Short title: Generative models of brain networks in schizophrenia Word Count (Abstract): 220/250 Word Count (Article Body): 3999/4000 Number of Figures: 3 Number of Tables: 1 Number of References: 62 This paper contains Supplementary Materials. AbstractBackground: Alterations in the structural connectome of schizophrenia patients have been widely characterized, but the mechanisms leading to those alterations remain largely unknown.Generative network models have recently been introduced as a tool to test the biological underpinnings of the formation of altered structural brain networks. Methods:We evaluated different generative network models to investigate the formation of structural brain networks in healthy controls (n=152), schizophrenia patients (n=66) and their unaffected first-degree relatives (n=32), and we identified spatial and topological factors contributing to network formation. We further investigated the association of these factors to cognition and to polygenic risk for schizophrenia.Results: Structural brain networks can be best accounted for by a two-factor model combining spatial constraints and topological neighborhood structure. The same wiring model explained brain network formation for all groups analyzed. However, relatives and schizophrenia patients exhibited significantly lower spatial constraints and lower topological facilitation compared to healthy controls. The model parameter for spatial constraint was correlated with the polygenic risk for schizophrenia and predicted reduced cognitive performance. Conclusions:Our results identify spatial constraints and local topological structure as two interrelated mechanisms contributing to normal brain development as well as altered connectomes in schizophrenia. Spatial constraints were linked to the genetic risk for schizophrenia and general cognitive functioning, thereby providing insights into their biological basis and behavioral relevance.

To understand the Lagrangian mathematical equation, the author proposes an application study of big data modeling in the village cultural industry model. The author takes 20 traditional villages in Beijing as the object, and through crawling relevant Internet cultural and tourism big data, using the theory of tourism consumer behavior, from the aspects of consumer motivation behavior, food, housing, shopping, and other experience behavior, consumer evaluation behavior, and consumer space-time behavior, carry out targeted content analysis, aggregation analysis, etc, fully reveal the behavioral portrait of tourists in Beijing’s traditional villages. The results show that the overall tourism experience of Beijing’s traditional villages is good, but the development level is quite different, the cultural and tourism supply pattern of “some have boutiques, some have highlights, and the overall influence is weak” is presented to improve the overall level. At the same time, the computer big data based on tourists’ feedback can reasonably reveal the inherent characteristics and problems of traditional village tourism, which is innovative in the current situation where village tourism data is difficult to obtain.