An fMRI hyperscanning dataset on cooperation and competition during strategic interactions

Abstract

Hyperscanning has emerged as a prominent technique in social neuroscience, enabling the simultaneous recording of neural activity from multiple individuals engaged in interactive tasks. Despite its potential, functional magnetic resonance imaging (fMRI) hyperscanning remains underutilized due to technical and logistical challenges in synchronizing data across scanners. To address these barriers, we developed an internet-based fMRI hyperscanning platform in southern Taiwan, enabling synchronized acquisition between two sites located 305 km apart. The present study involving 33 dyads examined social and economic decision-making tasks under cooperative and competitive conditions, using a Sender-Receiver paradigm where monetary outcomes hinged on the Receiver's choices and their alignment with the Sender's suggestions. This design elucidated neural mechanisms underlying cooperation, competition, deception, and strategic interactions. The resulting dataset enables analyses of neural synchrony, temporal dynamics of functional connectivity, and condition-specific network interactions. By introducing a novel methodological framework, this work provides a valuable dataset to advance research on social decision-making and interactive neural processes.

Fig. 1

The fMRI hyperscanning fMRI experiment involved both cooperation and competition, conducted simultaneously at two sites 305 km apart. The experiment consisted of 4 runs, each with 28 trials (14 cooperation and 14 competition). Player A and Player B alternated roles as Sender and Receiver. The Sender informed of the probability of the treasure box (NT$ 200 in the cooperation condition and NT$ 150 in the competition condition), suggested a box, while the Receiver made the final decision on which box to open.

Fig. 2

The T1-weighted temporal contrast-to-noise ratio (CNR) is used to evaluate the quality of structural data across two scanner sites. Positive CNR values indicate that the activation signal is greater than the noise level. Error bars represent the 95% confidence intervals, providing a measure of the variability and reliability of the CNR estimates.

Fig. 3

Framewise displacement (FD) across all fMRI volumes and participants. Each point represents the FD of a single fMRI volume, reflecting volume-to-volume head motion. FD was computed by summing absolute translational and rotational displacements (the latter converted to millimeters using a 50 mm head radius). Median FD was 0.051 mm at NCKU and 0.055 mm at NTU, with low overall motion across participants. Less than 5% of volumes exceeded the 95th percentile FD threshold and were flagged as outliers.

Fig. 4

ROI-based comparison of temporal signal-to-noise ratio (tSNR) and beta values across two sites. (a–c) Boxplots of rTPJ tSNR values for NCKU and NTU participants under the cooperation, competition, and combined conditions. No significant differences were observed between sites in any condition. (d–f) Histograms of trial-level rTPJ beta values across the two sites under the same three conditions. Distributions show similar means and standard deviations, with overlapping 95% confidence intervals, confirming data consistency across MRI scanners (GE MR750 at NCKU; Siemens Prisma at NTU). These results support the comparability and homogeneity of data across acquisition sites.