ArticleScientific reports2022
Hyperrealistic neural decoding for reconstructing faces from fMRI activations via the GAN latent space.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 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
12 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
- Article
- Large-scale foundation models and generative AI for BigData neuroscience.Neuroscience research · 2025Review
- Generative artificial intelligence and ethical considerations in health care: a scoping review and ethics checklist.The Lancet. Digital health · 2024Article
- Brain2GAN: Feature-disentangled neural encoding and decoding of visual perception in the primate brain.PLoS computational biology · 2024Article
- Dual-Guided Brain Diffusion Model: Natural Image Reconstruction from Human Visual Stimulus fMRI.Bioengineering (Basel, Switzerland) · 2023Article
- Natural scene reconstruction from fMRI signals using generative latent diffusion.Scientific reports · 2023Article
- Generative AI for brain image computing and brain network computing: a review.Frontiers in neuroscience · 2023Review
- A transformer model for learning spatiotemporal contextual representation in fMRI data.Network neuroscience (Cambridge, Mass.) · 2023Article
- On the encoding of natural music in computational models and human brains.Frontiers in neuroscience · 2022Review
- Brain2Pix: Fully convolutional naturalistic video frame reconstruction from brain activity.Frontiers in neuroscience · 2022Article
- Reconstruction of perceived face images from brain activities based on multi-attribute constraints.Frontiers in neuroscience · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Neural decoding can be conceptualized as the problem of mapping brain responses back to sensory stimuli via a feature space. We introduce (i) a novel experimental paradigm that uses well-controlled yet highly naturalistic stimuli with a priori known feature representations and (ii) an implementation thereof for HYPerrealistic reconstruction of PERception (HYPER) of faces from brain recordings. To this end, we embrace the use of generative adversarial networks (GANs) at the earliest step of our neural decoding pipeline by acquiring fMRI data as participants perceive face images synthesized by the generator network of a GAN. We show that the latent vectors used for generation effectively capture the same defining stimulus properties as the fMRI measurements. As such, these latents (conditioned on the GAN) are used as the in-between feature representations underlying the perceived images that can be predicted in neural decoding for (re-)generation of the originally perceived stimuli, leading to the most accurate reconstructions of perception to date.
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.