RT Journal Article
SR Electronic
T1 Decoding individual differences in mental information from human brain response predicted by convolutional neural networks
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2022.05.16.492029
DO 10.1101/2022.05.16.492029
A1 Kawahata, Kiichi
A1 Wang, Jiaxin
A1 Blanc, Antoine
A1 Maeda, Naoya
A1 Nishimoto, Shinji
A1 Nishida, Satoshi
YR 2023
UL http://biorxiv.org/content/early/2023/11/17/2022.05.16.492029.abstract
AB Recent advantages in brain decoding with functional magnetic resonance imaging (fMRI) have enabled us to estimate individual differences in mental information from brain responses to natural stimuli. However, the physical constraints and costs of fMRI measurements prevent brain decoding from achieving real-world applications. We address this issue by building a novel framework to decode individual differences in mental information under natural situations from brain responses predicted using convolutional neural networks (CNNs). Once the CNN-based prediction model is trained using measured response, mental information can be decoded from the predicted responses of individual brains with no need for additional fMRI measurements. The model was found to capture individual-difference patterns consistent with conventional decoding using measured responses in 81/87 items to be decoded. Our framework has great potential to decode personal mental information with low dependence on fMRI measurements, which could help substantially expand the applicability of brain decoding in daily life.Competing Interest StatementThis study was funded by NTT Data Corp. NM is an employee of NTT Data Corp.