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Re: Meaning of ABA brain region spreadsheet columns

From: William Bug <William.Bug@DrexelMed.edu>
Date: Sat, 3 Mar 2007 19:38:16 -0500
Message-Id: <859CDDBC-6C64-49F4-916E-3BB62699137C@DrexelMed.edu>
Cc: Alan Ruttenberg <alanruttenberg@gmail.com>, Nigam Shah <nigam@stanford.edu>, kc28 Cheung <kei.cheung@yale.edu>, June Kinoshita <junekino@media.mit.edu>, Gwen Wong <wonglabow@verizon.net>, Donald Doherty <donald.doherty@brainstage.com>, MaryAnn Martone <maryann@ncmir.ucsd.edu>, Luis Marenco <luis.marenco@yale.edu>, W3C HCLSIG hcls <public-semweb-lifesci@w3.org>
To: Mihail Bota <mbota@almaak-01.usc.edu>
Many thanks for the correction, Mihail.

As you can see from the URL below, as Mihail said, brain regions are  
indexed in BAMS using integer PKs:

	Thalamus:
		http://brancusi.usc.edu/bkms/brain/show-braing2.php?aidi=53

I was thrown off by the lack of such IDs in some of the XML exports -  
but these are not for BAMS per se, but for the source atlas  
terminologies BAMS distributes in XML format.

I did check some of the BAMS IDs. They do not appear to the IDs in  
that 2nd ID column (Col H).

You will note from this page on Thalamus, through it's focus on  
neuronal connectivity and chemoarchitectural experimental evidence,  
BAMS in addition to providing information on brain region  
mereological relations, provides info both on documented connections,  
as well as inferred connections and neurotransmitter profile.

Cheers,
Bill

On Mar 3, 2007, at 6:39 PM, Mihail Bota wrote:

