Brain Advance Access published online on September 18, 2007
Brain, doi:10.1093/brain/awm228
| ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Identification of Alzheimer and vascular lesion thresholds for mixed dementia
Departments of 1Geriatrics and 2Psychiatry, University of Geneva School of Medicine, Geneva and 3Division of Old Age Psychiatry, University of Lausanne School of Medicine, Lausanne, Switzerland
Correspondence to:
Dr Gabriel Gold, Department of Rehabilitation and Geriatrics, University of Geneva Hospitals, 1226 Thônex, Switzerland or Dr Panteleimon Giannakopoulos, Department of Psychiatry, University of Geneva Hospitals, 1225 Chêne-Bourg, Switzerland E-mail: gabriel.gold{at}hcuge.ch or panteleimon.giannakopoulos{at}hcuge.ch
| Summary |
|---|
|
|
|---|
To explore the pathological substrates of mixed dementia, we performed a detailed analysis of lacunar and microvascular pathology in 156 autopsied, elderly individuals with various degrees of Alzheimer's disease (AD) pathology. Cognitive status was assessed prospectively using the Clinical Dementia Rating (CDR) scale; neuropathological evaluation included Braak neurofibrillary tangle (NFT) and Aß-protein deposition staging and bilateral semi-quantitative assessment of microvascular ischaemic pathology and lacunes; statistics included univariate and multiple regression models controlling for age, and receiver-operating characteristic analysis. Sensitivity analysis was performed in a randomized derivation sub-sample and tested in a validation sub-sample. White matter lacunes, periventricular and diffuse white matter demyelination and focal and diffuse cortical gliosis were not associated with cognition. Braak NFT, Aß deposition, cortical microinfarcts (CMI) and thalamic and basal ganglia lacunes (TBGL) predicted 27% of CDR variability and 49% of the presence of dementia. Braak NFT, CMI and TBGL thresholds determined in a derivation sample yielded 0.88 sensitivity, 0.79 specificity and 0.85 correct classification rate for dementia in a validation sample. The same thresholds distinguished three groups of demented cases consistent with mixed dementia, pure vascular dementia and AD. These findings indicate that the clinical expression of the vascular component in mixed cases is highly dependent on lesion type and location as well as severity of concomitant AD-related pathology. Proposed thresholds for vascular and degenerative lesions predict the presence of dementia with great accuracy and provide a basis for distinguishing pure vascular dementia or AD from mixed cases.
Key Words: Alzheimer's disease; cognition; lacunar infarction; subcortical dementia; vascular dementia
Abbreviations: NFT, neurofibrillary tangles; CMI, cortical microinfarcts; CAA, cerebral amyloid angiopathy
.
Received May 24, 2007. Revised August 10, 2007. Accepted August 23, 2007.
| Introduction |
|---|
|
|
|---|
Brain ageing is characterized by the progressive development of both AD-related lesions and vascular pathology within the cerebral cortex of cognitively intact individuals. Clinically, these neuropathological changes remain silent in the vast majority of elderly people. Since Tomlinson's first observations (Tomlinson et al., 1968
The concept of mixed dementia covers a wide spectrum of combinations between AD and vascular pathology including at one extreme, cases with minimal AD-related pathology and substantial small macrovascular and microvascular changes and at the other cases with severe AD pathological changes and only slight vascular involvement. Mixed dementia was initially diagnosed in the presence of both AD pathology and large infarcts (Jellinger, 2005
), yet the importance of lacunes and small vessel disease for the development of clinically overt dementia in individuals who also presented with AD lesions is also well documented (Snowdon et al., 1997
; Esiri et al., 1999
). Despite considerable efforts, to date there are no widely accepted neuropathological criteria for this condition. Two main reasons may explain this lack. First, the marked heterogeneity of the type and location of microvascular lesions renders difficult the development of a simple and reliable approach to assess them in routine neuropathological settings. Second, the definition of threshold values for AD and microvascular lesions that may predict dementia needs the analysis of large autopsy series. We had the opportunity to investigate a series of 156 prospectively documented cases with various degrees of AD pathology, lacunes and different types of microvascular pathology (i.e. CMI, diffuse and focal gliosis, periventricular and deep white matter demyelination). Using systematic semi-quantitative assessment of various types of vascular lesions and multivariate models that control for the possible confounding effect of age, we report here the identification of threshold values for AD and microvascular pathology that permits a highly accurate diagnosis of mixed dementia.
