Recently, the BrainMap project has extended its focus from paradigm-based meta-analyses to investigations of large-scale, whole-database data mining. Two studies have been published that applied a multivariate analysis technique (independent component analysis) on the data archived in the BrainMap functional database:
- Smith et al., 2009:
Neural connections, providing the substrate for functional networks, exist whether or not they are functionally active at any given moment. However, it is not known to what extent brain regions are continuously interacting when the brain is ‘‘at rest.’’ In this work, we identify the major explicit activation networks by carrying out an image-based activation network analysis of thousands of separate activation maps derived from the BrainMap database of functional imaging studies, involving nearly 30,000 human subjects. Independently, we extract the major covarying networks in the resting brain, as imaged with functional magnetic resonance imaging in 36 subjects at rest. The sets of major brain networks, and their decompositions into subnetworks, show close correspondence between the independent analyses of resting and activation brain dynamics. We conclude that the full repertoire of functional networks utilized by the brain in action is continuously and dynamically ‘‘active’’ even when at ‘‘rest.’’
- Laird et al., 2011:
An increasingly large number of neuroimaging studies have investigated functionally connected networks during rest, providing insight into human brain architecture. Assessment of the functional qualities of resting state networks has been limited by the task-independent state, which results in an inability to relate these networks to specific mental functions. However, it was recently demonstrated that similar brain networks can be extracted from resting state data and data extracted from thousands of task-based neuroimaging experiments archived in the BrainMap database. Here, we present a full functional explication of these intrinsic connectivity networks at a standard low order decomposition using a neuroinformatics approach based on the BrainMap behavioral taxonomy, as well as a stratified, data-driven ordering of cognitive processes. Our results serve as a resource for functional interpretations of brain networks in resting state studies and future investigations into mental operations and the tasks that drive them.
The 20- and 70-dimensional ICA BrainMap and resting state components from this study can be downloaded from the FMRIB's website, courtesy of Steve Smith.
Image and Metadata Files for Laird et al., 2011
The network images and associated metadata matrices generated in this study can be downloaded below to aid interpretation of the functional significance of future resting state results. In addition, they may be useful as masks for seeding specific a priori cortical regions or networks of interest in prospective neuroimaging studies or as a technique for circumventing the inherent problems associated with double dipping.
- Image Files (20 ICA Components) - .zip, 2 MB
- Anatomical Template (Talairach space) - .nii, 8.2 MB
- Metadata Matrices (12 BrainMap Fields) - .zip, 40 kB
The above files were generated using an ICA model order of 20. Results derived from higher dimension analyses (i.e., model order of 70 or higher) will be made available in future releases.