The BrainMap strategy for standardization, sharing, and meta-analysis of neuroimaging data

Abstract

BackgroundNeuroimaging researchers have developed rigorous community data and metadata standards that encourage meta-analysis as a method for establishing robust and meaningful convergence of knowledge of human brain structure and function. Capitalizing on these standards, the BrainMap project offers databases, software applications, and other associated tools for supporting and promoting quantitative coordinate-based meta-analysis of the structural and functional neuroimaging literature.FindingsIn this report, we describe recent technical updates to the project and provide an educational description for performing meta-analyses in the BrainMap environment.ConclusionsThe BrainMap project will continue to evolve in response to the meta-analytic needs of biomedical researchers in the structural and functional neuroimaging communities. Future work on the BrainMap project regarding software and hardware advances are also discussed.

Comparison of structural covariance with functional connectivity approaches exemplified by an investigation of the left anterior insula

“…Secondly, task-based functional connectivity using meta-analytic co-activation modelling (MACM) has been established as another functional connectivity approach (Eickhoff et al, 2010; Laird et al, 2013). Here, co-activation of regions with a certain seed region across many experiments recorded in the BrainMap database (Fox and Lancaster, 2002; Laird et al, 2011, 2009, 2005) is used to identify functional networks. Furthermore, the meta-data specifying the kind of task and contrast employed by experiments activating the region of interest may be used to functionally characterize the resulting networks and thus reveal their functional implication.…”

Searching for behavior relating to grey matter volume in a-priori defined right dorsal premotor regions: Lessons learned

“…Recently, we identified five functional subregions within the right dorsal PM (PMd) by multi-modal connectivity-based parcellation (CBP) based on co-activations of right PMd voxels during thousands of activation (task-based fMRI and PET) studies. In order to characterize the delineated functional parcels of the right PMd in terms of associated behavioral functions, we examined hundreds of activation studies reporting activation peaks in the right PMd parcels using quantitative forward and reverse inferences based on the BrainMap (Laird et al 2011) database (Genon et al 2016a). As illustrated in Figure 1, this approach revealed a clear cognitive-motor gradient in terms of recruitment by fMRI tasks along the rostro-caudal axis.…”

Developing an Integrated Image Bank and Metadata for Large-scale Research in Cerebrovascular Disease: Our Experience from the Stroke Image Bank Project

“…Meanwhile, there would be numerous benefits that can be derived from semantically integrated data for various endeavors. Specifically, trials of new treatments for stroke require imaging data as part of the patient assessment (Wintermark et al, 2013), but the sample size needs to be large enough to obtain reliable results, particularly where treatment effects are likely to be modest (Lindley et al, 2015): the ability to combine image as well as clinical data facilitates meta-analyses (Laird et al, 2011). Furthermore, a semantically integrated patient database could be an efficient and cost-effective way to obtain data from many different centers and many different countries in order to obtain the sample size required to be able to observe a statistically significant difference between the subtypes of stroke and other key clinical variables or treatment effects in observational studies or clinical trials (Poldrack and Gorgolewski, 2014).…”