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  1. Evaluation Summary:

    This manuscript is of interest to readers interested in how brain gene expression patterns differ between humans and other animals. The authors develop an innovative approach to map correspondences between the gene expression profiles of human and mouse brains, finding that the profiles of sensorimotor areas are more similar than those of transmodal association cortex. This thus contributes to our understanding of the genetic mechanisms that may drive differences in brain organization across species. The study is methodologically sound and the key claims are supported by the data.

    (This preprint has been reviewed by eLife. We include the public reviews from the reviewers here; the authors also receive private feedback with suggested changes to the manuscript. Reviewer #2 agreed to share their name with the authors.)

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  2. Reviewer #1 (Public Review):

    This article tackles an interesting problem of using animal models in human neuroscience research through a comparative study of brain-wide gene expression patterns in mouse and human. One of the main strengths of this work is the analysis approach that builds from a set of relatively simple and well-defined assumptions and later is complemented with more sophisticated computational methods such as machine learning in order to tailor the methods for a more detailed investigation. The open and transparent use of publicly available datasets providing full data processing and analysis pipelines together with the realistic presentation and interpretation of the findings also strengthens the perceived trustworthiness of this work. Whereas the findings such as the greater similarity observed for sensorimotor compared to supramodal areas; or the fact that the introduction of the latent gene expression space in most cases only moderately improves identified regional correspondence between the species are not unexpected, they provide a novel outlook to well-known challenges in comparative neuroscience. Overall, the manuscript is methodologically sound, very well-written, and easy to follow, the key claims presented in the article are supported by the data.

    From the methodological point of view, this study is well-executed, the following are points to consider.

    Expression patterns across broad anatomical divisions such as the human cortex, subcortex, brainstem, and cerebellum demonstrate substantial differences. Similar tendencies are also observed in the mouse brain, where differences between neocortical and other brain areas tend to be much stronger compared to the differences within these divisions. The analyses presented in this work are performed on the combined datasets covering the whole brain and the resulting similarity metrics appear to be significantly skewed to the right with values broadly ranging from 0.7-1. It may be possible that transcriptional differences between broad anatomical divisions may attenuate/diminish the potential differences within these structures, e.g. within cortex/neocortex/subcortex/cerebellum.

    Currently, in the description of the processing of AHBA data there is no mention of within-donor normalization prior to data aggregation. It has been previously shown that samples acquired from the same donor tend to cluster together rather than reflecting anatomical divisions of the brain when samples across 6 brains are combined. Based on the current documentation, samples from all 6 brains are first aggregated into a sample x gene matrix and only then normalized for every gene across samples. This type of normalization retains expression differences between different donor brains and can bias the resulting sample x gene and region x gene datasets as well as subsequent analyses.

    Does the latent gene space method allows the identification of genes that are most informative in region identification?

    Some formal statistical evaluations should be presented when performing comparisons. For example, but not limited to, comparing maximal correlational values between sensimotor and supramodal areas (lines 277-280, Figure 5B).

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  3. Reviewer #2 (Public Review):

    The authors address a fascinating question: how can we map and identify homologous brain regions between the mouse brain (an important model organism) and the human brain. While previous studies have mostly focused on matching connectivity patterns or morphometric mapping, the authors propose a novel and imaginative approach: to directly register mouse and human brain into a common frame of reference using the spatial expression of homologous genes. To do this, they use the mouse and human whole-brain gene expression datasets from the Allen Institute. Using supervised learning, the authors show that matching gene expression patterns allows for finer-grained cross-species correspondence. In addition, they find that the sensorimotor cortex generally displays greater cross-species correspondence compared to the supramodal cortex.

    This is an important step forward in identifying cross-species correspondences. The findings are sure to be of wide interest to the field and will inspire a lot of follow-up work. The work is written up and presented very clearly. The methods are rigorous and I commend the authors for providing their code openly.

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