Published online before print December 24, 2008, doi: 10.1161/STROKEAHA.108.516286
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(Stroke. 2009;40:492.)
© 2009 American Heart Association, Inc.
Original Contributions |
MRI Biomarkers of Vascular Damage and Atrophy Predicting Mortality in a Memory Clinic Population
From Alzheimer Center, Department of Neurology (W.J.P.H., J.D.S., C.C., M.M.E.B., P.S., F.B., W.M.v.d.F.) and Department of Radiology (W.J.P.H., J.D.S., F.B.), VU University Medical Center, Amsterdam, The Netherlands; Department of Neurology and Stroke Department (C.C.), Lille University Hospital, France.
Correspondence to W.J.P. Henneman, MD, Department of Radiology and Alzheimer Center, VU University Medical Center, PO Box 7057, 1007 MB Amsterdam, The Netherlands. E-mail w.henneman{at}vumc.nl
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Abstract |
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Background and Purpose— MRI biomarkers play an important role in the diagnostic work-up of dementia, but their prognostic value is less well-understood. We investigated if simple MRI rating scales predict mortality in a memory clinic population.
Methods— We included 1138 consecutive patients attending our memory clinic. Diagnostic categories were: subjective complaints (n=220), mild cognitive impairment (n=160), Alzheimer disease (n=357), vascular dementia (n=46), other dementia (n=136), and other diagnosis (n=219). Baseline MRIs were assessed using visual rating scales for medial temporal lobe atrophy (range, 0–4), global cortical atrophy (range, 0–3), and white matter hyperintensities (range, 0–3). Number of microbleeds and presence of infarcts were recorded. Cox-regression models were used to calculate the risk of mortality.
Results— Mean follow-up duration was 2.6 (±1.9) years. In unadjusted models, all MRI markers except infarcts predicted mortality. After adjustment for age, sex, and diagnosis, white matter hyperintensities, and microbleeds predicted mortality (white matter hyperintensities: hazard ratio [HR], 1.2; 95% CI, 1.0–1.4; microbleeds: HR, 1.02 95% CI, 1.00–1.03; categorized: HR, 1.5; 95% CI, 1.1–2.0). The predictive effect of global cortical atrophy was restricted to younger subjects (HR, 1.7; 95% CI, 1.2–2.6). An interaction between microbleeds and global cortical atrophy indicated that mortality was especially high in patients with both microbleeds and global cortical atrophy.
Conclusion— Simple MRI biomarkers, in addition to their diagnostic use, have a prognostic value with respect to mortality in a memory clinic population. Microbleeds were the strongest predictor of mortality.
Key Words: dementia • magnetic resonance imaging • microbleeds • mortality • white matter
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Introduction |
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Predicting progression and outcome in dementia is clinically important. It provides important information for patients and caregivers and improves patient management. It also has scientific importance by giving insight in the possible pathological processes involved in progression of disease. The presence of dementia in general leads to an increased mortality, and several clinical and demographic characteristics have been identified that convey poor survival.1–3 Old age, male gender, and comorbidity are associated with higher mortality, both in a general population2 and among patients with dementia,1,2,4 although some studies report that mortality attributable to dementia is higher in young subjects compared to older subjects.5
MRI has an established role in the diagnostic work-up of dementia, beyond the exclusion of potentially treatable causes of dementia.6,7 MRI markers of atrophy can support a diagnosis of Alzheimer disease (AD)8 and markers of cerebrovascular disease are an essential part of the NINDS-AIREN criteria for vascular dementia.9 Moreover, global cortical atrophy (GCA) and medial temporal lobe atrophy (MTA) predict decline of cognitive function.10,11 The relation between MRI markers of vascular disease and cognitive function is less clear; however, some studies suggest an association12,13 but others fail to do so.10,14
Less is known about the value of MRI in predicting survival in memory clinic patients. A limited number of studies found a correlation between imaging markers and an increased risk of mortality, and only 1 of those studies4 was conducted in a dementia population, using CT rather than MRI. The latter study described an association of temporoparietal atrophy and mortality within a population of AD patients. Other studies that described associations between imaging markers and mortality were conducted either in an elderly population without dementia,15,16 or in other disease populations.17–19 The aim of the current study was to investigate the prognostic value with respect to mortality of 5 simple MRI markers related to neurodegenerative and vascular disease in a memory clinic population.
