Brain texture as a marker of transdiagnostic clinical profiles in patients with recent-onset psychosis and depression
Authors: Korda Alexandra I. , Andreou Christina , Ruef Anne , Hahn Lisa , Schmidt André , Dannlowski Udo , Kambeitz-Ilankovic Lana , Dwyer Dominic B. , Kambeitz Joseph , Wenzel Julian , Ruhrmann Stephan , Wood Stephen J. , Salokangas Raimo K. R. , Pantelis Christos , Schultze-Lutter Frauke , Meisenzahl Eva , Brambilla Paolo , Selvaggi Pierluigi , Bertolino Alessandro , Hietala Jarmo , Upthegrove Rachel , Lalousis Paris Alexandros , Riecher-Rössler Anita , Davatzikos Christos , Lencer Rebekka , Koutsouleris Nikolaos , Borgwardt Stefan ,   the PRONIA Consortium
Published: January 2024

AbstractThe inter-relationships of voxels can be captured by the radiomics texture features across multiple spatial scales. Prediction models of brain texture changes captured by the contrast texture feature in recent-onset psychosis (ROP) and recent-onset depression (ROD) have recently been proposed, although the validation of these models transdiagnostically at the individual level and the investigation of the variability in clinical profiles are lacking. Established prevention and treatment approaches focus on specific diagnoses and do not address the heterogeneity and manifold potential outcomes of patients. Here we aimed to investigate the cross-sectional utility of brain texture changes for (1) identification of the psychopathological state (ROP and ROD) and (2) the association of individualized brain texture maps with clinical symptom severity and outcome profiles. We developed transdiagnostic models based on structural magnetic resonance imaging data for 116 patients with ROD, 122 patients with ROP and 197 healthy control participants from the PRONIA (Personalized pROgNostic tools for early psychosIs mAnagement) study by applying a set of tools and frameworks to explain the classification decisions of the deep-learning algorithm (named explainable artificial intelligence) and clustering analysis. We investigated the contrast texture feature as the key feature for the identification of a general psychopathological state. The discrimination power of the trained prediction model was >72% and was validated in a second independent age- and sex-matched sample of 137 ROP, 94 ROD and 159 healthy control participants. Clustering analysis was implemented to map the changes in texture brain produced from an explainable artificial intelligence algorithm, in a group fashion. The explained individualized brain contrast map grouped into eight homogeneous clusters. In the clinical group, we investigated the association between the explained brain contrast texture map and clinical symptom severity as well as outcome profiles. Different patterns in the explained brain contrast texture map showed unique associations of brain alterations with clinical symptom severity and clinical outcomes, that is, age, positive, negative and depressive symptoms, and functionality. In some clusters, the mean explained brain contrast texture map values and/or brain contrast texture voxels that contributed significantly to the classification decision predicted accurately the PANSS (positive and negative symptom scale) scores, functionality and change in functionality over time. In conclusion, we created homogeneous clusters which predict the clinical severity and outcome profile in ROP and ROD patients.