Hostname: page-component-8448b6f56d-42gr6 Total loading time: 0 Render date: 2024-04-16T16:47:14.554Z Has data issue: false hasContentIssue false

Prediction of Free and Cued Selective Reminding Test Performance Using Volumetric and Amyloid-Based Biomarkers of Alzheimer’s Disease

Published online by Cambridge University Press:  01 December 2016

Lisa Quenon*
Affiliation:
Institute of Neuroscience, Université Catholique de Louvain, Brussels, Belgium
Laurence Dricot
Affiliation:
Institute of Neuroscience, Université Catholique de Louvain, Brussels, Belgium
John L. Woodard
Affiliation:
Institute of Neuroscience, Université Catholique de Louvain, Brussels, Belgium Psychology Department, Wayne State University, Detroit, Michigan
Bernard Hanseeuw
Affiliation:
Institute of Neuroscience, Université Catholique de Louvain, Brussels, Belgium Neurology Department, Saint Luc University Hospital, Université Catholique de Louvain, Brussels, Belgium Neurology Department, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts
Nathalie Gilis
Affiliation:
Neurosurgery Department, Citadelle Regional Hospital Center, Liège, Belgium
Renaud Lhommel
Affiliation:
Nuclear Medicine Department, Saint Luc University Hospital, Université Catholique de Louvain, Brussels, Belgium
Adrian Ivanoiu
Affiliation:
Institute of Neuroscience, Université Catholique de Louvain, Brussels, Belgium Neurology Department, Saint Luc University Hospital, Université Catholique de Louvain, Brussels, Belgium
*
Correspondence and reprint requests to: Lisa Quenon, Avenue Hippocrate 10, Centre de Revalidation Neuropsychologique, 1200 Woluwe-Saint-Lambert, Belgium. E-mail: lisa.quenon@uclouvain.be

Abstract

Objectives: Relatively few studies have investigated relationships between performance on clinical memory measures and indexes of underlying neuropathology related to Alzheimer’s disease (AD). This study investigated predictive relationships between Free and Cued Selective Reminding Test (FCSRT) cue efficiency (CE) and free-recall (FR) measures and brain amyloid levels, hippocampal volume (HV), and regional cortical thickness. Methods: Thirty-one older controls without memory complaints and 60 patients presenting memory complaints underwent the FCSRT, amyloid imaging using [F18]-flutemetamol positron emission tomography, and surface-based morphometry (SBM) using brain magnetic resonance imaging. Three groups were considered: patients with high (Aβ+P) and low (Aβ− P) amyloid load and controls with low amyloid load (Aβ− C). Results: Aβ+P showed lower CE than both Aβ− groups, but the Aβ− groups did not differ significantly. In contrast, FR discriminated all groups. SBM analyses revealed that CE indexes were correlated with the cortical thickness of a wider set of left-lateralized temporal and parietal regions than FR. Regression analyses demonstrated that amyloid load and left HV independently predicted FCSRT scores. Moreover, CE indexes were predicted by the cortical thickness of some regions involved in early AD, such as the entorhinal cortex. Conclusions: Compared to FR measures, CE indexes appear to be more specific for differentiating persons on the basis of amyloid load. Both CE and FR performance were predicted independently by brain amyloid load and reduced left HV. However, CE performance was also predicted by the cortical thickness of regions known to be atrophic early in AD. (JINS, 2016, 22, 991–1004)

