Radiology
HOME HELP FEEDBACK SUBSCRIPTIONS ARCHIVE SEARCH TABLE OF CONTENTS
QUICK SEARCH:   [advanced]


     

DOI: 10.1148/radiol.2211010086
This Article
Right arrow Abstract Freely available
Right arrow Figures Only
Right arrow Full Text (PDF)
Right arrow Submit a response
Right arrow Alert me when this article is cited
Right arrow Alert me when eLetters are posted
Right arrow Alert me if a correction is posted
Right arrow Citation Map
Services
Right arrow Email this article to a friend
Right arrow Similar articles in this journal
Right arrow Similar articles in PubMed
Right arrow Alert me to new issues of the journal
Right arrow Download to citation manager
Right arrow reprints & permissions
Citing Articles
Right arrow Citing Articles via HighWire
Right arrow Citing Articles via Google Scholar
Google Scholar
Right arrow Articles by Leaper, S. A.
Right arrow Articles by Whalley, L. J.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Leaper, S. A.
Right arrow Articles by Whalley, L. J.
(Radiology. 2001;221:51-55.)
© RSNA, 2001

Neuroradiology

Neuropsychologic Correlates of Brain White Matter Lesions Depicted on MR Images: 1921 Aberdeen Birth Cohort1

Steven A. Leaper, MA, Alison D. Murray, MD, FRCP, Helen A. Lemmon, MA, Roger T. Staff, PhD, Ian J. Deary, PhD, FRCPE, John R. Crawford, PhD and Lawrence J. Whalley, MD, FRCPE

1 From the Departments of Mental Health (S.A.L., H.A.L., L.J.W.), Radiology (A.D.M.), Biomedical Physics and Bioengineering (R.T.S.), and Psychology (J.R.C.), University of Aberdeen, Clinical Research Center, Royal Cornhill Hospital, Cornhill Rd, Aberdeen AB25 2ZJ, Scotland; and Department of Psychology, University of Edinburgh, Scotland (I.J.D.). From the 2000 RSNA scientific assembly. Received November 30, 2000; revision requested January 11, 2001; revision received March 9; accepted March 22. Supported by the Henry Smith Kensington Estates Charity, the Biotechnology and Biological Sciences Research Council, and the Chief Scientists Office of the Scottish Executive. Address correspondence to L.J.W. (e-mail: l.j.whalley.abdn.ac.uk).


   ABSTRACT
TOP
 ABSTRACT
INTRODUCTION
 MATERIALS AND METHODS
RESULTS
 DISCUSSION
 REFERENCES
 
PURPOSE: To examine relationships between brain white matter hyperintensities depicted at magnetic resonance (MR) imaging and performance on neuropsychologic tests in community-dwelling elderly adults.

MATERIALS AND METHODS: The 1921 Aberdeen Birth Cohort is a subsample of survivors of the Scottish Mental Survey of 1932 whose mental ability was tested at 11 years of age. Ninety-five of these subjects agreed to undergo brain MR imaging, an examination of general health, and a neuropsychologic evaluation. White matter hyperintensities detected at T2-weighted MR imaging were rated by using a semiquantitative method yielding two continuous variables: white matter lesions and periventricular lesions. Cognitive ability, including crystallized and fluid intelligence domains, was assessed with standard neuropsychologic tests.

RESULTS: Rating scores of white matter lesions were normally distributed (on a devised scale) with means of 1.14 for white matter lesions and 1.28 for periventricular lesions. Intra- and interobserver reliability coefficients for scores were high, generally above 0.7. There were significant correlations of medium effect size between the T2-weighted MR imaging–depicted white matter lesions and performance on tests of fluid-type intelligence. No significant correlation was demonstrated between white matter lesion ratings and tests of crystallized intelligence.

CONCLUSION: Lower fluid-type ("prevailing") intelligence test scores were associated with increased severity of white matter lesion ratings but not crystallized-type ("premorbid") intelligence test scores. This indicates that MR imaging–depicted white matter lesions are of clinical importance.

