Skip to main content
Download PDF

The effects of imaging markers on clinical trajectory in cerebral amyloid angiopathy: a longitudinal study in a memory clinic

Alzheimer's Research & Therapy volume 15, Article number: 14 (2023) Cite this article

Abstract

Background

We investigated the relevance of various imaging markers for the clinical trajectory of cerebral amyloid angiopathy (CAA) patients in a memory clinic.

Methods

A total of 226 patients with probable CAA were included in this study with a mean follow-up period of 3.5 ± 2.7 years. Although all had more than one follow-up visit, 173 underwent follow-up Mini-Mental Status Examination (MMSE) and Clinical Dementia Rating Sum of Boxes (CDR-SB) ranging from 2 to 15 time points. Among 226, 122 patients underwent amyloid-β (Aβ) PET imaging. The prevalence of intracerebral hemorrhage (ICH) and its imaging predictors was investigated. The effects of CAA imaging markers and Aβ PET positivity on longitudinal cognition based on the MMSE and CDR-SB were evaluated using mixed effects models.

Results

During the follow-up, 10 (4.4%) patients developed ICH: cortical superficial siderosis (cSS; hazard ratio [HR], 6.45) and previous lobar ICH (HR, 4.9), but lobar cerebral microbleeds (CMBs) were not predictors of ICH development. The presence of CMIs (p = 0.045) and Aβ positivity (p = 0.002) were associated with worse MMSE trajectory in CAA patients. Regarding CDR-SB trajectory, only Aβ positivity was marginally associated with worse longitudinal change (p = 0.050).

Conclusion

The results of the present study indicated that various imaging markers in CAA patients have different clinical relevance and predictive values for further clinical courses.

Introduction

Cerebral amyloid angiopathy (CAA) is characterized by amyloid β (Aβ) deposition in the small meningeal/cortical arteries [1] and clinically diagnosed using characteristic magnetic resonance imaging (MRI) markers such as strictly lobar intracerebral hemorrhage (ICH), lobar cerebral microbleeds (CMBs), and cortical superficial siderosis (cSS). Recently updated clinic-radiological criteria added additional characteristic imaging markers for CAA, which are enlarged perivascular space in the centrum semiovale (CSO-EPVS) and multi-spot patterned white matter hyperintensities (WMH) [2]. Because lobar ICH is the most common symptomatic presentation of CAA, the predictors of ICH development have been investigated in many studies. In particular, cSS has been considered the single most important risk factor for future ICH [3,4,5,6,7,8,9]. However, knowledge regarding predictors of clinical and cognitive decline, which are also regarded as important clinical phenotypes of CAA, is limited, especially in a memory clinic [10].

In our previous study, the hemorrhagic markers, lobar CMBs and cSS, were shown specifically associated with cognitive dysfunction and partially mediated by cortical thinning [11]. However, the cross-sectional study design may have limited the clinical implication in terms of prognosis prediction. Recently, we showed that Aβ PET positivity in CAA is associated with worse cognitive trajectory using longitudinal data, emphasizing the clinical implication of Aβ PET positivity [12]. However, we could not consider the effects of the other CAA-specific MRI markers as prognostic markers, such as cortical microinfarcts (CMIs), which have been investigated as another important contributor to cognition in CAA [13].

Therefore, in the present study, how various CAA-specific imaging markers affect the cognitive decline in a probable CAA cohort in a memory clinic was investigated. First, the prevalence of ICH and its imaging predictors was investigated in a memory clinic. In addition, the effects of the MRI markers as well as Aβ PET positivity on cognitive trajectory were investigated. We hypothesized that the presence of cSS, a higher number of CMBs, and CMIs affect cognitive decline regardless of ICH development in CAA patients in a memory clinic.

Materials and methods

Study participants

For the selection of study participants, we reviewed Neuroimaging Study Registries of patients who visited a memory clinic in Samsung Medical Center complaining of cognitive impairment. We first reviewed 5248 patients from MRI registries obtained in the memory clinic of Samsung Medical Center between July 2007 to December 2016. Since 2016, we also have developed the CAA imaging registry with compatible patients. Therefore, the present study included a total of 266 probable CAA participants from July 2007 to July 2020. The flowchart detailing the study participant inclusion is shown in Fig. 1. All participants underwent brain MRI at baseline and had at least two strictly lobar ICH/CMBs or one strictly lobar ICH/CMBs with cSS on MRI, meeting both the modified and the recent version 2.0 Boston criteria for CAA [2, 14,15,16]. All patients underwent baseline Mini-Mental State Examination (MMSE) and completed neuropsychological tests (if their MMSE score was not lower than 10).

