Skip to main content

Advertisement

Log in

Brain-behavior investigation of potential cognitive markers of Alzheimer’s disease in middle age: a multi-modal imaging study

  • Original Research
  • Published:
Brain Imaging and Behavior Aims and scope Submit manuscript

Abstract

Early detection of Alzheimer’s disease remains a challenge, and the development and validation of novel cognitive markers of Alzheimer’s disease is critical to earlier disease detection. The goal of the present study is to examine brain-behavior relationships of translational cognitive paradigms dependent on the medial temporal lobes and prefrontal cortices, regions that are first to undergo Alzheimer’s-associated changes. We employed multi-modal structural and functional MRI to examine brain-behavior relationships in a healthy, middle-aged sample (N = 133; 40–60 years). Participants completed two medial temporal lobe-dependent tasks (virtual Morris Water Task and Transverse Patterning Discriminations Task), and a prefrontal cortex-dependent task (Reversal Learning Task). No associations were found between various MRI measures of brain integrity and the Transverse Patterning or Reversal Learning tasks (p’s > .05). We report associations between virtual Morris Water Task performance and medial temporal lobe volume, hippocampal microstructural organization, fornix integrity, and functional connectivity within the executive control and frontoparietal control resting state networks (all p’s < 0.05; did not survive correction for multiple comparisons). This study suggests that virtual Morris Water Task performance is associated with medial temporal lobe integrity in middle age, a critical window for detection and prevention of Alzheimer’s disease, and may be useful as an early cognitive marker of Alzheimer’s disease risk.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

References

  • Acosta-Cabronero, J., & Nestor, P. J. (2014). Diffusion tensor imaging in Alzheimer's disease: Insights into the limbic-diencephalic network and methodological considerations. Frontiers in Aging Neuroscience, 6, 266. https://doi.org/10.3389/fnagi.2014.00266

    Article  PubMed  PubMed Central  Google Scholar 

  • Amlien, I. K., & Fjell, A. M. (2014). Diffusion tensor imaging of white matter degeneration in Alzheimer's disease and mild cognitive impairment. Neuroscience. https://doi.org/10.1016/j.neuroscience.2014.02.017

  • Andersson, J. L., & Sotiropoulos, S. N. (2016). An integrated approach to correction for off-resonance effects and subject movement in diffusion MR imaging. Neuroimage, 125, 1063–1078. https://doi.org/10.1016/j.neuroimage.2015.10.019

    Article  PubMed  Google Scholar 

  • Antonova, E., Parslow, D., Brammer, M., Dawson, G. R., Jackson, S. H., & Morris, R. G. (2009). Age-related neural activity during allocentric spatial memory. Memory, 17(2), 125–143. https://doi.org/10.1080/09658210802077348

    Article  CAS  PubMed  Google Scholar 

  • Astur, R. S., Ortiz, M. L., & Sutherland, R. J. (1998). A characterization of performance by men and women in a virtual Morris water task: A large and reliable sex difference. Behavioural Brain Research, 93(1–2), 185–190.

    Article  CAS  Google Scholar 

  • Barnes, J., Bartlett, J. W., van de Pol, L. A., Loy, C. T., Scahill, R. I., Frost, C., ... Fox, N. C. (2009). A meta-analysis of hippocampal atrophy rates in Alzheimer's disease. Neurobiology of Aging, 30(11), 1711–1723. https://doi.org/10.1016/j.neurobiolaging.2008.01.010.

  • Beckmann, C. F., DeLuca, M., Devlin, J. T., & Smith, S. M. (2005). Investigations into resting-state connectivity using independent component analysis. Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences, 360(1457), 1001–1013. https://doi.org/10.1098/rstb.2005.1634

    Article  PubMed  PubMed Central  Google Scholar 

  • Behrens, T. E., Woolrich, M. W., Jenkinson, M., Johansen-Berg, H., Nunes, R. G., Clare, S., ... Smith, S. M. (2003). Characterization and propagation of uncertainty in diffusion-weighted MR imaging. Magnetic Resonance in Medicine, 50(5), 1077–1088. https://doi.org/10.1002/mrm.10609.

