The Latent Factor Structure Underlying Regional Brain Volume Change and Its Relation to Cognitive Change in Older Adults

Overview

Journal Neuropsychology

Publisher American Psychological Association

Specialty Neurology

Date 2021 Jul 22

PMID 34292026

Citations 2

Authors

Brandon E Gavett

Evan Fletcher

Keith F Widaman

Sarah Tomaszewski Farias

Charles DeCarli

Dan Mungas

Affiliations

Soon will be listed here.

Abstract

Objective: Late-life changes in cognition and brain integrity are both highly multivariate, time-dependent processes that are essential for understanding cognitive aging and neurodegenerative disease outcomes. The present study seeks to identify a latent variable model capable of efficiently reducing a multitude of structural brain change magnetic resonance imaging (MRI) measurements into a smaller number of dimensions. We further seek to demonstrate the validity of this model by evaluating its ability to reproduce patterns of coordinated brain volume change and to explain the rate of cognitive decline over time.

Method: We used longitudinal cognitive data and structural MRI scans, obtained from a diverse sample of 358 participants ( = 74.81, = 7.17), to implement latent variable models for measuring brain change and to estimate the effects of these brain change factors on cognitive decline.

Results: Results supported a bifactor model for brain change with four group factors (prefrontal, temporolimbic, medial temporal, and posterior association) and one general change factor (global atrophy). Atrophy in the global (β = 0.434, = 0.070), temporolimbic (β = 0.275, = 0.085), and medial temporal (β = 0.240, = 0.085) factors were the strongest predictors of global cognitive decline. Overall, the brain change model explained 59% of the variance in global cognitive slope.

Conclusions: The current results suggest that brain change across 27 bilateral regions of interest can be grouped into five change factors, three of which (global gray matter, temporolimbic, and medial temporal lobe atrophy) are strongly associated with cognitive decline. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Citing Articles

The effects of CNS atrophy and ICVD on tests of executive function and functional status are mediated by intelligence.

Royall D, Palmer R Cereb Circ Cogn Behav. 2023; 5:100184.

PMID: 37811522 PMC: 10550593. DOI: 10.1016/j.cccb.2023.100184.

Episodic Memory and Executive Function Are Differentially Affected by Retests but Similarly Affected by Age in a Longitudinal Study of Normally-Aging Older Adults.

Glisky E, Woolverton C, McVeigh K, Grilli M Front Aging Neurosci. 2022; 14:863942.

PMID: 35493924 PMC: 9043807. DOI: 10.3389/fnagi.2022.863942.

References

Mungas D, Reed B, Farias S, DeCarli C . Criterion-referenced validity of a neuropsychological test battery: equivalent performance in elderly Hispanics and non-Hispanic Whites. J Int Neuropsychol Soc. 2005; 11(5):620-30. PMC: 3771317. DOI: 10.1017/S1355617705050745. View

Fletcher E, Gavett B, Harvey D, Farias S, Olichney J, Beckett L . Brain volume change and cognitive trajectories in aging. Neuropsychology. 2018; 32(4):436-449. PMC: 6525569. DOI: 10.1037/neu0000447. View

Persson K, Eldholm R, Barca M, Cavallin L, Ferreira D, Knapskog A . MRI-assessed atrophy subtypes in Alzheimer's disease and the cognitive reserve hypothesis. PLoS One. 2017; 12(10):e0186595. PMC: 5643102. DOI: 10.1371/journal.pone.0186595. View

Mungas D, Reed B, Haan M, Gonzalez H . Spanish and English neuropsychological assessment scales: relationship to demographics, language, cognition, and independent function. Neuropsychology. 2005; 19(4):466-75. DOI: 10.1037/0894-4105.19.4.466. View

Persson N, Ghisletta P, Dahle C, Bender A, Yang Y, Yuan P . Regional brain shrinkage and change in cognitive performance over two years: The bidirectional influences of the brain and cognitive reserve factors. Neuroimage. 2015; 126:15-26. PMC: 4733615. DOI: 10.1016/j.neuroimage.2015.11.028. View