Fig. 1
figure 1

The flowchart of the participant inclusion process. Abbreviations: SMC, Samsung medical center; MRI, magnetic resonance images; AD, Alzheimer’s disease; SCD, subjective cognitive decline; MCI, mild cognitive impairment; ADD, Alzheimer’s disease dementia; SVCI, subcortical vascular cognitive impairment; CAA, cerebral amyloid angiopathy

Full size image

Assessment of cerebral small vessel disease (CSVD) imaging markers on MRI

All participants underwent brain MRI including T2* GRE and FLAIR with or without 3-dimension (3D) T1 images at baseline. Imaging analysis was performed by neurologists who were trained in neuroimaging rating. All structural imaging markers of cerebral small vessel disease (CSVD) were rated in accordance with consensus guidelines [17]. Lobar CMBs were defined as homogenous and round lesions with signal loss (≤ 10 mm in diameter) on T2* GRE images, with a location in exclusively cortical and subcortical areas. Lesions with a diameter > 10 mm were counted as ICH. cSS was defined as linear hypointensities on T2* GRE images, consistent with chronic blood residues on the superficial cortical layers [18]. Four experienced neurologists who were blinded to clinical data rated lobar CMBs and cSS. The interobserver intraclass correlation coefficient (ICC) ranged from 0.87 to 0.91 for lobar CMBs and from 0.82 to 0.96 for cSS [19]. Lacunes were identified and counted in accordance with STandards for ReportIng Vascular changes on nEuroimaging (STRIVE) [17]. The severity of WMHs was rated using the modified Fazekas scale [20]. As the Boston criteria v2.0 introduced CSO-EPVS and WMH in a multi-spot pattern as major imaging characteristics of CAA, we also scored these two imaging markers. We used a validated Visual Rating Scale to score CSO-EPVS on the axial T2-weighted images: 0, no EPVS; 1, < 10 EPVS; 2, 10 to 20 EPVS; 3, 21 to 40 EPVS; and 4, > 40 EPVS [21], and defined severe CSO-EPVS as more than 20 visible EPVS (EPVS grade 3 or 4) according to the Boston criteria v2.0. In each case, the most affected hemisphere was rated, and in cases where the rating was difficult (e.g. due to movement or extensive WMH), we selected the closest category. The multi-spot patterned WMH was defined as the presence of at least 10 small circles or spots of WMH in subcortical areas as described in the previous studies [2, 22]. In addition, we visually rated the number of CMIs using FLAIR, T2-weighted, and T1-weighted images (mostly FLAIR and T1-weighted images). CMIs were defined as hyperintense/hypointense lesions on FLAIR/T1 images, which are less than 5 mm in diameter and restricted to the cortex. CMIs located in regions close to large cortical infarcts and appearing hypointense on GRE (hemorrhagic lesions) were discarded [23,24,25]. The experienced neurologist (H.J.) underwent ratings for EPVS grade, WMH multi-spot pattern, and CMIs for all images. For randomly selected 20 subjects’ images, an independent neurologist (C.M.Y.) rated all three imaging markers again, and interobserver κ were 0.62 for severe CSO-EPVS, 0.88 for a presence of multi-spot patterned WMH, and 0.51 for a presence of CMIs.

Aβ PET imaging acquisition, preprocessing, and interpretation

Among 226 participants, 122 underwent Aβ PET (26 11C-PiB, 86 18F-florbetaben, 10 18F-flutemetamol PET) using a Discovery STe PET/CT scanner (GE Medical Systems, Milwaukee, WI, USA) in a 3D scanning mode that examined 47 slices 3.3 mm in thickness spanning the entire brain. A 16-slice helical CT (140 keV, 80 mA; 3.75 mm section width) was performed for attenuation correction. For 11C-PiB PET, a 30-min emission static PET scan was performed 60 min after injection into an antecubital vein as a bolus of a mean dose of 420 MBq. For 18F-florbetaben PET and 18F-flutemetamol PET, a 20-min emission PET scan with dynamic mode (consisting of 4 × 5 min frames) was performed 90 min after injection of a mean dose of 311.5 MBq 18F-florbetaben and 197.7 MBq 18F-flutemetamol, respectively.

For PiB PET, both MR and PET images were co-registered with each other using the rigid body transformation. The T1-weighted MR image of each subject was aligned with the MNI-152 template using a non-linear deformation including translation, rotation, scaling, and shearing. After standard space registration, the grey matter was divided into 116 regions using the automated anatomical labeling (AAL) atlas [26]. To compute the standardized uptake value ratios (SUVRs), every voxel intensity was normalized by the mean intensity of cerebellar gray matter which was regarded as the reference region. Then, global PiB retention ratios were assessed from the volume-weighted average SUVR of 28 bilateral cerebral cortical volumes of interest (VOIs).