Type
Research Articles
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

Ahn, H. J., Seo, S. W., Chin, J., Suh, M. K., Lee, B. H., Kim, S. T., & Na, D. L. (2011). The cortical neuroanatomy of neuropsychological deficits in mild cognitive impairment and Alzheimer’s disease: A surface-based morphometric analysis. Neuropsychologia, 49(14), 39313945. doi:10.1016/j.neuropsychologia.2011.10.010 Google Scholar
Albert, M. S. (1996). Cognitive and neurobiologic markers of early Alzheimer disease. Proceedings of the National Academy of Sciences of the United States of America, 93(24), 1354713551.Google Scholar
Albert, M. S., DeKosky, S. T., Dickson, D., Dubois, B., Feldman, H. H., Fox, N. C., & Phelps, C. H. (2011). The diagnosis of mild cognitive impairment due to Alzheimer’s disease: Recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimer’s & Dementia, 7(3), 270279. doi:10.1016/j.jalz.2011.03.008 Google Scholar
American Psychiatric Association. (2000). Diagnostic and statistical manual of mental disorders: DSM-IV-TR (4th Ed.). Washington, DC: American Psychiatric Association.Google Scholar
Atienza, M., Atalaia-Silva, K. C., Gonzalez-Escamilla, G., Gil-Neciga, E., Suarez-Gonzalez, A., & Cantero, J. L. (2011). Associative memory deficits in mild cognitive impairment: The role of hippocampal formation. Neuroimage, 57(4), 13311342. doi:10.1016/j.neuroimage.2011.05.047 Google Scholar
Becker, J. A., Hedden, T., Carmasin, J., Maye, J., Rentz, D. M., Putcha, D., & Johnson, K. A. (2011). Amyloid-beta associated cortical thinning in clinically normal elderly. Annals of Neurology, 69(6), 10321042. doi:10.1002/ana.22333 Google Scholar
Blennow, K., Mattsson, N., Scholl, M., Hansson, O., & Zetterberg, H. (2015). Amyloid biomarkers in Alzheimer’s disease. Trends in Pharmacological Sciences, 36(5), 297309. doi:10.1016/j.tips.2015.03.002 Google Scholar
Bonner-Jackson, A., Mahmoud, S., Miller, J., & Banks, S. J. (2015). Verbal and non-verbal memory and hippocampal volumes in a memory clinic population. Alzheimer’s Research & Therapy, 7(1), 61. doi:10.1186/s13195-015-0147-9 Google Scholar
Braak, H., & Braak, E. (1991). Neuropathological stageing of Alzheimer-related changes. Acta Neuropatholigica, 82(4), 239259.CrossRefGoogle ScholarPubMed
Carlesimo, G. A., Perri, R., & Caltagirone, C. (2011). Category cued recall following controlled encoding as a neuropsychological tool in the diagnosis of Alzheimer’s disease: A review of the evidence. Neuropsychology Review, 21(1), 5465. doi:10.1007/s11065-010-9153-7 Google Scholar
Chen, P., Ratcliff, G., Belle, S. H., Cauley, J. A., DeKosky, S. T., & Ganguli, M. (2000). Cognitive tests that best discriminate between presymptomatic AD and those who remain nondemented. Neurology, 55(12), 18471853.Google Scholar
Crary, J. F., Trojanowski, J. Q., Schneider, J. A., Abisambra, J. F., Abner, E. L., Alafuzoff, I., & Nelson, P. T. (2014). Primary age-related tauopathy (PART): A common pathology associated with human aging. Acta Neuropatholigica, 128(6), 755766. doi:10.1007/s00401-014-1349-0 CrossRefGoogle 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), 968980. doi:10.1016/j.neuroimage.2006.01.021 Google Scholar
Dickerson, B. C., Bakkour, A., Salat, D. H., Feczko, E., Pacheco, J., Greve, D. N., & Buckner, R. L. (2009). The cortical signature of Alzheimer’s disease: Regionally specific cortical thinning relates to symptom severity in very mild to mild AD dementia and is detectable in asymptomatic amyloid-positive individuals. Cerebral Cortex, 19(3), 497510. doi:10.1093/cercor/bhn113 Google Scholar
