bioRxiv2024
Time-resolved functional network connectivity (trFNC) assesses the time-resolved coupling between brain regions using functional magnetic resonance imaging (fMRI) data. This study aims to compare two techniques used to estimate trFNC, to investigate their similarities and differences when applied to fMRI data. These techniques are the sliding window Pearson correlation (SWPC), an amplitude-based approach, and phase synchronization (PS), a phase-based technique. To accomplish our objective, we used resting-state fMRI data from the Human Connectome Project (HCP) with 827 subjects (repetition time: 0.7s) and the Function Biomedical Informatics Research Network (fBIRN) with 311 subjects (repetition time: 2s), which included 151 schizophrenia patients and 160 controls. Our simulations reveal distinct strengths in two connectivity methods: SWPC captures high-magnitude, low-frequency connectivity, while PS detects low-magnitude, high-frequency connectivity. Stronger correlations between SWPC and PS align with pronounced fMRI oscillations. For fMRI data, higher correlations between SWPC and PS occur with matched frequencies and smaller SWPC window sizes ([~]30s), but larger windows ([~]88s) sacrifice clinically relevant information. Both methods identify a schizophrenia-associated brain network state but show different patterns: SWPC highlights low anti-correlations between visual, subcortical, auditory, and sensory-motor networks, while PS shows reduced positive synchronization among these networks. In sum, our findings underscore the complementary nature of SWPC and PS, elucidating their respective strengths and limitations without implying the superiority of one over the other. Impact StatementThis study demonstrates that SWPC and PS provide complementary insights into dynamic functional connectivity, revealing different aspects of brain dynamics based on signal focus. For tasks involving slow dynamics, SWPC amplitude is ideal, while the PS phase is more suitable for transient dynamics. In schizophrenia, typically associated with general dysconnectivity, we uncover a dual dysconnectivity profile depending on phase or amplitude dynamics. This novel approach offers researchers a platform to explore task-specific dysconnectivity profiles, enabling more targeted interventions. These findings will guide methodology choices, deepen understanding of brain dynamics, and support the development of precise neuropsychiatric biomarkers. HighlightsO_LITime-resolved functional network connectivity (trFNC) is widely used; here we study two approaches often pit against one another: 1) phase synchrony (PS), a phase-based method, and 2) sliding window Pearson correlation (SWPC), an amplitude-based method. C_LIO_LISWPC is sensitive to the choice of window size, while PS requires a narrow frequency band. Both can result in the loss of relevant information. C_LIO_LIWe find through simulation that SWPC better captures high-magnitude slow-varying amplitude-encoded connectivity while PS better captures low-magnitude fast-varying phase-encoded connectivity. C_LIO_LIWe find that while both SWPC and PS detect disconnected states mostly associated with schizophrenia they exhibit unique complementary patterns. C_LIO_LIWe conclude that SWPC and PS are complementary techniques, each with distinct assumptions and constraints, which should be selected based on the focus of the study. C_LI