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Change in brain amyloid load and cognition in patients with amnestic mild cognitive impairment: a 3-year follow-up study

EJNMMI Research volume 12, Article number: 55 (2022) Cite this article

Abstract

Background

Our aim was to investigate the discriminative value of 18F-Flutemetamol PET in longitudinal assessment of amyloid beta accumulation in amnestic mild cognitive impairment (aMCI) patients, in relation to longitudinal cognitive changes.

Methods

We investigated the change in 18F-Flutemetamol uptake and cognitive impairment in aMCI patients over time up to 3 years which enabled us to investigate possible association between changes in brain amyloid load and cognition over time. Thirty-four patients with aMCI (mean age 73.4 years, SD 6.6) were examined with 18F-Flutemetamol PET scan, brain MRI and cognitive tests at baseline and after 3-year follow-up or earlier if the patient had converted to Alzheimer´s disease (AD). 18F-Flutemetamol data were analyzed both with automated region-of-interest analysis and voxel-based statistical parametric mapping.

Results

18F-flutemetamol uptake increased during the follow-up, and the increase was significantly higher in patients who were amyloid positive at baseline as compared to the amyloid-negative ones. At follow-up, there was a significant association between 18F-Flutemetamol uptake and MMSE, logical memory I (immediate recall), logical memory II (delayed recall) and verbal fluency. An association was seen between the increase in 18F-Flutemetamol uptake and decline in MMSE and logical memory I scores.

Conclusions

In the early phase of aMCI, presence of amyloid pathology at baseline strongly predicted amyloid accumulation during follow-up, which was further paralleled by cognitive declines. Inversely, some of our patients remained amyloid negative also at the end of the study without significant change in 18F-Flutemetamol uptake or cognition. Future studies with longer follow-up are needed to distinguish whether the underlying pathophysiology of aMCI in such patients is other than AD.

Introduction

It is known that about 60% of subjects with amnestic Mild Cognitive Impairment (aMCI) will convert to Alzheimer's disease (AD) [1]. Elevated brain amyloid load has been associated with subtle, but slightly more marked cognitive decline than what would be expected solely in “normal aging” [2]. Follow-up studies have shown differences in cognitive performance [3, 4] and brain amyloid accumulation [5,6,7] between those who convert to AD and those who do not. Those patients who converted to AD had lower cognitive measurements and higher uptake of amyloid binding 11C-PIB-PET ligand referring to greater accumulation of amyloid at baseline.

18F-Flutemetamol is a PET ligand which has high affinity for amyloid β. The use 18F-Flutemetamol ligand permits in vivo detection of amyloid deposition in the brain. Brain 18F-Flutemetamol uptake has been shown to be associated with the amount of beta-amyloid plaques [8,9,10]. In addition, it has been shown to differentiate between patients with AD and healthy controls [11]. In aMCI patients, 18F-Flutemetamol uptake has been positive approximately in half of the cases [11,12,13]. Amyloid positivity in 18F-Flutemetamol PET, low hippocampal volume, and cognitive status corresponded with a high probability of risk of progression from aMCI to probable AD within 36 months [13]. In earlier studies, the follow-up has performed with cognitive tests without amyloid PET scanning.

The aim of this study was to examine whether there is increase in 18F-Flutemetamol uptake during the course of aMCI and whether this increase is different between those aMCI subjects who were amyloid positive vs amyloid negative at baseline. Our study design also enabled us to analyze association between changes in brain amyloid load and cognition over time.

Material and methods

Subjects

Demographics of the patients are shown in Table 1. Altogether 34 patients were included in the study (23 men and 11 women). The mean age of the patients was 73.4 ± 6.6 (mean ± SD) at baseline. All patients met the criteria of amnestic MCI [13], and all patients gave their written informed consent, which was obtained according to the requirements of the Declaration of Helsinki. The study protocol was approved by the Ethics Committee of Hospital District of Southwest Finland.

