Neural architecture underlying classification of face perception paradigms

Abstract

We present a novel strategy for deriving a classification system of functional neuroimaging paradigms that relies on hierarchical clustering of experiments archived in the BrainMap database. The goal of our proof-of-concept application was to examine the underlying neural architecture of the face perception literature from a meta-analytic perspective, as these studies include a wide range of tasks. Task-based results exhibiting similar activation patterns were grouped as similar, while tasks activating different brain networks were classified as functionally distinct. We identified four sub-classes of face tasks: (1) Visuospatial Attention and Visuomotor Coordination to Faces, (2) Perception and Recognition of Faces, (3) Social Processing and Episodic Recall of Faces, and (4) Face Naming and Lexical Retrieval. Interpretation of these sub-classes supports an extension of a well-known model of face perception to include a core system for visual analysis and extended systems for personal information, emotion, and salience processing. Overall, these results demonstrate that a large-scale data mining approach can inform the evolution of theoretical cognitive models by probing the range of behavioral manipulations across experimental tasks.

Dissociable meta‐analytic brain networks contribute to coordinated emotional processing

“…To interrogate the affective processing literature to reveal differential meta-analytic network recruitment, we implemented a previously developed methodological approach (Laird et al, 2015), that was developed using the same techniques as in Co-Activation Based Parcellation (Cauda et al, 2012; Bzdok et al, 2014; Balsters et al, 2016). The only differentiating characteristics in the current approach are the method of experimental contrasts selection inclusive in the meta-analysis and the use of hierarchical (as opposed to k -means) clustering.…”

“…The “ average ” method was used in the present study to mitigate the problematic “chaining” effect in which increasing model order (i.e., number of clusters/MAGs) results in solutions differing only by the addition of one experiment. Solutions utilizing the correlation distance and average linkage parameterizations have been previously demonstrated with fMRI data (Liu, Zhu, Qiu, & Chen, 2012) and BrainMap-based meta-analytic maps (Laird et al, 2015). …”

“…The goal of coordinate-based meta-analyses is to provide quantitative reinforcement for consistent activation across similar studies. However, it has been demonstrated that the differentiating characteristics of task design have a neural basis (Barret & Satpute, 2013; Laird et al, 2015), and can provide insight regarding specialization of neural recruitment during the performance of a given task grouping. Advanced meta-analytic methodologies have emerged to probe these cognitive processes, resulting in new information characterizing meta-analytic networks and behavioral interpretations that provide support for complex psychological theories.…”

“…Meta-analytic connectivity modeling (MACM) has emerged as a useful tool for characterizing whole-brain networks co-activating with an individual brain region of interest (ROI) across various task domains (Laird et al, 2009a; Robinson, Laird, Glahn, Lovallo, & Fox, 2010; Eickhoff et al, 2010; Riedel et al, 2015). Extending this framework beyond a single isolated network co-activating with an individual seed ROI, a recent meta-analytic methodology leverages clustering techniques to characterize the recruitment of multiple, distinct networks across groups of studies (Laird et al, 2015). That previous work applied clustering techniques to modeled activation images associated with experiments within a heterogeneous task domain (i.e., facial processing; Laird et al, 2015), as opposed to clustering voxels within a user-defined ROI (connectivity-based parcellation; Neumann, von Cramon, & Lohmann, 2008; Cauda et al, 2012; Bzdok et al, 2015; Balsters, Mantini, Apps, Eickhoff, & Wenderoth, 2016).…”

Heterogeneous fractionation profiles of meta-analytic coactivation networks

“…Each value of the array indicated the number of fractionated child sub-networks observed at a given model order. The 20 canonical networks were subsequently grouped into clusters by applying hierarchical clustering analysis to this array using the average linkage algorithm and the euclidean distance metric (Laird et al, 2011a; Laird et al, 2015) (Figure 1, Step 5). Given the small number of variables, we identified a clustering solution using simple visual inspection of the resultant dendrogram.…”