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Multiple lines of research support the dysconnectivity hypothesis of schizophrenia. However, findings on white matter (WM) alterations in patients with schizophrenia are widespread and non-specific. Confounding factors from magnetic resonance image (MRI) processing, clinical diversity, antipsychotic exposure, and substance use may underlie some of the variability. By application of refined methodology and careful sampling, we rectified common confounders investigating WM and symptom correlates in a sample of strictly antipsychotic-naïve first-episode patients with schizophrenia. Eighty-six patients and 112 matched controls underwent diffusion MRI. Using fixel-based analysis (FBA), we extracted fibre-specific measures such as fibre density and fibre-bundle cross-section. Group differences on fixel-wise measures were examined with multivariate general linear modelling. Psychopathology was assessed with the Positive and Negative Syndrome Scale. We separately tested multivariate correlations between fixel-wise measures and predefined psychosis-specific versus anxio-depressive symptoms. Results were corrected for multiple comparisons. Patients displayed reduced fibre density in the body of corpus callosum and in the middle cerebellar peduncle. Fibre density and fibre-bundle cross-section of the corticospinal tract were positively correlated with suspiciousness/persecution, and negatively correlated with delusions. Fibre-bundle cross-section of isthmus of corpus callosum and hallucinatory behaviour were negatively correlated. Fibre density and fibre-bundle cross-section of genu and splenium of corpus callosum were negative correlated with anxio-depressive symptoms. FBA revealed fibre-specific properties of WM abnormalities in patients and differentiated associations between WM and psychosis-specific versus anxio-depressive symptoms. Our findings encourage an itemised approach to investigate the relationship between WM microstructure and clinical symptoms in patients with schizophrenia., (© 2023. The Author(s).)

Background: Validated clinical prediction models of short-term remission in psychosis are lacking. Our aim was to develop a clinical prediction model aimed at predicting 4-6-week remission following a first episode of psychosis., Method: Baseline clinical data from the Athens First Episode Research Study was used to develop a Support Vector Machine prediction model of 4-week symptom remission in first-episode psychosis patients using repeated nested cross-validation. This model was further tested to predict 6-week remission in a sample of two independent, consecutive Danish first-episode cohorts., Results: Of the 179 participants in Athens, 120 were male with an average age of 25.8 years and average duration of untreated psychosis of 32.8 weeks. 62.9% were antipsychotic-naïve. Fifty-seven percent attained remission after 4 weeks. In the Danish cohort, 31% attained remission. Eleven clinical scale items were selected in the Athens 4-week remission cohort. These included the Duration of Untreated Psychosis, Personal and Social Performance Scale, Global Assessment of Functioning and eight items from the Positive and Negative Syndrome Scale. This model significantly predicted 4-week remission status (area under the receiver operator characteristic curve (ROC-AUC) = 71.45, P < .0001). It also predicted 6-week remission status in the Danish cohort (ROC-AUC = 67.74, P < .0001), demonstrating reliability., Conclusions: Using items from common and validated clinical scales, our model significantly predicted early remission in patients with first-episode psychosis. Although replicated in an independent cohort, forward testing between machine learning models and clinicians' assessment should be undertaken to evaluate the possible utility as a routine clinical tool., (© The Author(s) 2021. Published by Oxford University Press on behalf of the Maryland Psychiatric Research Center.All rights reserved. For permissions, please email: journals.permissions@oup.com.)

Aim: Growing evidence suggests that subtle white matter (WM) alterations are associated with psychopathology in individuals at ultra-high risk for psychosis (UHR). However, the longitudinal relationship between symptom progression and WM changes over time remains under-explored. Here, we examine associations between changes in clinical symptoms and changes in WM over six months in a large UHR-cohort., Methods: 110 UHR-individuals and 59 healthy controls underwent diffusion weighted imaging at baseline and after six months. Group × time effects on fractional anisotropy (FA) were tested globally and in four predefined regions of interest (ROIs) bilaterally using linear modelling with repeated measures. Correlations between the changes in clinical symptoms and FA changes in the ROIs were examined with Pearson's correlation. A partial least squares correlation-technique (PLS-C) explored multivariate associations between patterns of changes in psychopathology, regional FA and additional WM indices., Results: At baseline, UHR-individuals displayed significantly lower FA globally (p = 0.018; F = 12.274), in right superior longitudinal fasciculus (p = 0.02; Adj R 2  = 0.07) and in left uncinate fasciculus (p = 0.048; Adj R 2  = 0.058) compared to controls (corrected). We identified a group × time interaction in global FA and right superior longitudinal fasciculus, but the finding did not survive multiple comparisons. However, an increase of negative symptoms in UHR-individuals correlated with FA increase in right superior longitudinal fasciculus (p = 0.048, corrected, r = 0.357), and this finding was supported by the multivariate PLS-C., Conclusion: We found a positive correlation with a moderate effect between change in negative symptoms and FA change over 6 months in right superior longitudinal fasciculus. This link appeared mainly to reflect a subgroup of UHR-individuals, which already at baseline presented as vulnerable., (Copyright © 2021 The Authors. Published by Elsevier B.V. All rights reserved.)

Objective: Psychosis spectrum disorders are associated with cerebral changes, but the prognostic value and clinical utility of these findings are unclear. Here, we applied a multivariate statistical model to examine the predictive accuracy of global white matter fractional anisotropy (FA) for transition to psychosis in individuals at ultra-high risk for psychosis (UHR)., Methods: 110 UHR individuals underwent 3 Tesla diffusion-weighted imaging and clinical assessments at baseline, and after 6 and 12 months. Using logistic regression, we examined the reliability of global FA at baseline as a predictor for psychosis transition after 12 months. We tested the predictive accuracy, sensitivity and specificity of global FA in a multivariate prediction model accounting for potential confounders to FA (head motion in scanner, age, gender, antipsychotic medication, parental socioeconomic status and activity level). In secondary analyses, we tested FA as a predictor of clinical symptoms and functional level using multivariate linear regression., Results: Ten UHR individuals had transitioned to psychosis after 12 months (9%). The model reliably predicted transition at 12 months (χ 2  = 17.595, p = 0.040), accounted for 15-33% of the variance in transition outcome with a sensitivity of 0.70, a specificity of 0.88 and AUC of 0.87. Global FA predicted level of UHR symptoms (R 2  = 0.055, F = 6.084, p = 0.016) and functional level (R 2  = 0.040, F = 4.57, p = 0.036) at 6 months, but not at 12 months., Conclusion: Global FA provided prognostic information on clinical outcome and symptom course of UHR individuals. Our findings suggest that the application of prediction models including neuroimaging data can inform clinical management on risk for psychosis transition., (© 2021 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.)