Index terms: Aging, 10.91, 10.92 • Brain, atrophy, 10.879, 10.91 • Brain, function, 10.91 • Brain, MR, 10.121411 • Brain, white matter, 10.879, 10.91


   INTRODUCTION
TOP
 ABSTRACT
 INTRODUCTION
MATERIALS AND METHODS
RESULTS
 DISCUSSION
 REFERENCES
 
Brain subcortical and deep white matter hyperintensities are a frequent finding at magnetic resonance (MR) imaging, as are periventricular white matter hyperintensities (1–3). Neuropathologic examination of subcortical and deep white matter brain lesions suggest that atherosclerosis of brain blood vessels is important in their formation (4–7). Well-established associations between white matter lesions (WMLs) and the presence of risk factors for cardiovascular disease (8–11) support this. However, there is sufficient evidence to suggest that the pathogenesis of periventricular white matter change is different (12–14). Fazekas et al (12) report that MR imaging–depicted hyperintensities in deep and/or subcortical white matter are likely to have a vascular origin.

In contrast, periventricular hyperintensities may be associated with disruption of the ependymal lining with subependymal gliosis and myelin degradation. The clinical significance of WMLs in an otherwise healthy (asymptomatic) sample of older adults is uncertain. There are some reports (15–17) of significant associations between WMLs and impaired cognitive function but the strength of these associations varies. This may be because researchers have not agreed on a definitive method for rating MR imaging–depicted WMLs (18,19) or assessing cognitive function (20). Recent research indicates that when WMLs are detected in certain brain areas, cognitive domains dependent on speed of mental processing appear sensitive to rated lesion severity (17). In support of this position, Garde and colleagues (20) reported a greater decline in performance intelligence quotient (fluid-type intelligence) than verbal intelligence quotient (crystallized-type intelligence) in an elderly community-dwelling sample of adults.

Authors investigating relationships between MR imaging–depicted WMLs and cognitive ability are limited by an absence of data collected before the onset of any putative disease process. We have access to a sample of elderly adults whose mental ability was estimated at age 11 years by using a valid measure of psychometric intelligence (21). This population sample allows comparisons to be made between past and present mental ability and MR imaging–depicted WMLs. The psychometric tests used in this study measure ability in the cognitive domains of crystallized-type and fluid-type intelligence, these being broad concepts that describe specialized cognitive skills underlying general ability (22). Fluid-type abilities decline from around age 60 years, whereas crystallized-type ability is maintained and may even increase (23,24) beyond this age. We hypothesized that measures of fluid-type intelligence would be inversely associated with the presence of WMLs, which increase with age (10,20). Thus, the purpose of this study was to examine relationships between WMLs and performance on tests of crystallized and fluid intelligence.


   MATERIALS AND METHODS
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
RESULTS
 DISCUSSION
 REFERENCES
 
Subjects
The Scottish Mental Survey of 1932 (25) measured the mental abilities of 87,498 children born in 1921 and attended Scottish schools on June 1, 1932. The intelligence test used, a version of the Moray House Test, contained a wide range of items that examined verbal reasoning, numerical and spatial abilities. In a representative subsample of 1,000 children (500 boys and 500 girls), test scores on the Moray House Test correlated (r = 0.8) with scores on the Stanford-Binet test (a valid measure of intelligence).

With the approval of the local ethics committee, we identified 427 living Aberdeen dwellers born in 1921 for whom a mental ability test score at age 11 years had been retained. We randomly invited 334 (78%) survivors to attend a general review of physical health and neuropsychologic function. Two hundred eighty-three (66%) community-dwelling individuals provided informed consent and agreed to participate in a research project known as the Aberdeen Birth Cohort 1921 study. Between June 1999 and June 2000, 133 (31%) individuals from this sample were randomly asked to participate in an MR imaging research project. Medical contraindications prevented 17 (4%) individuals from participation, and a further 21 (5%) refused. Ninety-five (22%) individuals provided informed consent and agreed to brain MR imaging examination. Fifty-eight were men, 37 were women, and all were Caucasian.

MR Imaging Examination
With the added specific approval of the local ethics committee, we obtained brain MR images, in the transverse plane, with a 1.0-T unit (Magnetom Impact; Siemens, Erlangen, Germany). A T2-weighted fast spin-echo sequence (4,000/96 [repetition time msec/echo time msec]; section thickness, 5 mm; acquisition time, 1 minute 53 seconds; intersection gap, 1.5 mm) was performed.