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Materials and Methods |
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Patients
The study population consisted of 1138 consecutive patients who visited the memory clinic of the Alzheimer Center, VU University Medical Center between 1993 and 2006, and underwent brain MRI. Brain MRI scans were performed as part of the routine clinical work-up. The other standardized clinical assessments included medical history, physical examination, neurological examination, laboratory tests, electroencephalography and neuropsychological examinations. Diagnoses were made in a multidisciplinary consensus meeting between neurologists, neuropsychologists, a clinical neurophysiologist, psychiatrist, geriatrician, and radiologist. We defined the following categories of diagnosis: subjective complaints (cognitive complaints in the absence of deficits on neuropsychological examination), mild cognitive impairment,20,21 AD using the NINCDS-ADRDA criteria,22 vascular dementia,9 other dementia (including frontotemporal lobar degeneration,23 dementia with Lewy bodies,24 and other neurodegenerative disorders), and other diagnosis (including neurological, psychiatric, or other diagnoses and cases in which diagnosis was postponed, eg, awaiting further examinations).
The study was approved by the local medical ethical committee, and all patients gave written informed consent for their clinical data to be used for research purposes at the time of their first visit. Questionnaires were sent out to patients’ general practitioners and follow-up was defined based on dates of survival or death on these questionnaires. When the questionnaire was not returned, last date of survival or date of death was retrieved from the patient’s clinical record (n=287). Information about medical history was retrieved from the patients’ clinical records.
MRI Protocol
The majority of MRI scans (n=998) were obtained on a 1.0-T system (Siemens Magnetom Impact Expert); 140 scans were obtained on a 1.5-T platform (Siemens Sonata Syngo or Siemens Vision). Scan protocol included: (1) 3-dimensional T1-weighted magnetization-prepared rapid acquisition gradient-echo sequence (coronal orientation, 148–176 slices; 1.5-mm slice thickness; field of view, 250 mm; 256x256 matrix; echo time (TE), 5.2–7.0 ms; repetition time (TR), 15 ms; inversion time (TI), 300 ms); (2) fluid-attenuated inversion recovery sequence (transverse orientation, 17 slices; slice thickness, 5 mm; slice gap, 1.5–2.0 mm; field of view, 250 mm; 256x256 matrix; TE, 101–105 ms; TR, 9000 ms; TI, 2200 ms); (3) T2-weighted turbo spin echo sequence (transverse orientation, 21 slices; slice thickness, 5 mm; slice gap, 1.5 mm; field of view, 250 mm; 256x256 matrix; TE, 119 ms; TR, 5775 ms); and (4) T2*-weighted gradient-echo sequence (transverse orientation, 19–21 slices; slice thickness, 5 mm; slice gap, 1.0 mm; field of view, 250 mm; 256x256 matrix; TE, 15–22 ms; TR, 600–800 ms; flip angle, 15°–20°).
MRI Assessment
Assessment of MRI consisted of 3 widely used visual rating scales (using previously described operationalization criteria25–27), a count of microbleeds, and assessment of the presence of large vessel infarcts. MTA was rated using a 5-point rating scale (0–4)27 using oblique reconstructions of the magnetization-prepared rapid acquisition gradient-echo sequences, perpendicular to the long axis of the hippocampus. In the analysis, we used the average of MTA score for the left and right sides. GCA (range, 0–3)26 and white matter hyperintensities (WMH) (range, 0–3)25 were assessed on fluid-attenuated inversion recovery images. We defined microbleeds as round lesions with low signal on T2*-weighted images within the brain parenchyma (diameter <10 mm).28 The total number of microbleeds was counted. Presence of 
1 infarcts, including both territorial and watershed infarctions, was recorded.