Dickerson, B. C., Feczko, E., Augustinack, J. C., Pacheco, J., Morris, J. C., Fischl, B., & Buckner, R. L. (2009). Differential effects of aging and Alzheimer’s disease on medial temporal lobe cortical thickness and surface area. Neurobiology of Aging, 30(3), 432440. doi:10.1016/j.neurobiolaging.2007.07.022 Google Scholar
Dickerson, B. C., Fenstermacher, E., Salat, D. H., Wolk, D. A., Maguire, R. P., Desikan, R., & Fischl, B. (2008). Detection of cortical thickness correlates of cognitive performance: Reliability across MRI scan sessions, scanners, and field strengths. Neuroimage, 39(1), 1018. doi:10.1016/j.neuroimage.2007.08.042 Google Scholar
Dierckx, E., Engelborghs, S., De Raedt, R., Van Buggenhout, M., De Deyn, P. P., Verte, D., & Ponjaert-Kristoffersen, I. (2009). Verbal cued recall as a predictor of conversion to Alzheimer’s disease in mild cognitive impairment. International Journal of Geriatric Psychiatry, 24(10), 10941100. doi:10.1002/gps.2228 Google Scholar
Dore, V., Villemagne, V. L., Bourgeat, P., Fripp, J., Acosta, O., Chetelat, G., & Rowe, C. C. (2013). Cross-sectional and longitudinal analysis of the relationship between Abeta deposition, cortical thickness, and memory in cognitively unimpaired individuals and in Alzheimer disease. JAMA Neurology, 70(7), 903911. doi:10.1001/jamaneurol.2013.1062 Google Scholar
Dubois, B., Feldman, H. H., Jacova, C., Dekosky, S. T., Barberger-Gateau, P., Cummings, J., & Scheltens, P. (2007). Research criteria for the diagnosis of Alzheimer’s disease: Revising the NINCDS-ADRDA criteria. Lancet Neurology, 6(8), 734746. doi:10.1016/S1474-4422(07)70178-3 Google Scholar
Ferguson, C. J. (2009). An effect size primer: A guide for clinicians and researchers. Professional Psychology: Research and Practice, 40(5), 532538. doi:10.1037/a0015808 Google Scholar
Fischl, B., Salat, D. H., van der Kouwe, A. J., Makris, N., Segonne, F., Quinn, B. T., & Dale, A. M. (2004). Sequence-independent segmentation of magnetic resonance images. Neuroimage, 23(Suppl 1), S69S84. doi:10.1016/j.neuroimage.2004.07.016 Google Scholar
Frisoni, G. B., Prestia, A., Zanetti, O., Galluzzi, S., Romano, M., Cotelli, M., &Geroldi, C. (2009). Markers of Alzheimer’s disease in a population attending a memory clinic. Alzheimer’s & Dementia, 5(4), 307317. doi:10.1016/j.jalz.2009.04.1235 Google Scholar
Grober, E., & Buschke, H. (1987). Genuine memory deficits in dementia. Developmental Neuropsychology, 3, 1336.CrossRefGoogle Scholar
Grober, E., Lipton, R. B., Hall, C., & Crystal, H. (2000). Memory impairment on free and cued selective reminding predicts dementia. Neurology, 54(4), 827832.CrossRefGoogle ScholarPubMed
Grober, E., Sanders, A. E., Hall, C., & Lipton, R. B. (2010). Free and cued selective reminding identifies very mild dementia in primary care. Alzheimer Disease & Associated Disorders, 24(3), 284290. doi:10.1097/WAD.0b013e3181cfc78b Google Scholar
Hanseeuw, B., Dricot, L., Kavec, M., Grandin, C., Seron, X., & Ivanoiu, A. (2011). Associative encoding deficits in amnestic mild cognitive impairment: A volumetric and functional MRI study. Neuroimage, 56(3), 17431748. doi:10.1016/j.neuroimage.2011.03.034 Google Scholar
Hurtz, S., Woo, E., Kebets, V., Green, A. E., Zoumalan, C., Wang, B., & Apostolova, L. G. (2014). Age effects on cortical thickness in cognitively normal elderly individuals. Dementia & Geriatric Cognitive Disorders Extra, 4(2), 221227. doi:10.1159/000362872 Google Scholar
