A generative model of the connectome with dynamic axon growth.

Connectome generative models, otherwise known as generative network models, provide insight into the wiring principles underpinning brain network organization. While these models can approximate numerous statistical properties of empirical networks, they typically fail to explicitly characterize an important contributor to brain organization - axonal growth. Emulating the chemoaffinity guided axonal growth, we provide a novel generative model in which axons dynamically steer the direction of propagation based on distance-dependent chemoattractive forces acting on their growth cones. This simple dynamic growth mechanism, despite being solely geometry-dependent, is shown to generate axonal fiber bundles with brain-like geometry and features of complex network architecture consistent with the human brain, including lognormally distributed connectivity weights, scale-free nodal degrees, small-worldness, and modularity. We demonstrate that our model parameters can be fitted to individual connectomes, enabling connectome dimensionality reduction and comparison of parameters between groups. Our work offers an opportunity to bridge studies of axon guidance and connectome development, providing new avenues for understanding neural development from a computational perspective.

Enhancing the accumulation of linoleic acid and α-linolenic acid through the pre-harvest ethylene treatment in Camellia oleifera.

Camellia oleifera Abel. (C. oleifera) is an important woody edible oil tree species in China. The quality of C. oleifera oil (tea oil) is mainly determined by the contents of linoleic acid (LA) and α-linolenic acid (ALA). However, how to increase the contents of LA and ALA in tea oil and the corresponding regulating mechanism have not been clarified. In the present study, we found that the LA and ALA contents in C. oleifera seeds were significant positively associated with the concentrations of ethephon and were decreased by ethylene inhibitor treatment. Furthermore, 1.5 g L-1 ethephon could receive an optimal LA and ALA contents without adverse effects to the growth of 'Huashuo' trees in this study. The ethephon treatment also increased the contents of 1-aminocyclopropane-1-carboxylic acid (ACC), sucrose, soluble sugar and reducing sugar contents in seeds. Transcriptome analysis further suggested that exogenous ethephon application enhanced the accumulation of LA and ALA via regulating genes involved in LA and ALA metabolism, plant hormone signal transduction pathways, and starch and sucrose metabolism. Our findings confirm the role of ethylene in LA and ALA regulation and provide new insights into the potential utilization of ethylene as a LA and ALA inducer in C. oleifera cultivation.

Parameter estimation for connectome generative models: Accuracy, reliability, and a fast parameter fitting method.

Generative models of the human connectome enable in silico generation of brain networks based on probabilistic wiring rules. These wiring rules are governed by a small number of parameters that are typically fitted to individual connectomes and quantify the extent to which geometry and topology shape the generative process. A significant shortcoming of generative modeling in large cohort studies is that parameter estimation is computationally burdensome, and the accuracy and reliability of current estimation methods remain untested. Here, we propose a fast, reliable, and accurate parameter estimation method for connectome generative models that is scalable to large sample sizes. Our method achieves improved estimation accuracy and reliability and reduces computational cost by orders of magnitude, compared to established methods. We demonstrate an inherent tradeoff between accuracy, reliability, and computational expense in parameter estimation and provide recommendations for leveraging this tradeoff. To enable power analyses in future studies, we empirically approximate the minimum sample size required to detect between-group differences in generative model parameters. While we focus on the classic two-parameter generative model based on connection length and the topological matching index, our method can be generalized to other growth-based generative models. Our work provides a statistical and practical guide to parameter estimation for connectome generative models.