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
Finally, although our in-house datasets provided detailed phenotypic information on the psychopathological, cognitive, and sensorimotor domains of SSD patients, this level of granularity is lacking in publicly available datasets. A validation using an independent, deeply phenotyped external cohort would have strengthened the robustness of our findings. Because cognitive testing and NSS were not performed in the HC group, it remains unclear whether the cognitive–motor profile observed in SSD patients is also present in healthy individuals. Future research efforts should focus on expanding and integrating well-characterized SSD cohorts, enabling more comprehensive analyses of the interplay between brain structure, cognition, and sensorimotor function in the disorder. Conclusion This multi-site Identified key predictive features, including GMV changes and ventricle enlargement, that shed light on the SSD’s pathophysiology and link structural deviations to functional outcomes, particularly in cognition and sensorimotor performance.
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
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Technique originates in neuroscience; functional analogues in psychology, criminal justice literature are absent.
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