arXiv subjects

Masoud Seraji

Publications and source records attributed to Masoud Seraji.

3 recordsLinked to original sources

arXiv2026

High-Order Triadic Functional Connectivity in the Brain and Beyond

Here, we report high-order functional network connectivity as a promising way for studying the brain connectome. Traditional functional connectivity approaches capture only pairwise relationships between brain regions, overlooking complex multivariate dependencies that underlie cognition and behavior. First, we demonstrated that high-order interactions capture more information and can distinguish between resting-state and task-state brain activity. Second, we introduce a matrix-based entropy-functional method for estimating triadic interactions, which are statistical dependencies among triplets of brain regions, and apply it to large-scale functional brain networks. The resulting triadic networks revealed distinct community patterns that complement those observed in traditional pairwise functional connectivity analyses and simultaneously capture additional connection information. Despite the potential combinatorial explosion of triadic configurations, the networks exhibited constrained and hierarchical structures that allowed computation and interpretation. These findings position triadic connectivity as a promising next-step functional connectivity framework for probing brain network organization and high-order neural interactions, while also highlighting key biological and technical challenges that require careful consideration.

q-bio.NC↗

arXiv2026

Complex-valued Phase Synchrony Reveals Directional Coupling in FMRI and Tracks Medication Effects

Understanding interactions in complex systems requires capturing the relative timing of coupling, not only its strength. Phase synchronization captures this timing, yet most methods either reduce the phase to its cosine or collapse it into scalar indices such as the phase-locking value, discarding relative timing. We propose a complex-valued phase synchrony (CVPS) framework that estimates phase with an adaptive Gabor wavelet and preserves both cosine and sine components. Simulations confirm that CVPS recovers true phase offsets and tracks non-stationary dynamics more faithfully than Hilbert-based methods. Because antipsychotics are known to modulate the timing of cortical interactions, they provide a rigorous context to evaluate whether CVPS can capture such pharmacological effects. CVPS further reveals cortical neuro-hemodynamic drivers, with occipital-to-parietal and prefrontal-to-striatal lead--lag flows consistent with known receptor targets, confirming its ability to capture pharmacological timing. CVPS, therefore, offers a robust, generalizable framework for detecting relative timing in complex systems such as the brain.

q-bio.NC↗

arXiv2025

Efficient Brain Network Estimation with Sparse ICA in Non-Human Primate Neuroimaging

Independent component analysis (ICA) is widely used to separate mixed signals and recover statistically independent components. However, in non-human primate neuroimaging studies, most ICA-recovered spatial maps are often dense. To extract the most relevant brain activation patterns, post-hoc thresholding is typically applied-though this approach is often imprecise and arbitrary. To address this limitation, we employed the Sparse ICA method, which enforces both sparsity and statistical independence, allowing it to extract the most relevant activation maps without requiring additional post-processing. Simulation experiments demonstrate that Sparse ICA performs competitively against 11 classical linear ICA methods. We further applied Sparse ICA to real non-human primate neuroimaging data, identifying several independent component networks spanning different brain networks. These spatial maps revealed clearly defined activation areas, providing further evidence that Sparse ICA is effective and reliable in practical applications.

stat.AP↗