You are here

  • Home
  • Archive
  • Volume 11, Issue 4
  • Investigation of hypertensive arteriopathy-related and cerebral amyloid angiopathy-related small vessel disease scores in patients from a memory clinic: a prospective single-centre study

Article Text

Article menu

Download PDFPDF

XML
Neurology
Original research
Investigation of hypertensive arteriopathy-related and cerebral amyloid angiopathy-related small vessel disease scores in patients from a memory clinic: a prospective single-centre study
  1. Kana Matsuda1,2,
  2. Akihiro Shindo2,3,
  3. Yuichiro Ii2,
  4. Ken-ichi Tabei3,
  5. Yukito Ueda1,
  6. Hidehiro Ishikawa2,
  7. Keita Matsuura2,
  8. Kimiko Yoshimaru3,
  9. Akira Taniguchi2,
  10. Natsuko Kato2,
  11. Masayuki Satoh4,
  12. Masayuki Maeda5,
  13. Hidekazu Tomimoto2
  1. 1Rehabilitation, Mie University Hospital, Tsu, Japan
  2. 2Neurology, Mie University Graduate School of Medicine Faculty of Medicine, Tsu, Japan
  3. 3Dementia Prevention and Therapeutics, Mie University Graduate School of Medicine Faculty of Medicine, Tsu, Japan
  4. 4Department of Dementia and Neuropsychology, Advanced Institute of Industrial Technology, Tokyo Metropolitan University, Shinagawa-Ku, Tokyo, Japan
  5. 5Neuroradiology, Mie University School of Medicine, Tsu, Japan
  1. Correspondence to Dr Akihiro Shindo; a-shindo{at}clin.medic.mie-u.ac.jp

Abstract

Objective The severity of cerebral small vessel disease (SVD) is assessed through neuroimaging findings, including hypertensive arteriopathy (HA)-SVD and cerebral amyloid angiopathy (CAA)-SVD. HA-SVD and CAA-SVD have been collectively estimated as total scores: the HA-SVD and CAA-SVD scores, respectively. Previous reports suggest that HA-SVD scores are associated with cognitive function; however, the relationship between CAA-SVD scores and cognitive function remains unclear. Therefore, we examined the association between CAA-SVD scores and cognitive function. Furthermore, we developed a modified CAA-SVD score considering cortical microinfarcts and posterior dominant white matter hyperintensities, which are imaging findings of CAA, and examined the association between these scores and cognitive function in the same patient group.

Design Prospective study.

Setting Single centre study from a memory clinic.

Participants Subjects were diagnosed with mild cognitive impairment (MCI) or mild dementia in our memory clinic between February 2017 and July 2019 and underwent clinical dementia rating scale and brain MRI assessment. A total of 42 patients (aged 75.3±9.12 years) were registered prospectively.

Primary and secondary outcome measures We evaluated intellectual function, memory, frontal lobe function and constructional ability. Furthermore, the relationship between each score and cognitive function was examined.

Results The CAA-SVD score showed significant associations with cognitive function (R2=0.63, p=0.016), but the HA-SVD score did not (R2=0.41, p=0.35). The modified CAA-SVD score was also significantly associated with cognitive function (R2=0.65, p=0.008).

Conclusion Cognitive function is associated with the CAA-SVD score, and more efficiently with the modified CAA-SVD score, in memory clinic patients. Although we have not validated the weighting of the modified CAA-SVD score, these scores can be a predictor of cognitive deterioration in patients with MCI and mild dementia.

  • delirium & cognitive disorders
  • dementia
  • hypertension
  • magnetic resonance imaging

Data availability statement

No data are available. No additional data available.

http://creativecommons.org/licenses/by-nc/4.0/

This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/.

https://doi.org/10.1136/bmjopen-2020-042550

Statistics from Altmetric.com

    Request Permissions

    If you wish to reuse any or all of this article please use the link below which will take you to the Copyright Clearance Center’s RightsLink service. You will be able to get a quick price and instant permission to reuse the content in many different ways.

    Strengths and limitations of this study

    • We examined the association between cognitive function and hypertensive arteriopathy-small vessel disease (SVD), and cerebral amyloid angiopathy (CAA)-SVD scores in patients from a memory clinic.

    • We developed a modified CAA-SVD score considering cortical microinfarcts and posterior dominant white matter hyperintensities, which are characteristic imaging findings of CAA, and examined the association between these scores and cognitive function.

    • Cognitive function was associated with the CAA-SVD score, and more efficiently with the modified CAA-SVD score. However, we have not validated the weighting of the modified CAA-SVD score.

    • This study included 42 cases; therefore, the results are based on a relatively small sample size.

    • This study included relatively large number of the patients with strictly lobar microbleeds, and this might be due to selection bias from including patients from a memory clinic.

