I suggest you just do it; boxplots look fine, Shapiro-Wilks does not have to be non-significant, the data should just look approximately normal. You can use ANOVA for unequal variances to deal with heteroskedasticity. The difference in variances between groups might not be just a nuisance but also something worth writing a paper about.

Although I think that ANOVA is enough, you can find a version of ANCOVA for unequal variances or just a heteroskedastic/robust linear model, to deal with gender. If you want to go non-parametric, you can use the Kruskal-Wallis test, or a permutation test, neuroimagers love permutation tests. See Winkler et al. 2014 Neuroimage https://www.sciencedirect.com/science/article/pii/S1053811914000913, where they also discuss heteroscedasticity and nuisance variable, so either you can put gender in your model or shuffle within gender. There is also an accompanying (Matlab?) package for it.

I suggest you just do it; boxplots look fine, Shapiro-Wilk does not have to be non-significant, the data should just look approximately normal. You can use ANOVA for unequal variances to deal with heteroskedasticity. The difference in variances between groups might not be just a nuisance but also something worth writing a paper about.

Although I think that ANOVA is enough, you can find a version of ANCOVA for unequal variances or just a heteroskedastic/robust linear model, to deal with gender. If you want to go non-parametric, you can use the Kruskal-Wallis test, or a permutation test, neuroimagers love permutation tests. See Winkler et al. 2014 Neuroimage https://www.sciencedirect.com/science/article/pii/S1053811914000913, where they also discuss heteroscedasticity and nuisance variable, so either you can put gender in your model or shuffle within gender. There is also an accompanying (Matlab?) package for it.