Poster No:
1262
Submission Type:
Abstract Submission
Authors:
Peter Fox1, William Allen2, Mohamad Habes1, Simon Eickhoff3
Institutions:
1University of Texas Health Science Center at San Antonio, San Antonio, TX, 2University of Texas at Austin, Austin, TX, 3Research Centre Jülich, Jülich, NRW
First Author:
Peter Fox, MD
University of Texas Health Science Center at San Antonio
San Antonio, TX
Co-Author(s):
Mohamad Habes, PhD
University of Texas Health Science Center at San Antonio
San Antonio, TX
Introduction:
BrainMap (brainmap.org) enables replication analyses in human neuroimaging by providing: 1) published data; 2) curated meta-data; and, 3) meta-analysis software (brainmap.org/software).Volumetric spatial normalization (Talairach or MNI) is the analytic framework supporting coordinate-based metaanalysis (CBMA). CBMA can be performed in a mass-univariate manner using activation likelihood estimation (ALE) or similar algorithms. Alternatively, CBMA can be performed in a mass-multivariate manner using algorithms including meta-analytic connectivity mapping (MACM), independent components analysis (ICA), and graph theory modeling (GTM). Utilization of BrainMap CBMA resources is substantial: > 1,200 peer-reviewed publications to date (BrainMap.org/pubs)
For multivariate and large-scale univariate CBMA, compute time and download limitations can be burdensome. The BrainMap Community Portal is funded by NIH MH074457 to overcome these limitations. CBMA can now be performed entirely within an online, high-performance computing (HPC) environment (portal.brainmap.org).
Methods:
The BrainMap Community Portal applies the community-portal (science gateway) architecture (Lawrence et al. 2015). Data, pipelined applications and HPC access are provided in an integrated environment as a standalone deployment of the Texas Advanced Computing Center (TACC) "Core Experience Portal" codebase. Computation is provided by TACC resources: Stampede2, Lonestar6, Maverick2, Frontera and Longhorn. Data and meta-data are accessed via a mirrored instance of the BrainMap DB, an SQL database implemented in Oracle®. BrainMap DB contains three sectors: task-activation (TA; Fox et al. 2005), voxel-based morphometry (VBM; Vanasse et al. 2018), and voxel-based physiology (VBP; Towne et al. in review). Containerized implementations of BrainMap tools are provided in both graphical and command line formats. Sleuth 3.0.4 performs data retrieval filtered by the meta-data taxonomy to create an editable workspace. GingerALE 3.0.2 performs mass-univariate CBMA on the workspace using the latest implementation of the ALE algorithm with best-practices default settings (Eickhoff et al. 2016; Frahm L et al., in review). Mango provides data visualization and regional interpretation of output by anatomy, function and disease. For multivariate analysis, five algorithms are implemented as pipelined, script-controlled, command-line applications. MACM is implemented de novo as mass-multivariate application of ALE, using a novel cooccurrence architecture. Connectivity-based parcellation (CBP; Bzdok et al. 2013) is implemented in an ALE-specific manner using shared code (Reuter et al., 2020) and the MACM co-occurrence architecture. ICA is implemented as a containerized instance of MELODIC (Beckmann et al. 2004) as adapted for CBMA (Smith et al., 2009). GTM (Cauda et al., 2018) and Author-topic modeling (Yeo et al. 2015; Ngo et al. 2019) are implemented in an ALE-specific format using code shared by the originators.
Results:
In-house and beta testing of the BrainMap Community Portal confirms that the portal interface, the BrainMap
database, CBMA applications are operational and ready for community access.
Conclusions:
The BrainMap Community Portal is ready for access at: portal.brainmap.org. We invite the community to explore this new resource and provide feedback Brainmap.org/forum). We encourage users to expand this resource by: 1) coding CBMA-compliant articles for entry; 2) sharing CBMA workspaces and other work products; 3) implementing new CBMA pipelines within the community portal (Yeung et al., 2023).