The rating was performed by 3 observers, blinded to the patients’ clinical data. The observers were trained using our standard training set (19 brains) to meet consistency requirements according to our standard operating procedure. The interrater-weighted Cohen 
scores were >0.90 for microbleeds, >0.80 for MTA and WMH scores, and >0.60 for GCA (against internally established gold standard). Intrarater-weighted Cohen scores were >0.90 for microbleeds, >0.80 for MTA, and >0.70 for GCA and WMH.
Because of missing sequences, or impaired quality of sequences, MTA could only be assessed in 1112 cases, GCA could only be assessed in 1090, and WMH could only be assessed in 1117. Because the T2*-weighted sequence has been included in the standard scan protocol more recently, microbleeds could be assessed in a smaller number of scans than the other measures (n=938).
Statistical Methods
For statistical analyses, SPSS version 12.0.1 for windows was used. Differences between groups were analyzed using 
2 test and Student t test. Kaplan-Meier survival curves were constructed for the scores of the visual rating scales. To estimate the risk of mortality associated with the MRI markers, Cox proportional hazards models were used. We performed the analyses for microbleeds in 2 ways, using the total number of microbleeds, and using a categorization (0, 1–2, 3), because the latter allows for easier comparison with the hazard ratios (HR) of the other MRI markers. Per MRI marker, we applied 5 models. Each MRI marker was entered univariately (model 1); adjusted for age and sex (model 2); adjusted for age, sex, and diagnosis (model 3); and additionally adjusted for history of hypertension, diabetes mellitus, hypercholesterolemia, and myocardial infarction (model 4). Finally, all MRI measures were entered simultaneously in model 5, with all covariates from model 4. To investigate the predictive effects of WMH and microbleeds in further detail, we repeated the analysis, entering them as categorical variables to model 3. To assess interactions between age and MRI markers, and mutual interactions between the MRI markers, we subsequently introduced bivariate interaction terms. For the assessment of interactions with age, we entered age as a dichotomous variable, based on median age (68 years). The models used to assess interactions between MRI markers contained 2 MRI markers and their interaction term with age, sex, and diagnosis as covariates. Finally, we repeated the analyses for the group of AD patients only, using the same 4 Cox proportional hazard models.
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Results |
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Baseline characteristics for all patients are shown in Table 1. The total number of patients who had died at follow-up was 153 (13%), after a mean follow-up duration of 2.6 (±1.9) years. The mean duration of follow-up did not differ between patients who had deceased and those who were alive at follow-up (P=1.0). There were 628 (55%) men and 510 (45%) women. Mortality was higher in men compared with women (P<0.001). Mean age at the time of MRI was 66 years (±11), and age at MRI was higher among patients who had deceased compared with those who were still alive at follow-up (P<0.001). The numbers of patients per category of diagnosis were: subjective symptoms, 220 (19%); mild cognitive impairment, 160(14%); AD, 357 (31%); vascular dementia, 46 (4%); other dementia, 136 (12%); and other diagnosis, 219 (19%). Mortality was higher among patients with dementia compared with the patients in the subjective symptoms and mild cognitive impairment groups (P<0.001 for AD and vascular dementia; P<0.005 for other dementia). Mean mini-mental state examination score was lower in the patients who had deceased at follow-up compared with those who were still alive. Mean mini-mental state examination, by diagnosis, ranged from 29 (subjective symptoms) to 21 (AD).
