Variability in Brain-Cognition Mapping Across the Sexes and across Menopausal stages
Poster No:
1477
Submission Type:
Abstract Submission
Authors:
Cole Gaines1, Lucas Keith1, Keith Jamison2, Amy Kuceyeski3, Ceren Tozlu4
Institutions:
1Cornell University, Ithaca, NY, 2Weill Cornell Medicine, New York, NY, 3Cornell, Ithaca, NY, 4Weill Cornell Medicine, NYC, NY
First Author:
Co-Author(s):
Introduction:
Menopausal transition (MT) is known to have systemic impacts on the body, marking a stage of major hormonal and reproductive changes. Studies have shown that MT can be identified as a neurological transition as brain anatomy and physiology as well as cognition change during MT (Mosconi et al., 2021). However, the mapping between the brain's functional networks and cognition has not been studied at different menopausal stages. Therefore, our study aims to apply a machine learning technique to identify the association between the brains' functional connectivity and cognition across pre-, peri-, and post-menopause stages.
Methods:
This study included 251 males and 343 females, separated into groups of 74 pre-, 42 peri-, and 227 post-menopause, from the Human Connectome Project-Aging (HCP-A) dataset to study brain-cognition mapping across different stages of menopause (Van Essen et al., 2013). Ridge regression was used to predict total cognition using regional functional connectivity (i.e. node strength or sum of positive values over the rows in the matrix), sex, age, and intracranial volume. Five-fold nested cross-validation was used to split the data into train and test sets where the train dataset was used to identify the optimal hyperparameter of the ridge regression and to fit the corresponding model, and the test dataset was used to measure prediction accuracy. We created one model for each of the five subsets of data (all females, all males, pre-, peri-, and post-menopause females) . Spearman's correlation was used to measure prediction accuracy and beta parameters were used as a proxy for variable importance. The relative importance of each feature in the ridge regression model was calculated as the average of the 50 beta parameters from 10 repeats of the outer loop of the 5-fold cross-validation procedure. The variable importance of brain regions was then averaged across the 7 Yeo functional brain networks (visual, somatomotor [SOM], dorsal and ventral attention [DAN and VAN], limbic, frontoparietal [FP], and default mode network [DMN]), plus subcortex and cerebellum.
Results:
Prediction accuracy was higher in females compared to males; however, the overall best prediction accuracy was obtained with the peri-menopausal group, followed by the post- and pre-menopause groups (see Fig 1). Males and females as well as females in different stages of menopause showed distinct brain-cognition mapping (See Fig 2). Decreased FC in the FP was associated with better total cognition in females, while increased FC in the DAN was associated with better total cognition in males. Decreased FC in DAN in peri-menopause and decreased FC of the cerebellum in post-menopause were associated with better total cognition.
Conclusions:
Our study reveals distinct patterns of brain-cognition associations across sexes and menopausal stages within females, suggesting varied neural underpinnings of cognition before, during, and after menopause. Interestingly, the peri-menopausal stage exhibited the most robust brain-cognition mapping, perhaps due to the joint impact of sudden hormonal changes on cognition and brain connectivity. Understanding how the neural substrates of cognition may change over menopause is essential if we are to treat menopause-related "brain fog".
Lifespan Development:
Aging 2
Modeling and Analysis Methods:
Connectivity (eg. functional, effective, structural)
fMRI Connectivity and Network Modeling 1
Keywords:
Aging
Cognition
Computing
Data analysis
FUNCTIONAL MRI
Modeling
Statistical Methods
Other - Menopause
1|2Indicates the priority used for review
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Provide references using APA citation style.
Van Essen, D. C., Smith, S. M., Barch, D. M., Behrens, T. E., Yacoub, E., Ugurbil, K., & WU-Minn HCP Consortium (2013). The WU-Minn Human Connectome Project: an overview. NeuroImage, 80, 62–79. https://doi.org/10.1016/j.neuroimage.2013.05.041