| Patients and Methods |
|---|
|
|
|---|
Patients
The initial autopsy series included 1355 patients who were autopsied at the Geriatric and Psychiatric Hospitals of the University of Geneva during the period 1993–2003. Three criteria were used to define the final sample. First, a cognitive assessment including the Clinical Dementia Rating (CDR) Scale had to be performed at most three months prior to death. The CDR is a validated scale that is widely used for the clinical staging of dementia.(Hughes et al., 1982
-synuclein and ubiquitin-positive inclusions as well as argyrophilic grains in the routine neuropathological examination were also excluded from the present series. The final sample included 156 patients aged 73 to 101 years. Gender and age distribution of the cases according to CDR score are listed in Table 1. All CDR 0 and 0.5 patients, 10 CDR1 and 25 CDR2 cases were admitted to the Geneva Geriatric Hospital for acute medical conditions such as bronchopneumonia (40%), cardiovascular (46%) and gastrointestinal disorders (14%). The remaining demented cases were admitted to the Psychiatric Hospital because of the presence of major behavioural disturbances such as psychomotor agitation, feeding difficulties, marked aggressiveness and delusional thoughts. There was no case with past history of psychiatric illnesses such as schizophrenia, major depression and bipolar disorder. The main causes of death were infectious disorders (38.7%), heart failure (37.7%), pulmonary embolism (15%) and cancer (8.6%).
|
Tissue processing
Brains obtained at autopsy were fixed in 15% formaldehyde for at least 4 weeks and cut into 1-cm-thick coronal slices. Lacunes, defined as small definitive ischaemic necrosis, ranging from 1 mm to 1.5 cm, located in the white matter or basal ganglia and thalamus, were identified on macroscopic examination and controlled on Luxol–van Gieson (LVG)-stained coronal sections (see later). To visualize CMI as well as focal cortical and white matter gliosis, 1-cm-thick tissue blocks from the anterior hippocampus, inferior temporal cortex (area 20), frontal cortex (area 9), and parietal cortex (area 40) bilaterally were cut into 20 µm-thick serial sections of approximately 3 x 2 cm2. Every 50 sections, one section was stained with Globus silver impregnation for a total of 10 sections per area which have been subsequently considered for semi-quantitative analysis (Vallet et al., 1992
-synuclein (1/20 000 courtesy of Dr Y. Charnay) and ubiquitin (1/100, Sigma). The tissues were incubated overnight at 4°C. Following incubation, sections were processed by the PAP method using 3,3'-diaminobenzidine as a chromogen (Vallet et al., 1992
Subsequently, all cases were classified neuropathologically according to Braak–NFT staging system (Braak and Braak, 1991
). Aß-protein deposition staging was performed according to the amyloid nomenclature proposed by Thal and collaborators (Thal et al., 2000
). Lacunes, CMI and focal cortical gliosis were assessed semiquantitatively in 10 sections per area using the following score: 0 (absence of such lesions), 1 (<3 lesions per slide), 2 (3–5 lesions per slide), 3 (>5 lesions per slide). Semi-quantitative assessment of white matter gliosis was made in the same number of sections using the following rating scale: 0 = absent, 1 = mild, 2 = moderate, 3 = severe. Although we cannot exclude that additional pathology may be present mainly in neocortical areas, the use of a high number of sections limits this possibility. For each of these lesions, a total score was obtained by adding the scores of each area. The severity of diffuse white matter and periventricular demyelination in each hemisphere was estimated in LVG-stained sections using the same semi-quantitative scale. Scores for each hemisphere were added to obtain a total score. The same semi-quantitative assessment of lacunes and microvascular pathology has already been used in our previous studies with a high inter-rater reliability (Fig. 1; Kövari et al., 2004
; Gold et al., 2005
). In the present study, both vascular pathology, Braak NFT, Aß-protein deposition staging (Braak and Braak, 1991
; Thal et al., 2000
) and CAA were assessed by two independent investigators (EK and CB), blind to the clinical findings, with a high inter-rater reliability (kappa values ranging from 0.88 to 0.95 for the different neuropathological variables). In case of disagreement between the two raters, the final determination was defined in a consensus meeting between both the raters.
