.png){kind=icon}
Return
BrainMap VBM: An environment for structural meta-analysis
.png){kind=icon}
OA
AI
DOI：10.1002/hbm.24078.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
The BrainMap database is a community resource that curates peer-reviewed, coordinate-based human neuroimaging literature. By pairing the results of neuroimaging studies with their relevant meta-data, BrainMap facilitates coordinate-based meta-analysis (CBMA) of the neuroimaging literature en masse or at the level of experimental paradigm, clinical disease, or anatomic location. Initially dedicated to the functional, task-activation literature, BrainMap is now expanding to include voxel-based morphometry (VBM) studies in a separate sector, titled: BrainMap VBM. VBM is a whole-brain, voxel-wise method that measures significant structural differences between or within groups which are reported as standardized, peak x-y-z coordinates. Here we describe BrainMap VBM, including the meta-data structure, current data volume, and automated reverse inference functions (region-to-disease profile) of this new community resource. CBMA offers a robust methodology for retaining true-positive and excluding false-positive findings across studies in the VBM literature. As with BrainMap's functional database, BrainMap VBM may be synthesized en masse or at the level of clinical disease or anatomic location. As a use-case scenario for BrainMap VBM, we illustrate a trans-diagnostic data-mining procedure wherein we explore the underlying network structure of 2,002 experiments representing over 53,000 subjects through independent components analysis (ICA). To reduce data-redundancy effects inherent to any database, we demonstrate two data-filtering approaches that proved helpful to ICA. Finally, we apply hierarchical clustering analysis (HCA) to measure network- and disease-specificity. This procedure distinguished psychiatric from neurological diseases. We invite the neuroscientific community to further exploit BrainMap VBM with other modeling approaches.
Keywords:
atrophy
data-mining
independent components analysis
networks
pattern analysis
structural covariance
structural magnetic resonance imaging
transdiagnostic
voxel-based morphometry
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
3.3
Papers:
6.8K
Citations:
2.6W
Organization
View more organizations
Cited Papers
Thalamic medial dorsal nucleus atrophy in medial temporal lobe epilepsy: A VBM meta-analysis.png)
NEUROIMAGE-CLINICALOAAI
NEUROIMAGE-CLINICALIF3.6
OAAIShareSaveThe hubs of the human connectome are generally implicated in the anatomy of brain disordersBRAINOAAI
BRAINIF11.7
OAAIShareSaveMetaanalytic Connectivity Modeling: Delineating the Functional Connectivity of the Human AmygdalaHUMAN BRAIN MAPPINGOAAI
HUMAN BRAIN MAPPINGIF3.3
OAAIShareSaveShareSaveShareSave View more