Hostname: page-component-cd9895bd7-gvvz8 Total loading time: 0 Render date: 2024-12-27T14:17:59.409Z Has data issue: false hasContentIssue false
  • English
  • Français

Diffusion Tensor Imaging Predictors of Episodic Memory Decline in Healthy Elders at Genetic Risk for Alzheimer’s Disease

Published online by Cambridge University Press:  01 December 2016

Melissa A. Lancaster ,
Michael Seidenberg ,
J. Carson Smith ,
Kristy A. Nielson ,
John L. Woodard ,
Sally Durgerian  and
Stephen M. Rao Open the ORCID record for Stephen M. Rao [Opens in a new window]
Melissa A. Lancaster
Affiliation:
Department of Psychology, Rosalind Franklin University of Medicine and Science, North Chicago, Illinois
Michael Seidenberg
Affiliation:
Department of Psychology, Rosalind Franklin University of Medicine and Science, North Chicago, Illinois
J. Carson Smith
Affiliation:
Department of Kinesiology, University of Maryland, College Park, Maryland
Kristy A. Nielson
Affiliation:
Department of Psychology, Marquette University, Milwaukee, Wisconsin Department of Neurology, Medical College of Wisconsin, Milwaukee, Wisconsin
John L. Woodard
Affiliation:
Department of Psychology, Wayne State University, Detroit, Wisconsin
Sally Durgerian
Affiliation:
Department of Neurology, Medical College of Wisconsin, Milwaukee, Wisconsin
Stephen M. Rao*
Affiliation:
Neurological Institute, Cleveland Clinic, Cleveland, Ohio
*
Correspondence and reprint requests to: Stephen M. Rao, Schey Center for Cognitive Neuroimaging, Lou Ruvo Center for Brain Health, Neurological Institute, Cleveland Clinic, 9500 Euclid Avenue / U10, Cleveland, OH 44195. E-mail: raos2@ccf.org
Get access Rights & Permissions [Opens in a new window]

Abstract

Objectives: White matter (WM) integrity within the mesial temporal lobe (MTL) is important for episodic memory (EM) functioning. The current study investigated the ability of diffusion tensor imaging (DTI) in MTL WM tracts to predict 3-year changes in EM performance in healthy elders at disproportionately higher genetic risk for Alzheimer’s disease (AD). Methods: Fifty-one cognitively intact elders (52% with family history (FH) of dementia and 33% possessing an Apolipoprotein E ε4 allelle) were administered the Rey Auditory Verbal Learning Test (RAVLT) at study entry and at 3-year follow-up. DTI scanning, conducted at study entry, examined fractional anisotropy and mean, radial and axial diffusion within three MTL WM tracts: uncinate fasciculus (UNC), cingulate-hippocampal (CHG), and fornix-stria terminalis (FxS). Correlations were performed between residualized change scores computed from RAVLT trials 1–5, immediate recall, and delayed recall scores and baseline DTI measures; MTL gray matter (GM) and WM volumes; demographics; and AD genetic and metabolic risk factors. Results: Higher MTL mean and axial diffusivity at baseline significantly predicted 3-year changes in EM, whereas baseline MTL GM and WM volumes, FH, and metabolic risk factors did not. Both ε4 status and DTI correlated with change in immediate recall. Conclusions: Longitudinal EM changes in cognitively intact, healthy elders can be predicted by disruption of the MTL WM microstructure. These results are derived from a sample with a disproportionately higher genetic risk for AD, suggesting that the observed WM disruption in MTL pathways may be related to early neuropathological changes associated with the preclinical stage of AD. (JINS, 2016, 22, 1005–1015)