|
Statistical analysis
Maximal likelihood ordered logistic regression with proportional odds was used to evaluate the association between CDR scores (the dependent variable) and neuropathological parameters (Braak NFT staging, Aß-protein deposition staging, presence of CAA and lacunes and microvascular pathology scores) in a univariate model. Subsequently, the same method was applied in a multiple model to take into account the effect of age as well as the interaction between the neuropathological variables. In addition, cases were dichotomized as demented (CDR 1 to 3) or non-demented (CDR 0 to 0.5) to build logistic regression models exploring the impact of lacunes and microvascular pathology on the presence of dementia. Braak NFT and Aß staging were entered as dummy variables in all regression models. A predictive model was derived from a randomized derivation sub-sample (50% of the cases, N = 78). Sensitivity analysis was performed in this sub-sample and corresponding receiver-operating characteristic (ROC) curves were constructed. The best threshold determined through this analysis of the derivation sub-sample, was then similarly tested in the validation sub-sample (remaining 50% of the cases, N = 78). Statistical analyses were performed using the Stata software package, release 9.2 (College Station, TX).
Ethical considerations
The present study has received the formal approval of the Local Ethics Committee of the University of Geneva Hospitals.
| Results |
|---|
|
|
|---|
Table 2 summarizes the distribution of AD-related pathology, lacunes and microvascular changes in the present series. In univariate analyses, five independent variables were significantly related to CDR scores. These included Braak NFT staging (P < 0.001), Aß deposition staging (P < 0.001), CMI score (P < 0.01) thalamic and basal ganglia lacune score (TBGL; P < 0.05) and age (P < 0.05). In contrast, presence of CAA, as well as white matter lacunes, periventricular and diffuse white matter demyelination scores as well as focal and diffuse cortical gliosis scores were not significantly related to CDR scores. We then tested a multiple model including all five variables that proved significant in the univariate approach. Four of the variables, Braak NFT staging, Aß deposition staging, CMI and TBGL scores remained significant predictors of cognitive status (Table 3). The concomitant assessment of these neuropathological variables predicted 27% of the CDR variability.
|
|
We then evaluated the relationship between the most important clinical outcome (presence or absence of dementia) and neuropathological parameters. In univariate analyses, the same five independent variables, Braak NFT staging, Aß deposition staging, CMI and TBGL scores and age, proved to be significant predictors of the presence of clinical dementia. Importantly, a multiple model which included these five variables revealed that age was no longer a significant predictor and that the four remaining neuropathological scores explained 48.9% of the presence of dementia. In a stepwise approach, the vascular scores (CMI and TBGL) explained 15% of the variability of the outcome variable (presence of dementia), Braak NFT staging 30.4% and Aß deposition staging 3.5%.
ROC curves were constructed using the vascular score (CMI + TBGL) and Braak NFT staging to determine the threshold value with the best combination of sensitivity and specificity. In the derivation sample, this corresponded to cut-off scores of 2 (>2) for both the vascular score and Braak NFT staging. The performance of this model was as follows: sensitivity 0.93, specificity 0.52, positive predictive value 0.82, negative predictive value 0.75 and correct classification rate of 0.81. The area under the ROC curve was 0.90 (Fig. 2A).
|
The same model was applied in the validation sample yielding an area under the ROC curve of 0.92. Use of the cut-off scores developed in the derivation model led to 0.88 sensitivity, 0.79 specificity, 0.88 positive predictive value, 0.79 negative predictive value and 0.85 correct classification rate (Fig. 2B).
