- From: William Bug <William.Bug@drexelmed.edu>
- Date: Tue, 6 Jun 2006 10:41:32 -0400
- To: public-semweb-lifesci@w3.org
Hi All, Sorry - I'd thought I'd already subscribed to this list, but apparently not - until now. The need for a mereotopologically-sound, neuroanatomical ontology is quite pressing across the community of neuroscientists involved in neuroinformatics projects most of which include a neuroimaging component. Generally there is only one thing neuroscientists are interested in when analyzing images at whatever resolution from the macromolecular (EM) on up to the macroscopic - i.e., identifying biologically relevant shapes. In order for these shapes to have any meaning in a context where one attempts to pool data and perform relevant data reduction operations, the shapes must exist within a shared coordinate space of some sort. For instance, if two separate labs are examining the change in the size of the Substantia Nigra during the course of Parkinsonian neurodegeneration, in order for them to compare their observations, they require several data integration/semantic frameworks: - a shared neuroanatomical terminology - a shared coordinate space (to place the shapes from their images in a comparable coordinate framework) - a shared, well-founded anatomical ontology which encapsulates mereotopological knowledge about shapes in - at least - 3D space. Other knowledge resources can be helpful in supplementing this array of tools, but, generally, these are the absolute minimum. [NOTE: the Wikipedia has a moderately clear definition of mereotopology (http://en.wikipedia.org/wiki/Mereotopology). Basically, it combines a formal, ontological theory of shapes and boundaries (mereology) with the mathematics of topology with the goal of providing a computational formalism to support applying logical operations to objects in space. As has been pointed out by others, a great deal of the work in this field of applied biomedical mereotopology derives from related work in the GIS field. Use of mereotopology by geographers has been going on for quite some time and is much more advanced. Work from GIS can be adapted for use in the biomedical domain, but it must be done with great care, as many of the assumptions behind the way researchers represent space and manner of information being represented can differ significantly across these disciplines.] The same is true as you scale this problem up to field-wide projects such as BIRN or The NeuroCommons. As several have mentioned in this thread, there are already existing resources that can begin to fill this need. 1) NeuroNames Kei, Olivier, Peter Mork, and others have already given sufficient references on NeuroNames in this thread, so that others can dig in deeper to the specifics if they like. Having worked with Doug Bowden, Mark Dubach, and their colleagues over the last year or so in an advisory capacity on the specific issue of use of NeuroNames for semantically-based, neuroanatomical data set integration, I can add a few important qualifying points: a) Doug et al. have been working on the extremely difficult task of unifying neuroanatomical terminologies across mammalian species for 20 years now. Embedded in Neuronames & Braininfo, there is a wealth of hard won empirical knowledge related to how one achieves this end. I think it would be ill-advised to try to duplicate their effort, as the myriad scientific problems related to this effort would surely present themselves again and only need to be worked out once one. b) Doug et al. are extremely collegial and quite receptive to feedback and collaboration - within the bounds of their limited resources. c) NeuroNames is a terminological resource - not a well-founded, spatial ontology of brain anatomy capable of supporting mereotopological reasoning. As with most research-based terminologies, there are many semantically-based relations embedded in the NeuroNames graphs, but as the primary goal of NN is to disambiguate and integrate across the neuroanatomical lexicon, the embedded semantic information can often lead to a logical dead end. For instance, many neuroanatomical terms critical to specifying location in the rodent brain have been placed in the NN category "ancillary terms," as they don't fit into the core hierarchy in an unambiguous way. This can make use of NN for annotating mouse brain gene & protein expression patterns (e.g., GENSAT, the Allen Brain Atlas, various BIRN projects) extremely problematic. d) The NN primary structures (http://braininfo.rprc.washington.edu/ indexabout.html) provide the closest thing to an ontology in NN. As Peter Mork pointed out, there has been an effort in the past to unite this core NN hierarchy with the FMA, which does provide a mereotopologically sound framework for anatomy. Barry Smith (formal ontologist who has worked for over a decade on problems in biomedical ontology - most especially, though hardly exclusively, in the area of mereotopological reasoning) and his colleagues have worked closely with the Cornelius Rosse and his colleagues at the FMA project to create in association with the work started in the FMA a foundational ontology for biomedicine (the Ontology of Biological