    Introduction

    Cerebral small vessel disease (SVD) is a comprehensive term that describes small vessel pathological conditions, including ischaemia and haemorrhage, in the brain. Patients with SVD share common pathological, clinical and neuroimaging features.1 Neuroradiological findings of SVD are examined using brain MRI, which shows various vascular lesions, including white matter hyperintensities (WMH), lacunar infarcts, enlargement of perivascular spaces (PVS), microbleeds (MBs), cortical superficial siderosis (cSS) and cortical microinfarcts (CMIs).1 2 SVD is the main cause of vascular dementia in older people, among which, SVD with dementia comprises nearly half of all patients with vascular dementia.3 Moreover, SVD is also present in Alzheimer’s disease (AD).4

    Although ageing is one of the main causes of SVD, several other diseases such as arteriosclerosis, cerebral amyloid angiopathy (CAA), genetic predispositions and inflammation also cause SVD.5 In particular, arteriosclerosis and CAA are the two major causes of SVD. SVD due to arteriosclerosis is particularly associated with hypertension (hypertensive arteriopathy; HA)6; this SVD type is also named sporadic non-amyloid microangiopathy.7 In contrast, CAA is characterised by the progressive deposition of amyloid beta (Aβ) protein in the cerebral vessels, and the major peptide isoforms of Aβ mainly consist of Aβ1-40 and Aβ1-42.5 Although both HA and CAA share common MRI features (figure 1), including WMH, enlargement of PVS and MBs, the location and distribution of these radiological findings are different. The anteroposterior distribution of WMH in CAA is posterior-dominant.8 The enlargement of PVS in the basal ganglia (BG-PVS) is associated with hypertension, and patients with CAA show centrum semiovale PVS (CSO-PVS).9 MBs located in the basal ganglia, thalamus or brainstem indicate HA (deep MBs) and MBs within the lobar brain compartment are associated with CAA.10 Moreover, lacunar infarcts are associated with hypertension, whereas cSS is a representative MRI biomarker in CAA.11 CMIs are caused by different pathological backgrounds, including CAA, arteriosclerosis and microembolism12; however, neuroradiological findings obtained using 3 Tesla (3T) MRI may enable distinction between CMIs related to CAA and those due to microembolisms.13

    Figure 1
    Figure 1

    Representative MRI findings of cerebral small vessel disease. The arrows show lobar cerebral microbleeds on SWI sequences. MRI in a patient with CAA (A). Cortical superficial siderosis was observed in SWI sequences in a patient with CAA (arrows, B). Centrum semiovale enlarged perivascular spaces on T2-weighted imaging in a patient with CAA (C). WMH assessed by fluid attenuated inversion recovery imaging. WMH in patients with CAA was posterior-dominant (D). Double inversion recovery imaging shows a CMI that localised within the cortex and was 3 mm in diameter (arrow). CMIs from patients with CAAs (E) showed that all lesions were localised within cortical structures, with a size of <5 mm.13 CAA, cerebral amyloid angiopathy; CMI, cortical microinfarct; SWI, susceptability-weighted image; WMH, white matter hyperintensities.

    Recently, two types of MRI-based assessment scores have been developed for SVD. Klarenbeek et al14 enrolled patients with lacunar stroke and assessed different MRI features, including lacunar infarct, MBs, BG-PVS and WMH. One point was awarded for the presence of each marker, producing a score between 0 and 4. This HA-SVD score was mainly used for the evaluation of patients with lacunar stroke and/or vascular risk factors,15 and was associated with intellectual function.16 Charidimou et al17 developed a novel SVD score for patients with CAA (CAA-SVD score), which was associated with clinical symptoms of transient focal neurological episodes.18 However, the relationship between CAA-SVD scores and cognitive function remains unclear.

    In this study, we investigated the relationship between the two types of SVD scores and cognitive function in patients who visited our memory clinic. Moreover, we added other radiological biomarkers of CAA to the CAA-SVD score and investigated its usefulness in evaluating cognitive function in patients with mild cognitive impairment (MCI) and mild dementia.

    Patients and methods

    Patients

    We prospectively registered patients who consulted our hospital’s memory clinic. Of the 50 subjects, 42 fulfilled the inclusion criteria. A complete description of all procedures was provided to patients, and written informed consent was obtained directly from them or from their caregivers. All patients were comprehensively examined by a neurologist with sufficient experience in examining patients with dementia. The Clinical Dementia Rating (CDR) and MRI were performed after obtaining written informed consent. We collected data from patients who fulfilled the following inclusion criteria: (1) consulted with our hospital’s memory clinic between February 2017 and July 2019, (2) underwent neuroimaging examinations using 3T MRI, (3) completed neuropsychological assessments and (4) had a global CDR score of 0.5 or 1.0. Neuropsychological tests and CDR were performed within 3 months of MRI. No neurological events occurred between these tests and MRI.

    We diagnosed MCI according to the National Institute on Aging–Alzheimer’s Association (NIA-AA) criteria for patients with MCI.19 MCI was classified into MCI due to AD or other types of MCI. The global CDR score was 0.5. We diagnosed AD according to the NIA-AA guidelines.20 Vascular dementia was diagnosed according to the criteria set forth by the American Heart Association/American Stroke Association.21

    Neuropsychological assessments

    The Mini-Mental State Examination (MMSE)22 and Japanese Raven’s Coloured Progressive Matrices (RCPM)23 were used to quantify intellectual function. Memory was evaluated using the Rivermead Behavioural Memory Test (RBMT). The scores included a standard profile score (SPS) and screening score (SS).24 Constructional ability was assessed using the Mie Constructional Apraxia Scale (MCAS).25 Frontal lobe function was assessed using two tasks: word fluency (WF) and trail making test (TMT) -A/-B.26 The WF test consisted of category and letter domains. In the category WF task (WF-category), participants were asked to name as many animals as possible in 1 min. In the letter WF task (WF-letter), participants were asked to name as many objects as possible in 1 min, beginning with each of the following four phonemes: ka, sa, ta and te. The average scores for these four phonemes were used for statistical analyses.

