White Matter Hyperintensities in Mild Cognitive Impairment: Clinical Impact of Location and Interaction with Lacunes and Medial Temporal Atrophy Bora Yoon, MD, PhD,* Yong S. Shim, MD, PhD,† Hae-Kwan Cheong, MD, PhD,‡ Yun-Jeong Hong, MD, MS,x Kwang-Soo Lee, MD, PhD,k Kee Hyung Park, MD, PhD,{ Kook Jin Ahn, MD, PhD,# Dai Jin Kim, MD, PhD,** Yong-Duk Kim, MD, PhD,* Seong Hye Choi, MD, PhD,†† and Dong-Won Yang, MD, PhDk
This study was to evaluate the influence on cognition and activities of daily living (ADL) by white matter hyperintensities (WMHs) based on the severity and location, as well as the interactions among WMHs, lacunes, and medial temporal atrophy (MTA). In 150 patients with amnestic mild cognitive impairment, WMHs were quantified with the use of a semiautomated volumetric method. Lacune counting and MTA assessment were performed by visual rating. The severer WMHs were, the more executive functions decreased. The influence on executive functions such as verbal fluency test and Stroop color reading test were greater in periventricular (PV) WMHs than deep WMHs, as well as bigger in anterior, middle, and posterior areas in order. The instrumental (I) ADL was strongly associated with the anterior (P 5 .028) and middle area (P 5 .014) of PVWMHs only. WMHs had synergistic interactions with lacunes in Controlled Oral Word Association Task-semantic (ß 5 21.12; R2 5 .24; P 5.039), Stroop color (ß 5 22.07; R2 5 .15; P 5.049), and IADL (ß 5 .23; R2 5 .20; P 5 .009). Anterior PVWMHs demonstrated the most powerful impact on frontal executive dysfunction and poor performance of IADL. WMHs had synergistic effects with the number of lacunes on them. Therefore, it is desirable to consider WMHs and lacunes simultaneously as potential imaging biomarkers for predicting cognition and IADL in aMCI. Key Words: White matter hyperintensities—subcortical ischemic vascular disease—lacunar infarct—mild cognitive impairment—executive function—activities of daily living—magnetic resonance imaging. Ó 2014 by National Stroke Association
From the *Department of Neurology, Konyang University Hospital, College of Medicine, Konyang University, Daejeon, Republic of Korea; †Department of Neurology, Bucheon St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Bucheon, Republic of Korea; ‡Department of Social and Preventive Medicine, School of Medicine, Sungkyunkwan University, Suwon, Republic of Korea; xDepartment of Neurology, Yongin Hyoja Geriatric Hospital, Yongin, Republic of Korea; kDepartment of Neurology, Seoul St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea; {Department of Neurology, Gachon University Gil Medical Center, Incheon, Republic of Korea; #Department of Radiology, Seoul St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea; **Department of Psychiatry, Seoul St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea; and ††Department of
Neurology, Inha University School of Medicine, Incheon, Republic of Korea. Received November 7, 2013; revision received December 17, 2013; accepted December 20, 2013. This study was supported by a grant of theKorea Healthcare Technology R&D project,Ministry of Health and Welfare, Republic of Korea(HI10C2020). Address correspondence to Dong-Won Yang, MD, PhD, Department of Neurology, College of Medicine, The Catholic University of Korea, Seoul St. Mary’s Hospital, 505 Banpo-dong, Seocho-gu, Seoul, 137-040, Republic of Korea. E-mail: [email protected]. 1052-3057/$ - see front matter Ó 2014 by National Stroke Association http://dx.doi.org/10.1016/j.jstrokecerebrovasdis.2013.12.040
Journal of Stroke and Cerebrovascular Diseases, Vol. 23, No. 5 (May-June), 2014: pp e365-e372
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Subcortical ischemic vascular disease (SIVD) accounts for the presence of periventricular (PV) and deep (D) white matter hyperintensities (WMHs) and lacunes on magnetic resonance imaging (MRI) of the brain.1 Chronic hypoperfusion by arteriosclerosis causes incomplete infarction, which appears as WMHs, whereas occlusion makes complete infarction, which is shown as lacunes on MRI.1 In numerous studies authors have attempted to elucidate the clinical significance of WMHs in healthy aging and dementia. In the Leukoaraiosis and Disability (LADIS) study, it generally has been recognized that WMHs affect cognitive decline,2-5 poorer performance on functional ability,6-8 gait disturbance,9,10 difficulty in urinating,11 and depression12-14 in nondemented elderly subjects; however, the previous results are inconsistent and mixed.15-18 Moreover, by subdividing WMHs by the location, researchers suggested that regional distribution of WMHs shows different patterns of cognitive decline in the normal elderly patient, those with amnestic mild cognitive impairment (aMCI), and those with dementia.19-24 It is still controversial whether WMHs would have a significant effect on cognition and functional ability according to location in aMCI regarded as prodromal Alzheimer’s dementia. Lacunes, which are considered further evidence of SIVD, have been demonstrated to be related to cognition,5,25,26 and the location of lacunes correlates with cognition.27 However, little is known about influence of coexisting WMHs and lacunes on cognition and functional ability, especially in patients with aMCI. We hypothesized that the volume of WMHs would correlate negatively with cognitive performance and functional ability and that the location of WMHs could differently affect these abilities among the anterior, middle, and posterior areas of WMHs, as well as between PVWMHs and DWMHs. To accurately confirm our hypothesis, we measured WMHs by using a semiautomatic volumetric method and divided WMHs according to location. In addition, it was thought that WMHs, lacunes, and medial temporal atrophy (MTA) could have interactive effects on each other. The aims of this study were to (1) evaluate the relationship between WMHs volume and cognition/activities of daily living (ADL); (2) investigate whether the location of WMHs could influence on them differently; and (3) determine the interactive effects among WMHs, lacunes, and MTA on them in patients with aMCI.
Materials and Methods Subjects This study was performed as a part of an ongoing, nationwide multicenter study on dementia: the Clinical Research Center for Dementia of South Korea (CREDOS) study (identifier on Clinical Trials: NCT01198093). We recruited 150 patients with aMCI who were registered be-
tween November 2008 and January 2010 consecutively in the CREDOS registry. We generally complied with the diagnostic criteria for aMCI defined by Petersen et al28 and Winblad et al29 The inclusion and exclusion criteria for aMCI have been detailed in a previous study.30 This study was approved by the institutional review board of each participant hospital, and written informed consent was obtained from patients or their caregivers who had received a complete description of this study.
Cognitive and Functional Assessments Clinical evaluations included basic demographics, medical history, neurologic examination, and Geriatric Depression Scale (GDS).31 General cognitive status was assessed by the Korean version Mini-Mental State Examination (K-MMSE)32 and general disease severity by the Clinical Dementia Rating-sum of boxes (CDRSB).33 Detailed cognitive functions were assessed by neuropsychologists, who used a standard comprehensive neuropsychologic battery, which includes 5 specific cognitive domains, such as attention, visuospatial function, language and related function, verbal and visual memory, and frontal executive functions.34 The neuropsychologic tests were detailed in a previous study23,35 (see the test items on supplementary data, Table S1). As for functional assessments, Barthel ADL (BADL)36 and Seoul Instrumental ADL (SIADL)37 were used to evaluate basic ADL and IADL.
WMHs Volume Measurements All subjects underwent MRI via a standard protocol. MRI was set as ideal parameters of their own at each center. Axial T2, T1, and fluid attenuation inversion recovery (FLAIR) images were obtained with 5-mm thickness without gap with anterior commissure-posterior commissure line as reference. WMHs were defined as circumscribed lesions of increased signal intensity within WM on both FLAIR and T2-weighted images. Infratentorial lesions were excluded. Lesions connected to the lateral ventricles were labeled as PVWMHs. Inferior and superior boundaries for PVWMHs were within 2 sections caudal to the most caudal section and cranial to the most cranial section that showed the lateral ventricles. Lesions separated from PVWMHs were labeled as DWMHs.38 They were also divided into anterior, middle, and posterior lesion based on the anterior and posterior commissure. Digital Imaging and Communications in Medicine (DICOM) files of FLAIR image were converted to Analyze file by the use of MRIcro software (available at http://www.mccauslandcenter.sc.edu/mricro/mricro/ index.html; McCausland Center for Brain Imaging, Columbia, SC). The axial FLAIR images were used to quantify WMHs with a semiautomated threshold method. The converted FLAIR imaging data were imported into the commercially available software program Analyze
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10.0 (Biomedical Imaging Resource, Mayo Foundation, Rochester, MN) where the skull was manually stripped leaving only the brain parenchyma. The inhomogeneity and artifacts of the signals were also corrected with Insight Segmentation and Registration Toolkit in Analyze 10.0Ò. After that, using the threshold tool, we semiautomatically isolated WMHs from the surrounding parenchyma for each imaging slice by adjusting the range of histogram values to include pixel intensity values consistent with signal abnormalities. Histograms of all slices of the FLAIR were generated to calculate the optimal voxel intensity of threshold, and the lowest voxel threshold was calculated as mean 6 1.4 SD. Each slice was visually reviewed by the trained rater and edited in case of being misclassified regions out of WM. The total volume of WMHs was generated by multiplying the area and the thickness of each slice. In addition, by using the neuroanatomical landmarks as described previously, not only were we able to separate and label WMHs according to the 6 regions (anterior PVWMHs/DWMHs, middle PVWMHs/DWMHs, posterior PVWMHs/DWMHs), but also each regional volume of WMHs was generated (see supplementary data, Fig S1 and S2). To correct interindividual variations in brain size, the total intracranial volume (TIV) was manually outlined and the ratio of the respective WMHs volume to TIV was referred to as normalized lesion volume. Volumetric measurement was repeated after an interval of 7 days to determine inter and intrarater reliability. Interrater reliability was .995 of intraclass correlation coefficient and test-retest reliability was .999 of intraclass correlation coefficient.
