Baseline white matter function predicts short-term treatment response in first-episode schizophrenia.

BACKGROUND AND PURPOSE: The detection and characterization of functional activities in the gray matter of schizophrenia (SZ) have been widely explored. However, the relationship between resting-state functional signals in the white matter of first-episode SZ and short-term treatment response remains unclear. METHODS: Thirty-six patients with first-episode SZ and 44 matched healthy controls were recruited in this study. Patients were classified as nonresponders and responders based on response to antipsychotic medication during a single hospitalization. The fractional amplitude of low-frequency fluctuation (fALFF), regional homogeneity (ReHo), and functional connectivity (FC) of white matter were calculated. The relationships between functional changes and clinical features were analyzed. In addition, voxel-based morphometry was performed to analyze the white matter volume. RESULTS: One-way analysis of variance showed significant differences of fALFF and ReHo in the left posterior thalamic radiation and left cingulum (hippocampus) in the patient group, and the areas were regarded as seeds. The FC was calculated between seeds and other white matter networks. Compared with responders, nonresponders showed significantly increased FC between the left cingulum (hippocampus) and left posterior thalamic radiation, splenium of corpus callosum, and left tapetum, and were associated with the changes of clinical assessment. However, there was no difference in white matter volume between groups. CONCLUSION: Our work provides a novel insight that psycho-neuroimaging-based white matter function holds promise for influencing the clinical diagnosis and treatment of SZ.

Abnormal dynamic functional connectivity after sleep deprivation from temporal variability perspective.

Sleep deprivation (SD) is very common in modern society and regarded as a potential causal mechanism of several clinical disorders. Previous neuroimaging studies have explored the neural mechanisms of SD using magnetic resonance imaging (MRI) from static (comparing two MRI sessions [one after SD and one after resting wakefulness]) and dynamic (using repeated MRI during one night of SD) perspectives. Recent SD researches have focused on the dynamic functional brain organization during the resting-state scan. Our present study adopted a novel metric (temporal variability), which has been successfully applied to many clinical diseases, to examine the dynamic functional connectivity after SD in 55 normal young subjects. We found that sleep-deprived subjects showed increased regional-level temporal variability in large-scale brain regions, and decreased regional-level temporal variability in several thalamus subregions. After SD, participants exhibited enhanced intra-network temporal variability in the default mode network (DMN) and increased inter-network temporal variability in numerous subnetwork pairs. Furthermore, we found that the inter-network temporal variability between visual network and DMN was negative related with the slowest 10% respond speed (ß = -.42, p = 5.57 × 10-4 ) of the psychomotor vigilance test after SD following the stepwise regression analysis. In conclusion, our findings suggested that sleep-deprived subjects showed abnormal dynamic brain functional configuration, which provides new insights into the neural underpinnings of SD and contributes to our understanding of the pathophysiology of clinical disorders.

The correlation between dynamic functional architecture and response to electroconvulsive therapy combined with antipsychotics in schizophrenia.

Attempts to determine why some patients respond to electroconvulsive therapy (ECT) are valuable in schizophrenia. Schizophrenia is associated with aberrant dynamic functional architecture, which might impact the efficacy of ECT. We aimed to explore the relationship between pre-treatment temporal variability and ECT acute efficacy. Forty-eight patients with schizophrenia and 30 healthy controls underwent functional magnetic resonance imaging to examine whether patterns of temporary variability of functional architecture differ between high responders (HR) and low responders (LR) at baseline. Compared with LR, HR exhibited significantly abnormal temporal variability in right inferior front gyrus (IFGtriang.R), left temporal pole (TPOsup.L) and right middle temporal gyrus (MTG.R). In the pooled patient group, ∆PANSS was correlated with the temporal variability of these regions. Patients with schizophrenia with a distinct dynamic functional architecture appear to reveal differential response to ECT. Our findings provide not only an understanding of the neural functional architecture patterns that are found in schizophrenia but also the possibility of using these measures as moderators for ECT selection.

