What are the advantages of functional localizers over traditional group-based methods?
There are several advantages to functionally defining regions of interest in individual subjects over using traditional group-based methods (that are based on voxel-level inter-subject overlap). We discuss these in detail in our papers, but here is a summary of the key advantages.
1. Greater sensitivity and functional resolution
The key problem with the traditional approach, which examines voxel-level activation overlap across subjects, is that individual subjects’ activations do not line up well. This is a consequence of inter-subject anatomical variability. Even the more advanced normalization methods that take into account the folding patterns (e.g., Fischl et al., 1999) are not good enough in cases where cytoarchitecture does not align well with the cortical folds, which is often the case for the association cortices (e.g., Brodmann, 1909; Frost & Goebel, 2011; Tahmasebi et al., 2011). This problem has two important ramifications. First, group-based methods are less sensitive: even if every subject shows activation in/around a particular anatomical location, this activation may be missed in a group analysis because of insufficient voxel-level spatial overlap across subjects. For example, the extensively studied fusiform face area (FFA) often does not emerge in group analyses even though it is robustly present in every individual.
Because of its greater sensitivity, the functional localization approach enables us to investigate small but interesting populations (where, in some cases, there may not be enough power for a traditional group analysis due to a small number of participants or due to an even higher level of variability in the precise loci of functional activations than in the healthy population). This is important, because such populations (e.g., patients with brain lesions or patients suffering from developmental or acquired disorders) have been a valuable source of evidence in understanding human cognition.
And second, group-based methods have lower functional resolution: nearby functionally distinct regions that differ in their absolute and/or relative locations in individual subjects may emerge - in the group analysis - as a single multi-functional region. This latter problem is especially serious if we are trying to discover functional specificity. (See Nieto-Castañon & Fedorenko, 2012, for an extensive discussion of lower sensitivity and functional resolution in group-based analyses.)
2. A cumulative research enterprise
Using the individual-subjects functional localization approach enables us to establish a cumulative research enterprise where we, as a field, work together to discover and characterize the key components of the language system. With the traditional (group-based) methods, it is difficult to compare findings across studies and therefore to build upon previous research. People fiercely argue about whether some bit of activation is the "same" or not across two studies. Having a standardized way to identify the components of the language system before investigating their functional properties ensures that we are talking about the same regions across studies and labs. This, in turn, leads to accumulation of knowledge and faster progress in understanding the functional architecture of the language system.
3. Brain-behavior and brain-genetics studies
Including a localizer task in every participant leads to accumulation of large datasets (with hundreds of participants) that allow us to relate neural variability (e.g., in the size or lateralization of the language system) to behavioral and genetic variability. Using functional neural markers may yield more power and reveal clearer patterns compared to purely anatomical markers based on properties of macroanatomical regions.
N.B.: Using functional localizers does not preclude you from analyzing your data using traditional analysis methods. In fact, it is sometimes a good idea to complement targeted fROI-based analyses with traditional analyses. However, if you don't include a functional localizer in your study a priori, you cannot benefit from the extra sensitivity and functional resolution that functional localizers yield, and from the ability to relate your results to those from other studies, after the fact (although see this FAQ).