MTA and Lacunes Rating MTA was rated by 2 raters on T1-weighted coronal images according to the Scheltens scale ranging from 0 to 4 on the left and right hemispheres.39 The average of these 2 scores was used in this study. Intrarater and interrater agreements for MTA were good (k 5 .694-.709). Lacunes were defined as cavities with a diameter of 3-10 mm with signal intensities similar to CSF in all scan sequences by using a combination of FLAIR, T1, and T2 images to distinguish lacunes from Virchow-Robin spaces and microbleeds.40 We subdivided mean MTA scores into 3 groups (0, 0.5-1, and .1) and lacune counts into 3 groups (0, 1-3, and .3) based on the tertiles in its distribution.
Statistical Analysis Categorical variables were analyzed using the chisquare or Fisher exact tests. The volume of WMHs was expressed as a proportion of TIV and log-transformed to normalize its distribution (log WMHs/TIV). We used lacunes and mean MTA as continuous variables. First, we conducted correlation analysis to evaluate the relationships between main predictor variables, between total and regional WMHs volume, lacunes, and MTA, and between results of each cognitive test, ADL/IADL, Clinical
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Dementia Rating-sum of boxes (CDRSB), and GDS by using the Pearson correlation coefficient for continuous, normalized variables, as well as Spearman correlation analysis for non-normalized variables, such as lacunes and mean MTA. Second, we performed linear regression analysis for WMHs volume, lacunes, and MTA as predictors, respectively, and cognitive tests as response variables. In univariate regression analysis, the association between each cognitive domain and total WMHs volume was evaluated. In multiple linear regression, we included these variables that were found to be statistically significant (defined as P ,.10) in the univariate linear regression analysis and that were biologically meaningful as covariates. A general linear model was performed by entering each log WMHs/TIV, lacunes, and MTA independently as a predictor and the results of cognitive tests as outcomes after controlling for age, gender and educational level. Third, after reviewing the results from the first and second step, we additionally constructed multiple linear regression analysis by using the general linear model by entering regional WMHs volume as a predictor and the results of cognitive tests which were statistically significant as outcomes after adjusting for age, gender, and educational level, to assess whether WMHs in a specific site would be related to specific cognitive domains. Finally, because WMHs, lacunes, and MTA had different patterns of correlation structure suggesting different biologic mechanisms, interaction terms (WMHs 3 lacunes, WMHs 3 MTA, lacunes 3 MTA, and WMHs 3 lacunes 3 MTA) were added as a predictor in the same method for evaluating the possibility of such interactions. Bonferroni correction was used for control type 1 error. The Statistical Package for the Social Sciences (SPSS) for Windows, version 17.0 (SPSS Institute, Inc, Chicago) was used for data analysis. Significance levels for all analyses were set at P , .05.
Results The subjects’ characteristics are presented in Table 1. The mean age of the participants was 72.9 years (SD, 6.1; range, 59-88 years) and 57.4% (86) of the subjects were female. The mean of total WMHs volume was 21.6 mL (SD, 19.3), the mean count of lacunes was 1.4 (SD, 2.5), and the mean of MTA rating was 1.0 (SD: .9). PVWMHs volume was greater than DWMHs volume, and anterior WMHs volume was greater than middle or posterior WMHs volumes. However, similar volumes were observed between left and right WMHs (Table 2).
Correlation of MRI Parameters with Cognition and ADL According to correlation analysis, total WMHs volume (r 5 .299, P , .001) PVWMHs volume (r 5 .329, P , .001), DWMHs volume (r 5 .215, P , .001), and MTA (r 5 .510, P , .001) correlated with age, whereas lacunes (r 5 .083, P . .05) did not correlate with age. WMHs and lacunes
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Table 1. Clinical characteristics of the 150 subjects Demographic factors
Mean 6 SD (range)
not correlate with DWMHs and lacunes. Unlike IADL, BADL and Korean version Mini-Mental State Examination did not correlate with all regional WMHs, lacunes, and MTA.
Age, yr Gender, female (%) Education, yr Vascular risk factors, no. (%) Hypertension Diabetes mellitus Hyperlipidemia Heart disease Current smoking Clinical stroke HIS Neuropsychologic features K-MMSE CDRSB BADL SIADL GDS
72.9 6 6.1 (59-88) 86 (57.4) 9.7 6 4.5 (3-20)
Main Effects of Total WMHs and Regional WMHs on Cognition and ADL
94 (62.7) 35 (23.3) 30 (20.0) 33 (20.1) 14 (9.3) 12 (7.3) 2.5 6 2.0 (0-11) 25.8 6 2.3 (21-30) 1.4 6 0.7 (0.5-3.0) 19.8 6 0.9 (19-20) 5.6 6 4.2 (0-13) 5.2 6 4.3 (0-15)
Abbreviations: BADL, Barthel Activities of Daily Living; CDRSB, Clinical Dementia Rating Scale-Sum of boxes; GDS, Geriatric Depression Scale; HIS, Hachinski Ischemic Score; K-MMSE, Korean version Mini-Mental State Examination; SIADL, Seoul Instrumental Activities of Daily Living. Values are given mean 6 SD (range: minimum-maximum) except gender and vascular risk factors.
demonstrated similar patterns that mainly correlated with frontal executive functions. Only IADL correlated with PVWMHs (r 5 .240, P , .001) and lacunes (r 5 .196, P , .005). Moreover, GDS correlated with PVWMHs (r 5 2.212, P , .001) and MTA (r 5 2.177, P , .05) but did Table 2. Magnetic resonance imaging measures of the 150 subjects Variables
Values
TIV Total WMHs PVWMHs DWMHs Rt WMHs Lt WMHs Ant WMHs Mid WMHs Post WMHs MTA, Rt MTA, Lt MTA, mean Lacunes
1423.3 6 126.5 (1194.55-1745.56) 21.6 6 19.3 (0.58-102.62) 14.9 6 13.6 (0.37-66.77) 6.6 6 7.0 (0.08-52.04) 11.0 6 10.0 (0.32-59.53) 10.5 6 9.5 (0.12-53.09) 8.7 6 8.5 (0.32-56.25) 6.7 6 6.6 (0.09-33.80) 6.2 6 5.9 (0.02-32.70) 1.1 6 1.0 (0-3) 1.0 6 0.9 (0-3) 1.0 6 0.9 (0.0-3.0) 1.4 6 2.5 (0-12)
Abbreviations: Ant/Mid/Post, anterior/middle/posterior; DWMHs, deep white matter hyperintensities; MTA, medial temporal atrophy; PVWMHs, periventricular white matter hyperintensities; Rt/Lt, right/left; TIV, total intracranial volume; WMHs, white matter hyperintensities. Values are given mean 6 SD (range: minimum-maximum). TIV and WMHs volumes are expressed in milliliters.
After we controlled for age, gender, and educational level (years), total WMHs volume was negatively associated with Controlled Oral Word Association Task (COWAT)-semantic (b 5 2.92; R2 5 .11; P 5 .004), COWAT-phonemic (b 5 21.81; R2 5 .21; P 5 .014), Stroop color reading (b 5 26.31; R2 5 .18; P 5 .003), and positively associated with IADL (b 5 .36; R2 5 .17; P 5 .042). Table 3 shows the association of regional WMHs volume and cognition/IADL. There were no significant differences in cognition between right and left WMHs. When WMHs were subdivided into just 2 regions— PVWMHs and DWMHs—both PVWMHs and DWMHs independently demonstrated significant associations with frontal executive functions, such as COWATsemantic, phonemic fluency and Stroop color reading; however, the effects of PVWMHs were greater than DWMHs when assumed in the same size of the lesion. Moreover, when WMHs were subdivided into 6 areas, PVWMHs were more associated with poor performance of the frontal executive function, and the influences tended to be greater in the anterior, middle and posterior parts in order. IADL was influenced by only the anterior (P 5 .028) and middle areas (P 5 .014) of PVWMHs.
Interactions among WMHs, Lacunes, and MTA in Cognition and ADL In multiple linear regression analysis entering interaction terms, significant interactions between WMHs volume and lacune counts were found in COWAT-semantic (b 5 21.12; R2 5 .24; P 5 .039), Stroop color reading (b 5 22.07; R2 5 .15; P 5 .049) and IADL (b 5 .23; R2 5 .20; P 5 .009), whereas the volume of WMHs and MTA had no interactions (see the supplementary data, Table S1). The synergistic effects between WMHs and lacunes were observed in the same tests (Fig 1). The more WMHs volumes and lacunes, the steeper decrease in COWAT performance and increase in IADL.
