title: Inter-subject neural code converter for visual image representation.
abstract: Brain activity patterns differ from person to person, even for an identical stimulus. In functional brain mapping studies, it is important to align brain activity patterns between subjects for group statistical analyses. While anatomical templates are widely used for inter-subject alignment in functional magnetic resonance imaging (fMRI) studies, they are not sufficient to identify the mapping between voxel-level functional responses representing specific mental contents. Recent work has suggested that statistical learning methods could be used to transform individual brain activity patterns into a common space while preserving representational contents. Here, we propose a flexible method for functional alignment, "neural code converter," which converts one subject's brain activity pattern into another's representing the same content. The neural code converter was designed to learn statistical relationships between fMRI activity patterns of paired subjects obtained while they saw an identical series of stimuli. It predicts the signal intensity of individual voxels of one subject from a pattern of multiple voxels of the other subject. To test this method, we used fMRI activity patterns measured while subjects observed visual images consisting of random and structured patches. We show that fMRI activity patterns for visual images not used for training the converter could be predicted from those of another subject where brain activity was recorded for the same stimuli. This confirms that visual images can be accurately reconstructed from the predicted activity patterns alone. Furthermore, we show that a classifier trained only on predicted fMRI activity patterns could accurately classify measured fMRI activity patterns. These results demonstrate that the neural code converter can translate neural codes between subjects while preserving contents related to visual images. While this method is useful for functional alignment and decoding, it may also provide a basis for brain-to-brain communication using the converted pattern for designing brain stimulation.
authors.affiliation: Fundamental Technology Research Center, Honda R&D Co., Ltd., Saitama 351-0188, Japan; ATR Computational Neuroscience Laboratories, Kyoto 619-0288, Japan.
authors.forename: Kentaro
authors.initials: K
authors.lastname: Yamada
authors.affiliation: National Institute of Information and Communications Technology, Kyoto 619-0288, Japan; ATR Computational Neuroscience Laboratories, Kyoto 619-0288, Japan; The University of Electro-Communications, Tokyo 182-8585, Japan.
authors.forename: Yoichi
authors.initials: Y
authors.lastname: Miyawaki
authors.affiliation: ATR Computational Neuroscience Laboratories, Kyoto 619-0288, Japan; Graduate School of Informatics, Kyoto University, Kyoto 606-8501, Japan. Electronic address: kmtn@atr.jp.
authors.forename: Yukiyasu
authors.initials: Y
authors.lastname: Kamitani
mesh_terms: D000328:Adult; D000465:Algorithms; D001921:Brain / Q000502:physiology*; D001931:Brain Mapping; D005260:Female; D007839:Functional Laterality / Q000502:physiology; D006801:Humans; D007091:Image Processing, Computer-Assisted; D007858:Learning; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D010775:Photic Stimulation; D015203:Reproducibility of Results; D012160:Retina / Q000033:anatomy & histology; D055815:Young Adult