Hostname: page-component-8448b6f56d-t5pn6 Total loading time: 0 Render date: 2024-04-23T12:05:43.666Z Has data issue: false hasContentIssue false
  • English
  • Français

Associations between Verbal Learning Slope and Neuroimaging Markers across the Cognitive Aging Spectrum

Published online by Cambridge University Press:  29 July 2015

Katherine A. Gifford ,
Jeffrey S. Phillips ,
Lauren R. Samuels ,
Elizabeth M. Lane ,
Susan P. Bell ,
Dandan Liu ,
Timothy J. Hohman ,
Raymond R. Romano III ,
Laura R. Fritzsche  and
Zengqi Lu
...Show all authors
Katherine A. Gifford
Affiliation:
Vanderbilt Memory & Alzheimer’s Center, Department of Neurology, Vanderbilt University Medical Center, Nashville, Tennessee
Jeffrey S. Phillips
Affiliation:
Vanderbilt Memory & Alzheimer’s Center, Department of Neurology, Vanderbilt University Medical Center, Nashville, Tennessee
Lauren R. Samuels
Affiliation:
Vanderbilt Memory & Alzheimer’s Center, Department of Neurology, Vanderbilt University Medical Center, Nashville, Tennessee Department of Biostatistics, Vanderbilt University Medical Center, Nashville, Tennessee
Elizabeth M. Lane
Affiliation:
Vanderbilt Memory & Alzheimer’s Center, Department of Neurology, Vanderbilt University Medical Center, Nashville, Tennessee
Susan P. Bell
Affiliation:
Vanderbilt Memory & Alzheimer’s Center, Department of Neurology, Vanderbilt University Medical Center, Nashville, Tennessee Division of Cardiovascular Medicine, Vanderbilt University Medical Center, Nashville, Tennessee
Dandan Liu
Affiliation:
Vanderbilt Memory & Alzheimer’s Center, Department of Neurology, Vanderbilt University Medical Center, Nashville, Tennessee Department of Biostatistics, Vanderbilt University Medical Center, Nashville, Tennessee
Timothy J. Hohman
Affiliation:
Vanderbilt Memory & Alzheimer’s Center, Department of Neurology, Vanderbilt University Medical Center, Nashville, Tennessee
Raymond R. Romano III
Affiliation:
Vanderbilt Memory & Alzheimer’s Center, Department of Neurology, Vanderbilt University Medical Center, Nashville, Tennessee
Laura R. Fritzsche
Affiliation:
Vanderbilt Memory & Alzheimer’s Center, Department of Neurology, Vanderbilt University Medical Center, Nashville, Tennessee
Zengqi Lu
Affiliation:
Department of Biostatistics, Vanderbilt University Medical Center, Nashville, Tennessee
Angela L. Jefferson*
Affiliation:
Vanderbilt Memory & Alzheimer’s Center, Department of Neurology, Vanderbilt University Medical Center, Nashville, Tennessee
*
Correspondence and reprint requests to: Angela L. Jefferson, Vanderbilt Memory & Alzheimer’s Center, Department of Neurology, Vanderbilt University Medical Center, 2525 West End Avenue, 12th Floor - Suite 1200, Nashville, TN 37203. E-mail: angela.jefferson@vanderbilt.edu
Get access Rights & Permissions [Opens in a new window]

Abstract

A symptom of mild cognitive impairment (MCI) and Alzheimer’s disease (AD) is a flat learning profile. Learning slope calculation methods vary, and the optimal method for capturing neuroanatomical changes associated with MCI and early AD pathology is unclear. This study cross-sectionally compared four different learning slope measures from the Rey Auditory Verbal Learning Test (simple slope, regression-based slope, two-slope method, peak slope) to structural neuroimaging markers of early AD neurodegeneration (hippocampal volume, cortical thickness in parahippocampal gyrus, precuneus, and lateral prefrontal cortex) across the cognitive aging spectrum [normal control (NC); (n=198; age=76±5), MCI (n=370; age=75±7), and AD (n=171; age=76±7)] in ADNI. Within diagnostic group, general linear models related slope methods individually to neuroimaging variables, adjusting for age, sex, education, and APOE4 status. Among MCI, better learning performance on simple slope, regression-based slope, and late slope (Trial 2–5) from the two-slope method related to larger parahippocampal thickness (all p-values<.01) and hippocampal volume (p<.01). Better regression-based slope (p<.01) and late slope (p<.01) were related to larger ventrolateral prefrontal cortex in MCI. No significant associations emerged between any slope and neuroimaging variables for NC (p-values ≥.05) or AD (p-values ≥.02). Better learning performances related to larger medial temporal lobe (i.e., hippocampal volume, parahippocampal gyrus thickness) and ventrolateral prefrontal cortex in MCI only. Regression-based and late slope were most highly correlated with neuroimaging markers and explained more variance above and beyond other common memory indices, such as total learning. Simple slope may offer an acceptable alternative given its ease of calculation. (JINS, 2015, 21, 455–467)

