By Shanna Kulik, Tianne Numan, Linda Douw
Update: this paper has now been published in Eneuro!
With great interest we read the recent BioRxiv contribution by Aerts and colleagues (2018). We would here like to informally offer some of our thoughts and suggestions on this piece of work. In this simulation study, Aerts and colleagues aimed to bridge the gap between pre-surgical planning for brain tumor resection and post-surgical functional outcome in terms of cognition. The Virtual Brain (VB) was used to simulate large-scale brain dynamics based on the structural connectome of individual patients. A global scaling factor was individually optimized by comparing the VB model with the individual patients’ functional connectome based on fMRI. Aerts and colleagues showed that the accuracy of simulated functional connectivity in reflecting the empirical data was significantly improved by individualized VB models. Moreover, the individualized model parameters correlated with cognition.
Gaining insight into mechanisms describing how tumor(-related) processes influence network topology and cognition is very important to obtain new insights into the disease and its symptomatology. Particularly predicting the cognitive outcome of a resection, one of the future directions of this work, is highly relevant to brain tumor patients from a clinical point of view: a better understanding of how post-surgical cognitive complaints come about will improve decision making in treatment strategy in this patient group. The relevance of this work can therefore not be underestimated.
Our thoughts primarily relate to the (details of) the methodology used and some of the results. We were surprised to see that the individually tuned model parameters in combination with the individual structural connectivity matrices did not result in better predictions of the individual functional connectivity patterns compared to individually tuned model parameters and the control average structural connectivity matrix. Although this finding is in line with previous work by Jirsa and colleagues (2017), it would be interesting to hear the authors’ own (speculative) explanations for this result after working with the model.
Furthermore, it is not completely clear why an average firing rate of ~3 Hz was applied. This is also not evident to us when reading the paper by Deco and colleagues (2014), who mention that in a large-scale model of interconnected brain areas, a range of 2.63-3.55 Hz should be applied. Therefore, we were wondering why only one firing rate has been applied instead of using a range of values, particularly as it is known that brain tumors may impact neurotransmitter levels around the tumor and possibly neuronal firing rates.
Finally, the relationship of the model parameters with cognitive functioning suggests a major step forward in getting a grip on explaining cognitive symptoms in brain tumor patients. We would like to suggest that it is also interesting to relate the available cognitive measures to the already calculated empirical functional network measures, to be able to assess how much variance in cognitive functioning can be explained by ‘simply’ using the model versus empirical data. Of course prospectively, it would be very interesting to relate the pre-surgical model parameters to post-surgical cognitive status, once longitudinal measurements are available as the authors imply. We are therefore looking forward see the prediction accuracy of the VB model using longitudinal cognitive data!
Multiscale Network Neuroscience 