Reviewer #2 (Public Review):
In the paper, the authors aimed to repurpose a previously developed Variational Autoencoder (VAE) trained on adult rsfMRI data to characterise the in vivo foetal-neonatal brain development. Although the attempts to understand both healthy and aberrant early functional development are becoming increasingly popular, the processing and interpretation of the foetal-neonatal rsfMRI remain challenging due to methodological difficulties and the extremely fast and complex nature of the early brain development itself. For this reason, the non-linear computational models, such as the proposed VAE, have the potential to represent the rsfMRI data and capture the early neurodevelopmental trajectories with higher accuracy compared to more prevalent linear methods such as ICA.
In this vein, the authors successfully apply the adult-trained VAE to compress the spatial representation of foetal-neonate rsfMRI cortical patterns into 256 latent features. Due to the non-linear nature of the VAE, this latent representation has the potential to yield more informative brain representations of rsfMRI data compared to other available methods making it a strength of the article.
Nevertheless, one important limitation is that the direct application of the model trained on adult data to early functional connectome and more importantly, the interpretation of the reconstructed latent space-based maps rests on a strong assumption that the adult connectome features are stable and recognisable in the very early period. Moreover, such a model trained on the adult data would also be incapable to reveal possible network structures that would be present in the developing but not in the adult brain.
The attempt to validate the method and assess its generalisability on two independent, fairly large datasets that include foetuses, and preterm- and term-born infants is commendable. However, the interpretation of the results in light of the subject, image acquisition, and processing (which is widely recognised to be very difficult, especially in foetuses) heterogeneity requires caution. For example, the VAE reconstruction error is positively correlated with the age at scan in dHCP, and DBI full-terms, but the relationship is very strong in the reverse direction in DBI foetuses. This suggests differences between the subgroups of subjects which might be driven by factors other than age. Thus, we cannot exclude the possibility that the high age-predictive power of the models based on the latent features is partly driven by those differences in addition to the age-dependant features of the infant functional connectome.
The approach for the extraction and mapping of the group-level brain resting state networks is interesting and has the potential to uncover new insights into the early connectome. However, some of the current results are rather surprising and put into question their biological plausibility. For example, the authors suggest observing the precursor of the default-mode network in the DBI but not the dHCP dataset. This is rather strange given the DBI subjects (including foetuses) were on average scanned earlier than the dHCP subjects. Also, the pattern similarity of the best matched extracted independent component ('brain network') in the full-term dHCP vs full-term DBI comparison is 0.6 which is rather low if expecting the same networks to be extracted in the age-matched comparison. Additionally, the network visualisations show large heterogeneity of the distribution of activation/deactivations within extracted independent components between the datasets (even after ordering them for pattern similarity) which contradicts the expectation that the extracted networks (if real) should be stable, if not along the whole development, then at least between the narrower age ranges within the datasets.
Overall, the interpretation of the current work is somewhat limited, and careful analysis of the latent representations derived from foetal-neonate data might be required to dissociate the effects of potential confounders from biological/developmental mechanisms. This might be difficult in the context of the highly complex and mostly black-box strategy such as VAE (this applies not only to the current method but to all novel methods proposed to study rsfMRI). Despite these limitations, the proposed approach could be very interesting methodologically with a potential impact on the future analysis of rsfMRI data. Overall, the authors achieved their aim of applying a novel VAE method to foetal-neonatal functional data and demonstrated that the extracted latent variables are predictive of brain age. However, careful evaluation of the latent representations and differences in predictive results and the mapped networks between the two datasets might be necessary to support the conclusion that the VAE-derived representations of foetal-neonatal rsfMRI carry informative neural signatures.