Masked features of task states found in individual brain networks

Abstract

Completing complex tasks requires that we flexibly integrate information across brain areas. While studies have shown how functional networks are altered during different tasks, this work has generally focused on a cross-subject approach, emphasizing features that are common across people. Here we used extended sampling "precision" fMRI data to test the extent to which task states generalize across people or are individually-specific. We trained classifiers to decode state using functional network data in single-person datasets across 5 diverse task states. Classifiers were then tested on either independent data from the same person or new individuals. Individualized classifiers were able to generalize to new participants. However, classification performance was significantly higher within a person, a pattern consistent across model types, people, tasks, feature subsets, and even for decoding very similar task conditions. Notably, these findings also replicated in a new independent dataset. These results suggest that individual-focused approaches can uncover robust features of brain states, including features obscured in cross-subject analyses. Individual-focused approaches have the potential to deepen our understanding of brain interactions during complex cognition. Citation Diversity StatementRecently, the field of neuroscience has reported a bias in citation practices such that papers from minority groups are more often under-cited relative to the number of papers in the field (Dworkin et al. 2020). The authors of this paper were proactive in consideration of selecting references that reflect diversity of the field in thought, contribution, and gender. Utilizing previously derived databases (Dworkin et al. 2020; Zhou et al. 2020) we obtained the predicted gender of authors referenced in this manuscript. By this measure (and excluding self-citations to the first and last authors of our current paper), our references contain 13.87% woman(first)/woman(last), 23.3% man/woman, 23.3% woman/man, and 39.53% man/man. This method is limited in that a) names, pronouns, and social media profiles used to construct the databases may not, in every case, be indicative of gender identity and b) it cannot account for intersex, non-binary, or transgender people. Second, we obtained the predicted racial/ethnic category of the first and last author of each reference by databases that store the probability of a first and last name being carried by an author of color(Ambekar et al. 2009). By this measure (and excluding self-citations), our references contain 10.83% author of color (first)/author of color(last), 10.64% white author/author of color, 23.55% author of color/white author, and 54.98% white author/white author. This method is limited in that a) names and Florida Voter Data to make the predictions may not be indicative of racial/ethnic identity, and b) it cannot account for Indigenous and mixed-race authors, or those who may face differential biases due to the ambiguous racialization or ethnicization of their names. We look forward to future work that could help us to better understand how to support equitable practices in science.