Differentiation of Neoplastic and Non-neoplastic Intracranial Enhancement Lesions Using Three-Dimensional Pseudo-Continuous Arterial Spin Labeling.

Background and Purpose: It is sometimes difficult to effectively distinguish non-neoplastic from neoplastic intracranial enhancement lesions using conventional magnetic resonance imaging (MRI). This study aimed to evaluate the diagnostic performance of three-dimensional pseudo-continuous arterial spin labeling (3D-pCASL) to differentiate non-neoplastic from neoplastic enhancement lesions intracranially. Materials and Methods: This prospective study included thirty-five patients with high-grade gliomas (HGG), twelve patients with brain metastasis, and fifteen non-neoplastic patients who underwent conventional, contrast enhancement and 3D-pCASL imaging at 3.0-T MR; all lesions were significantly enhanced. Quantitative parameters including cerebral blood flow (CBF) and relative cerebral blood flow (rCBF) were compared between neoplastic and non-neoplastic using Student's t-test. In addition, the area under the receiver operating characteristic (ROC) curve (AUC) was measured to assess the differentiation diagnostic performance of each parameter. Results: The non-neoplastic group demonstrated significantly lower rCBF values of lesions and perilesional edema compared with the neoplastic group. For the ROC analysis, both relative cerebral blood flow of lesion (rCBF-L) and relative cerebral blood flow of perilesional edema (rCBF-PE) had good diagnostic performance for discriminating non-neoplastic from neoplastic lesions, with an AUC of 0.994 and 0.846, respectively. Conclusion: 3D-pCASL may contribute to differentiation of non-neoplastic from neoplastic lesions.

Altered functional connectivity of the dentate nuclei in patients with schizophrenia.

Dentate nuclei (DN) are vital structures in the anatomical circuits that link the cerebellum to the cerebrum. However, the characteristics of DN functional connectivity (FC) in schizophrenia remain largely unknown. In this study, we investigated the FC of the DN in patients with schizophrenia and examined their possible clinical correlates using resting-state functional magnetic imaging data. We found that the patient group had greater DN FC with the parietal lobe (e.g., postcentral gyrus and superior parietal lobule) and less DN FC with the prefrontal cortex (e.g., superior frontal gyrus), posterior cingulate cortex, and regional cerebellum (e.g., vermis 4-5 and crus I) than did the control group. Furthermore, some abnormal connectivities of the DN with these regions significantly correlated with psychiatric symptoms. These results suggest that the DN circuits are disturbed and may participate in the pathophysiology of schizophrenia.

Triple network hypothesis-related disrupted connections in schizophrenia: A spectral dynamic causal modeling analysis with functional magnetic resonance imaging.

OBJECTIVE: The symptom-related neurobiology characteristic of schizophrenia in the brain from a network perspective is still poorly understood, leading to a lack of potential biologically-based markers and difficulty identifying therapeutic targets. We aim to test the dysregulated cross-network interactions among the Salience Network (SN), Central Executive Network (CEN) and Default Mode Network (DMN) and how they contributed to different symptoms in schizophrenia patients. METHODS: We examined network interactions among the SN, CEN and DMN in 76 patients with schizophrenia vs. 80 well-matched controls using dynamic causal modeling (DCM). We further analyzed the relation between network dynamics and Positive and Negative Syndrome Scale (PANSS). RESULTS: We observed that the DMN, CEN and SN across healthy controls and schizophrenia patients showed several similarities within or between-network pattern in the resting state. Comparing schizophrenia to controls, SN-centered cross-network interactions were most significantly reduced. Crucially, the strength of connections from CEN subnetwork 1 to DMN subnetwork 1 was positively correlated with the Positive Score of PANSS. The connection from the DMN subnetwork 2 to CEN subnetwork 2 was negatively correlated with the Negative Score of PANSS. CONCLUSIONS: Our study provides strong evidence for the dysregulation among SN, CEN and DMN in a triple-network perspective in schizophrenia. The connection between DMN and CEN could be clinically-relevant neurobiological signature of schizophrenia symptoms. Our study indicated that the description of brain triple network hypothesis could be a novel and possible bio-marker for schizophrenia.

