A brain-wide analysis maps structural evolution to distinct anatomical module

  1. Robert A Kozol
  2. Andrew J Conith
  3. Anders Yuiska
  4. Alexia Cree-Newman
  5. Bernadeth Tolentino
  6. Kasey Benesh
  7. Alexandra Paz
  8. Evan Lloyd
  9. Johanna E Kowalko
  10. Alex C Keene
  11. Craig Albertson
  12. Erik R Duboue

  • Curated by eLife

    eLife logo

    eLife assessment

    The authors ask if brain regions change based on the functional constraints or developmental constraints. To address this, they introduce an automated method for brain segmentation based on the zebrafish tool to study brain evolution in Astyanax. A caveat is that it is difficult to test the functional constraint hypothesis using this method, though it works well for testing developmental constraints.

Reviewed by

Read the full article See related articles

Listed in

Log in to save this article

Abstract

The vertebrate brain is highly conserved topologically, but less is known about neuroanatomical variation between individual brain regions. Neuroanatomical variation at the regional level is hypothesized to provide functional expansion, building upon ancestral anatomy needed for basic functions. Classically, animal models used to study evolution have lacked tools for detailed anatomical analysis that are widely used in zebrafish and mice, presenting a barrier to studying brain evolution at fine scales. In this study, we sought to investigate the evolution of brain anatomy using a single species of fish consisting of divergent surface and cave morphs, that permits functional genetic testing of regional volume and shape across the entire brain. We generated a high-resolution brain atlas for the blind Mexican cavefish Astyanax mexicanus and coupled the atlas with automated computational tools to directly assess variability in brain region shape and volume across all populations. We measured the volume and shape of every grossly defined neuroanatomical region of the brain and assessed correlations between anatomical regions in surface fish, cavefish, and surface × cave F 2 hybrids, whose phenotypes span the range of surface to cave. We find that dorsal regions of the brain are contracted, while ventral regions have expanded, with F 2 hybrid data providing support for developmental constraint along the dorsal-ventral axis. Furthermore, these dorsal-ventral relationships in anatomical variation show similar patterns for both volume and shape, suggesting that the anatomical evolution captured by these two parameters could be driven by similar developmental mechanisms. Together, these data demonstrate that A. mexicanus is a powerful system for functionally determining basic principles of brain evolution and will permit testing how genes influence early patterning events to drive brain-wide anatomical evolution.

Article activity feed

  1. Version published to 10.7554/elife.80777 on eLife
  2. eLife

    Author Response

    Reviewer #1 (Public Review):

    Kozol et al adapt an important tool, in the form of the atlas, to the Astyanax research community. While broadly the atlas appears to correctly identify large brain regions, it is unclear what is the significance of the finer divisions. The external confirmations are restricted to just a few large brain regions (by independent human observer: e.g., optic tectum, hypothalamus. By molecular marker: hypothalamus only.). As such, interpretations of results from as many as 180 small subregions should be interpreted sceptically.

    The authors also suggest that some brain regions have increased in size during cavefish evolution (e.g., hypothalamus, subpallium). The analysis of progeny from a genetic cross of cave and surface morphs suggest a complex genetic program has evolved to control this variant set of brain structures. With the development of genetic manipulation tools in this species, an exciting series of experiments may link causal variants with brain development differences.

    MAJOR ISSUES

    Line 85+. Segmentation accuracy is not well established by the authors. For example, Figure S2 states that the pixel correlation is high between Astyanax populations. But the details of how this cross-correlation was done are sparse. Is the Y- axis here showing the fraction of pixels that are shared in the morphs? While the annotation appears to function similarly across morphs, the 80% machine:human correlation is difficult to put into context. On the one hand, this seems low. For what values should one strive? Are there common "mistakes" or differences in human & machine annotations that lead to certain regions being excluded? A discussion of these is warranted and will be useful to others who wish to use this approach.

    Line 87. "such as" is misleading since these were the only two antibodies used to confirm molecular definitions of regions.

    But more to the point, additional markers should be used to confirm more than just the ISL+ hypothalamic divisions.

    This is particularly warranted, as Fig 1d is not convincing. I believe that the yellow label is ISL; this is difficult to see in the figures. ISL is not ideal since this is widespread in the hypothalamus. There are no ISL-negative regions depicted, which would be necessary to demonstrate that the resolution of this subregion labeling tool is high. A complementary approach would be to find molecular markers that are more restricted than ISL which label only subsets of hypothalamic regions.

