## 4 Discussion  
We found that DC measures are useful to discriminate PD subjects from controls. Importantly, we found that the latent space obtained by TL was more closely aligned with clinical indices related to movement, tremor, and body bradykinesia. It is perhaps unsurprising that the latent space, which will likely contain non-linear combinations of DC would more closely align with clinical performance, as presumably there will be a complex non-linear mapping between EEG connectivity measures and crude clinical performance  
Figure 6: Ten highest normalized directional connections’ values that cause maximum value in feature related to the maximum LASSO coefficient for latent space: (**A**) Right finger tap test; (**B**) Left finger tap test; (**C**) Right tremor test; (**D**) Left tremor test; (**E**) Bradykinesia. Figure 7: 10 highest normalized directional connections’ values that cause maximum value in feature related to the maximum LASSO coefficient for non-latent space: (**A**) Right finger tap test; (**B**) Left finger tap test. metrics. What is perhaps surprising is that TL, from a network trained on natural images (as the VGG-16 network used here is), results in a latent space that is more closely aligned to clinical indices. We are acutely aware that our study may potentially be under-powered. However, we note that (1) as is typical with patient populations, the number of subjects tends to be quite low, and as a result of the challenges associated with obtaining recordings in these populations, (2) we have spent particular effort in ensuring that overfitting has not taken place, and (3) we are using transfer learning, which is a key aspect of the novelty of the proposed approach. The whole point of transfer learning is to allow for smaller data sets to be employed to allow for informed descriptions of disease states, and (4) we augmented the data by a factor of four by generating independent heat maps for each frequency subband. The goal of using LASSO was to (1) select a sparse set of features and then (2) solve the regression problem to linearly map these sparse features to the target clinical scores. The beta coefficients are found by jointly minimizing the mean square error (MSE) between original and modeled values and applying an L1 norm constraint on the coefficients to promote sparsity. In Figure 5 we show that the LASSO method using the latent space values more closely predicts the clinical scores compared to the DC values used to develop the latent space. This proves that there exists a linear mapping from extracted features (of latent space) to the five clinical scores considered in this paper. It is interesting that the network was agnostic to the particular band used, yet still provided excellent classification performance. This implies that the presence of effective connectivity between pairs of channels may be more informative for disease discrimination than the particular frequencies involved. Note that we were still able, post-hoc, to infer which frequency bands were most informative by deducting which particular heat maps were most informative in the final latent space. It is also notable that many of the DC connections in the latest space appear ipsilateral to the hand in the finger tapping and tremor tasks (Figure 6). While mirror movements have been suggested as a reason for ipsilateral activation in PD subjects [40], it is important to note that the features we isolate best discriminate between PD and HC subjects. PD subjects tend to demonstrate more bilateral activation with unilateral movement than HC subjects [41]. Thus, the connections we found that best discriminate between PD and HC subjects will tend to be ipsilateral to the movement. We note that most of the discriminating features are in the beta-band (Table 7). This is hardly surprising, given the prominent role of altered beta-band oscillations in PD pathophysiology [42].