  • Behrens, T. E., Berg, H. J., Jbabdi, S., Rushworth, M. F., & Woolrich, M. W. (2007). Probabilistic diffusion tractography with multiple fibre orientations: What can we gain? Neuroimage, 34(1), 144–155. https://doi.org/10.1016/j.neuroimage.2006.09.018

    Article  CAS  PubMed  Google Scholar 

  • Bohbot, V. D., Lerch, J., Thorndycraft, B., Iaria, G., & Zijdenbos, A. P. (2007). Gray matter differences correlate with spontaneous strategies in a human virtual navigation task. The Journal of Neuroscience, 27(38), 10078–10083. https://doi.org/10.1523/JNEUROSCI.1763-07.2007

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Bondi, M. W., Edmonds, E. C., Jak, A. J., Clark, L. R., Delano-Wood, L., McDonald, C. R., ... Salmon, D. P. (2014). Neuropsychological criteria for mild cognitive impairment improves diagnostic precision, biomarker associations, and progression rates. Journal of Alzheimer's Disease, 42(1), 275–289. https://doi.org/10.3233/JAD-140276.

  • Cornwell, B. R., Johnson, L. L., Holroyd, T., Carver, F. W., & Grillon, C. (2008). Human hippocampal and parahippocampal theta during goal-directed spatial navigation predicts performance on a virtual Morris water maze. The Journal of Neuroscience, 28(23), 5983–5990. https://doi.org/10.1523/JNEUROSCI.5001-07.2008

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Cox, R. W. (1996). AFNI: Software for analysis and visualization of functional magnetic resonance neuroimages. Computers and Biomedical Research, 29(3), 162–173.

    Article  CAS  Google Scholar 

  • Dahmani, L., & Bohbot, V. D. (2015). Dissociable contributions of the prefrontal cortex to hippocampus- and caudate nucleus-dependent virtual navigation strategies. Neurobiology of Learning and Memory, 117, 42–50. https://doi.org/10.1016/j.nlm.2014.07.002

    Article  PubMed  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. https://doi.org/10.1006/nimg.1998.0395

    Article  CAS  PubMed  Google Scholar 

  • Dixon, R. M., Bradley, K. M., Budge, M. M., Styles, P., & Smith, A. D. (2002). Longitudinal quantitative proton magnetic resonance spectroscopy of the hippocampus in Alzheimer's disease. Brain, 125(Pt 10), 2332–2341.

    Article  Google Scholar 

  • Driscoll, I., Hamilton, D. A., Petropoulos, H., Yeo, R. A., Brooks, W. M., Baumgartner, R. N., & Sutherland, R. J. (2003). The aging hippocampus: Cognitive, biochemical and structural findings. Cerebral Cortex, 13(12), 1344–1351.

    Article  Google Scholar 

  • Driscoll, I., Hamilton, D. A., Yeo, R. A., Brooks, W. M., & Sutherland, R. J. (2005). Virtual navigation in humans: The impact of age, sex, and hormones on place learning. Hormones and Behavior, 47(3), 326–335. https://doi.org/10.1016/j.yhbeh.2004.11.013

    Article  CAS  PubMed  Google Scholar 

  • Fellows, L. K., & Farah, M. J. (2003). Ventromedial frontal cortex mediates affective shifting in humans: Evidence from a reversal learning paradigm. Brain, 126(Pt 8), 1830–1837. https://doi.org/10.1093/brain/awg180

    Article  PubMed  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.

  • Fischl, B., van der Kouwe, A., Destrieux, C., Halgren, E., Ségonne, F., Salat, D. H., ... Dale, A. M. (2004). Automatically parcellating the human cerebral cortex. Cerebral Cortex, 14(1), 11–22.

  • Florian, C., & Roullet, P. (2004). Hippocampal CA3-region is crucial for acquisition and memory consolidation in Morris water maze task in mice. Behavioural Brain Research, 154(2), 365–374. https://doi.org/10.1016/j.bbr.2004.03.003

    Article  PubMed  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.