Keywords

Diffusion tensor imagingAlzheimer’s diseaseEpisodic memoryAnatomical MRIApolipoprotein EMesial temporal lobe
Type
Research Articles
Information
Journal of the International Neuropsychological Society , Volume 22 , Special Issue 10: Special Issue: Preclinical Prediction , November 2016 , pp. 1005 - 1015
Copyright
Copyright © The International Neuropsychological Society 2016 

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

Acosta-Cabronero, J., Williams, G.B., Pengas, G., & Nestor, P.J. (2010). Absolute diffusivities define the landscape of white matter degeneration in Alzheimer’s disease. Brain, 133, 529–539.CrossRefGoogle ScholarPubMed
Agosta, F., Pievani, M., Sala, S., Geroldi, C., Galluzzi, S., Frisoni, G.B., & Filippi, M. (2011). White matter damage in Alzheimer disease and its relationship to gray matter atrophy. Radiology, 258(3), 853–863. doi: 10.1148/radiol.10101284 Google Scholar
Albert, M.S., Moss, M.B., Tanzi, R., & Jones, K. (2001). Preclinical prediction of AD using neuropsychological tests. Journal of the International Neuropsychological Society, 7(5), 631–639.Google Scholar
Alves, G.S., O’Dwyer, L., Jurcoane, A., Oertel-Knochel, V., Knochel, C., Prvulovic, D., & Laks, J. (2012). Different patterns of white matter degeneration using multiple diffusion indices and volumetric data in mild cognitive impairment and Alzheimer patients. PLoS One, 7(12), e52859. doi: 10.1371/journal.pone.0052859 CrossRefGoogle ScholarPubMed
Backman, L., Small, B.J., & Fratiglioni, L. (2001). Stability of the preclinical episodic memory deficit in Alzheimer’s disease. Brain, 124(Pt 1), 96–102.CrossRefGoogle ScholarPubMed
Balthazar, M.L., Yasuda, C.L., Cendes, F., & Damasceno, B.P. (2010). Learning, retrieval, and recognition are compromised in aMCI and mild AD: Are distinct episodic memory processes mediated by the same anatomical structures? Journal of the International Neuropsychological Society, 16(1), 205–209. doi: 10.1017/s1355617709990956 CrossRefGoogle ScholarPubMed
Bartzokis, G., Sultzer, D., Lu, P.H., Nuechterlein, K.H., Mintz, J., & Cummings, J.L. (2004). Heterogeneous age-related breakdown of white matter structural integrity: Implications for cortical “disconnection” in aging and Alzheimer’s disease. Neurobiology of Aging, 25, 843–851.Google Scholar
Behrens, M.W., Woolrich, M., Jenkinson, H., Johansen-Berg, R.G., Nunes, S., & Clare, P.M. (2003). Characterization and propagation of uncertainty in diffusion-weighted MR imaging. Magnetic Resonance in Medicine, 50, 1077–1088.Google Scholar
Benjamini, Y., & Hochberg, Y. (1995). Controlling the false discovery rate: A practical and powerful approach to multiple testing. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 57, 289–300.Google Scholar
Bennett, I.J., & Madden, D.J. (2014). Disconnected aging: Cerebral white matter integrity and age-related differences in cognition. Neuroscience, 276, 187–205. doi: 10.1016/j.neuroscience.2013.11.026 CrossRefGoogle ScholarPubMed
Blacker, D., Lee, H., Muzikansky, A., Martin, E.C., Tanzi, R., McArdle, J.J., & Albert, M. (2007). Neuropsychological measures in normal individuals that predict subsequent cognitive decline. Archives of Neurology, 64(6), 862–871. doi: 10.1001/archneur.64.6.862 Google Scholar
Bondi, M.W., Salmon, D.P., Galasko, D., Thomas, R.G., & Thal, L.J. (1999). Neuropsychological function and apolipoprotein E genotype in the preclinical detection of Alzheimer’s disease. Psychology and Aging, 14(2), 295–303.CrossRefGoogle ScholarPubMed
