## 3 Results
### Ground Truth 2  
With only one true connection and excluding self-connectivity, it was found that none of the methods, except for LSTM-NUE and TRGC (LSTM-NUE, TRGC: Sensitivity \(M\) = 1 \(\pm\) 0 in both dipole conditions), were able to detect this connection in none of the runs or datasets (Table 2). The results of a Scheirer-Ray-Hare Test with method and dipole condition as factors reveal, as expected, a main effect of connectivity method on Sensitivity (\(H\) (4,40) = 47.66, \(p\) < 0.001) as well as Precision (\(H\) (4,40) = 48.10, \(p\) < 0.001). The interaction between method and dipole condition, nor dipole condition itself were significant (Sensitivity: \(H\) (4,40) = 0.05, \(p\) = 0.99, \(H\) (1,40) = 0.01, \(p\) = 0.91, interaction and dipole condition effect, respectively; Precision: \(H\) (4,40) = 0.03, \(p\) = 0.99, \(H\) (1,40) = 0.00, \(p\) = 0.95, interaction and dipole condition effect, respectively). Looking into the effects of different connectivity methods using follow-up Mann-Whitney U tests (Bonferroni-corrected: alpha = 0.05, alpha adjusted = 0.005), significant differences in Sensitivity and Precision between LSTM-NUE/TRGC versus all other methods were found (\(p\) < 0.005). No differences in Sensitivity between \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Far-Superficial**} & \multicolumn{3}{c}{**Far-Deep**} \\ \cline{2-7}  & **Sensitivity** & **Precision** & **F1** & **Sensitivity** & **Precision** & **F1** \\ \hline TRGC & **1 \(\pm\) 0.00** & **0.71 \(\pm\) 0.29** & **0.79 \(\pm\) 0.21** & **1 \(\pm\) 0.00** & **0.70 \(\pm\) 0.29** & **0.79 \(\pm\) 0.21** \\ TCDF & 0 \(\pm\) 0.0 & 0 \(\pm\) 0.0 & 0 \(\pm\) 0 & 0 \(\pm\) 0.0 & 0 \(\pm\) 0.0 & 0 \(\pm\) 0 \\ LSTM-NEU & **1 \(\pm\) 0.0** & **0.37 \(\pm\) 0.13** & 0.53 \(\pm\) 0.14 & **1 \(\pm\) 0** & **0.43 \(\pm\) 0.09** & 0.60 \(\pm\) 0.09 \\ CONV2D * (TS=2) & 0 \(\pm\) 0.0 & 0 \(\pm\) 0.0 & 0 \(\pm\) 0.0 & 0.0 \(\pm\) 0.0 & 0.0 \(\pm\) 0.0 & 0.0 \(\pm\) 0.0 \\ Conv2D (TS=3) & 0 \(\pm\) 0.0 & 0 \(\pm\) 0.0 & 0 \(\pm\) 0.0 & 0 \(\pm\) 0.0 & 0 \(\pm\) 0.0 & 0 \(\pm\) 0.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Scores of the ANNs in comparison with TRGC, using Ground Truth 2. Results are based upon datasets where all 3 time series (TS) were included as predictors, with one exception: results from Conv2D with two time series as predictors, indicated with \({}^{\star}\), were also included. Figure 12: Runtime for the training of all ANNs, with 3 time series (TS = 3), L = 1500. An extra comparison showing the runtime of Conv2D with two time series as predictors (TS = 2) is shown (but all datasets contain 3 time series). ”Neurons” = the number of hidden layer neurons. TRGC (\(Mdn\) = 1) and LSTM-NUE (\(Mdn\) = 1) were found (\(p\) = 0.21), while Precision was significantly higher for TRGC (\(Mdn\) = 0.67) than for LSTM-NUE (\(Mdn\) = 0.42), \(p\) < 0.001). It is not surprising that comparisons of any other ANN method than LSTM-NUE with TRGC were significant, given that these methods had a Sensitivity and Precision of zero. Even though dipole condition did not turn out to exhibit a significant effect on Sensitivity nor Precision in our data sets, this distinction remains theoretically important. We summarized the qualitative differences below. Sensitivity and Precision were in both dipole conditions 0 while using TCDF and using two different configurations of Conv2D (once with two time series as predictors, once with three time series as predictors). In contrast, Precision was \(M\) = 0.37 (\(\pm\)0.13) for LSTM-NUE while TRGC obtained a precision of \(M\) = 0.71 (\(\pm\)0.29) in the Far-Superficial dipole condition. In the Far-Deep condition, performance of TRGC remained almost the same (Sensitivity \(M\) = 1 \(\pm\) 0, Precision \(M\) = 0.70 \(\pm\) 0.29) while it became slightly higher (in contrast to the Far-Superficial condition) for LSTM-NUE (Sensitivity \(M\) = 1 \(\pm\) 0, Precision \(M\) = 0.43 \(\pm\) 0.09).