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  1. Author Response:

    Thank you for taking the time to review the Digital Brain Bank, and for providing several suggestions to improve both the manuscript and website. We appreciate the positive comments surrounding our new resource, given the considerable effort that has been invested to date. Below, we provide a summary of the key changes that have been made to the Digital Brain Bank manuscript, reflecting the Editors’ and Reviewers’ suggestions.

    Resource Description

    We appreciate from the Reviewers’ comments that the description of the Digital Brain Bank as an “interactive data discovery and release platform” and a “cross-scale, cross-species investigation framework” does not reflect the current underlying functionality of the website. Although considerable effort has been made to enable users to visualise datasets directly on the website, the primary purpose of the Digital Brain Bank is a data release platform. We have adapted our wording to align with this, shifting the emphasis of the Digital Brain Bank as a data resource. We have additionally clarified the scope of datasets available in the resource, alongside the types of data available on the Digital Brain Bank website.

    **Context of Resource **

    The Reviewers noted that the original manuscript did not frame the Digital Brain Bank in the context of existing resources. In the revised manuscript, we have added a discussion of the Digital Brain Bank in terms of existing neuroimaging resources spanning multiple domains, including histology, transcriptomics, in vivo MRI & post-mortem MRI. We anticipate that the Digital Brain Bank will complement existing open-science initiatives in both human and non-human neuroimaging. We foresee the greatest overlap and integration between the Digital Brain Bank and existing in vivo and post-mortem MRI databases, where common signal-forming mechanisms facilitate comparisons.

    Web-based Image Viewer (Tview)

    Our web-based image viewer, Tview, provides visualisation of multi-scale (e.g. MRI & microscopy) data in a single 2D plane. This functionality was not readily available with existing viewers, requiring careful implementation due to the large size of the high-resolution microscopy datasets. The Reviewers note that Tview is only implemented for certain datasets in the first data release to the Digital Brain Bank. In the new manuscript we motivate this decision. Notably, several of the datasets in the first release are MRI-only. For these datasets, we found that a detailed static image was more suitable for visualisation.

    To further improve visualisation of these datasets, we are in the process of implementing a second web-based viewer to the Digital Brain Bank website, NiiVue. NiiVue is an open-source 3D volume viewer under active development. This will enable users to navigate 3D MRI datasets directly on the website, and supports overlays to localise the histology sampling location. These points are raised in the new manuscript, with an online NiiVue example available at https://niivue.github.io/niivue/features/overlay.multiplanar.html.

    Datasets

    Reviewer 1 raises that the Digital Anatomist and Pathologist categories have relatively few datasets. In the updated manuscript, we emphasise the uniqueness of the data available under these themes, and that the Digital Brain Bank represents one of the most substantial resources of its kind, providing data from 45 brains in total. We additionally provide further details of datasets which are intended for future release to the Digital Brain Bank. These are the Forget-Me-Not developing Human Connectome Project (dHCP) study - providing diffusion MRI datasets acquired in unfixed, post-mortem neonatal brains; BigMac dataset - providing in vivo MRI, post-mortem MRI, PLI and immunohistochemistry in a single, whole macaque brain; a cohort study combining multi-modal MRI and histology to investigate mouse models of ALS; and further primate species, alongside extensions into orders Carnivora and Rodentia.

    Corpus Callosum Analysis

    The corpus callosum analysis in Figure 3 has a small control cohort, and Reviewer 1 raises whether this analysis can produce meaningful results. We agree that the low number of controls and difficulty matching between groups is a major limitation of this analysis. Certainly, one would need to be cautious about interpreting any new observations based on our results. However, the purpose of this analysis was to demonstrate that we can use our data to replicate findings which have been previously reported in ALS (e.g. Chapman et al., 2014). This has been clarified in the new manuscript, alongside text to acknowledge the limitations of our analysis.

    MRI-Microscopy Registrations

    Co-registration between the MRI and microscopy data for the Human ALS MRI-Histology dataset is ongoing. As raised by Reviewer 1, coregistered MRI-microscopy datasets were previously available in only two brains. Since submission of the original preprint, we have additionally coregistered the PLP (myelin) staining data in multiple anatomical regions (5-8 regions per brain) for 13 brains in the Human ALS MRI-Histology dataset. These will now be available through the Digital Brain Bank.