    Finally, do the mid/hindbrain ISL labeled regions correspond to known ISL+ subregions?

    We agree with the reviewer that the Islet1/2 assessment was insufficient for demonstrating automated segmentation accuracy and that the labeling was difficult to visualize in the previous version of the figure. We have addressed this reviewers concern by adding new molecular markers for verification of segment accuracy and through a modified presentation of the original data. The first, and in our opinion most convincing, is the addition of more markers of known neuroanatomical regions. This required not only adding extra antibody stains to our brain atlas, but also optimizing Hybridization Chain Reaction (HCR) in situ protocol that could be coupled with immunohistochemistry, permitting automated segmentation via total ERK registration and brain atlas inverse registration. This novel protocol showed corresponding localization of markers, such as 5-hydroxytryptamine (5-HT), gastrulation brain homeobox 1 (gbx1), and oxytocin (oxt), in the expected neuroanatomical areas. It should be noted that these markers included both large neuroanatomical areas as well as small, well-defined areas such as the superior, and also labeled disparate neuroanatomical loci throughout the brain. We also modified our original figure to better illustrate the regions that islet+ staining labeled. These markers show that islet1/2 labels precise regions of the hypothalamus which correspond to known expression patterns. The updated methodology can be found in lines 422- 440, while the results can be found in lines 105-118 of the text, Figure 1 and Figure 1 – Figure Supplement 1a.

    We believe these two changes address the reviewers concerns, and suggest that the neuroanatomical labels generated in this study faithfully label the Astyanax brain.

    The molecular and human-observed confirmations of brain regions suggests that the annotated borders of gross anatomical regions are correctly identified by the algorithm. However, data is not presented that indicates whether the smaller regions correspond to biologically meaningful compartments.

    We agree with the reviewer that our assessment of regional accuracy for automated segmentation necessitated additional markers, which labeled smaller, more refined compartments. To address this, we developed an HCR in situ hybridization strategy that was compatible with our brain atlas, and used several markers that label smaller regions, such as the 5-HT positive neurons of the dorsal raphe and oxytocin positive neurons of the medial preoptic region. Together, these results were consistent with our previous finding that anatomical regions confirmed by human- observation and molecular staining did faithfully label the correct regions of the brain. These findings can be found in lines 105-118 in the text, along with Figure 1 and Figure 1 – Figure Supplement 1a-d. Together, we hope this shows that not only large neuroanatomical areas, but also finer areas are correctly labeled by CobraZ.

    Parameters used in CobraZ to perform the segmentation are not defined. More transparency is required here for others to replicate.

    We agree with the reviewer that parameters used for CobraZ and Advanced Normalization Tools (ANT) are necessary for reproducibility of our results. We have since added sentences to clarify that we did not change the original ANTs or CobraZ parameters from Gupta et al. 2018. (line 474- 475) and have added the CobraZ parameter file and ANTs bash scripts to our dryad depository.

    Reviewer #3 (Public Review):

    In this manuscript the authors use novel techniques and analytical methods on an up and coming animal model for brain evolution. The manuscript utilizes the cavefish Astyanax mexicanus, which can provide future important insights into the field of neurobiology and in evolution in general.

    The authors however, only argue that Astyanax is a powerful system for functionally determining basic principles of brain evolution (which clearly it will be), but fail to actually describe what brain evolution insights Astyanax gives. The data is in the paper, but the interpretation needs refinement. This would be a much more valuable paper with a thorough evolutionary context based on the already existing, extensive literature. I believe this manuscript has the potential to be extremely impactful.

    We thank the reviewer for her positive critique of our manuscript, and more broadly for the thoughtful comments, the challenge to re-evaluate the way we have thought about our own data, and for hinting us in a direction of scientific direction that is more impactful. We have spent a lot of time re-thinking this work to address this reviewers critique, and believe that it is a far better study for it.

  3. eLife

    eLife assessment

    The authors ask if brain regions change based on the functional constraints or developmental constraints. To address this, they introduce an automated method for brain segmentation based on the zebrafish tool to study brain evolution in Astyanax. A caveat is that it is difficult to test the functional constraint hypothesis using this method, though it works well for testing developmental constraints.

