Cognitive subtypes in recent onset psychosis: distinct neurobiological fingerprints?

In schizophrenia, neurocognitive subtypes can be distinguished based on cognitive performance and they are associated with neuroanatomical alterations. We investigated the existence of cognitive subtypes in shortly medicated recent onset psychosis patients, their underlying gray matter volume patterns and clinical characteristics. We used a K-means algorithm to cluster 108 psychosis patients from the multi-site EU PRONIA (Prognostic tools for early psychosis management) study based on cognitive performance and validated the solution independently (N = 53). Cognitive subgroups and healthy controls (HC; n = 195) were classified based on gray matter volume (GMV) using Support Vector Machine classification. A cognitively spared (N = 67) and impaired (N = 41) subgroup were revealed and partially independently validated (Nspared = 40, Nimpaired = 13). Impaired patients showed significantly increased negative symptomatology (pfdr = 0.003), reduced cognitive performance (pfdr < 0.001) and general functioning (pfdr < 0.035) in comparison to spared patients. Neurocognitive deficits of the impaired subgroup persist in both discovery and validation sample across several domains, including verbal memory and processing speed. A GMV pattern (balanced accuracy = 60.1%, p = 0.01) separating impaired patients from HC revealed increases and decreases across several fronto-temporal-parietal brain areas, including basal ganglia and cerebellum. Cognitive and functional disturbances alongside brain morphological changes in the impaired subgroup are consistent with a neurodevelopmental origin of psychosis. Our findings emphasize the relevance of tailored intervention early in the course of psychosis for patients suffering from the likely stronger neurodevelopmental character of the disease.

[Successful treatment in the day clinic of the Clinic for Psychiatry and Psychotherapy of LMU Munich. Experienced changes from the patients' perspective]. / Erfolgreiche Behandlung in der Tagklinik ­ erlebte Veränderungen aus Sicht der Patienten : Evaluierung der Arbeit der allgemeinen Tagklinik der Klinik für Psychiatrie und Psychotherapie der LMU München.

BACKGROUND: In the day clinic of the Department of Psychiatry and Psychotherapy of the LMU Munich, a multimodal therapy concept is offered. The goals of treatment are, in addition to the reduction of symptoms, an expansion of everyday life skills and the professional and social reintegration of patients. METHOD: The effectiveness of the therapeutic work was evaluated over a period of just under 3 years. Both the therapist and the patients themselves assessed the success of the treatment. RESULTS: Subjective measures: The affective (BDI: 17.45 at the beginning vs. 9.38 at the end) and the anxiety symptoms (STAI: 49.51 at the beginning vs. 42.11 at the end) decreased. The quality of life (WHOQOL-Bref: 40.96 at the beginning vs. 62.50 at the end) increased. Performance (COPM: 3.81 at start vs. 6.91 at end) and satisfaction with it (COPM: 3.48 at start vs. 7.08 at end) improved. Physician's judgment: The global assessment of functioning (GAF: start of therapy 54.91 vs. end of therapy 68.14) and the clinical global impression (CGI: start of therapy 4.48 vs. treatment end 3.03) showed an improvement. All results were significant. CONCLUSION: The day clinic's treatment concept seems to work comprehensively, both in patients with depressive disorder and in patients with schizophrenic disease.

Classifying Schizophrenia Using Multimodal Multivariate Pattern Recognition Analysis: Evaluating the Impact of Individual Clinical Profiles on the Neurodiagnostic Performance.

Previous studies have shown that structural brain changes are among the best-studied candidate markers for schizophrenia (SZ) along with functional connectivity (FC) alterations of resting-state (RS) patterns. This study aimed to investigate effects of clinical and sociodemographic variables on the classification by applying multivariate pattern analysis (MVPA) to both gray matter (GM) volume and FC measures in patients with SZ and healthy controls (HC). RS and structural magnetic resonance imaging data (sMRI) from 74 HC and 71 SZ patients were obtained from a Mind Research Network COBRE dataset available via COINS (http://coins.mrn.org/dx). We used a MVPA framework using support-vector machines embedded in a repeated, nested cross-validation to generate a multi-modal diagnostic system and evaluate its generalizability. The dependence of neurodiagnostic performance on clinical and sociodemographic variables was evaluated. The RS classifier showed a slightly higher accuracy (70.5%) compared to the structural classifier (69.7%). The combination of sMRI and RS outperformed single MRI modalities classification by reaching 75% accuracy. The RS based moderator analysis revealed that the neurodiagnostic performance was driven by older SZ patients with an earlier illness onset and more pronounced negative symptoms. In contrast, there was no linear relationship between the clinical variables and neuroanatomically derived group membership measures. This study achieved higher accuracy distinguishing HC from SZ patients by fusing 2 imaging modalities. In addition the results of RS based moderator analysis showed that age of patients, as well as their age at the illness onset were the most important clinical features.

