Comment on: ‘Sex differences in neural networks recruited by frontloaded binge alcohol drinking’

Abstract

We read the published article about ‘Sex differences in neural networks recruited by frontloaded binge alcohol drinking’ by Ardinger et al.,¹ and carried on the in-depth analysis of its content. Using whole brain imaging techniques and graph theory analysis methods, this paper revealed gender differences in alcohol preloading behaviour in brain network activity, providing new insights into gender differences in alcohol use disorders. However, we think there is still room for improvement in the following aspects.

First of all, the article focused on alcohol intake, and more behavioural indicators, such as anxiety, depression, and addictive behaviours, can be considered to more comprehensively assess the impact of alcohol preloading behaviours. Physiological indicators such as cortisol levels can also be considered to explore the relationship between alcohol preloading behaviour and stress response.

Second, C57BL/6J mice are commonly used animal models for alcohol research, but their alcohol intake behaviour may be different from that of humans. Other alcohol preference animal models, such as DBA/2 mice, could be considered to enhance the generalizability of the findings. At the same time, the DID paradigm can simulate human alcohol preloading behaviour, but other behavioural paradigms, such as operant conditioning, can be considered for more fine-grained control of alcohol intake behaviour.

Third, the paper clustered brain regions into modules based only on connection strength, and further analysis of the function of each module can be considered, such as using functional connection analysis or functional magnetic resonance imaging, to reveal the role of different modules in alcohol preloading behaviour. Dynamic network analysis methods, such as time series network analysis, can also be considered to explore the effect of alcohol preloading behaviour on dynamic changes in brain functional connectivity.

Then, for the results presentation part, we recommend more advanced network visualization tools, such as Gephi or Cytoscape, to more clearly demonstrate brain network structure and functional connectivity. Multivariate statistical analysis or machine learning algorithms can also be used to analyse the data more deeply.

Finally, the article mainly described the relationship between alcohol preloading behaviour and brain network activity, but lacks causal inference. Brain stimulation techniques or gene knockout techniques can be considered to explore the role of specific brain regions or neurotransmitter systems in alcohol preloading behaviour. The relationship between alcohol preloading behaviour and alcohol use disorder and its potential targets for intervention can also be explored.

We believe that with the above improvements, the article will shed more light on the neural mechanisms of alcohol preloading behaviour and provide new perspectives for understanding gender differences in alcohol use disorders.

Jiayue Xu and Hua Zhao drafted the letter. Ying Wang critically reviewed the article.

The authors declare no conflict of interest.