Image-based meta-and mega-analysis (IBMMA): A unified framework for large-scale, multi-site, neuroimaging data analysis
Identifikátory výsledku
Kód výsledku v IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00159816%3A_____%2F25%3A00082399" target="_blank" >RIV/00159816:_____/25:00082399 - isvavai.cz</a>
Nalezeny alternativní kódy
RIV/00216224:14740/25:00142813
Výsledek na webu
<a href="https://www.sciencedirect.com/science/article/pii/S1053811925005579" target="_blank" >https://www.sciencedirect.com/science/article/pii/S1053811925005579</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1016/j.neuroimage.2025.121554" target="_blank" >10.1016/j.neuroimage.2025.121554</a>
Alternativní jazyky
Jazyk výsledku
angličtina
Název v původním jazyce
Image-based meta-and mega-analysis (IBMMA): A unified framework for large-scale, multi-site, neuroimaging data analysis
Popis výsledku v původním jazyce
The increasing scale and complexity of neuroimaging datasets aggregated from multiple study sites present substantial analytic challenges, as existing statistical analysis tools struggle to handle missing voxel-data, suffer from limited computational speed and inefficient memory allocation, and are restricted in the types of statistical designs they are able to model. We introduce Image-Based Meta-& Mega-Analysis (IBMMA), a novel software package implemented in R and Python that provides a unified framework for analyzing diverse neuroimaging features, efficiently handles large-scale datasets through parallel processing, offers flexible statistical modeling options, and properly manages missing voxel-data commonly encountered in multi-site studies. IBMMA successfully analyzed a large-n dataset of several thousand participants and revealed findings in brain regions that some traditional software overlooked due to missing voxel-data resulting in gaps in brain coverage. IBMMA has the potential to accelerate discoveries in neuroscience and enhance the clinical utility of neuroimaging findings.
Název v anglickém jazyce
Image-based meta-and mega-analysis (IBMMA): A unified framework for large-scale, multi-site, neuroimaging data analysis
Popis výsledku anglicky
The increasing scale and complexity of neuroimaging datasets aggregated from multiple study sites present substantial analytic challenges, as existing statistical analysis tools struggle to handle missing voxel-data, suffer from limited computational speed and inefficient memory allocation, and are restricted in the types of statistical designs they are able to model. We introduce Image-Based Meta-& Mega-Analysis (IBMMA), a novel software package implemented in R and Python that provides a unified framework for analyzing diverse neuroimaging features, efficiently handles large-scale datasets through parallel processing, offers flexible statistical modeling options, and properly manages missing voxel-data commonly encountered in multi-site studies. IBMMA successfully analyzed a large-n dataset of several thousand participants and revealed findings in brain regions that some traditional software overlooked due to missing voxel-data resulting in gaps in brain coverage. IBMMA has the potential to accelerate discoveries in neuroscience and enhance the clinical utility of neuroimaging findings.
Klasifikace
Druh
J<sub>imp</sub> - Článek v periodiku v databázi Web of Science
CEP obor
—
OECD FORD obor
30103 - Neurosciences (including psychophysiology)
Návaznosti výsledku
Projekt
Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.
Návaznosti
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Ostatní
Rok uplatnění
2025
Kód důvěrnosti údajů
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Údaje specifické pro druh výsledku
Název periodika
NeuroImage
ISSN
1053-8119
e-ISSN
1095-9572
Svazek periodika
322
Číslo periodika v rámci svazku
Nov 2025
Stát vydavatele periodika
US - Spojené státy americké
Počet stran výsledku
12
Strana od-do
121554
Kód UT WoS článku
001607934700002
EID výsledku v databázi Scopus
—