Neuroimaging-based brain-age prediction of first-episode schizophrenia and the alteration of brain age after early medication.

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.

Altered resting-state networks may explain the executive impairment in young health immigrants into high-altitude area.

Executive function is a complex involving multiple advanced brain functions like planning, working memory, mental flexibility and psychomotor. Previous researches indicated that executive function may be impaired after acute or chronic high-altitude exposure, while the underlying neurobiological mechanism has not been totally clarified. In the present study, based on 69 young healthy volunteers immigrating to high-altitude, Stroop test was utilized to identify the potential impairment of executive function after two-year high-altitude exposure while resting-state functional MRI (rs-fMRI) technology was employed to observe the alteration of resting-state networks. Stroop test indicated that the subjects experienced significantly lower accuracies and prolonged responding time after two-year exposure. Resting-state network analysis displayed a significantly decreased degree of co-activation within the left/right frontoparietal network, sensorimotor network, and auditory network after exposure. In the frontoparietal network, decreased co-activation intensity was found in left angular gyrus, while in the right frontoparietal network, decreased co-activation intensity was found in left precentral gyrus and postcentral gyrus. Similarly, as for sensorimotor and auditory network, left middle frontal gyrus and left superior temporal gyrus was identified to have decreased co-activation, respectively. Moreover, the responding delays in ST (part II) were negatively correlated with the signal intensity alteration of the right frontoparietal network. All these evidences indicated that the high-altitude exposure induced alteration in above resting state networks may be the functional basis of executive control impairment.

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.

Alteration of Brain Gray Matter Density After 24 h of Sleep Deprivation in Healthy Adults.

It has been reported that one night of acute sleep deprivation (SD) could induce brain structural changes at the synaptic and neuronal levels in animal studies, and could lead to white matter microstructure and cortical thickness change in human neuroimaging studies. In this study, we focused on changes of brain gray matter density (GMD) after one night of acute SD, which has not been explored previously. Twenty-three normal young participants completed the experiment. Each participant underwent twice T1-weighted structural image scanning with one at 08:00 after normal sleep [resting wakeful (RW)] and the other at 08:00 after 24 h of SD. Using voxel-based morphometry (VBM) analysis by FSL-VBM software, we compared GMD between RW and SD. In addition, the gray matter volume (GMV) and cortical thickness (CT) were also calculated based on volumetric and surface measures with FreeSurfer software. The psychomotor vigilance test (PVT) and the Karolinska Sleepiness Scale (KSS) were performed and evaluated for correlation analysis with GMD, GMV, and CT of the significant regions. Our results showed that the GMD in the right frontal pole (FP), right superior frontal gyrus (SFG), and right middle frontal gyrus significantly increased and GMV and CT in the right temporal pole (TP) significantly decreased after 24 h of acute SD. SD-induced changes in GMD in the right middle frontal gyrus were positively correlated with the changes of KSS scores (Spearman's correlation r = 0.625, p = 0.0014, Bonferroni correction with p < 0.05/25). Taken together, our findings suggested that one night of acute SD could induce substantial brain structure changes and the alterations in GMD in the right middle frontal gyrus (MFG) might be implicated in sleepiness after SD.

Alteration in topological properties of brain functional network after 2-year high altitude exposure: A panel study.