  4. eLife

    Reviewer #1 (Public Review):

    Kozol et al adapt an important tool, in the form of the atlas, to the Astyanax research community. While broadly the atlas appears to correctly identify large brain regions, it is unclear what is the significance of the finer divisions. The external confirmations are restricted to just a few large brain regions (by independent human observer: e.g., optic tectum, hypothalamus. By molecular marker: hypothalamus only.). As such, interpretations of results from as many as 180 small subregions should be interpreted sceptically.
    The authors also suggest that some brain regions have increased in size during cavefish evolution (e.g., hypothalamus, subpallium). The analysis of progeny from a genetic cross of cave and surface morphs suggest a complex genetic program has evolved to control this variant set of brain structures. With the development of genetic manipulation tools in this species, an exciting series of experiments may link causal variants with brain development differences.

    MAJOR ISSUES
    Line 85+. Segmentation accuracy is not well established by the authors.
    For example, Figure S2 states that the pixel correlation is high between Astyanax populations. But the details of how this cross-correlation was done are sparse. Is the Y-axis here showing the fraction of pixels that are shared in the morphs? While the annotation appears to function similarly across morphs, the 80% machine:human correlation is difficult to put into context. On the one hand, this seems low. For what values should one strive? Are there common "mistakes" or differences in human & machine annotations that lead to certain regions being excluded? A discussion of these is warranted and will be useful to others who wish to use this approach.

    Line 87. "such as" is misleading since these were the only two antibodies used to confirm molecular definitions of regions.
    But more to the point, additional markers should be used to confirm more than just the ISL+ hypothalamic divisions.
    This is particularly warranted, as Fig 1d is not convincing. I believe that the yellow label is ISL; this is difficult to see in the figures. ISL is not ideal since this is widespread in the hypothalamus. There are no ISL-negative regions depicted, which would be necessary to demonstrate that the resolution of this subregion labeling tool is high. A complementary approach would be to find molecular markers that are more restricted than ISL which label only subsets of hypothalamic regions.
    Finally, do the mid/hindbrain ISL labeled regions correspond to known ISL+ subregions?

    The molecular and human-observed confirmations of brain regions suggests that the annotated borders of gross anatomical regions are correctly identified by the algorithm. However, data is not presented that indicates whether the smaller regions correspond to biologically meaningful compartments.

    Parameters used in CobraZ to perform the segmentation are not defined. More transparency is required here for others to replicate.

  5. eLife

    Reviewer #2 (Public Review):

    The authors tackled a longstanding question for brain evolution: if the brain regions change based on functional constraints or developmental constraints.

    The strength of this study is that the authors introduced an automated method for brain segmentation based on the zebrafish tool, which is a highly advanced technology. They also performed the volume and landmark-based shape analyses in a surface, cave and their F1 and F2 hybrid, highlighting genetic regulations, and revealed 3 genetically correlating clusters of brain regions, which are brand new as far as I know. This study needs intense effort, fine skills to conduct, and intellectual efforts to summarize the vast dataset. I simply admire how the authors achieve their study at this level.

    The weakness of this study is that the method/approach used in this study is difficult to test the functional constraint hypothesis although the authors nicely tested the developmental constraint hypothesis, which was highlighted in their correlation studies (volumetric and shape: Fig 4 and 5). I am also a little concerned with the accuracy of the automated segmentation algorithm shown in Figure 1-figure supplement 2. The original zebrafish paper (CobraZ) showed a similar accuracy (cross-correlation as 80%). If this level of accuracy is accepted in the field, I am OK with it.
    Their data support the conclusion 'brain-wide evolution occurs in distinct developmental modules' because of their correlation study. However, I am not so positive at the point that one of two central hypotheses were directly tested in this study: the functional constraint hypothesis - to test it, for example, the authors need to address the functional connectivities (calcium imaging, etc) and then test if the correlation between calcium-transients and the size/shape of each pair of brain regions.

  6. eLife

    Reviewer #3 (Public Review):

    In this manuscript the authors use novel techniques and analytical methods on an up and coming animal model for brain evolution. The manuscript utilizes the cavefish Astyanax mexicanus, which can provide future important insights into the field of neurobiology and in evolution in general.
    The authors however, only argue that Astyanax is a powerful system for functionally determining basic principles of brain evolution (which clearly it will be), but fail to actually describe what brain evolution insights Astyanax gives. The data is in the paper, but the interpretation needs refinement. This would be a much more valuable paper with a thorough evolutionary context based on the already existing, extensive literature. I believe this manuscript has the potential to be extremely impactful.

  7. Version published to 10.1101/2022.03.17.484801v1 on bioRxiv