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Dynamic Electrode-to-Image (DETI) mapping reveals the human brain's spatiotemporal code of visual information.
- Source :
-
PLoS computational biology [PLoS Comput Biol] 2021 Sep 27; Vol. 17 (9), pp. e1009456. Date of Electronic Publication: 2021 Sep 27 (Print Publication: 2021). - Publication Year :
- 2021
-
Abstract
- A number of neuroimaging techniques have been employed to understand how visual information is transformed along the visual pathway. Although each technique has spatial and temporal limitations, they can each provide important insights into the visual code. While the BOLD signal of fMRI can be quite informative, the visual code is not static and this can be obscured by fMRI's poor temporal resolution. In this study, we leveraged the high temporal resolution of EEG to develop an encoding technique based on the distribution of responses generated by a population of real-world scenes. This approach maps neural signals to each pixel within a given image and reveals location-specific transformations of the visual code, providing a spatiotemporal signature for the image at each electrode. Our analyses of the mapping results revealed that scenes undergo a series of nonuniform transformations that prioritize different spatial frequencies at different regions of scenes over time. This mapping technique offers a potential avenue for future studies to explore how dynamic feedforward and recurrent processes inform and refine high-level representations of our visual world.<br />Competing Interests: The authors have declared that no competing interests exist.
- Subjects :
- Adolescent
Brain Mapping instrumentation
Brain Mapping statistics & numerical data
Computational Biology
Electrodes
Electroencephalography instrumentation
Female
Functional Neuroimaging statistics & numerical data
Humans
Image Processing, Computer-Assisted
Magnetic Resonance Imaging statistics & numerical data
Male
Photic Stimulation
Spatio-Temporal Analysis
Visual Cortex physiology
Young Adult
Brain Mapping methods
Electroencephalography statistics & numerical data
Visual Pathways physiology
Subjects
Details
- Language :
- English
- ISSN :
- 1553-7358
- Volume :
- 17
- Issue :
- 9
- Database :
- MEDLINE
- Journal :
- PLoS computational biology
- Publication Type :
- Academic Journal
- Accession number :
- 34570753
- Full Text :
- https://doi.org/10.1371/journal.pcbi.1009456