P179 Introducing the Phase-Relationship Index (PRI): Transmission Delay Shapes In- and Anti-Phase Functional Connectivity in EEG Analysis and Simulation
William W Lytton*1, 2,3, Andrei Dragomir4, Ahmet Omurtag5
1Department of Physiology and Pharmacology, SUNY Downstate Health Sciences University, Brooklyn, New York
2Department of Neurology, SUNY Downstate Health Sciences University, Brooklyn, New York
3Department of Neurology, Kings County Hospital Center, Brooklyn, New York
4 Singapore Institute for Neurotechnology, National University of Singapore, Singapore
5Engineering Department, Nottingham Trent University, Nottingham, United Kingdom
*Email: billl@neurosim.downstate.edu
Introduction
Neural oscillations enable information processing via cortical network synchronization, yet EEG studies rarely examine precise phase relationships. Introducing the Phase-Relationship Index (PRI), we demonstrate in-phase clustering dominates at cortical distances <80 mm, shifting to anti-phase beyond this. Simulations of delay-coupled excitatory LeakyIntegrate-and-Fire (LIF)neurons reveal conduction delays as the mechanism underlying this distance-dependent EEG phase relationship pattern.
Methods
Analyzing 19-channel resting EEG from 31 healthy subjects [1], we computed inter-site phase clustering (ISPC/PLV) across 1–32 Hz frequencies for electrode pairs. Phase differences determined ISPC with PRI addressing the phase relationship. Cortical distances derived from MNI coordinates [2] were used for distance-dependent analyses. Simulations modeled two delay-coupled excitatory LIF neuron populations (N=200 each) with recurrent (gain G) and inter-population (gain g) connections, conduction delays (d and tau). Firing rates (analogous to EEG) underwent spectral analysis (Frequency Band Power), synchrony assessment (Order Parameter), and ISPC/PRI comparisons between simulated and empirical data.
Results
Analysis revealed PRI values predominantly near 0 or 1. For 16 Hz connections, cortical distance increased with PRI (Fig. 1A), transitioning sharply from in-phase (PRI≈0) to anti-phase (PRI≈1, mainly asymmetric long-range) at 85-120 mm (Fig. 1B-F). Simulations of LIF neuronal populations identified four dynamic regimes (Fig. 1H-K). Disconnected populations (g=0) showed irregular firing (Fig. 1H), transitioning to synchronous but un-clustered activity with increased intra-population connectivityG(Fig. 1I,L). Introducing inter-population connections (g>0) induced phase clustering (rising ISPC, Fig. 1M), switching from in-phase (small tau, Fig. 1J) to anti-phase at tau≈31 ms (Fig. 1K,N), accompanied by reduced synchrony (Fig. 1N) during the transition to anti-phase.
Discussion
Our findings link distance dependence of clustering (Fig. 1A) to delay-coupled neuronal population dynamics (Fig. 1N). Sparse inter-population connections which were sufficient to induce clustering (Fig. 1M) mirror sparse long-distance neuroanatomical connectivity [3], and derived conduction speeds (5.44-8 m/s) match myelinated axons [4]. We show, challenging prior assumptions, that zero-lag synchrony is genuine, not artefactual. PRI analysis also reveals anti-phase dominance (Fig. 1A; [5]), distinct topographies (Fig. 1E-F), and task-modulated dynamics, underscoring its biomarker potential.
Figure 1. Figure 1. Phase clustering in EEG (A-F) and simulated neurons (G-N). (A) ISPC, PRI, cortical distances. (B) z values on Argand plane for 3 electrode pairs. (C) angle(z) values. (D) PRI values. (E) Top 15 in-phase connections. (F) Top 15 anti-phase connections. (G) Schematic of populations. (H-K) Firing rate time series. (L) ISPC, FBP, Order Parameter vs G. (M) ISPC, FBP vs g. (N) ISPC, PRI vs tau.
Acknowledgements
None.
References
[1]https://doi.org/10.1038/s41598-020-69553-3
[2]https://doi.org/10.1002/brb3.2476
[3]https://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.3001575
[4]https://doi.org/10.1371/journal.pcbi.1007004
[5]https://doi.org/10.1002/jnr.24748