Table 1 Demographics of the study participants
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The patients were assessed with 18F-Flutemetamol PET scan, brain MRI and cognitive tests. Cognitive tests were administered yearly until conversion to Probable Alzheimer’s Disease or the end of the 3-year follow-up. Probable Alzheimer’s disease was diagnosed when the patients fulfilled the National Institute of Neurological and Communicative Disorders and Stroke-Alzheimer’s Disease and Related Disorders Association criteria as well as DSM-IV criteria for dementia of the Alzheimer type. MRI and 18F-flutemetamol PET were repeated after the subject had converted to AD or after 3 years had elapsed from the first 18F-Flutemetamol PET scan. 18F-Flutemetamol composite cortical uptake value ratio > 1.4 was used as a cut-off value of amyloid positivity [14]. Patients with amyloid-positive and amyloid-negative 18F-Flutemetamol scans at baseline did not differ regarding education and age at baseline (Table 1). Because the study was ongoing for several years, the PET scanner in the PET Centre changed during the study. Therefore, 14 patients were examined with one and 20 with the other scanner (see below for details). Baseline and follow-up scan was done with the same scanner in all but in 2 patients.

From the original 34 participants, seven dropped out the study after first scan and cognitive tests. Four of those 7 patients had positive baseline 18F-Flutemetamol PET scan. Five patients dropped out due to worsening of clinical condition, one participant died because of acute pulmonary embolism and one withdrew the consent due to personal reasons. Altogether 4 participants converted to AD during the study, 3 of them were amyloid positive and one was negative both at baseline and follow-up.

Additional 2 patients were excluded from the PET-analyses due to technical problems in image acquisition. Thus, both baseline and follow-up 18F-Flutemetamol PET data were available from 25 participants.

PET and MRI imaging

All patients underwent a 18F-Flutemetamol PET scan. They received approximately 185 MBq of intravenous 18F-Flutemetamol (187.8 ± 18.7 MBq, mean ± SD) and 90 min later underwent a 30-min brain scan. PET scan was done at baseline and approximately after 3-year follow-up or earlier if the patient converted to AD. The mean 18F-Flutemetamol scanning interval was 2.9 ± 0.4 years. Two patients’ follow-up PET scans were excluded from the analysis due to inadequate cerebellar imaging precluding reliable determination of reference region; one had positive and the other negative amyloid status at baseline.

First, the ECAT EXACT HR + (CTI/Siemens, Knoxville, TN, US) was used for PET imaging in 3D mode. The scanner has an axial field of view of 15.5 cm and a patient port of 56.2 cm, and physical performance evaluations of the scanner have shown radial and tangential average spatial resolution of 4.39 mm FWHM and axial resolution of 5.10 mm FWHM (16).

Later, both PET scans and follow-up scan in two patients, which were examined with ECAT scanner at baseline, were performed with GE Discovery 690 (GE Healthcare, Waukesha, WI, US) PET scanner because at that time ECAT EXACT HR + PET scanner was not in use anymore. GE Discovery 690 is a hybrid PET/CT scanner with the axial field of view of 15.7 cm and the patient port is 70.0 cm. Physical performance evaluations of the scanner have shown radial and tangential average spatial resolution of 4.70 mm FWHM and axial resolution of 4.74 mm FWHM [15].

All patients underwent brain MR imaging at baseline and after 3-year follow-up or earlier if the patient was converted to AD. MR imaging was done on Philips 1.5 T Gyroscan Intera CV Nova Dual MR scanner (Philips Medical Systems, Best, The Netherlands). A head coil (Philips Medical Systems, Best, The Netherlands) was used in the measurement. Whole-brain T1-weighted three-dimensional fast field echo data with 1-mm isotropic voxels were acquired in the transverse plane (acquisition parameters: repetition time 25 ms, echo time 5.5 ms, flip angle 30°, field of view 256 × 256 mm) yielding at least 160 contiguous slices to cover the whole head. In addition, routine axial T2-weighted and coronal FLAIR images were obtained.