Discussion The main purpose of this study was to confirm that the location of WMHs has different effects on cognition and functional ability in aMCI by use of the semiautomated WMHs volume measurement and to explore how WMHs influence cognition and ADL in cases in which lacunes or MTA coexists. The major findings of our study were as follows: (1) the severity of WMHs specifically in the anterior/middle PV regions was related to inferior executive performance and IADL; and (2) WMHs had
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Table 3. Association with regional WMHs and cognition/IADL PVWMHs
DWMHs
Rt WMHs
Lt WMHs
Variables
B (SE)
P
B (SE)
P
B (SE)
P
B (SE)
P
COWAT-semantic COWAT-phonemic Stroop color SIADL
2.94 (.32) 21.85 (.74) 27.09 (2.11) 0.75 (.37)
.004* .014* .001* .045*
20.64 (0.25) 21.15 (0.59) 23.56 (1.70) 0.20 (0.29)
.012* .055* .038* .505
20.88 (0.31) 21.55 (0.73) 25.94 (2.07) 0.47 (0.36)
.005* .036* .005* .198
20.89 (0.30) 21.96 (0.69) 26.22 (1.98) 0.63 (0.35)
.003* .005* .002* .069
Ant PVWMHs B(SE) 20.90 (0.31) 22.15 (0.74) 26.80 (2.11) 0.81 (0.37)
P
Ant DWMHs B (SE)
P
Mid PVWMHs B (SE)
P
Mid DWMHs B (SE)
P
Post PVWMHs B (SE)
P
Post DWMHs B (SE)
P
.005* 20.38 (0.20) .069 20.77 (0.29) .009* 20.25 (0.18) .093 20.57 (0.23) .014* 20.37 (0.24) .122 .004* 20.92 (0.48) .059 21.61 (0.67) .019* 21.35 (0.43) .002* 20.56 (0.53) .296 20.30 (0.57) .960 .002* 22.81 (1.36) .040* 25.16 (1.93) .009* 21.92 (1.25) .127 23.23 (1.52) .036* 21.24 (1.64) .453 .028* 0.22 (0.23) .352 0.83 (0.33) .014* 0.28 (0.22) .204 0.31 (0.27) .240 0.20 (0.28) .492
Abbreviations: DWMHs, deep white matter hyperintensities; COWAT, Controlled Oral Word Association Task; IDAL, instrumental activities of daily living; PVWMHs, periventricular white matter hyperintensities; SIADL, Seoul Instrumental Activities of Daily Living. *Statistically significant.
synergistic effects with the number of lacunes on frontal executive dysfunctions and poor performance of IADL. The associations between WMHs/lacunes and frontal executive functions were consistent with those reported by previous studies.3,25 Both WMHs and lacunes have been shown to be attributable to SIVD, and frontal executive dysfunctions that are simultaneously related to WMHs and lacunes may result from the disruption and disconnection of the frontal-subcortical circuit.4 In our study, we found that IADL was strongly associated with WMHs, which corresponds well with the results reported by previous studies.30,41 It would be reasonable to assume that BADL index is not sensitive enough to detect the subtle dysfunction of physical activities and that IADL represents more complex functional abilities which could be affected easily by WMHs.
Our results that PVWMHs had definitive effects on frontal executive dysfunction are similar to those of previous studies, showing that not DWMHs but PVWMHs strongly predict poorer performance in cognitive tests.20,21,23,42,43 One possible explanation could be that long association fibers, which connect several cognitionassociated areas, are most likely to be selectively affected by PVWMHs20 and the other explanation could be that PVWMHs interrupt the frontal subcortical circuit more profoundly.23,42,43 Moreover, our results indicate that the influences of PWMHs on frontal executive function tend to be greater in anterior, middle, and posterior areas in order. We postulated that WMHs appear more abundantly in the frontal lobes than in the other brain regions, and anterior WMHs disconnect the frontalsubcortical pathways profoundly.44 In addition, IADL
Figure 1. Interactions between the volume of white matter hyperintensities and lacunes. The graphs demonstrated the synergistic interactions between the volume of white matter hyperintensities and lacunes on verbal fluency test, Stroop color test and instrumental activities of daily living. Color dots and lines represent the groups of lacune counts (0, blue; 1-3, green; .3, red). The graphs indicate that the scatters of white matter hyperintensities are distributed toward severer volume of white matter hyperintensities as lacune levels increase. The lines also indicate that the more white matter hyperintensities and lacunes exist, the steeper decrease in Controlled Oral Word Association Task, Stroop color test performance, and steeper increase in instrumental activities of daily living.
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are mainly affected by the anterior and middle areas of PVWMHs but not by DWMHs and lacunes. It could be assumed that comprehensive frontal executive function is essential for good performance of IADL and is more related to PVWMs, indicating that poor performance of IADL is associated with PVWMHs rather than DWMHs. Another reason could be that the preferential effect of WMHs on the frontal cortex may be related to substantial convergence of the fiber pathways on the frontal lobes, so that lesions in any subcortical regions exert their effects on this large area of the association cortex and may have the functional importance of conectivity.44 There have been a few studies on interactions between WMHs and lacunes.45,46 It is noteworthy that WMHs and lacunes had synergistic interaction in inferior frontal executive function and IADL in our study. It is postulated that they could affect frontal executive dysfunction synergistically, because both WMHs and lacunes mainly affected the frontal-subcortical pathways. In our study, we found that WMHs and MTA had no synergistic effect on cognition and functional ability in aMCI, which was in contrast to the results of previous researches.47-51 The differences between the results of Shim et al49 based on the CREDOS registry and ours may be caused by methodological and disease group differences. Shim et al49 measured WMHs by using the visual rating scale instead of volume measurement and targeted AD dementia, which may have more various and severe degrees of MTA as well as impacts of MTA greater than that of aMCI. The differences also may be related to different pathophysiologic processes of WMHs and MTA. The strengths of this study are to analyze WMHs volume by the semiautomated volumetric method but not by visual rating and to measure WMHs volume, the number of lacunes, and MTA simultaneously to confirm their interactions. To avoid the possibility due to the ceiling effect of visual rating scales, we used volumetric measurement. Because this measurement is more accurate and associated with subtle cognitive decline, volumetric data are better appropriate for assessing differences in WMHs severity.52-54 However, this study has some limitations. First, this is a crosssectional study without clinicopathologic correlations between WMHs and pathologic findings. The general applicability of our results may be limited. Second, we did not use the same MRI machine because this was a multicenter study. Finally, we did not consider the location of the lacunes. Nevertheless, this study supports the hypothesis that both WMHs and lacunes on MRI which are known as important markers of SIVD led to cognitive and functional decline independently and synergistically. In conclusion, PVWMHs, especially their anterior area showed the most powerful impact on frontal executive dysfunction and poor performance of IADL in aMCI patients, therefore, the location of WMHs would be also
considered as an additional predictor of poor performance of frontal executive function and IADL. Furthermore, it is important to regard WMHs and lacunes as potential imaging biomarkers at the same time to predict cognition and IADL, based on the significantly more deleterious effect in aMCI patients. It would be highly desirable to pay more attention to IADL as well as frontal executive function when coexisting WMHs and lacunes.
Supplementary Data Supplementary data related to this article can be found at http://dx.doi.org/10.1016/j.jstrokecerebrovasdis.2013. 12.040.
References 1. Roman GC, Erkinjuntti T, Wallin A, et al. Subcortical ischaemic vascular dementia. Lancet Neurol 2002; 1:426-436. 2. The LADIS Study GroupPoggesi A, Pantoni L, Inzitari D, et al. 2001-2011: a decade of the LADIS (Leukoaraiosis And DISability) Study: what have we learned about white matter changes and small-vessel disease? Cerebrovasc Dis 2011;32:577-588. 3. Jokinen H, Kalska H, Ylikoski R, et al. MRI-defined subcortical ischemic vascular disease: baseline clinical and neuropsychological findings. The LADIS Study. Cerebrovasc Dis 2009;27:336-344. 4. Jokinen H, Kalska H, Ylikoski R, et al. Longitudinal cognitive decline in subcortical ischemic vascular disease–the LADIS Study. Cerebrovasc Dis 2009;27:384-391. 5. van der Flier WM, van Straaten EC, Barkhof F, et al. Small vessel disease and general cognitive function in nondisabled elderly: the LADIS study. Stroke 2005;36:2116-2120. 6. Inzitari D, Pracucci G, Poggesi A, et al. Changes in white matter as determinant of global functional decline in older independent outpatients: three year follow-up of LADIS (leukoaraiosis and disability) study cohort. BMJ 2009;339:b2477. 7. Inzitari D, Simoni M, Pracucci G, et al. Risk of rapid global functional decline in elderly patients with severe cerebral age-related white matter changes: the LADIS study. Arch Intern Med 2007;167:81-88. 8. Pantoni L, Poggesi A, Basile AM, et al. Leukoaraiosis predicts hidden global functioning impairment in nondisabled older people: The LADIS (leukoaraiosis and disability in the elderly) study. J Am Geriatr Soc 2006; 54:1095-1101. 9. Baezner H, Blahak C, Poggesi A, et al. Association of gait and balance disorders with age-related white matter changes: the LADIS study. Neurology 2008;70:935-942. 10. Blahak C, Baezner H, Pantoni L, et al. Deep frontal and periventricular age related white matter changes but not basal ganglia and infratentorial hyperintensities are associated with falls: cross sectional results from the LADIS study. J Neurol Neurosurg Psychiatry 2009; 80:608-613. 11. Poggesi A, Pracucci G, Chabriat H, et al. Urinary complaints in nondisabled elderly people with age-related white matter changes: the Leukoaraiosis And DISability (LADIS) Study. J Am Geriatr Soc 2008;56:1638-1643.
