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Background: Neuroimaging- and machine-learning-based brain-age prediction of schizophrenia is well established. However, the diagnostic significance and the effect of early medication on first-episode schizophrenia remains unclear. Aims: To explore whether predicted brain age can be used as a biomarker for schizophrenia diagnosis, and the relationship between clinical characteristics and brain-predicted age difference (PAD), and the effects of early medication on predicted brain age. Method: The predicted model was built on 523 diffusion tensor imaging magnetic resonance imaging scans from healthy controls. First, the brain-PAD of 60 patients with first-episode schizophrenia, 60 healthy controls and 21 follow-up patients from the principal data-set and 40 pairs of individuals in the replication data-set were calculated. Next, the brain-PAD between groups were compared and the correlations between brain-PAD and clinical measurements were analysed. Results: The patients showed a significant increase in brain-PAD compared with healthy controls. After early medication, the brain-PAD of patients decreased significantly compared with baseline (P < 0.001). The fractional anisotropy value of 31/33 white matter tract features, which related to the brain-PAD scores, had significantly statistical differences before and after measurements (P < 0.05, false discovery rate corrected). Correlation analysis showed that the age gap was negatively associated with the positive score on the Positive and Negative Syndrome Scale in the principal data-set (r = −0.326, P = 0.014). Conclusions: The brain age of patients with first-episode schizophrenia may be older than their chronological age. Early medication holds promise for improving the patient's brain ageing. Neuroimaging-based brain-age prediction can provide novel insights into the understanding of schizophrenia. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Background: In glioblastoma (GBM), promoter methylation of the DNA repair gene O-methylguanine-DNA methyltransferase (MGMT) is associated with beneficial chemotherapy.Purpose/hypothesis: To analyze radiomics features for utilizing the full potential of medical imaging as biomarkers of MGMT promoter methylation.Study Type: Retrospective.Population/subjects: In all, 98 GBM patients with known MGMT (48 methylated and 50 unmethylated tumors).Field Strength/sequence: 3.0T magnetic resonance (MR) images, containing T1 -weighted image (T1 WI), T2 -weighted image (T2 WI), and enhanced T1 WI.Assessment: A region of interest (ROI) of the tumor was delineated. A total of 1665 radiomics features were extracted and quantized, and were reduced using least absolute shrinkage and selection operator (LASSO) regularization.Statistical Testing: After the support vector machine construction, accuracy, sensitivity, and specificity were computed for different sequences. An independent validation cohort containing 20 GBM patients was utilized to further evaluate the radiomics model performance.Results: Radiomics features of T1 WI reached an accuracy of 67.54%. Enhanced T1 WI features reached an accuracy of 82.01%, while T2 WI reached an accuracy of 69.25%. The best classification system for predicting MGMT promoter methylation status originated from the combination of 36 T1 WI, T2 WI, and enhanced T1 WI images features, with an accuracy of 86.59%. Further validation on the independent cohort of 20 patients produced similar results, with an accuracy of 80%.Data Conclusion: Our results provide further evidence that radiomics MR features could predict MGMT methylation status in preoperative GBM. Multiple imaging modalities together can yield putative noninvasive biomarkers for the identification of MGMT.Level Of Evidence: 4 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018;47:1380-1387. [ABSTRACT FROM AUTHOR]

Rationale and Objectives: As a special movement disorder, hepatic myelopathy (HM) is characterized by spastic paraperesis and may be secondary to transjugular intrahepatic portosystemic shunt (TIPS). The prediction and diagnosis of HM is difficult due to largely unknown neuropathological underpinnings and a lack of specific biomarkers. We aimed to delve into the alterations in motor system of HM patients' brain and their potential clinical implication.Material and Methods: Twenty-three patients with HM and 23 without HM after TIPS and 24 demographically matched healthy controls were enrolled. High-spatial-resolution structural imaging and functional data at rest were acquired. Motor areas were included as seed regions for functional connectivity analysis. Then, we performed brain volume analysis.Results: We found decreased right supplementary motor area (SMA)-seeded functional connectivity with bilateral insula, thalamus and midbrain, left cerebellum and middle temporal gyrus, and right middle cingulate gyrus in HM compared to non-HM patients (p < 0.001). The right insula revealed decreased volume (p < 0.001), and white matter volume reduced in the right corona radiata beneath the right SMA (p < 0.001) in HM relative to non-HM patients. Furthermore, the strength of right SMA-seeded connectivity with insula was positively correlated with folic acid level in HM patients (r = 0.60, p = 0.03), showing an accuracy of 0.87 to distinguish HM from non-HM.Conclusion: Our study demonstrates the HM-specific dysconnectivity with an anatomical basis, and its correlation with laboratory findings and diagnostic value. Detecting these abnormalities might help to predict and diagnose post-TIPS HM. [ABSTRACT FROM AUTHOR]

Hepatic myelopathy is a complication seen in patients with chronic liver failure with physiologic or iatrogenic portosystemic shunting. The main symptom is progressive lower limb dyskinesia. The role of the brain motor control center in hepatic myelopathy is unknown. This study aimed to investigate the gray matter changes in patients with hepatic myelopathy secondary to transjugular intrahepatic portosystemic shunt and to examine their clinical relevance. This was a cross-sectional study. Twenty-three liver failure patients with hepatic myelopathy (hepatic myelopathy group), 23 liver failure patients without hepatic myelopathy (non-hepatic myelopathy group) after transjugular intrahepatic portosystemic shunt, and 23 demographically matched healthy volunteers were enrolled from March 2014 to November 2016 at Xijing Hospital, Air Force Military Medical University (Fourth Military Medical University), China. High-resolution magnetization-prepared rapid gradient-echo brain imaging was acquired. Group differences in regional gray matter were assessed using voxel-based morphometry analysis. The relationship between aberrant gray matter and motor characteristics was investigated. Results demonstrated that compared with the non-hepatic myelopathy group, gray matter volume abnormalities were asymmetric, with decreased volume in the left insula (P = 0.003), left thalamus (P = 0.029), left superior frontal gyrus (P = 0.006), and right middle cingulate cortex (P = 0.021), and increased volume in the right caudate nucleus (P = 0.017), corrected with open-source software. The volume of the right caudate nucleus in the hepatic myelopathy group negatively correlated with the lower limb clinical rating of the Fugl-Meyer Assessment (r = -0.53, P = 0.01). Compared with healthy controls, patients with and without hepatic myelopathy exhibited overall increased gray matter volume in both thalami, and decreased gray matter volume in both putamen, as well as in the globus pallidus, cerebellum, and vermis. The gray matter abnormalities we found predominantly involved motor-related regions, and may be associated with motor dysfunction. An enlarged right caudate nucleus might help to predict weak lower limb motor performance in patients with preclinical hepatic myelopathy after transjugular intrahepatic portosystemic shunt. This study was approved by the Ethics Committee of Xijing Hospital, Air Force Military Medical University (Fourth Military Medical University), China (approval No. 20140227-6) on February 27, 2014., Competing Interests: The authors declare that the article content was composed in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.