MR images were analyzed by using a semiquantitative rating scale devised by Fazekas et al (31). This method yields two separate brain WML scores: (a) subcortical and deep WML and (b) periventricular lesions (PVLs). Each variable was scored on a four-point scale of increasing severity. WMLs were scored by the following: 0, normal; 1, punctate; 2, coalescing; and 3, confluent. PVLs were scored by the following: 0, normal; 1, pencil lines and/or caps; 2, smooth haloes; and 3, irregular. T2-weighted images were scored by three independent observers (S.A.L., A.D.M., R.T.S.) and interobserver variation measured. Intraobserver variation was measured for observer A, who rated the T2-weighted images twice, the second rating was made being blinded to the results of the first. Examples of WMLs and PVLs detected at T2-weighted MR imaging are shown in Figures 1–3.



View larger version (174K):
[in this window]
[in a new window]
[Download PPT slide]
  		Figure 1. Grade 1 WMLs ({square}) and PVLs ({circ}). Transverse T2-weighted fast spin-echo MR image (4,000/96; acquisition time, 1 minute 53 seconds; section thickness, 5 mm; intersection gap, 1.5 mm).

 


View larger version (183K):
[in this window]
[in a new window]
[Download PPT slide]
  		Figure 2. Grade 2 WMLs ({square}) and PVLs ({circ}). Transverse T2-weighted fast spin-echo MR image (4,000/96; acquisition time, 1 minute 53 seconds; section thickness, 5 mm; intersection gap, 1.5 mm)

 


View larger version (178K):
[in this window]
[in a new window]
[Download PPT slide]
  		Figure 3. Grade 3 WMLs ({square}) and PVLs ({circ}). Transverse T2-weighted fast spin-echo MR image (4,000/96; acquisition time, 1 minute 53 seconds; section thickness, 5 mm; intersection gap, 1.5 mm)

 
A simple mean was calculated for each of the WML and PVL variables. The two ratings made by observer A on each variable were added to ratings made by observers B and C on the same variable. The total for each variable (four scores from three independent observers) was divided by the number of ratings to give a mean WML and PVL rating for each of the 95 MR imaging participants. This provided an overall mean for each MR imaging variable. The WML and PVL means were used to measure the degree of association between WML, PVL, and neuropsychologic test results and to investigate whether sex influenced WML and PVL ratings in the sample.

Neuropsychologic Evaluation
The 95 volunteers suitable for MR imaging assessment completed the Mini-Mental State Examination (26) and scored greater than 24. All completed the National Adult Reading Test (27) as a measure of crystallized-type intelligence (acquired knowledge or estimated "premorbid" intelligence quotient) and Raven’s Progressive Matrices (28) as a measure of fluid-type intelligence (the ability to solve novel problems with culturally reduced materials). In addition to Raven’s Progressive Matrices, individuals were asked to participate in a more comprehensive and voluntary neuropsychologic battery of fluid-type age-sensitive intelligence tests. These were Digit Symbol (29), Block Design (29), and Uses for Common Objects (30). Uses for Common Objects test scores were recorded as the total number of correct responses. Participants were not pressed to do any tests beyond the National Adult Reading Test and Raven’s Progressive Matrices. A total of 73 individuals provided data on all tests.

Statistical Adjustments and Analysis
All participants were born in 1921 with up to a 1-year age difference between the eldest and the youngest. For this reason all neuropsychologic test scores (1932 Moray House test, National Adult Reading Test, Raven’s Progressive Matrices, Digit Symbol, Block Design, and Uses for Common Objects) were standardized to adjust for age on the date of testing. By using data from the 1932 Moray House test, an independent-samples t test was used to examine differences between the intellectual abilities at age 11 years of Aberdeen volunteers (n = 261) and those eligible subjects who refused to take part (n = 51). Volunteers in the study were significantly more intellectually able at age 11 years than those who refused participation (t = 3.127; P < .002). An independent-samples t test was used to examine age 11 years old mental ability differences between volunteers who refused (n = 20) or agreed (n = 95) to undergo MR imaging examination. Participants who refused MR imaging examination on approach were no less intelligent at age 11 years than participants who agreed to MR imaging participation (t = .731; P < .47).

MR imaging rating data were normally distributed and were treated as continuous variables. An independent-samples t test was used to determine whether sex was associated with white matter lesion ratings. Data obtained from observers A, B, and C were examined by using a Pearson correlation to address questions related to intraobserver and interobserver reliability of the Fazekas scale. The associations between mean WML and PVL ratings and standardized neuropsychologic test results were tested by using the Pearson correlation coefficient.


