyperintensities white matter regions in AD. A previous PET study using increase with age and are significantly higher in patients with [11C]-PBR28 did not find an increase in TSPO binding in the AD.28,46 In our study, two AD patients (one HAB and one MAB) had whole brain white matter of patients with AD.32 However, a signif- severe white matter hyperintensities.46 The increase in [18F]-FEPPA icant increase of activated microglia and inflammatory markers binding in the AD group remained significant in both grey and have been detected throughout white matter.60 A study in a WM-ROIs when these two AD subjects were excluded from the

Molecular Psychiatry (2015), 1579 – 1587 © 2015 Macmillan Publishers Limited Neuroinflammation in Alzheimer’s disease I Suridjan et al 1585 analysis (Po 0.05). The associations between [18F]-FEPPA binding impairment, especially in the visuospatial domain. Our ability to and scores of white matter hyperintensities are summarized in the detect and quantify TSPO in the white matter regions will enable Supplementary Material (Supplementary Table S4). future examinations of the association between neuroinflamma- Previous studies have shown conflicting results regarding the tion and white matter microstructural abnormalities in AD. PET relationship between neuroinflammation and severity of cognitive imaging with [18F]-FEPPA might be used in conjunction with – impairment.10,12,31 33 However, a recent study showed that a other imaging biomarkers to evaluate the relationship between higher TSPO binding in the inferior parietal lobule, as measured neuroinflammation and AD neuropathology, and possibly with [11C]-PBR28 PET, was associated with an impaired perfor- determine the consequences of neuroinflammation at different 32 mance on Block design, a test that measures visuospatial ability. phases of the disease, particularly in patients at the pre- We found consistent results in the visuospatial domain (Bonferroni clinical stage. corrected) although our study had somewhat different sample population, and the visuospatial function was assessed using a different scale. In addition, we also explored correlations between CONFLICT OF INTEREST 18 [ F]-FEPPA binding in other brain regions and cognitive functions. The authors declare no conflict of interest However, because we do not have the statistical power to test all of these correlations, we only considered correlations as true associations if they remained significant after the very strict ACKNOWLEDGMENTS Bonferroni correction for multiple comparisons (as shown in This work was supported by the Alzheimer’s society of Canada and the Scottish Rite Supplementary Table S3). Previous PET studies measuring both Charitable Foundation. We thank Alan A. Wilson, Peter Bloomfield, Armando Garcia, beta-amyloid and TSPO in the same group of AD patients have Winston Stableford, Min Wong, Alvina Ng, Laura Nguyen and Wanna Mar for their generally shown significant associations between cognition assistance and expertise. and TSPO radioligand binding, but not [11C]-PIB binding.10,31 fl Thus, neuroin ammation may have a potential role as an imaging REFERENCES biomarker to predict cognitive impairment during the early phase 1 Akiyama H, Barger S, Barnum S, Bradt B, Bauer J, Cole GM et al. Inflammation and of the disease. Future studies should test the associations between 21 – neuroinflammation and cognition in a larger sample along the Alzheimer's disease. Neurobiol Aging 2000; :383 421. 2 Wegiel J, Wisniewski HM. The complex of microglial cells and amyloid star in continuum from normal to preclinical AD (that is, amnestic MCI) to three-dimensional reconstruction. Acta Neuropathol 1990; 81: 116–124. AD dementia. 