Brain Imaging and Behavior

, Volume 10, Issue 3, pp 640–651 | Cite as

Cortical brain atrophy and intra-individual variability in neuropsychological test performance in HIV disease

  • Lindsay J. Hines
  • Eric N. Miller
  • Charles H. Hinkin
  • Jeffery R. Alger
  • Peter Barker
  • Karl Goodkin
  • Eileen M. Martin
  • Victoria Maruca
  • Ann Ragin
  • Ned Sacktor
  • Joanne Sanders
  • Ola Selnes
  • James T. Becker
  • for the Multicenter AIDS Cohort Study
Original Research


To characterize the relationship between dispersion-based intra-individual variability (IIVd) in neuropsychological test performance and brain volume among HIV seropositive and seronegative men and to determine the effects of cardiovascular risk and HIV infection on this relationship. Magnetic Resonance Imaging (MRI) was used to acquire high-resolution neuroanatomic data from 147 men age 50 and over, including 80 HIV seropositive (HIV+) and 67 seronegative controls (HIV-) in this cross-sectional cohort study. Voxel Based Morphometry was used to derive volumetric measurements at the level of the individual voxel. These brain structure maps were analyzed using Statistical Parametric Mapping (SPM2). IIVd was measured by computing intra-individual standard deviations (ISD’s) from the standardized performance scores of five neuropsychological tests: Wechsler Memory Scale-III Visual Reproduction I and II, Logical Memory I and II, Wechsler Adult Intelligence Scale-III Letter Number Sequencing. Total gray matter (GM) volume was inversely associated with IIVd. Among all subjects, IIVd -related GM atrophy was observed primarily in: 1) the inferior frontal gyrus bilaterally, the left inferior temporal gyrus extending to the supramarginal gyrus, spanning the lateral sulcus; 2) the right superior parietal lobule and intraparietal sulcus; and, 3) dorsal/ventral regions of the posterior section of the transverse temporal gyrus. HIV status, biological, and cardiovascular disease (CVD) variables were not linked to IIVd -related GM atrophy. IIVd in neuropsychological test performance may be a sensitive marker of cortical integrity in older adults, regardless of HIV infection status or CVD risk factors, and degree of intra-individual variability links with volume loss in specific cortical regions; independent of mean-level performance on neuropsychological tests.


Imaging Cognition HIV Voxel-based morphometry Intra-individual variability 



The authors are grateful to the volunteers and the staff of the Multicenter AIDS Cohort Study for the time and effort that they contributed towards the successful completion of this project.

Compliance with ethical standards


Data in this manuscript were collected by the Multicenter AIDS Cohort Study (MACS) with centers at Baltimore (U01-AI35042): The Johns Hopkins University Bloomberg School of Public Health: Joseph B. Margolick (PI), Barbara Crain, Adrian Dobs, Homayoon Farzadegan, Joel Gallant, Lisette Johnson-Hill, Cynthia Munro, Michael W. Plankey, Ned Sacktor, James Shepard, Chloe Thio; Chicago (U01-AI35039): Feinberg School of Medicine, Northwestern University, and Cook County Bureau of Health Services: Steven M. Wolinsky (PI), John P. Phair, Sheila Badri, Maurice O’Gorman, David Ostrow, Frank Palella, Ann Ragin; Los Angeles (U01-AI35040): University of California, UCLA Schools of Public Health and Medicine: Roger Detels (PI), Otoniel Martínez-Maza (Co-PI), Aaron Aronow, Robert Bolan, Elizabeth Breen, Anthony Butch, Beth Jamieson, Eric N. Miller, John Oishi, Harry Vinters, Dorothy Wiley, Mallory Witt, Otto Yang, Stephen Young, Zuo Feng Zhang; Pittsburgh (U01-AI35041): University of Pittsburgh, Graduate School of Public Health: Charles R. Rinaldo (PI), Lawrence A. Kingsley (Co-PI), James T. Becker, Ross D. Cranston, Jeremy J. Martinson, John W. Mellors, Anthony J. Silvestre, Ronald D. Stall; and the Data Coordinating Center (UM1-AI35043): The Johns Hopkins University Bloomberg School of Public Health: Lisa P. Jacobson (PI), Alvaro Munoz (Co-PI), Alison, Abraham, Keri Althoff, Christopher Cox, Jennifer Deal, Gypsyamber D’Souza, Priya Duggal, Janet Schollenberger, Eric C. Seaberg, Sol Su, Pamela Surkan. The MACS is funded primarily by the National Institute of Allergy and Infectious Diseases (NIAID), with additional co-funding from the National Cancer Institute (NCI). Targeted supplemental funding for specific projects was also provided by the National Heart, Lung, and Blood Institute (NHLBI), and the National Institute on Deafness and Communication Disorders (NIDCD). MACS data collection is also supported by UL1-TR000424 (JHU CTSA). Website located at The contents of this publication are solely the responsibility of the authors and do not represent the official views of the National Institutes of Health (NIH).