WHITE MATTER HYPERINTENSITIES IN MCI 12. Firbank MJ, Teodorczuk A, van der Flier WM, et al. Relationship between progression of brain white matter changes and late-life depression: 3-year results from the LADIS study. Br J Psychiatry 2012;201:40-45. 13. Teodorczuk A, Firbank MJ, Pantoni L, et al. Relationship between baseline white-matter changes and development of late-life depressive symptoms: 3-year results from the LADIS study. Psychol Med 2010;40:603-610. 14. Teodorczuk A, O’Brien JT, Firbank MJ, et al. White matter changes and late-life depressive symptoms: longitudinal study. Br J Psychiatry 2007;191:212-217. 15. de Groot JC, de Leeuw FE, Oudkerk M, et al. Cerebral white matter lesions and cognitive function: the Rotterdam Scan Study. Ann Neurol 2000;47:145-151. 16. de Mendonca A, Ribeiro F, Guerreiro M, et al. Clinical significance of subcortical vascular disease in patients with mild cognitive impairment. Eur J Neurol 2005;12:125-130. 17. Debette S, Bombois S, Bruandet A, et al. Subcortical hyperintensities are associated with cognitive decline in patients with mild cognitive impairment. Stroke 2007; 38:2924-2930. 18. Mirsen TR, Lee DH, Wong CJ, et al. Clinical correlates of white-matter changes on magnetic resonance imaging scans of the brain. Arch Neurol 1991;48:1015-1021. 19. Delano-Wood L, Abeles N, Sacco JM, et al. Regional white matter pathology in mild cognitive impairment: differential influence of lesion type on neuropsychological functioning. Stroke 2008;39:794-799. 20. Kee Hyung P, Lee JY, Na DL, et al. Different associations of periventricular and deep white matter lesions with cognition, neuropsychiatric symptoms, and daily activities in dementia. J Geriatr Psychiatry Neurol 2011; 24:84-90. 21. Kim JH, Hwang KJ, Lee YH, et al. Regional white matter hyperintensities in normal aging, single domain amnestic mild cognitive impairment, and mild Alzheimer’s disease. J Clin Neurosci 2011;18:1101-1106. 22. Marquine MJ, Attix DK, Goldstein LB, et al. Differential patterns of cognitive decline in anterior and posterior white matter hyperintensity progression. Stroke 2010; 41:1946-1950. 23. Choi SH, Kim S, Han SH, et al. Neurologic signs in relation to cognitive function in subcortical ischemic vascular dementia: a CREDOS (Clinical Research Center for Dementia of South Korea) study. Neurol Sci 2012;33:839-846. 24. van Straaten EC, Harvey D, Scheltens P, et al. Periventricular white matter hyperintensities increase the likelihood of progression from amnestic mild cognitive impairment to dementia. J Neurol 2008;255:1302-1308. 25. Jokinen H, Gouw AA, Madureira S, et al. Incident lacunes influence cognitive decline: the LADIS study. Neurology 2011;76:1872-1878. 26. Arboix A. Lacunar infarct and cognitive decline. Expert Rev Neurother 2011;11:1251-1254. 27. Benisty S, Gouw AA, Porcher R, et al. Location of lacunar infarcts correlates with cognition in a sample of nondisabled subjects with age-related white-matter changes: the LADIS study. J Neurol Neurosurg Psychiatry 2009; 80:478-483. 28. Petersen RC, Stevens JC, Ganguli M, et al. Practice parameter: early detection of dementia: mild cognitive impairment (an evidence-based review). Report of the Quality Standards Subcommittee of the American Academy of Neurology. Neurology 2001;56:1133-1142. 29. Winblad B, Palmer K, Kivipelto M, et al. Mild cognitive impairment-beyond controversies, towards a consensus:
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30.
31.
32.
33. 34.
35.
36. 37.
38.
39.
40.
41.
42.
43.
44.
45.
46.
47.
48.
49.
report of the International Working Group on Mild Cognitive Impairment. J Intern Med 2004;256:240-246. Yoon B, Shim YS, Kim YD, et al. Correlation between instrumental activities of daily living and white matter hyperintensities in amnestic mild cognitive impairment: results of a cross-sectional study. Neurol Sci 2013;34:715-721. Cho M, Bae JN, Suh GH. Validation of geriatric depression scale, Korean version (GDS) in the assessment of DSM-III-R major depression. J Korean Neuropsychiatr Assoc 1999;38:48-63. Kang YW, Na DL, Hahn SH. A validity study on the Korean Mini-Mental State Examination (K-MMSE) in dementia patients. J Korean NeurolAssoc 1997;15:300-307. Morris JC. The Clinical Dementia Rating (CDR): current version and scoring rules. Neurology 1993;43:2412-2414. Kang Y, Na DL. Seoul Neuropsychological Screening Battery (SNSB). Seoul: Human Brain Research & Consulting Co. 2003. Ahn HJ, Chin J, Park A, 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;25:1071-1076. Mahoney FI, Barthel DW. Functional evaluation: The Barthel index. Md State Med J 1965;14:61-65. Ku H, Kim J, Kwon E, et al. A study on the reliability and validity of Seoul-instrumental activities of daily living (SIADL). J Korean Neuropsychiatr Assoc 2004;43:189-199. ten Dam VH, van den Heuvel DM, de Craen AJ, et al. Decline in total cerebral blood flow is linked with increase in periventricular but not deep white matter hyperintensities. Radiology 2007;243:198-203. Scheltens P, Launer LJ, Barkhof F, et al. Visual assessment of medial temporal lobe atrophy on magnetic resonance imaging: interobserver reliability. J Neurol 1995;242:557-560. Gouw AA, van der Flier WM, Fazekas F, et al. Progression of white matter hyperintensities and incidence of new lacunes over a 3-year period: the Leukoaraiosis and Disability study. Stroke 2008;39:1414-1420. Moon SY, Na DL, Seo SW, et al. Impact of white matter changes on activities of daily living in mild to moderate dementia. Eur Neurol 2011;65:223-230. De Groot JC, De Leeuw FE, Oudkerk M, et al. Periventricular cerebral white matter lesions predict rate of cognitive decline. Ann Neurol 2002;52:335-341. Seo SW, Lee JM, Im K, et al. Cortical thinning related to periventricular and deep white matter hyperintensities. Neurobiol Aging 2012;33:1156-1167. Tullberg M, Fletcher E, DeCarli C, et al. White matter lesions impair frontal lobe function regardless of their location. Neurology 2004;63:246-253. van de Pol LA, Korf ES, van der Flier WM, et al. Magnetic resonance imaging predictors of cognition in mild cognitive impairment. Arch Neurol 2007;64:1023-1028. Reed BR, Eberling JL, Mungas D, et al. Effects of white matter lesions and lacunes on cortical function. Arch Neurol 2004;61:1545-1550. van der Flier WM, van Straaten EC, Barkhof F, et al. Medial temporal lobe atrophy and white matter hyperintensities are associated with mild cognitive deficits in non-disabled elderly people: the LADIS study. J Neurol Neurosurg Psychiatry 2005;76:1497-1500. Jokinen H, Lipsanen J, Schmidt R, et al. Brain atrophy accelerates cognitive decline in cerebral small vessel disease: The LADIS study. Neurology 2012;78:1785-1792. Shim YS, Youn YC, Na DL, et al. Effects of medial temporal atrophy and white matter hyperintensities on the
e372 cognitive functions in patients with Alzheimer’s disease. Eur Neurol 2011;66:75-82. 50. Appel J, Potter E, Bhatia N, et al. Association of white matter hyperintensity measurements on brain MR imaging with cognitive status, medial temporal atrophy, and cardiovascular risk factors. AJNR Am J Neuroradiol 2009;30:1870-1876. 51. van der Flier WM, Middelkoop HA, WeverlingRijnsburger AW, et al. Interaction of medial temporal lobe atrophy and white matter hyperintensities in AD. Neurology 2004;62:1862-1864.
