Decoding cognitive task-sets from rostral prefrontal cortex functional connectivity patterns
Westphal, A.J., Reggente, N., Ito, K., Fortuna, W., & Rissman, J.
HBM 2013 Abstract
Resting state fMRI connectivity analyses have identified a number of distinct functional brain networks, including the fronto-parietal task control network (FPTCN), the dorsal attention network (DAN), and the default mode network (DMN) (e.g., Vincent et al., 2008, Power et al., 2011). While these networks are typically defined based on intrinsically correlated BOLD fluctuations during periods of undirected thought, engagement of these networks is also observed during goal-oriented cognition. For instance, the FPTCN has been shown to co-activate with the DMN to facilitate internally-focused mentation and with the DAN to promote externally-focused attention (Spreng et al., 2010). In the present investigation, we sought to evaluate the degree to which task-set representations, particularly those requiring relational integration such as analogical reasoning and episodic memory retrieval, could be decoded from functional connectivity patterns within and between these networks. We were most interested in examining the representational content of connections originating in the rostral prefrontal cortex (RPFC), since RPFC may play a key role in relational integration, in addition to supporting the maintenance of superordinate goal-states (e.g., Badre & D’Esposito, 2009).
20 subjects healthy adult subjects underwent fMRI scanning (3T Siemens Trim Trio scanner, TR = 2 s, voxel size = 3 x 3 x 3.7 mm), performing alternating blocks of analogical reasoning, episodic source memory retrieval, and visuospatial attention tasks. These tasks were closely matched for reaction times, response demands, and bottom-up visual stimulus processing (all trials involved 4-word arrays, with the tasks only differing in what subjects had to decide about these words). Our data analysis procedure involved calculating the pairwise correlations between the concatenated BOLD time-courses for each task for each of 264 functional areas (10 mm spheres, identified by Power et al., 2011). We then supplied a regularized logistic regression classification algorithm with the full connectivity matrix from a given network (within-network connectivity) or from the set of connections that linked a pair of networks (between-network connectivity). All classification analyses used a leave-one-subject-out procedure, such that the classifier was trained on the connectivity data from 19 of 20 subjects and then applied to predict the task-sets associated with the remaining connectivity matrices from the held-out subject.
Using correlations between all 264 nodes, our classifier was 100% accurate at differentiating between the three cognitive task-sets. When trained solely on the correlations between the 16 RPFC nodes, the classifier was unable to differentiate between the reasoning and memory task-sets, indicating that within-RPFC connectivity patterns are not necessarily diagnostic of task-set. However, when trained on the correlations between RPFC nodes and nodes outside of RPFC, classification accuracy was quite robust (Fig. 1), reaching accuracy levels of up to 85% depending on which network was paired with RPFC. This result provides novel evidence that RPFC flexibly adjusts its interactivity with all three of the core networks to facilitate both internally and externally-oriented cognition.
By measuring the pattern of correlations between distinct nodes in a subject’s brain, one can reliably decode information about that subject’s cognitive task-set, even when a classifier has not been trained on data from that subject. The connection strengths between RPFC nodes and nodes in other core brain networks can be used to predict whether a subject is engaged in analogical reasoning or episodic source memory retrieval, despite the common demands of these tasks for relational integration. Given its position at the apex of a rostral-caudal hierarchy (Badre & D’Esposito, 2009), these data suggest that RPFC may differentially collaborate with posterior networks depending on task goals.
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