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Accumulating evidence suggests that the brain is highly dynamic; thus, investigation of brain dynamics especially in brain connectivity would provide crucial information that stationary functional connectivity could miss. This study investigated temporal expressions of spatial modes within the default mode network (DMN), salience network (SN) and cognitive control network (CCN) using a reliable data-driven co-activation pattern (CAP) analysis in two independent data sets. We found enhanced CAP-to-CAP transitions of the SN in patients with MDD. Results suggested enhanced flexibility of this network in the patients. By contrast, we also found reduced spatial consistency and persistence of the DMN in the patients, indicating reduced variability and stability in individuals with MDD. In addition, the patients were characterized by prominent activation of mPFC. Moreover, further correlation analysis revealed that persistence and transitions of RCCN were associated with the severity of depression. Our findings suggest that functional connectivity in the patients may not be simply attenuated or potentiated, but just alternating faster or slower among more complex patterns. The aberrant temporal-spatial complexity of intrinsic fluctuations reflects functional diaschisis of resting-state networks as characteristic of patients with MDD.

Accumulating evidence suggested that the brain is highly dynamic, thus investigation of brain dynamics especially in brain connectivity would provide crucial information that stationary functional connectivity could miss. This study investigated temporal expressions of spatial modes within the default mode network (DMN), salience network (SN) and cognitive control network (CCN) using a reliable data-driven co-activation pattern (CAP) analysis. We found reduced number of CAPs, as well as transitions between different CAPs of the DMN and CCN, in patients with MDD. These results suggested reduced variability and flexibility of these two brain networks in the patients. By contrast, we also found increased number of CAPs of the SN in the patients, indicating enhanced variability of the SN in individuals with MDD. In addition, the patients were characterized by prominent activation of mPFC and insula. More importantly, we showed that our findings were robust and reproducible with another independent data set. Our findings suggest that functional connectivity in the patients may not be simply attenuated or potentiated, but just alternating faster or slower among more complex patterns. The aberrant temporal-spatial complexity of intrinsic fluctuations reflects functional diaschisis of resting-state networks as characteristic of patients with MDD.

Two different but interacting neural systems exist in the human brain: the task positive networks and task negative networks. One of the most important task positive networks is the central executive network (CEN), while the task negative network generally refers to the default mode network (DMN), which usually demonstrates task-induced deactivation. Although previous studies have clearly shown the association of both the CEN and DMN with major depressive disorder (MDD), how the causal interactions between these two networks change in depressed patients remains unclear. In the current study, 99 subjects (43 patients with MDD and 56 healthy controls) were recruited with their resting-state fMRI data collected. After data preprocessing, spectral dynamic causal modeling (spDCM) was used to investigate the causal interactions within and between the DMN and CEN. Group commonalities and differences in causal interaction patterns within and between the CEN and DMN in patients and controls were assessed by a parametric empirical Bayes (PEB) model. Both subject groups demonstrated significant effective connectivity between regions of the CEN and DMN. In particular, we detected inhibitory influences from the CEN to the DMN with node-level PEB analyses, which may help to explain the anticorrelations between these two networks consistently reported in previous studies. Compared with healthy controls, patients with MDD showed increased effective connectivity within the CEN and decreased connectivity from regions of the CEN to DMN, suggesting impaired control of the DMN by the CEN in these patients. These findings might provide new insights into the neural substrates of MDD.

BACKGROUND Noninvasive detection of isocitrate dehydrogenase (IDH) and TP53 mutations are meaningful for molecular stratification of lower-grade gliomas (LrGG). PURPOSE To explore potential MRI features reflecting IDH and TP53 mutations of LrGG, and propose a radiomics strategy for detecting them. STUDY TYPE Retrospective, radiomics. POPULATION/SUBJECTS A total of 103 LrGG patients were separated into development (n = 73) and validation (n = 30) cohorts. FIELD STRENGTH/SEQUENCE T1 -weighted (before and after contrast-enhanced), T2 -weighted, and fluid-attenuation inversion recovery images from 1.5T (n = 37) or 3T (n = 66) scanners. ASSESSMENT After data preprocessing, high-throughput features were derived from patients' volumes of interests of different sequences. The support vector machine-based recursive feature elimination (SVM-RFE) was adopted to find the optimal features for IDH and TP53 mutation detection. SVM models were trained and tested on development and validation cohort. The commonly used metric was used for assessing the efficiency. STATISTICAL TESTS One-way analysis of variance (ANOVA), chi-square, or Fisher's exact test were applied on clinical characteristics to confirm whether significant differences exist between three molecular subtypes decided by IDH and TP53 status. Intraclass correlation coefficients were calculated to assess the robustness of the radiomics features. RESULTS The constituent ratio of histopathologic subtypes was significantly different among three molecular subtypes (P = 0.017). SVM models for detecting IDH and TP53 mutation were established using 12 and 22 optimal features selected by SVM-RFE. The accuracies and area under the curves for IDH and TP53 mutations on the development cohort were 84.9%, 0.830, and 92.0%, 0.949, while on the validation cohort were 80.0%, 0.792, and 85.0%, 0.869, respectively. Furthermore, the stratified accuracies of three subtypes were 72.8%, 71.9%, and 70%, respectively. DATA CONCLUSION Using a radiomics approach integrating SVM model and multimodal MRI features, molecular subtype stratification of LGG patients was implemented through detecting IDH and TP53 mutations. The results suggested that the proposed approach has promising detecting efficiency and T2 -weighted image features are more important than features from other images. LEVEL OF EVIDENCE 3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018;48:916-926.