B. YOON ET AL. 52. Gouw AA, van der Flier WM, van Straaten EC, et al. Reliability and sensitivity of visual scales versus volumetry for evaluating white matter hyperintensity progression. Cerebrovasc Dis 2008;25:247-253. 53. Tiehuis AM, Vincken KL, Mali WP, et al. Automated and visual scoring methods of cerebral white matter hyperintensities: relation with age and cognitive function. Cerebrovasc Dis 2008;25:59-66. 54. van Straaten EC, Fazekas F, Rostrup E, et al. Impact of white matter hyperintensities scoring method on correlations with clinical data: the LADIS study. Stroke 2006;37:836-840.
This study was to evaluate the influence on cognition and activities of daily living (ADL) by white matter hyperintensities (WMHs) based on the severity and location, as well as the interactions among WMHs, lacunes, and medial temporal atrophy (MTA). In 150 patients with amnestic mild cognitive impairment, WMHs were quantified with the use of a semiautomated volumetric method. Lacune counting and MTA assessment were performed by visual rating. The severer WMHs were, the more executive functions decreased. The influence on executive functions such as verbal fluency test and Stroop color reading test were greater in periventricular (PV) WMHs than deep WMHs, as well as bigger in anterior, middle, and posterior areas in order. The instrumental (I) ADL was strongly associated with the anterior (P 5 .028) and middle area (P 5 .014) of PVWMHs only. WMHs had synergistic interactions with lacunes in Controlled Oral Word Association Task-semantic (ß 5 21.12; R2 5 .24; P 5.039), Stroop color (ß 5 22.07; R2 5 .15; P 5.049), and IADL (ß 5 .23; R2 5 .20; P 5 .009). Anterior PVWMHs demonstrated the most powerful impact on frontal executive dysfunction and poor performance of IADL. WMHs had synergistic effects with the number of lacunes on them. Therefore, it is desirable to consider WMHs and lacunes simultaneously as potential imaging biomarkers for predicting cognition and IADL in aMCI. Key Words: White matter hyperintensities—subcortical ischemic vascular disease—lacunar infarct—mild cognitive impairment—executive function—activities of daily living—magnetic resonance imaging. Ó 2014 by National Stroke Association
From the *Department of Neurology, Konyang University Hospital, College of Medicine, Konyang University, Daejeon, Republic of Korea; †Department of Neurology, Bucheon St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Bucheon, Republic of Korea; ‡Department of Social and Preventive Medicine, School of Medicine, Sungkyunkwan University, Suwon, Republic of Korea; xDepartment of Neurology, Yongin Hyoja Geriatric Hospital, Yongin, Republic of Korea; kDepartment of Neurology, Seoul St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea; {Department of Neurology, Gachon University Gil Medical Center, Incheon, Republic of Korea; #Department of Radiology, Seoul St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea; **Department of Psychiatry, Seoul St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea; and ††Department of
Neurology, Inha University School of Medicine, Incheon, Republic of Korea. Received November 7, 2013; revision received December 17, 2013; accepted December 20, 2013. This study was supported by a grant of theKorea Healthcare Technology R&D project,Ministry of Health and Welfare, Republic of Korea(HI10C2020). Address correspondence to Dong-Won Yang, MD, PhD, Department of Neurology, College of Medicine, The Catholic University of Korea, Seoul St. Mary’s Hospital, 505 Banpo-dong, Seocho-gu, Seoul, 137-040, Republic of Korea. E-mail: [email protected]. 1052-3057/$ - see front matter Ó 2014 by National Stroke Association http://dx.doi.org/10.1016/j.jstrokecerebrovasdis.2013.12.040
Journal of Stroke and Cerebrovascular Diseases, Vol. 23, No. 5 (May-June), 2014: pp e365-e372
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Subcortical ischemic vascular disease (SIVD) accounts for the presence of periventricular (PV) and deep (D) white matter hyperintensities (WMHs) and lacunes on magnetic resonance imaging (MRI) of the brain.1 Chronic hypoperfusion by arteriosclerosis causes incomplete infarction, which appears as WMHs, whereas occlusion makes complete infarction, which is shown as lacunes on MRI.1 In numerous studies authors have attempted to elucidate the clinical significance of WMHs in healthy aging and dementia. In the Leukoaraiosis and Disability (LADIS) study, it generally has been recognized that WMHs affect cognitive decline,2-5 poorer performance on functional ability,6-8 gait disturbance,9,10 difficulty in urinating,11 and depression12-14 in nondemented elderly subjects; however, the previous results are inconsistent and mixed.15-18 Moreover, by subdividing WMHs by the location, researchers suggested that regional distribution of WMHs shows different patterns of cognitive decline in the normal elderly patient, those with amnestic mild cognitive impairment (aMCI), and those with dementia.19-24 It is still controversial whether WMHs would have a significant effect on cognition and functional ability according to location in aMCI regarded as prodromal Alzheimer’s dementia. Lacunes, which are considered further evidence of SIVD, have been demonstrated to be related to cognition,5,25,26 and the location of lacunes correlates with cognition.27 However, little is known about influence of coexisting WMHs and lacunes on cognition and functional ability, especially in patients with aMCI. We hypothesized that the volume of WMHs would correlate negatively with cognitive performance and functional ability and that the location of WMHs could differently affect these abilities among the anterior, middle, and posterior areas of WMHs, as well as between PVWMHs and DWMHs. To accurately confirm our hypothesis, we measured WMHs by using a semiautomatic volumetric method and divided WMHs according to location. In addition, it was thought that WMHs, lacunes, and medial temporal atrophy (MTA) could have interactive effects on each other. The aims of this study were to (1) evaluate the relationship between WMHs volume and cognition/activities of daily living (ADL); (2) investigate whether the location of WMHs could influence on them differently; and (3) determine the interactive effects among WMHs, lacunes, and MTA on them in patients with aMCI.
Materials and Methods Subjects This study was performed as a part of an ongoing, nationwide multicenter study on dementia: the Clinical Research Center for Dementia of South Korea (CREDOS) study (identifier on Clinical Trials: NCT01198093). We recruited 150 patients with aMCI who were registered be-
tween November 2008 and January 2010 consecutively in the CREDOS registry. We generally complied with the diagnostic criteria for aMCI defined by Petersen et al28 and Winblad et al29 The inclusion and exclusion criteria for aMCI have been detailed in a previous study.30 This study was approved by the institutional review board of each participant hospital, and written informed consent was obtained from patients or their caregivers who had received a complete description of this study.
Cognitive and Functional Assessments Clinical evaluations included basic demographics, medical history, neurologic examination, and Geriatric Depression Scale (GDS).31 General cognitive status was assessed by the Korean version Mini-Mental State Examination (K-MMSE)32 and general disease severity by the Clinical Dementia Rating-sum of boxes (CDRSB).33 Detailed cognitive functions were assessed by neuropsychologists, who used a standard comprehensive neuropsychologic battery, which includes 5 specific cognitive domains, such as attention, visuospatial function, language and related function, verbal and visual memory, and frontal executive functions.34 The neuropsychologic tests were detailed in a previous study23,35 (see the test items on supplementary data, Table S1). As for functional assessments, Barthel ADL (BADL)36 and Seoul Instrumental ADL (SIADL)37 were used to evaluate basic ADL and IADL.
WMHs Volume Measurements All subjects underwent MRI via a standard protocol. MRI was set as ideal parameters of their own at each center. Axial T2, T1, and fluid attenuation inversion recovery (FLAIR) images were obtained with 5-mm thickness without gap with anterior commissure-posterior commissure line as reference. WMHs were defined as circumscribed lesions of increased signal intensity within WM on both FLAIR and T2-weighted images. Infratentorial lesions were excluded. Lesions connected to the lateral ventricles were labeled as PVWMHs. Inferior and superior boundaries for PVWMHs were within 2 sections caudal to the most caudal section and cranial to the most cranial section that showed the lateral ventricles. Lesions separated from PVWMHs were labeled as DWMHs.38 They were also divided into anterior, middle, and posterior lesion based on the anterior and posterior commissure. Digital Imaging and Communications in Medicine (DICOM) files of FLAIR image were converted to Analyze file by the use of MRIcro software (available at http://www.mccauslandcenter.sc.edu/mricro/mricro/ index.html; McCausland Center for Brain Imaging, Columbia, SC). The axial FLAIR images were used to quantify WMHs with a semiautomated threshold method. The converted FLAIR imaging data were imported into the commercially available software program Analyze
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10.0 (Biomedical Imaging Resource, Mayo Foundation, Rochester, MN) where the skull was manually stripped leaving only the brain parenchyma. The inhomogeneity and artifacts of the signals were also corrected with Insight Segmentation and Registration Toolkit in Analyze 10.0Ò. After that, using the threshold tool, we semiautomatically isolated WMHs from the surrounding parenchyma for each imaging slice by adjusting the range of histogram values to include pixel intensity values consistent with signal abnormalities. Histograms of all slices of the FLAIR were generated to calculate the optimal voxel intensity of threshold, and the lowest voxel threshold was calculated as mean 6 1.4 SD. Each slice was visually reviewed by the trained rater and edited in case of being misclassified regions out of WM. The total volume of WMHs was generated by multiplying the area and the thickness of each slice. In addition, by using the neuroanatomical landmarks as described previously, not only were we able to separate and label WMHs according to the 6 regions (anterior PVWMHs/DWMHs, middle PVWMHs/DWMHs, posterior PVWMHs/DWMHs), but also each regional volume of WMHs was generated (see supplementary data, Fig S1 and S2). To correct interindividual variations in brain size, the total intracranial volume (TIV) was manually outlined and the ratio of the respective WMHs volume to TIV was referred to as normalized lesion volume. Volumetric measurement was repeated after an interval of 7 days to determine inter and intrarater reliability. Interrater reliability was .995 of intraclass correlation coefficient and test-retest reliability was .999 of intraclass correlation coefficient.
