Early Treatment Response In Schizophrenia Research Articles

Thalamus Radiomics-Based Disease Identification and Prediction of Early Treatment Response for Schizophrenia.

BackgroundEmerging evidence suggests structural and functional disruptions of the thalamus in schizophrenia, but whether thalamus abnormalities are able to be used for disease identification and prediction of early treatment response in schizophrenia remains to be determined. This study aims at developing and validating a method of disease identification and prediction of treatment response by multi-dimensional thalamic features derived from magnetic resonance imaging in schizophrenia patients using radiomics approaches.MethodsA total of 390 subjects, including patients with schizophrenia and healthy controls, participated in this study, among which 109 out of 191 patients had clinical characteristics of early outcome (61 responders and 48 non-responders). Thalamus-based radiomics features were extracted and selected. The diagnostic and predictive capacity of multi-dimensional thalamic features was evaluated using radiomics approach.ResultsUsing radiomics features, the classifier accurately discriminated patients from healthy controls, with an accuracy of 68%. The features were further confirmed in prediction and random forest of treatment response, with an accuracy of 75%.ConclusionOur study demonstrates a radiomics approach by multiple thalamic features to identify schizophrenia and predict early treatment response. Thalamus-based classification could be promising to apply in schizophrenia definition and treatment selection.

Baseline structural and functional magnetic resonance imaging predicts early treatment response in schizophrenia with radiomics strategy.

Multimodal neuroimaging features provide opportunities for accurate classification and personalized treatment options in the psychiatric domain. This study aimed to investigate whether brain features predict responses to the overall treatment of schizophrenia at the end of the first or a single hospitalization. Structural and functional magnetic resonance imaging (MRI) data from two independent samples (N=85 and 63, separately) of schizophrenia patients at baseline were included. After treatment, patients were classified as responders and non-responders. Radiomics features of gray matter morphology and functional connectivity were extracted using Least Absolute Shrinkage and Selection Operator. Support vector machine was used to explore the predictive performance. Prediction models were based on structural features (cortical thickness, surface area, gray matter regional volume, mean curvature, metric distortion, and sulcal depth), functional features (functional connectivity), and combined features. There were 12 features after dimensionality reduction. The structural features involved the right precuneus, cuneus, and inferior parietal lobule. The functional features predominately included inter-hemispheric connectivity. We observed a prediction accuracy of 80.38% (sensitivity: 87.28%; specificity 82.47%) for the model using functional features, and 69.68% (sensitivity: 83.96%; specificity: 72.41%) for the one using structural features. Our model combining both structural and functional features achieved a higher accuracy of 85.03%, with 92.04% responder and 80.23% non-responders to the overall treatment to be correctly predicted. These results highlight the power of structural and functional MRI-derived radiomics features to predict early response to treatment in schizophrenia. Prediction models of the very early treatment response in schizophrenia could augment effective therapeutic strategies.

Prediction of early response to overall treatment for schizophrenia: A functional magnetic resonance imaging study.

IntroductionTreatment response at an early stage of schizophrenia is of considerable value with regard to future management of the disorder; however, there are currently no biomarkers that can inform physicians about the likelihood of response.ObjectsWe aim to develop and validate regional brain activity derived from functional magnetic resonance imaging (fMRI) as a potential signature to predict early treatment response in schizophrenia.MethodsAmplitude of low‐frequency fluctuation (ALFF) was measured at the start of the first/single episode resulting in hospitalization. Inpatients were included in a principal dataset (n = 79) and a replication dataset (n = 44). Two groups of healthy controls (n = 87; n = 106) were also recruited for each dataset. The clinical response was assessed at discharge from the hospital. The predictive capacity of normalized ALFF in patients by healthy controls, ALFFratio, was evaluated based on diagnostic tests and clinical correlates.ResultsIn the principal dataset, responders exhibited increased baseline ALFF in the left postcentral gyrus/inferior parietal lobule relative to non‐responders. ALFFratio of responders before treatment was significantly higher than that of non‐responders (p < 0.001). The area under the receiver operating characteristic curve was 0.746 for baseline ALFFratio to distinguish responders from non‐responders, and the sensitivity, specificity, and accuracy were 72.7%, 68.6%, and 70.9%, respectively. Similar results were found in the independent replication dataset.ConclusionsBaseline regional activity of the brain seems to be predictive of early response to treatment for schizophrenia. This study shows that psycho‐neuroimaging holds promise for influencing the clinical treatment and management of schizophrenia.

Can Computerised Tomography be Used to Predict Early Treatment Response in Schizophrenia?

The relationship between early treatment response and CT variables was investigated in 50 consecutively admitted patients with DSM-III schizophrenia. Treatment response was defined as the absolute BPRS improvement during patients' hospital stay and was found to be significantly correlated (P < 0.05) with the frontal horn ratio (r = -0.36), ventricular ratio (r = -0.32), width of the third ventricle (r = -0.37), width of the frontal interhemispheric fissure (r = -0.33), and average width of the three largest cortical sulci (r = -0.46). According to the results of multiple regression analysis, 35% of the variance in the treatment response was accounted for by the average width of the three largest cortical sulci, the ventricular ratio and the frontal horn ratio. Thus, cerebral abnormalities associated with poor treatment response do not seem to be limited to one particular morphological site, but involve different brain regions.