A Multimodal Multidimensional (4D) Map of the Mouse Brain
Project Overview
This is a project to develop a detailed multidimensional digital atlas of the mouse
nervous system. It will, for the first time, enable a unified framework for representing
brain maps and gene expression maps. It will provide the ability to chart the anatomy of
gene expression in the brain of adult and developing mice. It will establish the linkage
between genotype and phenotype. It will enable the comparison of gene expression maps
from different animals, laboratories and strains.
Our goals are to collect data from multiple modalities (MRI, PET, blockface
imaging and histology), reconstruct these data, place them in a defined coordinate
system, delineate and describe the anatomy and develop the appropriate informatics tools
to interact with these data. We will do this for adults and developing animals at various
maturational stages. We will use the C57BL/6J mouse strain. The product of these
efforts will be a series of comprehensive digital atlases describing the probabilistic
neuroanatomy of the mouse, a set of tools to import images of gene expression into the
atlas and tools for interacting with the atlas statistically and visually in 4D.
This is an enormous undertaking. We are cognizant of the technical, scientific
and labor intensive difficulties associated with this project. However, we are experienced
in all the appropriate neuroscientific and neuroinformatics disciplines. We have
performed preliminary experiments for every aspect of the research plan. We have
previously developed atlases of other species along with useable and distributable
software that demonstrates our ability to deliver mature products to the community.
The research plan includes three performance sites; UCLA, USC and CALTECH.
Each was chosen because of the participant’s ability to provide significant expertise in
each of the requisite elements of this proposal. Each complements the other resulting in a
powerful team of investigators with unique and relevant resources and experience
necessary for this project. We have already established productive working relationships.
Our plans also call for the use of workshops to help coordinate the research and
development between our efforts and those of other grantees as well as the gene mapping
community at large.
The structure of this proposal is of an integrated and unified project. In spite of
the participation from multiple institutions, the research plan describes the work without
identifying the responsible investigator. The budget and its justification provide details
regarding the allocation of funds and the specific activities supported.
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Specific Aims
The overall goals of this project are to develop and implement a probabilistic atlas
of the adult and developing C57BL/6J mouse. These atlases will describe the mouse
nervous system in detail within a well defined coordinate system. Since the atlas will be
based on multiple subjects, it will describe the morphological variability within this strain
as well as its development at maturational stages from in utero to adult. The data will be
comprised of in vivo and post-mortem imagery. Tools for visualizing, measuring,
spatially normalizing (deformation correction) and mapping gene expression data will be
created and validated.
In addition to these design driven goals, we will test several hypotheses using the
aforementioned atlases and tools. The product of this effort will be an atlas system for
the C57BL/6J mouse, tools for using it and evidence that it enables the linkage between
brain mapping and gene mapping.
Goals
1. To develop and implement an anatomic framework to map gene expression in
the brain. This framework will be comprised of novel imaging data including
MRI, PET, blockface imaging and histology.
2. To create a set of tools for colocalizing data from different markers, animals
and laboratories.
3. To collect multimodality data describing the brain of the C57BL/6J mouse
strain.
4. Dissemination of the map (and requisite interactive tools) enabling – output
(ability for others to use information/data) and input (ability to incorporate
data from other sources).
Hypotheses
1. There is a relationship between gene expression and morphology.
2. Patterns of gene expression co-vary with morphological changes during
development.
3. Anatomy from histological delineations accurately represents in vivo
morphology.
4. Within strain morphometric variability will be less than between strains.
The experiments necessary to test these hypotheses will utilize the atlases and associated
tools along with gene expression maps (GEMS) collected as part of this study. In this
way we can begin to test the utility and validity of our multimodality, multidimensional
mouse atlas.
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Background and Significance
Mapping the genome
The mouse is a living encyclopedia of known gene functions and a repository for the
unknown gene functions that produce a developing, metabolizing, reproducing mammal.
Consisting of up to one hundred thousand genes, the task of sequencing and cataloging
the entire mouse genome is one of enormous scope and complexity. However, as mouse
genome project advances, there has been a great demand for the structural, functional,
and anatomic correlates of gene expression. Genetic maps have localized genes to
specific sites on chromosomes, but their pattern of expression has only begun to be
touched upon. Even so, there exists no coherent framework for the cataloging and
comparison of gene expression. What is required is a Spatial and Temporal Atlas of
Gene Expression (STAGE) to coordinate the collection and analysis of the enormous
amount of data generated by the genome project.
Mice provide many advantages as a model system for mammalian genetics. Short
generation time, large litters, and relatively low cost of care, make it a pragmatic choice.
The existence of inbred strains of mice, where every individual (of the same sex) is
genetically identical, enhances the information content of STAGEs, allowing for the
collection of data from multiple individuals, secure in the knowledge that there will be no
variation due to genetic factors, something not readily accomplished or impossible in
other (outbred) species. The comparison across different strains of mice (i.e. C57BL/6
and Spret/Ei) will allow for analysis of discrete genetic differences which may already be
cataloged (Lyon, 1996). In addition, the generation of transgenic (gain-of-function) and
targeted knock-out (loss-of-function) mice represent a new kind of mapping directed
toward the elucidation of gene and genome function by the time-honored genetic
approach of comparing mutant and normal phenotypes. Combining genetic remodeling,
genetic maps, banks of genes and emerging methods for assessing gene expression on a
whole tissue level, the true potential of the mouse as a mammalian model is beginning to
emerge.
