Reconstructing imagined letters from early visual cortex reveals tight topographic correspondence between visual mental imagery and perception

• Published in: Brain Structure and Function Vol. 224; no. 3; pp. 1167 - 1183
• Main Authors: Senden, Mario, Emmerling, Thomas C., van Hoof, Rick, Frost, Martin A., Goebel, Rainer
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.04.2019; Springer Nature B.V
• Subjects: Biomedical and Life Sciences; Biomedicine; Cell Biology; Functional magnetic resonance imaging; Learning algorithms; Mental task performance; Neuroimaging; Neurology; Neurosciences; Original; Original Article; Receptive field; Retina; Spatial discrimination; Topography; Visual cortex; Visual discrimination learning; Visual field; Visual perception; Visual system
• ISSN: 1863-2653; 1863-2661; 1863-2661; 0340-2061
• DOI: 10.1007/s00429-019-01828-6

Visual mental imagery is the quasi-perceptual experience of “seeing in the mind’s eye”. While a tight correspondence between imagery and perception in terms of subjective experience is well established, their correspondence in terms of neural representations remains insufficiently understood. In the present study, we exploit the high spatial resolution of functional magnetic resonance imaging (fMRI) at 7T, the retinotopic organization of early visual cortex, and machine-learning techniques to investigate whether visual imagery of letter shapes preserves the topographic organization of perceived shapes. Sub-millimeter resolution fMRI images were obtained from early visual cortex in six subjects performing visual imagery of four different letter shapes. Predictions of imagery voxel activation patterns based on a population receptive field-encoding model and physical letter stimuli provided first evidence in favor of detailed topographic organization. Subsequent visual field reconstructions of imagery data based on the inversion of the encoding model further showed that visual imagery preserves the geometric profile of letter shapes. These results open new avenues for decoding, as we show that a denoising autoencoder can be used to pretrain a classifier purely based on perceptual data before fine-tuning it on imagery data. Finally, we show that the autoencoder can project imagery-related voxel activations onto their perceptual counterpart allowing for visually recognizable reconstructions even at the single-trial level. The latter may eventually be utilized for the development of content-based BCI letter-speller systems.