ArticleNeuroImage2016
The Automatic Neuroscientist: A framework for optimizing experimental design with closed-loop real-time fMRI.
Article in NeuroImage, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 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.
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
33 citing papers in PubMed, 94 citations in OpenAlex.
- AutoNeuro: An Open-Source fMRI Toolbox for Real-Time Neuroadaptive Task Design.bioRxiv : the preprint server for biology · 2026Article
- A State-Space Framework for Causal Detection of Hippocampal Ripple-Replay Events.IEEE transactions on bio-medical engineering · 2026Article
- Low-Rank Tensor Encoding Models Decompose Natural Speech Comprehension Processes.bioRxiv : the preprint server for biology · 2025Article
- Combining Real-Time Neuroimaging With Machine Learning to Study Attention to Familiar Faces During Infancy: A Proof of Principle Study.Developmental science · 2025Article
- Random rotational embedding Bayesian optimization for human-in-the-loop personalized music generation.PloS one · 2025Article
- Closed-loop fMRI at the mesoscopic scale of columns and layers: Can we do it and why would we want to?Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2024Article
- Dynamic layer-specific processing in the prefrontal cortex during working memory.Communications biology · 2024Article
- Reinforcement learning for closed-loop regulation of cardiovascular system with vagus nerve stimulation: a computational study.Journal of neural engineering · 2024Article
- Article
- Real-time monitoring of infant theta power during naturalistic social experiences.Developmental cognitive neuroscience · 2023Article
- Closing the loop between brain and electrical stimulation: towards precision neuromodulation treatments.Translational psychiatry · 2023Review
- How Machine Learning is Powering Neuroimaging to Improve Brain Health.Neuroinformatics · 2022Review
- Automated deep brain stimulation programming with safety constraints for tremor suppression in patients with Parkinson's disease and essential tremor.Journal of neural engineering · 2022Article
- Tissue volume estimation and age prediction using rapid structural brain scans.Scientific reports · 2022Article
- A guided multiverse study of neuroimaging analyses.Nature communications · 2022Article
- Behavioral Studies Using Large-Scale Brain Networks - Methods and Validations.Frontiers in human neuroscience · 2022Review
- Is it time to put rest to rest?Trends in cognitive sciences · 2021Review
- A Bayesian optimization approach for rapidly mapping residual network function in stroke.Brain : a journal of neurology · 2021Article
- Material characterization of thin planar structures using full-field harmonic vibration response measured with stroboscopic holography.International journal of mechanical sciences · 2021Article
- ADOpy: a python package for adaptive design optimization.Behavior research methods · 2021Article
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 at 3 institutions in 2 countries.
Funding
Abstract
Functional neuroimaging typically explores how a particular task activates a set of brain regions. Importantly though, the same neural system can be activated by inherently different tasks. To date, there is no approach available that systematically explores whether and how distinct tasks probe the same neural system. Here, we propose and validate an alternative framework, the Automatic Neuroscientist, which turns the standard fMRI approach on its head. We use real-time fMRI in combination with modern machine-learning techniques to automatically design the optimal experiment to evoke a desired target brain state. In this work, we present two proof-of-principle studies involving perceptual stimuli. In both studies optimization algorithms of varying complexity were employed; the first involved a stochastic approximation method while the second incorporated a more sophisticated Bayesian optimization technique. In the first study, we achieved convergence for the hypothesized optimum in 11 out of 14 runs in less than 10 min. Results of the second study showed how our closed-loop framework accurately and with high efficiency estimated the underlying relationship between stimuli and neural responses for each subject in one to two runs: with each run lasting 6.3 min. Moreover, we demonstrate that using only the first run produced a reliable solution at a group-level. Supporting simulation analyses provided evidence on the robustness of the Bayesian optimization approach for scenarios with low contrast-to-noise ratio. This framework is generalizable to numerous applications, ranging from optimizing stimuli in neuroimaging pilot studies to tailoring clinical rehabilitation therapy to patients and can be used with multiple imaging modalities in humans and animals.
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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.