IntroductionStructural MRI has long been used to characterize local morphological features of the human brain. Coordination patterns of the local morphological features among regions, however, are not well understood. Here, we constructed individual‐level morphological brain networks and systematically examined their topological organization and long‐term test–retest reliability under different analytical schemes of spatial smoothing, brain parcellation, and network type.MethodsThis study included 57 healthy participants and all participants completed two MRI scan sessions. Individual morphological brain networks were constructed by estimating interregional similarity in the distribution of regional gray matter volume in terms of the Kullback–Leibler divergence measure. Graph‐based global and nodal network measures were then calculated, followed by the statistical comparison and intra‐class correlation analysis.ResultsThe morphological brain networks were highly reproducible between sessions with significantly larger similarities for interhemispheric connections linking bilaterally homotopic regions. Further graph‐based analyses revealed that the morphological brain networks exhibited nonrandom topological organization of small‐worldness, high parallel efficiency and modular architecture regardless of the analytical choices of spatial smoothing, brain parcellation and network type. Moreover, several paralimbic and association regions were consistently revealed to be potential hubs. Nonetheless, the three studied factors particularly spatial smoothing significantly affected quantitative characterization of morphological brain networks. Further examination of long‐term reliability revealed that all the examined network topological properties showed fair to excellent reliability irrespective of the analytical strategies, but performing spatial smoothing significantly improved reliability. Interestingly, nodal centralities were positively correlated with their reliabilities, and nodal degree and efficiency outperformed nodal betweenness with respect to reliability.ConclusionsOur findings support single‐subject morphological network analysis as a meaningful and reliable method to characterize structural organization of the human brain; this method thus opens a new avenue toward understanding the substrate of intersubject variability in behavior and function and establishing morphological network biomarkers in brain disorders.

Accumulating evidence suggests that early improvement after two-week antidepressant treatment is predictive of later outcomes of patients with major depressive disorder (MDD); however, whether this early improvement is associated with baseline neural architecture remains largely unknown. Utilizing resting-state functional MRI data and graph-based network approaches, this study calculated voxel-wise degree centrality maps for 24 MDD patients at baseline and linked them with changes in the Hamilton Rating Scale for Depression (HAMD) scores after two weeks of medication. Six clusters exhibited significant correlations of their baseline degree centrality with treatment-induced HAMD changes for the patients, which were mainly categorized into the posterior default-mode network (i.e., the left precuneus, supramarginal gyrus, middle temporal gyrus, and right angular gyrus) and frontal regions. Receiver operating characteristic curve and logistic regression analyses convergently revealed excellent performance of these regions in discriminating the early improvement status for the patients, especially the angular gyrus (sensitivity and specificity of 100%). Moreover, the angular gyrus was identified as the optimal regressor as determined by stepwise regression. Interestingly, these regions possessed higher centrality than others in the brain (P < 10(-3)) although they were not the most highly connected hubs. Finally, we demonstrate a high reproducibility of our findings across several factors (e.g., threshold choice, anatomical distance, and temporal cutting) in our analyses. Together, these preliminary exploratory analyses demonstrate the potential of neuroimaging-based network analysis in predicting the early therapeutic improvement of MDD patients and have important implications in guiding earlier personalized therapeutic regimens for possible treatment-refractory depression.

Purpose To investigate the topological organization of functional brain networks in clinically isolated syndrome (CIS) and multiple sclerosis (MS) and examine the clinical relevance. Materials and Methods The institutional review board of Xuanwu Hospital, Capital Medical University, Beijing, People's Republic of China, approved the study, and written informed consent was obtained from each participant. Functional brain networks were constructed for 34 patients with MS, 34 patients with CIS, and 36 matched healthy control subjects by using resting-state functional magnetic resonance (MR) imaging data. Graph-based network measures were then calculated, followed by performance of between-group comparison and brain-behavior correlation analysis. Results Decreased whole-brain network efficiency was observed for patients with MS when compared with healthy control subjects, with intermediate values for the patients with CIS (P < .05, corrected). Regionally, both patient groups showed decreased nodal efficiency in the left rolandic operculum and insula and the superior temporal gyrus of the bilateral temporal pole (P < .05, corrected). Moreover, impaired functional connectivity involving the occipital, temporal, and frontal cortices and the insula was identified in MS (P = .007), and a similar but smaller component was observed in CIS (P = .032). The disrupted functional connectivity correlated with disease duration of the patients (r = 0.312, P = .011) and served to distinguish the patients from healthy control subjects with high performance (area under the curve for MS, 0.825 [P < .001]; area under the curve for CIS, 0.789 [P < .001]). These findings were reproducible across several different analytical strategies and were largely independent of white matter lesions and gray matter atrophy. Conclusion The results of this study demonstrate that disrupted network organization already emerges in CIS, with a lesser degree relative to MS. RSNA, 2016 Online supplemental material is available for this article.

Anxiety is a prevalent psychological disorder, in which the atypical expression of certain genes and the abnormality of amygdala are involved. Intermediate processes between genetic defects and anxiety, pathophysiological characteristics of neural network, remain unclear. Using behavioral task, two-photon cellular imaging and electrophysiology, we studied the characteristics of neural networks in basolateral amygdala and the influences of metabotropic glutamate receptor (mGluR) on their dynamics in DBA/2 mice showing anxiety-related genetic defects. Amygdala neurons in DBA/2 high anxiety mice express asynchronous activity and diverse excitability, and their GABAergic synapses demonstrate weak transmission, compared to those in low anxiety FVB/N mice. mGluR1,5 activation improves the anxiety-like behaviors of DBA/2 mice, synchronizes the activity of amygdala neurons and strengthens the transmission of GABAergic synapses. The activity asynchrony of amygdala neurons and the weakness of GABA synaptic transmission are associated with anxiety-like behavior.