    API, Metadata & Versioning

    Reviewer 3 raises that the resource is not currently designed for programmatic interactions or versioning. In the new manuscript we discuss why these are not yet implemented due to the current ad hoc nature of data access through signing MTAs via email. We have also taken the opportunity to outline our ambitions for incorporating these features in a future iteration of the Digital Brain Bank. Specifically, we intend on developing a new database to streamline data access and enable a programmatic interface. This database will perform user sign-up, authentication, and approval directly on the Digital Brain Bank website. This will enable approved users to access datasets directly on the website, which can readily incorporate stricter standards for linking data and dataset tracking.

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  2. Evaluation Summary:

    This paper will be of interest to a large class of neuroscientists who work with MRI. It presents the Digital Brain Bank website and project, which is an effort to curate and share high-quality post-mortem co-registered MRI and histology data of healthy human brains, pathological human brains, and brains from a variety of other species. These data allow investigators to address scientific questions that cannot be answered with in vivo imaging alone and are accompanied by an online browser-based viewer. The described datasets provide a highly valuable resource for multiscale investigations of brain architecture and comparative neuroanatomy, which is unique in its selection of modalities and species.

    (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 #1, Reviewer #2 and Reviewer #3 agreed to share their names with the authors.)

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  3. Reviewer #1 (Public Review):

    The authors collected various sets of post-mortem data that has been previously acquired at the University of Oxford and have mostly already contributed to different peer-reviewed publications. The paper and project categorise the data sets into neuroanatomical data sets (healthy humans), a digital zoo (nonhuman brains) and pathological data (human data). Together these form the 'Digital Brain Bank'. In this manuscript, the authors present the motivation for performing post-mortem MRI and histology experiments, describe the existing data sets, acquisition strategy and methodology. The core of this work is the website open.win.ox.ac.uk/DigitalBrainBank and the image viewer Tview that has been developed to allow anyone interested to explore the data directly in the browser without downloading them. To download complete datasets, official agreements with the University of Oxford have to be made.

    Strengths:

    The paper motivates well the use of post-mortem imaging in neuroscience and also discuss the technical challenges with data acquisition and solutions. Some of the data made available are unique without doubt, with regard to the brains and methodology used.

    Most of the described data have already been peer-reviewed and published. Researchers using them are provided with the respective references they can consult for details and cite.

    The digital zoo already has various different species and a concrete example is provided in the paper for approaches for how to use such data neuroscientifically (Figure 2).

    Weaknesses:

    Testing the website, it seemed to me that Tview is only implemented for certain datasets, and that upon clicking on most of the other datasets, only a screenshot from a certain axial view of the dataset is provided.

    The neuroanatomy database and neurology database do not have many data sets yet. In particular, the neuroanatomy database has three human corpus callosum sample and one whole brain. The pathologist data base only has ALS (+ control) data and no data from other pathologies yet. In Table 1 it states that some data are only available for "selected" brains, to me it is not clear how many brains were selected and how.

    Currently, I find the term "interactive data discovery and release platform", as used in the abstract, a little bit misleading. The interactivity is limited to viewing overlays of different images in a few of the datasets and the release option of one's own datasets is not established (yet).

    The integration of histology and MRI data seem to be a main component of the work, emphasizing the uniqueness and usefulness of the data to the community, and motivating the development of the Tview software and website itself. However, it is mentioned that the registration between these two data modalities has only been performed for two brains so far.

    An example neuroscientific application is demonstrated by statistically comparing FA in the corpus callosi of the ALS brains to the controls. Since there are only 3 controls, the sample size is very low, and I am sceptical to what extent it is even possible to match the two groups (e.g. for age, gender and tissue quality).

    I think the Digital Brain Project will be a very valuable resource to the community, especially if it is being extended and maintained. At the moment, the already available data are still limited (just one type of pathology, just two datasets with coregistered MRI and histology), however, the authors have demonstrated with selected examples what is possible with the developed website and software.

