Hostname: page-component-cd9895bd7-hc48f Total loading time: 0 Render date: 2024-12-25T18:46:50.400Z Has data issue: false hasContentIssue false

Multiple Cognitive and Behavioral Factors Link Association Between Brain Structure and Functional Impairment of Daily Instrumental Activities in Older Adults

Published online by Cambridge University Press:  26 July 2021

Seyul Kwak
Affiliation:
Department of Psychiatry, Seoul Metropolitan Government-Seoul National University College Boramae Medical Center, Seoul, Republic of Korea
Su Mi Park
Affiliation:
Department of Psychiatry, Seoul Metropolitan Government-Seoul National University College Boramae Medical Center, Seoul, Republic of Korea Department of Counseling Psychology, Hannam University, Daejeon, Republic of Korea
Yeong-Ju Jeon
Affiliation:
Department of Psychiatry, Seoul Metropolitan Government-Seoul National University College Boramae Medical Center, Seoul, Republic of Korea
Hyunwoong Ko
Affiliation:
Department of Psychiatry, Seoul Metropolitan Government-Seoul National University College Boramae Medical Center, Seoul, Republic of Korea Interdisciplinary Program in Cognitive Science, Seoul National University, Seoul, Republic of Korea
Dae Jong Oh
Affiliation:
Department of Psychiatry, Seoul Metropolitan Government-Seoul National University College Boramae Medical Center, Seoul, Republic of Korea
Jun-Young Lee*
Affiliation:
Department of Psychiatry, Seoul Metropolitan Government-Seoul National University College Boramae Medical Center, Seoul, Republic of Korea
*
*Correspondence and reprint requests to: Jun-Young Lee, Department of Psychiatry, Seoul National University College of Medicine & SMG-SNU Boramae Medical Center, Seoul07061, Republic of Korea. Email: benji@snu.ac.kr

Abstract

Objective:

Functional impairment in daily activity is a cornerstone in distinguishing the clinical progression of dementia. Multiple indicators based on neuroimaging and neuropsychological instruments are used to assess the levels of impairment and disease severity; however, it remains unclear how multivariate patterns of predictors uniquely predict the functional ability and how the relative importance of various predictors differs.

Method:

In this study, 881 older adults with subjective cognitive complaints, mild cognitive impairment (MCI), and dementia with Alzheimer’s type completed brain structural magnetic resonance imaging (MRI), neuropsychological assessment, and a survey of instrumental activities of daily living (IADL). We utilized the partial least square (PLS) method to identify latent components that are predictive of IADL.

Results:

The result showed distinct brain components (gray matter density of cerebellar, medial temporal, subcortical, limbic, and default network regions) and cognitive–behavioral components (general cognitive abilities, processing speed, and executive function, episodic memory, and neuropsychiatric symptoms) were predictive of IADL. Subsequent path analysis showed that the effect of brain structural components on IADL was largely mediated by cognitive and behavioral components. When comparing hierarchical regression models, the brain structural measures minimally added the explanatory power of cognitive and behavioral measures on IADL.

Conclusion:

Our finding suggests that cerebellar structure and orbitofrontal cortex, alongside with medial temporal lobe, play an important role in the maintenance of functional status in older adults with or without dementia. Moreover, the significance of brain structural volume affects real-life functional activities via disruptions in multiple cognitive and behavioral functions.

Type
Research Article
Copyright
Copyright © INS. Published by Cambridge University Press, 2021