Keywords

Alzheimer’s diseaseMild cognitive impairmentStructural imagingepisodic memoryVerbal learningPrefrontal cortexSimple slopeHippocampus
Type
Research Articles
Information
Journal of the International Neuropsychological Society , Volume 21 , Issue 6 , July 2015 , pp. 455 - 467
Copyright
Copyright © The International Neuropsychological Society 2015 

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.)

Footnotes

*

Data used in preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (adni.loni.usc.edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators can be found at: http://adni.loni.usc.edu/wp-content/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf.

References

Albert, M.S., Moss, M., Tanzi, R., & Jones, K. (2001). Preclinical prediction of AD using neuropsychological tests. [Research Support, U.S. Gov’t, P.H.S.]. Journal of the International Neuropsychological Society, 7(5), 631–639.Google Scholar
Apostolova, L.G., Dinov, I.D., Dutton, R.A., Hayashi, K.M., Toga, A.W., Cummings, J.L., & Thompson, P.M. (2006). 3D comparison of hippocampal atrophy in amnestic mild cognitive impairment and Alzheimer’s disease. [Comparative Study Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov’t Research Support, U.S. Gov’t, Non-P.H.S.]. Brain, 129(Pt 11), 2867–2873. doi: 10.1093/brain/awl274 CrossRefGoogle ScholarPubMed
Badre, D., & Wagner, A.D. (2007). Left ventrolateral prefrontal cortex and the cognitive control of memory. [Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov’t Research Support, U.S. Gov’t, Non-P.H.S. Review]. Neuropsychologia, 45(13), 2883–2901. doi: 10.1016/j.neuropsychologia.2007.06.015 CrossRefGoogle ScholarPubMed
Baldo, J.V., Delis, D., Kramer, J., & Shimamura, A.P. (2002). Memory performance on the California Verbal Learning Test-II: Findings from patients with focal frontal lesions. [Research Support, Non-U.S. Gov’t Research Support, U.S. Gov’t, P.H.S.]. Journal of the International Neuropsychological Society, 8(4), 539–546.Google Scholar
Blumenfeld, R.S., Parks, C.M., Yonelinas, A.P., & Ranganath, C. (2011). Putting the pieces together: The role of dorsolateral prefrontal cortex in relational memory encoding. [Research Support, N.I.H., Extramural]. Journal of Cognitive Neuroscience, 23(1), 257–265. doi: 10.1162/jocn.2010.21459 Google Scholar
Bondi, M.W., Monsch, A.U., Galasko, D., Butters, N., Salmon, D.P., & Delis, D.C. (1994). Preclinical cognitive markers of dementia of the Alzheimer type. Neuropsychology, 8(3), 374–384.CrossRefGoogle Scholar
Buckner, R.L., Head, D., Parker, J., Fotenos, A.F., Marcus, D., Morris, J.C., & Snyder, A.Z. (2004). A unified approach for morphometric and functional data analysis in young, old, and demented adults using automated atlas-based head size normalization: Reliability and validation against manual measurement of total intracranial volume. [Research Support, Non-U.S. Gov’t Research Support, U.S. Gov’t, P.H.S.]. Neuroimage, 23(2), 724–738. doi: 10.1016/j.neuroimage.2004.06.018 CrossRefGoogle ScholarPubMed
Chang, Y.L., Bondi, M.W., Fennema-Notestine, C., McEvoy, L.K., Hagler, D.J. Jr., & Jacobson, M.W., … Alzheimer’s Disease Neuroimaging, I. (2010). Brain substrates of learning and retention in mild cognitive impairment diagnosis and progression to Alzheimer’s disease. [Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov’t Research Support, U.S. Gov’t, Non-P.H.S.]. Neuropsychologia, 48(5), 1237–1247. doi: 10.1016/j.neuropsychologia.2009.12.024 Google Scholar
