Normative age-related structural brain deviations underlying psychopathology, cognitive impairment and neurological soft signs in schizophrenia spectrum disorders
PMC13039878
· 10.1038/s41398-026-03956-0
Gap Declaration
The leave-site-out CV approach validated the tool’s capacity for generalization, achieving results comparable to prior smaller-scale studies. Importantly, the identification of key age-dependent neuroanatomical features, such as total GMV and superior frontal sulcus, provided meaningful insights into the structural alterations in SSD. Additionally, the utilization of simple ML models, i.e., RF with almost standard hyperparameter settings further highlights a potential replication in future studies. The multivariate analysis further enhanced the understanding of brain-behavior relationships, offering a more integrated perspective on the disorder. Lastly, this is the first study that investigates the NSS and normative deviations of brain structures in different cohorts of SSD patients.
Abstract
Schizophrenia spectrum disorders (SSD) are marked by widespread structural brain abnormalities. Neuroanatomical normative modeling (NM) can quantify person-specific deviations from healthy variability, yet it remains unknown whether pre-trained, large-scale NM features support site-held-out classification and mechanistic brain–behavior mapping in SSD. Here, we applied a publicly available PCNtoolkit model (trained on ~57,000 healthy controls from 82 sites) to six independent cohorts (N = 831) to derive individual deviations in cortical thickness (CT) and subcortical volumes from T1-weighted MRI. Employing a random forest classifier with leave-site-out cross-validation, we achieved a balanced accuracy of 65%, which underscores the inherent complexity of SSD. Feature importance analysis iden…
Conclusions / Discussion
Discussion In this multi-dataset study, we used NM to quantify individual deviations from a reference brain profile and evaluated whether these deviation signatures (i) support clinically relevant classification of SSD and (ii) map onto symptom-relevant phenotypes, including psychopathology, cognition, and NSS. Importantly, we designed the analysis to reflect a realistic clinical deployment scenario: models were trained/fine-tuned on a subset of HC and then applied to SSD patients, and leave-site-out CV directly tested whether deviation-based inference generalizes across clinics rather than overfitting site-specific idiosyncrasies. This framing moves NM beyond a methodological exercise by explicitly interrogating its practical utility for cross-site biomarker development. This study yielded three main findings: First, using a NM approach (i.e., deviation scores), we achieved a mean balanced accuracy of 65% in predicting SSD. These features performed comparably to other SSD predictive models, and their robustness underscores the usefulness of tools like the PCNtoolkit in mitigating cross-site and cross-study variability. Feature importance analysis (MDI) revealed weak but consistent…
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Structural Hole
40% bridge
Technique originates in psychology; functional analogues in criminal justice, epidemiology literature are absent.
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