Fjell A, Walhovd K, Fennema-Notestine C, McEvoy L, Hagler D, Holland D . One-year brain atrophy evident in healthy aging. J Neurosci. 2009; 29(48):15223-31. PMC: 2827793. DOI: 10.1523/JNEUROSCI.3252-09.2009. View

Mungas D, Harvey D, Reed B, Jagust W, DeCarli C, Beckett L . Longitudinal volumetric MRI change and rate of cognitive decline. Neurology. 2005; 65(4):565-71. PMC: 1820871. DOI: 10.1212/01.wnl.0000172913.88973.0d. View

Raz N, Lindenberger U, Rodrigue K, Kennedy K, Head D, Williamson A . Regional brain changes in aging healthy adults: general trends, individual differences and modifiers. Cereb Cortex. 2005; 15(11):1676-89. DOI: 10.1093/cercor/bhi044. View

Vernet M, Quentin R, Chanes L, Mitsumasu A, Valero-Cabre A . Frontal eye field, where art thou? Anatomy, function, and non-invasive manipulation of frontal regions involved in eye movements and associated cognitive operations. Front Integr Neurosci. 2014; 8:66. PMC: 4141567. DOI: 10.3389/fnint.2014.00066. View

10.

Braak H, Braak E, Bohl J . Staging of Alzheimer-related cortical destruction. Eur Neurol. 1993; 33(6):403-8. DOI: 10.1159/000116984. View

11.

Winblad B, Palmer K, Kivipelto M, Jelic V, Fratiglioni L, Wahlund L . Mild cognitive impairment--beyond controversies, towards a consensus: report of the International Working Group on Mild Cognitive Impairment. J Intern Med. 2004; 256(3):240-6. DOI: 10.1111/j.1365-2796.2004.01380.x. View

12.

Braak H, Braak E . Neuropathological stageing of Alzheimer-related changes. Acta Neuropathol. 1991; 82(4):239-59. DOI: 10.1007/BF00308809. View

13.

Ashburner J, Friston K . Voxel-based morphometry--the methods. Neuroimage. 2000; 11(6 Pt 1):805-21. DOI: 10.1006/nimg.2000.0582. View

14.

Murray M, Graff-Radford N, Ross O, Petersen R, Duara R, Dickson D . Neuropathologically defined subtypes of Alzheimer's disease with distinct clinical characteristics: a retrospective study. Lancet Neurol. 2011; 10(9):785-96. PMC: 3175379. DOI: 10.1016/S1474-4422(11)70156-9. View

15.

Albert M, DeKosky S, Dickson D, Dubois B, Feldman H, Fox N . 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. Alzheimers Dement. 2011; 7(3):270-9. PMC: 3312027. DOI: 10.1016/j.jalz.2011.03.008. View

16.

Mwangi B, Tian T, Soares J . A review of feature reduction techniques in neuroimaging. Neuroinformatics. 2013; 12(2):229-44. PMC: 4040248. DOI: 10.1007/s12021-013-9204-3. View

17.

McArdle J, Hamgami F, Jones K, Jolesz F, Kikinis R, Spiro 3rd A . Structural modeling of dynamic changes in memory and brain structure using longitudinal data from the normative aging study. J Gerontol B Psychol Sci Soc Sci. 2004; 59(6):P294-304. DOI: 10.1093/geronb/59.6.p294. View

18.

Rueckert D, Aljabar P, Heckemann R, Hajnal J, Hammers A . Diffeomorphic registration using B-splines. Med Image Comput Comput Assist Interv. 2007; 9(Pt 2):702-9. DOI: 10.1007/11866763_86. View

19.

Iaccarino L, Tammewar G, Ayakta N, Baker S, Bejanin A, Boxer A . Local and distant relationships between amyloid, tau and neurodegeneration in Alzheimer's Disease. Neuroimage Clin. 2017; 17:452-464. PMC: 5684433. DOI: 10.1016/j.nicl.2017.09.016. View

20.

Fjell A, McEvoy L, Holland D, Dale A, Walhovd K . What is normal in normal aging? Effects of aging, amyloid and Alzheimer's disease on the cerebral cortex and the hippocampus. Prog Neurobiol. 2014; 117:20-40. PMC: 4343307. DOI: 10.1016/j.pneurobio.2014.02.004. View