Visual Semantic Encoding and Identification of Naturalistic Movies via High-Density Diffuse Optical Tomography.

ABSTRACT: Understanding how the brain represents meaning in real-world contexts is essential for both fundamental neuroscience and clinical applications. Brain encoding and decoding models from naturalistic stimuli provide a powerful window into semantic representations. Yet, existing approaches rely on a constrained scanning environment, or on conventional fNIRS, which has been limited to sparse sampling and/or block-design paradigms. Here, we tested whether high-density diffuse optical tomography (HD-DOT), an advanced high-density tomographic optical imaging method, can support semantic encoding and decoding using naturalistic movies. We collected 3.5 hours of naturalistic movie viewing data from six participants using stimuli labeled with 1,708 categories. Encoding models robustly predicted voxel

SUBMITTER: Fehner W 

PROVIDER: S-EPMC12713126 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature