Hostname: page-component-78c5997874-ndw9j Total loading time: 0 Render date: 2024-11-13T12:44:08.573Z Has data issue: false hasContentIssue false
  • English
  • Français

Cerebral volume loss, cognitive deficit and neuropsychological performance: Comparative measures of brain atrophy: I. Dementia

Published online by Cambridge University Press:  01 May 2004

ERIN D. BIGLER ,
E. SHANNON NEELEY ,
MICHAEL J. MILLER ,
DAVID F. TATE ,
SARA A. RICE ,
HOWARD CLEAVINGER ,
LARA WOLFSON ,
JOANN TSCHANZ  and
KATHLEEN WELSH-BOHMER
ERIN D. BIGLER
Affiliation:
Brigham Young University, Provo, Utah Department of Radiology, University of Utah, Salt Lake City, Utah LDS Hospital, Salt Lake City, Utah
E. SHANNON NEELEY
Affiliation:
Brigham Young University, Provo, Utah
MICHAEL J. MILLER
Affiliation:
Brigham Young University, Provo, Utah
DAVID F. TATE
Affiliation:
Brigham Young University, Provo, Utah
SARA A. RICE
Affiliation:
Brigham Young University, Provo, Utah
HOWARD CLEAVINGER
Affiliation:
Brigham Young University, Provo, Utah
LARA WOLFSON
Affiliation:
Brigham Young University, Provo, Utah
JOANN TSCHANZ
Affiliation:
Utah State University, Logan, Utah
KATHLEEN WELSH-BOHMER
Affiliation:
Duke University, Durham, North Carolina
Get access Rights & Permissions [Opens in a new window]

Abstract

There are several magnetic resonance (MR) imaging methods to measure brain volume and cerebral atrophy; however, the best measure for examining potential relationships between such measures and neuropsychological performance has not been established. Relationships between seven measures of MR derived brain volume or indices of atrophy and neuropsychological performance in the elderly subjects of the population-based Cache County, Utah Study of Aging and Memory (n = 195) were evaluated. The seven MR measures included uncorrected total brain volume (TBV), TBV corrected by total intracranial volume (TICV), TBV corrected by the ratio of the individuals TICV by group TICV (TBVC), a ventricle-to-brain ratio (VBR), total ventricular volume (TVV), TVV corrected by TICV, and a measure of parenchymal volume loss. The cases from the Cache County Study were comprised of elderly individuals classified into one of four subject groups based on a consensus diagnostic process, independent of quantitative MR imaging findings. The groups included subjects with Alzheimer's disease (AD, n = 85), no dementia but mild/ambiguous (M/A) deficits (n = 30), a group of subjects with non-AD dementia or neuropsychiatric disorder including vascular dementia (n = 60), and control subjects (n = 20). Neuropsychological performance was based on the Mini-Mental Status Exam (MMSE) and an expanded neuropsychological test battery (consortium to establish a registry for Alzheimer's disease (CERAD). The results demonstrated that the various quantitative MR measures were highly interrelated and no single measure was statistically superior. However, TBVC, TBV/TICV and VBR consistently exhibited the more robust relationships with neuropsychological performance. These results suggest that a single corrected brain volume measure or index is sufficient in studies examining global MR indicators of cerebral atrophy in relation to cognitive function and recommends use of either TBVC, TBV/TICV, or VBR. (JINS, 2004, 10, 442–452.)

Keywords

Cerebral volume lossCognitive deficitComparative measuresDementia
Type
Research Article
Information
Journal of the International Neuropsychological Society , Volume 10 , Issue 3 , May 2004 , pp. 442 - 452
Copyright
© 2004 The International Neuropsychological Society