INTRODUCTION: High altitude (HA) exposure leads to cognitive impairment while the underlying mechanism is still unclear. Brain functional network is crucial for advanced functions, and its alteration is implicated in cognitive decline in multiple diseases. The aim of current study was to investigate the topological changes in HA-exposed brain functional network. METHODS: Based on Shaanxi-Tibet immigrant cohort, neuropsychological tests and resting-state functional MRI were applied to evaluate the participants' cognitive function and functional connection (FC) changes, respectively. GRETNA toolbox was used to construct the brain functional network. The gray matter was parcellated into 116 anatomically defined regions according to Automated Anatomical Labeling atlas. Subsequently, the mean time series for each of the 116 regions were extracted and computed for Pearson's correlation coefficients. The relation matrix was further processed and seen as brain functional network. Correlation between functional network changes and neuropsychological results was also examined. RESULTS: The cognitive performance was impaired by HA exposure as indicated by neuropsychological test. HA exposure led to alterations of degree centrality and nodal efficiency in multiple brain regions. Moreover, two subnetworks were extracted in which the FCs significantly decreased after exposure. In addition, the alterations in FCs within above two subnetworks were significantly correlated with changes of memory and reaction time. CONCLUSIONS: Our results suggest that HA exposure modulates the topological property of functional network and FCs of some important regions, which may impair the attention, perception, memory, motion ignition, and modulation processes, finally decreasing cognitive performance in neuropsychological tests.

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

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.

Grading of Glioma: combined diagnostic value of amide proton transfer weighted, arterial spin labeling and diffusion weighted magnetic resonance imaging.

BACKGROUND: To investigate the ability of amide proton transfer (APT) weighted magnetic resonance imaging (MRI), arterial spin labeling (ASL), diffusion weighted imaging (DWI) and the combination for differentiating high-grade gliomas (HGGs) from low-grade gliomas (LGGs). METHODS: Twenty-seven patients including nine LGGs and eighteen HGGs underwent conventional, APT, ASL and DWI MRI with a 3.0-T MR scanner. Histogram analyses was performed and quantitative parameters including mean apparent diffusion coefficient (ADC mean), 20th-percentile ADC (ADC 20th), mean APT (APT mean), 90th-percentile APT (APT 90th), relative mean cerebral blood flow (rCBF mean) and relative 90th-percentile CBF (rCBF 90th) were compared between HGGs and LGGs. The diagnostic performance was evaluated with receiver operating characteristic (ROC) analysis of each parameter and their combination. Correlations were analyzed among the MRI parameters and Ki-67. RESULTS: The APT values were significantly higher in the HGGs compared to the LGGs (p <  0.005), whereas ADC values were significantly lower in HGGs than LGGs (P <  0.0001). The ADC 20th and APT mean had higher discrimination abilities compared with other single parameters, with the area under the ROC curve (AUC) of 0.877 and 0.840. Adding ADC parameter, the discrimination ability of APT and rCBF significantly improved. The ADC was negatively correlated with the APT and rCBF value, respectively, while APT value was positively correlated with rCBF value. Significant correlations between ADC values and Ki-67 were also observed. CONCLUSIONS: APT and DWI are valuable in differentiating HGGs from LGGs. The combination of APT, DWI and ASL imaging could improve the ability for discriminating HGGs from LGGs.

Learning curve of double-guidewire technique by trainees during hands-on endoscopic retrograde cholangiopancreatography training.

