Poster No:
136
Submission Type:
Abstract Submission
Authors:
Soyun Kim1, Jennna Adams1, Lea Stith1, Lisa Taylor1, Alyssa Harris1, Marielena Mendoza1, Liv McMillan1, Niels Janssen2, Michael Yassa1
Institutions:
1University of California Irvine, Irvine, CA, 2Universidad de La Laguna, Tenerife, Tenerife
First Author:
Soyun Kim
University of California Irvine
Irvine, CA
Co-Author(s):
Lea Stith
University of California Irvine
Irvine, CA
Introduction:
The cerebellum has long been recognized for its integral role in motor learning and control. However, recent findings suggest its involvement extends beyond motor functions, potentially impacting non-motor domains (i.e., cognition) and contributing to cognitive decline in Alzheimer's disease (AD). Despite its relative resilience to AD-related pathology, such as beta amyloid (Aβ) accumulation, previous studies indicate a decline in cerebellar volume over progression of AD. A few neuroimaging studies in AD have also demonstrated disrupted cerebellar-cortical functional networks that likely support cognitive functions. Nevertheless, our understanding of other changes in the cerebellum, such as subregional volume alterations, changes in cerebellar functional connectivity with various cortical networks, and variations in myelin content during aging and in AD, remains to be investigated.
Methods:
We analyzed cross-sectional as well as longitudinal neuroimaging data from the Alzheimer's Disease Neuroimaging Initiative (ADNI 3, N Sessions = 325, N Subjects = 109, 62 females). Structural and resting-state fMRI data were first processed with the Human Connectome Project pipeline (v4.7.0). Cerebellar subregional volumes were derived using an automated cerebellar parcellation method (Han et al., 2020). Functional connectivity between the cerebellum and cortical networks (Yeo et al., 2011) was computed by group independent component analysis and dual regression approaches. Aβ measures were obtained from [18F]-Florbetapir or [18F]-Florbetaben PET, and standardized uptake value ratio values were transformed to the Centiloid scale. Myelin content was estimated using the T1- and T2-weighted (T1W/T2W) ratio mapping (Glasser M. F. and Van Essen D. C., 2011). Linear mixed-effects models were used to investigate the effects of age or Aβ on cerebellar regional volume, cerebello-cerebral functional connectivity, and estimated cerebellar myelin content.
Results:
Cerebellar volume reduction was significantly associated with older age in areas Crus I, Crus II, or VI. Aβ was also significantly associated with atrophy in regions Crus I, Crus II, VIII A, and VIII B. Functional connectivity between the regions Crus II and vermis X and the cortical default mode network changed with age. Functional connectivity between the region VII B and cortical somatomotor network changed with Aβ. Estimated cerebellar myelin content was negatively related with age in regions Crus I and Crus II, but positively associated in the vermis. Estimated cerebellar myelin content was negatively associated with Aβ in regions Crus I, Crus II, VIII A and VIII B.
Conclusions:
Our findings underscore the intricate relationship between age-related changes, Aβ pathology, subregional atrophy, functional connectivity, and estimated myelin content in the cerebellum. Further understanding of these associations could potentially offer valuable insights into the role of the cerebellum in both aging and Alzheimer's disease.
Disorders of the Nervous System:
Neurodegenerative/ Late Life (eg. Parkinson’s, Alzheimer’s) 1
Learning and Memory:
Learning and Memory Other
Lifespan Development:
Aging 2
Modeling and Analysis Methods:
Task-Independent and Resting-State Analysis
Other Methods
Keywords:
Aging
Cerebellum
FUNCTIONAL MRI
Myelin
STRUCTURAL MRI
1|2Indicates the priority used for review
Provide references using author date format
Glasser, M.F., Van Essen, D.C. (2011), 'Mapping human cortical areas in vivo based on myelin content as revealed by T1- and T2-weighted MRI', Journal of Neuroscience, vol. 31, no. 32, pp. 11597-616.
Han, S., Carass, A., He, Y., Prince, J.L. (2020), 'Automatic cerebellum anatomical parcellation using U-Net with locally constrained optimization', NeuroImage, vol. 218, pp. 116819.
Yeo, B.T., Krienen, F.M., Sepulcre, J., Sabuncu, M.R., Lashkari, D., Hollinshead, M., Roffman, J.L., Smoller, J.W., Zöllei, L., Polimeni, J.R., Fischl, B., Liu, H., Buckner, R.L. (2011), 'The organization of the human cerebral cortex estimated by intrinsic functional connectivity', Journal of Neurophysiology, vol. 106, no. 3, pp. 1125-65.