Lancaster JL, Tordesillas-Gutierrez D, Martinez M, Salinas F, Evans A, Zilles K, Mazziotta JC, Fox PT. For citations concerning the icbm2tal transform: The hippocampal network model: A transdiagnostic metaconnectomic approach. Kotkowski E, Price LR, Fox PM, Vanasse TJ, Fox PT. The functional connectivity of the human caudate: An application of meta-analytic connectivity modeling with behavioral filtering. Robinson JL, Laird AR, Glahn DC, Blangero J, Sanghera MK, Pessoa L, Fox PM, Uecker AM, Friehs G, Young KA, Griffin JL, Lovallo WR, Fox PT. Investigating the Functional Heterogeneity of the Default Mode Network Using Coordinate-Based Meta-Analytic Modeling. Laird AR, Eickhoff SB, Li K, Robin DA, Glahn DC, Fox PT. Meta-analytic connectivity modeling: Delineating the functional connectivity of the human amygdala. Robinson JL, Laird AR, Glahn DC, Lovallo WR, Fox PT. For citations concerning Meta-Analytic Connectivity Modeling (MACM): Co-activation patterns distinguish cortical modules, their connectivity and functional differentiation. įor citations concerning the subtraction algorithm:Įickhoff SB, Bzdok D, Laird AR, Roski C, Caspers S, Zilles K, Fox PT. Activation likelihood estimation revisited. Įickhoff SB, Bzdok D, Laird AR, Kurth F, Fox PT. Minimizing within-experiment and within-group effects in activation likelihood estimation meta-analyses. Turkeltaub PE, Eickhoff SB, Laird AR, Fox M, Wiener M, Fox P. Coordinate-based activation likelihood estimation meta-analysis of neuroimaging data: A random-effects approach based on empirical estimates of spatial uncertainty. For citations concerning GingerALE:Įickhoff SB, Laird AR, Grefkes C, Wang LE, Zilles K, Fox PT. BrainMap: The social evolution of a functional neuroimaging database. BrainMap VBM: An Environment for Structural Meta-analysis. Vanasse T, Fox PM, Barron D, Robertson M, Eickhoff SB, Lancaster JL, Fox PT.
BrainMap taxonomy of experimental design: Description and evaluation. įox PT, Laird AR, Fox SP, Fox PM, Uecker AM, Crank M, Koenig SF, Lancaster JL. Mapping context and content: The BrainMap model. For citations concerning Sleuth, Scribe, and BrainMap meta-data:įox PT, Lancaster JL. If you have used the BrainMap database in your research, please cite one or more of the following papers in your references.
For more details, and the associated network images and metadata at a model order of 20, please click here. Given the vast amount of metadata archived in BrainMap, the functional significance of these intrinsic connectivity networks was quantitatively assessed by Laird et al. Networks resulting from an ICA decomposition of modeled activation images archived in BrainMap strongly correspond to resting state networks, as shown by Smith et al., 2009. We encourage collaborations that develop new tools for meta-analysis or use BrainMap data to develop or validate other neuroinformatics tools and strategies. We will provide guidance and assistance in the execution of meta-analyses upon request. The BrainMap development team welcomes collaborations.
#HUMAN BRAIN MAPPING JOURNAL SOFTWARE#
BrainMap provides not only data for meta-analyses and data mining, but also distributes software and concepts for quantitative integration of neuroimaging data. After more than 20 years of development, BrainMap has evolved into a much broader project whose software and data have been utilized in numerous publications. BrainMap was conceived in 1988 and originally developed as a web-based interface. The BrainMap Project is developed at the Research Imaging Institute of the University of Texas Health Science Center San Antonio. The goal of BrainMap is to develop software and tools to share neuroimaging results and enable meta-analysis of studies of human brain function and structure in healthy and diseased subjects. BrainMap is a database of published functional and structural neuroimaging experiments with coordinate-based results (x,y,z) in Talairach or MNI space.