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The Figure shows Kaplan-Meier survival curves for the MRI markers in the overall population. Cox proportional hazard models (Table 2) show that all MRI markers except infarcts predicted mortality in the univariate analysis and after adjustment for age and sex (models 1 and 2). Microbleeds had the strongest impact on mortality, which became especially clear after additional adjustment for diagnosis (model 3), while WMH also had a modest predictive effect. The effects were largely unchanged after entering history of hypertension, diabetes, hypercholesterolesterolemia, and myocardial infarction to the analysis. In the model in which all MRI measures were entered simultaneously, microbleeds remained a predictor of mortality, independent of the other MRI measures. To assess the predictive effects of microbleeds and WMH in more detail, we entered them as categorical variables to model 3. For both markers, the predictive effects mainly depended on the group with severe abnormality (severe WMH [no WMH=reference]: HR, 1.7; 95% CI, 1.0–2.8; P=0.06; many [3] microbleeds [no microbleeds=reference]: HR, 2.4; 95% CI, 1.4–4.3; P<0.05). The HR for 1 and 2 WMH (no WMH=reference) were 1.0 (95% CI, 0.6–1.5; P=0.94) and 1.0 (95% CI, 0.6-.8; P=0.90), respectively. For 1 to 2 microbleeds, HR was 0.8 (95% CI, 0.4–1.6; P=0.60).
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There was an interaction between age and GCA (P<0.01), but there was no significant interaction between age and the other MRI markers. To explore how the predictive effect of GCA was modified by age, we stratified the patients by median age (68 years). Adjusted for sex and diagnosis, GCA was a predictor of mortality in the younger patients (HR, 1.7; 95% CI, 1.1–2.6; P<0.05), but not in older patients (HR, 1.0; 95% CI, 0.8–1.3; P=0.80).
When entering bivariate interaction terms between pairs of MRI markers to model 3, we found 2 interactions. First, there was an interaction between MTA and GCA (P<0.05). The risk of mortality related to MTA was highest in patients with a low GCA score, and the risk of mortality related to GCA was highest in patients with low MTA scores. Second, we found an interaction between GCA and number of microbleeds (P<0.05). The risk of mortality for patients with microbleeds was highest in those who additionally had high GCA scores. The risk of mortality for patients with both severe cortical atrophy (GCA score=3) and 3 microbleeds was 6-times the risk found in patients without cortical atrophy and microbleeds (HR, 5.8; 95% CI, 1.5–22.9; P<0.05).
Finally, we repeated the analyses for the group of AD patients only (n=357). Mean age was 70 years (±9) and there were relatively more female (196; 55%) than male patients (161; 45%). Results were largely comparable with the results in the total population (Table 3). After adjustment for age and sex, the predictive effect of MTA and GCA disappeared, whereas the number of microbleeds predicted mortality in the group of AD patients independent of age and sex. The overall predictive effect of WMH did not reach significance in any of the models. The presence of infarcts did not predict mortality. The effects were not altered after adjusting for the additional covariates in model 3, and the predictive effect of microbleeds was independent of the other MRI variables.
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Discussion |
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The main finding of this study is that baseline MRI biomarkers, especially microbleeds and to a lesser extent WMH, predicted mortality in a memory clinic population. Large-vessel infarcts did not predict mortality. The predictive effect of cortical atrophy was restricted to the younger patients. When we restricted the analysis to AD patients only, findings were largely comparable.
Of the 5 MRI markers we investigated, presence of microbleeds was the strongest predictor of mortality. Where MTA and GCA are important markers in the diagnostic process, because they can both help differentiate neurodegenerative disease from controls8 and are related to cognitive function,10,11 we found that their value in predicting mortality is strongly related to diagnosis. Whereas the relation between markers of small-vessel disease and cognitive function remains less clear,10,12–14 and microbleeds at present do not have an important role the diagnostical process of dementia, we show that the number of microbleeds and, to a lesser extent, WMH are clinically relevant markers because they predict mortality in this population. Our finding that infarcts do not predict mortality within a memory clinic population is in line with findings by others.4
To our knowledge, this is the first study assessing the predictive value of microbleeds with respect to mortality in a population other than a stroke population. Recent studies have shown that the prevalence of microbleeds on MRI is relatively high within a dementia population, and that the presence of microbleeds is related to age, diagnosis, and the presence of other findings of vascular pathology on MRI.28–30 Although 2 studies have described a relation between the presence of microbleeds on MRI and severity of cognitive impairment within a vascular dementia 31 and a stroke32 population, the clinical significance of microbleeds is not fully clear at present. We add an important piece of information by showing that microbleeds are associated with poorer survival. Although microbleeds are highly prevalent among patients with intracerebral hemorrhages and ischemic stroke, literature about the risk of future cerebrovascular events is scarce.33,34 It is tempting to assume that the observed association between microbleeds and mortality is accounted for by cerebrovascular causes of death. However, because we were not able to collect data about cause of death of all deceased patients and because the reason of death was too often unknown, we were not able to perform analysis on these data. Further studies should address the important issue of the relation of microbleeds (and other MRI findings) and cause of death.