BACKGROUND AND AIMS: Double-guidewire technique (DWT) has been successfully performed by experts in difficult biliary cannulation as an advanced technique. This study aimed to define the learning curve and safety of DWT by trainees during hands-on endoscopic retrograde cholangiopancreatography (ERCP) training. METHODS: Patients were eligible for inclusion in the study if the biliary cannulation was difficult and the pancreatic duct was inadvertently cannulated. DWT was performed by two trainees randomly under trainers' guidance. The primary outcome was the success rate of DWT biliary cannulation of trainees. Cumulative sum analysis was used to generate visual learning curves. RESULTS: A total of 60 patients with difficult cannulation were enrolled. The main indications for ERCP were common bile duct stones (65%) and biliary stricture (31.7%). The learning curve analysis showed that to achieve a 70% rate of successful DWT, 12 procedures were needed for trainee A and 15 for trainee B. Higher targeted success rate of DWT could be achieved if the number of DWT procedures increased. Compared with the early stage of learning DWT (case 1 to 15 for each trainee), trainees had significantly higher DWT success rate in the late stage (36.7% [11/30] vs 80% [24/30], P = 0.001). The final success rate of cannulation was 98.3% (59/60). The overall rate of post-ERCP pancreatitis and adverse events was 6.7% (4/60) and 8.3% (5/60), respectively. CONCLUSIONS: Double-guidewire technique was safely performed by two novel trainees during hands-on ERCP training. Fifteen procedures may be enough for trainees to achieve the competency of performing DWT. (Clinicaltrials.gov number: NCT03707613).

Predicting responses to electroconvulsive therapy in schizophrenia patients undergoing antipsychotic treatment: Baseline functional connectivity among regions with strong electric field distributions.

This study explored imaging predictors of electroconvulsive therapy (ECT) outcome in schizophrenia patients based on pre-treatment functional connectivity (FC) within regions with strong ECT electric fields distribution. Forty-seven patients received standard antipsychotic drugs combined with ECT as well as two brain imaging sessions. Regions of interest (ROI) with strong electric field distribution were determined by ECT simulation. Using baseline functional connectivity between ROIs, a model was constructed to predict the percentage reduction of Positive and Negative Syndrome Scale (PANSS) scores. The strong electric fields were distributed in the orbital prefrontal lobe, medial temporal lobe, and other parts of the temporal lobe. Ten functional connectivity features within the electric field distribution areas showed a predictive ability for ECT outcome. The correlation coefficient between the predictive and real values of cross-validation was 0.7165. Among the predictive features, ECT induced a significant decrease in functional connectivity between the right amygdala and the left hippocampus. These results suggest that pretreatment functional connectivity patterns in brain regions with strong electric field distributions during ECT could be potential predictors of the efficacy of ECT augmentation in schizophrenia. These findings may help to improve individualized clinical treatment in the future.

MRI-based radiomics nomogram may predict the response to induction chemotherapy and survival in locally advanced nasopharyngeal carcinoma.

OBJECTIVES: To establish and validate a radiomics nomogram for prediction of induction chemotherapy (IC) response and survival in nasopharyngeal carcinoma (NPC) patients. METHODS: One hundred twenty-three NPC patients (100 in training and 23 in validation cohort) with multi-MR images were enrolled. A radiomics nomogram was established by integrating the clinical data and radiomics signature generated by support vector machine. RESULTS: The radiomics signature consisting of 19 selected features from the joint T1-weighted (T1-WI), T2-weighted (T2-WI), and contrast-enhanced T1-weighted MRI images (T1-C) showed good prognostic performance in terms of evaluating IC response in two cohorts. The radiomics nomogram established by integrating the radiomics signature with clinical data outperformed clinical nomogram alone (C-index in validation cohort, 0.863 vs 0.549; p < 0.01). Decision curve analysis demonstrated the clinical utility of the radiomics nomogram. Survival analysis showed that IC responders had significant better PFS (progression-free survival) than non-responders (3-year PFS 84.81% vs 39.75%, p < 0.001). Low-risk groups defined by radiomics signature had significant better PFS than high-risk groups (3-year PFS 76.24% vs 48.04%, p < 0.05). CONCLUSIONS: Multiparametric MRI-based radiomics could be helpful for personalized risk stratification and treatment in NPC patients receiving IC. KEY POINTS: • MRI Radiomics can predict IC response and survival in non-endemic NPC. • Radiomics signature in combination with clinical data showed excellent predictive performance. • Radiomics signature could separate patients into two groups with different prognosis.

The Prevalence of Cavum Septum Pellucidum in Mental Disorders Revealed by MRI: A Meta-Analysis.