Additional grant funding

Additional funding for this work was provided by the UCLA CFAR grant 5P30 AI028697, T32-MH019535, from the Department of Veteran Affairs (VA Merit Review), and from the National Institute on Aging (AG034852 to JTB).

Conflict of interest

All authors have declared that he or she has no conflict of interest.

Ethical approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

Informed consent

Informed consent was obtained from all individual participants included in the study.

Data analysis

The data were analyzed by L.J. Hines, J.T. Becker and V. Maruca, with assistance from J. Sanders.


  1. Allaire, J. C., & Marsiske, M. (2005). Intraindividual variability may not always indicate vulnerability in elders’ cognitive performance. Psychology and Aging, 20(3), 390–401. doi: 10.1037/0882-7974.20.3.390.CrossRefPubMedPubMedCentralGoogle Scholar
  2. Anstey, K. J., Mack, H. A., Christensen, H., Li, S.-C., Reglade-Meslin, C., Maller, J., et al. (2007). Corpus callosum size, reaction time speed and variability in mild cognitive disorders and in a normative sample. Neuropsychologia, 45(8), 1911–1920. doi: 10.1016/j.neuropsychologia.2006.11.020.CrossRefPubMedGoogle Scholar
  3. Antinori, A., Arendt, G., Becker, J. T., Brew, B. J., Byrd, D. A., Cherner, M., et al. (2007). Updated research nosology for HIV-associated neurocognitive disorders. Neurology, 69(18), 1789–1799.CrossRefPubMedPubMedCentralGoogle Scholar
  4. Ashburner, J., & Friston, K. J. (2000). Voxel-based morphometry–the methods. NeuroImage, 11(6 Pt 1), 805–821. doi: 10.1006/nimg.2000.0582.CrossRefPubMedGoogle Scholar
  5. Becker, J. T., Lopez, O. L., Dew, M. A., & Aizenstein, H. J. (2004). Prevalence of cognitive disorders differs as a function of age in HIV virus infection. [Research Support, U.S. Gov’t, P.H.S.] Aids, 18 Suppl 1, S11–S18.CrossRefPubMedGoogle Scholar
  6. Becker, J. T., Kingsley, L., Mullen, J., Cohen, B., Martin, E., Miller, E. N., et al. (2009). Vascular risk factors, HIV serostatus, and cognitive dysfunction in gay and bisexual men. [research support, N.I.H., extramural]. Neurology, 73(16), 1292–1299. doi: 10.1212/WNL.0b013e3181bd10e7.CrossRefPubMedPubMedCentralGoogle Scholar
  7. Becker, J. T., Sanders, J., Madsen, S. K., Ragin, A., Kingsley, L., Maruca, V., et al. (2011). Subcortical brain atrophy persists even in HAART-regulated HIV disease. Brain Imaging and Behavior, 5(2), 77–85. doi: 10.1007/s11682-011-9113-8.CrossRefPubMedPubMedCentralGoogle Scholar
  8. Becker, J. T., Bajo, R., Fabrizio, M., Sudre, G., Cuesta, P., Aizenstein, H. J., et al. (2012a). Functional connectivity measured with magnetoencephalography identifies persons with HIV disease. [research support, N.I.H., extramural]. Brain Imaging and Behavior, 6(3), 366–373. doi: 10.1007/s11682-012-9149-4.CrossRefPubMedPubMedCentralGoogle Scholar
  9. Becker, J. T., Cuesta, P., Fabrizio, M., Sudre, G., Vergis, E., Douaihy, A., et al. (2012b). Brain structural and functional recovery following initiation of combination antiretroviral therapy. Journal of Neurovirology, 18(5), 423–427. doi: 10.1007/s13365-012-0115-0.CrossRefPubMedPubMedCentralGoogle Scholar
  10. Becker, J. T., Maruca, V., Kingsley, L. A., Sanders, J. M., Alger, J. R., Barker, P. B., et al. (2012c). Factors affecting brain structure in men with HIV disease in the post-HAART era. Neuroradiology, 54(2), 113–121. doi: 10.1007/s00234-011-0854-2.CrossRefPubMedGoogle Scholar
  11. Becker, J. T., Kingsley, L. A., Molsberry, S., Reynolds, S., Aronow, A., Levine, A. J., et al. (2014). Cohort Profile: Recruitment cohorts in the neuropsychological substudy of the Multicenter AIDS Cohort Study. International Journal of Epidemiology, doi: 10.1093/ije/dyu092.