Reality) that is becoming increasingly important to all of the ontologies being monitored by NCBO and incorporated into the OBO site and the emerging OBO Foundary (http://obofoundry.org/). e) Doug and his colleagues have worked closely with Jack Park (a consulting scientist to SRI's AI Center - http://www.ai.sri.com/) to represent NN as a TopicMap (XTM). As many on this list may know, there has been a moderate amount of effort to integrate and/or reconcile XTM with RDF here at the W3C (search on "TopicMaps" at the main RDF page - http://www.w3.org/RDF/). I'm not certain how this effort will ultimately make NN more "semantic web" compliant, but the bottom line is a great deal of effort has already been expended to express NN in a semantically well-grounded formalism. f) Though - as Don points out - neuroanatomical representations are likely to significantly evolve over the coming decades, as the number of large scale gene & protein expression characterization studies focussed on the brain continue to accumulate. Having said that, the "conventional" view of neuroanatomy will likely remain relevant for a long while to come, not only because it has been used to characterize findings in the literature for the last 125+ years, but also because it did derive from a wealth of empirical observation which is likely to remain valid in many domains of neuroanatomical study. I would also modify Don's well informed comment regarding the derivation of "conventional" views of neuroanatomy. To a large extent they are related to functional studies of the brain - as well as lesion based studies of functional deficits dating back to the 19th century (think "Broca's Area"), but they are also very much based on a study of the morphology of the brain - both the external surface morphology (sulci, gyri, and lobes), as well as histological examination of internal structures. Many of these studies of structure in space are likely to stay with us for some time to come (and are well-founded in reality), though as Tim Clark & Don have pointed out in this thread, nomenclature is still a very significant problem even in this very "old" field. g) licensing of NN - Doug et al. formerly had a completely open policy to distributing NN. The only a reason a license was instituted was at some point about 5 years back another group sucked down the entirety of NN, reworked a lot of what was there - probably with very practical goals directed toward making NN more "correct" and effective in their problem domain - then "republished" their product as "NeuroNames". This lead to a great deal of confusion. The fact they chose to do this on sly also meant the work they did was not necessarily compatible with the work done by Doug et al.. In order to avoid this happening again, it was decided a license would be established to discourage this sort of behavior. As anyone who has developed a terminology and/or ontology, it is absolutely essential there remain a single curating authority, if the value of the resource is to remain in tact. The "vetting" performed by the central authority - as is extensively done by the curators of the Gene Ontology, for instance - is absolutely essential to the guaranteeing the integrity of the knowledge resource. This is not a "closed" or proprietary process, just a highly controlled one. Unfortunately, Doug Bowden's resources are MUCH MUCH smaller than those available to the curators/developers of GO, so the NN curation effort necessarily moves at a slower pace. 2) Working with the Neuroscience community As Kei, Don, and others have stated, it would be unwise to proceed in creating an "open source" neuroanatomical ontology without interacting with the researchers who've already put a lot of effort into this problem over the past decade or so. With this in mind, I have several suggestions: a) The 5 ways of knowing neuroanatomy: This is a pitch I've been making which I think helps to sum up the current ways various sub-fields have attempted to identify/label/ collate brain morphology i) Terminlogies - e.g., NN, BrainLex ii) Ontologies - e.g., Neuro-FMA (the project Peter Mork referred to) iii) Literature Informatics (CocoMac, BrainMap, NeuroScholar, BAMS, ArrowSmith, etc.). These are very mature projects. Some include their own mereotopological reasoning systems (e.g., CocoMac and BrainMap) in order to be able to pool and compare the relatedness of structures and connectivity across different studies in the literature. The goal in this category is to perform large-scale semantic mining of the literature to confirm/refute current knowledge and uncover new correlations - very much along the lines of what The NeuroCommons Project expects to achieve via use of semantic web technologies. Some researchers in this category are actually participating in The NeuroCommons Project (i.e., Gully Burns, who developed NeuroScholar). iv) voxel/pixel analysis: This approach applies computer vision algorithms to automatically - or semi-automatically - identify 2D & 3D shapes in digital anatomical images. This field is also extremely mature, though there are many significant caveats to exactly how much of this work can be effectively automated. v) parameterized models: Often these are derived from - or used to drive - the voxel/pixel based analysis described in 'iv' - though the spatial modeling is definitely a distinct approach from the pure voxel/pixel approach. None of studies you'd fit into these categories exclusively focus on their technique/tool alone without some aspect of the other "ways of knowing neuroanatomy" playing a role in what they do. However, it is clear much fundamental work in this area primarily focuses on one technique over the others. Having said that, when the neuroscience community makes use of this work to examine a specific biological problem, they will often draw significant tools and resources from more than one of these domains. b) NCBO/NCOR sponsored meeting focused on mereotopology in neuroanatomy: Barry Smith is working to bring together researchers working in the 5 domains described above. There is a very pressing need in large- scale, field-wide neuroinformatics projects such as what is being done in the BIRN project to have these 5 domains converge and work more cooperatively. Right now, a lot of manual effort has to be put out to bring them together. This is something BIRN has been pursuing. In the last 6 months, we have received a great deal of support and guidance on this effort from NCBO. Daniel Rubin interacts directly with the BIRN Ontology Task Force, and the work Barry Smith has been doing with FMA, OBO, FuGO, and PATO have very much begun to create a much more well-founded and computable path toward performing large-scale annotation of neuroimaging data. This meeting is on the NCBO/NCOR slate for 2007, but in the interim I hope to see more effort invested in the coming year across the 5 communities listed above toward the goal of integrating across these "ways of knowing" now that the need has been recognized. 3) Microarrays: Just as Don, Kei, Alan R., and others have pointed out, high- throughput assays - microarrays, BAC-based IHC, in situ studies using the Gene Paint technology employed by the Allen Institute of Brain Science to construct the Allen Brain Atlas of gene expression in the brain - are going to transform our understanding of neuroanatomy over the coming decades. This is just a given. There is a pressing need to derive a means to integrate spatially-mapped studies of gene & protein expression into a neuroimaging setting. The spatial resolution may be very coarse - e.g., "whole brain" - but they still provide sufficient spatial information to be usable in the context of a neuroanatomical coordinate system. We are working in the BIRN project to create a means for researchers to integrate these distinct approaches to studying the brain. As Alan R. pointed out, FuGO is working to put description of microarray experiments on a solid, formal footing, and I would expect one aspect of that will be to represent microarray data in RDF/OWL. This is not a trivial problem, given as much of the available data is merely MIAME-compliant - MIAME not even being a data format, but just a collection of minimal data requirements. One need only look at the great complexity of the data submission process at the NCBI GEO site to get an appreciation for how difficult this problem can be. A great deal of effort is being invested in the microarray field to come up with a better means handle this issue, and the FuGO effort will be a critical clearinghouse for this work. The important thing to remember when it comes to field-wide data pooling and re-analysis, it may sometimes be necessary to get right back to the microarray primary image files so as to reapply different criterial when performing the statistical tests and reductions on pooled data. Given this requirement - one we also see in the neuroimaging domain - I believe it is very important to proceed in a well-reasoned manner when seeking to integrate across microarray datasets using semantic web technologies. Alan R. and myself - possibly others too - on this list are on the FuGO Coordinators Committee, so hopefully we can help to keep those lines of communication open. Sorry to go on so, but this is a topic on which I've labored quite intensively over the past year. There is a lot being done on this issue, and I think all efforts will get much further more quickly - and in a way that will carry more street cred with practicing neuroscientists - if we all try to work together. Cheers, Bill Bill Bug Senior Analyst/Ontological Engineer Laboratory for Bioimaging & Anatomical Informatics www.neuroterrain.org Department of Neurobiology & Anatomy Drexel University College of Medicine 2900 Queen Lane Philadelphia, PA 19129 215 991 8430 (ph) 610 457 0443 (mobile) 215 843 9367 (fax) Please Note: I now have a new email - William.Bug@DrexelMed.edu This email and any accompany attachments are confidential. This information is intended solely for the use of the individual to whom it is addressed. Any review, disclosure, copying, distribution, or use of this email communication by others is strictly prohibited. If you are not the intended recipient please notify us immediately by returning this message to the sender and delete all copies. Thank you for your cooperation.
Received on Tuesday, 6 June 2006 14:41:49 UTC