    Article  CAS  Google Scholar 

  • Greenwood, P. M., Lambert, C., Sunderland, T., & Parasuraman, R. (2005). Effects of apolipoprotein E genotype on spatial attention, working memory, and their interaction in healthy, middle-aged adults: Results from the National Institute of Mental Health's BIOCARD study. Neuropsychology, 19(2), 199–211. https://doi.org/10.1037/0894-4105.19.2.199

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Greicius, M. D., Flores, B. H., Menon, V., Glover, G. H., Solvason, H. B., Kenna, H., ... Schatzberg, A. F. (2007). Resting-state functional connectivity in major depression: abnormally increased contributions from subgenual cingulate cortex and thalamus. Biological Psychiatry, 62(5), 429–437. https://doi.org/10.1016/j.biopsych.2006.09.020.

  • Heise, V., Filippini, N., Trachtenberg, A. J., Suri, S., Ebmeier, K. P., & Mackay, C. E. (2014). Apolipoprotein E genotype, gender and age modulate connectivity of the hippocampus in healthy adults. Neuroimage, 98, 23–30. https://doi.org/10.1016/j.neuroimage.2014.04.081

    Article  CAS  PubMed  Google Scholar 

  • Jack, C. R., Wiste, H. J., Weigand, S. D., Knopman, D. S., Vemuri, P., Mielke, M. M., ... Petersen, R. C. (2015). Age, sex, and APOE ε4 effects on memory, brain structure, and β-Amyloid across the adult life span. JAMA Neurology, 72(5), 511–519. https://doi.org/10.1001/jamaneurol.2014.4821.

  • Jack, C. R., Bennett, D. A., Blennow, K., Carrillo, M. C., Feldman, H. H., Frisoni, G. B., ... Dubois, B. (2016). A/T/N: An unbiased descriptive classification scheme for Alzheimer disease biomarkers. Neurology, 87(5), 539–547. https://doi.org/10.1212/WNL.0000000000002923.

  • Kesner, R. P., & Churchwell, J. C. (2011). An analysis of rat prefrontal cortex in mediating executive function. Neurobiology of Learning and Memory, 96(3), 417–431. https://doi.org/10.1016/j.nlm.2011.07.002

    Article  PubMed  Google Scholar 

  • Kiuchi, K., Morikawa, M., Taoka, T., Nagashima, T., Yamauchi, T., Makinodan, M., ... Kishimoto, T. (2009). Abnormalities of the uncinate fasciculus and posterior cingulate fasciculus in mild cognitive impairment and early Alzheimer's disease: a diffusion tensor tractography study. Brain Research, 1287, 184–191. https://doi.org/10.1016/j.brainres.2009.06.052.

  • Kok, E., Haikonen, S., Luoto, T., Huhtala, H., Goebeler, S., Haapasalo, H., & Karhunen, P. J. (2009). Apolipoprotein E-dependent accumulation of Alzheimer disease-related lesions begins in middle age. Annals of Neurology, 65(6), 650–657. https://doi.org/10.1002/ana.21696

    Article  CAS  PubMed  Google Scholar 

  • Korthauer, L. E., Nowak, N. T., Moffat, S. D., An, Y., Rowland, L. M., Barker, P. B., ... Driscoll, I. (2016). Correlates of virtual navigation performance in older adults. Neurobiology of Aging, 39, 118–127. https://doi.org/10.1016/j.neurobiolaging.2015.12.003.

  • Korthauer, L. E., Awe, E., Frahmand, M., & Driscoll, I. (2018). Genetic risk for age-related cognitive impairment does not predict cognitive performance in middle age. Journal of Alzheimer's Disease, 64(2), 459–471. https://doi.org/10.3233/JAD-171043

    Article  CAS  PubMed  Google Scholar 

  • Laczó, J., Vlcek, K., Vyhnálek, M., Vajnerová, O., Ort, M., Holmerová, I., ... Hort, J. (2009). Spatial navigation testing discriminates two types of amnestic mild cognitive impairment. Behavioural Brain Research, 202(2), 252–259. https://doi.org/10.1016/j.bbr.2009.03.041.