When the above threshold scores were applied to the entire study population, 90% of the demented cases were correctly classified. These could be divided into three distinct groups (Fig. 3). The first includes cases with a vascular score >2 and a Braak NFT score
2 in whom dementia is associated with vascular lesions. The second includes cases with a Braak NFT score >2 and a vascular score
2 and represents cases in whom dementia is associated with neurofibrillary tangle formation. The third groups consists of neuropathologically mixed cases, with both a Braak NFT score >2 and a vascular score >2, in whom dementia may be related to both vascular and degenerative disease.
|
| Discussion |
|---|
|
|
|---|
Completing our previous observations in cases with minimal to moderate NFT pathology [Braak NFT staging <4 (Kövari et al., 2004
Another significant determinant of cognition in mixed cases is the development of TBGL. The Nun study has first demonstrated that cognitive function was markedly influenced by thalamic, basal ganglia and deep white matter lacunes in individuals with AD neuropathology (Snowdon et al., 1997
). More recent studies challenged this point of view showing that many lacunes may have no cognitive repercussions (Jellinger and Attems, 2003
; Vermeer et al., 2003
). Giving additional support to the recent notion of subcortical vascular dementia (Reed et al., 2001
; Erkinjuntti, 2002
; Roman et al., 2002
), one recent neuropathological study of cases with minimal AD lesions revealed that the assessment of TBGL may predict as much as 17% of the cognitive variability (Gold et al., 2005
). In our series, a one-point increase in the 6-point semiquantitative scale used for the assessment of TBGL corresponded to a 1.8-fold increase in the risk for higher CDR scores, indicating that, in terms of cognition, the disruption of subcortical frontal circuits must be considered in mixed cases.
Given the unusually high number of autopsy cases in the present series, it was possible to define randomly two independent samples: that of derivation where the clinicopathological correlations and thresholds were established and that of validation in order to test the performance of the proposed semi-quantitative neuropathological approach. Based on a simple model that includes only Braak NFT staging and CMI + TBGL scores, we were able to identify correctly the vast majority of demented cases. The areas under the ROC curve reached 90% in the derivation sample and 92% in the validation sample implying that the concomitant consideration of these neuropathological variables is sufficient for a highly accurate discrimination of demented cases. Although significantly associated with cognitive decline, Aß deposits contributed only marginally to this discrimination since their assessment explained only an added 3.5% of the cognitive variability.
In routine neuropathological settings, the identification of a single cut-off value that can separate demented from non-demented cases in mixed conditions is a very challenging issue. We report here sensitivity values of 0.93 in the derivation sample and 0.88 in the validation sample when using a single cut-off point corresponding to Braak NFT II stage or CMI + TBGLs score of 2. The extremely high sensitivity value obtained with the simple semi-quantitative approach applied in the present study is encouraging in the perspective of developing widely accepted neuropathological criteria for mixed dementia. This is further supported by the quite high positive and negative predictive values in both derivation and validation samples that ranged from 0.75 to 0.88. Of course, it should be remembered that these latter values are also related to dementia prevalence.
Based on these observations, one can propose an operational definition of mixed dementia within the spectrum of degenerative and vascular changes occurring in brain ageing. Demented cases with Braak NFT staging >II and CMI + TBGL score >2 may be classified as having mixed dementia. Demented cases with Braak NFT staging
II and CMI + TBGL score >2 should be considered pure vascular dementia. Finally, a CMI + TBGL score
2 in the presence of substantial NFT pathology (Braak NFT staging >II) characterizes pure AD cases.
Several limitations should be considered when interpreting these data. First, our hospital-based neuropathological sample cannot be considered as fully representative of the whole spectrum of mixed dementia. Second, we excluded cases with macroinfarcts that correspond to a rarer and different type of vascular pathology and can make no conclusion in such cases. Third, although we carefully assessed microvascular changes in several neocortical association areas bilaterally, the obtained results are based on the sampling strategy used and needs further validation in other neuropathological centres. Fourth, the specificity values of the proposed cut-off values (i.e. 0.52 in the derivation sample and 0.79 in the validation sample) are suboptimal. Finally, a small number of patients were demented in the absence of both significant vascular and degenerative pathology (CMI + TBGL score
2 and Braak NFT staging
II), and the assessment of AD and vascular pathology explained
50% of the presence of dementia in our series. Methodological biases related to the semi-quantitative approach used may partly account for this (Giannakopoulos et al., 2003
). Alternatively, other neuropathological variables such as neuronal and synaptic loss or microvascular morphometry may also contribute to cognitive decline in these cases. In this respect, a recent stereological analysis of capillary morphometric parameters demonstrated that cortical capillary diameters may be a powerful and independent predictor of cognitive impairment in the elderly (Bouras et al., 2006
).
