Sex differences in the brain’s structural and functional connectivity across the lifespan
Poster No:
947
Submission Type:
Abstract Submission
Authors:
Ke Huang1, Amy Kuceyeski1
Institutions:
1Weill Cornell Medicine, New York, NY
First Author:
Co-Author:
Introduction:
Understanding sex differences in functional and structural brain connectivity across different age ranges is critical for unraveling the complex interplay between biological, developmental, and environmental factors that shape the human brain. Research has shown that males and females often exhibit distinct patterns of brain connectivity, which can influence cognitive abilities, behavior, and susceptibility to neurological and psychiatric conditions (Wierenga et al. (2022)). These differences, however, are not static; they evolve dynamically over the lifespan, reflecting the influence of key developmental stages such as puberty, reproductive maturity, and aging. By examining these patterns, we can identify critical periods where sex differences in connectivity are most pronounced and understand their contribution to developmental and aging processes. Additionally, identifying which regions or networks contribute most to sex differences has broader implications for neuroscience and both women's and men's brain health.
Methods:
We used functional connectivity (FC) and structural connectivity (SC) data from the Human Connectome Project - Development, Young Adult, Aging studies (Van Essen et al. [2013]). Fifteen different types of connectivity from different modalities, atlases and processing strategies were used. Individuals from 8-100 years of age were divided into 11 age bins containing approximately equal numbers of subjects. Logistic regression was applied to each of the 15 connectivity flavors and then ensembled. Haufe transformed logistic regression coefficients (Haufe et al. [2014]) were inspected and pairwise cosine similarities of these feature vectors were used to examine the similarity of brain connectivity features classifying sex across different age groups. We additionally applied the Krakencoder, a multi-modality connectome fusion and translation tool (Jamison et al., [in press])) to generate a low dimensional "fusion" latent space that captures all connectome flavors in a single compact, 128-dimension vector. Sensitivity analyses were conducted by taking all regions' connections in a network (Yeo et al. [2011]) and replacing it with the mean and performing sex classification on the ablated data to identify which brain networks are most critical in sex prediction.
Results:
The individual FC and SC models, as well as the connectome ensemble and Krakencoder models all show an inverted U shape for sex classification accuracy wherein it is more difficult at younger and older ages and easiest in young adulthood (Fig 1). Sex classification using the Krakencoder had higher accuracy compared to the ensemble model of each of the connectome flavors, highlighting the utility of this tool. Fig 2A provides the sensitivity analysis of Krakencoder model; the largest drops in sex classification accuracy occur when higher order default mode networks are excluded, particularly for younger and older ages, while young adults' sex classifications appear to rely more on lower order somatomotor and subcortex networks. Figure 2(B) illustrates the standardized Haufe-transformed coefficients for the sex classification using the best FC and SC models. Males have higher FC in the default mode and somatomotor network in older ages, while females have stronger FC in the cerebellum and visual system in older ages. Overall, stronger SC indicated female sex particularly in subcortex.
Conclusions:
Brain connectivity features are most distinct during middle age, particularly for structural connectivity. The Krakencoder's multi-modal connectome fusion representation may offer more sensitive sex classification abilities compared to the original connectomes. Higher-order networks, such as the default and control networks, play more critical roles in distinguishing sex compared with other networks, especially during periods of developmental or aging transitions. Understanding how sex differences change over the lifespan is critical to better supporting brain health for all.
Lifespan Development:
Early life, Adolescence, Aging 1
Modeling and Analysis Methods:
Connectivity (eg. functional, effective, structural) 2
fMRI Connectivity and Network Modeling
Keywords:
Data analysis
FUNCTIONAL MRI
Sexual Dimorphism
STRUCTURAL MRI
1|2Indicates the priority used for review
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Provide references using APA citation style.
Jamison, K. W., Gu, Z., Wang, Q., Sabuncu, M. R., and Kuceyeski, A. (2024). Release the krakencoder: A unified brain connectome translation and fusion tool. Nature Methods, in press.
Van Essen, D. C., Smith, S. M., Barch, D. M., Behrens, T. E., Yacoub, E., Ugurbil, K., Consortium,
W.-M. H., et al. (2013). The wu-minn human connectome project: an overview. Neuroimage, 80:62–79.
Wierenga, L. M., Doucet, G. E., Dima, D., Agartz, I., Aghajani, M., Akudjedu, T. N., Albajes-Eizagirre, A., Alnæs, D., Alpert, K. I., Andreassen, O. A., et al. (2022). Greater male than female variability in regional brain structure across the lifespan. Human brain mapping, 43(1):470–499.
Yeo, B. T., Krienen, F. M., Sepulcre, J., Sabuncu, M. R., Lashkari, D., Hollinshead, M., Roffman, J. L., Smoller, J. W., Z¨ollei, L., Polimeni, J. R., et al. (2011). The organization of the human cerebral cortex estimated by intrinsic functional connectivity. Journal of neurophysiology.