Canu, E., McLaren, D.G., Fitzgerald, M.E., Bendlin, B.B., Zoccatelli, G., Alessandrini, F., & Frisoni, G.B. (2010). Microstructural diffusion changes are independent of macrostructural volume loss in moderate to severe Alzheimer’s disease. Journal of Alzheimer’s Disease, 19(3), 963–976. doi: 10.3233/jad-2010-1295 CrossRefGoogle ScholarPubMed
Cardenas, V.A., Chao, L.L., Studholme, C., Yaffe, K., Miller, B.L., Madison, C., & Weiner, M.W. (2011). Brain atrophy associated with baseline and longitudinal measures of cognition. Neurobiology of Aging, 32(4), 572–580. doi: 10.1016/j.neurobiolaging.2009.04.011 Google Scholar
Caselli, R.J., Reiman, E.M., Osborne, D., Hentz, J.G., Baxter, L.C., Hernandez, J.L., & Alexander, G.G. (2004). Longitudinal changes in cognition and behavior in asymptomatic carriers of the APOE e4 allele. Neurology, 62(11), 1990–1995.Google Scholar
Christidi, F., Zalonis, I., Kyriazi, S., Rentzos, M., Karavasilis, E., Wilde, E.A., & Evdokimidis, I. (2014). Uncinate fasciculus microstructure and verbal episodic memory in amyotrophic lateral sclerosis: A diffusion tensor imaging and neuropsychological study. Brain Imaging and Behavior, 8(4), 497–505. doi: 10.1007/s11682-013-9271-y Google Scholar
Cox, R. (1996). AFNI: Sotware for analysis and visualization of functional magnetic resonance images. Computers and Biomedical Research, 29, 162–173.Google Scholar
den Heijer, T., Geerlings, M.I., Hoebeek, F.E., Hofman, A., Koudstaal, P.J., & Breteler, M.M. (2006). Use of hippocampal and amygdalar volumes on magnetic resonance imaging to predict dementia in cognitively intact elderly people. Archives of General Psychiatry, 63(1), 57–62. doi: 10.1001/archpsyc.63.1.57 Google Scholar
Dickerson, B.C., & Eichenbaum, H. (2010). The episodic memory system: Neurocircuitry and disorders. Neuropsychopharmacology, 35(1), 86–104. doi: 10.1038/npp.2009.126 CrossRefGoogle ScholarPubMed
Edmonds, E.C., Delano-Wood, L., Galasko, D.R., Salmon, D.P., & Bondi, M.W. (2015). Subtle cognitive decline and biomarker staging in preclinical Alzheimer’s disease. Journal of Alzheimer’s Disease, 47(1), 231–242. doi: 10.3233/jad-150128 Google Scholar
Ellis, K.A., Lim, Y.Y., Harrington, K., Ames, D., Bush, A.I., Darby, D., … AIBL Research Group. (2013). Decline in cognitive function over 18 months in healthy older adults with high amyloid-beta. Journal of Alzheimer’s Disease, 34(4), 861–871. doi: 10.3233/jad-122170 CrossRefGoogle ScholarPubMed
Ezzati, A., Katz, M.J., Lipton, M.L., Zimmerman, M.E., & Lipton, R.B. (2016). Hippocampal volume and cingulum bundle fractional anisotropy are independently associated with verbal memory in older adults. Brain Imaging and Behavior, 10, 652–659. doi: 10.1007/s11682-015-9452-y Google Scholar
Fagan, A.M., Roe, C.M., Xiong, C., Mintun, M.A., Morris, J.C., & Holtzman, D.M. (2007). Cerebrospinal fluid tau/beta-amyloid(42) ratio as a prediction of cognitive decline in nondemented older adults. Archives of Neurology, 64(3), 343–349. doi: 10.1001/archneur.64.3.noc60123 CrossRefGoogle ScholarPubMed
Fischl, B., Salat, D.H., Busa, E., Albert, M., Dieterich, M., & Haselgrove, C. (2004). Whole brain segmentation: Automated labeling of neuroanatomical structures in the human brain. Neuron, 33, 341–355.CrossRefGoogle Scholar
