Evidence map›Paper›PMID 32512115›Full record

ReviewProgress in neurobiology2021

Layer-dependent functional connectivity methods.

Laurentius Huber, Emily S Finn, Yuhui Chai, Rainer Goebel, Rüdiger Stirnberg, Tony Stöcker, Sean Marrett, Kamil Uludag, Seong-Gi Kim, SoHyun Han and 2 more

Abstract readReview
In one paragraph

Review in Progress in neurobiology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 75 papers.

0numbers the graph read from it
0cells of the map it votes in
75citing 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

75 citing papers in PubMed.

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15 more citing papers are in PubMed but not listed here.

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

12 authors.

Laurentius HuberMR-Methods group, MBIC, Faculty of Psychology and Neuroscience, Maastricht University, the Netherlands. Electronic address: renzohuber@gmail.com.
Emily S FinnNational Institute of Mental Health, NIH, Bethesda, MD, USA.
Yuhui ChaiNational Institute of Mental Health, NIH, Bethesda, MD, USA.
Rainer GoebelMR-Methods group, MBIC, Faculty of Psychology and Neuroscience, Maastricht University, the Netherlands.
Rüdiger StirnbergGerman Center for Neurodegenerative Diseases, Bonn, Germany.
Tony StöckerGerman Center for Neurodegenerative Diseases, Bonn, Germany; Department of Physics and Astronomy, University of Bonn, Bonn, Germany.
Sean MarrettNational Institute of Mental Health, NIH, Bethesda, MD, USA.
Kamil UludagCenter for Neuroscience Imaging Research, Institute for Basic Science & Department of Biomedial Engineering, Sungkyunkwan University, Suwon, Republic of Korea; Techna Institute & Koerner Scientist in MR Imaging, University Health Network, Toronto, Canada.
Seong-Gi KimCenter for Neuroscience Imaging Research, Institute for Basic Science & Department of Biomedial Engineering, Sungkyunkwan University, Suwon, Republic of Korea.
SoHyun HanCenter for Neuroscience Imaging Research, Institute for Basic Science & Department of Biomedial Engineering, Sungkyunkwan University, Suwon, Republic of Korea.
Peter A BandettiniNational Institute of Mental Health, NIH, Bethesda, MD, USA.
Benedikt A PoserMR-Methods group, MBIC, Faculty of Psychology and Neuroscience, Maastricht University, the Netherlands.

Funding

Functional MRI Method DevelopmentZIAMH002783 · NIMH · NATIONAL INSTITUTE OF MENTAL HEALTH · PI BANDETTINI, PETER · 2009 to 2025
$38.4M
Mapping the Human Connectome: Structure, Function, and HeritabilityU54MH091657 · NIMH · WASHINGTON UNIVERSITY · PI UGURBIL, KAMIL, VAN ESSEN, DAVID C · 2010 to 2014
$34.7M
Foundations of MRI Corticography for mesoscale organization and neuronal circuitryR01MH111444 · NIMH · UNIVERSITY OF CALIFORNIA BERKELEY · PI FEINBERG, DAVID ALAN · 2016 to 2020
$5.1M
Linking brain activity during naturalistic tasks to individual phenotypes on the depression spectrumR00MH120257 · NIMH · DARTMOUTH COLLEGE · PI FINN, EMILY SUZANNE · 2020 to 2022
$747k
Genetic Influences on Human Cortical DevelopmentK99MH102357 · NIMH · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI STEIN, JASON LOUIS · 2014 to 2015
$236k
Intramural NIH HHS Z99 MH999999Intramural NIH HHS ZIA MH002783NIMH NIH HHS K99 MH102357NIMH NIH HHS R00 MH120257NIMH NIH HHS R01 MH111444NIMH NIH HHS U54 MH091657
6 · The paper itself

Abstract

Recent methodological advances in fMRI contrast and readout strategies have allowed researchers to approach the mesoscopic spatial regime of cortical layers. This has revolutionized the ability to map cortical information processing within and across brain systems. However, until recently, most layer-fMRI studies have been confined to primary cortices using basic block-design tasks and macro-vascular-contaminated sequence contrasts. To become an established method for user-friendly applicability in neuroscience practice, layer-fMRI acquisition and analysis methods need to be extended to more flexible connectivity-based experiment designs; they must be able to capture subtle changes in brain networks of higher-order cognitive areas, and they should not be spatially biased with unwanted vein signals. In this article, we review the most pressing challenges of layer-dependent fMRI for large-scale neuroscientific applicability and describe recently developed acquisition methodologies that can resolve them. In doing so, we review technical tradeoffs and capabilities of modern MR-sequence approaches to achieve measurements that are free of locally unspecific vein signal, with whole-brain coverage, sub-second sampling, high resolutions, and with a combination of those capabilities. The presented approaches provide whole-brain layer-dependent connectivity data that open a new window to investigate brain network connections. We exemplify this by reviewing a number of candidate tools for connectivity analyses that will allow future studies to address new questions in network neuroscience. The considered network analysis tools include: hierarchy mapping, directional connectomics, source-specific connectivity mapping, and network sub-compartmentalization. We conclude: Whole-brain layer-fMRI without large-vessel contamination is applicable for human neuroscience and opens the door to investigate biological mechanisms behind any number of psychological and psychiatric phenomena, such as selective attention, hallucinations and delusions, and even conscious perception.

Indexed as

ConnectomeAttentionBrainBrain MappingCognitionHumansMagnetic Resonance Imaging7T fMRICBV-fMRIFunctional connectivityHuman connectomeLaminar fMRIlayer-fMRIMesoscopic fMRIVASOWhole brain submillimeter fMRI

Identifiers

PMID32512115
PMCPMC11800141

What OpenQuestion holds

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Registered trials

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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.