Wiafe, S.-L. | Search bioRxiv

bioRxiv2025

Mapping Dynamic Metabolic Energy Distribution in Brain Networks using fMRI: A Novel Dynamic Time Warping Framework

Schizophrenia has long been linked to impaired coordination of brain activity, yet most frameworks overlook two key dimensions: the amplitude of brain signals and the differing timescales on which regions operate. These factors are critical in disorders where neural activity is exaggerated and slowed. In healthy adults, networks compensate for mismatched processing speeds to maintain proportionate activity, but this process is poorly understood in schizophrenia. We developed a timescale-aligned, time-resolved framework that separates temporal distortions from genuine amplitude differences, enabling measurement of amplitude balance between networks across timescales. This approach was applied to large-scale fMRI datasets, including the Human Connectome Project and a multi-site schizophrenia cohort. Patients with schizophrenia showed greater amplitude imbalance, especially during fast fluctuations, along with more frequent re-entry into unbalanced states and slower recovery to stable coordination. We further identified a flexible intermediate state that patients occupied more often, and that predicted better working-memory performance. Across cohorts, amplitude imbalance was associated with greater symptom severity and poorer reasoning ability. These findings provide a new mechanistic view of dyscoordination in schizophrenia grounded in timescale-normalized amplitude dynamics, highlight aberrant recovery of amplitude balance as a core feature of the illness, and suggest that timescale-aligned amplitude imbalance may serve as a promising target for biomarker development.

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bioRxiv2024

Normalized Dynamic Time Warping Increases Sensitivity In Differentiating Functional Network Connectivity In Schizophrenia

Our study advances the application of dynamic time warping (DTW) as a functional connectivity measure by introducing a normalization technique which enhances the detection of schizophrenia effects in comparison to both standard DTW and traditional correlation methods. By rigorously examining the statistical validity of DTW and our proposed normalized DTW measure, we show that it effectively captures interdependencies between fMRI signals beyond linear correlation, offering a more robust, complementary and informative approach to functional connectivity analysis. Through comprehensive evaluations, we demonstrate that normalized DTW is more sensitive to differences in functional brain network connections between schizophrenia and controls, highlighting its potential to provide deeper insight into clinical research. Clinical RelevanceThis study enhances our understanding of the functional specificity of schizophrenia by emphasizing the importance of nonlinear relationships through the introduction of a normalization technique for the DTW metric.

neuroscience↗

bioRxiv2024

Studying time-resolved functional connectivity via communication theory: on the complementary nature of phase synchronization and sliding window Pearson correlation.

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

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