Radiomics Analysis of Quantitative Maps from Synthetic MRI for Predicting Grades and Molecular Subtypes of Diffuse Gliomas.

PURPOSE: To investigate the feasibility of using radiomics analysis of quantitative maps from synthetic MRI to preoperatively predict diffuse glioma grades, isocitrate dehydrogenase (IDH) subtypes, and 1p/19q codeletion status. METHODS: Data from 124 patients with diffuse glioma were used for analysis (n = 87 for training, n = 37 for testing). Quantitative T1, T2, and proton density (PD) maps were obtained using synthetic MRI. Enhancing tumour (ET), non-enhancing tumour and necrosis (NET), and peritumoral edema (PE) regions were segmented followed by manual fine-tuning. Features were extracted using PyRadiomics and then selected using Levene/T, BorutaShap and maximum relevance minimum redundancy algorithms. A support vector machine was adopted for classification. Receiver operating characteristic curve analysis and integrated discrimination improvement analysis were implemented to compare the performance of different radiomics models. RESULTS: Radiomics models constructed using features from multiple tumour subregions (ET + NET + PE) in the combined maps (T1 + T2 + PD) achieved the highest AUC in all three prediction tasks, among which the AUC for differentiating lower-grade and high-grade diffuse gliomas, predicting IDH mutation status and predicting 1p/19q codeletion status were 0.92, 0.95 and 0.86 respectively. Compared with those constructed on individual T1, T2, and PD maps, the discriminant ability of radiomics models constructed on the combined maps separately increased by 11, 17 and 10% in predicting glioma grades, 35, 52 and 19% in predicting IDH mutation status, and 16, 15 and 14% in predicting 1p/19q codeletion status (p < 0.05). CONCLUSION: Radiomics analysis of quantitative maps from synthetic MRI provides a new quantitative imaging tool for the preoperative prediction of grades and molecular subtypes in diffuse gliomas.

Aberrant resting-state co-activation network dynamics in major depressive disorder.

Major depressive disorder (MDD) is a globally prevalent and highly disabling disease characterized by dysfunction of large-scale brain networks. Previous studies have found that static functional connectivity is not sufficient to reflect the complicated and time-varying properties of the brain. The underlying dynamic interactions between brain functional networks of MDD remain largely unknown, and it is also unclear whether neuroimaging-based dynamic properties are sufficiently robust to discriminate individuals with MDD from healthy controls since the diagnosis of MDD mainly depends on symptom-based criteria evaluated by clinical observation. Resting-state functional magnetic resonance imaging (fMRI) data of 221 MDD patients and 215 healthy controls were shared by REST-meta-MDD consortium. We investigated the spatial-temporal dynamics of MDD using co-activation pattern analysis and made individual diagnoses using support vector machine (SVM). We found that MDD patients exhibited aberrant dynamic properties (such as dwell time, occurrence rate, transition probability, and entropy of Markov trajectories) in some transient networks including subcortical network (SCN), activated default mode network (DMN), de-activated SCN-cerebellum network, a joint network, activated attention network (ATN), and de-activated DMN-ATN, where some dynamic properties were indicative of depressive symptoms. The trajectories of other networks to deactivated DMN-ATN were more accessible in MDD patients. Subgroup analyses also showed subtle dynamic changes in first-episode drug-naïve (FEDN) MDD patients. Finally, SVM achieved preferable accuracies of 84.69%, 76.77%, and 88.10% in discriminating patients with MDD, FEDN MDD, and recurrent MDD from healthy controls with their dynamic metrics. Our findings reveal that MDD is characterized by aberrant dynamic fluctuations of brain network and the feasibility of discriminating MDD patients using dynamic properties, which provide novel insights into the neural mechanism of MDD.

Assessment of rhabdomyolysis-induced acute kidney injury with chemical exchange saturation transfer magnetic resonance imaging.