   RESULTS
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
RESULTS
DISCUSSION
 REFERENCES
 
The degree of agreement between observer A and A+ (the same individual assessing shuffled images blinded to original ratings several months apart) assessed intraobserver reliability of the Fazekas scale. Intraobserver reliability was r = 0.85 for WML and r = 0.85 for PVL (Table 1). Both correlations were significant to less than the 0.01 level (two-tailed). The degree of consensus between observers A, B, and C was used to assess interobserver reliability of the Fazekas scale (Table 1). This proved high, with Pearson correlations of 0.69, 0.72, and 0.74 among the three observers (Table 1).


View this table:
[in this window]
[in a new window]

  		TABLE 1. Descriptive Data among Observers on Fazekas Scores in 95 Subjects

 
Mean MR imaging lesion scores were entered into a Pearson correlation alongside standardized neuropsychologic test results (Table 2). Increased severity of subcortical and deep WML ratings was associated with lower test scores on all neuropsychologic measures. Increased severity of periventricular WML ratings was associated with lower Raven’s Progressive Matrices performance only.


View this table:
[in this window]
[in a new window]

  		TABLE 2. Pearson Correlations between WML Ratings and Tests of Crystallized and Fluid Intelligence

 
A correlation of r = .66 (P < .001, two-tailed) demonstrated a significant association of medium effect size between standardized current National Adult Reading Test scores and standardized 1932 Moray House test measurements at age 11 years. This is consistent with a report (32) we made in a larger sample of this cohort. Original 1932 mental ability test scores correlated significantly with Raven’s Progressive Matrices (r = .331; P < .001), the Digit Symbol test (r = .34; P < .001), and the number of correct responses made in the Uses for Common Objects test (r = .243; P < .024). Original 1932 mental ability test scores did not correlate with scores on the Block Design test. Sex had no effect on either mean WML (t = -1.66; P < .10) or mean PVL (t = .711; P < .48) ratings.


   DISCUSSION
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
RESULTS
 DISCUSSION
REFERENCES
 
We demonstrated associations between T2-weighted MR imaging–depicted WMLs and scores on tests of fluid-type, but not crystallized-type, intelligence. There were no significant sex differences in the observation of WMLs.

Fluid intelligence is the ability to generate solutions to problems by using culturally reduced materials, often with the pressure of time. In our sample, increased severity of WML ratings correlated with lower performance on fluid-type, age-sensitive intelligence tests. Increased severity of PVL ratings correlated with Raven’s Progressive Matrices performance only. Our findings are in agreement with those of recent studies (17,20). In the Rotterdam study, De Groot and colleagues (17) report that both WML and PVL severity are associated with worsening cognitive test scores when analyzed separately. The authors used a lesion rating system similar to the Fazekas method to rate PVLs but that deviated significantly in terms of WML ratings. This may account for their assertion that mainly periventricular WMLs are associated with lower cognitive test scores. Interestingly, with the exception of a memory test that is not a fluid intelligence measure (and showed no association with either WML or PVL ratings), the Rotterdam cognitive test battery can be described as a fluid-type intelligence test battery. Furthermore, by using another MR imaging–depicted lesion rating method, Garde et al (20) found no correlation between Wechsler Adult Intelligence Scale (29) subtest scores and PVL, and small associations between fluid-type WAIS subtests and WML in a sample of 80-year-olds. However, by using a cognitive decline measure based on longitudinal administration of the WAIS, Garde et al (20) demonstrated significant associations between fluid-type cognitive decline and both WML and PVL ratings.

The National Adult Reading Test is an estimate of premorbid mental ability in the presence of organic brain disease (33). Individuals with progressive dementia are known to retain the ability to pronounce words that are phonetically irregular and can no longer be defined, thus suggesting premorbid word familiarity (34). Knowledge of rules facilitating correct pronunciation of phonetically irregular words must have been acquired prior to any disease onset. Crystallized intelligence purports to be knowledge acquired throughout the lifetime (22) and the National Adult Reading Test appears to measure a form of acquired knowledge. This assertion is supported by a report of a strong association between 1932 Moray House Test scores and National Adult Reading Test scores in the larger 1921 Aberdeen cohort some 66 years later (32). Furthermore, it is the only test in our battery of neuropsychologic measures in which performance is not detrimentally affected by the presence of either WML or PVL.

