ArticleScientific reports2023
Natural scene reconstruction from fMRI signals using generative latent diffusion.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
32 citing papers in PubMed.
- Preferred visual experiences provide intuitive descriptions of the functional properties of the cortical navigation network.bioRxiv : the preprint server for biology · 2026Article
- Contrastive learning to fine-tune feature extraction models for the visual cortex.PLoS computational biology · 2026Article
- NeuroFusion: A Unified Framework for Generalized Visual Stimulus Decoding from fMRI Across Datasets and Subjects.Neuroinformatics · 2026Article
- Theoretical Framework, Technical Evolution, and Future Prospects of Cross-Modal Mapping and Controllable Image Generation Under Multi-Source Heterogeneous Collaboration.Sensors (Basel, Switzerland) · 2026Review
- Shared representations in brains and models reveal a two-route cortical organization during scene perception.Communications biology · 2026Article
- Dynamic Transformer Based on Wavelet and Diffusion Prior Guidance for Cardiac Cine MRI Reconstruction.Sensors (Basel, Switzerland) · 2026Article
- MIRAGE: Robust multi-modal architectures translate fMRI-to-image models from vision to mental imagery.PLoS computational biology · 2026Article
- Article
- Article
- Synthesis and perceptual scaling of high-resolution naturalistic images using Stable Diffusion.Behavior research methods · 2025Article
- Brain-imager: a multimodal framework for image reconstruction and captioning from human brain activity.Brain informatics · 2025Article
- DCBAN: A Dynamic Confidence Bayesian Adaptive Network for Reconstructing Visual Images from fMRI Signals.Brain sciences · 2025Article
- StyDiff: a refined style transfer method based on diffusion models.Scientific reports · 2025Article
- Evidence for compositionality in fMRI visual representations via Brain Algebra.Communications biology · 2025Article
- Large-scale foundation models and generative AI for BigData neuroscience.Neuroscience research · 2025Review
- Generative language reconstruction from brain recordings.Communications biology · 2025Article
- Improved image reconstruction from brain activity through automatic image captioning.Scientific reports · 2025Article
- Forecasting fMRI images from video sequences: linear model analysis.Health information science and systems · 2024Article
- Emerging Brain-to-Content Technologies from Generative AI and Deep Representation Learning.IEEE signal processing magazine · 2024Article
- Efficient Neural Decoding Based on Multimodal Training.Brain sciences · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In neural decoding research, one of the most intriguing topics is the reconstruction of perceived natural images based on fMRI signals. Previous studies have succeeded in re-creating different aspects of the visuals, such as low-level properties (shape, texture, layout) or high-level features (category of objects, descriptive semantics of scenes) but have typically failed to reconstruct these properties together for complex scene images. Generative AI has recently made a leap forward with latent diffusion models capable of generating high-complexity images. Here, we investigate how to take advantage of this innovative technology for brain decoding. We present a two-stage scene reconstruction framework called "Brain-Diffuser". In the first stage, starting from fMRI signals, we reconstruct images that capture low-level properties and overall layout using a VDVAE (Very Deep Variational Autoencoder) model. In the second stage, we use the image-to-image framework of a latent diffusion model (Versatile Diffusion) conditioned on predicted multimodal (text and visual) features, to generate final reconstructed images. On the publicly available Natural Scenes Dataset benchmark, our method outperforms previous models both qualitatively and quantitatively. When applied to synthetic fMRI patterns generated from individual ROI (region-of-interest) masks, our trained model creates compelling "ROI-optimal" scenes consistent with neuroscientific knowledge. Thus, the proposed methodology can have an impact on both applied (e.g. brain-computer interface) and fundamental neuroscience.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.