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  4. Reviewer #2 (Public Review):

    Tendler et al present carefully planned and well-executed resource offering the field access to a set of unique datasets. Highlights include high-resolution post-mortem diffusion data and PLI in humans along with images from a diverse range of primates and other species including the extinct thylacine. For samples where post-mortem data are available, the imaging data has been co-registered to facilitate cross-modal comparisons. The tailor-made online Tview tool enables easy initial visual inspection of the various datasets, including transparent layering of the various modalities. The Digital Pathologist theme is a particular asset as open human pathology datasets are still rare. Here the theme is currently limited to a single pathology - ALS - with no description of future planned studies expanding this to other pathologies. Hopefully the publication of the platform and resource will help to inspire the curation of similar datasets for other neurological/psychiatric diseases. There are additional data releases planned in the near future to include a multimodal macaque dataset and neonatal diffusion data.

    Overall the Digital Brain Bank's online platform and openly shared datasets represent a valuable resource to the neuroscientific community.

    I have a few minor comments that I believe would strengthen the manuscript:

    - There is a strong focus on diffusion-based imaging and corresponding PLI microscopy, which reflects the expertise and interests of the authors as well as addressing a gap in what other post-mortem datasets are currently available. It might be useful to place the Digital Brain Bank in the context of a few of these resources and platforms, such as the Allen Institute, which focuses on transcriptomic data, and Human Brain Project/BigBrain, which is predominantly histological.
    - It's a major strength that the MRI and histology have been coregistered and the online tool to view them is intuitive to use. The authors have chosen here to present the MRI data co-registered to the 2D histology and not vice versa. Sectioning can introduce morphological shifts in the tissue that might alter neuroanatomical findings relative to the original brain structure. Could the authors add a note to explain why they chose to present the registration this way round?

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  5. Reviewer #3 (Public Review):

    This paper presents a new online platform with releases of datasets from post-mortem imaging, currently providing access to 21 post-mortem whole-brain, high-resolution diffusion and structural MRI datasets of different species. The datasets are partly enriched by additional co-registered microscopy measurements.
    Some of the data are provided for the first time, and so the paper also describes in detail the challenges and strategies used for performing the high-resolution image aquisitions.

    Some other datasets have been described in previous publications.

    The data are organized into three categories: Datasets focusing on neuranatomical detail, datasets focusing on comparative cross-species anatomy, and datasets focusing on neuropathology.

    Datasets are released together with well curated descriptions and links to publications.

    A multi-resolution 2D online viewer allows to explore the different modalities in a selected image plane.

    Indeed, the platform provides access to a quite unique set of high-resolution postmortem MRI datasets in different species, partially together with co-registered microscopy data for certain sections or regions of interest. This data is a highly valuable resource for multiscale investigations of connectivity and brain architecture, and in particular for comparative anatomy investigations.

    The platform is intuitive to use, well structured and easily accessible. It provides well readable and fairly complete descriptions of the data.
    The multi-resolution 2D viewer gives a good feeling for the quality and type of underlying data, and is a very useful asset for browsing such datasets.

    Since the paper primarly presents a data sharing platform, I am missing more attention and comparison to some established systems with overlapping aims, like the data repositories offered by the Allen institute, HCP, or EBRAINS. It would be helpful to provide a basic overview of complementarity and commonalities with some of those, especially in terms of technical standards and scope of the datasets.

    While reading the paper, I found the platform itself not as feature-rich as I had expected after reading the abstract, where it is characterized as a "cross-scale, cross-species investigation framework". I expected to find features for performing such investigations directly on the platform, but it turned out that "investigation framework" refers rather to the datasets themselves than to the offered online functionality. While the multi-resolution viewer does allow to superimpose and explore the different modalities, and is indeed very helpful to get a first understanding of the underlying data, it is restricted to a pre-specified 2D plane or specific brain structures. I did not find a way to navigate to different sections or structures. Therefore, the main purpose of the platform seems to be finding and downloading the datasets - and without doubt are the data as such highly valuable for cross-scale and cross-species investigations.

    The platform seems at this stage not to be designed for programmatic interactions. It does not expose an API or foster strict metadata standards. This might make it difficult to link it with other repositories or online services, while I would expect significant interest for such programmatic links.

    Overall, the paper is well written, and the presented online resource will be of considerable interest to the neuroscience community. It represents a significant contribution towards filling the gap between the microscopic and whole-brain scale.

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