    CDR was performed by two speech therapists, and results were evaluated through a discussion between two neurologists and three speech therapists based on the CDR determination rules.27

    MRI protocol

    We followed the MRI protocol by Ii et al.28 Briefly, MRI studies were performed with a 3T MRI unit (Achieva, Philips Medical System, Best, the Netherlands) using an 8-channel or 32-channel phased-array head coil. We used T1-weighted and T2-weighted images and three-dimensional (3D)-fluid attenuated inversion recovery (FLAIR) images for the evaluation of WMH, lacunar infarcts and PVS. Susceptibility-weighted image (SWI) sequences were used for the detection of MBs and cSS. 3D-double inversion recovery (DIR) and 3D-FLAIR were used for the detection of CMIs. Axial DIR imaging was performed using two different inversion pulses. The long inversion time and the short inversion time were defined as the intervals between the 180° inversion pulse and the 90° excitation pulse, respectively, which had been optimised for human brain imaging and were provided by the vendor.

    Details of the two-dimensional (2D)-DIR and 3D-DIR protocols were as follows: field of view, 230 mm; matrix, 320×256 (512×512) after reconstruction; in-plane resolution, 0.45 mm×0.45 mm; section thickness, 3 mm with no intersection gap; no parallel imaging; repetition time (ms)/echo time (ms), 15 000/28; long inversion time (ms)/short inversion time (ms), 3400/325; number of signals acquired, two; and acquisition time, 4 min 30 s for 2D, and field of view, 250 mm; matrix, 208×163 (256×256) after reconstruction; in plane resolution, 0.98 mm×0.98 mm; section thickness, 0.65 mm with over contiguous slice; turbo spin echo factor (TSE) factor 173; repetition time (ms)/echo time (ms), 5500/247; long inversion time (ms)/short inversion time (ms), 2550/450; number of signals acquired, two; and acquisition time, 5 min 13 s for 3D.

    The SWI details were as follows: field of view, 230 mm; matrix, 320×251 (512×512) after reconstruction; in-plane resolution, 0.45 mm×0.45 mm; section thickness, 0.5 mm with over contiguous slice; repetition time (ms)/echo time (ms), 22/11.5 (in-phase), 33 (shifted); number of signals acquired, one; flip angle 20°; and acquisition time, 5 min 45 s. 3D-FLAIR imaging was obtained in a sagittal direction, and then the axial and coronal images were reconstructed. The 3D-FLAIR details were as follows: field of view, 260 mm; matrix, 288×288 (364×364) after reconstruction; in-plane resolution, 0.68 mm×0.67 mm; section thickness, 1 mm with 0.5 mm overlap; no parallel imaging; repetition time (ms)/echo time (ms), 6000/400; inversion time, 2000 ms; number of signals acquired, two; and acquisition time, 5 min 12 s.

    SVD scores

    The HA-SVD score was determined by Klarenbeek et al,14 where one point was awarded for each of the four markers (lacunar infarcts, MBs, BG-PVS and WMH), with a minimum score of 0 and a maximum score of 4. The CAA-SVD score was proposed by Charidimou et al17 (table 1), with one point awarded for each of the four markers (lobar MBs, cSS, CSO-PVS and WMH). For lobar MBs, one point was awarded if two to four MBs were present and two points for five or more MBs. The presence of cSS was awarded with one point if focal and two points if disseminated. The presence of CSO-PVSs was confirmed if there were moderate-to-severe (>20) PVSs (one point if present), with a minimum score of 0 and a maximum score of 6. Both scores were independently assessed by four raters.

    View this table:
    Table 1

    Cerebral amyloid angiopathy-small vessel disease (CAA-SVD) score and modified CAA-SVD score

    Modified CAA-SVD scores

    We tried to modify CAA-SVD scores by adding one point each in the presence of posteriorly dominant WMH and CMIs related to CAA (table 1).

    Tissue quantification was performed using a novel in-house software (FUsed Software for Imaging Of Nervous system: FUSION)29 that yielded an individualised volumetric brain tissue profile. The obtained T1-weighted and FLAIR images were imported from the Digital Imaging and Communications in Medicine format files for processing. To increase the accuracy of segmentation, we used the Lesion Segmentation Tool for lesion filling.30 Lesion filling was applied to T1-weighted images that were aligned with the lesion probability map. For pre-processing, the T1-weighted images were co-registered to the FLAIR images. Next, to separate out the white matter, segmentation was performed using the T1-weighted images and a mask covering the cerebral ventricles. The pre-processing function was based on SPM 8 (Wellcome Trust Centre for Neuroimaging, UCL). Second-level tissue segmentation was then performed to separate WMH from white matter using a semi-automated operation that extracted the pixels falling within a predetermined WMH value. The WMH volume, which appeared as hyperintense areas on FLAIR images, was quantified for each area. Brain tissue was classified into four areas based on the division of the longitudinal fissure of the cerebrum and central sulcus. WMH were automatically classified as periventricular hyperintensity or deep WMH, and their corrected volumes were quantified in cubic centimetres.29 The anteroposterior centre of WMH was calculated in the following way. To determine the reference point, we identified two anatomical landmarks (anterior, A and posterior, P). Point A was defined as the most anterior part on the wall of the frontal horn of the lateral ventricle. Point P was defined as the most posterior part of the dura mater covering the occipital cortex.8 If there was a large amount of posterior WMH, one point was added to the CAA-SVD score.

