ArticleScientific reports2022
Comprehensive decoding mental processes from Web repositories of functional brain images.
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 8 papers, 2 of them syntheses that pooled it.
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
8 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Striato-limbic and frontoparietal dysfunction underlying imbalance of reward-motivation and cognitive control in compulsive sexual behaviour disorder: A neuroimaging meta-analysis.Journal of behavioral addictions · 2026Pooled it
- Advancing image-based meta-analysis through systematic use of crowdsourced NeuroVault data.Scientific reports · 2025Pooled it
- Peaks2Image: Reconstructing fMRI maps from stereotactic coordinates to enhance cognitive meta-analysis.Imaging neuroscience (Cambridge, Mass.) · 2026Article
- NiCLIP: Neuroimaging contrastive language-image pretraining model for predicting text from brain activation images.bioRxiv : the preprint server for biology · 2025Article
- Inferring Mental States from Brain Data: Ethico-legal Questions about Social Uses of Brain Data.The Hastings Center report · 2025Article
- Methods for decoding cortical gradients of functional connectivity.Imaging neuroscience (Cambridge, Mass.) · 2024Article
- Should one go for individual- or group-level brain parcellations? A deep-phenotyping benchmark.Brain structure & function · 2024Article
- On the benefits of self-taught learning for brain decoding.GigaScience · 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
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Associating brain systems with mental processes requires statistical analysis of brain activity across many cognitive processes. These analyses typically face a difficult compromise between scope-from domain-specific to system-level analysis-and accuracy. Using all the functional Magnetic Resonance Imaging (fMRI) statistical maps of the largest data repository available, we trained machine-learning models that decode the cognitive concepts probed in unseen studies. For this, we leveraged two comprehensive resources: NeuroVault-an open repository of fMRI statistical maps with unconstrained annotations-and Cognitive Atlas-an ontology of cognition. We labeled NeuroVault images with Cognitive Atlas concepts occurring in their associated metadata. We trained neural networks to predict these cognitive labels on tens of thousands of brain images. Overcoming the heterogeneity, imbalance and noise in the training data, we successfully decoded more than 50 classes of mental processes on a large test set. This success demonstrates that image-based meta-analyses can be undertaken at scale and with minimal manual data curation. It enables broad reverse inferences, that is, concluding on mental processes given the observed brain activity.
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.