Ivanoiu, A., Adam, S., Van der Linden, M., Salmon, E., Juillerat, A. C., Mulligan, R., & Seron, X. (2005). Memory evaluation with a new cued recall test in patients with mild cognitive impairment and Alzheimer’s disease. Journal of Neurology, (1), 4755. doi:10.1007/s00415-005-0597-2 Google Scholar
Ivanoiu, A., Dricot, L., Gilis, N., Grandin, C., Lhommel, R., Quenon, L., &Hanseeuw, B. (2015). Classification of non-demented patients attending a memory clinic using the new diagnostic criteria for Alzheimer’s disease with disease-related biomarkers. Journal of Alzheimer’s Disease, 43(3), 835847. doi:10.3233/JAD-140651 Google Scholar
Jack, C. R. Jr. (2014). PART and SNAP. Acta Neuropatholigica, 128(6), 773776. doi:10.1007/s00401-014-1362-3 Google Scholar
Jack, C. R. Jr., Knopman, D. S., Jagust, W. J., Petersen, R. C., Weiner, M. W., Aisen, P. S., & Trojanowski, J. Q. (2013). Tracking pathophysiological processes in Alzheimer’s disease: An updated hypothetical model of dynamic biomarkers. Lancet Neurology, 12(2), 207216. doi:10.1016/S1474-4422(12)70291-0 Google Scholar
Jack, C. R. Jr., Lowe, V. J., Senjem, M. L., Weigand, S. D., Kemp, B. J., Shiung, M. M., & Petersen, R. C. (2008). 11C PiB and structural MRI provide complementary information in imaging of Alzheimer’s disease and amnestic mild cognitive impairment. Brain, 131(Pt 3), 665680. doi:10.1093/brain/awm336 Google Scholar
Jongbloed, W., Bruggink, K. A., Kester, M. I., Visser, P. J., Scheltens, P., Blankenstein, M. A., & Veerhuis, R. (2015). Amyloid-beta oligomers relate to cognitive decline in Alzheimer’s disease. Journal of Alzheimer’s Disease, 45(1), 3543. doi:10.3233/JAD-142136 Google Scholar
Lemaitre, H., Goldman, A. L., Sambataro, F., Verchinski, B. A., Meyer-Lindenberg, A., Weinberger, D. R., &Mattay, V. S. (2012). Normal age-related brain morphometric changes: Nonuniformity across cortical thickness, surface area and gray matter volume? Neurobiology of Aging, 33(3), 617.e1–9. doi:10.1016/j.neurobiolaging.2010.07.013 CrossRefGoogle ScholarPubMed
Lerch, J. P., Pruessner, J. C., Zijdenbos, A., Hampel, H., Teipel, S. J., & Evans, A. C. (2005). Focal decline of cortical thickness in Alzheimer’s disease identified by computational neuroanatomy. Cerebral Cortex, 15(7), 9951001. doi:10.1093/cercor/bhh200 Google Scholar
Llado-Saz, S., Atienza, M., & Cantero, J. L. (2015). Increased levels of plasma amyloid-beta are related to cortical thinning and cognitive decline in cognitively normal elderly subjects. Neurobiology of Aging, 36(10), 27912797. doi:10.1016/j.neurobiolaging.2015.06.023 Google Scholar
Markesbery, W. R. (2010). Neuropathologic alterations in mild cognitive impairment: A review. Journal of Alzheimer’s Disease, 19(1), 221228. doi:10.3233/JAD-2010-1220 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), 939944.Google Scholar
Mormino, E. C., Kluth, J. T., Madison, C. M., Rabinovici, G. D., Baker, S. L., Miller, B. L., … Alzheimer’s Disease Neuroimaging Initiative. (2009). Episodic memory loss is related to hippocampal-mediated beta-amyloid deposition in elderly subjects. Brain, 132(Pt 5), 13101323. doi:10.1093/brain/awn320 Google Scholar
Myers, R. (1990). Classical and modern regression with application (2nd Ed.). Boston, MA: Duxbury Press.Google Scholar
Nho, K., Risacher, S. L., Crane, P. K., DeCarli, C., Glymour, M. M., & Habeck, C., … Alzheimer’s Disease Neuroimaging Initiative. (2012). Voxel and surface-based topography of memory and executive deficits in mild cognitive impairment and Alzheimer’s disease. Brain Imaging and Behavior, 6(4), 551567. doi:10.1007/s11682-012-9203-2 Google Scholar
