Transfer learning to decode brain states reflecting the relationship between cognitive tasks
This work addresses the challenge of decoding brain states for cognitive tasks in neuroscience, providing guidance for source task selection in transfer learning, but it is incremental as it builds on existing transfer learning and neuroscience concepts.
The study tackled the problem of understanding relationships between cognitive tasks by proposing a transfer learning framework that reflects these relationships, showing that transfer learning performs better in task decoding with fMRI data when source and target tasks activate similar brain regions, with results aligning well with neurosynth-derived task relations.
Transfer learning improves the performance of the target task by leveraging the data of a specific source task: the closer the relationship between the source and the target tasks, the greater the performance improvement by transfer learning. In neuroscience, the relationship between cognitive tasks is usually represented by similarity of activated brain regions or neural representation. However, no study has linked transfer learning and neuroscience to reveal the relationship between cognitive tasks. In this study, we propose a transfer learning framework to reflect the relationship between cognitive tasks, and compare the task relations reflected by transfer learning and by the overlaps of brain regions (e.g., neurosynth). Our results of transfer learning create cognitive taskonomy to reflect the relationship between cognitive tasks which is well in line with the task relations derived from neurosynth. Transfer learning performs better in task decoding with fMRI data if the source and target cognitive tasks activate similar brain regions. Our study uncovers the relationship of multiple cognitive tasks and provides guidance for source task selection in transfer learning for neural decoding based on small-sample data.