Individual-subject Functional Localization Increases Univariate Activation but Not Multivariate Pattern Discriminability in the “Multiple-demand” Frontoparietal Network

• Published in: Journal of cognitive neuroscience Vol. 32; no. 7; pp. 1348 - 1368
• Main Authors: Shashidhara, Sneha, Spronkers, Floortje S, Erez, Yaara
• Format: Journal Article
• Language: English
• Published: One Rogers Street, Cambridge, MA 02142-1209, USA MIT Press 01.07.2020; MIT Press Journals, The
• Subjects: Activity patterns; Attention; Brain; Brain - diagnostic imaging; Brain architecture; Brain Mapping; Cognitive ability; Functional magnetic resonance imaging; Humans; Language; Localization; Magnetic Resonance Imaging; Memory; Memory, Short-Term; Mental task performance; Multivariate analysis; Neural coding; Short term memory; Spatial memory; Standardization
• ISSN: 0898-929X; 1530-8898; 1530-8898
• DOI: 10.1162/jocn_a_01554

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.