  • Laczó, J., Andel, R., Vyhnalek, M., Vlcek, K., Magerova, H., Varjassyova, A., ... Hort, J. (2010). Human analogue of the morris water maze for testing subjects at risk of Alzheimer's disease. Neurodegenerative Diseases, 7(1–3), 148–152. https://doi.org/10.1159/000289226.

  • Mattis, S. (1988). Dementia rating scale (DRS). Psychological Assessment Resources.

    Google Scholar 

  • Metzler-Baddeley, C., Baddeley, R. J., Jones, D. K., Aggleton, J. P., & O'Sullivan, M. J. (2013). Individual differences in fornix microstructure and body mass index. PLoS One, 8(3), e59849. https://doi.org/10.1371/journal.pone.0059849

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Moffat, S. D., Kennedy, K. M., Rodrigue, K. M., & Raz, N. (2007). Extrahippocampal contributions to age differences in human spatial navigation. Cerebral Cortex, 17(6), 1274–1282. https://doi.org/10.1093/cercor/bhl036

    Article  PubMed  Google Scholar 

  • Morris, R. G., Garrud, P., Rawlins, J. N., & O'Keefe, J. (1982). Place navigation impaired in rats with hippocampal lesions. Nature, 297(5868), 681–683.

    Article  CAS  Google Scholar 

  • Morris, J. C., Roe, C. M., Xiong, C., Fagan, A. M., Goate, A. M., Holtzman, D. M., & Mintun, M. A. (2010). APOE predicts amyloid-beta but not tau Alzheimer pathology in cognitively normal aging. Annals of Neurology, 67(1), 122–131. https://doi.org/10.1002/ana.21843

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Nickerson, L. D., Smith, S. M., Öngür, D., & Beckmann, C. F. (2017). Using dual regression to investigate network shape and amplitude in functional connectivity analyses. Frontiers in Neuroscience, 11, 115. https://doi.org/10.3389/fnins.2017.00115

    Article  PubMed  PubMed Central  Google Scholar 

  • Olsen, R. K., Moses, S. N., Riggs, L., & Ryan, J. D. (2012). The hippocampus supports multiple cognitive processes through relational binding and comparison. Frontiers in Human Neuroscience, 6, 146. https://doi.org/10.3389/fnhum.2012.00146

    Article  PubMed  PubMed Central  Google Scholar 

  • Ostreicher, M. L., Moses, S. N., Rosenbaum, R. S., & Ryan, J. D. (2010). Prior experience supports new learning of relations in aging. The Journals of Gerontology. Series B, Psychological Sciences and Social Sciences, 65B(1), 32–41. https://doi.org/10.1093/geronb/gbp081

    Article  PubMed  Google Scholar 

  • Parnetti, L., Lowenthal, D. T., Presciutti, O., Pelliccioli, G. P., Palumbo, R., Gobbi, G., ... Senin, U. (1996). 1H-MRS, MRI-based hippocampal volumetry, and 99mTc-HMPAO-SPECT in normal aging, age-associated memory impairment, and probable Alzheimer's disease. Journal of the American Geriatrics Society, 44(2), 133–138. https://doi.org/10.1111/j.1532-5415.1996.tb02428.x.

  • Provencher, S. W. (2001). Automatic quantitation of localized in vivo 1H spectra with LCModel. NMR in Biomedicine, 14(4), 260–264.

    Article  CAS  Google Scholar 

  • Redish, A. D., & Touretzky, D. S. (1998). The role of the hippocampus in solving the Morris water maze. Neural Computation, 10(1), 73–111.

    Article  CAS  Google Scholar 

  • Rickard, T. C., & Grafman, J. (1998). Losing their configural mind. Amnesic patients fail on transverse patterning. Journal of Cognitive Neuroscience, 10(4), 509–524.