Aβ PET positivity was defined when the global PiB SUVR was > 1.5, when florbetaben PET was visually rated as 2 or 3 on the brain Aβ plaque load (BAPL) scoring system [27], and when flutemetamol PET was visually assessed as positive in any 1 of the 5 brain regions (frontal, parietal, posterior cingulate/precuneus, striatum, and lateral temporal lobes) in either hemisphere [28]. Aβ PET images were reviewed by one nuclear medicine physician who was blinded to clinical information and one neurologist. They discussed the discrepant results of Aβ positivity to achieve consensus.

Neuropsychological tests

All patients underwent neuropsychological tests using the Seoul Neuropsychological Screening Battery (SNSB) [19, 29] which consists of tests for attention, language, visuoconstructive function, verbal and visual memory, and frontal/executive function. In the present study, a summary score was generated for each cognitive domain. An attention score was calculated by summing scores in digit span forward (range 0–9) and digit span backward (range 0–8). A memory domain score (memory score) was calculated by summing scores in verbal and visual memory tests including raw scores on the Seoul Verbal Learning Test (SVLT) immediate recall (range 0–36), delayed recall (range 0–12), and recognition (range 0–24), as well as raw scores on Rey–Osterrieth Complex Figure Test (RCFT) immediate recall (range 0–36), delayed recall (range 0–36), and recognition (range 0–24). A frontal-executive domain score (frontal score) was calculated by summing the scores in a category word generation task (animal), a phonemic word generation task, and the Stroop color-reading test (range 0–120). Raw scores on the Korean version of the Boston Naming Test (KBNT) and RCFT copy test were used to construct the language and visuospatial scores, respectively.

Follow-up

All participants had follow-up clinic visits at least once, and the mean follow-up period from the baseline MRI was 3.5 ± 2.7 years. Among 226 participants, 173 underwent follow-up MMSE or CDR-SB ranging from 2 to 15 time points.

Statistical analyses

Univariable logistic regression analysis was performed to determine the predictors associated with ICH development, including baseline age, presence of hypertension and hyperlipidemia, smoking status, anticoagulant use, presence of cSS, APOE2 and APOE4 genotype, number of lobar CMBs, previous lobar ICH (either symptomatic or asymptomatic), presence of severe CSO-EPVS, multi-spot patterned WMH, and severe WMH as independent variables. Independent variables which were significant with p-value < 0.2 were subsequently included in the multivariable logistic regression analysis. In addition, to investigate the effects of imaging markers cSS, lobar CMBs, CSO-EPVS, multi-spot patterned WMH, presence of CMIs, and Aβ positivity on longitudinal cognitive changes (MMSE and CDR-SB), linear mixed effects models were performed. In this analysis, lobar CMBs were log-transformed to ln (1 + lobar CMBs) due to skewed distribution. Fixed effects were each imaging marker, time from the baseline (time), age, sex, education years, baseline MMSE or CDR-SB scores, and the two-way interaction term for each imaging marker and time (imaging marker × time). Patients were included as random effects. All statistical analyses were performed with STATA/SE version 15.1. Statistical significance was defined as two-tailed p < 0.05.

Results

Demographics and clinical characteristics of probable CAA participants

A total of 266 probable CAA patients were included in the present study (Table 1). The mean age of CAA participants was 75.4 ± 7.0 years, and the prevalence of females was 54.4%. Regarding imaging markers, the median number of lobar CMBs was 5 (interquartile range 2–16), and 62 patients (27.4%) had cSS. Among 122 patients who underwent Aβ PET scans, 91 (74.6%) showed Aβ positivity. Among 266 CAA patients, the prevalence of APOE2 and APOE4 carriers was 28 (12.5%) and 98 (43.4%), respectively. The prevalence of lacunes, severe CSO-EPVS, CMIs, and previous ICH on MRI (either symptomatic or asymptomatic) was 37.2% (n = 84), 26.9% (n = 58), 31% (n = 70), and 15.9% (n = 36), respectively. In terms of WMH characteristics, the prevalence of multi-spot patterned WMH and severe WMH was 70.4% (n = 157) and 22.1% (n = 50), respectively.

Table 1 Clinical characteristics of probable CAA patients
Full size table

Predictors of lobar ICH development

During the mean follow-up period of 3.5 years, among 226 patients, 10 patients developed lobar ICH, 8 patients presented with ischemic stroke, and 1 patient deteriorated clinically with seizure disorder (Table 1).

In univariate analyses, hypertension (hazard ratio (HR), 0.24; confidence interval (CI), 0.05, 1.16), anticoagulant use (HR, 7.89; CI 0.75–83.5), presence of cSS (HR, 1.02; CI 0.93–1.12), APOE2 (HR, 5.33; CI 1.4–20.25), APOE4 (HR, 0.14; CI 0.02–1.0), and previous lobar ICH (HR, 9.3; CI 2.48– 34.9) were associated with ICH development. However, age, hyperlipidemia, smoking, e4 genotypes, lobar CMBs, severe CSO-EPVS, multi-spot patterned WMH, and severe WMH were not associated with ICH development.

