Dataset Information

Neuroanatomical Features That Predict Response to Electroconvulsive Therapy Combined With Antipsychotics in Schizophrenia: A Magnetic Resonance Imaging Study Using Radiomics Strategy.

ABSTRACT:

Objective

Neuroimaging-based brain signatures may be informative in identifying patients with psychosis who will respond to antipsychotics. However, signatures that inform the electroconvulsive therapy (ECT) health care professional about the response likelihood remain unclear in psychosis with radiomics strategy. This study investigated whether brain structure-based signature in the prediction of ECT response in a sample of schizophrenia patients using radiomics approach.

Methods

This high-resolution structural magnetic resonance imaging study included 57 patients at baseline. After ECT combined with antipsychotics, 28 and 29 patients were classified as responders and non-responders. Features of gray matter were extracted and compared. The logistic regression model/support vector machine (LRM/SVM) analysis was used to explore the predictive performance.

Results

The regularized multivariate LRM accurately discriminated responders from non-responders, with an accuracy of 90.91%. The structural features were further confirmed in the validating data set, resulting in an accuracy of 87.59%. The accuracy of the SVM in the training set was 90.91%, and the accuracy in the validation set was 91.78%.

Conclusion

Our results support the possible use of structural brain feature-based radiomics as a potential tool for predicting ECT response in patients with schizophrenia undergoing antipsychotics, paving the way for utilization of markers in psychosis.

SUBMITTER: Xi YB

PROVIDER: S-EPMC7253706 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Publications

Neuroanatomical Features That Predict Response to Electroconvulsive Therapy Combined With Antipsychotics in Schizophrenia: A Magnetic Resonance Imaging Study Using Radiomics Strategy.

Xi Yi-Bin YB Cui Long-Biao LB Gong Jie J Fu Yu-Fei YF Wu Xu-Sha XS Guo Fan F Yang Xuejuan X Li Chen C Wang Xing-Rui XR Li Ping P Qin Wei W Yin Hong H

Frontiers in psychiatry 20200521

<h4>Objective</h4>Neuroimaging-based brain signatures may be informative in identifying patients with psychosis who will respond to antipsychotics. However, signatures that inform the electroconvulsive therapy (ECT) health care professional about the response likelihood remain unclear in psychosis with radiomics strategy. This study investigated whether brain structure-based signature in the prediction of ECT response in a sample of schizophrenia patients using radiomics approach.<h4>Methods</h4 ...[more]

PMID: 32528327

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Project description:Schizophrenia symptoms can be conceptualized in terms of a breakdown of a balance between 1) activating, retrieving, and matching stored representations to incoming information (semantic memory-based processing) and 2) fully integrating activated semantic representations with one another and with other types of representations to form a gestalt representation of meaning (semantic integration). Semantic memory-based processes are relatively more dependent on inferior frontal and temporal cortices, whereas particularly demanding integrative processes additionally recruit the dorsolateral prefrontal cortex (DLPFC) and sometimes parietal cortices. We used functional magnetic resonance imaging (fMRI) to determine whether the modulation of temporal/inferior frontal cortices and the DLPFC can be neuroanatomically dissociated in schizophrenia, as semantic integration demands increase. Integration demands were manipulated by varying the nature (concrete vs. abstract) and the congruity (incongruous vs. congruous) of words within sentences.Sixteen right-handed schizophrenia patients and 16 healthy volunteers, matched on age and parental socioeconomic status, underwent event-related fMRI scanning while they read sentences. Blood oxygen level dependent (BOLD) effects were contrasted to words within sentences that were 1) concrete versus abstract and 2) semantically incongruous versus congruous with their preceding contexts.In both contrasts, large networks mediating the activation and retrieval of verbal and imagistic representations were normally modulated in patients. However, unlike control subjects, patients failed to recruit the DLPFC, medial frontal and parietal cortices to incongruous (relative to congruous) sentences, and failed to recruit the DLPFC to concrete (relative to abstract) sentences.As meaning is built from language, schizophrenia patients demonstrate a neuroanatomical dissociation in the modulation of temporal/inferior frontal cortices and the DLPFC.

Project description:ObjectiveTo explore the diagnostic value of radiomics model based on magnetic resonance T2-weighted imaging for predicting the recurrence of acute pancreatitis.MethodsWe retrospectively collected 190 patients with acute pancreatitis (AP), including 122 patients with initial acute pancreatitis (IAP) and 68 patients with recurrent acute pancreatitis (RAP). At the same time, the clinical characteristics of the two groups were collected. They were randomly divided into training group and validation group in the ratio of 7:3. One hundred thirty-four cases in the training group, including 86 cases of IAP and 48 cases of RAP. There were 56 cases in the validation group, including 36 cases of IAP and 20 cases of RAP. Least absolute shrinkage and selection operator (LASSO) were used for feature screening. Logistic regression was used to establish the radiomics model, clinical model and combined model for predicting AP recurrence. The predictive ability of the three models was evaluated by the area under the curve (AUC). The recurrence risk in patients with AP was assessed using the nomogram.ResultsThe AUCs of radiomics model in training group and validation group were 0.804 and 0.788, respectively. The AUCs of the combined model in the training group and the validation group were 0.833 and 0.799, respectively. The AUCs of the clinical model in training group and validation group were 0.677 and 0.572, respectively. The sensitivities of the radiomics model, combined model, and clinical model were 0.646, 0.691, and 0.765, respectively. The specificities of the radiomics model, combined model, and clinical model were 0.791, 0.828, and 0.590, respectively. There was no significant difference in AUC between the radiomics model and the combined model for predicting RAP (p = 0.067). The AUCs of the radiomics model and combined model were greater than those of the clinical model (p = 0.008 and p = 0.007, respectively).ConclusionsRadiomics features based on magnetic resonance T2WI could be used as biomarkers to predict the recurrence of AP, and radiomics model and combined model can provide new directions for predicting recurrence of acute pancreatitis.

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Dataset Information

Neuroanatomical Features That Predict Response to Electroconvulsive Therapy Combined With Antipsychotics in Schizophrenia: A Magnetic Resonance Imaging Study Using Radiomics Strategy.

Objective

Methods

Results

Conclusion

Publications

Neuroanatomical Features That Predict Response to Electroconvulsive Therapy Combined With Antipsychotics in Schizophrenia: A Magnetic Resonance Imaging Study Using Radiomics Strategy.

Similar Datasets

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