Despite their prevalence in the apparently healthy elderly subjects (10,20,35), WMLs detected at T2-weighted MR imaging are considered to be of uncertain clinical significance. Our result adds to a growing body of evidence (15–17) suggestive of an association between WML severity and cognitive performance. Our findings cannot be attributed to mental ability differences between participants before the onset of any putative disease process, since we demonstrated no association between a true premorbid measure of mental ability and the development of WMLs later in life. Early and later life mental abilities are highly but imperfectly correlated (21). Contributions to late life ability, independent of early life ability, may shed light on the causes of instability in mental function across adulthood. The unique nature of this sample suggests that any genetic and/or environmental contributions from early life ability and WMLs are independent.

The main criticisms of this study are related to sample size and bias, particularly survival bias; that is, only those who participated in the 1932 Moray House test and survived to 1999–2000 were able to participate. Yue et al (36) found that sex has an effect on the development of WMLs. In contrast, we were unable to demonstrate sex differences in this sample, which may be related to the relatively small size of our sample particularly when comparisons are made with the Cardiovascular Health Study (36) and the Rotterdam Study (17). Our MR imaging sample was drawn from the larger 1921 Aberdeen sample. Participants from the 1921 Aberdeen study have a significantly greater mean intelligence quotient at age 11 years than those who refused study participation. Comparisons between volunteers in the MR imaging study and volunteers who refused to undergo MR imaging examination showed no significant difference in cognitive ability at age 11 years. Although the 1921 Aberdeen sample is biased, the bias underestimates the degree to which our result can be applied to the general population. Those who refused participation in the present study are unlikely to be less susceptible to disease than participants. The results obtained from the MR imaging analysis may be more pertinent to nonparticipants, since it has been reported that superior cognitive ability is associated with longevity (37).

The Fazekas scale (31) is semiquantitative and applied to subjective assessment of MR images by a trained observer. The scale is easily applied, but previous studies have questioned its intra- and interobserver reliability. Leys et al (38) reported that the reliability of the Fazekas scale was poor. Scheltens et al (39) judged intraobserver reliability of Fazekas WML and PVL ratings to be moderate and interobserver reliability to be poor to moderate. Conversely, in an analysis of agreement between 13 various MR imaging visual rating scales, Mantyla et al (18) found that the Fazekas scale tends to be reliable. We tested the intraobserver and interobserver reliability of the Fazekas scale by having three independent observers rate the type and severity of WMLs. The results obtained in terms of intraobserver and interobserver reliability were good. Our results show that the Fazekas scale reliably yields robust scores that can be confidently related to measures of cognitive ability in a healthy (asymptomatic) sample of elderly people. However, volunteers in our MR imaging study had generally low WML and PVL ratings, and Scheltens et al (39) point out that obvious low scores, and indeed obvious high scores, on such variables are likely to increase the degree of interobserver reliability. Inconsistencies in the reported association of WMLs and cognitive function could be related to the use of various methods used to rate MR imaging hyperintensities. Researchers must agree on a standardized WML rating method so that progress can be made in this field of study. Alternatively, an automated image processing technique to quantify WML and PVL would eliminate intra- and interobserver inconsistencies.

We conclude that increased WML scores are associated with lower fluid-type intelligence through an as yet unknown process. In contrast, crystallized-type intelligence is not associated with WMLs and remains functionally intact in their presence, whatever their rated severity. Our results seem implicitly correct on review of the classic literature (23,24), since it shows that fluid-type intelligence declines with advancing old age, while crystallized-type intelligence remains relatively unaffected. It is possible that observed age-related cognitive change in fluid-type intelligence is attributable, at least in part (20), to the presence of WMLs and their rated severity.


   ACKNOWLEDGMENTS
 
The authors thank the University of Aberdeen Development Trust, 1921 Aberdeen Birth Cohort volunteers, Aberdeen city and county general practitioners, and MR radiographers Annette Lyburn, Laura Frisch, and Julie Walker.