Ong, K., Villemagne, V. L., Bahar-Fuchs, A., Lamb, F., Chetelat, G., Raniga, P., & Rowe, C. C. (2013). (18)F-florbetaben Abeta imaging in mild cognitive impairment. Alzheimer’s Research & Therapy, 5(1), 4. doi:10.1186/alzrt158 Google Scholar
Petersen, R. C. (2004). Mild cognitive impairment as a diagnostic entity. Journal of Internal Medicine, 256(3), 183194. doi:10.1111/j.1365-2796.2004.01388.x CrossRefGoogle ScholarPubMed
Pillon, B., Deweer, B., Michon, A., Malapani, C., Agid, Y., & Dubois, B. (1994). Are explicit memory disorders of progressive supranuclear palsy related to damage to striatofrontal circuits? Comparison with Alzheimer’s, Parkinson’s, and Huntington’s diseases. Neurology, 44(7), 12641270.Google Scholar
R Development Core Team. (2015). R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing.Google Scholar
Rabinovici, G. D., & Jagust, W. J. (2009). Amyloid imaging in aging and dementia: Testing the amyloid hypothesis in vivo. Behavioral Neurology, 21(1), 117128. doi:10.3233/BEN-2009-0232 Google Scholar
Rami, L., Fortea, J., Bosch, B., Sole-Padulles, C., Llado, A., Iranzo, A., & Molinuevo, J. L. (2011). Cerebrospinal fluid biomarkers and memory present distinct associations along the continuum from healthy subjects to AD patients. Journal of Alzheimer’s Disease, 23(2), 319326. doi:10.3233/JAD-2010-101422 Google Scholar
Rami, L., Sole-Padulles, C., Fortea, J., Bosch, B., Llado, A., Antonell, A., & Molinuevo, J. L. (2012). Applying the new research diagnostic criteria: MRI findings and neuropsychological correlations of prodromal AD. International Journal of Geriatric Psychiatry, 27(2), 127134. doi:10.1002/gps.2696 Google Scholar
Saka, E., Mihci, E., Topcuoglu, M. A., & Balkan, S. (2006). Enhanced cued recall has a high utility as a screening test in the diagnosis of Alzheimer’s disease and mild cognitive impairment in Turkish people. Archives of Clinical Neuropsychology, 21(7), 745751. doi:10.1016/j.acn.2006.08.007 Google Scholar
Sarazin, M., Berr, C., De Rotrou, J., Fabrigoule, C., Pasquier, F., Legrain, S., & Dubois, B. (2007). Amnestic syndrome of the medial temporal type identifies prodromal AD: A longitudinal study. Neurology, 69(19), 18591867. doi:10.1212/01.wnl.0000279336.36610.f7 Google Scholar
Sarazin, M., Chauvire, V., Gerardin, E., Colliot, O., Kinkingnehun, S., de Souza, L. C., & Dubois, B. (2010). The amnestic syndrome of hippocampal type in Alzheimer’s disease: An MRI study. Journal of Alzheimer’s Disease, 22(1), 285294. doi:10.3233/JAD-2010-091150 Google Scholar
Sperling, R. (2007). Functional MRI studies of associative encoding in normal aging, mild cognitive impairment, and Alzheimer’s disease. Annals of the New York Academy of Sciences, 1097, 146155. doi:10.1196/annals.1379.009 Google Scholar
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. Alzheimer’s & Dementia, 7(3), 280292. doi:10.1016/j.jalz.2011.03.003 Google Scholar
Stevens, J. P. (2002). Applied multivariate statistics for the social sciences (4th Ed.). Mahwah, NJ: Lawrence Erlbaum Associates.Google Scholar
Thambisetty, M., Wan, J., Carass, A., An, Y., Prince, J. L., & Resnick, S. M. (2010). Longitudinal changes in cortical thickness associated with normal aging. Neuroimage, 52(4), 12151223. doi:10.1016/j.neuroimage.2010.04.258 Google Scholar
Tounsi, H., Deweer, B., Ergis, A. M., Van der Linden, M., Pillon, B., Michon, A., & Dubois, B. (1999). Sensitivity to semantic cuing: An index of episodic memory dysfunction in early Alzheimer disease. Alzheimer Disease & Associated Disorders, 13(1), 3846.Google Scholar
