| Paper: | GS-5.1 | ||
| Session: | Contributed Talks 9-10 | ||
| Location: | H0104 | ||
| Session Time: | Monday, September 16, 09:50 - 10:30 | ||
| Presentation Time: | Monday, September 16, 09:50 - 10:10 | ||
| Presentation: | Oral | ||
| Publication: | 2019 Conference on Cognitive Computational Neuroscience, 13-16 September 2019, Berlin, Germany | ||
| Paper Title: | Functional Decoding using Convolutional Networks on Brain Graphs | ||
| Manuscript: | Click here to view manuscript | ||
| View Video: | Video | ||
| License: |  This work is licensed under a Creative Commons Attribution 3.0 Unported License. |
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| DOI: | https://doi.org/10.32470/CCN.2019.1384-0 | ||
| Authors: | Yu Zhang, Pierre Bellec, Chercheur Centre de recherche de l'institut Universitaire de gériatrie de Montréal (CRIUGM), Canada | ||
| Abstract: | A key goal in neuroscience is to understand brain mechanisms of cognitive functions. An emerging approach is the study of “brain states” dynamics using functional magnetic resonance imaging (fMRI). In this project, we applied graph convolutional networks (GCN) to decode brain activity over short time windows in a task fMRI dataset, i.e. associate a given window of fMRI time series with the task used. We investigated the performance of this GCN "cognitive state annotation" in the Human Connectome Project (HCP) database, which features 21 different experimental conditions spanning seven major cognitive domains, and high temporal resolution in task fMRI data. Using a 10-second window, the 21 cognitive states were identified with an excellent average test accuracy of 92\% (chance level 4.8\%). Performance remained good (60\%) even at a temporal resolution of one volume (720 ms of duration). As the HCP task battery was designed to selectively activate a wide range of specialized functional networks, we anticipate the GCN annotation to be applicable over a broad range of paradigms, including resting-state. | ||