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 approaches, which are based on examining voxel-level activation overlap across subjects in the normalized space, 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 going to be good enough in cases where cytoarchitecture does not align well with the cortical folds, which is often the case for association cortices (e.g., Brodmann, 1909). 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-CastaƱon & 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 often 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 across labs. This, in turn, leads to accumulation of knowledge and faster progress in understanding the functional architecture of the language system.

 

Note also, that using functional localizers does not preclude you from analyzing your data using traditional analysis methods. In fact, it is always 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 after the fact (although see FAQ2).

 

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