Our data suggest that not merely the presence, but rather the number, of microbleeds predicts the risk of mortality. The categorized (0, 1–2, 3 microbleeds) analysis shows that only the presence of many (3) microbleeds predicts mortality. It should, however, not be concluded that there is a strict threshold going from 2 to 3 microbleeds. Rather, our results indicate that the presence of multiple microbleeds does predict mortality, whereas the presence of some microbleeds is not associated with an increased risk of mortality.
The interaction between microbleeds and cortical atrophy resulted in a 6-fold increased risk of death for patients with severe cortical atrophy and multiple microbleeds in comparison with subjects without cortical atrophy and microbleeds. Literature shows that atrophy is present in vascular disease,35,36 and vascular damage often occurs in AD.37 Moreover, neuropathological studies have suggested that neurodegenerative disease and vascular pathology act in synergy, resulting in a higher risk of dementia and more severe cognitive impairment.38,39 In vivo studies using MRI have shown similar results.40,41 We extend on these findings by showing that patients expressing both types of pathology on MRI are at a far higher risk of mortality than patients with either of them. Although one might hypothesize that microbleeds and cortical atrophy could coexist as a representation of amyloid-angiopathy, recent findings that microbleeds seem to have no or only little influence on cerebral atrophy in CADASIL36 suggest that different pathological pathways might be involved as well. Age modified the relation between cortical atrophy and mortality. In the younger patients GCA predicts mortality, but it does not do so in the older patients. This might explain why the overall effect of GCA did not reach statistical significance in the overall population.
Among the limitations of this study is the fact that, rather than an epidemiological population-based study in which a given number of subjects is followed for a fixed number of years, our study is a cohort study in a clinical setting. The consequentially high variability in duration of follow-up was, nevertheless, accounted for in the statistical analyses. Second, although the cohort consists of a clinically important selection of patients, the results might not be applicable to a general population. In the Cox-proportional hazards models, age, sex, diagnosis, and vascular risk factors were included as covariates. We were not able to include other predictors of mortality that have been described, such as (nonvascular) comorbidity and physical performance, because these data, derived from clinical files, were not uniformly assessed and were less complete than data on vascular risk factors. No conclusions about the comparison between the predictive values of MRI variables and other predictors of mortality, such as age, diagnosis, and comorbidity, can be drawn from our results. Taken into account that MRI is at present widely available, as it is used in the diagnostic work-up of memory clinic patients, our finding that simple MRI markers, in addition to their diagnostic value, have prognostic value in predicting mortality is clinically important. The information obtained from MRI should be weighted together with other clinical variables such as comorbidity in planning of patient care and information given to patients and caregivers about prognosis.
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Acknowledgments |
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Marian van ‘t Oever, Anita Dekker and Agnes Sanders are gratefully acknowledged for their efforts in collecting the survival data.
Sources of Funding
W.J.P. Henneman is supported by the Image Analysis Center, Amsterdam. The Alzheimer Center VU Medical Center is supported by Alzheimer Nederland and Stichting VU Medical Center fonds. The clinical database structure was developed with funding from Stichting Dioraphte.
Disclosure
None.
Received January 30, 2008; revision received July 3, 2008; accepted July 22, 2008.
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References |
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1. Brookmeyer R, Corrada MM, Curriero FC, Kawas C. Survival following a diagnosis of Alzheimer disease. Arch Neurol. 2002; 59: 1764–1767.