Multivariable analyses including significant predictors from univariate analyses showed that only the presence of cSS (HR, 7.38; CI 1.42–38.44) and previous lobar ICH (HR, 5.07; CI 1.23–20.87) were associated with ICH development but not hypertension, smoking, and APOE genotypes (Table 2).

Table 2 Univariable and multivariable logistic regression analysis results for predictors of lobar ICH development
Full size table

Effects of imaging markers on cognitive decline in CAA patients

In linear mixed effects models to investigate the effects of CAA imaging markers on cognitive decline, most of the imaging markers including the presence of cSS, the number of lobar CMBs, severe CSO-EPVS, and multi-spot patterned WMH were not associated with MMSE decline, while only the presence of CMIs (p = 0.045) and Aβ positivity (p = 0.001) were associated with a decline in MMSE score. However, Aβ positivity (p = 0.050) was marginally associated with increased CDR-SB score (Table 3, Fig. 2).

Table 3 Effects of imaging markers on longitudinal cognitive trajectory obtained from the mixed effects model
Full size table
Fig. 2
figure 2

Distinctive MMSE decline based on various imaging markers. a CMI. b Aβ positivity. Abbreviations: MMSE, Mini-Mental State Examination; CMI, cortical microinfarcts; Aβ, amyloid β. Y-axis represents the predicted MMSE scores for each follow-up year derived from the predicted model equation using a linear mixed effect model

Full size image

Discussion

In the present study, clinical characteristics of probable CAA patients based on their CAA-specific MRI markers and their effects on clinical trajectory were investigated using a longitudinal CAA cohort in a memory clinic. The main findings were as follows: First, even in CAA patients from a memory clinic, ICH was an important clinical outcome for which cSS and previous lobar ICH were predictors. Second, only the presence of CMIs and Aβ positivity was associated with the worse trajectory in general cognition in CAA patients. Taken together, the findings indicated that various imaging markers in CAA patients have different clinical relevance and predictive values for further clinical course.

The first major finding was that 4.4% of probable CAA patients in the memory clinic developed ICH, and 3.5% developed stroke during the mean follow-up period of 3.5 years. This prevalence is lower than that in previous reports [5, 8, 30]. This discrepancy may be explained by participants from different CAA cohorts (primarily patients with a relatively low prevalence of previous ICH or cSS recruited from a memory clinic in the present sample versus patients with symptomatic lobar ICH in previous studies), considering that CAA with and without lobar ICH might have different pathophysiologic mechanisms [31]. In the univariate analyses, we found hypertension, anticoagulant use, APOE genotype, presence of cSS, and previous lobar ICH were associated with lobar ICH development (p < 0.2). However, in the final model, we found that only cSS and previous ICH were predictors for further ICH development in memory clinic settings, which is consistent with previous studies [3,4,5,6,7,8,9, 32]. However, the number of lobar CMBs was not a significant predictor of ICH in the present study. In several previous studies, two different imaging phenotypes in CAA were suggested, macrohemorrhagic (cSS and ICH) and microhemorrhagic (many lobar CMBs) types [33,34,35]. Although the negative association of the lobar CMBs and ICH development in this study might be due to the lack of power, it might alternatively indicate that the presence of lobar CMBs and cSS/ICH in a memory clinic cohort may not be potentially mutually associated and occur simultaneously. Thus, in patients who have many lobar CMBs but no cSS or previous ICH, the need for a reliable indicator of future ICH should be further studied.

Second, only the presence of CMIs was associated with general cognition decline represented by MMSE score among various imaging markers on MRI. CMIs are found commonly in the brains with AD and CAA pathologies, known to be associated with CAA severity [36, 37]. As an important imaging marker in CAA, we found that 31% of study participants had CMIs, which is similar with the prevalence reported in the previous CAA cohort [24]. Our finding that the presence of CMIs was associated with MMSE decline corroborates the previous knowledge that CMIs are important in cognition [24, 38]. However, in this study, we failed to demonstrate the association between the other imaging markers (particularly, the presence of cSS and the number of lobar CMBs) and cognitive trajectory. In terms of the association between hemorrhagic markers and longitudinal clinical follow-up, cSS was mainly studied as the key predictive marker for future ICH [3,4,5,6,7,8,9]. Our study finding suggests that although cSS is strongly associated with ICH occurrence, it does not necessarily cause a detrimental effect on cognition. In terms of CMBs, several longitudinal studies as well as cross-sectional studies investigated the association between lobar CMBs and cognitive deterioration [11, 39,40,41,42,43], but most of these studies were population-based studies which could not reveal the dose-effect relationship between lobar CMBs and cognitive change specifically in probable CAA patients, which our current study tried to investigate. Therefore, we consider that in probable CAA patients in a memory clinic, the number of lobar CMBs might not be directly associated with cognitive decline.