Modeling and Analysis Methods:
Connectivity (eg. functional, effective, structural) 1
Methods Development 2
Keywords:
Computational Neuroscience
Meta- Analysis
Statistical Methods
Workflows
1|2Indicates the priority used for review
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Propose your OHBM abstract(s) as source work for future OHBM meetings by selecting one of the following options:
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Please indicate below if your study was a "resting state" or "task-activation” study.
Other
Healthy subjects only or patients (note that patient studies may also involve healthy subjects):
Patients
Was this research conducted in the United States?
Yes
Are you Internal Review Board (IRB) certified?
Please note: Failure to have IRB, if applicable will lead to automatic rejection of abstract.
Not applicable
Were any human subjects research approved by the relevant Institutional Review Board or ethics panel?
NOTE: Any human subjects studies without IRB approval will be automatically rejected.
Not applicable
Were any animal research approved by the relevant IACUC or other animal research panel?
NOTE: Any animal studies without IACUC approval will be automatically rejected.
Not applicable
Please indicate which methods were used in your research:
PET
Functional MRI
Structural MRI
Diffusion MRI
TMS
Behavior
Neuropsychological testing
Computational modeling
For human MRI, what field strength scanner do you use?
If Other, please list
-
Multiple
Which processing packages did you use for your study?
Other, Please list
-
ALE, MELODIC, GTA, Other
Provide references using APA citation style.
Beckmann CF (2004) Probabilistic Independent Component Analysis for Functional Magnetic Resonance
Imaging. IEEE Transaction on Medical Imaging Vol 23 (2) 137-152.
Bzdok D (2013) An investigation of the structural, connectional and functional subspecialization in the human
amygdala. Hum Brain Mapp 34(12_:3247-3266.
Eickhoff SB (2016) Behavior, sensitivity, and power of activation likelihood estimation characterized by massive
empirical simulation. Neuroimage 137:70-85.
Fox PT (2005) BrainMap Taxonomy of Experimental Design: Description and Evaluation. Hum Brain Mapp
25:185-198.
Fox PT (2014) Meta-Analysis in Human Neuroimaging: Computation Modeling of Large-Scale Databases.
Annual Review of Neurosciences 37:409-34.
Frahm L (in review) ALE meta-analysis of voxel-based morphometry Studies: Parameter validation via largescale
simulation.
Lawrence KA Science gateways today and tomorrow: positive perspective of nearly 5000 members of the
research community. Concurrency Computat: Pract. Exper. 2015: 27:4252-4268.
Ngo G (2019) Beyond consensus: Embracing heterogeneity in curated neuroimaging meta-analysis.
NeuroImage 300:142-158.
Reuter N (2020) CBPtools: a Python package for regional connectivity-based parcellation. Brain Structure &
Function 225:1261-1275.
Robinson JR (2010) Meta-analytic connectivity modeling: Delineating the Functional Connectivity of the Human
Amygdala. Hum Brain Mapp 31:173-184.
Smith SM (2009) The functional architecture of the human brain: Correspondence between resting FMRI and
task-activation studies. Proc Natl Acad Sci USA 106:13040-13045.
Towne J (in review) BrainMap VBP: a resource for meta-analysis of voxel-based physiological literature.
Turkeltaub P (2002) Meta-analysis of the functional neuroanatomy of single-word reading: Method and
Validation. NeuroImage 16: 765-780.
Vanasse T (2018) BrainMap VBM: An environment for structural meta-analysis. Human Brain Mapp 39(8) 3308-
3325.
Yeo BTT (2015) Functional specialization and flexibility in human association cortex. Cerebr Cortex 25:3654-
3672.
Yeung AWK et al. (2023) Trends in the sample size, statistics, and contributions to the BrainMap database of
activation likelihood estimation meta-analyses: An empirical study of 10-year data. Human Brain Mapp .
No