Chao, L.L., & Knight, R.T. (1995). Human prefrontal lesions increase distractibility to irrelevant sensory inputs. [Research Support, U.S. Gov’t, Non-P.H.S. Research Support, U.S. Gov’t, P.H.S.]. Neuroreport, 6(12), 1605–1610.Google Scholar
Cosgrove, K.P., Mazure, C.M., & Staley, J.K. (2007). Evolving knowledge of sex differences in brain structure, function, and chemistry. [Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov’t Review]. Biological Psychiatry, 62(8), 847–855. doi: 10.1016/j.biopsych.2007.03.001 Google Scholar
D’Esposito, M., Postle, B.R., Ballard, D., & Lease, J. (1999). Maintenance versus manipulation of information held in working memory: An event-related fMRI study. [Research Support, Non-U.S. Gov’t Research Support, U.S. Gov’t, P.H.S.]. Brain and Cognition, 41(1), 66–86. doi: 10.1006/brcg.1999.1096 Google Scholar
Dale, A.M., Fischl, B., & Sereno, M.I. (1999). Cortical surface-based analysis: I. Segmentation and surface reconstruction. Neuroimage, 9(2), 179–194. doi: S1053-8119(98)90395-0 [pii] 10.1006/nimg.1998.0395.Google Scholar
Delis, D.C., Kramer, J.H., Kaplan, E., & Ober, B.A. (2000). California Verbal Learning Test (2nd ed.)., San Antonio, TX: Psychological Corporation.Google Scholar
Desikan, R.S., Segonne, F., Fischl, B., Quinn, B.T., Dickerson, B.C., Blacker, D., Killiany, R.J. (2006). An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. Neuroimage, 31(3), 968–980. doi: S1053-8119(06)00043-7 [pii] 10.1016/j.neuroimage.2006.01.021CrossRefGoogle ScholarPubMed
Destrieux, C., Fischl, B., Dale, A., & Halgren, E. (2009). A sulcal depth-based anatomical parcellation of the cerebral cortex. Neuroimage, 47(S1), S151.Google Scholar
Fischl, B., & Dale, A.M. (2000). Measuring the thickness of the human cerebral cortex from magnetic resonance images. Proceedings of the National Academy of Sciences of the United States of America, 97(20), 11050–11055. doi: 10.1073/pnas.200033797 Google Scholar
Fischl, B., Salat, D.H., Busa, E., Albert, M., Dieterich, M., Haselgrove, C., Dale, A.M. (2002). Whole brain segmentation: Automated labeling of neuroanatomical structures in the human brain. Neuron, 33(3), 341–355. doi: S089662730200569X [pii].Google Scholar
Fischl, B., Sereno, M.I., & Dale, A.M. (1999). Cortical surface-based analysis. II: Inflation, flattening, and a surface-based coordinate system. Neuroimage, 9(2), 195–207. doi: S1053-8119(98)90396-2 [pii] 10.1006/nimg.1998.0396.Google Scholar
Fischl, B., Sereno, M.I., Tootell, R.B., & Dale, A.M. (1999). High-resolution intersubject averaging and a coordinate system for the cortical surface. Human Brain Mapping, 8(4), 272–284. doi: 10.1002/(SICI)1097-0193(1999)8:4<272::AID-HBM10>3.0.CO;2-4 [pii].Google Scholar
Flory, J.D., Manuck, S.B., Ferrell, R.E., Ryan, C.M., & Muldoon, M.F. (2000). Memory performance and the apolipoprotein E polymorphism in a community sample of middle-aged adults. American Journal of Medical Genetics, 96(6), 707–711.Google 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 the clinician. Journal of Psychiatric Research, 12(3), 189–198.Google Scholar