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

Arndt, S., Cohen, G., Alliger, R.J., Swayze, V.W., & Andreasen, N.C. (1991). Problems with ratio and proportion measures of imaged cerebral structures. Psychiatry Research, 40, 79–90.CrossRefGoogle Scholar
Atkins, M.S. & Mackiewich, B.T. (2000). Fully automated hybrid segmentation of the brain. In I.N. Bankman (Ed.), Handbook of medical imaging (pp. 171–183). San Diego, CA: Academic Press.
Baare, W.F.C., Hulshoff Pol, H.E., Boomsma, D.I., Postuma, D., de Geus, E.J.C., Schnack, H.G., van Haren, N.E.M., van Oel, C.J., & Kahn, R.S. (2001a). Quantitative genetic modeling of variation in human brain morphology. Cerebral Cortex, 11, 816–824.Google Scholar
Baare, W.F.C., van Oel, C.J., Hulshoff Pol, H.E., Schnack, H.G., Durston, S., Sitskoorn, M.M., & Kahn, R.S. (2001b). Volumes of brain structures in twins discordant for Schizophrenia. Archives of General Psychiatry, 58, 33–40.Google Scholar
Bigler, E.D. (2001). Premorbid brain volume and dementia. Archives of Neurology, 58, 831–833.CrossRefGoogle Scholar
Bigler, E.D., Kerr, B., Victoroff, J., Tate, D., & Breitner, J.C.S. (2002a). White matter lesions, quantitative MRI and dementia. Alzheimer Disease and Associated Disorders, 16, 161–170.Google Scholar
Bigler, E.D., Lowry, C.M., Anderson, C.V., Johnson, S.C., Terry, J., & Steed, M. (2000). Dementia, quantitative neuroimaging, and Apolipoprotein E genotype. American Journal of Neuroradiology, 21, 1857–1868.Google Scholar
Bigler, E.D., Lowry, C.M., Kerr, B., Tate, D.F., Hessel, C.D., Earl, H.D., Miller, M.J., Smith, K.H., Tschanz, J.T., Welsh-Bohmer, K.A., Plassman, B.L., & Victoroff, J. (2003). Role of white matter lesions, cerebral atrophy, and APOE on cognition in older persons with and without dementia: The Cache County, Utah, study of memory and aging. Neuropsychology, 17, 339–352.CrossRefGoogle Scholar
Bigler, E.D. & Tate, D.F. (2001). Brain volume, intracranial volume and dementia. Investigative Radiology, 36, 539–546.CrossRefGoogle Scholar
Bigler, E.D., Tate, D.F., Miller, M.J., Rice, S.A., Hessel, C.D., Heath, D.E., Tschanz, J.T., Plassman, B.L., & Welsh-Bohmer, K.A. (2002b). Dementia, asymmetry of temporal lobe structures, and Apolipoprotein E genotype: Relationships to cerebral atrophy and neuropsychological impairment. Journal of the International Neuropsychological Society, 8, 925–933.Google Scholar
Blatter, D.D., Bigler, E.D., Gale, S.C., Johnson, S.C., Anderson, C.V., Burnett, B.M., Parker, N., Kurth, S., & Horn, S. (1995). Quantitative volumetric analysis of brain MR: Normative database spanning five decades of life. American Journal of Neuroradiology, 16, 241–251.Google Scholar
Blinkov, S.M. & Glezer, I.I. (1968). The human brain in figures and tables: A quantitative handbook. New York: Plenum Press.
Bradley, W.G. & Orrison, W.W. (2000). Hydrocephalus and cerebrospinal fluid flow. In W.W. Orrison (Ed.), Neuroimaging (pp. 704–1716). Philadelphia: W.B. Saunders.
Breitner, J.C.S., Wyse, B.W., Anthony, J.C., Welsh-Bohmer, K.A., Steffens, D.C., Norton, M.C., Tschanz, J.T., Plassman, B.L., Meyer, M.R., Skoog, I., & Khachaturian, A. (1999). APOE-4 count predicts age when prevalence of AD increases, then declines: The Cache County Study. Neurology, 53, 321–331.CrossRefGoogle Scholar
Cahn, D.A., Sullivan, E.V., Shear, p.K., Marsh, L., Fama, R., Lim, K.O., Yesavage, J.A., Tinklenberg, J.R., & Pfefferbaum, A. (1998). Structural MRI correlates of recognition memory in Alzheimer's disease. Journal of the International Neuropsychological Society, 4, 106–114.CrossRefGoogle Scholar