Fletcher, E., Raman, M., Huebner, P., Liu, A., Mungas, D., Carmichael, O., & DeCarli, C. (2013). Loss of fornix white matter volume as a predictor of cognitive impairment in cognitively normal elderly individuals. JAMA Neurology, 70(11), 1389–1395. doi: 10.1001/jamaneurol.2013.3263 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 Psychiatry Research, 12, 189–198.Google Scholar
Fu, J.L., Liu, Y., Li, Y.M., Chang, C., & Li, W.B. (2014). Use of diffusion tensor imaging for evaluating changes in the microstructural integrity of white matter over 3 years in patients with amnesic-type mild cognitive impairment converting to Alzheimer’s disease. Journal of Neuroimaging, 24(4), 343–348. doi: 10.1111/jon.12061 CrossRefGoogle ScholarPubMed
Geschwind, N. (1965a). Disconnexion syndromes in animals and man. I. Brain, 88(2), 237–294.Google Scholar
Geschwind, N. (1965b). Disconnexion syndromes in animals and man. II. Brain, 88(3), 585–644.Google Scholar
Golomb, J., Kluger, A., de Leon, M.J., Ferris, S.H., Mittelman, M., Cohen, J., & George, A.E. (1996). Hippocampal formation size predicts declining memory performance in normal aging. Neurology, 47(3), 810–813.Google Scholar
Hamel, R., Kohler, S., Sistermans, N., Koene, T., Pijnenburg, Y., van der Flier, W., & Ramakers, I. (2015). The trajectory of cognitive decline in the pre-dementia phase in memory clinic visitors: Findings from the 4C-MCI study. Psychological Medicine, 45(7), 1509–1519. doi: 10.1017/s0033291714002645 Google Scholar
Hiyoshi-Taniguchi, K., Oishi, N., Namiki, C., Miyata, J., Murai, T., Cichocki, A., & Fukuyama, H. (2015). The uncinate fasciculus as a predictor of conversion from aMCI to Alzheimer disease. Journal of Neuroimaging, 25, 748–753. doi: 10.1111/jon.12196 Google Scholar
Jurica, P.J., Leittten, C.L., & Mattis, S. (2001). Dementia Rating Scale-2 professional manual. Lutz, FL: Psychological Assessment Resources.Google Scholar
Lawton, M.P., & Brody, E.M. (1969). Assessment of older people: Self-maintaining instrumental activities of daily living. Gerontologist, 9, 179–186.CrossRefGoogle ScholarPubMed
Li, W., Muftuler, L.T., Chen, G., Ward, B.D., Budde, M.D., Jones, J.L., & Goveas, J.S. (2014). Effects of the coexistence of late-life depression and mild cognitive impairment on white matter microstructure. Journal of the Neurological Sciences, 338(1-2), 46–56. doi: 10.1016/j.jns.2013.12.016 Google Scholar
Lim, Y.Y., Pietrzak, R.H., Ellis, K.A., Jaeger, J., Harrington, K., Ashwood, T., & Maruff, P. (2013). Rapid decline in episodic memory in healthy older adults with high amyloid-beta. Journal of Alzheimer’s Disease, 33(3), 675–679. doi: 10.3233/jad-2012-121516 CrossRefGoogle ScholarPubMed
Loewenstein, D.A., Barker, W.W., Chang, J.Y., Apicella, A., Yoshii, F., Kothari, P., & Duara, R. (1989). Predominant left hemisphere metabolic dysfunction in dementia. Archives of Neurology, 46(2), 146–152.Google Scholar
Mattsson, P., Forsberg, A., Persson, J., Nyberg, L., Nilsson, L.G., Halldin, C., & Farde, L. (2015). beta-Amyloid binding in elderly subjects with declining or stable episodic memory function measured with PET and [(1)(1)C]AZD2184. European Journal of Nuclear Medicine and Molecular Imaging, 42(10), 1507–1511. doi: 10.1007/s00259-015-3103-9 CrossRefGoogle Scholar
McSweeny, A.J., Naugle, R.I., Chelune, G.J., & Luders, H. (1993). “T scores for change”: An illustration of a regression approach to depicting change in clinical neuropsychology. The Clinical Neuropsychologist, 7, 300–312.CrossRefGoogle Scholar
