The frontoparietal “multiple-demand” (MD) control network plays a key role in goal-directed behavior. Recent developments of multivoxel pattern analysis (MVPA) for fMRI data allow for more fine-grained investigations into the functionality and properties of brain systems. In particular, MVPA in the MD network was used to gain better understanding of control processes such as attentional effects, adaptive coding, and representation of multiple task-relevant features, but overall low decoding levels have limited its use for this network. A common practice of applying MVPA is by investigating pattern discriminability within a ROI using a template mask, thus ensuring that the same brain areas are studied in all participants. This approach offers high sensitivity but does not take into account differences between individuals in the spatial organization of brain regions. An alternative approach uses independent localizer data for each subject to select the most responsive voxels and define individual ROIs within the boundaries of a group template. Such an approach allows for a refined and targeted localization based on the unique pattern of activity of individual subjects while ensuring that functionally similar brain regions are studied for all subjects. In the current study, we tested whether using individual ROIs leads to changes in decodability of task-related neural representations as well as univariate activity across the MD network compared with when using a group template. We used three localizer tasks to separately define subject-specific ROIs: spatial working memory, verbal working memory, and a Stroop task. We then systematically assessed univariate and multivariate results in a separate rule-based criterion task. All the localizer tasks robustly recruited the MD network and evoked highly reliable activity patterns in individual subjects. Consistent with previous studies, we found a clear benefit of the subject-specific ROIs for univariate results from the criterion task, with increased activity in the individual ROIs based on the localizers' data, compared with the activity observed when using the group template. In contrast, there was no benefit of the subject-specific ROIs for the multivariate results in the form of increased discriminability, as well as no cost of reduced discriminability. Both univariate and multivariate results were similar in the subject-specific ROIs defined by each of the three localizers. Our results provide important empirical evidence for researchers in the field of cognitive control for the use of individual ROIs in the frontoparietal network for both univariate and multivariate analysis of fMRI data and serve as another step toward standardization and increased comparability across studies.

A distributed, frontoparietal “multiple-demand” (MD) network is involved in tasks of many different kinds. Integrated activity across this network may be needed to bind together the multiple features of a mental control program (Duncan, 2013). Previous data suggest that, especially with low cognitive load, there may be some differentiation between MD regions (e.g., anterior vs. posterior regions of lateral frontal cortex), but with increasing load, there is progressive recruitment of the entire network. Differentiation may reflect preferential access to different task features, whereas co-recruitment may reflect information exchange and integration. To examine these patterns, we used manipulations of complexity, time pressure, and reward while participants solved a spatial maze. Complexity was manipulated by combining two simple tasks. Time pressure was added by fading away the maze during route planning, and on some of these trials, there was the further possibility of a substantial reward. Simple tasks evoked activity only in posterior MD regions, including posterior lateral frontal cortex, pre-supplementary motor area/anterior cingulate, and intraparietal sulcus. With increasing complexity, time pressure, and reward, increases in activity were broadly distributed across the MD network, though with quantitative variations. Across the MD network, the results show a degree of functional differentiation, especially at low load, but strong co-recruitment with increased challenge or incentive.

Target objects required for goal-directed behavior are typically embedded within multiple irrelevant objects that may interfere with their encoding. Most neuroimaging studies of high-level visual cortex have examined the representation of isolated objects, and therefore, little is known about how surrounding objects influence the neural representation of target objects. To investigate the effect of different types of clutter on the distributed responses to target objects in high-level visual areas, we used fMRI and manipulated the type of clutter. Specifically, target objects (i.e., a face and a house) were presented either in isolation, in the presence of a homogeneous (identical objects from another category) clutter (“pop-out” display), or in the presence of a heterogeneous (different objects) clutter, while participants performed a target identification task. Using multivoxel pattern analysis (MVPA) we found that in the posterior fusiform object area a heterogeneous but not homogeneous clutter interfered with decoding of the target objects. Furthermore, multivoxel patterns evoked by isolated objects were more similar to multivoxel patterns evoked by homogenous compared with heterogeneous clutter in the lateral occipital and posterior fusiform object areas. Interestingly, there was no effect of clutter on the neural representation of the target objects in their category-selective areas, such as the fusiform face area and the parahippocampal place area. Our findings show that the variation among irrelevant surrounding objects influences the neural representation of target objects in the object general area, but not in object category-selective cortex, where the representation of target objects is invariant to their surroundings.