    Article  CAS  Google Scholar 

  • Smith, S. M. (2002). Fast robust automated brain extraction. Human Brain Mapping, 17(3), 143–155. https://doi.org/10.1002/hbm.10062

    Article  PubMed  PubMed Central  Google Scholar 

  • Smith, S. M., Jenkinson, M., Woolrich, M. W., Beckmann, C. F., Behrens, T. E., Johansen-Berg, H., ... Matthews, P. M. (2004). Advances in functional and structural MR image analysis and implementation as FSL. Neuroimage, 23(Suppl 1), S208–S219. https://doi.org/10.1016/j.neuroimage.2004.07.051.

  • Smith, S. M., Beckmann, C. F., Andersson, J., Auerbach, E. J., Bijsterbosch, J., Douaud, G., ... Consortium, W.-M.H. (2013). Resting-state fMRI in the Human Connectome Project. Neuroimage, 80, 144–168. https://doi.org/10.1016/j.neuroimage.2013.05.039.

  • Strange, B. A., Witter, M. P., Lein, E. S., & Moser, E. I. (2014). Functional organization of the hippocampal longitudinal axis. Nature Reviews. Neuroscience, 15(10), 655–669. https://doi.org/10.1038/nrn3785

    Article  CAS  PubMed  Google Scholar 

  • Wang, L., Zang, Y., He, Y., Liang, M., Zhang, X., Tian, L., ... Li, K. (2006). Changes in hippocampal connectivity in the early stages of Alzheimer's disease: evidence from resting state fMRI. Neuroimage, 31(2), 496–504. https://doi.org/10.1016/j.neuroimage.2005.12.033.

  • Wang, K., Liang, M., Wang, L., Tian, L., Zhang, X., Li, K., & Jiang, T. (2007). Altered functional connectivity in early Alzheimer's disease: A resting-state fMRI study. Human Brain Mapping, 28(10), 967–978. https://doi.org/10.1002/hbm.20324

    Article  PubMed  Google Scholar 

  • Winkler, A. M., Ridgway, G. R., Webster, M. A., Smith, S. M., & Nichols, T. E. (2014). Permutation inference for the general linear model. Neuroimage, 92, 381–397. https://doi.org/10.1016/j.neuroimage.2014.01.060

    Article  PubMed  Google Scholar 

  • Zarei, M., Beckmann, C. F., Binnewijzend, M. A., Schoonheim, M. M., Oghabian, M. A., Sanz-Arigita, E. J., ... Barkhof, F. (2013). Functional segmentation of the hippocampus in the healthy human brain and in Alzheimer's disease. Neuroimage, 66, 28–35. https://doi.org/10.1016/j.neuroimage.2012.10.071.

Download references

Funding

This work was supported by National Institute on Aging (NIA) R00-AG032361 (Driscoll) and F31-AG050407 (Korthauer).

Author information

Authors and Affiliations

Authors

Contributions

Author contributions included conception and study design (ID), data collection and acquisition (LEK, EA, MF, RP, ID), statistical analysis (LEK, JKB), interpretation of results (LEK, JKB, ID), drafting the manuscript and revising it critically for important intellectual content (LEK, JKB, ID), and approval of the final version to be published and agreement to be accountable for the integrity and accuracy of all aspects of the work (all authors).

Corresponding author

Correspondence to Ira Driscoll.

Ethics declarations

All participants provided written informed consent in accordance with guidelines set by the institutional review boards at the University of Wisconsin-Milwaukee and Medical College of Wisconsin.

Conflict of interest/Disclosure statement

The authors have no conflict of interest to report.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Korthauer, L.E., Blujus, J.K., Awe, E. et al. Brain-behavior investigation of potential cognitive markers of Alzheimer’s disease in middle age: a multi-modal imaging study. Brain Imaging and Behavior 16, 1098–1105 (2022). https://doi.org/10.1007/s11682-021-00573-x

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11682-021-00573-x

Keywords

Navigation