Golomb, J., de Leon, M.J., Kluger, A., George, A.E., Tarshish, C., & Ferris, S.H. (1993). Hippocampal atrophy in normal aging. An association with recent memory impairment. [Research Support, Non-U.S. Gov’t Research Support, U.S. Gov’t, P.H.S.]. Archives of Neurology, 50(9), 967–973.Google Scholar
Hebert, L.E., Scherr, P.A., Bienias, J.L., Bennett, D.A., & Evans, D.A. (2003). Alzheimer disease in the US population: prevalence estimates using the 2000 census. Archives of Neurology, 60(8), 1119–1122.Google Scholar
Herlitz, A., Nilsson, L.G., & Backman, L. (1997). Gender differences in episodic memory. [Research Support, Non-U.S. Gov’t]. Memory & Cognition, 25(6), 801–811.Google Scholar
Jedynak, B.M., Lang, A., Liu, B., Katz, E., Zhang, Y., Wyman, B.T., &Prince, J.L. (2012). A computational neurodegenerative disease progression score: Method and results with the Alzheimer’s disease Neuroimaging Initiative cohort. [Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov’t]. Neuroimage, 63(3), 1478–1486. doi: 10.1016/j.neuroimage.2012.07.059 Google Scholar
Jones, R.N., Rosenberg, A.L., Morris, J.N., Allaire, J.C., McCoy, K.J., Marsiske, M., & Malloy, P.F. (2005). A growth curve model of learning acquisition among cognitively normal older adults. [Clinical Conference Comparative Study Research Support, N.I.H., Extramural Research Support, U.S. Gov’t, P.H.S.]. Experimental Aging Research, 31(3), 291–312. doi: 10.1080/03610730590948195 Google Scholar
Kizilbash, A.H., Vanderploeg, R.D., & Curtiss, G. (2002). The effects of depression and anxiety on memory performance. Archives of Clinical Neuropsychology, 17(1), 57–67.Google Scholar
Leube, D.T., Weis, S., Freymann, K., Erb, M., Jessen, F., Heun, R., &Kircher, T.T. (2008). Neural correlates of verbal episodic memory in patients with MCI and Alzheimer’s disease--A VBM study. International Journal of Geriatric Psychiatry, 23(11), 1114–1118. doi: 10.1002/gps.2036 Google Scholar
McKhann, G., Drachman, D., Folstein, M., Katzman, R., Price, D., & Stadlan, E.M. (1984). Clinical diagnosis of Alzheimer’s disease: Report of the NINCDS-ADRDA Work Group under the auspices of Department of Health and Human Services Task Force on Alzheimer’s Disease. Neurology, 34(7), 939–944.CrossRefGoogle ScholarPubMed
McMinn, M., Wiens, A., & Crossen, J. (1988). Rey auditory-verbal learning test: Development of norms for healthy young adults. Clinical Neuropsychologist, 2(1), 67–87. doi: 10.1080/13854048808520087 Google Scholar
Mormino, E.C., Kluth, J.T., Madison, C.M., Rabinovici, G.D., Baker, S.L., Miller, B.L., &Jagust, W.J. (2009). Episodic memory loss is related to hippocampal-mediated beta-amyloid deposition in elderly subjects. [Comparative Study Research Support, N.I.H., Extramural]. Brain, 132(Pt 5), 1310–1323. doi: 10.1093/brain/awn320 Google Scholar
Morris, J.C. (1993). The Clinical Dementia Rating (CDR): Current version and scoring rules. Neurology, 43(11), 2412–2414.Google Scholar
O’Dwyer, L., Lamberton, F., Matura, S., Tanner, C., Scheibe, M., Miller, J., &Hampel, H. (2012). Reduced hippocampal volume in healthy young ApoE4 carriers: An MRI study. [Research Support, Non-U.S. Gov’t]. PLoS One, 7(11), e48895 doi: 10.1371/journal.pone.0048895 Google Scholar
Park, H., & Rugg, M.D. (2008). Neural correlates of successful encoding of semantically and phonologically mediated inter-item associations. [Research Support, N.I.H., Extramural]. Neuroimage, 43(1), 165–172. doi: 10.1016/j.neuroimage.2008.06.044 Google Scholar
