Reviewer #2 (Public Review):
Gyrencephaly has been linked to the split of the subventricular zone (SVZ) and the formation of an outer subventricular zone (OSVZ) during neurogenesis. This paper proposes a convincing multizone computational model of neurogenesis allowing exploration of the role of this OSVZ in the folding dynamics. This model is a bridge between knowledge of cell proliferation and migration and the physics of growth.
Strengths<br />
• The computational model described in this paper is probably the most ambitious to date. It succeeds in translating the complexity of microscopic biological phenomena that describe cell proliferation and migration into physical phenomena from continuum mechanics. It is truly a tour de force.<br />
• The description of neurogenesis is particularly clear, within the reach of a naive reader despite its complexity. The figure illustrating the chronology of the phenomena at work is a success.<br />
• The paper builds on impressive efforts to estimate from real human brain sections some of the complex parameters of the model such as the density of cells at different stages of migration.<br />
• The physical model is able to show ripples in the deep zones of proliferation that seem induced by the folding of the cortex. This observation is consistent with feedback from folding on the organization of the migration, as these ripples are not part of the model. I do not know to what extent these ripples have been demonstrated in reality.<br />
• The model shows that significant proliferation in the OSVZ leads to a doubling of the frequency of folding, a phenomenon observed in reality in large brains, which gives rise to allometric laws between folding and brain size (see Toro et al., Germanaud et al.)<br />
• The paper includes an experiment based on heterogeneous proliferation in the OSVZ, which is difficult to model in more classical models such as Tallinen's one. This is a particularly interesting possibility for modelling spatial heterogeneity in the expression of genes that modulate neurogenesis (see Llinares-Benadero et al.).
Weaknesses<br />
• To account for the complexity of biological phenomena, the model relies on a large number of ad hoc choices whose consequences are difficult to predict.<br />
• The physical model description is highly technical and out of reach for a non-specialist.<br />
• The description of neurogenesis shows three zones of cell proliferation, each inhabited by a specific cell type. Despite its realism, the proposed model does not take into account the ISVZ where the intermediate progenitors operate.<br />
• The experiment of comparing several regimes derived from the relative importance of proliferation in the VZ and OSVZ is not very clear. It leads to the observation of the evolution of cell density maxima over time, which seems insufficient to conclude the importance of the OSVZ for folding. One wonders whether the key parameter that leads to folding is the rate of OSVZ proliferation or simply the total quantity of neurons generated by the two or even the three zones.<br />
• The experiment on the heterogeneity of proliferation in the OSVZ is a bit frustrating. I would like to see a set-up corresponding to the mosaics found in ferrets and closely associated with folding patterns.<br />
• It would be interesting to elaborate a little on the possibility of extending the model in 3D, which seems imperative to evaluate the nature of the folding pattern generated. Comparing them to reality is an essential step in gauging the credibility of the model. For instance, it would be interesting to test to which extent the model can father the type of variability observed in the general population (Mangin et al.). It will also be particularly interesting to work on the inverse model between the real folding patterns and the heterogeneous proliferation maps that can generate them.
Conclusion
The computational model of neurogenesis described in this paper is the most sophisticated model proposed to date. It is a convincing step towards a model that could one day simulate perturbations of neurogenesis that may give rise to the gyration abnormalities observed in certain developmental pathologies. A better understanding of the genesis of these anomalies could contribute to their use as a signature of hidden deleterious events occuring during neurogenesis.
References
Toro, R., Perron, M., Pike, B., Richer, L., Veillette, S., Pausova, Z., & Paus, T. (2008). Brain size and folding of the human cerebral cortex. Cerebral cortex, 18(10), 2352-2357.<br />
Germanaud, D., Lefèvre, J., Toro, R., Fischer, C., Dubois, J., Hertz-Pannier, L., & Mangin, J. F. (2012). Larger is twistier: spectral analysis of gyrification (SPANGY) applied to adult brain size polymorphism. NeuroImage, 63(3), 1257-1272.<br />
Tallinen, T., Chung, J. Y., Rousseau, F., Girard, N., Lefèvre, J., & Mahadevan, L. (2016). On the growth and form of cortical convolutions. Nature Physics, 12(6), 588-593.<br />
Llinares-Benadero, C., & Borrell, V. (2019). Deconstructing cortical folding: genetic, cellular and mechanical determinants. Nature Reviews Neuroscience, 20(3), 161-176.<br />
Mangin, J. F., Le Guen, Y., Labra, N., Grigis, A., Frouin, V., Guevara, M., ... & Sun, Z. Y. (2019). "Plis de passage" deserve a role in models of the cortical folding process. Brain topography, 32(6), 1035-1048.