Mesulam, M.M. (1990). Large-scale neurocognitive networks and distributed processing for attention, language, and memory. Annals of Neurology, 28(5), 597–613. doi: 10.1002/ana.410280502 CrossRefGoogle ScholarPubMed
Metzler-Baddeley, C., Hunt, S., Jones, D.K., Leemans, A., Aggleton, J.P., & O’Sullivan, M.J. (2012). Temporal association tracts and the breakdown of episodic memory in mild cognitive impairment. Neurology, 79(23), 2233–2240. doi: 10.1212/WNL.0b013e31827689e8 Google Scholar
Mistridis, P., Krumm, S., Monsch, A.U., Berres, M., & Taylor, K.I. (2015). The 12 years preceding mild cognitive impairment due to Alzheimer’s disease: The temporal emergence of cognitive decline. Journal of Alzheimer’s Disease, 48(4), 1095–1107. doi: 10.3233/jad-150137 CrossRefGoogle ScholarPubMed
Moghekar, A., Li, S., Lu, Y., Li, M., Wang, M.C., & Albert, M., … Biocard Research Team. (2013). CSF biomarker changes precede symptom onset of mild cognitive impairment. Neurology, 81(20), 1753–1758. doi: 10.1212/01.wnl.0000435558.98447.17 Google Scholar
Mori, S., Oishi, K., Jiang, H., Jiang, L., Li, X., Akhter, K., & Mazziotta, J. (2008). Stereotaxic white matter atlas based on diffusion tensor imaging in an ICBM template. Neuroimage, 40(2), 570–582. doi: 10.1016/j.neuroimage.2007.12.035 Google Scholar
Nir, T.M., Jahanshad, N., Villalon-Reina, J.E., Toga, A.W., Jack, C.R., & Weiner, M.W., … Alzheimer’s Disease Neuroimaging Initiative. (2013). Effectiveness of regional DTI measures in distinguishing Alzheimer’s disease, MCI, and normal aging. Neuroimage: Clinical, 3, 180–195. doi: 10.1016/j.nicl.2013.07.006 Google Scholar
O’Dwyer, L., Lamberton, F., Bokde, A.L., Ewers, M., Faluyi, Y.O., Tanner, C., & Hampel, H. (2011). Multiple indices of diffusion identifies white matter damage in mild cognitive impairment and Alzheimer’s disease. PLoS One, 6(6), e21745. doi: 10.1371/journal.pone.0021745 Google Scholar
Oishi, K., Faria, A., van Zijl, P.C.M., & Mori, S. (2011). MRI atlas of human white matter, (2nd ed.). London: Elsevier.Google Scholar
Papp, K.V., Amariglio, R.E., Mormino, E.C., Hedden, T., Dekhytar, M., Johnson, K.A., & Rentz, D.M. (2015). Free and cued memory in relation to biomarker-defined abnormalities in clinically normal older adults and those at risk for Alzheimer’s disease. Neuropsychologia, 73, 169–175. doi: 10.1016/j.neuropsychologia.2015.04.034 CrossRefGoogle ScholarPubMed
Pierpaoli, C., Barnett, A., Pajevic, S., Chen, R., Penix, L.R., Virta, A., & Basser, P. (2001). Water diffusion changes in Wallerian degeneration and their dependence on white matter architecture. Neuroimage, 13(6 Pt 1), 1174–1185. doi: 10.1006/nimg.2001.0765 Google Scholar
Ray, N.J., Metzler-Baddeley, C., Khondoker, M.R., Grothe, M.J., Teipel, S., Wright, P., & O’Sullivan, M.J. (2015). Cholinergic basal forebrain structure influences the reconfiguration of white matter connections to support residual memory in mild cognitive impairment. Journal of Neuroscience, 35(2), 739–747. doi: 10.1523/jneurosci.3617-14.2015 CrossRefGoogle ScholarPubMed
Raz, N., & Rodrigue, K.M. (2006). Differential aging of the brain: Patterns, cognitive correlates and modifiers. Neuroscience and Biobehavioral Reviews, 30(6), 730–748. doi: 10.1016/j.neubiorev.2006.07.001 Google Scholar
Remy, F., Vayssiere, N., Saint-Aubert, L., Barbeau, E., & Pariente, J. (2015). White matter disruption at the prodromal stage of Alzheimer’s disease: Relationships with hippocampal atrophy and episodic memory performance. Neuroimage: Clinical, 7, 482–492. doi: 10.1016/j.nicl.2015.01.014 Google Scholar