3 Luber-Narod J, Rogers J. Immune system associated antigens expressed by cells of 18 We did not detect significant associations between [ F]-FEPPA the human central nervous system. Neurosci Lett 1988; 94:17–22. and severity or length of illness. The lack of association is 4 McGeer PL, Itagaki S, McGeer EG. Expression of the histocompatibility glyco- consistent with the notion that elevated neuroinflammation is protein HLA-DR in neurological disease. Acta Neuropathol 1988; 76:550–557. an early event in AD,9,67,68 which might increase with severity 5 Kreutzberg GW. Mic roglia: a sensor for pathological events in the CNS. Trends of dementia, but reach plateau in moderately affected cases.67 Neurosci 1996; 19: 312–318. Similarly, elevated TSPO expression was detected in the early 6 Chen MK, Guilarte TR. Translocator protein 18 kDa (TSPO): molecular sensor of 118 – phase of AD9,12 before the occurrence of brain atrophy,9,69 or brain injury and repair. Pharmacol Ther 2008; :1 17. 70 7 Cosenza-Nashat M, Zhao ML, Suh HS, Morgan J, Natividad R, Morgello S et al. synaptic loss. Finally, the majority of our patients were in mode- Expression of the translocator protein of 18 kDa by microglia, macrophages and rate stage of AD, a narrow severity range, which limits our ability astrocytes based on immunohistochemical localization in abnormal human brain. 18 to test the associations between [ F]-FEPPA binding and clinical Neuropathol Appl Neurobiol 2009; 35:306–328. severity. 8 Venneti S, Wiley CA, Kofler J. Imaging microglial activation during neuro- As it is common with molecular imaging studies in humans, inflammation and Alzheimer's disease. J Neuroimmune Pharmacol 2009; 4: some caveats should be considered. First, as shown in Table 1, the 227–243. amount of [18F]-FEPPA injected was significantly higher in 9 Cagnin A, Brooks DJ, Kennedy AM, Gunn RN, Myers R, Turkheimer FE et al. In-vivo participants with AD than healthy controls (F = 4.092, P = 0.05). measurement of activated microglia in dementia. Lancet 2001; 358: 461–467. However, there were no correlations between [18F]-FEPPA binding 10 Edison P, Archer HA, Gerhard A, Hinz R, Pavese N, Turkheimer FE et al. Microglia, amyloid, and cognition in Alzheimer's disease: An [11C](R)PK11195-PET and [11C] and the amount injected in all the GM and WM-ROI examined 32 – ⩽ − 4 PIB-PET study. Neurobiol Dis 2008; :412 419. (r 0.1 and P 0.05 for all regions). Importantly, there were no 11 Schuitemaker A, Kropholler MA, Boellaard R, van der Flier WM, Kloet RW, van der fi differences in the speci c activity and mass injected between the Doef TF et al. Microglial activation in Alzheimer's disease: an (R)-[(1)(1)C]PK11195 groups. Second, like many previous TSPO PET studies, we did not positron emission tomography study. Neurobiol Aging 2013; 34:128–136. exclude patients with AD who were treated with a stable dose of 12 Okello A, Edison P, Archer HA, Turkheimer FE, Kennedy J, Bullock R et al. Microglial memantine or acetylcholinesterase inhibitors. Although none of activation and amyloid deposition in mild cognitive impairment: a PET study. the participants with AD meet diagnostic criteria for mood Neurology 2009; 72:56–62. disorders, almost half were taking anti-depressants. In-vitro 13 Wiley CA, Lopresti BJ, Venneti S, Price J, Klunk WE, DeKosky ST et al. Carbon 11- labeled Pittsburgh Compound B and carbon 11-labeled (R)-PK11195 positron evidence suggested that anti-depressants may have some anti- 66 – inflammatory effect on microglia response to inflammatory emission tomographic imaging in Alzheimer disease. Arch Neurol 2009; :60 67. 71 14 Chauveau F, Boutin H, Van Camp N, Dolle F, Tavitian B. Nuclear imaging of stimuli. However, there is so far no evidence on the effect of neuroinflammation: a comprehensive review of [11C]PK11195 challengers. anti-depressants or donepezil on TSPO radioligand binding. Our Eur J Nucl Med Mol Imaging 2008; 35: 2304–2319. 18 post-hoc analyses showed that the group differences in [ F]- 15 Wilson AA, Garcia A, Parkes J, McCormick P, Stephenson KA, Houle S et al. FEPPA binding remained significant for all grey matter ROIs Radiosynthesis and initial evaluation of [18F]-FEPPA for PET imaging of peripheral (Po0.01) and WM-ROI (Po0.05), even excluding those who were benzodiazepine receptors. Nucl Med Biol 2008; 35: 305–314. taking anti-depressants. Finally, we did not estimate the plasma 16 Rusjan PM, Wilson AA, Bloomfield PM, Vitcu I, Meyer JH, Houle S et al. Quanti- 18 tation of translocator protein binding in human brain with the novel radioligand free fraction (fp) values for [ F]-FEPPA in the present study. However, V values uncorrected for f were still able to distinguish [(18)F]-FEPPA and positron emission tomography. J Cereb Blood Flow Metab 2011; T p 31 – the AD group from controls. Further studies are required to : 1807 1816. 