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., Zhu, Y., Moghekar, A., Mori, S., Miller, M.I., Soldan, A., … Wang, M.C. (2018). Predicting progression from normal cognition to mild cognitive impairment for individuals at 5 years. Brain, 141(3), 877887. https://doi.org/10.1093/brain/awx365 CrossRefGoogle ScholarPubMed
Amieva, H., Mokri, H., Le Goff, M., Meillon, C., Jacqmin-Gadda, H., Foubert-Samier, A., … Dartigues, J.F. (2014). Compensatory mechanisms in higher-educated subjects with Alzheimer’s disease: A study of 20 years of cognitive decline. Brain, 137(4), 11671175. https://doi.org/10.1093/brain/awu035 Google ScholarPubMed
Arenaza-Urquijo, E.M. & Vemuri, P. (2018). Resistance vs resilience to Alzheimer disease. Neurology, 90(15), 695703. https://doi.org/10.1212/WNL.0000000000005303 CrossRefGoogle ScholarPubMed
Belleville, S., Fouquet, C., Hudon, C., Zomahoun, H.T.V., & Croteau, J. (2017). Neuropsychological Measures that Predict Progression from Mild Cognitive Impairment to Alzheimer’s type dementia in Older Adults: a Systematic Review and Meta-Analysis. Neuropsychology Review, 27(4), 328353. https://doi.org/10.1007/s11065-017-9361-5 CrossRefGoogle ScholarPubMed
Bilder, R.M. & Reise, S.P. (2019). Neuropsychological tests of the future: How do we get there from here? The Clinical Neuropsychologist, 33(2), 220245. https://doi.org/10.1080/13854046.2018.1521993 CrossRefGoogle Scholar
Borges, M.K., Canevelli, M., Cesari, M., & Aprahamian, I. (2019). Frailty as a predictor of cognitive disorders: A systematic review and meta-analysis. Frontiers in Medicine. https://doi.org/10.3389/fmed.2019.00026 Google Scholar
Burton, R.L., O’Connell, M.E., & Morgan, D.G. (2018). Cognitive and neuropsychiatric correlates of functional impairment across the continuum of no cognitive impairment to dementia. Archives of Clinical Neuropsychology, 33(7), 795807. https://doi.org/10.1093/arclin/acx112 CrossRefGoogle Scholar
Bzdok, D. & Meyer-Lindenberg, A. (2018). Machine learning for precision psychiatry: opportunities and challenges. Biological Psychiatry: Cognitive Neuroscience and Neuroimaging, 3(3), 223230. https://doi.org/10.1016/j.bpsc.2017.11.007 Google ScholarPubMed
Chang, Y.L., Bondi, M.W., McEvoy, L.K., Fennema-Notestine, C., Salmon, D.P., Galasko, D., … Dale, A.M. (2011). Global clinical dementia rating of 0.5 in MCI masks variability related to level of function. Neurology. https://doi.org/10.1212/WNL.0b013e31820ce6a5 CrossRefGoogle Scholar
Choi, S.H., Na, D.L., Kwon, H.M., Yoon, S.J., Jeong, J.H., & Ha, C.K. (2000). The Korean version of the Neuropsychiatric Inventory: A scoring tool for neuropsychiatric disturbance in dementia patients. Journal of Korean Medical Science. https://doi.org/10.3346/jkms.2000.15.6.609 CrossRefGoogle Scholar
Cummings, J.L., Mega, M., Gray, K., Rosenberg-Thompson, S., Carusi, D.A., & Gornbein, J. (1994). The neuropsychiatric inventory: Comprehensive assessment of psychopathology in -dementia. Neurology. https://doi.org/10.1212/wnl.44.12.2308 CrossRefGoogle Scholar
de Paula, J.J., Diniz, B.S., Bicalho, M.A., Albuquerque, M.R., Nicolato, R., de Moraes, E.N., … Malloy-Diniz, L.F. (2015). Specific cognitive functions and depressive symptoms as predictors of activities of daily living in older adults with heterogeneous cognitive backgrounds. Frontiers in Aging Neuroscience. https://doi.org/10.3389/fnagi.2015.00139 CrossRefGoogle Scholar