18 F-Flutemetamol image analysis

To obtain quantitative regional values of 18F-Flutemetamol uptake, an automated volume of interest (VOI) analysis was conducted as described previously [16]. Briefly, parametric images representing 18F-Flutemetamol uptake in each pixel were calculated as a region-to-cerebellum ratio of the radioactivity concentration over 90–120 min after 18F-Flutemetamol injection. In order to compensate for head motion during PET imaging, the three ten-minute frames of 18F-Flutemetamol uptake were registered to each other prior to parametric image calculation. A rigid image registration algorithm implemented in statistical parametric mapping (SPM, version 8) software in MATLAB was employed. The motion-corrected data were subsequently summed and co-registered with subject-specific T1-weighted MR images in native space. Finally, the unified segmentation algorithm in SPM8 [17] was used for spatially normalizing the MR and PET images into the standard Montreal Neurological Institute (MNI) coordinate space. Automated VOI-delineation in the MNI space was defined on the basis of automated anatomical labeling [18] atlas, and gray matter masking using individual gray matter segments thresholded at 25%. Standard VOIs of cerebellar gray matter, frontal cortex, parietal cortex, lateral temporal cortex, anterior and posterior cingulate and precuneus were used in the analysis. A composite cortical amyloid uptake score was formed by combining the VOIs of frontal, parietal and lateral temporal cortices and posterior cingulate, similar to our earlier investigations [19]. Average regional VOI-values were extracted from spatially normalized 18F-Flutemetamol uptake ratio images within the above-mentioned VOIs, except in the cerebellar cortex which served as a reference region.

Cognitive testing

General cognition was assessed using Mini-Mental State Examination (MMSE). A subset of tests defined in Wechsler Memory Scale revised (WMS-R [20]) were conducted to assess immediate recall (logical memory I), delayed recall (logical memory II) and verbal fluency. All tests were delivered yearly to follow-up conversion to AD. Scores from the test occasions closest in time with baseline and follow-up PET imaging were used in the analysis.

Statistical analysis

VOI-based statistical analysis was conducted using R (version 4.0.3). Descriptive statistics included mean and standard deviations of continuous variables and counts for categorical variables. Student’s t test was employed for paired and Welcher two-sample t test for unpaired analysis. Pearson product moment correlation coefficients were used for examining the relationships between cognitive and imaging measures. Significance level was set at p < 0.05 (trend-level p < 0.1).

Confirmatory voxel-wise analysis was conducted using SPM8. Briefly, the spatially normalized 18F-Flutemetamol uptake ratio images were first smoothed using a kernel of 8 mm (FWHM; 3D) and, secondly, analyzed with paired t test to detect group-level changes in 18F-Flutemetamol uptake ratios over time. The resulting voxel-wise maps of T-statistics were inspected for regional changes of 18F-Flutemetamol uptake ratios using a liberal threshold of T ≥ 2.5, corresponding to approximately p < 0.01 (uncorrected). The use of a liberal threshold allowed detection of clusters that were potentially uncovered using VOI-analysis.

Results

The patients were classified as either positive (uptake ratio ≥ 1.4) or negative (uptake ratio < 1.4) based on the 18F-Flutemetamol composite cortical uptake score (see Table 2). At baseline, 15 patients (44%) had a positive and 19 patients (56%) had a negative 18F-Flutemetamol scan. The mean 18F-Flutemetamol composite score at baseline was 1.49 ± 0.36 and at follow-up 1.54 ± 0.39. Statistically significant increases in 18F-Flutemetamol uptake ratio were observed in the composite score, as well as in the prefrontal cortex, parietal cortex, lateral temporal cortex, and precuneus, for all patients together, as well as for patients with positive or negative baseline scans alone (Table 2). However, the increase in 18F-Flutemetamol uptake during follow-up was larger in patients who were 18F-Flutemetamol positive at baseline than in those who were negative [increase 0.15 ± 0.10 p < 0.001 and 0.05 ± 0.08 p < 0.08, respectively, Welcher two-sample t test t(16) = 2.64*]. Voxel-wise maps of 18F-Flutemetamol uptake ratios at baseline revealed a pattern of more wide-spread cortical uptake in amyloid-positive patients as compared to the amyloid-negative patients (Fig. 1a). Furthermore, voxel-wise statistical parametric mapping showed large clusters of increased follow-up 18F-Flutemetamol uptake ratios in patients with amyloid-positive baseline scans in the prefrontal, parietal, and lateral temporal regions, in agreement with the VOI-analysis (Fig. 1b). No significant clusters of increased 18F-Flutemetamol uptake ratios were observed in amyloid-negative patients (Fig. 1c).

