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Brain imaging technologies provide critical insights into both the structure and function of the human brain, enabling medical professionals and researchers to diagnose, study, and treat neurological disorders or psychiatric disorders more effectively.
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Related Experiment Video

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Author Spotlight: Insights into Visual Cortex Research Through Wide-View fMRI Mapping
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Optimized AI-based neural decoding from BOLD fMRI signal for analyzing visual and semantic ROIs in the human visual

Lorenzo Veronese1, Andrea Moglia1, Nicolo Pecco2

  • 1Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy.

Journal of Neural Engineering
|August 14, 2025
PubMed
Summary

This study introduces a more efficient AI model for reconstructing visual perception from brain activity (fMRI). The new model simplifies the process while maintaining high accuracy in visual decoding.

Keywords:
fMRI imagerygenerative artificial intelligenceneural decodingvisual stimuls

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Area of Science:

  • Neuroscience
  • Artificial Intelligence
  • Computer Vision

Background:

  • AI-driven neural decoding aims to reconstruct visual perception from fMRI brain activity using generative models.
  • Current methods often involve complex, multi-stage processes with variational autoencoders (VAE) and latent diffusion models (LDM).
  • Challenges include fMRI data complexity, noise, and optimizing the interplay between reconstruction stages.

Purpose of the Study:

  • To develop and evaluate an optimized two-stage AI architecture for neural decoding of visual stimuli from fMRI data.
  • To address gaps in existing two-stage models by implementing non-linear mappings, optimizing latent space dimensionality, and analyzing stage contributions.
  • To investigate the impact of different brain regions of interest (ROIs) on reconstruction quality.

Main Methods:

  • Implemented a gated recurrent unit (GRU) architecture for non-linear fMRI to VAE latent space mapping.
  • Optimized the VAE latent space dimensionality.
  • Conducted systematic evaluations of the first reconstruction stage's contribution and analyzed ROI-specific performance.
  • Utilized the Natural Scenes Dataset (NSD) with fMRI data from eight subjects.

Main Results:

  • The proposed architecture achieved competitive performance with an 85% reduction in the first stage's complexity.
  • Sensitivity analysis confirmed the first stage's crucial role in maintaining structural similarity.
  • Excluding early visual areas and focusing on semantic ROIs preserved semantics but reduced visual coherence.
  • High inter-subject repeatability was observed (92% for visual, 98% for semantic metrics).

Conclusions:

  • The study presents a key advancement in optimized neural decoding architectures for stimulus prediction using non-linear models.
  • Analysis highlights the critical interplay between the two reconstruction stages.
  • ROI-based analysis supports the two-stage AI model's reflection of the brain's hierarchical visual processing.