MTA and Lacunes Rating MTA was rated by 2 raters on T1-weighted coronal images according to the Scheltens scale ranging from 0 to 4 on the left and right hemispheres.39 The average of these 2 scores was used in this study. Intrarater and interrater agreements for MTA were good (k 5 .694-.709). Lacunes were defined as cavities with a diameter of 3-10 mm with signal intensities similar to CSF in all scan sequences by using a combination of FLAIR, T1, and T2 images to distinguish lacunes from Virchow-Robin spaces and microbleeds.40 We subdivided mean MTA scores into 3 groups (0, 0.5-1, and .1) and lacune counts into 3 groups (0, 1-3, and .3) based on the tertiles in its distribution.
Statistical Analysis Categorical variables were analyzed using the chisquare or Fisher exact tests. The volume of WMHs was expressed as a proportion of TIV and log-transformed to normalize its distribution (log WMHs/TIV). We used lacunes and mean MTA as continuous variables. First, we conducted correlation analysis to evaluate the relationships between main predictor variables, between total and regional WMHs volume, lacunes, and MTA, and between results of each cognitive test, ADL/IADL, Clinical
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Dementia Rating-sum of boxes (CDRSB), and GDS by using the Pearson correlation coefficient for continuous, normalized variables, as well as Spearman correlation analysis for non-normalized variables, such as lacunes and mean MTA. Second, we performed linear regression analysis for WMHs volume, lacunes, and MTA as predictors, respectively, and cognitive tests as response variables. In univariate regression analysis, the association between each cognitive domain and total WMHs volume was evaluated. In multiple linear regression, we included these variables that were found to be statistically significant (defined as P ,.10) in the univariate linear regression analysis and that were biologically meaningful as covariates. A general linear model was performed by entering each log WMHs/TIV, lacunes, and MTA independently as a predictor and the results of cognitive tests as outcomes after controlling for age, gender and educational level. Third, after reviewing the results from the first and second step, we additionally constructed multiple linear regression analysis by using the general linear model by entering regional WMHs volume as a predictor and the results of cognitive tests which were statistically significant as outcomes after adjusting for age, gender, and educational level, to assess whether WMHs in a specific site would be related to specific cognitive domains. Finally, because WMHs, lacunes, and MTA had different patterns of correlation structure suggesting different biologic mechanisms, interaction terms (WMHs 3 lacunes, WMHs 3 MTA, lacunes 3 MTA, and WMHs 3 lacunes 3 MTA) were added as a predictor in the same method for evaluating the possibility of such interactions. Bonferroni correction was used for control type 1 error. The Statistical Package for the Social Sciences (SPSS) for Windows, version 17.0 (SPSS Institute, Inc, Chicago) was used for data analysis. Significance levels for all analyses were set at P , .05.
Results The subjects’ characteristics are presented in Table 1. The mean age of the participants was 72.9 years (SD, 6.1; range, 59-88 years) and 57.4% (86) of the subjects were female. The mean of total WMHs volume was 21.6 mL (SD, 19.3), the mean count of lacunes was 1.4 (SD, 2.5), and the mean of MTA rating was 1.0 (SD: .9). PVWMHs volume was greater than DWMHs volume, and anterior WMHs volume was greater than middle or posterior WMHs volumes. However, similar volumes were observed between left and right WMHs (Table 2).
Correlation of MRI Parameters with Cognition and ADL According to correlation analysis, total WMHs volume (r 5 .299, P , .001) PVWMHs volume (r 5 .329, P , .001), DWMHs volume (r 5 .215, P , .001), and MTA (r 5 .510, P , .001) correlated with age, whereas lacunes (r 5 .083, P . .05) did not correlate with age. WMHs and lacunes
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Table 1. Clinical characteristics of the 150 subjects Demographic factors
Mean 6 SD (range)
not correlate with DWMHs and lacunes. Unlike IADL, BADL and Korean version Mini-Mental State Examination did not correlate with all regional WMHs, lacunes, and MTA.
Age, yr Gender, female (%) Education, yr Vascular risk factors, no. (%) Hypertension Diabetes mellitus Hyperlipidemia Heart disease Current smoking Clinical stroke HIS Neuropsychologic features K-MMSE CDRSB BADL SIADL GDS
72.9 6 6.1 (59-88) 86 (57.4) 9.7 6 4.5 (3-20)
Main Effects of Total WMHs and Regional WMHs on Cognition and ADL
94 (62.7) 35 (23.3) 30 (20.0) 33 (20.1) 14 (9.3) 12 (7.3) 2.5 6 2.0 (0-11) 25.8 6 2.3 (21-30) 1.4 6 0.7 (0.5-3.0) 19.8 6 0.9 (19-20) 5.6 6 4.2 (0-13) 5.2 6 4.3 (0-15)
Abbreviations: BADL, Barthel Activities of Daily Living; CDRSB, Clinical Dementia Rating Scale-Sum of boxes; GDS, Geriatric Depression Scale; HIS, Hachinski Ischemic Score; K-MMSE, Korean version Mini-Mental State Examination; SIADL, Seoul Instrumental Activities of Daily Living. Values are given mean 6 SD (range: minimum-maximum) except gender and vascular risk factors.
demonstrated similar patterns that mainly correlated with frontal executive functions. Only IADL correlated with PVWMHs (r 5 .240, P , .001) and lacunes (r 5 .196, P , .005). Moreover, GDS correlated with PVWMHs (r 5 2.212, P , .001) and MTA (r 5 2.177, P , .05) but did Table 2. Magnetic resonance imaging measures of the 150 subjects Variables
Values
TIV Total WMHs PVWMHs DWMHs Rt WMHs Lt WMHs Ant WMHs Mid WMHs Post WMHs MTA, Rt MTA, Lt MTA, mean Lacunes
1423.3 6 126.5 (1194.55-1745.56) 21.6 6 19.3 (0.58-102.62) 14.9 6 13.6 (0.37-66.77) 6.6 6 7.0 (0.08-52.04) 11.0 6 10.0 (0.32-59.53) 10.5 6 9.5 (0.12-53.09) 8.7 6 8.5 (0.32-56.25) 6.7 6 6.6 (0.09-33.80) 6.2 6 5.9 (0.02-32.70) 1.1 6 1.0 (0-3) 1.0 6 0.9 (0-3) 1.0 6 0.9 (0.0-3.0) 1.4 6 2.5 (0-12)
Abbreviations: Ant/Mid/Post, anterior/middle/posterior; DWMHs, deep white matter hyperintensities; MTA, medial temporal atrophy; PVWMHs, periventricular white matter hyperintensities; Rt/Lt, right/left; TIV, total intracranial volume; WMHs, white matter hyperintensities. Values are given mean 6 SD (range: minimum-maximum). TIV and WMHs volumes are expressed in milliliters.
After we controlled for age, gender, and educational level (years), total WMHs volume was negatively associated with Controlled Oral Word Association Task (COWAT)-semantic (b 5 2.92; R2 5 .11; P 5 .004), COWAT-phonemic (b 5 21.81; R2 5 .21; P 5 .014), Stroop color reading (b 5 26.31; R2 5 .18; P 5 .003), and positively associated with IADL (b 5 .36; R2 5 .17; P 5 .042). Table 3 shows the association of regional WMHs volume and cognition/IADL. There were no significant differences in cognition between right and left WMHs. When WMHs were subdivided into just 2 regions— PVWMHs and DWMHs—both PVWMHs and DWMHs independently demonstrated significant associations with frontal executive functions, such as COWATsemantic, phonemic fluency and Stroop color reading; however, the effects of PVWMHs were greater than DWMHs when assumed in the same size of the lesion. Moreover, when WMHs were subdivided into 6 areas, PVWMHs were more associated with poor performance of the frontal executive function, and the influences tended to be greater in the anterior, middle and posterior parts in order. IADL was influenced by only the anterior (P 5 .028) and middle areas (P 5 .014) of PVWMHs.
Interactions among WMHs, Lacunes, and MTA in Cognition and ADL In multiple linear regression analysis entering interaction terms, significant interactions between WMHs volume and lacune counts were found in COWAT-semantic (b 5 21.12; R2 5 .24; P 5 .039), Stroop color reading (b 5 22.07; R2 5 .15; P 5 .049) and IADL (b 5 .23; R2 5 .20; P 5 .009), whereas the volume of WMHs and MTA had no interactions (see the supplementary data, Table S1). The synergistic effects between WMHs and lacunes were observed in the same tests (Fig 1). The more WMHs volumes and lacunes, the steeper decrease in COWAT performance and increase in IADL.