Table 2 PET imaging results stratified by test time and amyloid status
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Fig. 1
figure 1

A Average voxel-wise maps of baseline and follow-up.18F-Flutemetamol PET uptake ratios (warmer colors represent higher uptake ratios) in patients with amyloid-positive (N = 15/10) and amyloid-negative (N = 19/15) baseline scans. Patients with amyloid-positive baseline scans showed increased cortical 18F-Flutemetamol uptake. In contrast, in patients with amyloid-negative baseline scans, negligible 18F-Flutemetamol uptake was seen in gray matter at baseline or follow-up. B Voxel-wise statistical mapping was conducted to assess longitudinal change. Widespread clusters of positive change were observed in amyloid-positive (top panel), but not in amyloid-negative patients (bottom panel). Statistical parametric maps are expressed in T-values using a threshold T ≥ 2.5 (cluster-forming threshold zero), corresponding to approximately p < 0.01 (uncorrected)

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Cognitive test performances of the two groups at baseline and follow-up are presented in Table 3. The mean Mini-Mental State Examination (MMSE) score was 27.2 ± 1.8 (range 24–30) and the mean logical memory I score (reflecting immediate recall) of Wechsler Memory scale revised (WMS-R, [20]) was 8.7 ± 3.6 and logical memory II score (reflecting delayed recall) was 6.6 ± 3.9 at baseline (see Table 3).

Table 3 Cognitive test results stratified by test time and amyloid status
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Patients with a positive 18F-Flutemetamol scan at baseline (N = 15; Table 3) showed a statistically significant decrement in MMSE, but not in the other cognitive measures over follow-up period, although numerically a decline in the scores of these tests was seen. Patients with negative baseline scans (N = 19) did not exhibit statistically significant mean decline in any of the cognitive measures. At baseline, amyloid-positive patients tended to perform more poorly than amyloid-negative patients in all cognitive tests, but statistically significant group-wise difference was observed only in the logical memory I test (Table 3). At follow-up, group-wise differences in cognition reached statistical significance (Table 3) for all cognitive tests. Moreover, the patients that were amyloid positive at baseline experienced more decline in MMSE, but not in other tests, as compared to the amyloid-negative patients.

Associations between 18F-Flutemetamol uptake ratios and cognition were explored using Pearson’s correlation coefficients. Statistically significant associations were absent at baseline, but at follow-up, all cognitive measures were negatively associated with composite 18F-Flutemetamol uptake ratios (rs =  − 0.60, p < 0.01 to − 0.41, p = 0.047; Table 4, Fig. 2). Furthermore, increases in composite 18F-Flutemetamol uptake ratio were associated with decreases in MMSE and logical memory I test scores (rs =  − 0.57, p < 0.01 to − 0.46, p = 0.023; Table 4, Fig. 2). The negative change-change association in MMSE was manifested in amyloid-positive patients but not in the amyloid-negative patients (rs =  − 0.78, p < 0.01 and − 0.18, n.s., respectively).

Table 4 Associations between 18F-Flutemetamol composite scores and cognitive test results
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Fig. 2
figure 2

Composite 18F-Flutemetamol uptake ratio and MMSE score were negatively associated at follow-up (A), and in terms of change over time from baseline to the end of follow-up (B)

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Discussion

In this study, we found that 15 of 34 (44%) aMCI patients had amyloid-positive 18F-Flutemetamol PET at baseline, and they showed statistically significant increase in 18F-Flutemetamol uptake during follow-up compared to patients with amyloid-negative baseline PET scan.

We also showed a statistically significant association between 18F-Flutemetamol increase and decrease in a general cognitive measure (MMSE) and a measure of episodic memory immediate recall (logical memory I) over time in individuals who were amyloid positive at baseline. There was also a trend-level association between increase in 18F-Flutemetamol uptake and decline in delayed episodic memory recall (logical memory II), but this did not reach statistical significance. In this study, the scores in the delayed recall measure (logical memory II) were already relatively low at baseline, leaving relatively little room for decline. Thus, a floor effect may explain why the association did not reach statistical significance. Contrary to our study, in earlier studies, the follow-up has been performed with clinical evaluation without amyloid PET scanning. In this study, as an extension to previous findings, following patients both clinically and with amyloid PET scans enabled us to analyze association between cognition and brain amyloid load over time.