  12. Bellgrove, M. A., Hester, R., & Garavan, H. (2004). The functional neuroanatomical correlates of response variability: evidence from a response inhibition task. Neuropsychologia, 42(14), 1910–1916. doi: 10.1016/j.neuropsychologia.2004.05.007.CrossRefPubMedGoogle Scholar
  13. Brew, B. J. (2004). Evidence for a change in AIDS dementia complex in the era of highly active antiretroviral therapy and the possibility of new forms of AIDS dementia complex. [Review]. Aids, 18 Suppl 1, S75-S78.Google Scholar
  14. Budka, A. (1991). Neuropathology of human immunodeficiency virus infection. Brain Pathology, 1, 163–175.CrossRefPubMedGoogle Scholar
  15. Bunce, D., Anstey, K., Christensen, H., Dear, K., Wen, W., & Sachdev, P. (2007). White matter hyperintensities and within-person variability in community-dwelling adults aged 60–64 years. Neuropsychologia, 45(9), 2009–2015. doi: 10.1016/j.neuropsychologia.2007.02.006.CrossRefPubMedGoogle Scholar
  16. Butters, N., Grant, I., Haxby, J., Judd, L. L., Martin, A., McClelland, J., et al. (1990). Assessment of AIDS-related cognitive changes: recommendations of the NIMH workshop on neuropsychological assessment approaches. Journal of Clinical and Experimental Neuropsychology, 12(6), 963–978. doi: 10.1080/01688639008401035.CrossRefPubMedGoogle Scholar
  17. Christensen, H., Mackinnon, A. J., Korten, A. E., Jorm, A. F., Henderson, A. S., & Jacomb, P. (1999). Dispersion in cognitive ability as a function of age: a longitudinal study of an elderly community sample. Aging, Neuropsychology, and Cognition (Neuropsychology, Development and Cognition: Section B), 6(3), 214–228. doi: 10.1076/anec. Scholar
  18. Cysique, L. A., Maruff, P., & Brew, B. J. (2004). Prevalence and pattern of neuropsychological impairment in human immunodeficiency virus–infected/acquired immunodeficiency syndrome (HIV/AIDS) patients across pre- and post-highly active antiretroviral therapy eras: a combined study of two cohorts clinical report. Journal of Neurovirology, 10(6), 350–357. doi: 10.1080/13550280490521078.CrossRefPubMedGoogle Scholar
  19. Dawes, S., Suarez, P., Casey, C. Y., Cherner, M., Marcotte, T. D., Letendre, S., et al. (2008). Variable patterns of neuropsychological performance in HIV-1 infection. [research support, N.I.H., extramural]. Journal of Clinical and Experimental Neuropsychology, 30(6), 613–626. doi: 10.1080/13803390701565225.CrossRefPubMedPubMedCentralGoogle Scholar
  20. Ettenhofer, M. L., Foley, J., Behdin, N., Levine, A. J., Castellon, S. A., & Hinkin, C. H. (2010). Reaction time variability in HIV-positive individuals. Archives of Clinical Neuropsychology, 25(8), 791–798. doi: 10.1093/arclin/acq064.CrossRefPubMedPubMedCentralGoogle Scholar
  21. Everall, I. P., Hansen, L. A., & Masliah, E. (2005). The shifting patterns of HIV encephalitis neuropathology. Neurotoxicity Research, 8(1–2), 51–61.CrossRefPubMedGoogle Scholar
  22. Fernandez-Duque, D., & Posner, M. I. (2001). Brain imaging of attentional networks in normal and pathological states. Journal of Clinical and Experimental Neuropsychology, 23(1), 74–93. doi: 10.1076/jcen. Scholar
  23. Genovese, C. R., Lazar, N. A., & Nichols, T. (2002). Thresholding of statistical maps in functional neuroimaging using the false discovery rate. [research support, U.S. Gov’t, Non-P.H.S.]. NeuroImage, 15(4), 870–878. doi: 10.1006/nimg.2001.1037.CrossRefPubMedGoogle Scholar
