Evidence map›Paper›PMID 35102154›Full record

ArticleScientific data2022

Dataset of human intracranial recordings during famous landmark identification.

Oscar Woolnough, Cihan M Kadipasaoglu, Christopher R Conner, Kiefer J Forseth, Patrick S Rollo, Matthew J Rollo, Vatche G Baboyan, Nitin Tandon

Abstract readDataset
In one paragraph

Article in Scientific data, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from 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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Oscar WoolnoughVivian L. Smith Department of Neurosurgery, McGovern Medical School at UT Health Houston, Houston, TX, 77030, United States of America.ORCID 0000-0002-5878-6865
Cihan M KadipasaogluVivian L. Smith Department of Neurosurgery, McGovern Medical School at UT Health Houston, Houston, TX, 77030, United States of America.ORCID 0000-0001-5390-372X
Christopher R ConnerVivian L. Smith Department of Neurosurgery, McGovern Medical School at UT Health Houston, Houston, TX, 77030, United States of America.ORCID 0000-0003-0176-9025
Kiefer J ForsethVivian L. Smith Department of Neurosurgery, McGovern Medical School at UT Health Houston, Houston, TX, 77030, United States of America.ORCID 0000-0003-1624-8329
Patrick S RolloVivian L. Smith Department of Neurosurgery, McGovern Medical School at UT Health Houston, Houston, TX, 77030, United States of America.
Matthew J RolloVivian L. Smith Department of Neurosurgery, McGovern Medical School at UT Health Houston, Houston, TX, 77030, United States of America.
Vatche G BaboyanVivian L. Smith Department of Neurosurgery, McGovern Medical School at UT Health Houston, Houston, TX, 77030, United States of America.
Nitin TandonVivian L. Smith Department of Neurosurgery, McGovern Medical School at UT Health Houston, Houston, TX, 77030, United States of America. nitin.tandon@uth.tmc.edu.ORCID 0000-0002-2752-2365

Funding

Data Archive for the Brain Initiative (DABI)R24MH114796 · NIMH · UNIVERSITY OF SOUTHERN CALIFORNIA · PI DUNCAN, DOMINIQUE, POURATIAN, NADER · 2018 to 2023
$6.2M
Functional Architecture of Speech Motor CortexU01NS098971 · NINDS · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI CHANG, EDWARD · 2016 to 2018
$3.5M
A unified cognitive network model of languageU01NS098981 · NINDS · UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON · PI CRONE, NATHAN E, TANDON, NITIN · 2016 to 2018
$3.0M
Brain Networks of Noun GenerationR01DC014589 · NIDCD · UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON · PI TANDON, NITIN · 2015 to 2019
$2.0M
Cortical Dynamics during Speech ProductionF30DC017083 · NIDCD · UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON · PI FORSETH, KIEFER · 2018 to 2020
$64k
NIDCD NIH HHS F30 DC017083NIDCD NIH HHS R01 DC014589NIMH NIH HHS R24 MH114796NINDS NIH HHS U01 NS098971NINDS NIH HHS U01 NS098981
6 · The paper itself

Abstract

For most people, recalling information about familiar items in a visual scene is an effortless task, but it is one that depends on coordinated interactions of multiple, distributed neural components. We leveraged the high spatiotemporal resolution of direct intracranial recordings to better delineate the network dynamics underpinning visual scene recognition. We present a dataset of recordings from a large cohort of humans while they identified images of famous landmarks (50 individuals, 52 recording sessions, 6,775 electrodes, 6,541 trials). This dataset contains local field potential recordings derived from subdural and penetrating electrodes covering broad areas of cortex across both hemispheres. We provide this pre-processed data with behavioural metrics (correct/incorrect, response times) and electrode localisation in a population-normalised cortical surface space. This rich dataset will allow further investigation into the spatiotemporal progression of multiple neural processes underlying visual processing, scene recognition and cued memory recall.

Indexed as

ElectroencephalographyMemoryCognitionHumansMental RecallVisual Perception

Identifiers

PMID35102154
PMCPMC8803828

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.