> For the sake of completeness, Swanson lab did not define any  
> nomenclature,
> or part of it for ABA. As I said previously, ABA nomenclature and its
> hierarchy are inspired from Swanson-2004. So, is exactly opposite.  
> I am
> not sure whether ABA used primarily or solely Paxinos/Franklin  
> mouse atlas.
>
> Regarding "cols G and H", I cannot say anything, because I haven't  
> seen
> the spreadsheet and I don't know what a PK is. Thus, I don't know what
> "thalamus" ID is about in Bill's message.
> Anyway, the unique identification or regions in BAMS is made by  
> integer
> ID's and not by abbreviations.
>
> Mihai
> On Sat, 3 Mar 2007, William Bug wrote:
>
>> Sorry, Alan - got swamped with BIRN Ontology & Mouse BIRN AHM mtg
>> preparations for next week.
>>
>> You are right - you & I reviewed details related to ABA image meta
>> data last weekend - NOT brain region level meta data.
>>
>> I'd bet a lot of what Nigam lays out below  - RGB LUT values and PK -
>> is correct.
>>
>>
>> Region Abbrev (Cols B & C):
>> 	'CNU' can be found in the Swanson (2004) XML file ("Cerebral
>> nuclei").  Despite the ABA atlas gray scale image plates having been
>> derived from the Franklin & Paxinos adult C57Bl/6 atlas, as Mihail
>> mentioned, the Swanson lab did some region classification for ABA,
>> and they lumped this at a higher level in a region they called
>> "Cerebral Nuclei" - though, again as Mihail points out, Striatum
>> immediately maps in rodent to a structure referred to as the
>> "Caudoputamen" (http://brancusi.usc.edu/bkms/brain/show-braing2.php?
>> aidi=129).  Caudoputamen [very important for the PD use case] and
>> other structures in the base of the telencephalon are a part_of a
>> larger structure they've defined for ABA as "Cerebral Nuclei (CNU)
>> 	The one fly-in-the-ointment here is the phrase "...they've defined
>> for ABA...".  This doesn't necessarily map to any of the other brain
>> region classification schemes/CVs used elsewhere - not a trivial
>> process - but not impossible - both the NN group and BIRN groups are
>> working on this - as is Mihail - as he mentioned).  As Mihail points
>> out - and you can see in the XML files he distributes - it does link
>> into the vocabulary used in Swanson 1998/2004 for rat.  It most
>> likely does not deterministically map to the CVs used for brain
>> region by GENSAT (believe that somehow derived from some combination
>> of NN and regions as given in the Franklin&Paxinos mouse atlas -
>> believe the GENSAT segmentation was performed by someone from George
>> Paxinos's lab who went to work with the Rockefeller GENSAT group).
>> SenseLab has much less brain region detail.  It MAY be using the
>> Swanson nomenclature.  Given SenseLab has just a subset of the
>> regions you'd find in an atlas, it's possible someone there at Yale
>> could fairly quickly provide a lookup table mapping their region
>> terms to one of the other atlases (Luis may even have done this
>> already in the context of some of the neuroinformatics repository
>> integration work he has done over the last several years).
>>
>>
>> Region color (Cols D, E, F):
>> 	All digital atlas have a color LUT for regions.  These are generally
>> just 8-bit (only because few atlas projects have foreseen having the
>> expert personnel resources to manually segment > 256 regions) In the
>> atlases, the regions derive from very laborious manual segmentation
>> done in tools like AMIRA by specially trained, highly knowledgeable
>> neuroanatomists.  The manual segmentation is performed on 2D
>> sections, assembled into 3D volumes, smoothed, then added to the 3D
>> atlas voxel data (many atlases are not actually TRUE 3D data sets -
>> e.g., the Paxinos atlas used at ABA - so the re-assembly, smoothing
>> and integration with voxels isn't required in that case).
>> 	Anyway - ABA is obviously being forward looking and using 24-bit
>> values for their region LUT.  Besides, when using Paxinos, they are
>> GIVEN the region segmentation, so the manual effort is potentially
>> eliminated (though the only electronic version of the region
>> segmentation typically must be obtained through the atlas publisher -
>> Elsevier in this case - and generally all the regions for a given
>> Paxinos image (sagittal or coronal) are just lumped into a single,
>> bit-mapped file.  This means you must take that bitmap, run
>> algorithms to identify the individual regions (usually based on color
>> - e.g., just as you see here, each region has a specified color in
>> the bit map).  The isolated regions can then be converted to a
>> geometric object format (from simple point list on to quad-tree or
>> oct-tree) and this is then stored separately in a RDBMS.  This is
>> EXACTLY what the SMART Atlas project in BIRN (from Maryann Martone's
>> NCMIR group at UCSD - source of CCDB, too) has done.  This way, each
>> individual region is defined as a geometry in a specified coordinate
>> space AND - most importantly - can then be used to support SPATIAL
>> queries on the atlas (e.g., "Show me all the defined brain regions
>> that lie within this shape I just drew on image X that you've
>> registered to your atlas coordinate space).
>>
>>
>> Other Region numbers:
>>
>> 	A)  Cols G & H
>> 		I would guess these are BOTH PKs of some sort as they both contain
>> rather small and unique integers.  Given column G is listed in order,
>> I'd guess that is the ABA internal PK for that region.  The other ID
>> is probably a cross-reference to another brain region classification
>> scheme.  A search of the various atlas classifications on the
>> Mihail's BAMS site doesn't appear to provide such equivalent IDs.
>> I've searched in NN, but those IDs don't correspond either (e.g., the
>> Col H. for "Thalamus" - 351 - does not correspond to the NN ID for
>> "Thalamus" - 283).
>> 	One interesting note - if you sort the spreadsheet on Col H - you
>> will find the rows are ordered in nearly perfect alphabetical order
>> by brain region abbreviation.  This indicates to me these Col H
>> values are likely to relate to IDs created for these regions by
>> Mihail/Swanson when they did this classification work for ABA.  There
>> are no integer PKs given in the BAMS XML files from the Swanson lab
>> that match these numbers, so only Mihail can vet this hunch.  I'd
>> guess they are expecting to use the "brain region abbreviation" as
>> their immutable, unique link.
>>
>> 	B) Col I:
>> 	Typically, the whole purpose of registering brain image stacks into
>> the coordinate space of a digital atlas (such as has been done for
>> the 20,000+ ABA image stacks for the individual, gene-specific,