Cherniak, C. (1990). The bounded brain: Toward quantitative neuroanatomy. Journal of Cognitive Neuroscience, 2, 58–68.CrossRefGoogle Scholar
Conover, W.J. (1999). Practical Nonparametric Statistics. New York: John Wiley & Sons, Inc.
Courchesne, E., Chisum, H.J., Townsend, J., Cowles, A., Covington, J., Egaas, B., Harwood, M., Hinds, S., & Press, G.A. (2000). Normal brain development and aging: Quantitative analysis at in vivo MR imaging in healthy volunteers. Radiology, 216, 672–682.CrossRefGoogle Scholar
Dekaban, A.S. & Sadowsky, D. (1978). Changes in brain weight during the span of human life: Relation of brain weights to body heights and body weights. Annals of Neurology, 4, 345–356.CrossRefGoogle Scholar
Fama, R., Sullivan, E.V., Shear, P.K., Cahn-Weiner, D.A., Marsh, L., Lim, K.O., Yesavage, J.A., & Rinklenberg, J.R. (2000). Structural brain correlates of verbal and nonverbal fluency measures in Alzheimer's disease. Neuropsychology, 14, 29–40.CrossRefGoogle Scholar
Finlay, B.L. & Darlington, R.B. (1995). Linked regularities in the development and evolution of mammalian brains. Science, 268, 1578–1584.CrossRefGoogle Scholar
Folstein, M.F., Folstein, S.E., & McHugh, P.R. (1975). “Mini-Mental State.” A practical method for grading the cognitive state of patients for clinician. Journal of Psychiatric Research, 12, 189–198.CrossRefGoogle Scholar
Forstl, H., Sattel, H., Besthorn, C., Daniel, S., Geiger-Kabisch, C., Hentschel, F., Sarochan, M., & Zerfab, R. (1996). Longitudinal cognitive, electroencephalographic and morphological brain changes in ageing and Alzheimer's disease. British Journal of Psychiatry, 168, 280–286.CrossRefGoogle Scholar
Fox, N.C., Cousens, S., Scahill, R., Harvey, R.J., & Rossor, M.N. (2000). Using serial registered brain magnetic resonance imaging to measure disease progression in Alzheimer disease. Archives of Neurology, 57, 339–344.CrossRefGoogle Scholar
Gale, S.D., Bigler, E.D., Johnson, S., & Blatter, D.D. (1995). Global degeneration following traumatic brain injury: Anatomic and neuropsychologic correlates. Journal of the International Neuropsychological Society, 1, 162.CrossRefGoogle Scholar
Gur, R.E., Turetsky, B.I., Cowell, P.E., Finkelman, C., Maany, V., Grossman, R.I., Arnold, S.E., Bilker, W.B., & Gur, R.C. (2000). Temporolimbic volume reductions in schizophrenia. Archives of General Psychiatry, 57, 769–775.CrossRefGoogle Scholar
Haug, J.O. (1962). Pneumoencephalographic studies in mental disease. Acta Psychiatrica Scandinavica (suppl.), 165, 1–114.Google Scholar
Jack, C.R., Petersen, R.C., Xu, Y., O'Brien, P.C., Smith, G.e., Ivnik, R.J., Tangalos, E.G., & Kokmen, E.F. (1998). Rate of medial temporal lobe atrophy in typical aging and Alzheimer's disease. Neurology, 51, 993–999.CrossRefGoogle Scholar
Jack, C.R., Petersen, R.C., Xu, Y.C., O'Brien, P.C., Smith, G.E., Ivnik, R.J., Boeve, B.F., Waring, S.C., Tangalos, E.G., & Kokmen, E. (1999). Prediction of AD with MRI-based hippocampal volume in mild cognitive impairment. Neurology, 52, 1397–1403.CrossRefGoogle Scholar
Jack, C.R., Theodore, W.H., Cook, M., & McCarthy, G. (1995). MRI-based hippocampal volumetrics: Data acquisition, normal ranges, and optimal protocol. Magnetic Resonance Imaging, 13, 1057–1064.CrossRefGoogle Scholar
Jenkins, R., Fox, N.C., Rossor, A.M., Harvey, R.J., & Rosser, M.N. (2000). Intracranial volume and Alzheimer disease: Evidence against the cerebral reserve hypothesis. Archives of Neurology, 57, 220–224.CrossRefGoogle Scholar
Kidron, D., Black, S.E., Stanchev, P., Buck, B., Szalai, J.P., Parker, J., Szekely, C., & Bronskill, M.J. (1997). Quantitative MR volumetry in Alzheimer's disease: Topographic markers and the effects of sex and education. Neurology, 49, 1504–1512.CrossRefGoogle Scholar
Killiany, R.J., Gomez-Isla, T., Moss, M., Kikinis, R., Sandor, T., Jolesz, F., Tanzi, R., Jones, K., Hyman, B.T., & Albert, M.S. (2000). Use of structural magnetic resonance imaging to predict who will get Alzheimer's disease. Annals of Neurology, 47, 430–439.3.0.CO;2-I>CrossRefGoogle Scholar
Lancaster, J.L., Woldorff, M.G., Parsons, L.M., Liotti, M., Freitas, C.S., Rainey, L., Kochunov, P.V., Nickerson, D., Mikiten, S.A., & Fox, P.T. (2000). Automated Talairach atlas labels for functional brain mapping. Human Brain Mapping, 10, 120–131.3.0.CO;2-8>CrossRefGoogle Scholar
Mathalon, D.H., Sullivan, E.V., Rawles, J.M., & Pfefferbaum, A. (1993). Correction for head size in brain-imaging measurements. Psychiatry Research, 50, 121–139.CrossRefGoogle Scholar
Mathalon, D.H., Sullivan, E.V., Rawles, J.M., & Pfefferbaum, A. (1994). Correction for head size in brain-imaging measurements: Correction. Psychiatry Research, 55, 179–180.Google Scholar
Matsumae, M., Kikinis, R., Morocz, I.A., Lorenzo, A.V., Sandor, T., Albert, M.A., Black, P.M., & Jolesz, F.A. (1996). Age-related changes in intracranial compartment volumes in normal adults assessed by magnetic resonance imaging. Journal of Neurosurgery, 84, 982–991.CrossRefGoogle Scholar
Nellhaus, G. (1968). Head circumference from birth to eighteen years. Pediatrics, 41, 106–114.Google Scholar
Norton, M.C., Tschanz, J.T., Fan, X., Plassman, B.L., Welsh-Bohmer, K.A., N., W., Wyse, B.W., & Breitner, J.C.S. (1999). Telephone adaptation of the Modified Mini-Mental State Exam. Neuropsychiatry, Neuropsychology and Behavioral Neurology, 12, 270–276.Google Scholar
Peterson, B.S., Staib, L.H., Scahill, L., Zhang, H., Anderson, C.V., Leckman, J.F., Cohen, D.J., Gore, J.C., Albert, J., & Webster, R. (2001). Regional brain and ventricular volumes in Tourette syndrome. Archives of General Psychiatry, 58, 427–440.CrossRefGoogle Scholar
Peterson, B.S., Vohr, B., Staib, L.H., Cannistraci, C.J., A.Dolberg, A., Schneider, K.C., Katz, K.H., Westerveld, M., Sparrow, S., Anderson, A.W., Duncan, C.C., Makuch, R.W., Gore, J.C., & Ment, L.R. (2000). Regional brain volume abnormalities and long-term cognitive outcome in preterm infants. Journal of the American Medical Association, 284, 1939–1947.CrossRefGoogle Scholar
Pfefferbaum, A., Lim, K.O., Rosenbloom, M.J., & Zipursky, R.B. (1990). Brain magnetic resonance imaging: Approaches for investigating schizophrenia. Schizophrenia Bulletin, 16, 453–476.CrossRefGoogle Scholar
Raz, N., Raz, S., & Bigler, E.D. (1988a). Ventriculomegaly in schizophrenia, the role of control groups and the perils of dichotomous thinking: A reply to Smith and Iacono. Psychiatry Research, 26, 245–248.Google Scholar
Raz, S., Raz, N., & Bigler, E.D. (1988b). Ventriculomegaly in schizophrenia: Is the choice of controls important? Psychiatry Research, 24, 71–77.Google Scholar
Reiss, A.L., Abrams, M.T., Singer, H.S., Ross, J.L., & Denkla, M.B. (1996). Brain development, gender and IQ in children: A volumetric imaging study. Brain, 119, 1763–1774.CrossRefGoogle Scholar
