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Clarifying the mechanisms of altered brain network communication in Parkinson's disease is crucial, yet conventional connectivity measures do not distinguish whether directed information flow is driven by phase synchrony or spectral amplitude modulation. To address this, we introduce Decomposed Transfer Entropy (DTE), which represents directed information flow as a mechanistic profile comprising phase, spectral, and interaction components. Simulations indicate that DTE identifies distinct coupling types and remains robust under noise; we further benchmark DTE against representative baseline measures and assess parameter sensitivity. Applied to $\theta $ band resting EEG, DTE reveals that the unmedicated (PD-OFF) state is characterized by a phase-dominant reweighting originating from the frontal midline hub Fz; medication shifts this mechanistic composition toward that of healthy reference. Hub-centric composition metrics derived from DTE discriminate PD-OFF from healthy controls, and the normalization of phase dominance relates to motor improvement ( $\Delta $ UPDRS-III). Overall, DTE offers a mechanism-aware perspective on PD pathophysiology, suggesting that therapeutic benefit aligns with normalization of mechanistic composition rather than simple changes in connection strength, and provides a promising route toward mechanism-oriented biomarkers.
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