    CMIs were defined as small cortical hyperintense lesions non-adjacent to WMH. When CMIs were localised within the cortex, predominantly in the occipital lobe, they were smaller than 5 mm in diameter, and had fewer than three lesions, they were defined as CMIs related to CAA (Ishikawa score).13 When there were any CMIs related to CAA, we added one point to the CAA-SVD score.

    Statistical analyses

    The association between each SVD score (dependent variable) and cognitive function (independent variable) was analysed using linear regression analysis. Clinical and radiological characteristics are presented as numbers with percentages and means with SD. Statistical analyses were performed using IBM SPSS statistics software V.20. Differences with p<0.05 were considered statistically significant.

    Patient and public involvement

    There was no patient involvement.

    Results

    Patients

    In total, 50 patients were registered for this study, and 42 fulfilled the inclusion criteria. Clinical characteristics, neuropsychological test results and MRI findings of the participants are shown in table 2. The mean age was 75.3±9.12 years, and there were 23 men (54.7%). Regarding vascular risk factors, 22 patients had hypertension (52.3%), 4 had diabetes mellitus (9.5%) and 11 smoked and had dyslipidaemia (26.1%). Fourteen patients had a history of lacunar stroke (33.3%) and 24 patients (57.1%) met the modified Boston criteria (V.1.5).

    View this table:
    Table 2

    Participant characteristics

    The global CDR score was 0.5 for 30 patients (71.4%) and 1.0 for 12 patients (28.6%). Of the 12 patients with a global CDR score of 1.0, 10 met the criteria reflecting probable AD and 2 had vascular dementia. Among 30 patients with MCI, 20 had MCI due to AD and 10 had other types of MCI. Regarding MRI findings, 31 patients had ≥1 MBs (73.8%), 16 had ≥2 and ≤4 lobar MBs (38.0%) and 10 had ≥5 lobar MBs (23.8%). Three patients had focal cSS (7.1%), 25 had >20 BG-PVSs (59.5%), 30 had >20 CSO-PVSs (71.4%), 26 had deep WMH (Fazekas 2 or 3) (61.9%) and 11 had periventricular WMH (Fazekas 3) (26.1%).

    WMH were divided according to whether they were anterior or posterior and were analysed using FUSION. There were seven posterior superiorities (16.6%). CMIs related to CAA were detected in three patients (7.1%), and two of these patients met the modified Boston criteria for probable CAA. The patients with CMIs did not have any evidence of CAA except for CMIs, such as atrial fibrillation and cerebral artery stenosis.

    Results of each SVD score

    As for each SVD score (table 3), the HA-SVD score was 0 in 3 patients (7.1%), 1 in 7 patients (16.6%), 2 in 14 patients (33.3%), 3 in 11 patients (26.1%) and 4 in 7 patients (16.6%). The CAA-SVD score was 0 in 5 patients (11.9%), 1 in 6 patients (14.2%), 2 in 13 patients (30.9%), 3 in 12 patients (28.5%) and 4 in 6 patients (14.2%). Moreover, the modified CAA-SVD score was 0 in 1 patient (2.3%), 1 in 6 patients (14.2%), 2 in 8 patients (19%), 3 in 13 patients (30.9%), 4 in 11 patients (26.1%), 5 in 2 patients (4.7%) and 6 in 1 patient (2.3%). A significant difference was observed when the HA-SVD scores and CAA-SVD scores were analysed using Pearson’s χ2 test (p=0.000).

    View this table:
    Table 3

    Cerebral small vessel disease score

    Cognitive function and the three types of SVD scores

    HA-SVD score

    With regard to the relationship between each cognitive function and the HA-SVD score, no significant difference was found across any function (table 4), such as MMSE (p=0.52), RCPM (p=0.47), RBMT-SPS (p=0.15), RBMT-SS (p=0.11), TMT-A (p=0.85), TMT-B (p=0.23), WF-category (p=0.10), WF-letter (p=0.17) or MCAS (p=0.05). Additionally, the linear regression models of the associations between the HA-SVD scores and cognitive function revealed that the coefficient of determination was R2=0.409 (p=0.35), and the regression equation did not hold. The Akaike’s information criterion (AIC) was 122.493.

    View this table:
    Table 4

    Linear regression models of associations between cognitive function and SVD score

    CAA-SVD score

    With regard to the relationship between each cognitive function and the CAA-SVD score, a significant difference was found in three out of nine items (table 4), including MMSE (p=0.006), WF-category (p=0.04) and MCAS (p=0.03), while there was no significant difference in six out of nine items, including RCPM (p=0.15), RBMT-SP (p=0.20), RBMT-SS (p=0.06), TMT-A (p=0.69), TMT-B (p=0.05) and WF-letter (p=0.71). The results of the linear regression models of the associations between CAA-SVD scores and cognitive function demonstrated that the coefficient of determination was R2=0.639 (p=0.016) and the AIC was 104.269.

    Modified CAA-SVD score

    With regard to the relationship between each cognitive function and the modified CAA-SVD score, a significant difference was found in four out of nine items (table 4), including MMSE (p=0.001), RBMT-SS (p=0.04), WF-category (p=0.02) and MCAS (p=0.03), while no significant difference was found in five out of nine items, including RCPM (p=0.38), RBMT-SPS (p=0.33), TMT-A (p=0.48), TMT-B (p=0.11) and WF-letter (p=0.63). The results of the linear regression models of the associations between the CAA-SVD scores and cognitive function revealed that the coefficient of determination was R2=0.645 (p=0.008) and the AIC was 103.43.

