A distribution model of functional connectome based on criticality and energy constraints

Raising the Connectome: The Emergence of Neuronal Activity and Behavior in Caenorhabditis elegans

The differentiation of neurons and formation of connections between cells is the basis of both the adult phenotype and behaviors tied to cognition, perception, reproduction, and survival. Such behaviors are associated with local (circuits) and global (connectome) brain networks. A solid understanding of how these networks emerge is critical. This opinion piece features a guided tour of early developmental events in the emerging connectome, which is crucial to a new view on the connectogenetic process. Connectogenesis includes associating cell identities with broader functional and developmental relationships. During this process, the transition from developmental cells to terminally differentiated cells is defined by an accumulation of traits that ultimately results in neuronal-driven behavior. The well-characterized developmental and cell biology of Caenorhabditis elegans will be used to build a synthesis of developmental events that result in a functioning connectome. Specifically, our view of connectogenesis enables a first-mover model of synaptic connectivity to be demonstrated using data representing larval synaptogenesis. In a first-mover model of Stackelberg competition, potential pre- and postsynaptic relationships are shown to yield various strategies for establishing various types of synaptic connections. By comparing these results to what is known regarding principles for establishing complex network connectivity, these strategies are generalizable to other species and developmental systems. In conclusion, we will discuss the broader implications of this approach, as what is presented here informs an understanding of behavioral emergence and the ability to simulate related biological phenomena.

Principles of Mutual Information Maximization and Energy Minimization Affect the Activation Patterns of Large Scale Networks in the Brain

Successive patterns of activation and deactivation in local areas of the brain indicate the mechanisms of information processing in the brain. It is possible that this process can be optimized by principles, such as the maximization of mutual information and the minimization of energy consumption. In the present paper, I showed evidence for this argument by demonstrating the correlation among mutual information, the energy of the activation, and the activation patterns. Modeling the information processing based on the functional connectome datasets of the human brain, I simulated information transfer in this network structure. Evaluating the statistical quantities of the different network states, I clarified the correlation between them. First, I showed that mutual information and network energy have a close relationship, and that the values are maximized and minimized around a same network state. This implies that there is an optimal network state in the brain that is organized according to the principles regarding mutual information and energy. On the other hand, the evaluation of the network structure revealed that the characteristic network structure known as the criticality also emerges around this state. These results imply that the characteristic features of the functional network are also affected strongly by these principles. To assess the functional aspects of this state, I investigated the output activation patterns in response to random input stimuli. Measuring the redundancy of the responses in terms of the number of overlapping activation patterns, the results indicate that there is a negative correlation between mutual information and the redundancy in the patterns, suggesting that there is a trade-off between communication efficiency and robustness due to redundancy, and the principles of mutual information and network energy are important to network formation and its function in the human brain.

The Prognostic Impact of the Metastatic Lymph Nodes Ratio in Colorectal Cancer

Background: This study was designed to validate the prognostic significance of the ratio of positive to examined lymph nodes (LNR) in patients with colorectal cancer. Methods: 218,314 patients from the SEER database and 1,811 patients from the three independent multicenter were included in this study. The patients were divided into 5 groups on a basis of previous published LNR: LNR0, patients with no metastatic lymph nodes; LNR1, patients with the LNR between 0.1 and 0.17; LNR2, patients with the LNR between 0.18 and 0.41; LNR3, patients with the LNR between 0.42 and 0.69; LNR4, patients with the LNR >0.7. The 5-year OS and CSS rate were estimated using Kaplan-Meier method and the survival difference was tested using log-rank test. Multivariate Cox analysis was used to further assess the influence of the LNR on patients' outcome. Results: The 5-year OS rate of patients within LNR0 to LNR4 group was 71.2, 55.8, 39.3, 22.6, and 14.6%, respectively (p < 0.001) in the SEER database. While the 5-year OS rate of those with LNR0 to LNR4 was 75.2, 66.1, 48.0, 34.0, and 17.7%, respectively (p < 0.001) in the international multicenter cohort. In the multivariate analysis, LNR was demonstrated to be a strong prognostic factor in patients with < 12 and ≥12 metastatic lymph nodes. Furthermore, the LNR had a similar impact on the patients' prognosis in colon cancer and rectal cancer. Conclusion: The LNR allowed better prognostic stratification than the positive node (pN) in patients with colorectal cancer and the cut-off values were well validated.

Information-Based Principle Induces Small-World Topology and Self-Organized Criticality in a Large Scale Brain Network

The information processing in the large scale network of the human brain is related to its cognitive functions. Due to requirements for adaptation to changing environments under biological constraints, these processes in the brain can be hypothesized to be optimized. The principles based on the information optimization are expected to play a central role in affecting the dynamics and topological structure of the brain network. Recent studies on the functional connectivity between brain regions, referred to as the functional connectome, reveal characteristics of their networks, such as self-organized criticality of brain dynamics and small-world topology. However, these important attributes are established separately, and their relations to the principle of the information optimization are unclear. Here, we show that the maximization principle of the mutual information entropy induces the optimal state, at which the small-world network topology and the criticality in the activation dynamics emerge. Our findings, based on the functional connectome analyses, show that according to the increasing mutual information entropy, the coactivation pattern converges to the state of self-organized criticality, and a phase transition of the network topology, which is responsible for the small-world topology, arises simultaneously at the same point. The coincidence of these phase transitions at the same critical point indicates that the criticality of the dynamics and the phase transition of the network topology are essentially rooted in the same phenomenon driven by the mutual information maximization. As a consequence, the two different attributes of the brain, self-organized criticality and small-world topology, can be understood within a unified perspective under the information-based principle. Thus, our study provides an insight into the mechanism underlying the information processing in the brain.