Prediction of outcome in the psychosis prodrome using neuroanatomical pattern classification.

To date, research into the biomarker-aided early recognition of psychosis has focused on predicting the transition likelihood of clinically defined individuals with different at-risk mental states (ARMS) based on structural (and functional) brain changes. However, it is currently unknown whether neuroimaging patterns could be identified to facilitate the individualized prediction of symptomatic and functional recovery. Therefore, we investigated whether cortical surface alterations analyzed by means of multivariate pattern recognition methods could enable the single-subject identification of functional outcomes in twenty-seven ARMS individuals. Subjects were dichotomized into 'good' vs. 'poor' outcome groups on average 4years after the baseline MRI scan using a Global Assessment of Functioning (GAF) threshold of 70. Cortical surface-based pattern classification predicted good (N=14) vs. poor outcome status (N=13) at follow-up with an accuracy of 82% as determined by nested leave-one-cross-validation. Neuroanatomical prediction involved cortical area reductions in superior temporal, inferior frontal and inferior parietal areas and was not confounded by functional impairment at baseline, or antipsychotic medication and transition status over the follow-up period. The prediction model's decision scores were correlated with positive and general symptom scores in the ARMS group at follow-up, whereas negative symptoms were not linked to predicted poorer functional outcome. These findings suggest that poorer functional outcomes are associated with non-resolving attenuated psychosis and could be predicted at the single-subject level using multivariate neuroanatomical risk stratification methods. However, the generalizability and specificity of the suggested prediction model should be thoroughly investigated in future large-scale and cross-diagnostic MRI studies.

Detecting the psychosis prodrome across high-risk populations using neuroanatomical biomarkers.

To date, the MRI-based individualized prediction of psychosis has only been demonstrated in single-site studies. It remains unclear if MRI biomarkers generalize across different centers and MR scanners and represent accurate surrogates of the risk for developing this devastating illness. Therefore, we assessed whether a MRI-based prediction system identified patients with a later disease transition among 73 clinically defined high-risk persons recruited at two different early recognition centers. Prognostic performance was measured using cross-validation, independent test validation, and Kaplan-Meier survival analysis. Transition outcomes were correctly predicted in 80% of test cases (sensitivity: 76%, specificity: 85%, positive likelihood ratio: 5.1). Thus, given a 54-month transition risk of 45% across both centers, MRI-based predictors provided a 36%-increase of prognostic certainty. After stratifying individuals into low-, intermediate-, and high-risk groups using the predictor's decision score, the high- vs low-risk groups had median psychosis-free survival times of 5 vs 51 months and transition rates of 88% vs 8%. The predictor's decision function involved gray matter volume alterations in prefrontal, perisylvian, and subcortical structures. Our results support the existence of a cross-center neuroanatomical signature of emerging psychosis enabling individualized risk staging across different high-risk populations. Supplementary results revealed that (1) potentially confounding between-site differences were effectively mitigated using statistical correction methods, and (2) the detection of the prodromal signature considerably depended on the available sample sizes. These observations pave the way for future multicenter studies, which may ultimately facilitate the neurobiological refinement of risk criteria and personalized preventive therapies based on individualized risk profiling tools.

Sensory and striatal areas integrate auditory and visual signals into behavioral benefits during motion discrimination.

For effective interactions with our dynamic environment, it is critical for the brain to integrate motion information from the visual and auditory senses. Combining fMRI and psychophysics, this study investigated how the human brain integrates auditory and visual motion into benefits in motion discrimination. Subjects discriminated the motion direction of audiovisual stimuli that contained directional motion signal in the auditory, visual, audiovisual, or no modality at two levels of signal reliability. Therefore, this 2 × 2 × 2 factorial design manipulated: (1) auditory motion information (signal vs noise), (2) visual motion information (signal vs noise), and (3) reliability of motion signal (intact vs degraded). Behaviorally, subjects benefited significantly from audiovisual integration primarily for degraded auditory and visual motion signals while obtaining near ceiling performance for "unisensory" signals when these were reliable and intact. At the neural level, we show audiovisual motion integration bilaterally in the visual motion areas hMT+/V5+ and implicate the posterior superior temporal gyrus/planum temporale in auditory motion processing. Moreover, we show that the putamen integrates audiovisual signals into more accurate motion discrimination responses. Our results suggest audiovisual integration processes at both the sensory and response selection levels. In all of these regions, the operational profile of audiovisual integration followed the principle of inverse effectiveness, in which audiovisual response suppression for intact stimuli turns into response enhancements for degraded stimuli. This response profile parallels behavioral indices of audiovisual integration, in which subjects benefit significantly from audiovisual integration only for the degraded conditions.