    On assessing the relationship between each cognitive function and each SVD score, a significant difference was found in MMSE, WF-category, MCAS and RBMT-SS. Among these four items, the WF-category had the highest coefficient of determination for the HA-SVD score (R2=0.0004), and the RBMT-SS had the highest coefficient of determination for the CAA-SVD (R2=0.0142) and modified CAA-SVD scores (R2=0.0161). In the linear regression models of the associations between each SVD score and RBMT-SS, the coefficient of determination was found to increase in the following order: HA-SVD score<CAA SVD score<modified CAA-SVD score (figure 2).

    Figure 2
    Figure 2

    Linear regression models of the associations between each cerebral small vessel disease (SVD) score and the Rivermead Behavioural Memory Test-screening score. CAA, cerebral amyloid angiopathy; HA, hypertensive arteriopathy.

    Discussion

    This study demonstrated a novel association between the CAA-SVD score and cognitive function in memory clinic patients, whereas no significant association was found between the HA-SVD score and cognitive function. Additionally, there was a significant difference between the HA-SVD score and CAA-SVD score; that is, WF-category had the highest coefficient of determination for the HA-SVD score, and the RBMT-SS had the highest coefficient of determination for the CAA-SVD and modified CAA-SVD scores. Moreover, it is plausible that the modified CAA-SVD score, in addition to the analysis of the posterior distribution of WMH and CMIs, may be a useful tool for evaluating patients with MCI or mild dementia.

    Taken together, our study showed that there was a significant difference in each cognitive domain between the HA-SVD score and CAA-SVD score, and a significant association between the CAA-SVD score and cognitive function. This result indicates that the CAA-SVD score may reflect the cognitive function in patients of a memory clinic. Although a previous report showed that the HA-SVD score showed significant associations with intellectual function in patients having had a lacunar stroke and/or with hypertension,16 our study did not show any such significant association. This may be attributed to the patients’ background, such as older age and lower prevalence of vascular factors. The mean age of patients in the previous study was 63.1 years, while the mean age of patients in our study was 75.3 years. Moreover, in our study, 22 patients had hypertension (52.3%) and 14 patients had a lacunar stroke (33.3%) compared with 84.1% and 68.7%, respectively, in a previous study.

    The HA-SVD score and CAA-SVD score share common components including WMH, PVS and MBs. The HA-SVD score includes lacunar infarcts, whereas the CAA-SVD score includes cSS. Moreover, the location of PVS and MBs differ between the HA-SVD and CAA-SVD scores. Previous reports have shown that CSO-PVS is negatively correlated with memory and that BG-PVS is negatively correlated with processing speed, executive function and memory.16 Additionally, the presence and number of MBs have been associated with cognitive impairment.31 The incidence of cSS is extremely low and difficult to study in healthy individuals32; however, cSS is highly-specific for CAA. As described above, the CAA-SVD score was produced by adding cSS to the WMH and region-specific MBs and PVS and was more related to cognitive function than the HA-SVD score.

    The modified CAA-SVD score improved the prediction accuracy of the regression equation, reduced the AIC and slightly improved the prediction accuracy compared with the CAA-SVD score. CMIs are an important risk factor for dementia, and it has been reported that the presence of CMIs approximately doubles the risk of dementia.32 One of the major causes of CMIs is CAA.33 Additionally, several reports have described the relationship between WMH and cognitive function,34 and WMH due to CAA have been reported to be posterior-dominant.8 Therefore, it was thought that incorporation of these two markers may have affected relationship with cognitive function in an additive manner.

    On observing the results for each test item, the CAA-SVD score was found to have significant associations with constructional ability and memory. This observation is in line with the diagnostic criteria of NIA-AA, which includes constructional ability and memory as an essential cognitive domain.35

    These results in our study may be dependent on the background of the patients in our memory clinic. In this study, 24 patients (57.1%) met the modified Boston criteria (V.1.5), 10 of 12 patients with mild dementia had AD and MCI due to AD was present in 20 out of 30 patients with MCI. MCI due to AD has been reported to have a high rate of progression to AD.36 Low prevalence of vascular risk and advanced ageing in the present study may indicate that our memory clinic’s patients had a higher burden of amyloid pathology. Therefore, the CAA-SVD score and modified CAA-SVD score may reflect the pathological background of AD. The CAA-SVD score may be a useful tool for memory clinic patients whereas the SVD scores may not, rather being suited for the patients with vascular risk factors. Additionally, there may be a possibility that cognitive dysfunction can be detected earlier by evaluating patients with a score that is well-tailored to them, thereby enabling appropriate subsequent patient treatment.

    This study had several limitations. First, it was based on a relatively small sample size. Second, deep MBs is common in Japan,37 but the patients included in this study mostly had strictly lobar MBs, and we believe that there was selection bias due to recruiting patients from a memory clinic. Third, we were unable to carry out pathological examinations. The patient who did not meet the modified Boston criteria but meet the CAA due to CMI criteria are scored as CAA-related CMI. In the previous report, 17% of the pathological patients had a CAA but a CAA score of 0, and most of the pathological changes were mild.17 CMI is also detected by mild CAA.38 The Ishikawa score is based on the characteristics of patients with CAA, and we considered that there is no problem with this addition, but this case also requires pathological findings.