17 Kreisl WC, Jenko KJ, Hines CS, Hyoung Lyoo C, Corona W, Morse CL et al. A genetic determine if correcting VT values for fp further improve the ability 18 polymorphism for translocator protein 18 kDa affects both in vitro and in vivo of [ F]-FEPPA to distinguish increases in TSPO in AD. radioligand binding in human brain to this putative biomarker of neuroin- In conclusion, AD patients have significantly elevated neuro- flammation. J Cereb Blood Flow Metab 2013; 33:53–58. inflammation throughout grey and white matter. The higher 18 Mizrahi R, Rusjan PM, Kennedy J, Pollock B, Mulsant B, Suridjan I et al. Trans- level of neuroinflammation was associated with greater cognitive locator protein (18 kDa) polymorphism (rs6971) explains in-vivo brain binding

© 2015 Macmillan Publishers Limited Molecular Psychiatry (2015), 1579 – 1587 Neuroinflammation in Alzheimer’s disease I Suridjan et al 1586 affinity of the PET radioligand [(18)F]-FEPPA. J Cereb Blood Flow Metab 2012; 32: 43 Cummings JL, Mega M, Gray K, Rosenberg-Thompson S, Carusi DA, Gornbein J. 968–972. The Neuropsychiatric Inventory: comprehensive assessment of psychopathology 19 Guo Q, Owen DR, Rabiner EA, Turkheimer FE, Gunn RN. Identifying improved in dementia. Neurology 1994; 44: 2308–2314. TSPO PET imaging probes through biomathematics: The impact of multiple TSPO 44 Alexopoulos GS, Abrams RC, Young RC, Shamoian CA. Cornell Scale for Depres- binding sites in vivo. Neuroimage 2012; 60: 902–910. sion in Dementia. Biol Psychiatry 1988; 23: 271–284. 20 Owen DR, Howell OW, Tang SP, Wells LA, Bennacef I, Bergstrom M et al. 45 Suridjan I, Rusjan PM, Voineskos AN, Selvanathan T, Setiawan E, Strafella AP et al. Two binding sites for [3H]PBR28 in human brain: implications for TSPO Neuroinflammation in healthy aging: a PET study using a novel Translocator PET imaging of neuroinflammation. J Cereb Blood Flow Metab 2010; 30: Protein 18kDa (TSPO) radioligand, [(18)F]-FEPPA. Neuroimage 2014; 84: 1608–1618. 868–875. 21 Owen DR, Gunn RN, Rabiner EA, Bennacef I, Fujita M, Kreisl WC et al. Mixed-affinity 46 Scheltens P, Barkhof F, Valk J, Algra PR, van der Hoop RG, Nauta J et al. White binding in humans with 18-kDa translocator protein ligands. J Nucl Med 2011; 52: matter lesions on magnetic resonance imaging in clinically diagnosed Alzheimer's 24–32. disease. Evidence for heterogeneity. Brain 1992; 115: 735–748. 22 Sexton CE, Kalu UG, Filippini N, Mackay CE, Ebmeier KP. A meta-analysis of dif- 47 Rusjan P, Mamo D, Ginovart N, Hussey D, Vitcu I, Yasuno F et al. An automated fusion tensor imaging in mild cognitive impairment and Alzheimer's disease. method for the extraction of regional data from PET images. Psychiatry Res 2006; Neurobiol Aging 2010; 32: 2322.e5–e18. 147:79–89. 23 Canu E, Agosta F, Spinelli EG, Magnani G, Marcone A, Scola E et al. White matter 48 Braak H, Braak E. Neuropathological stageing of Alzheimer-related changes. microstructural damage in Alzheimer's disease at different ages of onset. Neuro- Acta Neuropathol 1991; 82:239–259. biol Aging 2013; 34: 2331–2340. 