Background: Rhabdomyolysis (RM)-induced acute kidney injury (AKI) is a common renal disease with low survival rate and inadequate prognosis. In this study, we investigate the feasibility of chemical exchange saturation transfer (CEST) magnetic resonance imaging (MRI) for assessing the progression of RM-induced AKI in a mouse model. Methods: AKI was induced in C57BL/6J mice via intramuscular injection of 7.5 mL/kg glycerol (n=30). Subsequently, serum creatinine (SCr), blood urea nitrogen (BUN), and hematoxylin-eosin (HE) and Masson staining, were performed. Longitudinal CEST-MRI was conducted on days 1, 3, 7, 15, and 30 after AKI induction using a 7.0-T MRI system. CEST-MRI quantification parameters including magnetization transfer ratio (MTR), MTR asymmetric analysis (MTRasym), apparent amide proton transfer (APT*), and apparent relayed nuclear Overhauser effect (rNOE*) were used to investigate the feasibility of detecting RM-induced renal damage. Results: Significant increases of SCr and BUN demonstrated established AKI. The HE staining revealed various degrees of tubular damage, and Masson staining indicted an increase in the degree of fibrosis in the injured kidneys. Among CEST parameters, the cortical MTR presented a significant difference, and it also showed the best diagnostic performance for AKI [area under the receiver operating characteristic curve (AUC) =0.915] and moderate negative correlations with SCr and BUN. On the first day of renal damage, MTR was significantly reduced in cortex (22.7%±0.04%, P=0.013), outer stripe of outer medulla (24.7%±1.6%, P<0.001), and inner stripe of outer medulla (27.0%±1.5%, P<0.001) compared to the control group. Longitudinally, MTR increased steadily with AKI progression. Conclusions: The MTR obtained from CEST-MRI is sensitive to the pathological changes in RM-induced AKI, indicating its potential clinical utility for the assessment of kidney diseases.

Astrocyte dysfunction drives abnormal resting-state functional connectivity in depression.

Major depressive disorder (MDD) is a devastating mental disorder that affects up to 17% of the population worldwide. Although brain-wide network-level abnormalities in MDD patients via resting-state functional magnetic resonance imaging (rsfMRI) exist, the mechanisms underlying these network changes are unknown, despite their immense potential for depression diagnosis and management. Here, we show that the astrocytic calcium-deficient mice, inositol 1,4,5-trisphosphate-type-2 receptor knockout mice (Itpr2-/- mice), display abnormal rsfMRI functional connectivity (rsFC) in depression-related networks, especially decreased rsFC in medial prefrontal cortex (mPFC)-related pathways. We further uncover rsFC decreases in MDD patients highly consistent with those of Itpr2-/- mice, especially in mPFC-related pathways. Optogenetic activation of mPFC astrocytes partially enhances rsFC in depression-related networks in both Itpr2-/- and wild-type mice. Optogenetic activation of the mPFC neurons or mPFC-striatum pathway rescues disrupted rsFC and depressive-like behaviors in Itpr2-/- mice. Our results identify the previously unknown role of astrocyte dysfunction in driving rsFC abnormalities in depression.

Longitudinal study of irradiation-induced brain functional network alterations in patients with nasopharyngeal carcinoma.

BACKGROUND: To investigate radiotherapy (RT)-related brain network changes in patients with nasopharyngeal carcinoma (NPC) over time and develop least absolute shrinkage and selection operator (LASSO)-based multivariable normal tissue complication probability (NTCP) models to predict RT-related brain network changes. METHODS: 36 NPC patients were followed up at four timepoints: baseline, within 3 months (acute), 6 months (subacute), and 12 months (delayed) post-RT. 15 comparable healthy controls (HCs) were finally included and followed up in parallel. Functional neuroimaging data, dose-volume parameters of bilateral temporal lobes and Montreal Cognitive Assessment (MoCA) were acquired. Graph theoretical analysis and mixed-design analysis of variance were performed to investigate how the brain global and nodal changes were affected by RT. Multivariate logistic regression NTCP models were developed. LASSO with nested cross-validation strategy was used to select features. The relationships between network changes and MoCA changes were also examined. RESULTS: Significant changes were detected in nodal efficiency (NE) in NPC patients but not in HCs over time. Altered NE was distributed in the bilateral frontal, temporal lobes and the right insula, which showed a "decrease-increase/recovery" pattern over time. Among all models, the model for predicting NE changes of STG.R showed a relatively good performance (area under the receiver operating curve: 0.68), and D20cc and V20 to right temporal lobe outperformed in this model. CONCLUSION: Our findings indicate that RT-induced brain injury begin at the acute period and follow a recovery over time. Furthermore, our study presents prediction models for brain dysfunction based on the dosimetric and clinical parameters.