title: Dynadiff: Single-stage Decoding of Images from Continuously Evolving
    fMRI
summary: Brain-to-image decoding has been recently propelled by the progress in
  generative AI models and the availability of large ultra-high field functional
  Magnetic Resonance Imaging (fMRI). However, current approaches depend on
  complicated multi-stage pipelines and preprocessing steps that typically
  collapse the temporal dimension of brain recordings, thereby limiting
  time-resolved brain decoders. Here, we introduce Dynadiff (Dynamic Neural
  Activity Diffusion for Image Reconstruction), a new single-stage diffusion
  model designed for reconstructing images from dynamically evolving fMRI
  recordings. Our approach offers three main contributions. First, Dynadiff
  simplifies training as compared to existing approaches. Second, our model
  outperforms state-of-the-art models on time-resolved fMRI signals, especially
  on high-level semantic image reconstruction metrics, while remaining
  competitive on preprocessed fMRI data that collapse time. Third, this approach
  allows a precise characterization of the evolution of image representations in
  brain activity. Overall, this work lays the foundation for time-resolved
  brain-to-image decoding.
paper_authors._id: 682ddb634e8859195a89ee05
paper_authors.name: Marlène Careil
paper_authors._id: 682ddb634e8859195a89ee06
paper_authors.name: Yohann Benchetrit
paper_authors.status: extracted_pending
paper_authors.statusLastChangedAt: 2025-05-21T13:55:52.729Z
paper_authors._id: 6679bc0467be4d7b18a2896d
paper_authors.avatarUrl: /avatars/9cba4040a9e90f2230d8b3c9412ecc26.svg
paper_authors.fullname: Yohann Benchetrit
paper_authors.type: user
paper_authors.user: ybenchetrit
paper_authors._id: 682ddb634e8859195a89ee07
paper_authors.name: Jean-Rémi King
paper_title: Dynadiff: Single-stage Decoding of Images from Continuously Evolving
    fMRI
paper_summary: Brain-to-image decoding has been recently propelled by the progress in
  generative AI models and the availability of large ultra-high field functional
  Magnetic Resonance Imaging (fMRI). However, current approaches depend on
  complicated multi-stage pipelines and preprocessing steps that typically
  collapse the temporal dimension of brain recordings, thereby limiting
  time-resolved brain decoders. Here, we introduce Dynadiff (Dynamic Neural
  Activity Diffusion for Image Reconstruction), a new single-stage diffusion
  model designed for reconstructing images from dynamically evolving fMRI
  recordings. Our approach offers three main contributions. First, Dynadiff
  simplifies training as compared to existing approaches. Second, our model
  outperforms state-of-the-art models on time-resolved fMRI signals, especially
  on high-level semantic image reconstruction metrics, while remaining
  competitive on preprocessed fMRI data that collapse time. Third, this approach
  allows a precise characterization of the evolution of image representations in
  brain activity. Overall, this work lays the foundation for time-resolved
  brain-to-image decoding.
paper_ai_summary: Dynadiff, a single-stage diffusion model, enhances time-resolved brain-to-image decoding from fMRI recordings by simplifying training and improving high-level semantic image reconstruction.
paper_ai_keywords: diffusion model Dynadiff fMRI brain-to-image decoding time-resolved image reconstruction semantic image reconstruction