In conclusion, the present findings demonstrate that a systematic semi-quantitative assessment of CMI and TBGL coupled with the traditional Braak NFT staging not only makes it possible to predict with a high sensitivity the presence of dementia in cases with various combinations of vascular and degenerative changes but can serve to distinguish mixed dementia from AD and pure vascular dementia. Additional studies in independent prospectively assessed autopsy series are needed to confirm the validity of the proposed approach and define easily applicable and consensual neuropathological procedures to improve its specificity.
| Footnotes |
|---|
*These authors contributed equally to this work.
| Acknowledgements |
|---|
This work was supported by an unrestricted grant from the Jérôme Tissières Foundation (to P.G.).
| References |
|---|
|
|
|---|
Barber R, Scheltens P, Gholkar A, Ballard C, McKeith I, Ince P, et al. White matter lesions on magnetic resonance imaging in dementia with lewy bodies, Alzheimer's disease, vascular dementia, and normal aging. J Neurol Neurosurg Psychiatry (1999) 67:66–72.
Bouras C, Kövari E, Herrmann FR, Rivara CB, Bailey TL, von Gunten A, et al. Stereologic analysis of microvascular morphology in the elderly: Alzheimer disease pathology and cognitive status. J Neuropathol Exp Neurol (2006) 65:235–44.[Web of Science][Medline]
Braak H, Braak E. Neuropathological stageing of Alzheimer-related changes. Acta Neuropathol (Berl) (1991) 82:239–59.[CrossRef][Medline]
Bussière T, Friend PD, Sadeghi N, Wicinski B, Lin GI, Bouras C, et al. Stereologic assessment of the total cortical volume occupied by amyloid deposits and its relationship with cognitive status in aging and Alzheimer's disease. Neuroscience (2002) 112:75–91.[CrossRef][Web of Science][Medline]
de Groot JC, de Leeuw FE, Oudkerk M, Hofman A, Jolles J, Breteler MM. Cerebral white matter lesions and subjective cognitive dysfunction: the Rotterdam Scan Study. Neurology (2001) 56:1539–45.
de Groot JC, de Leeuw FE, Oudkerk M, van Gijn J, Hofman A, Jolles J, et al. Cerebral white matter lesions and cognitive function: the Rotterdam Scan Study. Ann Neurol (2000) 47:145–51.[CrossRef][Web of Science][Medline]
de Groot JC, de Leeuw FE, Oudkerk M, Van Gijn J, Hofman A, Jolles J, et al. Periventricular cerebral white matter lesions predict rate of cognitive decline. Ann Neurol (2002) 52:335–41.[CrossRef][Web of Science][Medline]
Erkinjuntti T. Subcortical vascular dementia. Cerebrovasc Dis (2002) 13(Suppl 2):58–60.[CrossRef][Web of Science][Medline]
Esiri MM, Nagy Z, Smith MZ, Barnetson L, Smith AD. Cerebrovascular disease and threshold for dementia in the early stages of Alzheimer's disease. Lancet (1999) 354:919–20.[CrossRef][Web of Science][Medline]
Garde E, Mortensen EL, Krabbe K, Rostrup E, Larsson HB. Relation between age-related decline in intelligence and cerebral white-matter hyperintensities in healthy octogenarians: a longitudinal study. Lancet (2000) 356:628–34.[CrossRef][Web of Science][Medline]
Giannakopoulos P, Gold G, Kövari E, von Gunten A, Imhof A, Bouras C, et al. Assessing the cognitive impact of Alzheimer disease pathology and vascular burden in the aging brain: the Geneva experience. Acta Neuropathol (Berl) (2007) 113:1–12.[Medline]
Giannakopoulos P, Herrmann FR, Bussière T, Bouras C, Kövari E, Perl DP, et al. Tangle and neuron numbers, but not amyloid load, predict cognitive status in Alzheimer's disease. Neurology (2003) 60:1495–500.