   FOOTNOTES
 
Abbreviations: PVL = periventricular lesion, WML = white matter lesion

Author contributions: Guarantor of integrity of entire study, L.J.W.; study concepts, L.J.W., I.J.D., A.D.M.; study design, L.J.W., S.A.L.; literature research, S.A.L., A.D.M.; clinical studies, S.A.L., H.A.L., A.D.M., L.J.W.; data acquisition and analysis/interpretation, S.A.L., A.D.M., R.T.S.; statistical analysis, J.R.C., S.A.L.; manuscript preparation, S.A.L., A.D.M.; manuscript definition of intellectual content, S.A.L., L.J.W.; manuscript editing, revision/review, and final version approval, all authors.


   REFERENCES
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
RESULTS
 DISCUSSION
 REFERENCES
 

  1. Schmidt R, Fazekas F, Kleinert G, et al. Magnetic resonance image signal hyperintensities in the deep and subcortical white matter. Arch Neurol 1992; 49:825-827.[Abstract/Free Full Text]
  2. Longstreth WT, Jr, Manolio TA, Arnold A, et al. Clinical correlates of white matter findings on cranial magnetic resonance imaging of 3301 elderly people: the Cardiovascular Health Study. Stroke 1996; 27:1274-1282.[Abstract/Free Full Text]
  3. Schmidt R, Hayn M, Fazekas F, Kapellar P, Esterbauer H. Magnetic resonance imaging white matter hyperintensities in clinically normal elderly individuals. Stroke 1996; 27:2043-2047.[Abstract/Free Full Text]
  4. Hachinski VC, Potter P, Merskey H. Leuko-araiosis. Neurology 1987; 44:21-23.
  5. Shintani S, Shiigai T, Arinami T. Subclinical cerebral lesion accumulation on serial magnetic resonance imaging (MRI) in patients with hypertension. Acta Neurol Scand 1998; 97:251-256.[Medline]
  6. Fazekas F, Niederhorn K, Schmidt R, et al. White matter signal abnormalities in normal individuals: correlation with carotid ultrasonography, cerebral blood flow measurements and cerebrovascular risk factors. Stroke 1988; 19:1285-1288.[Abstract/Free Full Text]
  7. Lechner H, Schmidt R, Bertha G, Justich E, Offenbacher H, Schneider G. Nuclear magnetic resonance image white matter lesions and risk factors for stroke in normal individuals. Stroke 1988; 19:263-265.[Abstract/Free Full Text]
  8. Breteler MMB, van Swieten JC, Bots ML, et al. Cerebral white matter lesions, vascular risk factors and cognitive function in a population-based study: the Rotterdam study. Neurology 1994; 44:1246-1252.[Abstract/Free Full Text]
  9. Veldink JH, Scheltens P, Jonker C, Launer JJ. Progression of cerebral white matter hyperintensities on MRI is related to diastolic blood pressure. Neurology 1998; 51:319-320.[Free Full Text]
  10. Liao D, Cooper L, Cai J, et al. The prevalence and severity of white matter lesions, their relationship with age, ethnicity, gender, and cardiovascular disease risk factors: the ARIC Study. Neuroepidemiology 1997; 16:149-162.[Medline]
  11. De Leeuw FE, De Groot JC, Oudkerk M, et al. A Follow-up study of blood pressure and cerebral white matter lesions. Ann Neurol 1999; 46:827-833.[CrossRef][Medline]
  12. Fazekas F, Kleinert R, Offenbacher H, et al. Pathologic correlates of incidental MRI white matter signal hyperintensities. Neurology 1993; 43:1683-1689.[Abstract/Free Full Text]
  13. Pantoni L, Garcia JH. Pathogenesis of leukoaraiosis: a review. Stroke 1997; 28:652-659.[Abstract/Free Full Text]
  14. Fazekas F, Schmidt R, Scheltens P. Pathophysiologic mechanisms in the development of age-related white matter changes of the brain. Dement Geriatr Cogn Disord 1998; 9(suppl 1):2-5.
  15. Breteler MMB, van Amerongen NM, van Swieten JC, et al. Cognitive correlates of ventricular enlargement and cerebral white matter lesions on magnetic resonance imaging: the Rotterdam Study. Stroke 1994; 25:1109-1115.[Abstract]
  16. De Groot JC, De Leeuw FE, Breteler MMB. Cognitive correlates of cerebral white matter changes. J Neural Transm Suppl 1998; 53:41-67.[Medline]
  17. 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.[CrossRef][Medline]
