ArticleCerebral cortex (New York, N.Y. : 1991)2020
A Gaussian Process Model of Human Electrocorticographic Data.
Article in Cerebral cortex (New York, N.Y. : 1991), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 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.
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Who cites it
9 citing papers in PubMed, 16 citations in OpenAlex.
- Spatially Masked Regression Reveals Local and Distributed Predictability in Electrophysiological Recordings.ArXiv · 2026Article
- Aligning brains into a shared space improves their alignment with large language models.Nature computational science · 2026Article
- From Bedside to Desktop: A Data Protocol for Normative Intracranial EEG and Abnormality Mapping.Bio-protocol · 2025Article
- High-level cognition is supported by information-rich but compressible brain activity patterns.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Macroscopic resting-state brain dynamics are best described by linear models.Nature biomedical engineering · 2024Article
- Normative brain mapping of interictal intracranial EEG to localize epileptogenic tissue.Brain : a journal of neurology · 2022Article
- Distributed Subnetworks of Depression Defined by Direct Intracranial Neurophysiology.Frontiers in human neuroscience · 2021Article
- Stable maintenance of multiple representational formats in human visual short-term memory.Proceedings of the National Academy of Sciences of the United States of America · 2020Article
- Insights into human cognition from intracranial EEG: A review of audition, memory, internal cognition, and causality.Journal of neural engineering · 2020Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 2 institutions in 1 country.
Funding
Abstract
We present a model-based method for inferring full-brain neural activity at millimeter-scale spatial resolutions and millisecond-scale temporal resolutions using standard human intracranial recordings. Our approach makes the simplifying assumptions that different people's brains exhibit similar correlational structure, and that activity and correlation patterns vary smoothly over space. One can then ask, for an arbitrary individual's brain: given recordings from a limited set of locations in that individual's brain, along with the observed spatial correlations learned from other people's recordings, how much can be inferred about ongoing activity at other locations throughout that individual's brain? We show that our approach generalizes across people and tasks, thereby providing a person- and task-general means of inferring high spatiotemporal resolution full-brain neural dynamics from standard low-density intracranial recordings.
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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.