SynthesisFrontiers in neuroscience2021
Natural Image Reconstruction From fMRI Using Deep Learning: A Survey.
Synthesis in Frontiers in neuroscience, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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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.
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Who cites it
6 citing papers in PubMed, 41 citations in OpenAlex.
- Article
- Brain-AI Alignment in Naturalistic Movies.bioRxiv : the preprint server for biology · 2025Article
- DCBAN: A Dynamic Confidence Bayesian Adaptive Network for Reconstructing Visual Images from fMRI Signals.Brain sciences · 2025Article
- Natural Image Reconstruction from fMRI Based on Node-Edge Interaction and Multi-Scale Constraint.Brain sciences · 2024Article
- Dual-Guided Brain Diffusion Model: Natural Image Reconstruction from Human Visual Stimulus fMRI.Bioengineering (Basel, Switzerland) · 2023Article
- Mapping the movie-watching brain with AI-derived semantics.Imaging neuroscience (Cambridge, Mass.)Article
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
With the advent of brain imaging techniques and machine learning tools, much effort has been devoted to building computational models to capture the encoding of visual information in the human brain. One of the most challenging brain decoding tasks is the accurate reconstruction of the perceived natural images from brain activities measured by functional magnetic resonance imaging (fMRI). In this work, we survey the most recent deep learning methods for natural image reconstruction from fMRI. We examine these methods in terms of architectural design, benchmark datasets, and evaluation metrics and present a fair performance evaluation across standardized evaluation metrics. Finally, we discuss the strengths and limitations of existing studies and present potential future directions.
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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.