Those patients who were amyloid positive at baseline performed more poorly in all cognitive tests both at the beginning of the study and at follow-up compared to amyloid-negative patients. Moreover, those patients with amyloid-positive PET scan at baseline had statistically significant decrease in MMSE test and also decline in other cognitive tests. Instead, patients with negative amyloid PET scan at baseline did not show statistically significant decrement in any cognitive tests. It is possible that the small increase (3–5.8%) in brain amyloid load during 3-year follow-up in the individuals who were amyloid negative at baseline could at least partially be due to the known age-related increase in brain amyloid load. Since the diagnosis of aMCI was based on clinical criteria without involvement of biomarkers, it is possible that the amyloid-negative aMCI patients at baseline probably have a different pathophysiology behind their MCI than those with amyloid-positive PET scan at baseline. In previous studies, the correlation between amyloid binding PET tracer retention and cognition has been generally fairly weak which is consistent with the notion that amyloid plaque distribution does not correlate well with clinical symptoms in AD. In addition, a few longitudinal 11C-PIB-PET publications indicate a lack of significant progression of 11C-PIB uptake in MCI and AD [19, 21,22,23]. AD patients apparently reach almost a plateau in 11C-PIB retention (and hence beta-amyloid accumulation) despite progression of their clinical symptoms. The lack of longitudinal increase in brain amyloid load in AD patients in previous studies suggests that amyloid tracer deposition most likely is an early event during the disease process. It has been suggested that amyloid biomarkers follow a sigmoid-shaped trajectory over time [24]. Therefore, in our study, aMCI patients with amyloid-positive PET scan were probably in the accelerating phase in the curve. At more advanced stage of MCI approaching AD stage, the increase in amyloid load would be less pronounced with probably weak association with the change in cognition. In the amyloid-negative aMCI patients, it is probable that the pathophysiology of their aMCI could be other than AD pathology, although theoretically they might turn amyloid positive later, but this is not supported by our findings with relatively short 3-year follow-up. In a previous study [25], 207 patients with memory problems were examined with 18F-Flutemetamol PET scan. 131 patients had diagnosis of MCI and 41 patients had diagnosis of AD before scanning. Most of the MCI and AD patients were 18F-Flutemetamol PET-positive (63 patients, 53% and 28 patients, 68%, respectively). In those patients with negative PET scan, the diagnosis changed to dementia due to non-AD disorders (including vascular dementia, progressive supranuclear palsy, dementia with Lewy bodies or frontotemporal dementia) or dementia of unclear etiology (dementia NOS) or led to retention/changed to MCI diagnosis.

18F-Flutemetamol and 11C-PIB are both brain amyloid binding PET ligands. They have been shown to differentiate between MCI patients and healthy controls [11, 26] and predict the progression of MCI to AD [5, 7, 13]. 11C-PIB is the first developed human amyloid imaging PET ligand and is widely used. 18F-Flutemetamol has some advantages over 11C-PIB. The half-life of 18F (ca. 110 min) enables wider accessibility for clinical and research use, enables shipment of the tracer from the production site to several, even remote, imaging sites, enables to scan several patients from a single synthesis batch and also enables longer PET acquisition times. The shorter 11C-PIB half-life (ca. 20 min) requires the use of on-site cyclotron and radiotracer production [16].

In this study, two different PET scanners were used. First, PET scanning of 14 patients was performed with ECAT EXACT HR + (CTI/Siemens, Knoxville, TN, US) camera. Later, both scans of 20 patients and a follow-up scan of two patients scans were performed with GE Discovery 690 (GE Healthcare, Waukesha, WI, US) PET scanner because at that time the other scanner was not in use anymore. Resolution and sensitivity of both scanners are very close to each other (see “PET and MRI imaging” section). In addition, the design of comparing the follow-up scan result to that of the baseline scan using the same scanner takes into account the possible difference in absolute uptake values between the scanners. Therefore, change of the scanner did not most probably have a significant effect on the results, especially since all but two patients were scanned with the same scanner both at baseline and follow-up.

