Evidence map›Paper›PMID 33580320›Full record

ArticleBrain structure & function2021

A probabilistic atlas of the human ventral tegmental area (VTA) based on 7 Tesla MRI data.

Anne C Trutti, Laura Fontanesi, Martijn J Mulder, Pierre-Louis Bazin, Bernhard Hommel, Birte U Forstmann

Open access · hybridAbstract read
In one paragraph

Article in Brain structure & function, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers.

0numbers the graph read from it
0cells of the map it votes in
38citing papers in PubMed
5.3field-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

38 citing papers in PubMed, 65 citations in OpenAlex.

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  20. Leveraging ultra-high field (7T) MRI in psychiatric research.Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology · 2024
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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 at 4 institutions in 3 countries.

Anne C TruttiIntegrative Model-Based Cognitive Neuroscience Research Unit, University of Amsterdam, Nieuwe Achtergracht 129, 1018 WS, Amsterdam, The Netherlands. a.c.trutti@uva.nl.ORCID http://orcid.org/0000-0002-0044-4846
Laura FontanesiPsychology Department, Center for Economic Psychology and Decision Neuroscience, University of Basel, Basel, Switzerland.
Martijn J MulderIntegrative Model-Based Cognitive Neuroscience Research Unit, University of Amsterdam, Nieuwe Achtergracht 129, 1018 WS, Amsterdam, The Netherlands.
Pierre-Louis BazinIntegrative Model-Based Cognitive Neuroscience Research Unit, University of Amsterdam, Nieuwe Achtergracht 129, 1018 WS, Amsterdam, The Netherlands.
Bernhard HommelCognitive Psychology Unit and Leiden Institute for Brain and Cognition, Leiden University, Leiden, The Netherlands.
Birte U ForstmannIntegrative Model-Based Cognitive Neuroscience Research Unit, University of Amsterdam, Nieuwe Achtergracht 129, 1018 WS, Amsterdam, The Netherlands.
Leiden University · NLMax Planck Institute for Human Cognitive and Brain Sciences · DEUniversity of Basel · CHUtrecht University · NL

Funding

European Research Council ERC-2012-Stg-313481European Research Council ERC-2015-AdG- 694722Koninklijke Nederlandse Akademie van Wetenschappen 452-11-008
6 · The paper itself

Abstract

Functional magnetic resonance imaging (fMRI) BOLD signal is commonly localized by using neuroanatomical atlases, which can also serve for region of interest analyses. Yet, the available MRI atlases have serious limitations when it comes to imaging subcortical structures: only 7% of the 455 subcortical nuclei are captured by current atlases. This highlights the general difficulty in mapping smaller nuclei deep in the brain, which can be addressed using ultra-high field 7 Tesla (T) MRI. The ventral tegmental area (VTA) is a subcortical structure that plays a pivotal role in reward processing, learning and memory. Despite the significant interest in this nucleus in cognitive neuroscience, there are currently no available, anatomically precise VTA atlases derived from 7 T MRI data that cover the full region of the VTA. Here, we first provide a protocol for multimodal VTA imaging and delineation. We then provide a data description of a probabilistic VTA atlas based on in vivo 7 T MRI data.

Indexed as

Magnetic Resonance ImagingVentral Tegmental AreaBrain MappingHumansReward7 T MRIMidbrainProbabilistic atlasSubcortexVTA

Identifiers

PMID33580320
PMCPMC8036186
OpenAlexW3126484596

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.