Hostname: page-component-5d59c44645-lfgmx Total loading time: 0 Render date: 2024-03-03T12:40:49.875Z Has data issue: false hasContentIssue false

White Matter Correlates of Cognitive Performance on the UCSF Brain Health Assessment

Published online by Cambridge University Press:  26 April 2019

Andrea G. Alioto*
Affiliation:
Alzheimer’s Disease Center- East Bay, University of California, Davis, CA 94598, USA
Paige Mumford
Affiliation:
London Institute of Neurology, University College London, London WC1E6BT, UK
Amy Wolf
Affiliation:
Memory and Aging Center, University of California, San Francisco, CA 94158, USA
Kaitlin B. Casaletto
Affiliation:
Memory and Aging Center, University of California, San Francisco, CA 94158, USA
Sabrina Erlhoff
Affiliation:
Memory and Aging Center, University of California, San Francisco, CA 94158, USA
Tacie Moskowitz
Affiliation:
Memory and Aging Center, University of California, San Francisco, CA 94158, USA
Joel H. Kramer
Affiliation:
Memory and Aging Center, University of California, San Francisco, CA 94158, USA
Katherine P. Rankin
Affiliation:
Memory and Aging Center, University of California, San Francisco, CA 94158, USA
Katherine L. Possin
Affiliation:
Memory and Aging Center, University of California, San Francisco, CA 94158, USA
*
Correspondence and reprint requests to: Andrea G. Alioto, Alzheimer’s Disease Center- East Bay, University of California, Davis 100 N. Wiget Lane, Suite 150, Walnut Creek, CA 94598. E-mail: agalioto@ucdavis.edu

Abstract

Objective: White matter (WM) microstructural changes are increasingly recognized as a mechanism of age-related cognitive differences. This study examined the associations between patterns of WM microstructure and cognitive performance on the University of California, San Francisco (UCSF) Brain Health Assessment (BHA) subtests of memory (Favorites), executive functions and speed (Match), and visuospatial skills (Line Orientation) within a sample of older adults. Method: Fractional anisotropy (FA) in WM tracts and BHA performance were examined in 84 older adults diagnosed as neurologically healthy (47), with mild cognitive impairment (19), or with dementia (18). The relationships between FA and subtest performances were evaluated using regression analyses. We then explored whether regional WM predicted performance after accounting for variance explained by global FA. Results: Memory performance was associated with FA of the fornix and the superior cerebellar peduncle; and executive functions and speed, with the body of the corpus callosum. The fornix–memory association and the corpus callosum–executive association remained significant after accounting for global FA. Neither tract-based nor global FA was associated with visuospatial performance. Conclusions: Memory and executive functions are associated with different patterns of WM diffusivity. Findings add insight into WM alterations underlying age- and disease-related cognitive decline.

Type
Brief Communication
Copyright
Copyright © INS. Published by Cambridge University Press, 2019. 