Our finding demonstrated that Aβ positivity had the single most important effect on cognitive decline (presented by MMSE and marginally by CDR-SB) in CAA in a memory clinic setting. First of all, the prevalence of Aβ positivity (74.6%) in this study was similar to those of 60% [44] or 70 %[45] in previous studies, although other studies showed relatively high sensitivity over 80% of Aβ PET in probable CAA [46,47,48,49]. The clinical significance of Aβ positivity in CAA is consistent with our previous study showing that Aβ positivity was associated with cognitive decline in CAA patients, although the effects of other hemorrhagic markers were not previously considered [12]. As discussed in the previous study, Aβ positivity in CAA patients may indicate combined AD neuropathological changes or advanced CAA pathology even without parenchymal Aβ [12]. Thus, the synergistic effect of combined AD or worse vascular injury due to severe CAA may contribute to cognitive decline [50, 51]. However, in this study, not all patients included did not undergo Aβ PET, which limits the relevance of the study finding. Therefore, a future study with a more complete dataset is required.

Limitation

The present study’s strengths include a relatively large probable CAA cohort from a memory clinic compared with previous studies [44,45,46,47,48,49]. Although our findings on the predictors of lobar ICH development or cognitive decline in CAA might be predictable based on many previous studies, our study has generated additional evidence supporting the previous knowledge, especially using the real memory clinic cohort. However, several limitations should be acknowledged. First, this study was not confirmed by pathological data. This could be a critical limitation of this study, because the modified Boston criteria (v1.5) have not been fully validated in a memory clinic. Second, the retrospectively recruited cohort could have led to underestimated ICH occurrence or cognitive decline because patients with rapid deterioration might have been lost to follow-up. Third, there might be possibilities that some potential CAA patients were not included because v1.5 is less sensitive than the recent version (v2.0) of the Boston criteria and if individual specialists missed the CAA cases in the clinic while screening for probable CAA. Fourth, a few patients developed ICH or stroke during the follow-up period, where negative associations with the presence of lobar CMBs or Aβ positivity on PET simply might be simply due to a lack of power. Finally, Aβ PET was not performed in all participants which may limit the study findings.

Conclusions

The results of the present study are noteworthy because the effects of CAA-related imaging markers on longitudinal cognition in memory clinics were reported for the first time. The results indicated that cSS and previous lobar ICH were associated with lobar ICH development, while CMIs and Aβ positivity which indicates AD pathology in the parenchyma were associated with cognitive decline in a memory clinic setting.

Availability of data and materials

The data are publicly available and provided upon request.

References

  1. Biffi A, Greenberg SM. Cerebral amyloid angiopathy: a systematic review. J Clin Neurol. 2011;7(1):1–9.

    Article  Google Scholar 

  2. Charidimou A, Boulouis G, Frosch MP, Baron J-C, Pasi M, Albucher JF, et al. The Boston criteria version 2.0 for cerebral amyloid angiopathy: a multicentre, retrospective, MRI–neuropathology diagnostic accuracy study. Lancet Neurol. 2022;21(8):714–25.

    Article  Google Scholar 

  3. Beitzke M, Enzinger C, Wunsch G, Asslaber M, Gattringer T, Fazekas F. Contribution of convexal subarachnoid hemorrhage to disease progression in cerebral amyloid angiopathy. Stroke. 2015;46(6):1533–40.

    Article  Google Scholar 

  4. Charidimou A, Linn J, Vernooij MW, Opherk C, Akoudad S, Baron JC, et al. Cortical superficial siderosis: detection and clinical significance in cerebral amyloid angiopathy and related conditions. Brain. 2015;138(Pt 8):2126–39.

    Article  Google Scholar 

  5. Raposo N, Charidimou A, Roongpiboonsopit D, Onyekaba M, Gurol ME, Rosand J, et al. Convexity subarachnoid hemorrhage in lobar intracerebral hemorrhage: a prognostic marker. Neurology. 2020;94(9):e968–e77.

    Article  Google Scholar 

  6. Charidimou A, Boulouis G, Xiong L, Jessel MJ, Roongpiboonsopit D, Ayres A, et al. Cortical superficial siderosis and first-ever cerebral hemorrhage in cerebral amyloid angiopathy. Neurology. 2017;88(17):1607–14.

    Article  Google Scholar 

  7. Charidimou A, Boulouis G, Greenberg SM, Viswanathan A. Cortical superficial siderosis and bleeding risk in cerebral amyloid angiopathy: a meta-analysis. Neurology. 2019;93(24):e2192–e202.

    Article  Google Scholar 

  8. Charidimou A, Peeters AP, Jäger R, Fox Z, Vandermeeren Y, Laloux P, et al. Cortical superficial siderosis and intracerebral hemorrhage risk in cerebral amyloid angiopathy. Neurology. 2013;81(19):1666–73.

