Schizophrenia spectrum disorders show frequency- specific reorganization of postural sway geometry

Postural instability is a disabling yet insufficiently characterized feature of Schizophrenia Spectrum Disorders (SSD). Quantitative posturography has consistently demonstrated increased body sway in SSD, which correlates with negative symptom severity and functional impairment; however, the sensorimotor mechanisms underlying these abnormalities remain poorly understood. This gap arises in part because conventional sway metrics neglect the multiscale, orientation-dependent organization of postural control. To overcome these limitations, we introduce Frequency-Specific Oriented Fractal Scaling Component Analysis (FS-OFSCA), a novel method which partitions center-of-pressure (CoP) trajectories into physiologically motivated frequency bands associated with proprioceptive and visual-vestibular processing. Within each band, FS-OFSCA identifies the planar directions of strongest and weakest long-range temporal correlations. We applied this approach to CoP recordings obtained from 42 individuals with SSD and 33 Healthy Participants (HP), across 12 stance conditions. The central result is that SSD is characterized less by how much participants sway than by a stance- and band-specific reorganization of the directional geometry of sway. Using linear mixed-effects models of the inter-axial angle ( Δ θ ), SSD showed a compression of Δ θ that was statistically supported at the overall-group level in the global band ( $$p = 0.001$$ ; Holm-adjusted $$p = 0.003$$ ); in the visual-vestibular and proprioceptive bands the overall group effect was not significant and the difference was stance-dependent. The coupling between Δ θ and directional fractal descriptors ( H₁ , H₂ , SDH ) further differed between groups in a band-dependent manner. By comparison, conventional geometric, spectral, and entropy-based metrics also separated the groups, with small-to-moderate effect sizes (Hedges’ g ≈ 0.1-0.5 ) concentrated under sensory-challenging conditions; FS-OFSCA captures a complementary, directional aspect of postural control rather than information categorically unavailable to those metrics. To support reproducible posturography, we release STABLE, an open-source Python toolkit integrating linear, spectral, nonlinear, and anisotropic analysis methods.