Prefrontal Brain-to-Brain Synchrony in Group Hunting (n=32)

This repository contains the multimodal data pipeline and analysis scripts for the study: "Prefrontal brain-to-brain synchrony during human group hunting: Evidence from fNIRS hyperscanning" (bioRxiv, 2026).

🔬 Key Findings

• Behavioral Predictors: Success in group hunting is significantly predicted by "speed-matching" and maintaining closer interpersonal distance with co-predators.
• PFC Synchrony: We found significantly greater brain-to-brain synchrony in the medial and lateral Prefrontal Cortex during hunting compared to chance levels.
• Social Coordination: Findings suggest the PFC plays a critical role in real-time social tracking and tactical coordination in dynamic virtual environments.

📁 Files

🛠 Minecraft Data Acquisition (Java/Maven)

• Main.java, plugin.yml, pom.xml, & .gitignore: Custom Minecraft plugin infrastructure developed to extract high-fidelity game data for behavioral analysis.

🧠 Neural Analysis & Synchrony (MATLAB)

• Preprocessing & Pipeline: spmfnirsflow.m, PreprocessingfNIRSEmre.m, spm_fnirs_spm.m, spm_fnirs_spatialpreproc_ui.m, spm_fMRI_design.m, Organisefolders.m, getfolders.m, and spm_get_ons.m handle the core brain data preprocessing workflow.
• Synchrony Analysis: process_dyads.m, Waveletcoherenceanalysis.m, phase_scramble.m, and block_time_scramble.m for dyadic coupling and null distribution generation.
• Activation Analysis: Run_GLM_snirf.m (creating design matrices for single-subject brain data) and singlesubjectanalysis.m (single-subject brain activity analysis).
• Quality Control: Beforepreprocessingcheck.m, Excludetable.m, and Confirmdateoftesting.m ensure data integrity and subject inclusion/exclusion.

📊 Behavioral Modeling & Visualization (Python/R)

• Data Preprocessing: Behaviouralplotsstats.ipynb and PreparingbehaviourdataforfNIRS.ipynb for cleaning and formatting raw behavioral data.
• Statistical Analysis: fNIRSanalysis.ipynb (Running fNIRS and physiology data stats) and FNIRSDistancemodel.R (Analyzing behavioral stats).
• Visualization: CreatefNIRStemplate.ipynb for fNIRS-specific data visualizations.

🛠 Skills Demonstrated

• Multimodal Sensor Fusion: Synchronizing discrete game-engine events (Minecraft) with continuous neural streams (fNIRS) at high temporal resolution.
• Advanced Signal Processing: Implementing Wavelet Coherence and non-parametric permutation testing (Phase/Time scrambling) for dyadic data analysis.
• Full-Stack Research Engineering: Building the data acquisition tools (Java), the analysis engine (MATLAB), and the statistical modeling (R/Python).