A generalizable normative deep autoencoder for brain morphological anomaly detection: application to the multi-site StratiBip dataset on bipolar disorder in an external validation framework - Sampaio, Inês Won; Tassi, Emma; Bellani, Marcella; Benedetti, Francesco; Nenadić, Igor; Phillips, Mary L.; Piras, Fabrizio; Yatham, Lakshmi; Bianchi, Anna Maria; Brambilla, Paolo; Maggioni, Eleonora (Artificial intelligence in medicine, 2025)
The heterogeneity of psychiatric disorders makes researching disorder-specific neurobiological markers an ill-posed problem. Here, we face the need for disease stratification models by presenting a generalizable multivariate normative modelling framework for characterizing brain morphology, applied to bipolar disorder (BD). We used deep autoencoders in an anomaly detection framework, combined for the first time with a confounder removal step that integrates training and external validation. The model was trained with healthy control (HC) data from the human connectome project and applied to multi-site external data of HC and BD individuals. We found that brain deviating scores were greater, more heterogeneous, and with increased extreme values in the BD group, with volumes prominently from the basal ganglia, hippocampus, and adjacent regions emerging as significantly deviating. Similarly, individual brain deviating maps based on modified z scores expressed higher abnormalities occurrences, but their overall spatial overlap was lower compared to HCs. Our generalizable framework enabled the identification of brain deviating patterns differing between the subject and the group levels, a step forward towards the development of more effective and personalized clinical decision support systems and patient stratification in psychiatry. [Display omitted] •A normative autoencoder model is used to study BD brain deviations.•Our end-to-end pipeline includes harmonization of external test sets with training set.•We investigated both group- and individual-level brain deviations.•BD showed higher deviations, heterogeneity, and extreme values compared to controls.•Individual-level brain deviating maps showed lower overlap in BD than in controls.