  24. Good, C. D., Johnsrude, I. S., Ashburner, J., Henson, R. N., Friston, K. J., & Frackowiak, R. S. (2001). A voxel-based morphometric study of ageing in 465 normal adult human brains. [research support, Non-U.S. Gov’t]. NeuroImage, 14(1 Pt 1), 21–36. doi: 10.1006/nimg.2001.0786.CrossRefPubMedGoogle Scholar
  25. Heaton, R., Marcotte, T. D., Mindt, M. R., Sadek, J., Moore, D. J., Bentley, H., et al. (2004). The impact of HIV-associated neuropsychological impairment on everyday functioning. Journal of the International Neuropsychological Society : JINS, 10(3), 317–331. doi: 10.1017/S1355617704102130.CrossRefPubMedGoogle Scholar
  26. Hilborn, J. V., Strauss, E., Hultsch, D. F., & Hunter, M. A. (2009). Intraindividual variability across cognitive domains: investigation of dispersion levels and performance profiles in older adults. Journal of Clinical and Experimental Neuropsychology, 31(4), 412–424. doi: 10.1080/13803390802232659.CrossRefPubMedGoogle Scholar
  27. Holmes, C. J., Hoge, R., Collins, L., Woods, R., Toga, A. W., & Evans, A. C. (1998). Enhancement of MR images using registration for signal averaging. Journal of Computer Assisted Tomography, 22(2), 324–333.CrossRefPubMedGoogle Scholar
  28. Holtzer, R., Verghese, J., Wang, C., Hall, C. B., & Lipton, R. B. (2008). Within-person across-neuropsychological test variability and incident dementia. JAMA, 300(7), 823–830. doi: 10.1001/jama.300.7.823.CrossRefPubMedPubMedCentralGoogle Scholar
  29. Hultsch, D. F., MacDonald, S. W., Hunter, M. A., Levy-Bencheton, J., & Strauss, E. (2000). Intraindividual variability in cognitive performance in older adults: comparison of adults with mild dementia, adults with arthritis, and healthy adults. Neuropsychology, 14(4), 588–598.CrossRefPubMedGoogle Scholar
  30. Hultsch, D. F., MacDonald, S. W., & Dixon, R. A. (2002). Variability in reaction time performance of younger and older adults. Journal of Gerontology: Series B Psychological adn Social Sciences, 57(2), P101–P115.CrossRefGoogle Scholar
  31. Kelly, A. M., Uddin, L. Q., Biswal, B. B., Castellanos, F. X., & Milham, M. P. (2008). Competition between functional brain networks mediates behavioral variability. NeuroImage, 39(1), 527–537. doi: 10.1016/j.neuroimage.2007.08.008.CrossRefPubMedGoogle Scholar
  32. Kieburtz, K., Ketonen, L., Cox, C., Grossman, H., Holloway, R., Booth, H., et al. (1996). Cognitive performance and regional brain volume in human immunodeficiency virus type 1 infection. Archives of Neurology, 53(2), 155–158.CrossRefPubMedGoogle Scholar
  33. Kingsley, L. A., Cuervo-Rojas, J., Munoz, A., Palella, F. J., Post, W., Witt, M. D., et al. (2008). Subclinical coronary atherosclerosis, HIV infection and antiretroviral therapy: multicenter AIDS cohort study. AIDS, 22(13), 1589–1599.CrossRefPubMedPubMedCentralGoogle Scholar
  34. Kuper, M., Rabe, K., Esser, S., Gizewski, E. R., Husstedt, I. W., Maschke, M., et al. (2011). Structural gray and white matter changes in patients with HIV. Journal of Neurology. doi: 10.1007/s00415-010-5883-y.PubMedGoogle Scholar
  35. Letendre, S., Marquie-Beck, J., Capparelli, E., Best, B., Clifford, D., Collier, A. C., et al. (2008). Validation of the CNS penetration-effectiveness rank for quantifying antiretroviral penetration into the central nervous system. Archives of Neurology, 65(1), 65–70, doi: 10.1001/archneurol.2007.31.
  36. Levine, A. J., Hardy, D. J., Barclay, T. R., Reinhard, M. J., Cole, M. M., & Hinkin, C. H. (2008). Elements of attention in HIV-infected adults: evaluation of an existing model. Journal of Clinical and Experimental Neuropsychology, 30(1), 53–62. doi: 10.1080/13803390601186684.CrossRefPubMedGoogle Scholar
