Evidence map›Paper›PMID 31570628›Full record

ArticleProceedings of the National Academy of Sciences of the United States of America2019

Laminar specific fMRI reveals directed interactions in distributed networks during language processing.

Daniel Sharoh, Tim van Mourik, Lauren J Bains, Katrien Segaert, Kirsten Weber, Peter Hagoort, David G Norris

Open access · bronzeAbstract read
In one paragraph

Article in Proceedings of the National Academy of Sciences of the United States of America, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 45 papers.

0numbers the graph read from it
0cells of the map it votes in
45citing papers in PubMed
6.1field-weighted citation impact, top 3% of its field
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

45 citing papers in PubMed, 86 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Review
  6. Article
  7. Article
  8. 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 · 2024
    Article
  9. Article
  10. The neocortical infrastructure for language involves region-specific patterns of laminar gene expression.Proceedings of the National Academy of Sciences of the United States of America · 2024
    Article
  11. Article
  12. Article
  13. Article
  14. Article
  15. Article
  16. Article
  17. Identifying the distinct spectral dynamics of laminar-specific interhemispheric connectivity with bilateral line-scanning fMRI.Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism · 2023
    Article
  18. Review
  19. Article
  20. 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

7 authors at 3 institutions in 3 countries.

Daniel SharohDonders Institute for Brain Cognition and Behaviour, Radoud University, NL-6500 HB Nijmegen, The Netherlands.ORCID 0000-0002-6941-5680
Tim van MourikDonders Institute for Brain Cognition and Behaviour, Radoud University, NL-6500 HB Nijmegen, The Netherlands.
Lauren J BainsDonders Institute for Brain Cognition and Behaviour, Radoud University, NL-6500 HB Nijmegen, The Netherlands.
Katrien SegaertSchool of Psychology, University of Birmingham, Birmingham B15 2SA, United Kingdom.
Kirsten WeberDonders Institute for Brain Cognition and Behaviour, Radoud University, NL-6500 HB Nijmegen, The Netherlands.
Peter HagoortDonders Institute for Brain Cognition and Behaviour, Radoud University, NL-6500 HB Nijmegen, The Netherlands; peter.hagoort@mpi.nl.
David G NorrisDonders Institute for Brain Cognition and Behaviour, Radoud University, NL-6500 HB Nijmegen, The Netherlands.
Radboud University Nijmegen · NLErwin L. Hahn Institute for Magnetic Resonance Imaging · DEUniversity of Birmingham · GB

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Interactions between top-down and bottom-up information streams are integral to brain function but challenging to measure noninvasively. Laminar resolution, functional MRI (lfMRI) is sensitive to depth-dependent properties of the blood oxygen level-dependent (BOLD) response, which can be potentially related to top-down and bottom-up signal contributions. In this work, we used lfMRI to dissociate the top-down and bottom-up signal contributions to the left occipitotemporal sulcus (LOTS) during word reading. We further demonstrate that laminar resolution measurements could be used to identify condition-specific distributed networks on the basis of whole-brain connectivity patterns specific to the depth-dependent BOLD signal. The networks corresponded to top-down and bottom-up signal pathways targeting the LOTS during word reading. We show that reading increased the top-down BOLD signal observed in the deep layers of the LOTS and that this signal uniquely related to the BOLD response in other language-critical regions. These results demonstrate that lfMRI can reveal important patterns of activation that are obscured at standard resolution. In addition to differences in activation strength as a function of depth, we also show meaningful differences in the interaction between signals originating from different depths both within a region and with the rest of the brain. We thus show that lfMRI allows the noninvasive measurement of directed interaction between brain regions and is capable of resolving different connectivity patterns at submillimeter resolution, something previously considered to be exclusively in the domain of invasive recordings.

Indexed as

BrainBrain MappingHumansLanguageMagnetic Resonance ImagingReadingSignal TransductionBOLD biophysicsdirected connectivitylaminar fMRIlanguagesystems neuroscience

Identifiers

PMID31570628
PMCPMC6800353
OpenAlexW2977834749

What OpenQuestion holds

Textmetadata
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.