    These issues should be addressed in future studies. Fourth, currently, FUSION has its limits and cannot distinguish small infarcts and enlarged PVS. At present, the radiologist visually confirmed whether the results of FUSION were likely to be affected, and it was determined that the results were not affected. We aim to improve the software so that it can distinguish small infarcts and enlarged PVS in the future. Fifth, we have not validated the weighting of the modified CAA-SVD score; this needs further investigation. Finally, there was no significant association between the HA-SVD score and cognitive function in this study, possibly due to the limited number of patients with hypertension included in this study. Furthermore, even though there is a possibility that a larger number of cases may allow a significant correlation, further and lager studies would be required to validate this.

    Despite these limitations, our study shows that patients with MCI or mild dementia should be evaluated with the CAA-SVD score. The modified CAA-SVD score may also be applicable to these patients.

    Data availability statement

    No data are available. No additional data available.

    Ethics statements

    Ethics approval

    All procedures followed the Clinical Study Guidelines of the Ethics Committee of Mie University Hospital and were approved by the internal review board (Registration number: 1596).

    References

    1. ↵
      1. Wardlaw JM,
      2. Smith EE,
      3. Biessels GJ, et al
      . Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. Lancet Neurol 2013;12:822–38.doi:10.1016/S1474-4422(13)70124-8pmid:http://www.ncbi.nlm.nih.gov/pubmed/23867200
      OpenUrlCrossRefPubMedWeb of Science
    2. ↵
      1. Wardlaw JM,
      2. Smith C,
      3. Dichgans M
      . Small vessel disease: mechanisms and clinical implications. Lancet Neurol 2019;18:684–96.doi:10.1016/S1474-4422(19)30079-1pmid:http://www.ncbi.nlm.nih.gov/pubmed/31097385
      OpenUrlCrossRefPubMed
    3. ↵
      1. Chui H,
      2. dementia V
      . Vascular dementia, a new beginning: shifting focus from clinical phenotype to ischemic brain injury. Neurol Clin 2000;18:951–78.doi:10.1016/s0733-8619(05)70234-6pmid:http://www.ncbi.nlm.nih.gov/pubmed/11072269
      OpenUrlCrossRefPubMedWeb of Science
    4. ↵
      1. Tomimoto H
      . Subcortical vascular dementia. Neurosci Res 2011;71:193–9.doi:10.1016/j.neures.2011.07.1820pmid:http://www.ncbi.nlm.nih.gov/pubmed/21821070
      OpenUrlCrossRefPubMed
    5. ↵
      1. Pantoni L
      . Cerebral small vessel disease: from pathogenesis and clinical characteristics to therapeutic challenges. Lancet Neurol 2010;9:689–701.doi:10.1016/S1474-4422(10)70104-6pmid:http://www.ncbi.nlm.nih.gov/pubmed/20610345
      OpenUrlCrossRefPubMedWeb of Science
    6. ↵
      1. Charidimou A,
      2. Pantoni L,
      3. Love S
      . The concept of sporadic cerebral small vessel disease: a road map on key definitions and current concepts. Int J Stroke 2016;11:6–18.doi:10.1177/1747493015607485pmid:http://www.ncbi.nlm.nih.gov/pubmed/26763016
      OpenUrlCrossRefPubMed
    7. ↵
      1. Furuta A,
      2. Ishii N,
      3. Nishihara Y,
      4. Horie A, et al
      . Medullary arteries in aging and dementia. Stroke 1991;22:442–6.doi:10.1161/01.STR.22.4.442pmid:http://www.ncbi.nlm.nih.gov/pubmed/2024272
      OpenUrlAbstract/FREE Full Text
    8. ↵
      1. Thanprasertsuk S,
      2. Martinez-Ramirez S,
      3. Pontes-Neto OM, et al
      . Posterior white matter disease distribution as a predictor of amyloid angiopathy. Neurology 2014;83:794–800.doi:10.1212/WNL.0000000000000732pmid:http://www.ncbi.nlm.nih.gov/pubmed/25063759
      OpenUrlPubMed
    9. ↵
      1. Charidimou A,
      2. Jaunmuktane Z,
      3. Baron J-C, et al
      . White matter perivascular spaces: an MRI marker in pathology-proven cerebral amyloid angiopathy? Neurology 2014;82:57–62.doi:10.1212/01.wnl.0000438225.02729.04pmid:http://www.ncbi.nlm.nih.gov/pubmed/24285616
      OpenUrlCrossRefPubMed
    10. ↵
      1. Greenberg SM,
      2. Vernooij MW,
      3. Cordonnier C, et al
      . Cerebral microbleeds: a guide to detection and interpretation. Lancet Neurol 2009;8:165–74.doi:10.1016/S1474-4422(09)70013-4pmid:http://www.ncbi.nlm.nih.gov/pubmed/19161908
      OpenUrlCrossRefPubMedWeb of Science
    11. ↵
      1. Linn J,
      2. Halpin A,
      3. Demaerel P, et al
      . Prevalence of superficial siderosis in patients with cerebral amyloid angiopathy. Neurology 2010;74:1346–50.doi:10.1212/WNL.0b013e3181dad605pmid:http://www.ncbi.nlm.nih.gov/pubmed/20421578
