Poster No:
1480
Submission Type:
Abstract Submission
Authors:
Divesh Thaploo1, Adebiyi Sobitan1, Atsuko Kurosu1, Kristen Wingert1, Nadia Biassou2
Institutions:
1National Institutes on Deafness and Other Communication Disorders, Bethesda, MD, 2National Institutes of Health, Bethesda, MD
First Author:
Divesh Thaploo, PhD
National Institutes on Deafness and Other Communication Disorders
Bethesda, MD
Co-Author(s):
Atsuko Kurosu, PhD
National Institutes on Deafness and Other Communication Disorders
Bethesda, MD
Kristen Wingert, MS
National Institutes on Deafness and Other Communication Disorders
Bethesda, MD
Introduction:
Gender-based differences in brain structure and function are well-documented (Zhang et al., 2020) with various implications, especially in language processing. However, the exact nature where these differences arise, in terms, of functional imaging are poorly understood. One of these aspects could be brain regions or nodes itself (Kurth et al., 2021). This study focuses on comparing various brain centrality measures between males and females to understand whether nodes or brain processing units differ.
Methods:
This study focused on the task-based fMRI from the publicly available Human Connectome Project (HCP) dataset, where participants performed an auditory language comprehension task while being in an 3T MRI scanner (Essen et al., 2013). Specifically, subjects listened to a story and then they were auditorily presented with a word that represented the gist of the story and asked if the word correctely described the gist of the story that they had heard. They responded yes or no. Data from 114 healthy subjects were analyzed (61 females and 53 males). Preprocessing was conducted using AFNI, with surface-based parcellation performed using FreeSurfer and the Desikan-Killiany atlas. To examine the brain networks dynamically, we employed a dynamic functional connectivity approach, utilizing Jackknife resampling followed by Pearson correlation to assess connections between nodes over time. Node2Vec algorithm was used to calculate centrality measures, which include, eigenvector centrality, degree centrality and betweenness centrality. We utilized statistical packages in R (v4.4.2) for statistical comparison.
Results:
Eigenvector centrality accounted for the maximum variance in the data, followed by degree and betweenness centrality measures, in that order. This ranking suggests that eigenvector centrality, which captures the influence of a node based on its connections to other highly connected nodes, may provide the most comprehensive insight into the overall structure of the brain network. Degree centrality, representing the number of direct connections, and betweenness centrality, reflecting the role of nodes in facilitating communication across the network, also contributed to understanding the network's topology, albeit to a lesser extent. Despite these distinctions in explanatory power, independent samples t-test showed that the brain nodes are essentially constant between males and females. This lack of differentiation suggests that gender-based variations in brain connectivity might not be driven by the centrality of individual nodes within the network.
Conclusions:
Node-based centrality measures do not appear to dynamically differentiate between males and females. One possible explanation for this could be that the orientation and connectivity of brain nodes are not only structurally stable but also exhibit temporal stability across time. This stability suggests that the fundamental organization of brain networks may be conserved, regardless of gender, during dynamic functional processes.
Language:
Language Comprehension and Semantics 2
Modeling and Analysis Methods:
fMRI Connectivity and Network Modeling 1
Novel Imaging Acquisition Methods:
BOLD fMRI
Keywords:
Computational Neuroscience
FUNCTIONAL MRI
Hemispheric Specialization
Language
NORMAL HUMAN
1|2Indicates the priority used for review
By submitting your proposal, you grant permission for the Organization for Human Brain Mapping (OHBM) to distribute your work in any format, including video, audio print and electronic text through OHBM OnDemand, social media channels, the OHBM website, or other electronic publications and media.
I accept
The Open Science Special Interest Group (OSSIG) is introducing a reproducibility challenge for OHBM 2025. This new initiative aims to enhance the reproducibility of scientific results and foster collaborations between labs. Teams will consist of a “source” party and a “reproducing” party, and will be evaluated on the success of their replication, the openness of the source work, and additional deliverables. Click here for more information.
Propose your OHBM abstract(s) as source work for future OHBM meetings by selecting one of the following options:
I do not want to participate in the reproducibility challenge.
Please indicate below if your study was a "resting state" or "task-activation” study.
Task-activation
Healthy subjects only or patients (note that patient studies may also involve healthy subjects):
Healthy subjects
Was this research conducted in the United States?
Yes
Are you Internal Review Board (IRB) certified?
Please note: Failure to have IRB, if applicable will lead to automatic rejection of abstract.
Not applicable
Were any human subjects research approved by the relevant Institutional Review Board or ethics panel?
NOTE: Any human subjects studies without IRB approval will be automatically rejected.
Not applicable
Were any animal research approved by the relevant IACUC or other animal research panel?
NOTE: Any animal studies without IACUC approval will be automatically rejected.
Not applicable
Please indicate which methods were used in your research:
Functional MRI
Computational modeling
For human MRI, what field strength scanner do you use?
3.0T
Which processing packages did you use for your study?
AFNI
Provide references using APA citation style.
1. Zhang, X., Liang, M., Qin, W., Wan, B., Yu, C., & Ming, D. (2020). Gender Differences Are Encoded Differently in the Structure and Function of the Human Brain Revealed by Multimodal MRI. Frontiers in human neuroscience, 14, 244. https://doi.org/10.3389/fnhum.2020.00244.
2. Kurth, F., Gaser, C., & Luders, E. (2021). Development of sex differences in the human brain. Cognitive neuroscience, 12(3-4), 155–162. https://doi.org/10.1080/17588928.2020.1800617.
3. David C. Van Essen, Stephen M. Smith, Deanna M. Barch, Timothy E.J. Behrens, Essa Yacoub, Kamil Ugurbil, for the WU-Minn HCP Consortium. (2013). The WU-Minn Human Connectome Project: An overview. NeuroImage 80(2013):62-79.
No