How do functional localizers work?
In the individual-subjects functional localization approach, a region or a set of regions is defined in each subject using a contrast targeting the cognitive process of interest. For example, to identify face-selective brain regions, a contrast between faces and objects is commonly used (e.g., Kanwisher, McDermott & Chun, 1997). Once a localizer task has been developed and validated, in each subsequent study every participant is scanned on the localizer task and on the "task of interest", i.e., a task designed to evaluate a particular hypothesis about the functional profile of the region(s) in question. (For example, with respect to face-selective brain regions, one might want to know whether these regions respond more strongly to upright vs. inverted faces (e.g., Kanwisher, Tong & Nakayama, 1998), or how they respond to a wide range of visual objects (Downing et al., 2006).)
There are two challenges that face researchers who want to adopt this approach for studying high-level cognitive processes, such as language. First, it is non-trivial to decide on a contrast that would target all and only regions supporting the cognitive process of interest. And second, many high-level cognitive tasks elicit robust and distributed activations, which sometimes makes it difficult to decide (a) what counts as a "region", and (b) how parts of activations correspond across subjects. Here are the solutions we came up with.
Deciding on the localizer contrast
When dealing with high-level cognitive processes it is probably impossible to devise a single localizer task that would target all and only the relevant brain regions. We started with a relatively broad functional contrast between sentences and pronounceable nonwords (like florp). This contrast targets regions engaged in retrieving the meanings of individual words and in combining these lexical-level meanings into more complex meaning/structural representations. This contrast identifies a set of brain regions previously implicated in linguistic processing, including the left frontal and left temporo-parietal regions. Our current set of fROIs therefore seems like a good start. However, as we discuss in more detail in Fedorenko et al. (2010), future research may tell us that some of these fROIs should be abandoned, some should be split into multiple sub-regions, others should be combined, and yet other new ones (derived from new functional contrasts) should be added. We always complement our fROI analyses with individual-subject whole-brain analyses, which would help us see structure within our fROIs as well as detect activations outside the borders of our fROIs. Furthermore, we have developed an analysis method that enables detecting and examining functionally heterogeneous subsets of voxels within the fROIs (drop me an email if you want to know more).
We continue to work on developing possible additional localizers targeting particular aspects of language more narrowly, and we will make these available as soon as they are ready.
Discovering regions and their correspondence across subjects
The traditional way to select subject-specific voxels for a particular ROI is by examining an individual subject's activation map for the localizer contrast and defining the fROI(s) by hand, using macroanatomy as a guide. This method works well in cases where regions activated by the localizer contrast are far away from each other, so that there is no confusion as to what part of the activation reflects the activity of a particular brain region, so that it is easy to establish correspondence across different brains. Because (i) this method would not obviously work for language due to the distributed nature of the activations, and because (ii) we were seeking a more objective way to define subject-specific fROIs, we developed a new procedure.
This procedure - that we termed "group-constrained subject-specific" (GSS; formerly known as GcSS) analysis - consists of several steps (described in detail in Fedorenko et al., 2010). These steps are schematically illustrated here.
In particular, the GSS analysis involves thresholding individual activation maps for some contrast of interest at a specified level, overlaying these individual maps on top of one another in a common space to create a probabilistic overlap map (where each voxel contains information about how many subjects show a significant effect in that voxel), and using an image parcellation algorithm to divide the map into “functional parcels (or partitions)”, following the map’s topography. These parcels are then used as spatial constraints to select subject-specific voxels for each region. Finally, the response is extracted from each set of subject-specific voxels (using a subset of the data that was not used in defining the ROIs) and averaged across subjects for each region.
The parcels that have a non-zero intersection with a substantial proportion of individual subjects (a non-zero intersection means that a subject has at least one supra-threshold voxel within the borders of the partition) and that show a replicable effect in an independent subset of the data can be treated as meaningful and used in future studies to constrain the selection of subject-specific voxels in defining fROIs.
If you use a version of our localizer task, you can download and use our parcels (that were created based on an overlap map from 25 subjects). However, this method can also be applied in developing new localizers (for language or other domains), or to perform group-level analyses on datasets where a traditional random-effects analysis doesn't yield strong/clear results (see the toolbox page for additional information).