Delgado, C., Vergara, R.C., Martínez, M., Musa, G., Henríquez, F., & Slachevsky, A. (2019). Neuropsychiatric symptoms in Alzheimer’s disease are the main determinants of functional impairment in advanced everyday activities. Journal of Alzheimer’s Disease. https://doi.org/10.3233/JAD-180771 CrossRefGoogle Scholar
Donders, J. (2019). The incremental value of neuropsychological assessment: A critical review. The Clinical Neuropsychologist, 132. https://doi.org/10.1080/13854046.2019.1575471 CrossRefGoogle Scholar
Dong, A., Toledo, J.B., Honnorat, N., Doshi, J., Varol, E., Sotiras, A., … Davatzikos, C. (2017). Heterogeneity of neuroanatomical patterns in prodromal Alzheimer’s disease: Links to cognition, progression and biomarkers. Brain, 145(3), 732747. https://doi.org/10.1093/brain/aww319 Google Scholar
Fields, J.A., Ferman, T.J., Boeve, B.F., & Smith, G.E. (2011). Neuropsychological assessment of patients with dementing illness. Nature Reviews Neurology, 7(12), 677687. https://doi.org/10.1038/nrneurol.2011.173 CrossRefGoogle ScholarPubMed
Fields, J.A., Machulda, M., Aakre, J., Ivnik, R.J., Boeve, B.F., Knopman, D.S., … Smith, G.E. (2010). Utility of the drs for predicting problems in day-to-day functioning. Clinical Neuropsychologist, 24(7), 11671180. https://doi.org/10.1080/13854046.2010.514865 CrossRefGoogle ScholarPubMed
Fjell, A.M., Westlye, L.T., Grydeland, H., Amlien, I., Espeseth, T., Reinvang, I., … Walhovd, K.B. (2013). Neurobiology of Aging Critical ages in the life course of the adult brain : nonlinear subcortical aging. Neurobiology of Aging, 34, 22392247. https://doi.org/10.1016/j.neurobiolaging.2013.04.006 CrossRefGoogle ScholarPubMed
Giorgio, J., Landau, S.M., Jagust, W.J., Tino, P., & Kourtzi, Z. (2020). Modelling prognostic trajectories of cognitive decline due to Alzheimer’s disease. NeuroImage: Clinical. https://doi.org/10.1016/j.nicl.2020.102199 CrossRefGoogle Scholar
Guo, C.C., Tan, R., Hodges, J.R., Hu, X., Sami, S., & Hornberger, M. (2016). Network-selective vulnerability of the human cerebellum to Alzheimer’s disease and frontotemporal dementia. Brain. https://doi.org/10.1093/brain/aww003 CrossRefGoogle Scholar
Harrison, J., Minassian, S.L., Jenkins, L., Black, R.S., Koller, M., & Grundman, M. (2007). A neuropsychological test battery for use in alzheimer disease clinical trials. Archives of Neurology. https://doi.org/10.1001/archneur.64.9.1323 CrossRefGoogle Scholar
Howieson, D. (2019). Current limitations of neuropsychological tests and assessment procedures. The Clinical Neuropsychologist, 33(2), 200208. https://doi.org/10.1080/13854046.2018.1552762 CrossRefGoogle ScholarPubMed
Jack, C.R., Bennett, D.A., Blennow, K., Carrillo, M.C., Dunn, B., Haeberlein, S.B., … Silverberg, N. (2018). NIA-AA Research Framework: Toward a biological definition of Alzheimer’s disease. Alzheimer’s and Dementia, 14(4), 535562. https://doi.org/10.1016/j.jalz.2018.02.018 CrossRefGoogle Scholar
Jack, C.R. & Holtzman, D.M. (2013). Biomarker modeling of alzheimer’s disease. Neuron, 80(6), 13471358. https://doi.org/10.1016/j.neuron.2013.12.003 CrossRefGoogle ScholarPubMed
Jacobs, H.I.L., Hopkins, D.A., Mayrhofer, H.C., Bruner, E., Van Leeuwen, F.W., Raaijmakers, W., & Schmahmann, J.D. (2018). The cerebellum in Alzheimer’s disease: Evaluating its role in cognitive decline. Brain. https://doi.org/10.1093/brain/awx194 CrossRefGoogle Scholar