Rey, A. (1958). L’examen clinique en psychologie. Paris: Presses Universitaires de France.Google Scholar
Rosen, A.C., Prull, M.W., Gabrieli, J.D., Stoub, T., O’Hara, R., Friedman, L., & deToledo-Morrell, L. (2003). Differential associations between entorhinal and hippocampal volumes and memory performance in older adults. Behavioral Neuroscience, 117(6), 1150–1160. doi: 10.1037/0735-7044.117.6.1150 CrossRefGoogle ScholarPubMed
Salat, D.H., Tuch, D.S., van der Kouwe, A.J., Greve, D.N., Pappu, V., & Lee, S.Y. (2010). White matter pathology isolates the hippocampal formation in Alzheimer’s disease. Neurobiology of Aging, 31, 244–256.CrossRefGoogle ScholarPubMed
Saunders, A.M., Hulette, O., Welsh-Bohmer, K.A., Schmechel, D.E., Crain, B., & Burke, J.R. (1996). Specificity, sensitivity, and predictive value of apolipoprotein-E genotyping for sporadic Alzheimer’s disease. Lancet, 348, 90–93.Google Scholar
Schaeffer, D.J., Krafft, C.E., Schwarz, N.F., Chi, L., Rodrigue, A.L., Pierce, J.E., & McDowell, J.E. (2014). The relationship between uncinate fasciculus white matter integrity and verbal memory proficiency in children. Neuroreport, 25(12), 921–925. doi: 10.1097/wnr.0000000000000204 Google Scholar
Seidenberg, M., Guidotti, L., Nielson, K.A., Woodard, J.L., Durgerian, S., Antuono, P., & Rao, S.M. (2009). Semantic memory activation in individuals at risk for developing Alzheimer disease. Neurology, 73(8), 612–620. doi: 10.1212/WNL.0b013e3181b389ad Google Scholar
Smith, S.M. (2002). Fast robust automated brain extraction. Human Brain Mapping, 17, 143–155.Google Scholar
Smith, S.M., Jenkinson, M., Johansen-Berg, H., Ruckert, D., Nichols, T.E., & Mackay, C.E. (2006). Tract-based spatial statistics: Voxelwise analysis of multi-subject diffusion data. Neuroimage, 31, 1487–1505.CrossRefGoogle ScholarPubMed
Song, S.K., Sun, S.W., Ju, W.K., Lin, S.J., Cross, A.H., & Neufeld, A.H. (2003). Diffusion tensor imaging detects and differentiates axon and myelin degeneration in mouse optic nerve after retinal ischemia. Neuroimage, 20, 1714–1722.Google Scholar
Song, S.K., Sun, S.W., Ramsbottom, M.J., Chang, C., Russell, J., & Cross, A.H. (2002). Dysmyelination revealed through MRI as increased radial (but unchanged axial) diffusion of water. Neuroimage, 17(3), 1429–1436.CrossRefGoogle ScholarPubMed
Sperling, R.A., Aisen, P.S., Beckett, L.A., Bennett, D.A., Craft, S., Fagan, A.M., & Phelps, C.H. (2011). Toward defining the preclinical stages of Alzheimer’s disease: Recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers and Dementia, 7(3), 280–292. doi: 10.1016/j.jalz.2011.03.003 CrossRefGoogle ScholarPubMed
Thai, C., Lim, Y.Y., Villemagne, V.L., Laws, S.M., Ames, D., & Ellis, K.A., … Australian Imaging, Biomarkers and Lifestyle (AIBL) Research Group. (2015). Amyloid-related memory decline in preclinical Alzheimer’s disease is dependent on APOE epsilon4 and is detectable over 18-months. PLoS One, 10(10), e0139082. doi: 10.1371/journal.pone.0139082 CrossRefGoogle ScholarPubMed
Thompson, P.M., Hayashi, K.M., de Zubicaray, G., Janke, A.L., Rose, S.E., Semple, J., & Toga, A.W. (2003). Dynamics of gray matter loss in Alzheimer’s disease. Journal of Neuroscience, 23(3), 994–1005.Google Scholar
Tucker, L.R., Damarin, F., & Messick, S. (1966). A base-free measure of change. Psychometrika, 31(4), 457–473.Google Scholar