Conclusions

In this study, we found that in the early phase of aMCI, 18F-Flutemetamol uptake significantly increased during the follow-up in amyloid-positive patients and was associated with a decline in MMSE score. Some of our patients were amyloid negative also at end of the study. In those patients, the 18F-flutemetamol uptake did not increase significantly over time. Most probably their aMCI is due to pathology other that AD, but the 3-year follow-up is too short to determine this. Future studies with longer follow-up are needed.

Availability of data and materials

Due to the consent given by study participants and the high degree of identifiability, data cannot be made publicly available. Pseudonymized data may be shared with authorized researchers, upon researcher’s reasonable request, who have IRB/ethics approval and an institutionally approved study plan.

References

  1. Petersen RC, Smith GE, Waring SC, Ivnik RJ, Tangalos EG, Kokmen E. Mild cognitive impairment: clinical characterization and outcome. Arch Neurol. 1999;56:303–8.

    Article  CAS  Google Scholar 

  2. Mormino EC, Papp KV. Amyloid Accumulation and cognitive decline in clinically normal older individuals: implications for aging and early Alzheimer’s disease. J Alzheimer’s Dis. 2018;64:S633–46.

    Article  CAS  Google Scholar 

  3. Landau SM, Harvey D, Madison CM, Reiman EM, Foster NL, Aisen PS, et al. Comparing predictors of conversion and decline in mild cognitive impairment. Neurology. 2010;75:230–8.

    Article  CAS  Google Scholar 

  4. Fleisher AS, Sun S, Taylor C, Ward CP, Gamst AC, Petersen RC, et al. Volumetric MRI vs clinical predictors of Alzheimer disease in mild cognitive impairment. Neurology. 2008;70:191–9.

    Article  CAS  Google Scholar 

  5. Okello A, Koivunen J, Edison P, Archer HA, Turkheimer FE, Någren K, et al. Conversion of amyloid positive and negative MCI to AD over 3 years: an 11C-PiB PET study. Neurology. 2009;73:754–60.

    Article  CAS  Google Scholar 

  6. Koivunen J, Scheinin N, Virta JR, Aalto S, Vahlberg T, Någren K, et al. Amyloid PET imaging in patients with mild cognitive impairment: a 2-year follow-up study. Neurology. 2011;76:1085–90.

    Article  CAS  Google Scholar 

  7. Brück A, Virta JR, Koivunen J, Koikkalainen J, Scheinin NM, Helenius H, et al. PIB, [18F]FDG and MR imaging in patients with mild cognitive impairment. Eur J Nucl Med Mol Imaging. 2013;40:1567–72.

    Article  Google Scholar 

  8. Ikonomovic MD, Buckley CJ, Heurling K, Sherwin P, Jones PA, Zanette M, Mathis CA, Klunk WE, Chakrabarty A, Ironside J, Ismail A, Smith C, Thal DR, Beach TG, Farrar G, Smith APL. Post-mortem histopathology underlying β-amyloid PET imaging following flutemetamol F 18 injection. Acta Neuropathol Commun. 2016. https://doi.org/10.1186/s40478-016-0399-z.

    Article  PubMed  PubMed Central  Google Scholar 

  9. Curtis C, Gamez JE, Singh U, Sadowsky CH, Villena T, Sabbagh MN, et al. Phase 3 trial of flutemetamol labeled with radioactive fluorine 18 imaging and neuritic plaque density. JAMA Neurol. 2015;72:287–94.

    Article  Google Scholar 

  10. Thal DR, Beach TG, Zanette M, Lilja J, Heurling K, Chakrabarty A, Ismail A, Farrar G, Buckley C, Smith APL. Estimation of amyloid distribution by [18F]flutemetamol PET predicts the neuropathological phase of amyloid β-protein deposition. Acta Neuropathol. 2018;136(4):557–67. https://doi.org/10.1007/s00401-018-1897-9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Thurfjell L, Lötöjnen J, Lundqvist R, Koikkalainen J, Soininen H, Waldemar G, et al. Combination of biomarkers: PET [18F]flutemetamol imaging and structural MRI in dementia and mild cognitive impairment. Neurodegener Dis. 2012;10:246–9.