Trojanowski, J. Q., Vandeerstichele, H., Korecka, M., Clark, C. M., Aisen, P. S., Petersen, R. C., … Alzheimer’s Disease Neuroimaging Initiative. (2010). Update on the biomarker core of the Alzheimer’s Disease Neuroimaging Initiative subjects. Alzheimer’s & Dementia, 6(3), 230238. doi:10.1016/j.jalz.2010.03.008 Google Scholar
Troyer, A. K., Murphy, K. J., Anderson, N. D., Craik, F. I., Moscovitch, M., Maione, A., &Gao, F. (2012). Associative recognition in mild cognitive impairment: Relationship to hippocampal volume and apolipoprotein E. Neuropsychologia, 50(14), 37213728. doi:10.1016/j.neuropsychologia.2012.10.018 Google Scholar
Troyer, A. K., Murphy, K. J., Anderson, N. D., Hayman-Abello, B. A., Craik, F. I., & Moscovitch, M. (2008). Item and associative memory in amnestic mild cognitive impairment: Performance on standardized memory tests. Neuropsychology, 22(1), 1016. doi:10.1037/0894-4105.22.1.10 Google Scholar
Van der Linden, M., Coyette, F., Poitrenaud, J., Kalafat, M., Calicis, F., Wyns, C., … GRENEM. (2004). L’épreuve de rappel libre/rappel indicé à 16 items (RL/RI-16). In M. Van der Linden, S. Adam, A. Agniel, C. Baisset Mouly, F. Bardet, F. Coyette, B. Desgranges, B. Deweer, A.-M. Ergis, M.-C. Gély-Nargeot, L. Grimomprez, A. C. Juillerat, M. Kalafat, J. Poitrenaud, F. Sellal, & C. Thomas-Antérion (Eds.), L’évaluation des troubles de la mémoire. Présentation de quatre tests de mémoire épisodique (avec leur étalonnage). Marseille: Solal.Google Scholar
Vandenberghe, R., Van Laere, K., Ivanoiu, A., Salmon, E., Bastin, C., Triau, E., & Brooks, D. J. (2010). 18F-flutemetamol amyloid imaging in Alzheimer disease and mild cognitive impairment: A phase 2 trial. Annals of Neurology, 68(3), 319329. doi:10.1002/ana.22068 Google Scholar
Villemagne, V. L., Burnham, S., Bourgeat, P., Brown, B., Ellis, K. A., Salvado, O., … Australian Lifestyle Research Group. (2013). Amyloid beta deposition, neurodegeneration, and cognitive decline in sporadic Alzheimer’s disease: A prospective cohort study. Lancet Neurology, 12(4), 357367. doi:10.1016/S1474-4422(13)70044-9 Google Scholar
Villemagne, V. L., Pike, K. E., Chetelat, G., Ellis, K. A., Mulligan, R. S., Bourgeat, P., & Rowe, C. C. (2011). Longitudinal assessment of Abeta and cognition in aging and Alzheimer disease. Annals of Neurology, 69(1), 181192. doi:10.1002/ana.22248 Google Scholar
Wagner, M., Wolf, S., Reischies, F. M., Daerr, M., Wolfsgruber, S., Jessen, F., & Wiltfang, J. (2012). Biomarker validation of a cued recall memory deficit in prodromal Alzheimer disease. Neurology, 78(6), 379386. doi:10.1212/WNL.0b013e318245f447 Google Scholar
Wisse, L. E., Butala, N., Das, S. R., Davatzikos, C., Dickerson, B. C., Vaishnavi, S. N., … Alzheimer’s Disease Neuroimaging Initiative. (2015). Suspected non-AD pathology in mild cognitive impairment. Neurobiolology of Aging, 36(12), 31523162. doi:10.1016/j.neurobiolaging.2015.08.029 Google Scholar
Xie, J., Gabelle, A., Dorey, A., Garnier-Crussard, A., Perret-Liaudet, A., Delphin-Combe, F., & Krolak-Salmon, P. (2014). Initial memory deficit profiles in patients with a cerebrospinal fluid Alzheimer’s disease signature. Journal of Alzheimer’s Disease, 41(4), 11091116. doi:10.3233/JAD-131916 Google Scholar
Supplementary material: PDF

Quenon supplementary material

Quenon supplementary material 1

Download Quenon supplementary material(PDF)
PDF 12.1 MB
Supplementary material: PDF

Quenon supplementary material

Quenon supplementary material 2

Download Quenon supplementary material(PDF)
PDF 6.1 MB
Supplementary material: File

Quenon supplementary material

Quenon supplementary material 3

Download Quenon supplementary material(File)
File 90.6 KB