## 4 Discussion
Also, the spatial pattern of the figurative connectivity network showed a pronounced right hemisphere dominance at 500 ms in the theta, alpha and beta bands, matching results reported in past studies [43; 44]. In comparison, our other (abstract > figurative) condition showed more widespread connectivity across different brain areas, with more prominent occipital region [50] activations in the theta, alpha and beta bands at 500 ms, which may reflect the stronger reliance on purely visual elements and the lack of visual memory and recognition tasks that might characterize abstract painting processing. The novelty of our work is the demonstration that the presented functional connectivity analysis is able to detect significant differences and changes in connectivity patterns at distinct time points instead of temporal averages. Our study has also demonstrated that phase synchronization methods in general, and dwPLI in particular is a reliable way to extract functional connectivity networks. Using dwPLI removes the need to apply additional spatial filtering techniques to mitigate volume conduction effects to avoid the detection of spurious connections. The work presented here has some limitations. We used distinct selected time points in the analysis instead of looking at continuous temporal changes in connectivity. The reasons for this were (i) partly inherent in the method since dwPLI uses spectrum-based calculation that requires window-based operations that make it problematic to obtain instantaneous phase information, and (ii) it would have generated severe computational issues (i.e., very long runtimes and an extreme amount of memory space requirements). For similar computational reasons, we have not looked into the calculation of effective connectivity, which could provide additional causality information on connections, which obviously would be very important for understanding the intricacies of the underlying cortical mechanisms. In our study, we used two rather general art categories. In the future, relying on the demonstrated sensitivity of our method, one could create more conditions and then perform comparisons between more specific groups of paintings (e.g., portraits, landscapes, still life, abstract figurative and fully non-representational) to allow for a fine-grained analysis on how different artistic elements influence neural connectivity. In addition, further analyses of the effects of visual properties such as color, contrast, complexity and symmetry on the neural processing of art could be another area to investigate. Symmetry, for instance, is often associated with positive aesthetic judgments and emotional responses that contribute to the perception of beauty in both visual and auditory art forms [51]. An additional area left unexplored in this study is the effect of art expertise on the neural correlates of art perception. It has been shown [52; 53; 54; 55] that art novices generally have a preference towards representative art. Since the subjects in our study did not have formal art education, the connectivity differences might have been influenced by the lower level of art expertise. A future study should compare the connectivity network differences between artists and non-artists to study the effect of art expertise on cognitive processing and to shed light on how they engage with abstract and figurative paintings. Finally, in our current study, we have only looked at the edge weight and node strength parameters of the connectivity networks. However, there are many other graph measures that can be used for a more detailed comparison of the detected networks, e.g., node centrality, modularity, efficiency measures, etc. Future studies should focus on these as well. Additionally, the node strength seems to be a more sensitive measure of node activation than event-related potential or power, and consequently could be explored as a potential alternative to traditional ERP analysis methods.