Goedert M, Jakes R, Vanmechelen E. Monoclonal antibody AT8 recognises tau protein phosphorylated at both serine 202 and threonine 205. Neurosci Lett (1995) 189:167–9.[CrossRef][Web of Science][Medline]
Gold G, Bouras C, Kövari E, Canuto A, Glaria BG, Malky A, et al. Clinical validity of Braak neuropathological staging in the oldest-old. Acta Neuropathol (Berl) (2000) 99:579–82.[CrossRef][Medline]
Gold G, Kövari E, Herrmann FR, Canuto A, Hof PR, Michel JP, et al. Cognitive consequences of thalamic, basal ganglia, and deep white matter lacunes in brain aging and dementia. Stroke (2005) 36:1184–8.
Hachinski VD, Lassen NA, Marshall J. Multi-infarct dementia. A cause of mental deterioration in the elderly. Lancet (1974) 2:207–10.[CrossRef][Web of Science][Medline]
Hughes CP, Berg L, Danziger WL, Coben LA, Martin RL. A new clinical scale for the staging of dementia. Brit J Psychiat (1982) 140:566–72.
Jellinger KA. Understanding the pathology of vascular cognitive impairment. J Neurol Sci (2005) 229–230:57–63.
Jellinger KA. Clinicopathological analysis of dementia disorders in the elderly–an update. J Alzheimers Dis (2006) 9:61–70.[Medline]
Jellinger KA, Attems J. Incidence of cerebrovascular lesions in Alzheimer's disease: a postmortem study. Acta Neuropathol (Berl) (2003) 105:14–7.[Medline]
Jellinger KA, Attems J. Neuropathological evaluation of mixed dementia. J Neurol Sci (2007) 257:80–7.[CrossRef][Web of Science][Medline]
Kalaria RN, Kenny RA, Ballard CG, Perry R, Ince P, Polvikoski T. Towards defining the neuropathological substrates of vascular dementia. J Neurol Sci (2004) 226:75–80.[CrossRef][Web of Science][Medline]
Knopman DS, Parisi JE, Salviati A, Floriach-Robert M, Boeve BF, Ivnik RJ, et al. Neuropathology of cognitively normal elderly. J Neuropathol Exp Neurol (2003) 62:1087–95.[Web of Science][Medline]
Kövari E, Gold G, Herrmann FR, Canuto A, Hof PR, Bouras C, et al. Cortical microinfarcts and demylination affect cognition in cases at high risk for dementia. Neurology (2007) 68:927–31.
Kövari E, Gold G, Herrmann FR, Canuto A, Hof PR, Michel JP, et al. Cortical microinfarcts and demyelination significantly affect cognition in brain aging. Stroke (2004) 35:410–4.
Morris JC. The Clinical Dementia Rating (CDR): current version and scoring rules. Neurology (1993) 43:2412–4.
Neuropathology Group of the Medical Research Council Cognitive Function, and Aging Study (MRC CFAS). Pathological correlates of late-onset dementia in a multicentre, community-based population in England and Wales. Lancet (2001) 357:169–75.[CrossRef][Web of Science][Medline]
Pantoni L, Sarti C, Alafuzoff I, Jellinger K, Munoz DG, Ogata J, et al. Postmortem examination of vascular lesions in cognitive impairment: a survey among neuropathological services. Stroke (2006) 37:1005–9.
Price JL, Morris JC. Tangles and plaques in nondemented aging and "preclinical" Alzheimer's disease. Ann Neurol (1999) 45:358–68.[CrossRef][Web of Science][Medline]
Reed BR, Eberling JL, Mungas D, Weiner M, Jagust WJ. Frontal lobe hypometabolism predicts cognitive decline in patients with lacunar infarcts. Arch Neurol (2001) 58:493–7.
Roman GC, Erkinjuntti T, Wallin A, Pantoni L, Chui HC. Subcortical ischaemic vascular dementia. Lancet Neurol (2002) 1:426–36.[CrossRef][Web of Science][Medline]
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) 277:813–7.