    Article  CAS  Google Scholar 

  12. Rabinovici GD, Gatsonis C, Apgar C, Chaudhary K, Gareen I, Hanna L, et al. Association of amyloid positron emission tomography with subsequent change in clinical management among medicare beneficiaries with mild cognitive impairment or dementia. JAMA J Am Med Assoc. 2019;321:1286–94.

    Article  Google Scholar 

  13. Wolk DA, Sadowsky C, Safirstein B, Rinne JO, Duara R, Perry R, et al. Use of flutemetamol F18-labeled positron emission tomography and other biomarkers to assess risk of clinical progression in patients with amnestic mild cognitive impairment. JAMA Neurol. 2018;75:1114–23.

    Article  Google Scholar 

  14. Duara R, Loewenstein DA, Shen Q, Barker W, Potter E, Varon D, et al. Amyloid positron emission tomography with 18F-flutemetamol and structural magnetic resonance imaging in the classification of mild cognitive impairment and Alzheimer’s disease. Alzheimer’s Dement. 2013;9:295–301. https://doi.org/10.1016/j.jalz.2012.01.006.

    Article  Google Scholar 

  15. Bettinardi V, Presotto L, Rapisarda E, Picchio M, Gianolli L, Gilardi MC. Physical performance of the new hybrid PETCT Discovery-690. Med Phys. 2011;38:5394–411.

    Article  CAS  Google Scholar 

  16. Vandenberghe R, Van Laere K, Ivanoiu A, Salmon E, Bastin C, Triau E, et al. 18F-flutemetamol amyloid imaging in Alzheimer disease and mild cognitive impairment a phase 2 trial. Ann Neurol. 2010;68:319–29.

    Article  Google Scholar 

  17. Ashburner J, Friston KJ. Unified segmentation. Neuroimage. 2005;26(3):839–51. https://doi.org/10.1016/j.neuroimage.2005.02.018.

    Article  PubMed  Google Scholar 

  18. Tzourio-Mazoyer N, Landeau B, Papathanassiou D, Crivello F, Etard O, Delcroix N, et al. Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain. Neuroimage. 2002;15:273–89.

    Article  CAS  Google Scholar 

  19. Scheinin NM, Aalto S, Koikkalainen J, Lötjönen J, Karrasch M, Kemppainen N, et al. Follow-up of [11C]PIB uptake and brain volume in patients with alzheimer disease and controls. Neurology. 2009;73:1186–92.

    Article  CAS  Google Scholar 

  20. Wechsler D. Manual for the wechsler memory scale-revised. San Antonio: The Psychological Corporation; 1987.

    Google Scholar 

  21. Jack CR, Lowe VJ, Weigand SD, Wiste HJ, Senjem ML, Knopman DS, et al. Serial PIB and MRI in normal, mild cognitive impairment and Alzheimers disease: implications for sequence of pathological events in Alzheimers disease. Brain. 2009;132:1355–65.

    Article  Google Scholar 

  22. Engler H, Forsberg A, Almkvist O, Blomquist G, Larsson E, Savitcheva I, et al. Two-year follow-up of amyloid deposition in patients with Alzheimer’s disease. Brain. 2006;129:2856–66.

    Article  Google Scholar 

  23. Villemagne VL, Burnham S, Bourgeat P, Brown B, Ellis KA, Salvado O, Cassandra Szoeke S, Macaulay L, Martins R, Maruff P, Ames D, Rowe CC, Masters CL. Amyloid β deposition, neurodegeneration, and cognitive decline in sporadic Alzheimer’s disease: a prospective cohort study. Lancet Neurol. 2013;12(4):357–67. https://doi.org/10.1016/S1474-4422(13)70044-9.

    Article  CAS  PubMed  Google Scholar 

  24. Jack CR, Knopman DS, Jagust WJ, Petersen RC, Weiner MW, Aisen PS, et al. Tracking pathophysiological processes in Alzheimer’s disease: an updated hypothetical model of dynamic biomarkers. Lancet Neurol. 2013;12:207–16.

