Age-related changes in gray matter asymmetry: Will brains get more (a)symmetric?
Poster No:
1173
Submission Type:
Abstract Submission
Authors:
Florian Kurth1, Nicolas Cherbuin2, Christian Gaser3, Eileen Luders1
Institutions:
1University of Auckland, Auckland, New Zealand, 2National Centre for Epidemiology and Population Health, Canberra, Australia, 3Jena University Hospital, Jena, Germany
First Author:
Co-Author(s):
Introduction:
Structural and functional differences between the hemispheres are known to change over time (Ocklenburg and Gunturkun, 2018; Toga et al., 2009). Some theories suggest a progressive recruitment of homotopic contralateral brain regions with increasing age, which may manifest as decreases in asymmetry (Cabeza, 2002). Other theories support the assumption of an accelerated atrophy of one hemisphere compared to the other (Kong et al., 2018; Minkova et al., 2017; Thompson et al., 2003), which may manifest as increases in asymmetry. Here we set out to explore age-related changes in gray matter asymmetry using a longitudinal design in a large sample of 2,324 participants (1151 women / 1173 men) spanning a wide age range (47 – 80 years).
Methods:
T1-weighted brain images were obtained from the UK Biobank, only including participants who were scanned at two time points and without a history of neuropsychiatric conditions, cancer, or stroke. The time between baseline and follow-up scans ranged between 1 and 7 years (mean ± SD: 2.39 ± 0.82). All brain images were preprocessed using the CAT12 toolbox (Gaser et al., 2022) applying the longitudinal workflow for age effects. The resulting tissue segments were registered to MNI space using affine transformations, flipped in the x-axis, and both original and flipped tissue segments were then warped to a symmetric Shooting Template in MNI space and modulated (Kurth et al., 2015). Subsequently, the asymmetry index was calculated as AI = (right-left)/(0.5 x [right+left]), duplicate information in the left hemisphere was discarded and the AI values within the remaining right hemisphere were smoothed using an 8 mm FWHM kernel (Kurth et al., 2015). Finally, all AI values were converted into absolute values and the absolute AI values at baseline were subtracted from the absolute AI values at follow-up. The resulting difference maps served as the dependent variable in the statistical model. Changes in asymmetry were assessed in a general linear model with sex and brain volume as covariates. Results were corrected for multiple comparisons on cluster level by controlling the family-wise error, using a cluster-forming threshold at p≤0.001 and correcting for non-stationarity (Hayasaka et al., 2004).
Results:
Our study revealed brain regions where (a) asymmetry remains stable over time, (b) significantly decreases over time (Figure 1), or (c) significantly increases over time (Figure 2). More specifically, decreases in asymmetry were detected in the temporal lobe, extending into inferior parietal and occipital regions. In contrast, increases in asymmetry were evident in the frontal cortex and orbitofrontal regions as well as the insula, posterior parietal and medial frontal/parietal regions.
·Figure 1. Decreases in asymmetry.
·Figure 2. Increases in asymmetry.
Conclusions:
Our study revealed that asymmetry remains stable over time in most parts of the brain. However, there were some regions where asymmetry significantly increased or decreased. As far as this change over time is concerned, there seems to be a predominant decrease of leftward asymmetries as well as an increase of rightward asymmetries. Overall, the observed effects suggest a more pronounced gray matter loss in the left hemisphere compared to the right, supporting previous reports (Thompson et al., 2003).
Lifespan Development:
Aging 1
Modeling and Analysis Methods:
Image Registration and Computational Anatomy
Neuroanatomy, Physiology, Metabolism and Neurotransmission:
Neuroanatomy Other 2
Novel Imaging Acquisition Methods:
Anatomical MRI
Keywords:
Aging
MRI
STRUCTURAL MRI
Other - Asymmetry, Gray Matter
1|2Indicates the priority used for review
Provide references using author date format
Gaser, C., Dahnke, R., Thompson, P.M., Kurth, F., Luders, E. (2022) CAT – A Computational Anatomy Toolbox for the Analysis of Structural MRI Data. bioRxiv, 2022.06.11.495736.
Hayasaka, S., Phan, K.L., Liberzon, I., Worsley, K.J., Nichols, T.E. (2004) Nonstationary cluster-size inference with random field and permutation methods. Neuroimage, 22:676-87.
Kong, X.Z., Mathias, S.R., Guadalupe, T., Group, E.L.W., Glahn, D.C., Franke, B., Crivello, F., Tzourio-Mazoyer, N., Fisher, S.E., Thompson, P.M., Francks, C. (2018) Mapping cortical brain asymmetry in 17,141 healthy individuals worldwide via the ENIGMA Consortium. Proc Natl Acad Sci U S A, 115:E5154-E5163.
Kurth, F., Gaser, C., Luders, E. (2015) A 12-step user guide for analyzing voxel-wise gray matter asymmetries in statistical parametric mapping (SPM). Nat Protoc, 10:293-304.
Minkova, L., Habich, A., Peter, J., Kaller, C.P., Eickhoff, S.B., Kloppel, S. (2017) Gray matter asymmetries in aging and neurodegeneration: A review and meta-analysis. Hum Brain Mapp, 38:5890-5904.
Ocklenburg, S., Gunturkun, O. (2018) Hemispheric Asymmetries Over the Lifespan. The lateralized brain: the neuroscience and evolution of hemispheric asymmetries. London: Academic Press. p 263-288.
Thompson, P.M., Hayashi, K.M., de Zubicaray, G., Janke, A.L., Rose, S.E., Semple, J., Herman, D., Hong, M.S., Dittmer, S.S., Doddrell, D.M., Toga, A.W. (2003) Dynamics of gray matter loss in Alzheimer's disease. J Neurosci, 23:994-1005.
Toga, A.W., Narr, K.L., Thompson, P.M., Luders, E. (2009) Brain Asymmetry: Evolution. In: Squire, L.R., editor. Encyclopedia of Neuroscience. Oxford: Academic Press. p 303-311.