Thal DR, Rüb U, Schultz C, Sassin I, Ghebremedhin E, Del Tredici K, et al. Sequence of Abeta-protein deposition in the human medial temporal lobe. J Neuropathol Exp Neurol (2000) 59:733–48.[Web of Science][Medline]
Tomlinson BE, Blessed G, Roth M. Observations on the brains of non-demented old people. J Neurol Sci (1968) 7:331–56.[CrossRef][Web of Science][Medline]
Tomlinson BE, Blessed G, Roth M. Observations on the brains of demented old people. J Neurol Sci (1970) 11:205–42.[CrossRef][Web of Science][Medline]
Vallet PG, Guntern R, Hof PR, Golaz J, Delacourte A, Robakis NK, et al. A comparative study of histological and immunohistochemical methods for neurofibrillary tangles and senile plaques in Alzheimer's disease. Acta Neuropathol (1992) 83:170–8.[CrossRef][Medline]
van der Flier WM, van Straaten EC, Barkhof F, Verdelho A, Madureira S, Pantoni L, et al. Small vessel disease and general cognitive function in nondisabled elderly: the LADIS study. Stroke (2005) 36:2116–20.
Vermeer SE, Prins ND, den Heijer T, Hofman A, Koudstaal PJ, Breteler MM. Silent brain infarcts and the risk of dementia and cognitive decline. N Engl J Med (2003) 348:1215–22.
Vinters HV, Ellis WG, Zarow C, Zaias BW, Jagust WJ, Mack WJ, et al. Neuropathologic substrates of ischemic vascular dementia. J Neuropathol Exp Neurol (2000) 59:931–45.[Web of Science][Medline]
Ylikoski A, Erkinjuntti T, Raininko R, Sarna S, Sulkava R, Tilvis R. White matter hyperintensities on MRI in the neurologically nondiseased elderly. Analysis of cohorts of consecutive subjects aged 55 to 85 years living at home. Stroke (1995) 26:1171–7.
![]()
CiteULike
Connotea
Del.icio.us What's this?
This article has been cited by other articles:
![]() |
M. Santos, G. Gold, E. Kovari, F. R. Herrmann, V. P. Bozikas, C. Bouras, and P. Giannakopoulos Differential Impact of Lacunes and Microvascular Lesions on Poststroke Depression Stroke, November 1, 2009; 40(11): 3557 - 3562. [Abstract] [Full Text] [PDF] |
||||
![]() |
N. Purandare Preventing dementia: role of vascular risk factors and cerebral emboli Br. Med. Bull., September 1, 2009; 91(1): 49 - 59. [Abstract] [Full Text] [PDF] |
||||
![]() |
L. C. Silbert, D. B. Howieson, H. Dodge, and J. A. Kaye Cognitive impairment risk: White matter hyperintensity progression matters Neurology, July 14, 2009; 73(2): 120 - 125. [Abstract] [Full Text] [PDF] |
||||
![]() |
C. H Rojas-Fernandez and P. Moorhouse Current Concepts in Vascular Cognitive Impairment and Pharmacotherapeutic Implications Ann. Pharmacother., July 1, 2009; 43(7): 1310 - 1323. [Abstract] [Full Text] [PDF] |
||||
![]() |
P. Giannakopoulos, E. Kovari, F. R. Herrmann, P. R. Hof, and C. Bouras Interhemispheric Distribution of Alzheimer Disease and Vascular Pathology in Brain Aging Stroke, March 1, 2009; 40(3): 983 - 986. [Abstract] [Full Text] [PDF] |
||||
![]() |
J. V. Guadagno, P. S. Jones, F. I. Aigbirhio, D. Wang, T. D. Fryer, D. J. Day, N. Antoun, I. Nimmo-Smith, E. A. Warburton, and J. C. Baron Selective neuronal loss in rescued penumbra relates to initial hypoperfusion Brain, October 1, 2008; 131(10): 2666 - 2678. [Abstract] [Full Text] [PDF] |
||||
![]() |
D. J. Libon and K. M. Heilman Assessing the Impact of Vascular Disease in Demented and Nondemented Patients Stroke, March 1, 2008; 39(3): 783 - 784. [Full Text] [PDF] |
||||
| ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||







