Introduction: Structural 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.Methods: This 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.Results: The 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.Conclusions: Our 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.

Morphological brain networks, in particular those at the individual level, have become an important approach for studying the human brain connectome; however, relevant methodology is far from being well-established in their formation, description and reproducibility. Here, we extended our previous study by constructing and characterizing single-subject morphological similarity networks from brain volume to surface space and systematically evaluated their reproducibility with respect to effects of different choices of morphological index, brain parcellation atlas and similarity measure, sample size-varying stability and test-retest reliability. Using the Human Connectome Project dataset, we found that surface-based single-subject morphological similarity networks shared common small-world organization, high parallel efficiency, modular architecture and bilaterally distributed hubs regardless of different analytical strategies. Nevertheless, quantitative values of all interregional similarities, global network measures and nodal centralities were significantly affected by choices of morphological index, brain parcellation atlas and similarity measure. Moreover, the morphological similarity networks varied along with the number of participants and approached stability until the sample size exceeded ~70. Using an independent test-retest dataset, we found fair to good, even excellent, reliability for most interregional similarities and network measures, which were also modulated by different analytical strategies, in particular choices of morphological index. Specifically, fractal dimension and sulcal depth outperformed gyrification index and cortical thickness, higher-resolution atlases outperformed lower-resolution atlases, and Jensen-Shannon divergence-based similarity outperformed Kullback-Leibler divergence-based similarity. Altogether, our findings propose surface-based single-subject morphological similarity networks as a reliable method to characterize the human brain connectome and provide methodological recommendations and guidance for future research.

Multiple sclerosis is associated network disorganization of the functional connectome at multiple levels, and some of them are already detectable in clinically isolated syndrome. PurposeTo investigate the topological organization of functional brain networks in clinically isolated syndrome (CIS) and multiple sclerosis (MS) and examine the clinical relevance.Materials and MethodsThe 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.ResultsDecreased 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...

The pathogenesis of schizophrenia (SCH) is associated with the dysfunction of monoamine neurotransmitters, the synthesis and release of which are mainly regulated by a key structure, the habenular (Hb) nucleus. However, little is known regarding whether SCH is associated with structural or functional alterations in the Hb. In this study, we combined structural and resting-state functional magnetic resonance imaging to investigate the changes in volume and functional connectivity of the Hb in 15 patients with SCH vs. 16 age- and gender-matched healthy controls (HCs). Morphologically, the absolute volume of the bilateral Hb was significantly lower in the SCH patients than in the HCs. Functionally, the bilateral Hb showed significantly enhanced functional connectivity with the left medial prefrontal cortex (mPFC) in the SCH patients. Additionally, the SCH patients exhibited increased functional connectivity of the left Hb with the left lingual gyrus and right inferior frontal gyrus (IFG). A further exploratory analysis revealed that the SCH patients showed increased functional connectivity between the right Hb and several subcortical regions related to dopaminergic pathways, including the left ventral striatum, caudate and putamen. Finally, the increased functional connectivity of the right Hb with the mPFC was positively correlated with the Brief Psychiatric Rating Scale (BPRS) scores in the patients. Together, these results suggest that the altered volume and functional connectivity of the Hb may be involved in the pathogenesis of SCH and thus that the Hb may serve as a potential target in developing new therapeutic strategies in SCH.

Empirical networks exhibit significant heterogeneity in node connections, resulting in a few vertices playing critical roles in various scenarios, including decision-making, viral marketing, and population immunization. Thus, identifying key vertices is a fundamental research problem in Network Science. In this paper, we introduce vertex entanglement (VE), an entanglement-based metric capable of quantifying the perturbations caused by individual vertices on spectral entropy, residing at the intersection of quantum information and network science. Our analytical analysis reveals that VE is closely related to network robustness and information transmission ability. As an application, VE offers an approach to the challenging problem of optimal network dismantling, and empirical experiments demonstrate its superiority over state-of-the-art algorithms. Furthermore, VE also contributes to the diagnosis of autism spectrum disorder (ASD), with significant distinctions in hub disruption indices based on VE between ASD and typical controls, promising a diagnostic role for VE in ASD assessment.