Shoji, T.; Nakaki, R.

Psychiatric disorders, such as attention-deficit/hyperactivity disorder, autism spectrum disorder, and schizophrenia, are clinically heterogeneous and lack objective biomarkers for reliable diagnosis. Although blood transcriptomic data have been proposed as a potential source of diagnostic information, their generalizability across independent cohorts remains unclear. This study aimed to assess whether biologically informed measures of dynamic instability enhance the reproducibility and generalizability of psychiatric classifications based on peripheral blood data by integrating publicly available blood transcriptomic datasets from multiple cohorts and evaluating classification performance using individual-level cross-validation and study-level holdout validation. To investigate the underlying biological structure, we applied a dynamic systems framework, including pseudotime-based vector field inference and attractor analysis. Additionally, we introduced temporal entropy as a measure of dynamic instability in the inferred transcriptomic trajectories. High classification performance was observed in individual-level cross-validation (area under the receiver operating characteristic [AUROC] > 0.8 across several comparisons); however, performance decreased substantially in study-level validation (AUROC {approx} 0.5-0.7), indicating limited generalizability. Attractor analysis revealed that transcriptomic states formed continuous and overlapping structures rather than distinct diagnostic clusters. Stratification based on temporal entropy identified a subset of individuals with unstable transcriptomic dynamics, and excluding these individuals improved the classification performance across most diagnostic pairs (AUROC > 0.7). These findings suggest that transcriptomic variability and dynamic instability contribute to the limited reproducibility of psychiatric classifications. Incorporating temporal entropy as a measure of system-level instability may enhance the robustness and interpretability of biomarker-based models and provide a new perspective on psychiatric disorders as dynamic systems.