2. Ganguli M, Dodge HH, Shen C, Pandav RS, DeKosky ST. Alzheimer disease and mortality: a 15-year epidemiological study. Arch Neurol. 2005; 62: 779–784.
3. Tschanz JT, Corcoran C, Skoog I, Khachaturian AS, Herrick J, Hayden KM, Welsh-Bohmer KA, Calvert T, Norton MC, Zandi P, Breitner JC. Dementia: the leading predictor of death in a defined elderly population: the Cache County Study. Neurology. 2004; 62: 1156–1162.
4. Claus JJ, van Gool WA, Teunisse S, Walstra GJ, Kwa VI, Hijdra A, Verbeeten B Jr, Koelman JH, Bour LJ, Ongerboer DV. Predicting survival in patients with early Alzheimer’s disease. Dement Geriatr Cogn Disord. 1998; 9: 284–293.[CrossRef][Medline] [Order article via Infotrieve]
5. Helmer C, Joly P, Letenneur L, Commenges D, Dartigues JF. Mortality with dementia: results from a French prospective community-based cohort. Am J Epidemiol. 2001; 154: 642–648.
6. Knopman DS, DeKosky ST, Cummings JL, Chui H, Corey-Bloom J, Relkin N, Small GW, Miller B, Stevens JC. Practice parameter: diagnosis of dementia (an evidence-based review). Report of the Quality Standards Subcommittee of the Am Academy of Neurology. Neurology. 2001; 56: 1143–1153.
7. Scheltens P, Fox N, Barkhof F, De Carli C. Structural magnetic resonance imaging in the practical assessment of dementia: beyond exclusion. Lancet Neurol. 2002; 1: 13–21.[CrossRef][Medline] [Order article via Infotrieve]
8. Jack CR Jr, Petersen RC, Xu Y, O'Brien PC, Smith GE, Ivnik RJ, Tangalos EG, Kokmen E. Rate of medial temporal lobe atrophy in typical aging and Alzheimer’s disease. Neurology. 1998; 51: 993–999.
9. Roman GC, Tatemichi TK, Erkinjuntti T, Cummings JL, Masdeu JC, Garcia JH, Amaducci L, Orgogozo JM, Brun A, Hofman A. Vascular dementia: diagnostic criteria for research studies. Report of the NINDS-AIREN International Workshop. Neurology. 1993; 43: 250–260.
10. Mungas D, Reed BR, Jagust WJ, DeCarli C, Mack WJ, Kramer JH, Weiner MW, Schuff N, Chui HC. Volumetric MRI predicts rate of cognitive decline related to AD and cerebrovascular disease. Neurology. 2002; 59: 867–873.
11. Petersen RC, Jack CR Jr, Xu YC, Waring SC, O'Brien PC, Smith GE, Ivnik RJ, Tangalos EG, Boeve BF, Kokmen E. Memory and MRI-based hippocampal volumes in aging and AD. Neurology. 2000; 54: 581–587.
12. Au R, Massaro JM, Wolf PA, Young ME, Beiser A, Seshadri S, D'Agostino RB, DeCarli C. Association of white matter hyperintensity volume with decreased cognitive functioning: the Framingham Heart Study. Arch Neurol. 2006; 63: 246–250.
13. van der Flier WM, van Straaten ECW, Barkhof F, Verdelho A, Madureira S, Pantoni L, Inzitari D, Erkinjuntti T, Crisby M, Waldemar G, Schmidt R, Fazekas F, Scheltens P. Small vessel disease and general cognitive function in nondisabled elderly - The LADIS study. Stroke. 2005; 36: 2116–2120.
14. Hirono N, Kitagaki H, Kazui H, Hashimoto M, Mori E. Impact of white matter changes on clinical manifestation of Alzheimer’s disease: A quantitative study. Stroke. 2000; 31: 2182–2188.
