ReviewThe Journal of neuroscience : the official journal of the Society for Neuroscience2018
A Shared Vision for Machine Learning in Neuroscience.
Review in The Journal of neuroscience : the official journal of the Society for Neuroscience, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 72 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
72 citing papers in PubMed.
- Anatomic & functional brain atlases: connecting functional flexibility with fixed infrastructure.Brain structure & function · 2026Review
- Optimization of academic performance and mental health in college students through an AI-driven personalized physical exercise and mindfulness intervention system.Scientific reports · 2026Article
- State-Dependent CNN-GRU Reinforcement Framework for Robust EEG-Based Sleep Stage Classification.Biomimetics (Basel, Switzerland) · 2026Article
- Exploring synergies: Advancing neuroscience with machine learning.Signal processing · 2026Article
- Neurodatascience: Past, Present, and Future.Data science in science · 2026Article
- Multimodal investigation of the neurocognitive deficits underlying dyslexia in adulthood.Cerebral cortex (New York, N.Y. : 1991) · 2025Article
- The NERVE-ML (neural engineering reproducibility and validity essentials for machine learning) checklist: ensuring machine learning advances neural engineeringJournal of neural engineering · 2025Review
- Eeg Microstates and Balance Parameters for Stroke Discrimination: A Machine Learning Approach.Brain topography · 2025Article
- In vivo microelectrode arrays for neuroscience.Nature reviews. Methods primers · 2025Article
- Clustering Electrophysiological Predisposition to Binge Drinking: An Unsupervised Machine Learning Analysis.Brain and behavior · 2024Article
- Simple Behavioral Analysis (SimBA) as a platform for explainable machine learning in behavioral neuroscience.Nature neuroscience · 2024Article
- An unbiased method to partition diverse neuronal responses into functional ensembles reveals interpretable population dynamics during innate social behavior.bioRxiv : the preprint server for biology · 2024Article
- A primer on the use of machine learning to distil knowledge from data in biological psychiatry.Molecular psychiatry · 2024Review
- Electome network factors: Capturing emotional brain networks related to health and disease.Cell reports methods · 2024Review
- Through their eyes: Multi-subject brain decoding with simple alignment techniques.Imaging neuroscience (Cambridge, Mass.) · 2024Article
- Predicting interindividual response to theta burst stimulation in the lower limb motor cortex using machine learning.Frontiers in neuroscience · 2024Article
- The application of eXplainable artificial intelligence in studying cognition: A scoping review.Ibrain · 2024Article
- Neuroscience Needs Network Science.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2023Article
- Decoding Mental Effort in a Quasi-Realistic Scenario: A Feasibility Study on Multimodal Data Fusion and Classification.Sensors (Basel, Switzerland) · 2023Article
- Differences in brain connectivity of meditators during assessing neurocognition via gamified experimental logic task: A machine learning approach.The neuroradiology journal · 2023Article
12 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
13 authors.
Funding
Abstract
With ever-increasing advancements in technology, neuroscientists are able to collect data in greater volumes and with finer resolution. The bottleneck in understanding how the brain works is consequently shifting away from the amount and type of data we can collect and toward what we actually do with the data. There has been a growing interest in leveraging this vast volume of data across levels of analysis, measurement techniques, and experimental paradigms to gain more insight into brain function. Such efforts are visible at an international scale, with the emergence of big data neuroscience initiatives, such as the BRAIN initiative (Bargmann et al., 2014), the Human Brain Project, the Human Connectome Project, and the National Institute of Mental Health's Research Domain Criteria initiative. With these large-scale projects, much thought has been given to data-sharing across groups (Poldrack and Gorgolewski, 2014; Sejnowski et al., 2014); however, even with such data-sharing initiatives, funding mechanisms, and infrastructure, there still exists the challenge of how to cohesively integrate all the data. At multiple stages and levels of neuroscience investigation, machine learning holds great promise as an addition to the arsenal of analysis tools for discovering how the brain works.
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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.