  18. Mantyla R, Erkinjuntti T, Salonon O, et al. Variable agreement between visual rating scales for white matter hyperintensities on MRI: comparison of 13 rating scales in a poststroke cohort. Stroke 1997; 28:1614-1623.[Abstract/Free Full Text]
  19. Scheltens P, Erkinjuntti T, Leys D, et al. White matter changes on CT and MRI: an overview of visual rating scales. Eur Neurol 1998; 39:80-89.[CrossRef][Medline]
  20. Garde E, Mortensen EL, Krabbe K, Rostrup E, Larsson HBW. Relation between age-related decline in intelligence and cerebral white-matter hyperintensities in healthy octogenarians: a longitudinal study. Lancet 2000; 356:628-634.[CrossRef][Medline]
  21. Deary IJ, Whalley LJ, Lemmon H, Crawford JR, Starr JM. The stability of individual differences in mental ability from childhood to old age: follow-up of the 1932 Scottish Mental Survey. Intelligence 2000; 28:49-55.[CrossRef]
  22. Carroll JB. Human cognitive abilities: a survey of factor-analytic studies Cambridge, England: Cambridge University Press, 1993.
  23. Schaie KW. Intellectual development in adulthood: the Seattle longitudinal study Cambridge, England: Cambridge University Press, 1996.
  24. Horn JL, Cattell RB. Age differences in fluid and crystallised intelligence. Acta Psychologia 1967; 26:107-129.
  25. Scottish Council for Research in Education. The intelligence of Scottish school children: a national survey of an age group London, England: University of London Press, 1933.
  26. Folstein MF, Folstein SE, McHugh PR. "Mini-mental state": a practical method for grading the cognitive state of patients for the clinician. J Psychiatr Res 1975; 12:189-198.[CrossRef][Medline]
  27. Nelson HE. The National Adult Reading Test (NART): test manual Windsor, England: NFER-Nelson, 1982.
  28. Raven JC. Guide to the standard progressive matrices London, England: Lewis, 1960.
  29. Wechsler D. WAIS-R manual New York, NY: The Psychological Corporation, 1981.
  30. Guildford JP, Christensen PR, Merrifield PR, Wilson JC. Alternate uses: manual of instructions and interpretation Orange, Calif: Sheridan Psychological Service, 1978.
  31. 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.[Abstract/Free Full Text]
  32. Crawford JR, Deary IJ, Starr J, Whalley LJ. The NART as an index of prior intellectual functioning: a retrospective validity study covering a 66-year interval. Psychol Med 2001; 31:451-458.[Medline]
  33. Crawford J, Parker DM, Besson LAO. Estimation of premorbid intelligence in organic conditions. Br J Psychiatry 1988; 153:178-181.[Abstract/Free Full Text]
  34. Lezak MD. Verbal functions and language skills In: Neuropsychological assessment. New York, NY: Oxford University Press, 1995.
  35. Fazekas F, Schmidt R, Kleinert R, Kapellar P, Roob G, Flooh E. The spectrum of age-associated brain abnormalities: their measurement and histopathological correlates. J Neural Transm Suppl 1998; 53:31-39.[Medline]
  36. Yue NC, Arnold AM, Longstreth WT, Jr, et al. Sulcal, ventricular, and white matter changes at MR imaging in the aging brain: data from the cardiovascular health study. Radiology 1997; 202:33-39.[Abstract/Free Full Text]
  37. Maier H, Smith J. Psychological predictors of mortality in old age. J Gerontol B Psychol Sci Soc Sci 2000; 54:P44-P54.
  38. Leys D, Soetaert G, Petit H, Fauquette A, Pruvo JP, Steinling M. Periventricular and white matter magnetic resonance imaging hyperintensities do not differ between Alzheimer’s disease and normal aging. Arch Neurol 1990; 47:524-527.[Abstract/Free Full Text]
  39. Scheltens P, Barkhof F, Leys D, et al. A semi-quantitative rating scale for the assessment of signal hyperintensities on magnetic resonance imaging. J Neurol Sci 1993; 114:7-12.[CrossRef][Medline]



This article has been cited by other articles:


Home page
 Biol Res NursHome page
K. C. Insel, S. L. Reminger, and C.-P. Hsiao
White Matter Hyperintensities and Medication Adherence
Biol Res Nurs, October 1, 2008; 10(2): 121 - 127.
[Abstract] [PDF]