Jekel, K., Damian, M., Wattmo, C., Hausner, L., Bullock, R., Connelly, P.J., … Frölich, L. (2015). Mild cognitive impairment and deficits in instrumental activities of daily living: A systematic review. Alzheimer’s Research and Therapy. https://doi.org/10.1186/s13195-015-0099-0 CrossRefGoogle Scholar
Johnson, J.K., Lui, L.-Y., & Yaffe, K. (2007). Executive function, more than global cognition, predicts functional decline and mortality in elderly women. The Journals of Gerontology Series A: Biological Sciences and Medical Sciences, 62(10), 11341141. https://doi.org/10.1093/gerona/62.10.1134 CrossRefGoogle ScholarPubMed
Kievit, R.A., Davis, S.W., Mitchell, D.J., Taylor, J.R., Duncan, J., & Henson, R.N.A. (2014). Distinct aspects of frontal lobe structure mediate age-related differences in fluid intelligence and multitasking. Nature Communications, 5, 5658. https://doi.org/10.1038/ncomms6658 CrossRefGoogle ScholarPubMed
Kim, S., Won, J., & Cho, K. (2005). The validity and reliability of korean version of lawton IADL index. Journal of the Korean Geriatrics Society, 9(1), 2329.Google Scholar
Krishnan, A., Williams, L.J., McIntosh, A.R., & Abdi, H. (2011). Partial Least Squares (PLS) methods for neuroimaging: A tutorial and review. NeuroImage, 56(2), 455475. https://doi.org/10.1016/j.neuroimage.2010.07.034 CrossRefGoogle ScholarPubMed
Kuhn, M. (2015). A short introduction to the caret package. R Foundation for Statistical Computing, 1–10. Retrieved from cran.r-project.org/web/packages/caret/vignettes/caret.pdf Google Scholar
Lawton, M.P. & Brody, E.M. (1969). Assessment of older people: Self-maintaining and instrumental activities of daily living. Gerontologist. https://doi.org/10.1093/geront/9.3_Part_1.179 CrossRefGoogle Scholar
Lee, J.H., Lee, K.U., Lee, D.Y., Kim, K.W., Jhoo, J.H., Kim, J.H., … Woo, J.I. (2002). Development of the Korean version of the consortium to establish a registry for Alzheimer’s disease assessment packet (CERAD-K): Clinical and neuropsychological assessment batteries. The Journals of Gerontology Series B: Psychological Sciences and Social Sciences, 57(1), P47P53. https://doi.org/10.1093/geronb/57.1.P47 CrossRefGoogle Scholar
Lindbergh, C.A., Dishman, R.K., & Miller, L.S. (2016). Functional disability in mild cognitive impairment: A systematic review and meta-analysis. Neuropsychology Review. https://doi.org/10.1007/s11065-016-9321-5 CrossRefGoogle Scholar
Manjón, J.V., Coupé, P., Martí-Bonmatí, L., Collins, D.L., & Robles, M. (2010). Adaptive non-local means denoising of MR images with spatially varying noise levels. Journal of Magnetic Resonance Imaging, 31(1), 192203. https://doi.org/10.1002/jmri.22003 CrossRefGoogle ScholarPubMed
Marek, S., Siegel, J.S., Gordon, E.M., Raut, R.V., Gratton, C., Newbold, D.J., … Dosenbach, N.U.F. (2018). Spatial and temporal organization of the individual human cerebellum. Neuron, 100(4), 977993.e7. https://doi.org/10.1016/j.neuron.2018.10.010 CrossRefGoogle ScholarPubMed
Masouleh, K.S., Eickhoff, S.B., Hoffstaedter, F., & Genon, S. (2019). Empirical examination of the replicability of associations between brain structure and psychological variables. ELife, 8, 125. https://doi.org/10.7554/elife.43464 Google Scholar
McKhann, G.M., 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. https://doi.org/10.1212/WNL.34.7.939 CrossRefGoogle ScholarPubMed