  37. Lin, J. J., Salamon, N., Dutton, R. A., Lee, A. D., Geaga, J. A., Hayashi, K. M., et al. (2005). Three-dimensional preoperative maps of hippocampal atrophy predict surgical outcomes in temporal lobe epilepsy. Neurology, 65(7), 1094–1097. doi: 10.1212/01.wnl.0000179003.95838.71.CrossRefPubMedPubMedCentralGoogle Scholar
  38. Lindenberger, U., & Baltes, P. B. (1997). Intellectual functioning in old and very old age: cross-sectional results from the berlin aging study. Psychology and Aging, 12(3), 410–432.CrossRefPubMedGoogle Scholar
  39. MacDonald, S. W., Nyberg, L., Sandblom, J., Fischer, H., & Backman, L. (2008). Increased response-time variability is associated with reduced inferior parietal activation during episodic recognition in aging. [research support, Non-U.S. Gov’t]. Journal of Cognitive Neuroscience, 20(5), 779–786. doi: 10.1162/jocn.2008.20502.CrossRefPubMedGoogle Scholar
  40. MacDonald, S. W., Li, S. C., & Backman, L. (2009). Neural underpinnings of within-person variability in cognitive functioning. Psychology and Aging, 24(4), 792–808. doi: 10.1037/a0017798.CrossRefPubMedGoogle Scholar
  41. Marcotte, T. D., Wolfson, T., Rosenthal, T. J., Heaton, R. K., Gonzalez, R., Ellis, R. J., et al. (2004). A multimodal assessment of driving performance in HIV infection. Neurology, 63(8), 1417–1422.CrossRefPubMedGoogle Scholar
  42. Martin, M., & Hofer, S. M. (2004). Intraindividual variability, change, and aging: conceptual and analytical issues. Gerontology, 50(1), 7–11. doi: 10.1159/000074382.CrossRefPubMedGoogle Scholar
  43. Mazerolle, E. L., Wojtowicz, M. A., Omisade, A., & Fisk, J. D. (2013). Intra-individual variability in information processing speed reflects white matter microstructure in multiple sclerosis. NeuroImage. Clinical, 2, 894–902. doi: 10.1016/j.nicl.2013.06.012.CrossRefPubMedPubMedCentralGoogle Scholar
  44. Miller, E. N., Satz, P., & Visscher, B. (1991). Computerized and conventional neuropsychological assessment of HIV-1-infected homosexual men. [research support, U.S. Gov’t, P.H.S.]. Neurology, 41(10), 1608–1616.CrossRefPubMedGoogle Scholar
  45. Moore, D. J., Masliah, E., Rippeth, J. D., Gonzalez, R., Carey, C. L., Cherner, M., et al. (2006). Cortical and subcortical neurodegeneration is associated with HIV neurocognitive impairment. AIDS, 20(6), 879–887. doi: 10.1097/01.aids.0000218552.69834.00.CrossRefPubMedGoogle Scholar
  46. Morgan, E. E., Woods, S. P., Delano-Wood, L., Bondi, M. W., & Grant, I. (2011). Intraindividual variability in HIV infection: evidence for greater neurocognitive dispersion in older HIV seropositive adults. [research support, N.I.H., extramural]. Neuropsychology, 25(5), 645–654. doi: 10.1037/a0023792.CrossRefPubMedPubMedCentralGoogle Scholar
  47. Morgan, E. E., Woods, S. P., & Grant, I. (2012). Intra-individual neurocognitive variability confers risk of dependence in activities of daily living among HIV-seropositive individuals without HIV-associated neurocognitive disorders. [research support, N.I.H., extramural]. Archives of clinical neuropsychology : the official journal of the National Academy of Neuropsychologists, 27(3), 293–303. doi: 10.1093/arclin/acs003.CrossRefGoogle Scholar
  48. Murtha, S., Cismaru, R., Waechter, R., & Chertkow, H. (2002). Increased variability accompanies frontal lobe damage in dementia. Journal of the International Neuropsychological Society, 8(3), 360–372.CrossRefPubMedGoogle Scholar
  49. Navia, B. A., Cho, E. S., Petito, C. K., & Price, R. W. (1986). The AIDS dementia complex: II. Neuropathology. Annals of Neurology, 19(6), 525–535. doi: 10.1002/ana.410190603.CrossRefPubMedGoogle Scholar