Home page
 Br. J. Radiol.Home page
S MILLS, J CAIN, N PURANDARE, and A JACKSON
Biomarkers of cerebrovascular disease in dementia
Br. J. Radiol., December 1, 2007; 80(Special_Issue_2): S128 - S145.
[Abstract] [Full Text] [PDF]

Home page
 ImagingHome page
A D MURRAY
Imaging in dementia
Imaging, June 1, 2007; 19(2): 133 - 141.
[Abstract] [Full Text] [PDF]

Home page
 RadiologyHome page
A. D. Murray, R. T. Staff, S. D. Shenkin, I. J. Deary, J. M. Starr, and L. J. Whalley
Brain White Matter Hyperintensities: Relative Importance of Vascular Risk Factors in Nondemented Elderly People
Radiology, October 1, 2005; 237(1): 251 - 257.
[Abstract] [Full Text] [PDF]

Home page
 StrokeHome page
C. Bocti, R. H. Swartz, F.-Q. Gao, D. J. Sahlas, P. Behl, and S. E. Black
A New Visual Rating Scale to Assess Strategic White Matter Hyperintensities Within Cholinergic Pathways in Dementia
Stroke, October 1, 2005; 36(10): 2126 - 2131.
[Abstract] [Full Text] [PDF]

Home page
 J. Neurol. Neurosurg. PsychiatryHome page
E Garde, E Lykke Mortensen, E Rostrup, and O B Paulson
Decline in intelligence is associated with progression in white matter hyperintensity volume
J. Neurol. Neurosurg. Psychiatry, September 1, 2005; 76(9): 1289 - 1291.
[Abstract] [Full Text] [PDF]

Home page
 NeurologyHome page
E. E. Smith, M. E. Gurol, J. A. Eng, C. R. Engel, T. N. Nguyen, J. Rosand, and S. M. Greenberg
White matter lesions, cognition, and recurrent hemorrhage in lobar intracerebral hemorrhage
Neurology, November 9, 2004; 63(9): 1606 - 1612.
[Abstract] [Full Text] [PDF]

Home page
 J. Neurol. Neurosurg. PsychiatryHome page
R A Kenny, F E Shaw, J T O'Brien, P H Scheltens, R Kalaria, and C Ballard
Carotid sinus syndrome is common in dementia with Lewy bodies and correlates with deep white matter lesions
J. Neurol. Neurosurg. Psychiatry, July 1, 2004; 75(7): 966 - 971.
[Abstract] [Full Text] [PDF]

Home page
 BrainHome page
R. T. Staff, A. D. Murray, I. J. Deary, and L. J. Whalley
What provides cerebral reserve?
Brain, May 1, 2004; 127(5): 1191 - 1199.
[Abstract] [Full Text] [PDF]

Home page
 StrokeHome page
M. Ihara, H. Tomimoto, K. Ishizu, T. Mukai, H. Yoshida, N. Sawamoto, M. Inoue, T. Doi, K. Hashikawa, J. Konishi, et al.
Decrease in Cortical Benzodiazepine Receptors in Symptomatic Patients With Leukoaraiosis: A Positron Emission Tomography Study
Stroke, April 1, 2004; 35(4): 942 - 947.
[Abstract] [Full Text] [PDF]

This Article
Right arrow Abstract Freely available
Right arrow Figures Only
Right arrow Full Text (PDF)
Right arrow Submit a response
Right arrow Alert me when this article is cited
Right arrow Alert me when eLetters are posted
Right arrow Alert me if a correction is posted
Right arrow Citation Map
Services
Right arrow Email this article to a friend
Right arrow Similar articles in this journal
Right arrow Similar articles in PubMed
Right arrow Alert me to new issues of the journal
Right arrow Download to citation manager
Right arrow reprints & permissions
Citing Articles
Right arrow Citing Articles via HighWire
Right arrow Citing Articles via Google Scholar
Google Scholar
Right arrow Articles by Leaper, S. A.
Right arrow Articles by Whalley, L. J.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Leaper, S. A.
Right arrow Articles by Whalley, L. J.

HOME HELP FEEDBACK SUBSCRIPTIONS ARCHIVE SEARCH TABLE OF CONTENTS
RADIOLOGY RADIOGRAPHICS RSNA JOURNALS ONLINE