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Deep Neural Encoder-Decoder Model to Relate fMRI Brain Activity with Naturalistic Stimuli.

Florian David, Michael Chan, Elenor Morgenroth

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |December 3, 2025
    PubMed
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    We developed a deep learning model to decode brain activity from fMRI scans during movie watching. This model reconstructs visual stimuli, revealing key brain regions involved in processing visual information.

    Area of Science:

    • Neuroscience
    • Artificial Intelligence
    • Computer Vision

    Background:

    • Functional magnetic resonance imaging (fMRI) captures brain activity but has temporal resolution limitations.
    • Naturalistic stimuli like movies offer rich visual information for studying brain function.
    • Bridging the gap between stimulus presentation and neural recording is crucial for accurate brain decoding.

    Purpose of the Study:

    • To develop an end-to-end deep neural encoder-decoder model for encoding and decoding brain activity from fMRI data.
    • To reconstruct visual stimuli from neural activity and identify brain regions involved in visual processing.
    • To leverage deep learning models as a proxy for understanding visual processing in the brain.

    Main Methods:

    • An end-to-end deep neural encoder-decoder model was proposed.

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  • Temporal convolutional layers were employed to handle the temporal resolution gap between movie stimuli and fMRI.
  • Saliency maps were used to investigate brain regions contributing to visual decoding.

    • Main Results:

    • The model successfully predicted voxel activity in and around the visual cortex.
    • Reconstruction of visual inputs from neural activity was achieved, including edges, faces, and contrasts.
    • Key brain regions identified include the middle occipital area (shape perception), fusiform area (complex recognition, faces), and calcarine (basic visual features).

    Conclusions:

    • Deep learning models can effectively decode brain activity related to visual stimuli from fMRI data.
    • The model's ability to reconstruct visual features aligns with the known functions of identified brain regions.
    • Deep learning models serve as a valuable proxy for probing and understanding visual processing in the brain during naturalistic viewing.