  50. Osowiecki, D. M., Cohen, R. A., Morrow, K. M., Paul, R. H., Carpenter, C. C., Flanigan, T., et al. (2000). Neurocognitive and psychological contributions to quality of life in HIV-1-infected women. AIDS, 14(10), 1327–1332.CrossRefPubMedGoogle Scholar
  51. Posner, M. I., & Petersen, S. E. (1990). The attention system of the human brain. Annual Review of Neuroscience, 13, 25–42. doi: 10.1146/ Scholar
  52. Radloff, L. S. (1977). The CES-D scale. A self-report depression scale for research in the general population. Applied Psychological Measurement, 1(3), 385–401. doi: 10.1177/014662167700100306.CrossRefGoogle Scholar
  53. Rapp, M. A., Schnaider-Beeri, M., Sano, M., Silverman, J. M., & Haroutunian, V. (2005). Cross-domain variability of cognitive performance in very old nursing home residents and community dwellers: relationship to functional status. Gerontology, 51(3), 206–212. doi: 10.1159/000083995.CrossRefPubMedGoogle Scholar
  54. Ratcliff, R. (1979). Group reaction time distributions and an analysis of distribution statistics. Psychological Bulletin, 86(3), 446–461.CrossRefPubMedGoogle Scholar
  55. Sacktor, N., Lyles, R. H., Skolasky, R. L., Anderson, D. E., McArthur, J. C., McFarlane, G., et al. (1999). Combination antiretroviral therapy improves psychomotor speed performance in HIV-seropositive homosexual men. Multicenter AIDS cohort study (MACS). [clinical trial, multicenter study, research support, U.S. Gov’t, P.H.S.]. Neurology, 52(8), 1640–1647.CrossRefPubMedGoogle Scholar
  56. Sacktor, N., Tarwater, P. M., Skolasky, R. L., McArthur, J. C., Selnes, O. A., Becker, J., et al. (2001). CSF antiretroviral drug penetrance and the treatment of HIV-associated psychomotor slowing. [research support, U.S. Gov’t, P.H.S.]. Neurology, 57(3), 542–544.CrossRefPubMedGoogle Scholar
  57. Schmiedek, F., Lovden, M., & Lindenberger, U. (2009). On the relation of mean reaction time and intraindividual reaction time variability. Psychology and Aging, 24(4), 841–857. doi: 10.1037/a0017799.CrossRefPubMedGoogle Scholar
  58. Schretlen, D. J., Munro, C. A., Anthony, J. C., & Pearlson, G. D. (2003). Examining the range of normal intraindividual variability in neuropsychological test performance. Journal of the International Neuropsychological Society, 9(6), 864–870. doi: 10.1017/S1355617703960061.CrossRefPubMedGoogle Scholar
  59. Sled, J. G., Zijdenbos, A. P., & Evans, A. C. (1998). A nonparametric method for automatic correction of intensity nonuniformity in MRI data. IEEE Transactions on Medical Imaging, 17(1), 87–97. doi: 10.1109/42.668698.CrossRefPubMedGoogle Scholar
  60. Sowell, E. R., Peterson, B. S., Thompson, P. M., Welcome, S. E., Henkenius, A. L., & Toga, A. W. (2003). Mapping cortical change across the human life span. Nature Neuroscience, 6(3), 309–315. doi: 10.1038/nn1008.CrossRefPubMedGoogle Scholar
  61. Stuss, D. T., Murphy, K. J., Binns, M. A., & Alexander, M. P. (2003). Staying on the job: the frontal lobes control individual performance variability. Brain, 126(Pt 11), 2363–2380. doi: 10.1093/brain/awg237.CrossRefPubMedGoogle Scholar
  62. Thaler, N. S., Sayegh, P., Arentoft, A., Thames, A. D., Castellon, S. A., & Hinkin, C. H. (2015). Increased neurocognitive intra-individual variability is associated with declines in medication adherence in HIV-infected adults. Neuropsychology. doi: 10.1037/neu0000191.Google Scholar
  63. Thompson, P. M., Schwartz, C., & Toga, A. W. (1996). High-resolution random mesh algorithms for creating a probabilistic 3D surface atlas of the human brain. NeuroImage, 3(1), 19–34. doi: 10.1006/nimg.1996.0003.CrossRefPubMedGoogle Scholar