Von Der Heide, R.J., Skipper, L.M., Klobusicky, E., & Olson, I.R. (2013). Dissecting the uncinate fasciculus: Disorders, controversies and a hypothesis. Brain, 136(6), 1692–1707. doi: 10.1093/brain/awt094 CrossRefGoogle ScholarPubMed
Vyhnalek, M., Nikolai, T., Andel, R., Nedelska, Z., Rubinova, E., Markova, H., & Hort, J. (2014). Neuropsychological correlates of hippocampal atrophy in memory testing in nondemented older adults. Journal of Alzheimer’s Disease, 42(Suppl 3), S81–S90. doi: 10.3233/jad-132642 Google Scholar
Wang, R., Fratiglioni, L., Laukka, E.J., Lovden, M., Kalpouzos, G., Keller, L., & Qiu, C. (2015). Effects of vascular risk factors and APOE epsilon4 on white matter integrity and cognitive decline. Neurology, 84(11), 1128–1135. doi: 10.1212/wnl.0000000000001379 CrossRefGoogle ScholarPubMed
Wheeler-Kingshott, C.A., & Cercignani, M. (2009). About “axial” and “radial” diffusivities. Magnetic Resonance in Medicine, 61(5), 1255–1260. doi: 10.1002/mrm.21965 CrossRefGoogle Scholar
Wisse, L.E., Reijmer, Y.D., ter Telgte, A., Kuijf, H.J., Leemans, A., & Luijten, P.R., … Utrecht Vascular Cognitive Impairment Study Group. (2015). Hippocampal disconnection in early Alzheimer’s disease: A 7 tesla MRI study. Journal of Alzheimer’s Disease, 45(4), 1247–1256. doi: 10.3233/jad-142994 Google Scholar
Woodard, J.L., Seidenberg, M., Nielson, K.A., Antuono, P., Guidotti, L., Durgerian, S., & Rao, S.M. (2009). Semantic memory activation in amnestic mild cognitive impairment. Brain, 132(Pt 8), 2068–2078. doi: 10.1093/brain/awp157 Google Scholar
Woodard, J.L., Seidenberg, M., Nielson, K.A., Smith, J.C., Antuono, P., Durgerian, S., & Rao, S.M. (2010). Prediction of cognitive decline in healthy older adults using fMRI. Journal of Alzheimer’s Disease, 21, 871–885.Google Scholar
Woodard, J.L., Sugarman, M.A., Nielson, K.A., Smith, J.C., Seidenberg, M., Durgerian, S., & Rao, S.M. (2012). Lifestyle and genetic contributions to cognitive decline and hippocampal structure and function in healthy aging. Current Alzheimer’s Research, 9(4), 436–446.CrossRefGoogle ScholarPubMed
Yasmin, H., Nakata, Y., Aoki, S., Abe, O., Sato, N., Nemoto, K., & Ohtomo, K. (2008). Diffusion abnormalities of the uncinate fasciculus in Alzheimer’s disease: Diffusion tensor tract-specific analysis using a new method to measure the core of the tract. Neuroradiology, 50(4), 293–299. doi: 10.1007/s00234-007-0353-7 Google Scholar
Yesavage, J.A., Brink, T.L., Rose, T.L., Lum, O., Huang, V., & Adey, M. (1983). Development and validation of a geriatric depression screening scale: A preliminary report. Journal of Psychiatry Research, 17, 37–49.CrossRefGoogle Scholar
Zhang, Y., Schuff, N., Jahng, G.H., Bayne, W., Mori, S., Schad, L., & Weiner, M.W. (2007). Diffusion tensor imaging of cingulum fibers in mild cognitive impairment and Alzheimer disease. Neurology, 68(1), 13–19. doi: 10.1212/01.wnl.0000250326.77323.01 Google Scholar
Zhuang, L., Sachdev, P.S., Trollor, J.N., Kochan, N.A., Reppermund, S., Brodaty, H., & Wen, W. (2012). Microstructural white matter changes in cognitively normal individuals at risk of amnestic MCI. Neurology, 79(8), 748–754. doi: 10.1212/WNL.0b013e3182661f4d CrossRefGoogle ScholarPubMed
Zhuang, L., Sachdev, P.S., Trollor, J.N., Reppermund, S., Kochan, N.A., Brodaty, H., & Wen, W. (2013). Microstructural white matter changes, not hippocampal atrophy, detect early amnestic mild cognitive impairment. PLoS One, 8(3), e58887. doi: 10.1371/journal.pone.0058887 Google Scholar