Human Brain Structural Connectivity Matrices-Ready for Modelling

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985807%3A_____%2F22%3A00560334" target="_blank" >RIV/67985807:_____/22:00560334 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/00023752:_____/22:43920899 RIV/00023001:_____/22:00083444 RIV/68407700:21230/22:00358939

Výsledek na webu

<a href="https://dx.doi.org/10.1038/s41597-022-01596-9" target="_blank" >https://dx.doi.org/10.1038/s41597-022-01596-9</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1038/s41597-022-01596-9" target="_blank" >10.1038/s41597-022-01596-9</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Human Brain Structural Connectivity Matrices-Ready for Modelling

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Human Brain Structural Connectivity Matrices-Ready for Modelling

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2022

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Scientific Data

ISSN

2052-4463

e-ISSN

2052-4463

Svazek periodika

9

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

9

Strana od-do

486

Kód UT WoS článku

000838094100001

EID výsledku v databázi Scopus

2-s2.0-85135728092