  64. Thompson, P. M., Dutton, R. A., Hayashi, K. M., Toga, A. W., Lopez, O. L., Aizenstein, H. J., et al. (2005). Thinning of the cerebral cortex visualized in HIV/AIDS reflects CD4+ T lymphocyte decline. Proceedings of the National Academy of Sciences of the United States of America, 102(43), 15647–15652. doi: 10.1073/pnas.0502548102.CrossRefPubMedPubMedCentralGoogle Scholar
  65. Wechsler, D. (1987). WMS-R : Wechsler Memory Scale–Revised : manual. San Antonio: Psychological Corp. : Harcourt Brace JovanovichGoogle Scholar
  66. Wechsler, D. (1997). Wechsler Adult Intelligence Scale—3rd Edition (WAIS-3®). San Antonio, TX:Harcourt Assessment.Google Scholar
  67. West, R., Murphy, K. J., Armilio, M. L., Craik, F. I., & Stuss, D. T. (2002). Lapses of intention and performance variability reveal age-related increases in fluctuations of executive control. Brain and Cognition, 49(3), 402–419.CrossRefPubMedGoogle Scholar
  68. Wetter, S. R., Delis, D. C., Houston, W. S., Jacobson, M. W., Lansing, A., Cobell, K., et al. (2006). Heterogeneity in verbal memory: a marker of preclinical alzheimer’s disease? Neuropsychology, development, and cognition. Section B, Aging, neuropsychology and cognition, 13(3–4), 503–515. doi: 10.1080/138255890969492.CrossRefGoogle Scholar
  69. Woods, S. P., Morgan, E. E., Dawson, M., Cobb Scott, J., Grant, I., & Group, H. I. V. N. R. C. (2006). Action (verb) fluency predicts dependence in instrumental activities of daily living in persons infected with HIV-1. Journal of Clinical and Experimental Neuropsychology, 28(6), 1030–1042. doi: 10.1080/13803390500350985.CrossRefGoogle Scholar
  70. Zamrini, E., Maestu, F., Pekkonen, E., Funke, M., Makela, J., Riley, M., et al. (2011). Magnetoencephalography as a putative biomarker for alzheimer’s disease. International journal of Alzheimer’s disease, 2011, 280289. doi: 10.4061/2011/280289.PubMedPubMedCentralGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  • Lindsay J. Hines
    • 1
    • 3
    • 4
  • Eric N. Miller
    • 1
  • Charles H. Hinkin
    • 1
  • Jeffery R. Alger
    • 2
  • Peter Barker
    • 5
  • Karl Goodkin
    • 7
  • Eileen M. Martin
    • 8
  • Victoria Maruca
    • 9
  • Ann Ragin
    • 10
  • Ned Sacktor
    • 6
  • Joanne Sanders
    • 11
  • Ola Selnes
    • 11
  • James T. Becker
    • 12
    • 13
    • 14
  • for the Multicenter AIDS Cohort Study
  1. 1.Semel Institute for NeurosciencesUniversity of California Los AngelesLos AngelesUSA
  2. 2.The Department of NeurologyUniversity of California Los AngelesLos AngelesUSA
  3. 3.Sanford Brain and Spine CenterSanford HealthFargoUSA
  4. 4.Department of PsychologyUniversity of North DakotaFargoUSA
  5. 5.Department of RadiologyThe Johns Hopkins University School of MedicineBaltimoreUSA
  6. 6.Department of NeurologyThe Johns Hopkins University School of MedicineBaltimoreUSA
  7. 7.Department of Psychiatry and Behavioral SciencesEast Tennessee State UniversityJohnson CityUSA
  8. 8.Department of PsychiatryRush UniversityChicagoUSA
  9. 9.Department of PsychologySpalding UniversityLouisvilleUSA
  10. 10.Department of Neurology, Feinberg School of MedicineNorthwestern UniversityEvanstonUSA
  11. 11.Department of Epidemiology, Bloomberg School of Public HealthThe Johns Hopkins UniversityBaltimoreUSA
  12. 12.Department of PsychiatryUniversity of Pittsburgh School of MedicinePittsburghUSA
  13. 13.Department of NeurologyUniversity of Pittsburgh School of MedicinePittsburghUSA
  14. 14.Department of PsychologyUniversity of Pittsburgh School of MedicinePittsburghUSA

Personalised recommendations