Previous studies have suggested that major depressive disorder was associated with topological properties of impaired white matter. However, most related studies only use one property of nerve fibers to construct whole-brain structural brain network. Considering white matter changes variously, We hypothesized whether the alternations of white matter topological properties could reflect different impairment of white matter integrity. In addition, it is still unknown whether impaired integrity of the white matter fiber tracts has relationship with abnormal topological properties in MDD. This study investigated the impaired white matter by using graph theoretic analyses in a cohort of 37 MDD patients and 38 matched control subjects. In addition, we further investigated fiber tracts differences in three interregional connectivity matrixes of significant different topological regions in MDD. Our graph theoretic analyses demonstrated that 7 different regions were observed for the local measures in patients with MDD compared with control groups. These regions were the central nodes of cortical-limbic network, frontal-cingulate network, default mode network (DMN), cognitive control network(CCN)and affective network (AN). In addition, two impaired white matter pathways which included inferior longitudinal fasciculus (ILF) and cingulum were observed in MDD using fiber tracts analysis. We speculate impaired integrity of ILF is due to the alternations in the number of axons or myelination. The results further demonstrated that the number of fiber tracts of anterior cingulum was associated with the depression scores in MDD.

Background Disturbances in emotion regulation are the hallmarks of major depressive disorder (MDD). The incapacity to control negative emotion in patients has been associated with abnormal hyperactivation of the limbic system and hypoactivation of the frontal cortex. The amygdala and orbital frontal cortex (OFC) are two critical regions of the emotion regulation neural systems. Methods This study investigated the anatomical basis of abnormal emotion regulation by tracking the fiber tracts connecting the amygdala and OFC. In addition, using dynamic casual modeling on resting-state fMRI data of 20 MDD patients and equivalent controls, we investigated the exact neural mechanism through which abnormal communications between these two nodes were mediated in MDD. Key results The results revealed disrupted white matter integrity of fiber tracts in MDD, suggesting that functional abnormalities were accompanied by underlying anatomical basis. We also detected a failure of inhibition of the OFC on the activity of the amygdala in MDD, suggesting dysconnectivity was mediated through "top-down" influences from the frontal cortex to the amygdala. Following 8 weeks of antidepressant treatment, the patients showed significant clinical improvement and normalization of the abnormal OFC-amygdala structural and effective connectivity in the left hemisphere. Conclusions & inferences Our findings suggest that pathways connecting these two nodes may be core targets of the antidepressant treatment. In particular, it raised the intriguing question: Does the reversal of structural markers of connectivity reflect a response to antidepressant medication or activity-dependent myelination following a therapeutic restoration of effective connectivity?

Noninvasive detection of isocitrate dehydrogenase (IDH) and TP53 mutations are meaningful for molecular stratification of lower-grade gliomas (LrGG).To explore potential MRI features reflecting IDH and TP53 mutations of LrGG, and propose a radiomics strategy for detecting them.Retrospective, radiomics.A total of 103 LrGG patients were separated into development (n = 73) and validation (n = 30) cohorts.TAfter data preprocessing, high-throughput features were derived from patients' volumes of interests of different sequences. The support vector machine-based recursive feature elimination (SVM-RFE) was adopted to find the optimal features for IDH and TP53 mutation detection. SVM models were trained and tested on development and validation cohort. The commonly used metric was used for assessing the efficiency.One-way analysis of variance (ANOVA), chi-square, or Fisher's exact test were applied on clinical characteristics to confirm whether significant differences exist between three molecular subtypes decided by IDH and TP53 status. Intraclass correlation coefficients were calculated to assess the robustness of the radiomics features.The constituent ratio of histopathologic subtypes was significantly different among three molecular subtypes (P = 0.017). SVM models for detecting IDH and TP53 mutation were established using 12 and 22 optimal features selected by SVM-RFE. The accuracies and area under the curves for IDH and TP53 mutations on the development cohort were 84.9%, 0.830, and 92.0%, 0.949, while on the validation cohort were 80.0%, 0.792, and 85.0%, 0.869, respectively. Furthermore, the stratified accuracies of three subtypes were 72.8%, 71.9%, and 70%, respectively.Using a radiomics approach integrating SVM model and multimodal MRI features, molecular subtype stratification of LGG patients was implemented through detecting IDH and TP53 mutations. The results suggested that the proposed approach has promising detecting efficiency and T3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018;48:916-926.