15. Kerber KA, Whitman GT, Brown DL, Baloh RW. Increased risk of death in community-dwelling older people with white matter hyperintensities on MRI. J Neurol Sci. 2006; 250: 33–38.[CrossRef][Medline] [Order article via Infotrieve]
16. Kuller LH, Arnold AM, Longstreth WT Jr, Manolio TA, O'Leary DH, Burke GL, Fried LP, Newman AB. White matter grade and ventricular volume on brain MRI as markers of longevity in the cardiovascular health study. Neurobiol Aging. 2007; 28: 1307–1315.[CrossRef][Medline] [Order article via Infotrieve]
17. Briley DP, Haroon S, Sergent SM, Thomas S. Does leukoariosis predict morbidity and mortality? Neurology. 2000; 54: 90–94.
18. Fu JH, Lu CZ, Hong Z, Dong Q, Luo Y, Wong KS. Extent of white matter lesions is related to acute subcortical infarcts and predicts further stroke risk in patients with first ever ischaemic stroke. J Neurol Neurosurg Psychiatry. 2005; 76: 793–796.
19. van W, I, Kappelle LJ, van Gijn J, Koudstaal PJ, Franke CL, Vermeulen M, Gorter JW, Algra A. Long-term survival and vascular event risk after transient ischaemic attack or minor ischaemic stroke: a cohort study. Lancet. 2005; 365: 2098–2104.[CrossRef][Medline] [Order article via Infotrieve]
20. Petersen RC, Smith GE, Waring SC, Ivnik RJ, Tangalos EG, Kokmen E. Mild cognitive impairment: clinical characterization and outcome. Arch Neurol. 1999; 56: 303–308.
21. Petersen RC, Morris JC. Mild cognitive impairment as a clinical entity and treatment target. Arch Neurol. 2005; 62: 1160–1163.
22. McKhann G, Drachman D, Folstein M, Katzman R, Price D, Stadlan EM. Clinical diagnosis of Alzheimer’s disease: report of the NINCDS-ADRDA Work Group under the auspices of Department of Health and Human Services Task Force on Alzheimer’s Disease. Neurology. 1984; 34: 939–944.
23. Neary D, Snowden JS, Gustafson L, Passant U, Stuss D, Black S, Freedman M, Kertesz A, Robert PH, Albert M, Boone K, Miller BL, Cummings J, Benson DF. Frontotemporal lobar degeneration: a consensus on clinical diagnostic criteria. Neurology. 1998; 51: 1546–1554.
24. McKeith IG, Galasko D, Kosaka K, Perry EK, Dickson DW, Hansen LA, Salmon DP, Lowe J, Mirra SS, Byrne EJ, Lennox G, Quinn NP, Edwardson JA, Ince PG, Bergeron C, Burns A, Miller BL, Lovestone S, Collerton D, Jansen EN, Ballard C, de Vos RA, Wilcock GK, Jellinger KA, Perry RH. Consensus guidelines for the clinical and pathologic diagnosis of dementia with Lewy bodies (DLB): report of the consortium on DLB international workshop. Neurology. 1996; 47: 1113–1124.
25. Fazekas F, Chawluk JB, Alavi A, Hurtig HI, Zimmerman RA. MR signal abnormalities at 1.5 T in Alzheimer’s dementia and normal aging. AJR Am J Roentgenol. 1987; 149: 351–356.
26. Pasquier F, Leys D, Weerts JG, Mounier-Vehier F, Barkhof F, Scheltens P. Inter- and intraobserver reproducibility of cerebral atrophy assessment on MRI scans with hemispheric infarcts. Eur Neurol. 1996; 36: 268–272.[Medline] [Order article via Infotrieve]
27. Scheltens P, Launer LJ, Barkhof F, Weinstein HC, van Gool WA. Visual assessment of medial temporal lobe atrophy on magnetic resonance imaging: interobserver reliability. J Neurol. 1995; 242: 557–560.[CrossRef][Medline] [Order article via Infotrieve]
28. Cordonnier C, van der Flier WM, Sluimer JD, Leys D, Barkhof F, Scheltens P. Prevalence and severity of microbleeds in a memory clinic setting. Neurology. 2006; 66: 1356–1360.