Discussion The main purpose of this study was to confirm that the location of WMHs has different effects on cognition and functional ability in aMCI by use of the semiautomated WMHs volume measurement and to explore how WMHs influence cognition and ADL in cases in which lacunes or MTA coexists. The major findings of our study were as follows: (1) the severity of WMHs specifically in the anterior/middle PV regions was related to inferior executive performance and IADL; and (2) WMHs had
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Table 3. Association with regional WMHs and cognition/IADL PVWMHs
DWMHs
Rt WMHs
Lt WMHs
Variables
B (SE)
P
B (SE)
P
B (SE)
P
B (SE)
P
COWAT-semantic COWAT-phonemic Stroop color SIADL
2.94 (.32) 21.85 (.74) 27.09 (2.11) 0.75 (.37)
.004* .014* .001* .045*
20.64 (0.25) 21.15 (0.59) 23.56 (1.70) 0.20 (0.29)
.012* .055* .038* .505
20.88 (0.31) 21.55 (0.73) 25.94 (2.07) 0.47 (0.36)
.005* .036* .005* .198
20.89 (0.30) 21.96 (0.69) 26.22 (1.98) 0.63 (0.35)
.003* .005* .002* .069
Ant PVWMHs B(SE) 20.90 (0.31) 22.15 (0.74) 26.80 (2.11) 0.81 (0.37)
P
Ant DWMHs B (SE)
P
Mid PVWMHs B (SE)
P
Mid DWMHs B (SE)
P
Post PVWMHs B (SE)
P
Post DWMHs B (SE)
P
.005* 20.38 (0.20) .069 20.77 (0.29) .009* 20.25 (0.18) .093 20.57 (0.23) .014* 20.37 (0.24) .122 .004* 20.92 (0.48) .059 21.61 (0.67) .019* 21.35 (0.43) .002* 20.56 (0.53) .296 20.30 (0.57) .960 .002* 22.81 (1.36) .040* 25.16 (1.93) .009* 21.92 (1.25) .127 23.23 (1.52) .036* 21.24 (1.64) .453 .028* 0.22 (0.23) .352 0.83 (0.33) .014* 0.28 (0.22) .204 0.31 (0.27) .240 0.20 (0.28) .492
Abbreviations: DWMHs, deep white matter hyperintensities; COWAT, Controlled Oral Word Association Task; IDAL, instrumental activities of daily living; PVWMHs, periventricular white matter hyperintensities; SIADL, Seoul Instrumental Activities of Daily Living. *Statistically significant.
synergistic effects with the number of lacunes on frontal executive dysfunctions and poor performance of IADL. The associations between WMHs/lacunes and frontal executive functions were consistent with those reported by previous studies.3,25 Both WMHs and lacunes have been shown to be attributable to SIVD, and frontal executive dysfunctions that are simultaneously related to WMHs and lacunes may result from the disruption and disconnection of the frontal-subcortical circuit.4 In our study, we found that IADL was strongly associated with WMHs, which corresponds well with the results reported by previous studies.30,41 It would be reasonable to assume that BADL index is not sensitive enough to detect the subtle dysfunction of physical activities and that IADL represents more complex functional abilities which could be affected easily by WMHs.
Our results that PVWMHs had definitive effects on frontal executive dysfunction are similar to those of previous studies, showing that not DWMHs but PVWMHs strongly predict poorer performance in cognitive tests.20,21,23,42,43 One possible explanation could be that long association fibers, which connect several cognitionassociated areas, are most likely to be selectively affected by PVWMHs20 and the other explanation could be that PVWMHs interrupt the frontal subcortical circuit more profoundly.23,42,43 Moreover, our results indicate that the influences of PWMHs on frontal executive function tend to be greater in anterior, middle, and posterior areas in order. We postulated that WMHs appear more abundantly in the frontal lobes than in the other brain regions, and anterior WMHs disconnect the frontalsubcortical pathways profoundly.44 In addition, IADL
Figure 1. Interactions between the volume of white matter hyperintensities and lacunes. The graphs demonstrated the synergistic interactions between the volume of white matter hyperintensities and lacunes on verbal fluency test, Stroop color test and instrumental activities of daily living. Color dots and lines represent the groups of lacune counts (0, blue; 1-3, green; .3, red). The graphs indicate that the scatters of white matter hyperintensities are distributed toward severer volume of white matter hyperintensities as lacune levels increase. The lines also indicate that the more white matter hyperintensities and lacunes exist, the steeper decrease in Controlled Oral Word Association Task, Stroop color test performance, and steeper increase in instrumental activities of daily living.
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are mainly affected by the anterior and middle areas of PVWMHs but not by DWMHs and lacunes. It could be assumed that comprehensive frontal executive function is essential for good performance of IADL and is more related to PVWMs, indicating that poor performance of IADL is associated with PVWMHs rather than DWMHs. Another reason could be that the preferential effect of WMHs on the frontal cortex may be related to substantial convergence of the fiber pathways on the frontal lobes, so that lesions in any subcortical regions exert their effects on this large area of the association cortex and may have the functional importance of conectivity.44 There have been a few studies on interactions between WMHs and lacunes.45,46 It is noteworthy that WMHs and lacunes had synergistic interaction in inferior frontal executive function and IADL in our study. It is postulated that they could affect frontal executive dysfunction synergistically, because both WMHs and lacunes mainly affected the frontal-subcortical pathways. In our study, we found that WMHs and MTA had no synergistic effect on cognition and functional ability in aMCI, which was in contrast to the results of previous researches.47-51 The differences between the results of Shim et al49 based on the CREDOS registry and ours may be caused by methodological and disease group differences. Shim et al49 measured WMHs by using the visual rating scale instead of volume measurement and targeted AD dementia, which may have more various and severe degrees of MTA as well as impacts of MTA greater than that of aMCI. The differences also may be related to different pathophysiologic processes of WMHs and MTA. The strengths of this study are to analyze WMHs volume by the semiautomated volumetric method but not by visual rating and to measure WMHs volume, the number of lacunes, and MTA simultaneously to confirm their interactions. To avoid the possibility due to the ceiling effect of visual rating scales, we used volumetric measurement. Because this measurement is more accurate and associated with subtle cognitive decline, volumetric data are better appropriate for assessing differences in WMHs severity.52-54 However, this study has some limitations. First, this is a crosssectional study without clinicopathologic correlations between WMHs and pathologic findings. The general applicability of our results may be limited. Second, we did not use the same MRI machine because this was a multicenter study. Finally, we did not consider the location of the lacunes. Nevertheless, this study supports the hypothesis that both WMHs and lacunes on MRI which are known as important markers of SIVD led to cognitive and functional decline independently and synergistically. In conclusion, PVWMHs, especially their anterior area showed the most powerful impact on frontal executive dysfunction and poor performance of IADL in aMCI patients, therefore, the location of WMHs would be also
considered as an additional predictor of poor performance of frontal executive function and IADL. Furthermore, it is important to regard WMHs and lacunes as potential imaging biomarkers at the same time to predict cognition and IADL, based on the significantly more deleterious effect in aMCI patients. It would be highly desirable to pay more attention to IADL as well as frontal executive function when coexisting WMHs and lacunes.
Supplementary Data Supplementary data related to this article can be found at http://dx.doi.org/10.1016/j.jstrokecerebrovasdis.2013. 12.040.
References 1. Roman GC, Erkinjuntti T, Wallin A, et al. Subcortical ischaemic vascular dementia. Lancet Neurol 2002; 1:426-436. 2. The LADIS Study GroupPoggesi A, Pantoni L, Inzitari D, et al. 2001-2011: a decade of the LADIS (Leukoaraiosis And DISability) Study: what have we learned about white matter changes and small-vessel disease? Cerebrovasc Dis 2011;32:577-588. 3. Jokinen H, Kalska H, Ylikoski R, et al. MRI-defined subcortical ischemic vascular disease: baseline clinical and neuropsychological findings. The LADIS Study. Cerebrovasc Dis 2009;27:336-344. 4. Jokinen H, Kalska H, Ylikoski R, et al. Longitudinal cognitive decline in subcortical ischemic vascular disease–the LADIS Study. Cerebrovasc Dis 2009;27:384-391. 5. van der Flier WM, van Straaten EC, Barkhof F, et al. Small vessel disease and general cognitive function in nondisabled elderly: the LADIS study. Stroke 2005;36:2116-2120. 6. Inzitari D, Pracucci G, Poggesi A, et al. Changes in white matter as determinant of global functional decline in older independent outpatients: three year follow-up of LADIS (leukoaraiosis and disability) study cohort. BMJ 2009;339:b2477. 7. Inzitari D, Simoni M, Pracucci G, et al. Risk of rapid global functional decline in elderly patients with severe cerebral age-related white matter changes: the LADIS study. Arch Intern Med 2007;167:81-88. 8. Pantoni L, Poggesi A, Basile AM, et al. Leukoaraiosis predicts hidden global functioning impairment in nondisabled older people: The LADIS (leukoaraiosis and disability in the elderly) study. J Am Geriatr Soc 2006; 54:1095-1101. 9. Baezner H, Blahak C, Poggesi A, et al. Association of gait and balance disorders with age-related white matter changes: the LADIS study. Neurology 2008;70:935-942. 10. Blahak C, Baezner H, Pantoni L, et al. Deep frontal and periventricular age related white matter changes but not basal ganglia and infratentorial hyperintensities are associated with falls: cross sectional results from the LADIS study. J Neurol Neurosurg Psychiatry 2009; 80:608-613. 11. Poggesi A, Pracucci G, Chabriat H, et al. Urinary complaints in nondisabled elderly people with age-related white matter changes: the Leukoaraiosis And DISability (LADIS) Study. J Am Geriatr Soc 2008;56:1638-1643.
