Multivariate Association Between Cognitive Function and Brain Tissue in Healthy Older Adults
Poster No:
1165
Submission Type:
Abstract Submission
Authors:
Christophe Phillips1, Daphné Chylinski2, Maxime Van Egroo3, Justinas Narbutas4, Eric Salmon2, Pierre Maquet2, Fabienne Collette2, Gilles Vandewalle5, Christine Bastin2, Soodeh Moallemian2
Institutions:
1University of Liège, Liège, Belgium, 2University of Liège, Liège, Liege, 3Maastricht University, Maastricht, Netherlands, 4Leibnitz research centre for working environment and human factors, Dortmund, Germany, 5Sleep and Chronobiology Lab, GIGA-Institute, CRC-In Vivo Imaging Unit, University of Liège, Liège, Belgium
First Author:
Co-Author(s):
Justinas Narbutas, PhD
Leibnitz research centre for working environment and human factors
Dortmund, Germany
Leibnitz research centre for working environment and human factors
Dortmund, Germany
Gilles Vandewalle
Sleep and Chronobiology Lab, GIGA-Institute, CRC-In Vivo Imaging Unit, University of Liège
Liège, Belgium
Sleep and Chronobiology Lab, GIGA-Institute, CRC-In Vivo Imaging Unit, University of Liège
Liège, Belgium
Introduction:
The aging process is often accompanied by cognitive alterations, collectively known as cognitive aging, which can lead to a decline in functional capacity [1]. Normal aging is also accompanied by macro- and micro- structural changes in the brain, such as gray matter (GM) and white matter (WM) atrophy [2]–[4], iron accumulation, and demyelination [5]–[7]. Microstructural changes in the brain are interconnected; for instance, elevation in iron content is associated to demyelination, collectively contributing to synaptic density loss and brain atrophy [8]–[10]. Therefore, a comprehensive examination of these concurrent brain microstructural properties with respect to cognitive aging is imperative. This exploration can reveal regions in the brain that undergo changes at an earlier stage, potentially serving as early indicators preceding the onset of cognitive issues.
Methods:
This study investigates the association between cognition and various brain micro- and macro-structural properties, as assessed by multiparametric quantitative MRI maps, in healthy older adults (baseline: n=101, 31.68% male, follow-up: n=67, 32.84% male). Participants underwent cognitive assessments at baseline and after 2 years, resulting in composite scores for attention, executive function, and memory. The preclinical Alzheimer's cognitive composite (PACC5) was calculated for all participants [11]. Quantitative MRI data were obtained at baseline using a multiparametric mapping protocol. The association between cognitive composite scores and tissue properties, both at baseline and for the rate of cognitive decline over 2 years, was tested using univariate and multivariate general linear models.
Results:
The univariate analyses conducted at baseline revealed several significant associations between cognition and brain structural properties. Executive function showed a positive correlation with GM volume in the cerebellum, while memory exhibited positive associations with myelin content in the cerebellum and hippocampus. GM iron levels were linked to lower memory scores in the right insula. A significant positive correlation emerged between WM myelin content and PACC5 in the left middle temporal region. Conversely, higher iron levels in the medial orbitofrontal cortex were associated with smaller PACC5 values. Results from the univariate regression analysis are presented in Table 1. As illustrated in Figure 1, the multivariate regression analyses at baseline revealed significant associations between executive function and the combination of macro- and microstructural changes in the cerebellum, as well as between memory and combined changes in the cingulate gyrus and insula (See Table 2 for detailed results). Finally, multivariate regression did not reveal any significant correlations between the different maps and the rate of decline in cognition. Moreover, it is important to note that, throughout the study duration, we did not observe a decline in cognition among the subjects.
·Statistical parametric maps (SPM) for the multivariate regression analyses within gray and white matter.
·Univariate and multivariate regression results at the baseline
Conclusions:
In summary, these findings highlight the intricate connections between cognition and brain micro- and macro-structural properties in aging, with a particular emphasis on the role of the cerebellum in cognitive aging. However, a more prolonged study is needed to further explore the association between the decline in cognition and concurrent changes in the brains.
Higher Cognitive Functions:
Executive Function, Cognitive Control and Decision Making
Lifespan Development:
Aging 1
Modeling and Analysis Methods:
Multivariate Approaches 2
Novel Imaging Acquisition Methods:
Multi-Modal Imaging
Keywords:
MRI
Multivariate
Other - cognitive aging, aging, quantitative MRI, MTsat, R2*, PD, memory, PACC5, Attention, Executive function
1|2Indicates the priority used for review
Provide references using author date format
[2] S. Ramanoël et al., “Gray Matter Volume and Cognitive Performance During Normal Aging. A Voxel-Based Morphometry Study,” Front. Aging Neurosci., vol. 10, p. 235, Aug. 2018, doi: 10.3389/fnagi.2018.00235.
[3] C. D. Good, I. S. Johnsrude, J. Ashburner, R. N. A. Henson, K. J. Friston, and R. S. J. Frackowiak, “A Voxel-Based Morphometric Study of Ageing in 465 Normal Adult Human Brains,” NeuroImage, vol. 14, no. 1, pp. 21–36, Jul. 2001, doi: 10.1006/nimg.2001.0786.
[4] L. Pini et al., “Brain atrophy in Alzheimer’s Disease and aging,” Ageing Res. Rev., vol. 30, pp. 25–48, Sep. 2016, doi: 10.1016/j.arr.2016.01.002.
[5] M. F. Callaghan et al., “Widespread age-related differences in the human brain microstructure revealed by quantitative magnetic resonance imaging,” Neurobiol. Aging, vol. 35, no. 8, pp. 1862–1872, Aug. 2014, doi: 10.1016/j.neurobiolaging.2014.02.008.
[6] M. Taubert et al., “Converging patterns of aging-associated brain volume loss and tissue microstructure differences,” Neurobiol. Aging, vol. 88, pp. 108–118, Apr. 2020, doi: 10.1016/j.neurobiolaging.2020.01.006.
[7] J. Acosta-Cabronero, M. J. Betts, A. Cardenas-Blanco, S. Yang, and P. J. Nestor, “In Vivo MRI Mapping of Brain Iron Deposition across the Adult Lifespan,” J. Neurosci., vol. 36, no. 2, pp. 364–374, Jan. 2016, doi: 10.1523/JNEUROSCI.1907-15.2016.
[8] S. Moallemian et al., “Multimodal imaging of microstructural cerebral alterations and loss of synaptic density in Alzheimer’s disease,” Neurobiol. Aging, vol. 132, pp. 24–35, Dec. 2023, doi: 10.1016/j.neurobiolaging.2023.08.001.
[9] T. K. Steiger, N. Weiskopf, and N. Bunzeck, “Iron Level and Myelin Content in the Ventral Striatum Predict Memory Performance in the Aging Brain,” J. Neurosci., vol. 36, no. 12, pp. 3552–3558, Mar. 2016, doi: 10.1523/JNEUROSCI.3617-15.2016.
[10] G. Bartzokis, “Alzheimer’s disease as homeostatic responses to age-related myelin breakdown,” Neurobiol. Aging, vol. 32, no. 8, pp. 1341–1371, Aug. 2011, doi: 10.1016/j.neurobiolaging.2009.08.007.
[11] M. C. Donohue et al., “The Preclinical Alzheimer Cognitive Composite: Measuring Amyloid-Related Decline,” JAMA Neurol., vol. 71, no. 8, p. 961, Aug. 2014, doi: 10.1001/jamaneurol.2014.803.