49 Duvernoy H. The Human Brain: Surface, Blood Supply and Three Dimensional 24 Migliaccio R, Agosta F, Possin KL, Rabinovici GD, Miller BL, Gorno-Tempini ML. Sectional Anatomy. SpringerWien: New York, 1999. White matter atrophy in Alzheimer's disease variants. Alzheimers Dement 2012; 8, 50 Mori S, Oishi K, Jiang H, Jiang L, Li X, Akhter K et al. Stereotaxic white matter atlas S78-87, e71–e72. based on diffusion tensor imaging in an ICBM template. Neuroimage 2008; 40: 25 Rose SE, Chen F, Chalk JB, Zelaya FO, Strugnell WE, Benson M et al. Loss of 570–582. connectivity in Alzheimer's disease: an evaluation of white matter tract integrity 51 Suridjan I, Rusjan P, Kenk M, Verhoeff NP, Voineskos AN, Rotenberg D et al. with colour coded MR diffusion tensor imaging. J Neurol Neurosurg Psychiatry Quantitative imaging of neuroinflammation in human white matter: a positron 2000; 69: 528–530. emission tomography (PET) study with translocator protein 18kDA (TSPO) 26 Duan JH, Wang HQ, Xu J, Lin X, Chen SQ, Kang Z et al. White matter damage of radioligand, [18F]-FEPPA. Synapse 2014; 68:536–547. patients with Alzheimer's disease correlated with the decreased cognitive 52 Muller-Gartner HW, Links JM, Prince JL, Bryan RN, McVeigh E, Leal JP et al. function. Surg Radiol Anat 2006; 28: 150–156. Measurement of radiotracer concentration in brain gray matter using positron 27 Zhang Y, Schuff N, Jahng GH, Bayne W, Mori S, Schad L et al. Diffusion tensor emission tomography: MRI-based correction for partial volume effects. J Cereb imaging of cingulum fibers in mild cognitive impairment and Alzheimer disease. Blood Flow Metab 1992; 12: 571–583. Neurology 2007; 68:13–19. 53 Bencherif B, Stumpf MJ, Links JM, Frost JJ. Application of MRI-based partial- 28 Scheltens P, Barkhof F, Leys D, Wolters EC, Ravid R, Kamphorst W. Histopathologic volume correction to the analysis of PET images of mu-opioid receptors using correlates of white matter changes on MRI in Alzheimer's disease and statistical parametric mapping. J Nucl Med 2004; 45:402–408. normal aging. Neurology 1995; 45: 883–888. 54 Turkheimer FE, Banati RB, Visvikis D, Aston JA, Gunn RN, Cunningham VJ. 29 Sjobeck M, Haglund M, Englund E. Decreasing myelin density reflected increasing Modeling dynamic PET-SPECT studies in the wavelet domain. J Cereb Blood Flow white matter pathology in Alzheimer's disease – a neuropathological study. Int J Metab 2000; 20:879–893. Geriatr Psychiatry 2005; 20:919–926. 55 Lahiri DK, Nurnberger JI Jr. A rapid non-enzymatic method for the prepa- 30 Sjobeck M, Englund E. Glial levels determine severity of white matter disease in ration of HMW DNA from blood for RFLP studies. Nucleic Acids Res 1991; Alzheimer's disease: a neuropathological study of glial changes. Neuropathol Appl 19: 5444. Neurobiol 2003; 29:159–169. 56 Owen DR, Yeo AJ, Gunn RN, Song K, Wadsworth G, Lewis A et al. An 18-kDa 31 Yokokura M, Mori N, Yagi S, Yoshikawa E, Kikuchi M, Yoshihara Y et al. In vivo Translocator Protein (TSPO) polymorphism explains differences in binding changes in microglial activation and amyloid deposits in brain regions with affinity of the PET radioligand PBR28. J Cereb Blood Flow Metab 2011; 32: hypometabolism in Alzheimer's disease. Eur J Nucl Med Mol Imaging 2011; 38: 1–5. 343–351. 57 Gulyas B, Makkai B, Kasa P, Gulya K, Bakota L, Varszegi S et al. A compa- 32 Kreisl WC, Lyoo CH, McGwier M, Snow J, Jenko KJ, Kimura N et al. In vivo radi- rative autoradiography study in post mortem whole hemisphere human oligand binding to translocator protein correlates with severity of Alzheimer's brain slices taken from Alzheimer patients and age-matched controls disease. Brain 2013; 136:2228–2238. using two radiolabelled DAA1106 analogues with high affinity to the peri- 33 Yasuno F, Ota M, Kosaka J, Ito H, Higuchi M, Doronbekov TK et al. Increased pheral benzodiazepine receptor (PBR) system. Neurochem Int 2009; 54: binding of peripheral benzodiazepine receptor in Alzheimer's disease measured 28–36. by positron emission tomography with [11C]DAA1106. Biol Psychiatry 2008; 64: 58 Diorio D, Welner SA, Butterworth RF, Meaney MJ, Suranyi-Cadotte BE. Peripheral 835–841. benzodiazepine binding sites in Alzheimer's disease frontal and temporal cortex. 34 McKhann G, Drachman D, Folstein M, Katzman R, Price D, Stadlan EM. Clinical Neurobiol Aging 1991; 12: 255–258. diagnosis of Alzheimer's disease: report of the NINCDS-ADRDA Work Group under 59 Venneti S, Wang G, Nguyen J, Wiley CA. The positron emission tomography ligand the auspices of Department of Health and Human Services Task Force on DAA1106 binds with high affinity to activated microglia in human neurological Alzheimer's Disease. Neurology 1984; 34:939–944. disorders. J Neuropathol Exp Neurol 2008; 67: 1001–1010. 