Poster No:
657
Submission Type:
Abstract Submission
Authors:
Kirandeep Kaur1, Katerina Michalaki1, Charlotte Pennington1, Klaus Kessler2, Daniel Shaw1
Institutions:
1Aston University, Birmingham, United Kingdom, 2University College Dublin, Dublin, Ireland
First Author:
Co-Author(s):
Introduction:
Interacting effectively with others and conducting ourselves appropriately in social contexts is essential for establishing and maintaining meaningful interpersonal relationships. Recent research suggests that executive functions (e.g., working memory) and their associated functional brain networks (e.g., fronto-parietal network) combine systematically with one another in dynamic ways to coordinate interactive behaviour (e.g., Shaw et al., 2023), but this has not yet been investigated directly. The present study developed a novel interactive ("second-person") paradigm to determine precisely if and how such domain-general neurocognitive systems support interactive behaviour.
Methods:
Fifty healthy young adults underwent functional magnetic resonance imaging (fMRI) with multiband acquisition whilst performing four tasks designed to measure discrete executive functions or interpersonal behaviours: the Colour-Shape Switching Task (CSST) assessed task switching, the Keep Track Task (KTT) assessed memory updating, the Stroop Task (ST) assessed response inhibition, and a novel Joint Action Task (JAT) assessed their cooperative or competitive interactions with an experimenter located outside of the scanner. To the behavioural data acquired on the JAT, we applied cross-recurrence quantification analysis (CRQA; Wallot et al., 2018) – a technique capable of capturing expressions of discrete interactive behaviours during every exchange, such as interactants' ability to predict and adapt to their co-player's actions. After localising the brain networks supporting each executive function, we then applied a state-based analysis of dynamic functional connectivity (Bayesian Mixtures of Factor Analysers; BMFA) to the functional brain images acquired during the JAT to determine if and how they integrate with one another systematically during social exchanges. To further characterise their involvement during distinct interpersonal behaviours, we applied Partial Least Squares (PLS) analysis to define patterns of dynamic connectivity within and between the functional brain networks that were specific or common to discrete types of interactive behaviour.
Results:
BMFA revealed several states that occurred reliably during social interactions on the JAT, each characterised by a unique covariance pattern that comprised integrations and segregations among the functional brain networks supporting executive functions. Each state was characterised by three metrics of dynamic connectivity: its probability of occurrence, duration (lifetime) and transition with other states. Applied to these metrics, PLS revealed that the three functional brain networks integrate in different time-varying ways according to the type of interactive behaviour shown. Expressions of reciprocity, for example, were associated with occurrences of a state characterised by connectivity among the brain network supporting task switching, while leader-follower dynamics were associated with transitions among states capturing differential integrations of the brain networks supporting memory updating and response inhibition.
Conclusions:
Our study shows that to coordinate effective interpersonal behaviour, the brain deploys and switches flexibly between functional brain networks that support domain-general executive functions. Future studies should utilise neuroimaging techniques with high temporal resolution to identify patterns of time-varying effective connectivity among these brain networks. This might help to identify the pathoconnectome underlying dysfunctional social cognition and interpersonal behaviour that characterises many neurological (Cotter et al., 2018) and psychiatric disorders (Schilbach et al., 2016).
Emotion, Motivation and Social Neuroscience:
Social Interaction 1
Higher Cognitive Functions:
Executive Function, Cognitive Control and Decision Making
Modeling and Analysis Methods:
fMRI Connectivity and Network Modeling 2
Keywords:
FUNCTIONAL MRI
Social Interactions
Other - Brain states
1|2Indicates the priority used for review
By submitting your proposal, you grant permission for the Organization for Human Brain Mapping (OHBM) to distribute your work in any format, including video, audio print and electronic text through OHBM OnDemand, social media channels, the OHBM website, or other electronic publications and media.
I accept
The Open Science Special Interest Group (OSSIG) is introducing a reproducibility challenge for OHBM 2025. This new initiative aims to enhance the reproducibility of scientific results and foster collaborations between labs. Teams will consist of a “source” party and a “reproducing” party, and will be evaluated on the success of their replication, the openness of the source work, and additional deliverables. Click here for more information.
Propose your OHBM abstract(s) as source work for future OHBM meetings by selecting one of the following options:
I do not want to participate in the reproducibility challenge.
Please indicate below if your study was a "resting state" or "task-activation” study.
Task-activation
Healthy subjects only or patients (note that patient studies may also involve healthy subjects):
Healthy subjects
Was this research conducted in the United States?
No
Were any human subjects research approved by the relevant Institutional Review Board or ethics panel?
NOTE: Any human subjects studies without IRB approval will be automatically rejected.
Yes
Were any animal research approved by the relevant IACUC or other animal research panel?
NOTE: Any animal studies without IACUC approval will be automatically rejected.
Not applicable
Please indicate which methods were used in your research:
Functional MRI
For human MRI, what field strength scanner do you use?
3.0T
Which processing packages did you use for your study?
SPM
FSL
Provide references using APA citation style.
Cotter, J., Granger, K., Backx, R., Hobbs, M., Looi, C. Y., & Barnett, J. H. (2018). Social cognitive dysfunction as a clinical marker: A systematic review of meta-analyses across 30 clinical conditions. Neuroscience & Biobehavioral Reviews, 84, 92-99.
Ghahramani, Z. & Beai, M. J. (2000). Variational inference for Bayesian mixtures of factor analysers. Advances in Neural Information Processing Systems, 12, 449-45
Schilbach, L. (2016). Towards a second-person neuropsychiatry. Philosophical Transactions of the Royal Society B: Biological Sciences, 371(1686), 20150081.
Shaw, D. J., Czekoova, K., Mareček, R., Špiláková, B. H., & Brázdil, M. (2023). The interacting brain: dynamic functional connectivity among canonical brain networks dissociates cooperative from competitive social interactions. NeuroImage, 269, 119933.
Wallot, S., & Leonardi, G. (2018). Analyzing multivariate dynamics using cross-recurrence quantification analysis (crqa), diagonal-cross-recurrence profiles (dcrp), and multidimensional recurrence quantification analysis (mdrqa)–a tutorial in r. Frontiers in psychology, 9, 2232.
No