    Article  Google Scholar 

  9. Roongpiboonsopit D, Charidimou A, William CM, Lauer A, Falcone GJ, Martinez-Ramirez S, et al. Cortical superficial siderosis predicts early recurrent lobar hemorrhage. Neurology. 2016;87(18):1863–70.

    Article  CAS  Google Scholar 

  10. Banerjee G, Carare R, Cordonnier C, Greenberg SM, Schneider JA, Smith EE, et al. The increasing impact of cerebral amyloid angiopathy: essential new insights for clinical practice. J Neurol Neurosurg Psychiatry. 2017.

  11. Jang YK, Kim HJ, Lee JS, Kim YJ, Kim KW, Kim Y, et al. Distinctive clinical effects of haemorrhagic markers in cerebral amyloid angiopathy. Sci Rep. 2017;7(1):15984.

    Article  Google Scholar 

  12. Jang H, Jang YK, Kim HJ, Werring DJ, Lee JS, Choe YS, et al. Clinical significance of amyloid beta positivity in patients with probable cerebral amyloid angiopathy markers. Eur J Nucl Med Mol Imaging. 2019;46(6):1287–98.

    Article  CAS  Google Scholar 

  13. Lauer A, van Veluw SJ, William CM, Charidimou A, Roongpiboonsopit D, Vashkevich A, et al. Microbleeds on MRI are associated with microinfarcts on autopsy in cerebral amyloid angiopathy. Neurology. 2016;87(14):1488–92.

    Article  Google Scholar 

  14. Linn J, Halpin A, Demaerel P, Ruhland J, Giese AD, Dichgans M, et al. Prevalence of superficial siderosis in patients with cerebral amyloid angiopathy. Neurology. 2010;74(17):1346–50.

    Article  CAS  Google Scholar 

  15. Greenberg SM, Charidimou A. Diagnosis of cerebral amyloid angiopathy: evolution of the Boston criteria. Stroke. 2018;49(2):491–7.

    Article  Google Scholar 

  16. Knudsen KA, Rosand J, Karluk D, Greenberg SM. Clinical diagnosis of cerebral amyloid angiopathy: validation of the Boston criteria. Neurology. 2001;56(4):537–9.

    Article  CAS  Google Scholar 

  17. Wardlaw JM, Smith EE, Biessels GJ, Cordonnier C, Fazekas F, Frayne R, et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. Lancet Neurol. 2013;12(8):822–38.

    Article  Google Scholar 

  18. Linn J, Herms J, Dichgans M, Bruckmann H, Fesl G, Freilinger T, et al. Subarachnoid hemosiderosis and superficial cortical hemosiderosis in cerebral amyloid angiopathy. AJNR Am J Neuroradiol. 2008;29(1):184–6.

    Article  CAS  Google Scholar 

  19. Kang Y, Na DL. Seoul Neuropsychological Screening Battery (SNSB). Incheon: Human Brain Research & Consulting Co.; 2003.

    Google Scholar 

  20. Fazekas F, Kleinert R, Offenbacher H, Schmidt R, Kleinert G, Payer F, et al. Pathologic correlates of incidental MRI white matter signal hyperintensities. Neurology. 1993;43(9):1683–9.

    Article  CAS  Google Scholar 

  21. Maclullich AM, Wardlaw JM, Ferguson KJ, Starr JM, Seckl JR, Deary IJ. Enlarged perivascular spaces are associated with cognitive function in healthy elderly men. J Neurol Neurosurg Psychiatry. 2004;75(11):1519–23.

    Article  CAS  Google Scholar 

  22. Charidimou A, Boulouis G, Haley K, Auriel E, van Etten ES, Fotiadis P, et al. White matter hyperintensity patterns in cerebral amyloid angiopathy and hypertensive arteriopathy. Neurology. 2016;86(6):505–11.

    Article  CAS  Google Scholar 

  23. Hilal S, Sikking E, Shaik MA, Chan QL, van Veluw SJ, Vrooman H, et al. Cortical cerebral microinfarcts on 3T MRI. Neurology. 2016;87(15):1583.

    Article  Google Scholar 

  24. Xiong L, Van Veluw SJ, Bounemia N, Charidimou A, Pasi M, Boulouis G, et al. Cerebral cortical microinfarcts on magnetic resonance imaging and their association with cognition in cerebral amyloid angiopathy. Stroke. 2018;49(10):2330–6.

    Article  Google Scholar 

  25. Hilal S, Sikking E, Shaik MA, Chan QL, Van Veluw SJ, Vrooman H, et al. Cortical cerebral microinfarcts on 3T MRI: a novel marker of cerebrovascular disease. Neurology. 2016;87(15):1583–90.

    Article  Google Scholar 

  26. 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(1):273–89.

