Decoding Resting Minds: How Mind Wandering Explains Brain Connectivity and Behavioral Phenotypes
Poster No:
1662
Submission Type:
Abstract Submission
Authors:
Junhong Yu1, Charly Billaud1
Institutions:
1Nanyang Technological University, Singapore, NA
First Author:
Co-Author:
Introduction:
Resting-state functional connectivity (rsFC) has been widely studied in relation to individual differences in behavioral phenotypes, to draw inferences on psychological processes implicated in the brain. These inferences have been criticized for being too far-fetched and difficult to relate since subjects do not engage in any tasks that elicit these psychological processes during rest (Finn, 2021). We argue that this is not entirely true. Mind wandering occurs during the scan; its thought contents are psychologically meaningful and can be used to meaningfully relate rsFC to behavioral phenotypes. To these ends, the current study sought to study 1) the relationship between rsFC fingerprints and mind wandering contents during rest, 2) the functional network connectivity correlates of various dimensions of mind wandering contents and 3) given the intimate relationship between mind wandering and rsFC, can mind wandering-related features perform similarly well, relative to rsFC features, in predicting a diverse range of behavioral phenotypes?
Methods:
765 resting-state runs from 164 subjects (i.e., 3 to 4 runs per subject) from the MPI-Leipzig Mind-Brain-Body database (Mendes et al., 2019) were analyzed. Subjects completed the Short-form New York Cognition Questionnaire (SNYCQ; Gorgolewski et al., 2015) after each run to assess the mind wandering contents during the scan. For the first aim, the between-run difference in FC fingerprints (i.e., in the form of 219 x 219 FC matrices) and SNYCQ scores were calculated between every unique pair of runs for each subject. Linear mixed-effect models were used to examine if between-run differences in FC fingerprint and SNYCQ scores were significantly related. As for the second aim, we mapped out the network-to-network connections (within runs) that are significantly predicted by each of the 12 SNYCQ items, at the within-subject level with the use of linear mixed effects models. Finally, using a machine learning approach, two sets of ridge regression models were fitted using across-run averaged rsFC features or SNYCQ scores, to predict a diverse range of 65 behavioral phenotypes, one at a time, in unseen subjects. The accuracy of these predictions, as assessed via predicted-actual correlations, was compared between the two sets of models.
Results:
Similarity in SNYCQ scores significantly predicted similarity in rsFC fingerprints β=.23, 95%CI [.15, .32]. Further analyses showed that the similarity in the vigilance item score contributed mostly to the similarity in rsFC fingerprints (see fig 1a). Next, the SNYCQ dimensions of 'positive', 'negative', 'surroundings', 'vigilance', and 'specific' could be significantly predicted by one or more network-to-network connections after correcting for false discovery rate at the item level (see Fig 1b). Finally, our machine learning analyses revealed that the SNYCQ score outperformed (i.e., higher predicted-actual correlations) rsFC features in the prediction of most behavioral phenotypes (see fig 2).
·Fig 1
·Fig 2
Conclusions:
These findings suggest a robust relationship between rsFC features and mind wandering contents, at the within-subject level and mind wandering-related features fared similarly or if not better than rsFC in predicting most behavioral phenotypes. Overall, these findings provide a strong empirical basis for the use of mind wandering contents to bridge the study of rsFC and behavioral phenotypes. By anchoring rsFC features to mind wandering contents, this would help researchers draw inferences on the psychological processes implicated in a diverse range of behavioral phenotypes and clinical populations. Our findings also suggest that if a subject's state of vigilance varies across resting state sessions, this will significantly confound the estimates of rsFC.
Modeling and Analysis Methods:
Connectivity (eg. functional, effective, structural) 2
Task-Independent and Resting-State Analysis 1
Keywords:
Consciousness
Machine Learning
Other - mind wandering; resting-state; functional connectivity
1|2Indicates the priority used for review
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Provide references using APA citation style.
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