Petersen, R.C., Aisen, P.S., Beckett, L.A., Donohue, M.C., Gamst, A.C., Harvey, D.J., &Weiner, M.W. (2010). Alzheimer’s Disease Neuroimaging Initiative (ADNI): Clinical characterization. Neurology, 74(3), 201–209. doi: WNL.0b013e3181cb3e25 [pii] 10.1212/WNL.0b013e3181cb3e25Google Scholar
Petersen, R.C., Jack, C.R. Jr., Xu, Y.C., Waring, S.C., O’Brien, P.C., Smith, G.E., &Kokmen, E. (2000). Memory and MRI-based hippocampal volumes in aging and AD. [Research Support, Non-U.S. Gov’t Research Support, U.S. Gov’t, P.H.S.]. Neurology, 54(3), 581–587.Google Scholar
Rey, A. (1964). L’examen clinique en psychologie. Paris: Presses Universitaires de France.Google Scholar
Rodgers, J., & Nicewander, A. (1988). Thirteen ways to look at the correlation coefficient. The American Statistician, 42(1), 59–66. doi: 10.2307/2685263 CrossRefGoogle Scholar
Rosas, H.D., Liu, A.K., Hersch, S., Glessner, M., Ferrante, R.J., Salat, D.H., &Fischl, B. (2002). Regional and progressive thinning of the cortical ribbon in Huntington’s disease. Neurology, 58(5), 695–701.CrossRefGoogle ScholarPubMed
Salat, D.H., Buckner, R.L., Snyder, A.Z., Greve, D.N., Desikan, R.S., Busa, E., &Fischl, B. (2004). Thinning of the cerebral cortex in aging. Cerebral Cortex, 14(7), 721–730. doi: 10.1093/cercor/bhh032 bhh032 [pii]Google Scholar
Salthouse, T.A. (1996). The processing-speed theory of adult age differences in cognition. Psychological Review, 103(3), 403–428.Google Scholar
Scheltens, P., Leys, D., Barkhof, F., Huglo, D., Weinstein, H.C., Vermersch, P., &Valk, J. (1992). Atrophy of medial temporal lobes on MRI in “probable” Alzheimer’s disease and normal ageing: Diagnostic value and neuropsychological correlates. [Research Support, Non-U.S. Gov’t]. Journal of Neurology, Neurosurgery, and Psychiatry, 55(10), 967–972.Google Scholar
Stern, Y. (2002). What is cognitive reserve? Theory and research application of the reserve concept. Journal of the International Neuropsychological Society, 8(3), 448–460.Google Scholar
Stout, J.C., Bondi, M.W., Jernigan, T.L., Archibald, S.L., Delis, D.C., & Salmon, D.P. (1999). Regional cerebral volume loss associated with verbal learning and memory in dementia of the Alzheimer type. [Clinical Trial Research Support, U.S. Gov’t, Non-P.H.S. Research Support, U.S. Gov’t, P.H.S.]. Neuropsychology, 13(2), 188–197.Google Scholar
Tierney, M., Nores, A., Snow, W., Fisher, R., Zorzitto, M., & Reid, D. (1994). Use of the Rey Auditory Verbal Learning Test in differentiating normal aging from Alzheimer’s and Parkinson’s dementia. Psychological Assessment, 6(2), 129–134. doi: 10.1037/1040-3590.6.2.129 Google Scholar
Tierney, M., Yao, C., Kiss, A., & McDowell, I. (2005). Neuropsychological tests accurately predict incident Alzheimer disease after 5 and 10 years. Neurology, 64(11), 1853–1859. doi: 10.1212/01.WNL.0000163773.21794.0B Google Scholar
Tulving, E. (1964). Intratrial and intertrial retention: Notes towards a theory of free recall verbal learning. Psychology Review, 71, 219–237.Google Scholar
Weiner, M.W., Aisen, P.S., Jack, C.R. Jr., Jagust, W.J., Trojanowski, J.Q., Shaw, L., &Schmidt, M. (2010). The Alzheimer’s disease neuroimaging initiative: Progress report and future plans. Alzheimer’s & Dementia, 6(3), 202–211. e207. doi: S1552-5260(10)00067-1 [pii] 10.1016/j.jalz.2010.03.007CrossRefGoogle Scholar