McKhann, G.M., Knopman, D.S., Chertkow, H., Hyman, B.T., Jack, C.R., Kawas, C.H., … Phelps, C.H. (2011). The diagnosis of dementia 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), 263269. https://doi.org/10.1016/j.jalz.2011.03.005 CrossRefGoogle Scholar
Möller, C., Vrenken, H., Jiskoot, L., Versteeg, A., Barkhof, F., Scheltens, P., & van der Flier, W.M. (2013). Different patterns of gray matter atrophy in early- and late-onset Alzheimer’s disease. Neurobiology of Aging. https://doi.org/10.1016/j.neurobiolaging.2013.02.013 CrossRefGoogle Scholar
Ossenkoppele, R., Pijnenburg, Y.A.L., Perry, D.C., Cohn-Sheehy, B.I., Scheltens, N.M.E., Vogel, J.W., … Rabinovici, G.D. (2015). The behavioural/dysexecutive variant of Alzheimer’s disease: Clinical, neuroimaging and pathological features. Brain, 138(9), 27322749. https://doi.org/10.1093/brain/awv191 Google ScholarPubMed
Overdorp, E.J., Kessels, R.P.C., Claassen, J.A., & Oosterman, J.M. (2016). The combined effect of neuropsychological and neuropathological deficits on instrumental activities of daily living in older adults: A systematic review. Neuropsychology Review, 26(1), 92106. https://doi.org/10.1007/s11065-015-9312-y CrossRefGoogle ScholarPubMed
Pellegrini, E., Ballerini, L., Hernandez, M. del C.V., Chappell, F.M., González-Castro, V., Anblagan, D., … Wardlaw, J.M. (2018). Machine learning of neuroimaging for assisted diagnosis of cognitive impairment and dementia: A systematic review. Alzheimer’s and Dementia: Diagnosis, Assessment and Disease Monitoring. https://doi.org/10.1016/j.dadm.2018.07.004 CrossRefGoogle Scholar
Petersen, R.C., Smith, G.E., Waring, S.C., Ivnik, R.J., Tangalos, E.G., & Kokmen, E. (1999). Mild cognitive impairment. Archives of Neurology, 56(3), 303. https://doi.org/10.1001/archneur.56.3.303 CrossRefGoogle ScholarPubMed
Rathore, S., Habes, M., Iftikhar, M.A., Shacklett, A., & Davatzikos, C. (2017). A review on neuroimaging-based classification studies and associated feature extraction methods for Alzheimer’s disease and its prodromal stages. NeuroImage, 155(March), 530548. https://doi.org/10.1016/j.neuroimage.2017.03.057 CrossRefGoogle ScholarPubMed
Reiman, E.M., Quiroz, Y.T., Fleisher, A.S., Chen, K., Velez-Pardo, C., Jimenez-Del-Rio, M., … Lopera, F. (2012). Brain imaging and fluid biomarker analysis in young adults at genetic risk for autosomal dominant Alzheimer’s disease in the presenilin 1 E280A kindred: A case-control study. The Lancet Neurology. https://doi.org/10.1016/S1474-4422(12)70228-4 CrossRefGoogle Scholar
Rosseel, Y. (2012). lavaan : An R package for structural equation modeling. Journal of Statistical Software, 48(2). https://doi.org/10.18637/jss.v048.i02 CrossRefGoogle Scholar
Royall, D.R., Lauterbach, E.C., Kaufer, D., Malloy, P., Coburn, K.L., & Black, K.J. (2007). The cognitive correlates of functional status: A review from the committee on research of the American neuropsychiatric association. The Journal of Neuropsychiatry and Clinical Neurosciences, 19(3), 249265. https://doi.org/10.1176/jnp.2007.19.3.249 CrossRefGoogle Scholar
Ruan, Q., D’Onofrio, G., Sancarlo, D., Bao, Z., Greco, A., & Yu, Z. (2016). Potential neuroimaging biomarkers of pathologic brain changes in Mild Cognitive Impairment and Alzheimer’s disease: A systematic review. BMC Geriatrics. https://doi.org/10.1186/s12877-016-0281-7 CrossRefGoogle Scholar