OBJECTIVE: The prevalence of cavum septum pellucidum (CSP) in mental disorders, particularly schizophrenia spectrum disorders and mood disorders, remains uncertain. The authors used a meta-analytical approach to determine the prevalence of CSP in mental disorders and to compare these with the prevalence of CSP in psychiatrically healthy comparison subjects. METHODS: PubMed and Embase were systematically searched for relevant articles published as of January 9, 2018. After a quality assessment of individual studies using the Newcastle-Ottawa Scale, a random-effects model within Stata statistical software was used to synthesize 25 eligible studies that included 2,392 patients with mental disorders and 1,445 psychiatrically healthy comparison subjects. RESULTS: The prevalence of CSP of any size and large CSP was found to be significantly higher in individuals with mental disorders compared with healthy comparison subjects, and the prevalence of CSP in schizophrenia spectrum and mood disorders did not differ between the groups. CONCLUSIONS: The meta-regression with predefined covariance indicated that imaging parameters were not associated with the heterogeneity among original studies; however, the mean age of enrolled subjects was identified as a possible source of heterogeneity. No publication bias was found.

Predicting response to electroconvulsive therapy combined with antipsychotics in schizophrenia using multi-parametric magnetic resonance imaging.

Electroconvulsive therapy (ECT) has been shown to be effective in schizophrenia, particularly when rapid symptom reduction is needed or in cases of resistance to drug treatment. However, there are no markers available to predict response to ECT. Here, we examine whether multi-parametric magnetic resonance imaging (MRI)-based radiomic features can predict response to ECT for individual patients. A total of 57 treatment-resistant schizophrenia patients, or schizophrenia patients with an acute episode or suicide attempts were randomly divided into primary (42 patients) and test (15 patients) cohorts. We collected T1-weighted structural MRI and diffusion MRI for 57 patients before receiving ECT and extracted 600 radiomic features for feature selection and prediction. To predict a continuous improvement in symptoms (ΔPANSS), the prediction process was performed with a support vector regression model based on a leave-one-out cross-validation framework in primary cohort and was tested in test cohort. The multi-parametric MRI-based radiomic model, including four structural MRI feature from left inferior frontal gyrus, right insula, left middle temporal gyrus and right superior temporal gyrus respectively and six diffusion MRI features from tracts connecting frontal or temporal gyrus possessed a low root mean square error of 15.183 in primary cohort and 14.980 in test cohort. The Pearson's correlation coefficients between predicted and actual values were 0.671 and 0.777 respectively. These results demonstrate that multi-parametric MRI-based radiomic features may predict response to ECT for individual patients. Such features could serve as prognostic neuroimaging biomarkers that provide a critical step toward individualized treatment response prediction in schizophrenia.

Voxel-based morphometry results in first-episode schizophrenia: a comparison of publicly available software packages.

Investigations of brain structure in schizophrenia using magnetic resonance imaging (MRI) have identified variations in regional grey matter (GM) volume throughout the brain but the results are mixed. This study aims to investigate whether the inconsistent voxel-based morphometry (VBM) findings in schizophrenia are due to the use of different software packages. T1 MRI images were obtained from 86 first-episode schizophrenia (FESZ) patients and 86 age- and gender-matched Healthy controls (HCs). VBM analysis was carried out using FMRIB software library (FSL) 5.0 and statistical parametric mapping 8 (SPM8). All images were processed using the default parameter settings as provided by these software packages. FSL-VBM revealed widespread GM volume reductions in FESZ patients compared with HCs, however, for SPM-VBM, only increased and circumscribed GM volume changes were found, both software revealed increased GM volume within cerebellum. Significant correlations between Positive and Negative Syndrome Scale (PANSS) and GM volume were mainly found in frontal regions. Algorithms of GM tissue segmentation, image registration and statistical strategies might contribute to these disparate results.