29. Cordonnier C, Al-Shahi SR, Wardlaw J. Spontaneous brain microbleeds: systematic review, subgroup analyses and standards for study design and reporting. Brain. 2007; 130: 1988–2003.
30. Koennecke HC. Cerebral microbleeds on MRI - Prevalence, associations, and potential clinical implications. Neurology. 2006; 66: 165–171.
31. Won SS, Hwa LB, Kim EJ, Chin J, Sun CY, Yoon U, Na DL. Clinical significance of microbleeds in subcortical vascular dementia. Stroke. 2007; 38: 1949–1951.
32. Werring DJ, Frazer DW, Coward LJ, Losseff NA, Watt H, Cipolotti L, Brown MM, Jager HR. Cognitive dysfunction in patients with cerebral microbleeds on T2*-weighted gradient-echo MRI. Brain. 2004; 127: 2265–2275.
33. Greenberg SM, Eng JA, Ning M, Smith EE, Rosand J. Hemorrhage burden predicts recurrent intracerebral hemorrhage after lobar hemorrhage. Stroke. 2004; 35: 1415–1420.
34. Imaizumi T, Horita Y, Hashimoto Y, Niwa J. Dotlike hemosiderin spots on T2*-weighted magnetic resonance imaging as a predictor of stroke recurrence: a prospective study. J Neurosurg. 2004; 101: 915–920.[Medline] [Order article via Infotrieve]
35. Kril JJ, Patel S, Harding AJ, Halliday GM. Patients with vascular dementia due to microvascular pathology have significant hippocampal neuronal loss. J Neurol Neurosurg Psychiatry. 2002; 72: 747–751.
36. Jouvent E, Viswanathan A, Mangin JF, O'Sullivan M, Guichard JP, Gschwendtner A, Cumurciuc R, Buffon F, Peters N, Pachai C, Bousser MG, Dichgans M, Chabriat H. Brain atrophy is related to lacunar lesions and tissue microstructural changes in CADASIL. Stroke. 2007; 38: 1786–1790.
37. Tian J, Shi J, Bailey K, Lendon CL, Pickering-Brown SM, Mann DM. Association between apolipoprotein E e4 allele and arteriosclerosis, cerebral amyloid angiopathy, and cerebral white matter damage in Alzheimer’s disease. J Neurol Neurosurg Psychiatry. 2004; 75: 696–699.
38. Pathological correlates of late-onset dementia in a multicentre, community-based population in England and Wales. Neuropathology Group of the Medical Research Council Cognitive Function and Ageing Study (MRC CFAS). Lancet. 2001; 357: 169–175.[CrossRef][Medline] [Order article via Infotrieve]
39. Snowdon DA, Greiner LH, Mortimer JA, Riley KP, Greiner PA, Markesbery WR. Brain infarction and the clinical expression of Alzheimer disease. The Nun Study. JAMA. 1997; 277: 813–817.
40. van der Flier WM, Middelkoop HA, Weverling-Rijnsburger AW, dmiraal-Behloul F, Spilt A, Bollen EL, Westendorp RG, van Buchem MA. Interaction of medial temporal lobe atrophy and white matter hyperintensities in AD. Neurology. 2004; 62: 1862–1864.
41. Vermeer SE, Prins ND, den HT, Hofman A, Koudstaal PJ, Breteler MM. Silent brain infarcts and the risk of dementia and cognitive decline. N Engl J Med. 2003; 348: 1215–1222.
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  S. M. Gregoire, U. J. Chaudhary, M. M. Brown, T. A. Yousry, C. Kallis, H. R. Jager, and D. J. Werring The Microbleed Anatomical Rating Scale (MARS): Reliability of a tool to map brain microbleeds Neurology, November 24, 2009; 73(21): 1759 - 1766. [Abstract] [Full Text] [PDF]
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