Robb, R. (1995a). ANALYZE: Three-dimensional biomedical imaging. New York: VCH Publishers.
Robb, R. (1995b). Three-dimensional biomedical imaging. New York: VCH Publishers.
Robb, R.A. (2001). ANALYZE: The biomedical imaging resource at Mayo Clinic. IEEE Transactions on Medical Imaging, 20, 854–867.CrossRefGoogle Scholar
Schlaepfer, T.E., Harris, G.J., Tien, A.Y., L., P., & Lee, S. (1995). Structural differences in the cerebral cortex of healthy female and male subjects: A magnetic resonance imaging study. Psychiatry Research, 61, 129–135.CrossRefGoogle Scholar
Schultz, R.T. & Chakraborty, A. (1996). Magnetic resonance image analysis. In E.D. Bigler (Ed.), Neuroimaging I: Basic science (Vol. I, pp. 9–51). New York: Plenum Press.
Shear, P.K., Sullivan, E.V., Mathalon, D.H., Lim, K.O., Davis, L.F., Yesavage, J.A., Tinklenberg, J.R., & Pfefferbaum, A. (1995). Longitudinal volumetric computed tomographic analysis of regional brain changes in normal aging and Alzheimer's disease. Archives of Neurology, 52, 392–402.CrossRefGoogle Scholar
Smith, C.D., Snowden, D.A., Wang, H., & Markesbery, W.R. (2000). White matter volumes and periventricular white matter hyperintensities in aging and dementia. Neurology, 54, 838–842.CrossRefGoogle Scholar
Tanabe, J.L., Amend, D., Schuff, N., DiSclafani, V., Ezekiel, F., Norman, D., Fein, G., & Weiner, M.W. (1997). Tissue segmentation of the brain in Alzheimer's disease. American Journal of Neuroradiology, 18, 115–123.Google Scholar
Thompson, P.M., Cannon, T.D., Narr, K.L., van Erp, T., Poutanen, V.-P., Huttenen, M., Lonnqvist, J., Standertskjold-Nordenstam, C.-G., Kaprio, J., Khaledy, M., Dail, R., Zoumalan, C.I., & Toga, A.W. (2001a). Genetic influences on brain structure. Neuroscience, 4, 1253–1258.Google Scholar
Thompson, P.M., Moussai, J., Zohoori, S., Goldkorn, A., Khan, A.A., Mega, M.S., Small, G.W., Cummings, J.L., & Toga, A.W. (1998). Cortical variability and asymmetry in normal aging and Alzheimer's disease. Cerebral Cortex, 8, 492–509.CrossRefGoogle Scholar
Thompson, P.M., Vidal, C., Giedd, J.N., Gochman, P., Blumenthal, J., Nicolson, R., Toga, A.W., & Rapoport, J.L. (2001b). Mapping adolescent brain change reveals dynamic wave of accelerated gray matter loss in very early-onset schizophrenia. Proceedings of the National Academy of Sciences of the United States of America, 98, 11650–11655.Google Scholar
Tschanz, J.T., Welsh-Bohmer, K.A., Skoog, I., West, N., Norton, M.C., Wyse, B.W., Nickles, R., & Breitner, J.C.S. (2000). Dementia diagnoses from clinical and neuropsychological data compared: The Cache County study. Neurology, 54, 1290–1296.CrossRefGoogle Scholar
Welsh, K.A., Butters, N., Mohs, R.C., Beekly, D., Edland, S., Fillenbaum, G., & Heyman, A. (1994). The Consortium to Establish a Registry of Alzheimer's Disease (CERAD). Part V: A normative study of the neuropsychological battery. Neurology, 44, 609–614.Google Scholar
Wilson, R.S., Sullivan, M., de Toledo-Morrell, L., Stebbins, G.T., Bennett, D.A., & Morrell, F. (1996). Association of memory and cognition in Alzheimer's disease with volumetric estimates of temporal lobe structures. Neuropsychology, 10, 459–463.CrossRefGoogle Scholar
Xu, Y., Jack, C.R., O'brien, P.C., Kokmen, E., Smith, G.E., Ivnik, R.J., Boeve, B.F., Tangalos, R.G., & Petersen, R.C. (2000). Usefulness of MRI measures of entorhinal cortex versus hippocampus in AD. Neurology, 54, 1760–1767.CrossRefGoogle Scholar
Zar, J.H. (1996). Biostatistical analysis. Englewood Cliffs, NJ: Prentice Hall.