      OpenUrlCrossRefPubMed
    12. ↵
      1. Kövari E,
      2. Herrmann FR,
      3. Gold G, et al
      . Association of cortical microinfarcts and cerebral small vessel pathology in the ageing brain. Neuropathol Appl Neurobiol 2017;43:505–13.doi:10.1111/nan.12366pmid:http://www.ncbi.nlm.nih.gov/pubmed/27783888
      OpenUrlCrossRefPubMed
    13. ↵
      1. Ishikawa H,
      2. Ii Y,
      3. Shindo A, et al
      . Cortical microinfarcts detected by 3-tesla magnetic resonance imaging. Stroke 2020;51:1010–3.doi:10.1161/STROKEAHA.119.028202
      OpenUrl
    14. ↵
      1. Klarenbeek P,
      2. van Oostenbrugge RJ,
      3. Rouhl RPW, et al
      . Ambulatory blood pressure in patients with lacunar stroke: association with total MRI burden of cerebral small vessel disease. Stroke 2013;44:2995–9.doi:10.1161/STROKEAHA.113.002545pmid:http://www.ncbi.nlm.nih.gov/pubmed/23982717
      OpenUrlAbstract/FREE Full Text
    15. ↵
      1. Staals J,
      2. Makin SDJ,
      3. Doubal FN, et al
      . Stroke subtype, vascular risk factors, and total MRI brain small-vessel disease burden. Neurology 2014;83:1228–34.doi:10.1212/WNL.0000000000000837pmid:http://www.ncbi.nlm.nih.gov/pubmed/25165388
      OpenUrlCrossRefPubMed
    16. ↵
      1. Huijts M,
      2. Duits A,
      3. van Oostenbrugge RJ, et al
      . Accumulation of MRI markers of cerebral small vessel disease is associated with decreased cognitive function. A study in first-ever lacunar stroke and hypertensive patients. Front Aging Neurosci 2013;5:72. doi:10.3389/fnagi.2013.00072pmid:http://www.ncbi.nlm.nih.gov/pubmed/24223555
      OpenUrlCrossRefPubMed
    17. ↵
      1. Charidimou A,
      2. Martinez-Ramirez S,
      3. Reijmer YD, et al
      . Total magnetic resonance imaging burden of small vessel disease in cerebral amyloid angiopathy: an imaging-pathologic study of concept validation. JAMA Neurol 2016;73:994–1001.doi:10.1001/jamaneurol.2016.0832pmid:http://www.ncbi.nlm.nih.gov/pubmed/27366898
      OpenUrlPubMed
    18. ↵
      1. Boulouis G,
      2. Charidimou A,
      3. Jessel MJ, et al
      . Small vessel disease burden in cerebral amyloid angiopathy without symptomatic hemorrhage. Neurology 2017;88:878–84.doi:10.1212/WNL.0000000000003655pmid:http://www.ncbi.nlm.nih.gov/pubmed/28130469
      OpenUrlPubMed
    19. ↵
      1. Albert MS,
      2. DeKosky ST,
      3. Dickson D, et al
      . The diagnosis of mild cognitive impairment due to Alzheimer's disease: recommendations from the National Institute on Aging-Alzheimer's association workgroups on diagnostic guidelines for Alzheimer's disease. Alzheimers Dement 2011;7:270–9.doi:10.1016/j.jalz.2011.03.008pmid:http://www.ncbi.nlm.nih.gov/pubmed/21514249
      OpenUrlCrossRefPubMedWeb of Science
    20. ↵
      1. McKhann GM,
      2. Knopman DS,
      3. Chertkow H, et al
      . The diagnosis of dementia due to Alzheimer’s disease: Recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimer's & Dementia 2011;7:263–9.doi:10.1016/j.jalz.2011.03.005
      OpenUrl
    21. ↵
      1. Gorelick PB,
      2. Scuteri A,
      3. Black SE, et al
      . Vascular contributions to cognitive impairment and dementia: a statement for healthcare professionals from the American heart association/american stroke association. Stroke 2011;42:2672–713.doi:10.1161/STR.0b013e3182299496pmid:http://www.ncbi.nlm.nih.gov/pubmed/21778438
      OpenUrlAbstract/FREE Full Text
    22. ↵
      1. Mori E,
      2. Mitani Y,
      3. Yamadori A
      . Usefulness of a Japanese version of the Mini-Mental state test in neurological patients. Jpn J Neuropsychol 1985;1:82–90.
      OpenUrl
    23. ↵
      1. Raven JC
      . Coloured progressive matrices, sets a, AB, B. London: H.K. Lewis, 1962.
    24. ↵
      1. Wilson B,
      2. Cockburn J,
      3. Baddeley A, et al
      . The development and validation of a test battery for detecting and monitoring everyday memory problems. J Clin Exp Neuropsychol 1989;11:855–70.doi:10.1080/01688638908400940pmid:http://www.ncbi.nlm.nih.gov/pubmed/2592527
      OpenUrlCrossRefPubMedWeb of Science
    25. ↵
      1. Satoh M,
      2. Mori C,
      3. Matsuda K, et al
      . Improved Necker cube drawing-based assessment battery for constructional apraxia: the Mie Constructional apraxia scale (MCAS). Dement Geriatr Cogn Dis Extra 2016;6:424–36.doi:10.1159/000449245pmid:http://www.ncbi.nlm.nih.gov/pubmed/27790241
      OpenUrlPubMed
    26. ↵
      1. Abe M,
      2. Suzuki K,
      3. Okada K, et al
      . [Normative data on tests for frontal lobe functions: Trail Making Test, Verbal fluency, Wisconsin Card Sorting Test (Keio version)]. No To Shinkei 2004;56:567–74.pmid:http://www.ncbi.nlm.nih.gov/pubmed/15379283