35 Folstein MF, Folstein SE, McHugh PR. ‘Mini-mental state’. A practical method for 60 Wirths O, Breyhan H, Marcello A, Cotel MC, Bruck W, Bayer TA. Inflammatory grading the cognitive state of patients for the clinician. J Psychiatr Res 1975; 12: changes are tightly associated with neurodegeneration in the brain and spinal 189–198. cord of the APP/PS1KI mouse model of Alzheimer's disease. Neurobiol Aging 2010; 36 Smith T, Gildeh N, Holmes C. The Montreal Cognitive Assessment: validity and 31:747–757. utility in a memory clinic setting. Can J Psychiatry 2007; 52:329–332. 61 Ou-Yang MH, Van Nostrand WE. The absence of myelin basic protein promotes 37 Tombaugh TN. Trail Making Test A and B: normative data stratified by age and neuroinflammation and reduces amyloid beta-protein accumulation in education. Arch Clin Neuropsychol 2004; 19:203–214. Tg-5xFAD mice. J Neuroinflammation 2013; 10: 134. 38 Trenerry MR. Stroop Neuropsychological Screening Test Manual. Psycho- 62 Bai F, Zhang Z, Watson DR, Yu H, Shi Y, Yuan Y et al. Abnormal integrity of logical Assessment Resources: Odessa, FL, 1989. association fiber tracts in amnestic mild cognitive impairment. J Neurol Sci 2009; 39 Wechsler D. Wechsler Adult Intelligence Scale– Third Edition (WAIS–III). Texas: 278:102–106. Pearson Corp, San Antonio, 1997. 63 Fellgiebel A, Schermuly I, Gerhard A, Keller I, Albrecht J, Weibrich C et al. Func- 40 Duff K, Humphreys Clark JD, O'Bryant SE, Mold JW, Schiffer RB, Sutker PB. Utility of tional relevant loss of long association fibre tracts integrity in early Alzheimer's the RBANS in detecting cognitive impairment associated with Alzheimer's dis- disease. Neuropsychologia 2008; 46: 1698–1706. ease: sensitivity, specificity, and positive and negative predictive powers. Arch Clin 64 Meng JZ, Guo LW, Cheng H, Chen YJ, Fang L, Qi M et al. Correlation Neuropsychol 2008; 23:603–612. between cognitive function and the association fibers in patients with 41 Morris JC. The Clinical Dementia Rating (CDR): current version and scoring rules. Alzheimer's disease using diffusion tensor imaging. J Clin Neurosci 2012; 19: Neurology 1993; 43: 2412–2414. 1659–1663. 42 Gelinas I, Gauthier L, McIntyre M, Gauthier S. Development of a functional mea- 65 Xie S, Xiao JX, Wang YH, Wu HK, Gong GL, Jiang XX. Evaluation of bilateral sure for persons with Alzheimer's disease: the disability assessment for dementia. cingulum with tractography in patients with Alzheimer's disease. Neuroreport Am J Occup Ther 1999; 53:471–481. 2005; 16: 1275–1278.

Molecular Psychiatry (2015), 1579 – 1587 © 2015 Macmillan Publishers Limited Neuroinflammation in Alzheimer’s disease I Suridjan et al 1587 66 Fellgiebel A, Muller MJ, Wille P, Dellani PR, Scheurich A, Schmidt LG et al. pathologies in mouse models of Alzheimer's disease and related disorders. Color-coded diffusion-tensor-imaging of posterior cingulate fiber tracts in mild J Neurosci 2011; 31: 4720–4730. cognitive impairment. Neurobiol Aging 2005; 26:1193–1198. 70 Yoshiyama Y, Higuchi M, Zhang B, Huang SM, Iwata N, Saido TC et al. Synapse loss 67 Arends YM, Duyckaerts C, Rozemuller JM, Eikelenboom P, Hauw JJ. Microglia, amyloid and microglial activation precede tangles in a P301S tauopathy mouse model. and dementia in alzheimer disease. A correlative study. Neurobiol Aging 2000; 21:39–47. Neuron 2007; 53:337–351. 68 Vehmas AK, Kawas CH, Stewart WF, Troncoso JC. Immune reactive cells in senile plaques 71 Tynan RJ, Weidenhofer J, Hinwood M, Cairns MJ, Day TA, Walker FR. A com- and cognitive decline in Alzheimer's disease. Neurobiol Aging 2003; 24:321–331. parative examination of the anti-inflammatory effects of SSRI and SNRI 69 Maeda J, Zhang MR, Okauchi T, Ji B, Ono M, Hattori S et al. In vivo positron antidepressants on LPS stimulated microglia. Brain Behav Immun 2012; 26: emission tomographic imaging of glial responses to amyloid-beta and tau 469–479.

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