WHITE MATTER HYPERINTENSITIES IN MCI 12. Firbank MJ, Teodorczuk A, van der Flier WM, et al. Relationship between progression of brain white matter changes and late-life depression: 3-year results from the LADIS study. Br J Psychiatry 2012;201:40-45. 13. Teodorczuk A, Firbank MJ, Pantoni L, et al. Relationship between baseline white-matter changes and development of late-life depressive symptoms: 3-year results from the LADIS study. Psychol Med 2010;40:603-610. 14. Teodorczuk A, O’Brien JT, Firbank MJ, et al. White matter changes and late-life depressive symptoms: longitudinal study. Br J Psychiatry 2007;191:212-217. 15. de Groot JC, de Leeuw FE, Oudkerk M, et al. Cerebral white matter lesions and cognitive function: the Rotterdam Scan Study. Ann Neurol 2000;47:145-151. 16. de Mendonca A, Ribeiro F, Guerreiro M, et al. Clinical significance of subcortical vascular disease in patients with mild cognitive impairment. Eur J Neurol 2005;12:125-130. 17. Debette S, Bombois S, Bruandet A, et al. Subcortical hyperintensities are associated with cognitive decline in patients with mild cognitive impairment. Stroke 2007; 38:2924-2930. 18. Mirsen TR, Lee DH, Wong CJ, et al. Clinical correlates of white-matter changes on magnetic resonance imaging scans of the brain. Arch Neurol 1991;48:1015-1021. 19. Delano-Wood L, Abeles N, Sacco JM, et al. Regional white matter pathology in mild cognitive impairment: differential influence of lesion type on neuropsychological functioning. Stroke 2008;39:794-799. 20. Kee Hyung P, Lee JY, Na DL, et al. Different associations of periventricular and deep white matter lesions with cognition, neuropsychiatric symptoms, and daily activities in dementia. J Geriatr Psychiatry Neurol 2011; 24:84-90. 21. Kim JH, Hwang KJ, Lee YH, et al. Regional white matter hyperintensities in normal aging, single domain amnestic mild cognitive impairment, and mild Alzheimer’s disease. J Clin Neurosci 2011;18:1101-1106. 22. Marquine MJ, Attix DK, Goldstein LB, et al. Differential patterns of cognitive decline in anterior and posterior white matter hyperintensity progression. Stroke 2010; 41:1946-1950. 23. Choi SH, Kim S, Han SH, et al. Neurologic signs in relation to cognitive function in subcortical ischemic vascular dementia: a CREDOS (Clinical Research Center for Dementia of South Korea) study. Neurol Sci 2012;33:839-846. 24. van Straaten EC, Harvey D, Scheltens P, et al. Periventricular white matter hyperintensities increase the likelihood of progression from amnestic mild cognitive impairment to dementia. J Neurol 2008;255:1302-1308. 25. Jokinen H, Gouw AA, Madureira S, et al. Incident lacunes influence cognitive decline: the LADIS study. Neurology 2011;76:1872-1878. 26. Arboix A. Lacunar infarct and cognitive decline. Expert Rev Neurother 2011;11:1251-1254. 27. Benisty S, Gouw AA, Porcher R, et al. Location of lacunar infarcts correlates with cognition in a sample of nondisabled subjects with age-related white-matter changes: the LADIS study. J Neurol Neurosurg Psychiatry 2009; 80:478-483. 28. Petersen RC, Stevens JC, Ganguli M, et al. Practice parameter: early detection of dementia: mild cognitive impairment (an evidence-based review). Report of the Quality Standards Subcommittee of the American Academy of Neurology. Neurology 2001;56:1133-1142. 29. Winblad B, Palmer K, Kivipelto M, et al. Mild cognitive impairment-beyond controversies, towards a consensus:
e371
30.
31.
32.
33. 34.
35.
36. 37.
38.
39.
40.
41.
42.
43.
44.
45.
46.
47.
48.
49.
report of the International Working Group on Mild Cognitive Impairment. J Intern Med 2004;256:240-246. Yoon B, Shim YS, Kim YD, et al. Correlation between instrumental activities of daily living and white matter hyperintensities in amnestic mild cognitive impairment: results of a cross-sectional study. Neurol Sci 2013;34:715-721. Cho M, Bae JN, Suh GH. Validation of geriatric depression scale, Korean version (GDS) in the assessment of DSM-III-R major depression. J Korean Neuropsychiatr Assoc 1999;38:48-63. Kang YW, Na DL, Hahn SH. A validity study on the Korean Mini-Mental State Examination (K-MMSE) in dementia patients. J Korean NeurolAssoc 1997;15:300-307. Morris JC. The Clinical Dementia Rating (CDR): current version and scoring rules. Neurology 1993;43:2412-2414. Kang Y, Na DL. Seoul Neuropsychological Screening Battery (SNSB). Seoul: Human Brain Research & Consulting Co. 2003. Ahn HJ, Chin J, Park A, 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;25:1071-1076. Mahoney FI, Barthel DW. Functional evaluation: The Barthel index. Md State Med J 1965;14:61-65. Ku H, Kim J, Kwon E, et al. A study on the reliability and validity of Seoul-instrumental activities of daily living (SIADL). J Korean Neuropsychiatr Assoc 2004;43:189-199. ten Dam VH, van den Heuvel DM, de Craen AJ, et al. Decline in total cerebral blood flow is linked with increase in periventricular but not deep white matter hyperintensities. Radiology 2007;243:198-203. Scheltens P, Launer LJ, Barkhof F, et al. Visual assessment of medial temporal lobe atrophy on magnetic resonance imaging: interobserver reliability. J Neurol 1995;242:557-560. Gouw AA, van der Flier WM, Fazekas F, et al. Progression of white matter hyperintensities and incidence of new lacunes over a 3-year period: the Leukoaraiosis and Disability study. Stroke 2008;39:1414-1420. Moon SY, Na DL, Seo SW, et al. Impact of white matter changes on activities of daily living in mild to moderate dementia. Eur Neurol 2011;65:223-230. De Groot JC, De Leeuw FE, Oudkerk M, et al. Periventricular cerebral white matter lesions predict rate of cognitive decline. Ann Neurol 2002;52:335-341. Seo SW, Lee JM, Im K, et al. Cortical thinning related to periventricular and deep white matter hyperintensities. Neurobiol Aging 2012;33:1156-1167. Tullberg M, Fletcher E, DeCarli C, et al. White matter lesions impair frontal lobe function regardless of their location. Neurology 2004;63:246-253. van de Pol LA, Korf ES, van der Flier WM, et al. Magnetic resonance imaging predictors of cognition in mild cognitive impairment. Arch Neurol 2007;64:1023-1028. Reed BR, Eberling JL, Mungas D, et al. Effects of white matter lesions and lacunes on cortical function. Arch Neurol 2004;61:1545-1550. van der Flier WM, van Straaten EC, Barkhof F, et al. Medial temporal lobe atrophy and white matter hyperintensities are associated with mild cognitive deficits in non-disabled elderly people: the LADIS study. J Neurol Neurosurg Psychiatry 2005;76:1497-1500. Jokinen H, Lipsanen J, Schmidt R, et al. Brain atrophy accelerates cognitive decline in cerebral small vessel disease: The LADIS study. Neurology 2012;78:1785-1792. Shim YS, Youn YC, Na DL, et al. Effects of medial temporal atrophy and white matter hyperintensities on the
e372 cognitive functions in patients with Alzheimer’s disease. Eur Neurol 2011;66:75-82. 50. Appel J, Potter E, Bhatia N, et al. Association of white matter hyperintensity measurements on brain MR imaging with cognitive status, medial temporal atrophy, and cardiovascular risk factors. AJNR Am J Neuroradiol 2009;30:1870-1876. 51. van der Flier WM, Middelkoop HA, WeverlingRijnsburger AW, et al. Interaction of medial temporal lobe atrophy and white matter hyperintensities in AD. Neurology 2004;62:1862-1864.
B. YOON ET AL. 52. Gouw AA, van der Flier WM, van Straaten EC, et al. Reliability and sensitivity of visual scales versus volumetry for evaluating white matter hyperintensity progression. Cerebrovasc Dis 2008;25:247-253. 53. Tiehuis AM, Vincken KL, Mali WP, et al. Automated and visual scoring methods of cerebral white matter hyperintensities: relation with age and cognitive function. Cerebrovasc Dis 2008;25:59-66. 54. van Straaten EC, Fazekas F, Rostrup E, et al. Impact of white matter hyperintensities scoring method on correlations with clinical data: the LADIS study. Stroke 2006;37:836-840.