>> GenePaint-ed brains) is so the expert segmented regions from the
>> digital atlas can be used to drive QUANTITATIVE ANALYSIS of the
>> registered image stacks in a consistent and comparable manner.  Being
>> able to visualize the atlas regions overlayed onto individual images
>> from a given registered stack is useful for making qualitative
>> observations - or as a pedagogical aid - BUT it can't drive automated
>> analysis unless:
>> 		- the atlas comes with a coordinate system and the segmented brain
>> regions have been deterministically mapped into that space
>> 		- the image stacks have been registered to the same coordinate  
>> space.
>> 	In the case of the ABA, the atlas is the Franklin&Paxinos 2001 adult
>> C57Bl/6 brain atlas and the coordinate space is their interpretation
>> of stereotaxic coordinate mapping.  The registration process has some
>> error (for ABA, I believe that is 300 microns [probably different for
>> in-plane registration - i.e., 2D to 2D alignment - vs. the third
>> dimension between images in a given dissection axis (i.e., coronal or
>> saggital for ABA)]).
>> 	SO - for Cols J,K,L, they likely refer to the location of the brain
>> regions in the coordinate space.  In a true 3D atlas, all you'd have
>> to do is give 3D geometric definition of the region shape (e.g., as
>> an oct-tree), and give the location of the centroid for that shape in
>> the coordinate system.  Since the F&P atlas is NOT a true 3D atlas
>> but rather a series of 2D images, you don't have a 3D geometric
>> definition of the region.  With that in mind, the way to specify
>> WHERE in the atlas a given region lies is to give the FIRST and LAST
>> atlas image plate the extents of that region lie in when viewed along
>> a specific dissection axis.  For very convoluted structures such as
>> the hippocampal formation, this can be a bit problematic to use
>> computationally, but it's usually sufficient for the types of tasks a
>> 2D atlas can support.
>> 	In terms of what this set up can support, it is likely one of the
>> things they are looking to do is to support users segmenting the  
>> gene-
>> specific images (GenePaint-ed images) then comparing those gene
>> specific segmentations to the brain segmentations.  Given the limits
>> of the 2D realm, you can't really do true 3D volumetric
>> intersection.  However, what you can do is determine to what extent
>> regions-of-interest (ROI) created by users to identify expression
>> patterns on a given GenePainted image overlap with the 2D sections
>> through the brain regions that appear on the corresponding,
>> registered Paxinos plate.  You would essentially try to estimate an
>> intersection of the user drawn 2D ROIs with the 2D atlas region
>> shapes for each region that appears on that Paxinos plate.  When
>> sorting those numbers by atlas brain region, you'd then go through
>> ALL the atlas images that contain a given region, and add up the
>> total intersection with the user drawn ROIs across all the images
>> from BRAIN X that user drew ROIs on.  This would be normalized to the
>> total approx. pixel volume for that brain region across all the atlas
>> plates where it appears - resulting in an APPROXIMATE volume ratio of
>> a given gene stained for in BRAIN X with a given region defined in
>> the atlas.  The large number - Col I - appears to scale with the
>> approximate size of the region - e.g., "Olfactory areas" is quite
>> large (838206), whereas one of the smaller regions included within
>> OLF is much smaller (Nucleus of the lateral olfactory tract [NLOT] -
>> 7407) - so I would guess this column represents that altas-defined
>> approx. region volume (i.e., sum of all the 2D areas defined for that
>> brain region across all the F&P atlas images).
>>
>> 	B) Cols J, K, L:
>> 		It's quite likely ABA calculated an approx. 3D location value for a
>> region probably truncated based on the existing locations of the
>> Paxinos plates within the stereotaxic coordinate space.  Those
>> coordinates would be specified either with:
>> 			* a 2D coordinate location within a F&P atlas image plate (either
>> as unitless PIXELS or as stereotaxically-defined MICRONS) + a unique
>> ID for that F&P atlas image plate derived from a specific dissection
>> plane axis (e.g., F&P Coronal plate 23)
>> 			* a 3D coordinate location that somehow places some morphological
>> property of the region in the stereotaxic coordinate space - e.g.
>> front-upper-left point for the approx. 3D bounding box for that
>> region, centroid for the approx. 3D bounding box for that region,  
>> etc.
>>
>> That's all I have time for now.  Must get back to meeting prep.  It's
>> possible reading the ABA Nature paper from January would get you a
>> more specific answer - or - better yet - just drop an email to the
>> guy you spoke with at ABA.
>>
>> Hope that helps.
>>
>> Cheers,
>> Bill
>>
>>
>> On Mar 2, 2007, at 5:15 PM, Alan Ruttenberg wrote:
>>
>>> Don't recall doing this, though it's certainly possible that I've
>>> forgotten.
>>> Just to clarify, each of these lines is for a brain region, not for
>>> an image.
>>> If you want to do this later this evening with me, give me a call
>>> at home after about 10.
>>> -Alan
>>>
>>> On Mar 2, 2007, at 5:10 PM, William Bug wrote:
>>>
>>>> Alan,
>>>>
>>>> Didn't you and I review this already at the ABA site.
>>>>
>>>> All one would need to do is bring up one of these images at the
>>>> ABA site, go through the "noodling" we did, and look at the
>>>> corresponding entries in the spreadsheet to match up a "meaning"
>>>> to each column (probably nearly all those columns).
>>>>
>>>> Cheers,
>>>> Bill
>>>>
>>>> On Mar 2, 2007, at 2:15 PM, Nigam Shah wrote:
>>>>
>>>>>> BTW, if someone has a theory of what the other number in
>>>>>> ontology.xls are, I'm all ears.
>>>>>
>>>>> Okay, pure guesses:
>>>>>
>>>>> Line 4 = Cerebral
>>>>> cortex,CTX,CH,176,255,184,3, 85,4141526,61.647,29.999,33.711
>>>>>
>>>>> 176,255,184 seem like RGB values (they all range from 2 to 255)  
>>>>> for
>>>>> that region in the image.
>>>>> 3 is a serial number or internal id.
>>>>> 85 - no clue
>>>>> 4141526 - no clue
>>>>> 61.647,29.999,33.711 seem like 3D voxel coordinates.
>>>>>
>>>>> --Nigam.
>>>>>
>>>>
>>>> Bill Bug
>>>> Senior Research 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
>>>>
>>>>
>>>>
>>>>
>>>
>>
>> Bill Bug
>> Senior Research 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
>>
>>
>>
>>
>>
>

Bill Bug
Senior Research 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
Received on Sunday, 4 March 2007 00:38:44 UTC

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