Community detection in the human connectome: Method types, differences and their impact on inference.

Community structure is a fundamental topological characteristic of optimally organized brain networks. Currently, there is no clear standard or systematic approach for selecting the most appropriate community detection method. Furthermore, the impact of method choice on the accuracy and robustness of estimated communities (and network modularity), as well as method-dependent relationships between network communities and cognitive and other individual measures, are not well understood. This study analyzed large datasets of real brain networks (estimated from resting-state fMRI from n $$ n $$ = 5251 pre/early adolescents in the adolescent brain cognitive development [ABCD] study), and n $$ n $$ = 5338 synthetic networks with heterogeneous, data-inspired topologies, with the goal to investigate and compare three classes of community detection methods: (i) modularity maximization-based (Newman and Louvain), (ii) probabilistic (Bayesian inference within the framework of stochastic block modeling (SBM)), and (iii) geometric (based on graph Ricci flow). Extensive comparisons between methods and their individual accuracy (relative to the ground truth in synthetic networks), and reliability (when applied to multiple fMRI runs from the same brains) suggest that the underlying brain network topology plays a critical role in the accuracy, reliability and agreement of community detection methods. Consistent method (dis)similarities, and their correlations with topological properties, were estimated across fMRI runs. Based on synthetic graphs, most methods performed similarly and had comparable high accuracy only in some topological regimes, specifically those corresponding to developed connectomes with at least quasi-optimal community organization. In contrast, in densely and/or weakly connected networks with difficult to detect communities, the methods yielded highly dissimilar results, with Bayesian inference within SBM having significantly higher accuracy compared to all others. Associations between method-specific modularity and demographic, anthropometric, physiological and cognitive parameters showed mostly method invariance but some method dependence as well. Although method sensitivity to different levels of community structure may in part explain method-dependent associations between modularity estimates and parameters of interest, method dependence also highlights potential issues of reliability and reproducibility. These findings suggest that a probabilistic approach, such as Bayesian inference in the framework of SBM, may provide consistently reliable estimates of community structure across network topologies. In addition, to maximize robustness of biological inferences, identified network communities and their cognitive, behavioral and other correlates should be confirmed with multiple reliable detection methods.

Sonosensitizer-Functionalized Graphene Nanoribbons for Adhesion Blocking and Sonodynamic Ablation of Ovarian Cancer Spheroids.

Advanced stage ovarian cancer is challenging to treat due to widespread seeding of tumor spheroids throughout the mesothelial lining of the peritoneal cavity. In this work, a therapeutic strategy using graphene nanoribbons (GNR) functionalized with 4-arm polyethylene glycol (PEG) and chlorin e6 (Ce6), a sonosensitizer, to target metastatic ovarian cancer spheroids is reported. GNR-PEG-Ce6 adsorbs onto the spheroids and disrupts their adhesion to extracellular matrix proteins or LP-9 mesothelial cells. Furthermore, for spheroids that do adhere, GNR-PEG-Ce6 delays spheroid disaggregation and spreading as well as mesothelial clearance, key metastatic processes following adhesion. Owing to the sonodynamic effects of Ce6 and its localized delivery via the biomaterial, GNR-PEG-Ce6 can kill ovarian cancer spheroids adhered to LP-9 cell monolayers when combined with mild ultrasound irradiation. The interaction with GNR-PEG-Ce6 also loosens cell-cell adhesions within the spheroids, rendering them more susceptible to treatment with the chemotherapeutic agents cisplatin and paclitaxel, which typically have difficulty in penetrating ovarian cancer spheroids. Thus, this material can facilitate effective chemotherapeutic and sonodynamic combination therapies. Finally, the adhesion inhibiting and sonodynamic effects of GNR-PEG-Ce6 are also validated with tumor spheroids derived from the ascites fluid of ovarian cancer patients, providing evidence of the translational potential of this biomaterial approach.