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BRIEF RESEARCH REPORT article

Front. Comput. Neurosci.
Volume 18 - 2024 | doi: 10.3389/fncom.2024.1471229

Modelling Functional Connectivity Changes During an Auditory Language Task Using Line Graph Neural Networks

Provisionally accepted
Stein Acker Stein Acker 1Jinqing Liang Jinqing Liang 1Ninet Sinaii Ninet Sinaii 2Kristen Wingert Kristen Wingert 1Atsuko Kurosu Atsuko Kurosu 1Sunder Rajan Sunder Rajan 3Sara K. Inati Sara K. Inati 4William Theodore William Theodore 4Nadia Biassou Nadia Biassou 1,3*
  • 1 Integrative Neuroscience of Communication Research Unit, National Institute on Deafness and Other Communication Disorders (NIH), Bethesda, Maryland, United States
  • 2 Biostatistics and Clinical Epidemiology Service, Clinical Center (NIH), Bethesda, Maryland, United States
  • 3 Department of Radiology and Imaging Sciences, Clinical Center (NIH), Bethesda, Maryland, United States
  • 4 Clinical Epilepsy Service, National Institute of Neurological Disorders and Stroke (NIH), Bethesda, Maryland, United States

The final, formatted version of the article will be published soon.

    Functional connectivity (FC) refers to the activation correlation between different brain regions. FC networks as typically represented as graphs with brain regions of interest (ROIs) as nodes and functional connections as edges. Graph neural networks (GNNs) are machine learning architectures used to analyze FC graphs. However, traditional GNNs are limited in their ability to characterize FC edge attributes because they typically emphasize the importance of ROI node-based brain activation data. Line GNNs convert the edges of the original graph to nodes in the transformed graph, thereby emphasizing the FC between brain regions. We hypothesize that line GNNs will outperform traditional GNNs in FC applications. We investigated the performance of two common GNN architectures (GraphSAGE and GCN) trained on line and traditional graphs predicting task-associated FC changes across two datasets. The first dataset was from the Human Connectome Project (HCP) with 205 participants, the second was a dataset with 12 participants. The HCP dataset detailed FC changes in participants during a story-listening task, while the second dataset included the FC changes in a different auditory language task. Our findings from the HCP dataset indicated that line GNNs achieved lower mean squared error compared to traditional GNNs, with the line GraphSAGE model outperforming the traditional GraphSAGE by 18% (p<0.0001). When applying the same models to the second dataset, both line GNNs also showed statistically significant improvements over their traditional counterparts with little to no overfitting. We believe this shows that line GNN models demonstrate promising utility in FC studies.

    Keywords: graph theory, Graph neural network, Line graph, functional connectivity, machine learning, functional MRI were included. We

    Received: 26 Jul 2024; Accepted: 15 Oct 2024.

    Copyright: © 2024 Acker, Liang, Sinaii, Wingert, Kurosu, Rajan, Inati, Theodore and Biassou. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Nadia Biassou, Integrative Neuroscience of Communication Research Unit, National Institute on Deafness and Other Communication Disorders (NIH), Bethesda, MSC 2320, Maryland, United States

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.