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

Albert, M.S., DeKosky, S.T., Dickson, D., Dubois, B., Feldman, H.H., Fox, N.C., Gamst, A., Holtzman, D.M., Jagust, W.J., Petersen, P.C., Snyder, P.J., Carrillo, M.C., Thies, B., & Phelps, C.H. (2011). The diagnosis of mild cognitive impairment due to Alzheimer’s disease: recommendations from the National Institute on Aging- Alzhiemer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimer’s and Dementia, 7(3), 270279. doi: 10.1016/j.jalz.2011.03.008 CrossRefGoogle Scholar
American Psychiatric Association. (2013). Diagnostic and statistical manual of mental disorders: DSM-5 (5th ed.). Arlington, VA: American Psychiatric Association.Google Scholar
Bennett, I.J. & Madden, D.J. (2014). Disconnected aging: cerebral white matter integrity and age-related differences in cognition. Neuroscience, 276, 187205. doi: 10.1016/j.neuroscience.2013.11.026 CrossRefGoogle ScholarPubMed
Bettcher, B.M., Mungas, D., Patel, N., Elofson, J., Dutt, S., Wynn, M., Watson, C.L., Stephens, M., Walsh, C.M., & Kramer, J.H. (2016). Neuroanatomical substrates of executive functions: beyond prefrontal structures. Neuropsychologia, 85, 100109. doi: 10.1016/j.neuropsychologia.2016.03.001 CrossRefGoogle ScholarPubMed
Brickman, A.M., Honig, L.S., Scarmeas, N., Tatarina, O., Sanders, L., Albert, M.S., Brandt, J., Blacker, D., & Stern, Y. (2008). Measuring cerebral atrophy and white matter hyperintensity burden to predict the rate of cognitive decline in Alzheimer disease. Archives of Neurology, 65(9), 12021208. doi: 10.1001/archneur.65.9.1202 CrossRefGoogle ScholarPubMed
Cohen, J. (1992). A power primer. Psychological Bulletin, 112, 155159.CrossRefGoogle ScholarPubMed
Fischer, F.U., Wolf, D., Scheurich, A., Fellgiebel, A., & Alzheimer’s Disease Neuroimaging Initiative. (2015). Altered whole-brain white matter networks in preclinical Alzheimer’s disease. NeuroImage: Clinical, 8, 660666. doi: 10.1016/j.nicl.2015.06.007 CrossRefGoogle ScholarPubMed
Fletcher, P.C. & Henson, R.N.A. (2001). Frontal lobes and human memory: insights from functional neuroimaging. Brain, 124(5), 849881. doi: 10.1093/brain/124.5.849 CrossRefGoogle ScholarPubMed
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), 189198.CrossRefGoogle ScholarPubMed
Garyfallidis, E., Bret, M., Amirbekian, B., Rokem, A., van der Walt, S., Descoteaux, M., & Nimmo-Smith, I. (2014). Dipy, a library for the analysis of diffusion MRI data. Frontiers in Neuroimformatics, 8, 8. doi: 10.3389/fnif.2014.00008 Google ScholarPubMed
Hochberg, Y. (1988). A sharper Bonferroni procedure for multiple tests of significance. Biometrika, 75(4), 800802.CrossRefGoogle Scholar
Jacobs, H.I.L., Leritz, E.C., Williams, V.J., Van Boxtel, M.P.J., van der Elst, W., Jolles, J., Verhey, F.R.J., McGlinchey, R.E., Milberg, W.P., & Salat, D.H. (2013). Association between white matter microstructure, executive functioning, and processing speed in older adults: the impact of vascular health. Human Brain Mapping, 34(1), 7795. doi: 10.1002/hbm.21412 CrossRefGoogle ScholarPubMed
Mori, S., Wakana, S, van Zijl, P.C.M., & Nagae-Poetscher, L.M. (2005). MRI atlas of human white matter (1st ed.). Amsterdam: Elsevier.Google Scholar
Morris, J.C. (1993). The Clinical Dementia Rating (CDR): current version and scoring rules. Neurology, 43(11), 24122412. doi: 10.1212/WNL.43.11.2412-a CrossRefGoogle ScholarPubMed
Nowrangi, M.A. & Rosenberg, P.B. (2015). The fornix in mild cognitive impairment and Alzheimer’s disease. Frontiers in Aging Neuroscience, 7, 17. doi:10.3389/fnagi.2015.00001 CrossRefGoogle ScholarPubMed
Possin, K.L., Moskowitz, T., Erlhoff, S.J., Rogers, K., Johnson, E., Steele, N.Z.R., Higgins, J.J., Stiver, J., Alioto, A.G., Farias, S.T., Miller, B.L., & Rankin, K.R. (2018). The Brain Health Assessment for detecting and diagnosing neurocognitive disorders. Journal of the American Geriatrics Society, 66(1), 150156. doi: 10.1111/jgs.15208 CrossRefGoogle Scholar
Sexton, C.E., Ukwuori, G.K., Flippini, N., Mackay, C.E., & Ebmeier, K.P. (2011). A meta-analysis of diffusion tensor imaging in mild cognitive impairment and Alzheimer’s disease. Neurobiology of Aging, 32(12), 2322.e5–2322.318. doi: 10.1016/j.neurobiolaging.2010.05.019 CrossRefGoogle ScholarPubMed
Tranel, D., Vianna, E., Manzel, K., Damasio, H., & Grabowski, T. (2009). Neuroanatomical correlates of the Benton Facial Recognition Test and Judgment of Line Orientation Test. Journal of Clinical and Experimental Neuropsychology, 31(2), 219233. doi: 10.1080/13803390802317542 CrossRefGoogle ScholarPubMed
Vernooij, M.W., Ikram, M.A., Vrooman, H.A., Wielopolski, P.A., Krestin, G.P., Hofman, A., Niessen, W.J., Van der Lugt, A., & Breteler, M.M.B. (2009). White matter microstructural integrity and cognitive function in a general elderly population. Archives of General Psychiatry, 66(60), 545553. doi: 10.1001/archgenpsychiatry.2009.5 CrossRefGoogle Scholar
Zhang, H., Yushkevich, P.A., Alexander, D.C., & Gee, J.C. (2006). Deformable registration of diffusion tensor MR images with explicit orientation optimization. Medical Image Analysis, 10(5), 764785. doi: 10.1016/j.media.2006.06.004 CrossRefGoogle ScholarPubMed
Supplementary material: File

Alioto et al. supplementary material

Appendix S1
Download Alioto et al. supplementary material(File)
File 500 KB