    Article  CAS  Google Scholar 

  27. Barthel H, Gertz HJ, Dresel S, Peters O, Bartenstein P, Buerger K, et al. Cerebral amyloid-beta PET with florbetaben (18F) in patients with Alzheimer’s disease and healthy controls: a multicentre phase 2 diagnostic study. Lancet Neurol. 2011;10(5):424–35.

    Article  CAS  Google Scholar 

  28. Kim SE, Woo S, Kim SW, Chin J, Kim HJ, Lee BI, et al. A nomogram for predicting amyloid PET positivity in amnestic mild cognitive impairment. J Alzheimers Dis. 2018;66(2):681–91.

    Article  CAS  Google Scholar 

  29. Ahn HJ, Chin J, Park A, Lee BH, Suh MK, Seo SW, et al. Seoul Neuropsychological Screening Battery-dementia version (SNSB-D): a useful tool for assessing and monitoring cognitive impairments in dementia patients. J Korean Med Sci. 2010;25(7):1071–6.

    Article  Google Scholar 

  30. Charidimou A, Boulouis G, Roongpiboonsopit D, Auriel E, Pasi M, Haley K, et al. Cortical superficial siderosis multifocality in cerebral amyloid angiopathy: a prospective study. Neurology. 2017;89(21):2128–35.

    Article  Google Scholar 

  31. Charidimou A, Martinez-Ramirez S, Shoamanesh A, Oliveira-Filho J, Frosch M, Vashkevich A, et al. Cerebral amyloid angiopathy with and without hemorrhage: evidence for different disease phenotypes. Neurology. 2015;84(12):1206–12.

    Article  CAS  Google Scholar 

  32. Ni J, Auriel E, Jindal J, Ayres A, Schwab KM, Martinez-Ramirez S, et al. The characteristics of superficial siderosis and convexity subarachnoid hemorrhage and clinical relevance in suspected cerebral amyloid angiopathy. Cerebrovasc Dis. 2015;39(5-6):278–86.

    Article  Google Scholar 

  33. Charidimou A, Werring DJ. Cerebral microbleeds as a predictor of macrobleeds: what is the evidence? Int J Stroke. 2014;9(4):457–9.

    Article  Google Scholar 

  34. Greenberg SM, Nandigam RK, Delgado P, Betensky RA, Rosand J, Viswanathan A, et al. Microbleeds versus macrobleeds: evidence for distinct entities. Stroke. 2009;40(7):2382–6.

    Article  Google Scholar 

  35. Shoamanesh A, Martinez-Ramirez S, Oliveira-Filho J, Reijmer Y, Falcone GJ, Ayres A, et al. Interrelationship of superficial siderosis and microbleeds in cerebral amyloid angiopathy. Neurology. 2014;83(20):1838–43.

    Article  Google Scholar 

  36. Okamoto Y, Yamamoto T, Kalaria RN, Senzaki H, Maki T, Hase Y, et al. Cerebral hypoperfusion accelerates cerebral amyloid angiopathy and promotes cortical microinfarcts. Acta neuropathologica. 2012;123(3):381–94.

    Article  CAS  Google Scholar 

  37. Haglund M, Passant U, Sjöbeck M, Ghebremedhin E, Englund E. Cerebral amyloid angiopathy and cortical microinfarcts as putative substrates of vascular dementia. Int J Geriatr Psychiatry. 2006;21(7):681–7.

    Article  Google Scholar 

  38. Van Veluw SJ, Hilal S, Kuijf HJ, Ikram MK, Xin X, Yeow TB, et al. Cortical microinfarcts on 3T MRI: clinical correlates in memory-clinic patients. Alzheimer’s Dementia. 2015;11(12):1500–9.

    Article  Google Scholar 

  39. Akoudad S, Wolters FJ, Viswanathan A, de Bruijn RF, van der Lugt A, Hofman A, et al. Association of cerebral microbleeds with cognitive decline and dementia. JAMA Neurol. 2016;73(8):934–43.

    Article  Google Scholar 

  40. Ding J, Sigurðsson S, Jónsson PV, Eiriksdottir G, Meirelles O, Kjartansson O, et al. Space and location of cerebral microbleeds, cognitive decline, and dementia in the community. Neurology. 2017;88(22):2089.

    Article  Google Scholar 

  41. Poels MMF, Ikram MA, van der Lugt A, Hofman A, Niessen WJ, Krestin GP, et al. Cerebral microbleeds are associated with worse cognitive function. Neurology. 2012;78(5):326.

    Article  CAS  Google Scholar 

  42. Charidimou A, Werring DJ. Cerebral microbleeds and cognition in cerebrovascular disease: an update. J Neurol Sci. 2012;322(1-2):50–5.

    Article  Google Scholar 

  43. Martinez-Ramirez S, Greenberg SM, Viswanathan A. Cerebral microbleeds: overview and implications in cognitive impairment. Alzheimer’s Res Ther. 2014;6(3):1–7.