      OpenUrlPubMed
    27. ↵
      1. Hughes CP,
      2. Berg L,
      3. Danziger WL, et al
      . A new clinical scale for the staging of dementia. Br J Psychiatry 1982;140:566–72.doi:10.1192/bjp.140.6.566pmid:http://www.ncbi.nlm.nih.gov/pubmed/7104545
      OpenUrlAbstract/FREE Full Text
    28. ↵
      1. Ii Y,
      2. Maeda M,
      3. Ishikawa H, et al
      . Cortical microinfarcts in patients with multiple lobar microbleeds on 3 T MRI. J Neurol 2019;266:1887–96.doi:10.1007/s00415-019-09350-9pmid:http://www.ncbi.nlm.nih.gov/pubmed/31049727
      OpenUrlPubMed
    29. ↵
      1. Tabei K-I,
      2. Kida H,
      3. Hosoya T, et al
      . Prediction of cognitive decline from white matter hyperintensity and single-photon emission computed tomography in Alzheimer's disease. Front Neurol 2017;8:408–18.doi:10.3389/fneur.2017.00408pmid:http://www.ncbi.nlm.nih.gov/pubmed/28928704
      OpenUrlPubMed
    30. ↵
      1. Schmidt P,
      2. Gaser C,
      3. Arsic M, et al
      . An automated tool for detection of FLAIR-hyperintense white-matter lesions in multiple sclerosis. Neuroimage 2012;59:3774–83.doi:10.1016/j.neuroimage.2011.11.032pmid:http://www.ncbi.nlm.nih.gov/pubmed/22119648
      OpenUrlCrossRefPubMedWeb of Science
    31. ↵
      1. Gregoire SM,
      2. Scheffler G,
      3. Jäger HR, et al
      . Strictly lobar microbleeds are associated with executive impairment in patients with ischemic stroke or transient ischemic attack. Stroke 2013;44:1267–72.doi:10.1161/STROKEAHA.111.000245pmid:http://www.ncbi.nlm.nih.gov/pubmed/23482601
      OpenUrlAbstract/FREE Full Text
    32. ↵
      1. Vernooij MW,
      2. Ikram MA,
      3. Hofman A, et al
      . Superficial siderosis in the general population. Neurology 2009;73:202–5.doi:10.1212/WNL.0b013e3181ae7c5epmid:http://www.ncbi.nlm.nih.gov/pubmed/19620607
      OpenUrlCrossRefPubMed
    33. ↵
      1. Kövari E,
      2. Gold G,
      3. Herrmann FR, et al
      . Cortical microinfarcts and demyelination affect cognition in cases at high risk for dementia. Neurology 2007;68:927–31.doi:10.1212/01.wnl.0000257094.10655.9apmid:http://www.ncbi.nlm.nih.gov/pubmed/17372128
      OpenUrlCrossRefPubMed
    34. ↵
      1. van der Flier WM,
      2. van Straaten ECW,
      3. Barkhof F, et al
      . Small vessel disease and general cognitive function in nondisabled elderly: the LADIS study. Stroke 2005;36:2116–20.doi:10.1161/01.STR.0000179092.59909.42pmid:http://www.ncbi.nlm.nih.gov/pubmed/16141425
      OpenUrlAbstract/FREE Full Text
    35. ↵
      1. McKhann GM,
      2. Knopman DS,
      3. Chertkow H, et al
      . The diagnosis of dementia due to Alzheimer's disease: recommendations from the National Institute on Aging-Alzheimer's association workgroups on diagnostic guidelines for Alzheimer's disease. Alzheimers Dement 2011;7:263–9.doi:10.1016/j.jalz.2011.03.005pmid:http://www.ncbi.nlm.nih.gov/pubmed/21514250
      OpenUrlCrossRefPubMedWeb of Science
    36. ↵
      1. Bennett DA,
      2. Schneider JA,
      3. Bienias JL, et al
      . Mild cognitive impairment is related to Alzheimer disease pathology and cerebral infarctions. Neurology 2005;64:834–41.doi:10.1212/01.WNL.0000152982.47274.9Epmid:http://www.ncbi.nlm.nih.gov/pubmed/15753419
      OpenUrlCrossRefPubMed
    37. ↵
      1. Yakushiji Y,
      2. Wilson D,
      3. Ambler G
      . Distribution of cerebral microbleeds in the East and West-Individual participant meta-analysis. Neurology 2019;93:e1086–97.
      OpenUrl
    38. ↵
      1. Arvanitakis Z,
      2. Capuano AW,
      3. Leurgans SE, et al
      . The relationship of cerebral vessel pathology to brain microinfarcts. Brain Pathol 2017;27:77–85.doi:10.1111/bpa.12365pmid:http://www.ncbi.nlm.nih.gov/pubmed/26844934
      OpenUrlPubMed

    Footnotes

    • Contributors KMatsuda: draft of manuscript, acquisition of data and analysis. AS: revision of manuscript, interpretation of data and study supervision. K-IT and YI: revision of manuscript and interpretation of data. YU, HI, KMatsuura, KY, AT, NK, MS and MM: acquisition of data and interpretation of data. HT: revision of the manuscript, study concept and design and study supervision.

    • Funding The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.

    • Competing interests None declared.

    • Patient and public involvement Patients and/or the public were not involved in the design, or conduct, or reporting, or dissemination plans of this research.

    • Provenance and peer review Not commissioned; externally peer reviewed.