Evidence map›Paper›PMID 35945231›Full record

ArticleScientific data2022

Human brain structural connectivity matrices-ready for modelling.

Antonín Škoch, Barbora Rehák Bučková, Jan Mareš, Jaroslav Tintěra, Pavel Sanda, Lucia Jajcay, Jiří Horáček, Filip Španiel, Jaroslav Hlinka

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 24 papers.

0numbers the graph read from it
0cells of the map it votes in
24citing 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

24 citing papers in PubMed.

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  11. Learning Networks from Wide-Sense Stationary Stochastic Processes.IEEE transactions on signal and information processing over networks · 2025
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  16. A Novel Directed Seed-Based Connectivity Analysis Toolbox Applied to Human and Marmoset Resting-State FMRI.The Journal of neuroscience : the official journal of the Society for Neuroscience · 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

9 authors.

Antonín ŠkochNational Institute of Mental Health, Klecany, Czech Republic.ORCID 0000-0002-1739-3256
Barbora Rehák Bučková *National Institute of Mental Health, Klecany, Czech Republic.ORCID 0000-0001-5619-3946
Jan Mareš *National Institute of Mental Health, Klecany, Czech Republic.
Jaroslav TintěraNational Institute of Mental Health, Klecany, Czech Republic.
Pavel SandaInstitute of Computer Science of the Czech Academy of Sciences, Prague, Czech Republic.
Lucia JajcayNational Institute of Mental Health, Klecany, Czech Republic.ORCID 0000-0001-9068-3826
Jiří HoráčekNational Institute of Mental Health, Klecany, Czech Republic.ORCID 0000-0003-0114-7306
Filip ŠpanielNational Institute of Mental Health, Klecany, Czech Republic.
Jaroslav HlinkaNational Institute of Mental Health, Klecany, Czech Republic. hlinka@cs.cas.cz.ORCID 0000-0003-1402-1470

Funding

Agentura Pro Zdravotnický Výzkum České Republiky (Czech Health Research Council) NU21-08-00432Akademie Věd České Republiky (Academy of Sciences of the Czech Republic) Strategy AV21České Vysoké Učení Technické v Praze (Czech Technical University in Prague) SGS20/172/OHK3/3T/13Grantová Agentura České Republiky (Grant Agency of the Czech Republic) 21-32608SMinisterstvo Zdravotnictví Ceské Republiky (Ministry of Health of the Czech Republic) DRO 2021 National Institute of Mental Health - NIMH IN 00023752
6 · The paper itself

Abstract

The human brain represents a complex computational system, the function and structure of which may be measured using various neuroimaging techniques focusing on separate properties of the brain tissue and activity. We capture the organization of white matter fibers acquired by diffusion-weighted imaging using probabilistic diffusion tractography. By segmenting the results of tractography into larger anatomical units, it is possible to draw inferences about the structural relationships between these parts of the system. This pipeline results in a structural connectivity matrix, which contains an estimate of connection strength among all regions. However, raw data processing is complex, computationally intensive, and requires expert quality control, which may be discouraging for researchers with less experience in the field. We thus provide brain structural connectivity matrices in a form ready for modelling and analysis and thus usable by a wide community of scientists. The presented dataset contains brain structural connectivity matrices together with the underlying raw diffusion and structural data, as well as basic demographic data of 88 healthy subjects.

Indexed as

BrainImage Processing, Computer-AssistedWhite MatterConnectomeDiffusion Magnetic Resonance ImagingDiffusion Tensor ImagingHumans

Identifiers

PMID35945231
PMCPMC9363436

What OpenQuestion holds

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LicenceCC BY
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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.