Predicting an individual's dorsal attention network activity from functional connectivity fingerprints

Abstract

The cortical dorsal attention network (DAN) is a set of parietal and frontal regions that support a wide variety of attentionally demanding tasks. Whereas attentional deployment reliably drives DAN activity across subjects, there is a large degree of variation in the activation pattern in individual subjects. We hypothesize that a subject's own idiosyncratic pattern of cortical DAN activity can be predicted from that subject's own unique pattern of functional connectivity. By modeling task activation as a function of whole brain connectivity patterns, we are able to define the connectivity fingerprints for the frontal and parietal DAN, and use it to predict a subject's characteristic DAN activation pattern with high accuracy. These predictions outperform the standard group-average benchmark and predict a subject's own activation pattern above and beyond predictions from another subject's connectivity pattern. Thus an individual's distinctive connectivity pattern accounts for substantial variance in DAN functional responses. Last, we show that the set of connections that predict cortical DAN responses, the frontal and parietal DAN connectivity fingerprints, is predominantly composed of other coactive regions, including regions outside of the DAN including occipital and temporal visual cortices. These connectivity fingerprints represent defining computational characteristics of the DAN, delineating which voxels are or are not capable of exerting top-down attentional bias to other regions of the brain. NEW & NOTEWORTHY The dorsal attention network (DAN) is a set of regions in frontoparietal cortex that reliably activate during attentional tasks. We designed computational models that predict the degree of an individual's DAN activation using their resting-state connectivity pattern alone. This uncovered the connectivity fingerprints of the DAN, which define it so well that we can predict how a voxel will respond to an attentional task given only its pattern of connectivity, with outstanding accuracy.

Keywords: attention; connectivity fingerprint; functional connectivity; vision.

Fig. 1.

Task and methods schematic. A: blood oxygen level-dependent (BOLD) data were acquired while subjects performed a change detection task. Participants were instructed to attend to the orientation of the red target items and to ignore irrelevant distractors (blue). After a short delay, participants indicated whether any of the attended items had changed orientation from the sample to the probe display. B: the multimodal parcellation from Glasser et al. (2016) was used to define targets and search spaces. The frontal and parietal search spaces are outlined in white. All parcels not part of a search space were included as targets. C: dorsal attention network activation and functional connectivity were modeled with a nested leave-one-subject-out cross validation routine, using ridge regression. All vertices were iteratively held out for a single subject, and the lambda hyperparameter (λ; regularization coefficient) of the ridge was optimized within an additional loop of cross-validation across the remaining subjects. The coefficients from the optimal inner-loop models were averaged and then used to generate predictions for the outer-loop subject using only that subject’s connectivity pattern. Sub, subject.

Fig. 2.

Predicted dorsal attention network (DAN) activation for each subject. Predictions for all 9 subjects are shown, juxtaposed with their actual observed responses, for both frontal and parietal search spaces. Color scale is in Z scores. Note the variation in DAN activation profiles across individual subjects, which is accurately accounted for by the connectivity-based predictions. fcMRI, functional connectivity magnetic resonance imaging.

Fig. 3.

Prediction accuracy depends on subjects’ unique connectivity pattern. For each subject and for each region of interest, predictions were generated from a subject’s own connectivity (red) or from each other subject’s connectivity (blue). In all but one case, a subject’s unique pattern of dorsal attention network responses is best predicted from his or her own connectivity (right hemisphere parietal region of subject 2).

Fig. 4.

Connectivity fingerprints for the dorsal attention network (DAN). A: the connectivity fingerprints for frontal and parietal DANs are shown at left and right, respectively, with the lateral view at top and medial view at bottom. Because the connectivity fingerprints of both hemispheres were extremely similar, they are averaged for visualization purposes. Hot colors depict regions whose connectivity predicts higher DAN activation, whereas cool colors depict regions whose connectivity predicts lower DAN activation. The empty regions on the lateral surfaces are the frontal and parietal search spaces where DAN activation was predicted (within-search spaces were excluded as targets). Color scale for these model coefficients is in arbitrary units. B: unthresholded group-average map with regional boundaries overlaid in black and search spaces in white. Color scale is in Z scores, as in Fig. 2. C: the DAN connectivity fingerprints strongly correlate with blood oxygen level-dependent (BOLD) responses. The model coefficients and mean BOLD responses of each parcel outside of the search spaces are plotted for the frontal (left) and parietal (right) DANs. Thick red lines denote best linear fit, and thin lines are 95% confidence intervals. Note that the responses of these regions are completely independent of the model itself, but nonetheless, the most responsive regions are the best predictors.