    Article  CAS  Google Scholar 

  25. Leuzy A, Savitcheva I, Chiotis K, Lilja J, Andersen P, Bogdanovic N, Jelic V, Nordberg A. Clinical impact of [18F]flutemetamol PET among memory clinic patients with an unclear diagnosis. Eur J Nucl Med Mol Imaging. 2019;46(6):1276–86. https://doi.org/10.1007/s00259-019-04297-5.

    Article  PubMed  PubMed Central  Google Scholar 

  26. Kemppainen NM, Aalto S, Wilson IA, Någren K, Helin S, Brück A, et al. PET amyloid ligand [11C]PIB uptake is increased in mild cognitive impairment. Neurology. 2007;68:1603–6.

    Article  CAS  Google Scholar 

Download references

Acknowledgements

The authors thank all the study volunteers for their generous contribution. The skillful assistance of the staff of Turku PET Centre is gratefully acknowledged. We acknowledge GE Healthcare (GEHC) for the provision of [18F]flutemetamol doses

Registration

This was an academic study with no drug or other intervention, and therefore, the study was not registered in any registries.

Funding

This work was financially supported by the Finnish Cultural Foundation, the Paulo Foundation, the Finnish Medicine Foundation, the Maire Taponen Foundation, the Maud Kuistila Memorial Foundation, Finnish Academy (project # 310962), the Sigrid Juselius Foundation and Finnish State Research Funding (VTR).

Author information

Author notes

  1. Elina Rauhala and Jarkko Johansson contributed equally

Authors and Affiliations

  1. Clinical Neurosciences, Faculty of Medicine, Turku University Hospital, University of Turku and Neurocenter, Turku, Finland

    Elina Rauhala

  2. Turku PET Centre, Turku University Hospital, Turku, Finland

    Jarkko Johansson, Kirsi A. Virtanen & Juha O. Rinne

  3. Department of Radiation Sciences, Umeå University, Umeå, Sweden

    Jarkko Johansson

  4. Department of Psychology, Åbo Akademi University, Turku, Finland

    Mira Karrasch

  5. Turku PET Centre, University of Turku, Turku, Finland

    Olli Eskola & Tuula Tolvanen

  6. Department of Medical Physics, Turku University Hospital, Turku, Finland

    Tuula Tolvanen

  7. Department of Radiology, University of Turku and Turku University Hospital, Turku, Finland

    Riitta Parkkola

  8. InFLAMES Research Flagship Center, University of Turku, Turku, Finland

    Juha O. Rinne

Authors
  1. Elina Rauhala
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  2. Jarkko Johansson
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  3. Mira Karrasch
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  4. Olli Eskola
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  5. Tuula Tolvanen
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  6. Riitta Parkkola
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  7. Kirsi A. Virtanen
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  8. Juha O. Rinne
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Contributions

ER, JJ, MK and JOR contributed to the study conception and design. Material preparation was performed by OE and TT, and data collection and analysis were performed by ER, JJ, MK, RP and KAV. The first draft of the manuscript was written by ER and JJ and MK, OE, TT, RP, KAV and JOR critically commented the first draft of the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Juha O. Rinne.

Ethics declarations

Ethics approval and consent to participate

This study was performed in line with the principles of the Declaration of Helsinki. Approval was granted by the Ethics Committee of Hospital District of Southwest Finland on the 15th of February, 2011. Informed consent was obtained from all individual participants included in the study.

Consent for publication

Informed consent was obtained from all individual participants included in the study, including that imaging data can be used in research purposes and can be published in international scientific journals without possibility to identify individual participant.

Competing interests

Dr. Rinne serves as an neurology consultant at CRST (Clinical Research Services Turku) and in Global Expert Panel for Novo Nordisk and in data monitoring committee in a clinical drug trial conducted by Lundbeck.

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Rauhala, E., Johansson, J., Karrasch, M. et al. Change in brain amyloid load and cognition in patients with amnestic mild cognitive impairment: a 3-year follow-up study. EJNMMI Res 12, 55 (2022). https://doi.org/10.1186/s13550-022-00928-5

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