Evidence map›Paper›PMID 30459391›Full record

ArticleScientific reports2018

Resolving laminar activation in human V1 using ultra-high spatial resolution fMRI at 7T.

Sriranga Kashyap, Dimo Ivanov, Martin Havlicek, Shubharthi Sengupta, Benedikt A Poser, Kâmil Uludağ

Abstract read
In one paragraph

Article in Scientific reports, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers.

0numbers the graph read from it
0cells of the map it votes in
35citing 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

35 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. 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
  7. Article
  8. Article
  9. Article
  10. Review
  11. 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
  12. Review
  13. Article
  14. Article
  15. Article
  16. Article
  17. 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

6 authors.

Sriranga KashyapDepartment of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, 6229 EV, Maastricht, Netherlands. sriranga.kashyap@maastrichtuniversity.nl.ORCID http://orcid.org/0000-0003-0307-9773
Dimo IvanovDepartment of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, 6229 EV, Maastricht, Netherlands.ORCID http://orcid.org/0000-0001-9387-5823
Martin HavlicekDepartment of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, 6229 EV, Maastricht, Netherlands.ORCID http://orcid.org/0000-0001-9217-8268
Shubharthi SenguptaDepartment of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, 6229 EV, Maastricht, Netherlands.ORCID http://orcid.org/0000-0001-5033-0110
Benedikt A PoserDepartment of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, 6229 EV, Maastricht, Netherlands.ORCID http://orcid.org/0000-0001-8190-4367
Kâmil UludağDepartment of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, 6229 EV, Maastricht, Netherlands. kamil.uludag@maastrichtuniversity.nl.ORCID http://orcid.org/0000-0002-2813-5930

Funding

Foundations of MRI Corticography for mesoscale organization and neuronal circuitryR01MH111444 · NIMH · UNIVERSITY OF CALIFORNIA BERKELEY · PI FEINBERG, DAVID ALAN · 2016 to 2020
$5.1M
Nederlandse Organisatie voor Wetenschappelijk Onderzoek (Netherlands Organisation for Scientific Research) 016-178-052Nederlandse Organisatie voor Wetenschappelijk Onderzoek (Netherlands Organisation for Scientific Research) 452-11-002NIMH NIH HHS R01 MH111444U.S. Department of Health & Human Services | National Institutes of Health (NIH) R01MH111444
6 · The paper itself

Abstract

The mesoscopic organization of the human neocortex is of great interest for cognitive neuroscience. However, fMRI in humans typically maps the functional units of cognitive processing on a macroscopic level. With the advent of ultra-high field MRI (≥7T), it has become possible to acquire fMRI data with sub-millimetre resolution, enabling probing the laminar and columnar circuitry in humans. Currently, laminar BOLD responses are not directly observed but inferred via data analysis, due to coarse spatial resolution of fMRI (e.g. 0.7-0.8 mm isotropic) relative to the extent of histological laminae. In this study, we introduce a novel approach for mapping the cortical BOLD response at the spatial scale of cortical layers and columns at 7T (an unprecedented 0.1 mm, either in the laminar or columnar direction). We demonstrate experimentally and using simulations, the superiority of the novel approach compared to standard approaches for human laminar fMRI in terms of effective spatial resolution in either laminar or columnar direction. In addition, we provide evidence that the laminar BOLD signal profile is not homogeneous even over short patches of cortex. In summary, the proposed novel approach affords the ability to directly study the mesoscopic organization of the human cortex, thus, bridging the gap between human cognitive neuroscience and invasive animal studies.

Indexed as

AdultBrain MappingFemaleFunctional NeuroimagingHumansImage Processing, Computer-AssistedMagnetic Resonance ImagingMaleVisual Cortex

Identifiers

PMID30459391
PMCPMC6244001

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.