    Google Scholar 

  44. Gurol ME, Viswanathan A, Gidicsin C, Hedden T, Martinez-Ramirez S, Dumas A, et al. Cerebral amyloid angiopathy burden associated with leukoaraiosis: a positron emission tomography/magnetic resonance imaging study. Ann Neurol. 2013;73(4):529–36.

    Article  Google Scholar 

  45. Raposo N, Planton M, Peran P, Payoux P, Bonneville F, Lyoubi A, et al. Florbetapir imaging in cerebral amyloid angiopathy-related hemorrhages. Neurology. 2017;89(7):697–704.

    Article  CAS  Google Scholar 

  46. Ly J, Donnan GA, Villemagne VL, Zavala J, Ma H, O’keefe G, et al. 11C-PIB binding is increased in patients with cerebral amyloid angiopathy–related hemorrhage. Neurology. 2010;74(6):487–93.

    Article  CAS  Google Scholar 

  47. Johnson KA, Gregas M, Becker JA, Kinnecom C, Salat DH, Moran EK, et al. Imaging of amyloid burden and distribution in cerebral amyloid angiopathy. Ann Neurol. 2007;62(3):229–34.

    Article  Google Scholar 

  48. Gurol ME, Becker JA, Fotiadis P, Riley G, Schwab K, Johnson KA, et al. Florbetapir-PET to diagnose cerebral amyloid angiopathy: a prospective study. Neurology. 2016;87(19):2043–9.

    Article  Google Scholar 

  49. Baron JC, Farid K, Dolan E, Turc G, Marrapu ST, O’Brien E, et al. Diagnostic utility of amyloid PET in cerebral amyloid angiopathy-related symptomatic intracerebral hemorrhage. J Cereb Blood Flow Metab. 2014;34(5):753–8.

    Article  CAS  Google Scholar 

  50. Pfeifer LA, White LR, Ross GW, Petrovitch H, Launer LJ. Cerebral amyloid angiopathy and cognitive function: the HAAS autopsy study. Neurology. 2002;58(11):1629–34.

    Article  CAS  Google Scholar 

  51. Boyle PA, Yu L, Wilson RS, Leurgans SE, Schneider JA, Bennett DA. Person-specific contribution of neuropathologies to cognitive loss in old age. Ann Neurol. 2018;83(1):74–83.

    Article  CAS  Google Scholar 

Download references

Acknowledgements

This work was supported by a grant from the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health Welfare, Republic of Korea (grant number: HR21C0885), and Future Medicine 2030 Project of the Samsung Medical Center [#SMX1210771]. This work was also supported by the Korea Health Industry Development Institute (No. HU22C0052) and the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI) and Korea Dementia Research Center (KDRC), funded by the Ministry of Health & Welfare and Ministry of Science and ICT, Republic of Korea (HU20C0414).

Funding

This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT) (NRF-2020R1A2C1009778).

Author information

Authors and Affiliations

  1. Samsung Alzheimer’s Convergence Research Center, Samsung Medical Center, Seoul, South Korea

    Hyemin Jang & Sang Won Seo

  2. Departments of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, South Korea

    Hyemin Jang, Min Young Chun, Hee Jin Kim, Duk L. Na & Sang Won Seo

  3. Neuroscience Center, Samsung Medical Center, Seoul, South Korea

    Hyemin Jang, Min Young Chun, Hee Jin Kim & Sang Won Seo

  4. Department of Health Sciences and Technology, SAIHST, Sungkyunkwan University, Seoul, South Korea

    Hyemin Jang & Sang Won Seo

  5. Happymind Clinic, Seoul, South Korea

    Duk L. Na

Authors
  1. Hyemin Jang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Min Young Chun
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Hee Jin Kim
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Duk L. Na
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Sang Won Seo
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

HJ analyzed and interpreted the data and drafted the manuscript for intellectual content. MYC and HJK contributed to the acquisition of the data. DLN interpreted the data and revised the manuscript for intellectual content. SWS conceptualized the study, interpreted the data, and revised the manuscript for intellectual content. The authors read and approved the final manuscript.

Corresponding authors

Correspondence to Hyemin Jang or Sang Won Seo.

Ethics declarations

Ethics approval and consent to participate

The Institutional Review Board of Samsung Medical Center approved the study protocol, and the need for written consent from patients was waived due to the retrospective nature of the study.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and Permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Jang, H., Chun, M.Y., Kim, H.J. et al. The effects of imaging markers on clinical trajectory in cerebral amyloid angiopathy: a longitudinal study in a memory clinic. Alz Res Therapy 15, 14 (2023). https://doi.org/10.1186/s13195-023-01161-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s13195-023-01161-5

Keywords

  • Cerebral amyloid angiopathy
  • Prognosis
  • Cognition
  • Amyloid β
  • Microbleed
  • Cortical superficial siderosis

Alzheimer's Research & Therapy

ISSN: 1758-9193