Transdiagnostic subgroups of cognitive impairment in early affective and psychotic illness

Abstract

Cognitively impaired and spared patient subgroups were identified in psychosis and depression, and in clinical high-risk for psychosis (CHR). Studies suggest differences in underlying brain structural and functional characteristics. It is unclear whether cognitive subgroups are transdiagnostic phenomena in early stages of psychotic and affective disorder which can be validated on the neural level. Patients with recent-onset psychosis (ROP; N = 140; female = 54), recent-onset depression (ROD; N = 130; female = 73), CHR (N = 128; female = 61) and healthy controls (HC; N = 270; female = 165) were recruited through the multi-site study PRONIA. The transdiagnostic sample and individual study groups were clustered into subgroups based on their performance in eight cognitive domains and characterized by gray matter volume (sMRI) and resting-state functional connectivity (rsFC) using support vector machine (SVM) classification. We identified an impaired subgroup (N_ROP = 79, N_ROD = 30, N_CHR = 37) showing cognitive impairment in executive functioning, working memory, processing speed and verbal learning (all p < 0.001). A spared subgroup (N_ROP = 61, N_ROD = 100, N_CHR = 91) performed comparable to HC. Single-disease subgroups indicated that cognitive impairment is stronger pronounced in impaired ROP compared to impaired ROD and CHR. Subgroups in ROP and ROD showed specific symptom- and functioning-patterns. rsFC showed superior accuracy compared to sMRI in differentiating transdiagnostic subgroups from HC (BAC_impaired = 58.5%; BAC_spared = 61.7%, both: p < 0.01). Cognitive findings were validated in the PRONIA replication sample (N = 409). Individual cognitive subgroups in ROP, ROD and CHR are more informative than transdiagnostic subgroups as they map onto individual cognitive impairment and specific functioning- and symptom-patterns which show limited overlap in sMRI and rsFC. CLINICAL TRIAL REGISTRY NAME: German Clinical Trials Register (DRKS). Clinical trial registry URL: https://www.drks.de/drks_web/ . Clinical trial registry number: DRKS00005042.

Fig. 1. Cognitive characteristics of clusters based on the transdiagnostic and individual clustering analyses in the discovery sample.

A represents the cognitive performances of impaired (blue) and spared clusters (green) for the transdiagnostic cluster solution. B represents the cognitive performances of impaired (shades of blue) and spared (shades of green) clusters for the clusterings based on recent-onset depression patients (ROD), recent-onset psychosis patients (ROP) and clinical high-risk individuals (CHR) separately. For comparison impaired and spared clusters of the transdiagnostic cluster solution are shown in gray. For both sections: High principal component (PCA) scores represent high performance. Abbreviations: vismem visual memory, soccog social cognition, wm working memory, proc processing speed, exfun executive functioning, att attention, sal salience, verbmem verbal memory.

Fig. 2. Functional characteristics and characteristics with respect to symptoms of the clusters based on the transdiagnostic and individual clustering analyses in the discovery sample.

Functioning differences (A) and psychotic and depressive symptom differences (B, C) of impaired (shades of blue) and spared (shades of green) clusters for the clusterings based on recent-onset depression patients (ROD), recent-onset psychosis patients (ROP) and clinical high-risk individuals (CHR) separately. For comparison impaired and spared clusters of the transdiagnostic cluster solution are shown in gray. Abbreviations: GAF S global assessment of functioning (symptom scale), PANSS positive and negative syndrome scale, BDI Beck’s depression inventory.

Fig. 3. Reliability maps of significant sMRI and rsFC classification models as measured by the cross-validation ratio (CV ratio).

The upper row in panel (A) shows the predictive connectivity patterns for the ‘impaired vs HC’ rsFC model. The lower row in (A) shows the predictive connectivity patterns for the ‘spared vs HC’ rsFc model. The ten most predictive connectivity patterns for HC status are marked in blue and the ten most predictive connectivity patterns for impaired/spared cluster status are marked in red. A list containing the predictive features is given in supplementary Table S12. Figures were generated using the BrainNet Viewer. B shows the voxel reliability maps for the significant ‘spared vs HC’ sMRI model. Voxels predictive of spared status are represented by positive CV ratio ( = warm colors) and voxels predictive of HC status are represented by negative CV ratio ( = cool colors). Reliability maps are thresholded at the 99th percentile for both positive and negative CV ratio.