Rudolph, M.D., Graham, A.M., Feczko, E., Miranda-Dominguez, O., Rasmussen, J.M., Nardos, R., … Fair, D.A. (2018). Maternal IL-6 during pregnancy can be estimated from newborn brain connectivity and predicts future working memory in offspring. Nature Neuroscience, 21(May), 18. https://doi.org/10.1038/s41593-018-0128-y CrossRefGoogle ScholarPubMed
Schaefer, A., Kong, R., Gordon, E.M., Laumann, T.O., Zuo, X.-N., Holmes, A.J., … Yeo, B.T.T. (2018). Local-global parcellation of the human cerebral cortex from intrinsic functional connectivity MRI. Cerebral Cortex, 28(9), 30953114. https://doi.org/10.1093/cercor/bhx179 CrossRefGoogle ScholarPubMed
Seo, E.H., Lee, D.Y., Kim, K.W., Lee, J.H., Jhoo, J.H., Youn, J.C., … Woo, J.I. (2006). A normative study of the Trail Making Test in Korean elders. International Journal of Geriatric Psychiatry, 21(9), 844852. https://doi.org/10.1002/gps.1570 CrossRefGoogle ScholarPubMed
Stern, Y., Arenaza-Urquijo, E.M., Bartrés-Faz, D., Belleville, S., Cantilon, M., Chetelat, G., … Vuoksimaa, E. (2018). Whitepaper: Defining and investigating cognitive reserve, brain reserve, and brain maintenance. Alzheimer’s & Dementia, 17. https://doi.org/10.1016/j.jmarsys.2011.03.015 CrossRefGoogle Scholar
Thomann, P.A., Schläfer, C., Seidl, U., Santos, V. Dos, Essig, M., & Schröder, J. (2008). The cerebellum in mild cognitive impairment and Alzheimer’s disease - A structural MRI study. Journal of Psychiatric Research. https://doi.org/10.1016/j.jpsychires.2007.12.002 CrossRefGoogle Scholar
Varkuti, B., Cavusoglu, M., Kullik, A., Schiffler, B., Veit, R., Yilmaz, O., … Sitaram, R. (2011). Quantifying the link between anatomical connectivity, gray matter volume and regional cerebral blood flow: An integrative MRI study. PLoS One, 6(4), e14801. https://doi.org/10.1371/journal.pone.0014801 CrossRefGoogle ScholarPubMed
Vemuri, P., Lesnick, T.G., Przybelski, S.A., Knopman, D.S., Lowe, V.J., Graff-Radford, J., … Jack, C.R. (2017). Age, vascular health, and Alzheimer disease biomarkers in an elderly sample. Annals of Neurology. https://doi.org/10.1002/ana.25071 CrossRefGoogle Scholar
Verbrugge, L.M. & Jette, A.M. (1994). The disablement process. Social Science and Medicine. https://doi.org/10.1016/0277-9536(94)90294-1 CrossRefGoogle Scholar
Wade, D.T. & Collin, C. (1988). The barthel ADL index: A standard measure of physical disability? Disability and Rehabilitation. https://doi.org/10.3109/09638288809164105 CrossRefGoogle Scholar
Yarkoni, T. & Westfall, J. (2017). Choosing prediction over explanation in psychology: lessons from machine learning. Perspectives on Psychological Science, 12(6), 11001122. https://doi.org/10.1177/1745691617693393 CrossRefGoogle ScholarPubMed
Yeo, T., Krienen, F.M., Sepulcre, J., Sabuncu, M.R., Lashkari, D., Hollinshead, M., … Buckner, R.L. (2011). The organization of the human cerebral cortex estimated by intrinsic functional connectivity. Journal of Neurophysiology, 106(3), 11251165. https://doi.org/10.1152/jn.00338.2011 Google ScholarPubMed
You, S.C., Walsh, C.M., Chiodo, L.A., Ketelle, R., & Miller, B.L. (2015). Neuropsychiatric Symptoms Predict Functional Status in Alzheimer’s Disease. Journal of Alzheimer’s Disease, 48, 863869. https://doi.org/10.3233/JAD-150018 CrossRefGoogle ScholarPubMed
Younes, L., Albert, M., Moghekar, A., Soldan, A., Pettigrew, C., & Miller, M.I. (2019). Identifying changepoints in biomarkers during the preclinical phase of Alzheimer’s disease. Frontiers in Aging Neuroscience. https://doi.org/10.3389/fnagi.2019.00074 CrossRefGoogle Scholar