Gene expression can be studied in a number of ways. Measurements of mRNA within a
tissue can be done by traditional electrophoretic techniques such as Northern blotting and
RT-PCR. Both are technically simple, but limited by the crudity of harvesting tissue
manually. Cytochemical methods such as in situ hybridization can also measure the
expression of a specific mRNA, but in turn are limited by their qualitative nature.
Furthermore, gene expression can also be measured by the level of protein expressed.
These measurements can be made either electrophoretically by Western blot or by
cytochemical methods such as immunohistochemistry. In both cases protein levels are
measured by the binding of an antibody specific for the protein itself, making this
perhaps the best indicator of gene expression. (Should microPET be mentioned
here?)YES AND POINT TO PRELIM RESULTS
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Atlases and Maps
Atlases of normal mouse development have immense pedagogical value and
provide researchers studying normal, mutant, and transgenic mice a standard against
which specific examples may be compared and contrasted. Standard methods of atlas
construction typically involve sacrificing, fixing, sectioning, staining, then recording
photomicrographs of individual sections. Photographic plates are the raw material of
most atlases that contain three additional critical elements: 1) annotation in the form of
graphical reconstructions highlighting important detail; 2) nomenclature in the form of
descriptions and names of discrete structures; and 3) imposition of a 3D coordinate
system so that anatomy can be referred to using a standardized atlas. Atlases of this type
for the mouse have been presented by Rugh (1990), Theiler (1989) and Kaufman (1992).
The advent of powerful inexpensive computers coupled with the ability to conveniently
transport large amounts of data (via CD-ROM or over the Internet) are bringing about
changes in the way atlases are constructed and in the ways they can be used. When in
book form, the intrinsically 3D animal must be viewed as a series of 2D sections.
Moreover, the orientations available to the viewer are limited to samples of standard
planes of section (e.g. sagittal, coronal, axial). These restrictions make it difficult to
follow complex 3D structures and hinder comparison of one's own 'oblique' sections with
the 'perpendicular' sections found in the atlases. Digital atlases have the potential to
obviate both of these vexing problems (Williams & Doyle, 1996; Kaufman, et al., 1997;
Gibaud et al., 1997 and Toga et al., 1995). With the section data reconstructed into three
dimensions, highlighting complex structures and computationally sectioning at arbitrary
angles becomes possible. Quantitative morphological measurements (volumes, distances,
angles) can be accomplished and maps can be generated that amalgamate data from
various experimental techniques. Temporal and spatial gene and protein expression
patterns, axonal trajectories, patterns of vasculature, and specific neuronal responses to
stimuli can all be combined to obtain a canonical organism or system. Such a data set
could potentially embody all quantitative information known about the animal in a
concise framework.
Informatics
Motivated by such benefits, several efforts are underway to generate digital
atlases. There is at least one commercially available CD-ROM rat atlas (Paxinos and
Watson, 1991) and other less ambitious CD-ROM undertakings (Ghosh, et al., 1994;
Smith, et al., 1996). A number of World Wide Web sites present a variety of two
dimensional data (www.rodents) and some aim towards being three dimensional atlases
(www_atlases; Toga et al., 1995). Based upon the atlas of the developing mouse
(Kaufman, 1992) the Edinburgh group has embarked on a significant effort to create a
database to house gene expression (Ringwald et al., 1994). In our own laboratories, the
ICBM effort at mapping the human brain, is based upon a digital 3D representation of a
population’s anatomy. The spatial normalization, warping, morphometrics, visualization,
databasing and related informatics efforts included in brain mapping have made
enormous progress in the last few years (Koslow & Huerta, 1998; Toga & Mazziotta,
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1996,1999; Toga, 1999). Many of these advancements have direct relevance to the
present project and specific examples are provided below.
Name
Reference
Features
Muritech
UT Memphis
Edinburgh
Chemoarchitectonic Atlas of the
Developing Mouse Brain
Jacobowitz & Abbott, 1997
Atlas of the Prenatal Mouse
Brain
Shambra, Lauder & Silver,
1992
Calretinin, calbindin, serotonin,
tyrosine hydroxylase, AChE, 336
dpi/20 mm
H & E; 10mm
Technology
High resolution MR. The use of magnetic resonance imaging has revolutionized the
noninvasive investigation of neuroanatomy and function, and is an integral component of
digital atlasing. Recent reports of the observation of subtle intracortical structure such as
the stria of Gennari (Clark et al., 1992), the histological confirmation of the normal MR
distribution of the corticospinal tract (Yagishita et al, 1994) and the ability to selectively
image myelin (MacKay et al, 1994) support efforts to apply MR techniques to the
imaging of anatomy. The combination of higher field magnets and post processing
techniques for image enhancement (Kui Ying et al, 1996; Holmes et al, 1997) has also
recently revealed structures as fine as thalamic nuclei, the origin of the thalamocortical
tracts, and there is evidence that we can discriminate in vivo cortical architectonic
regions. These technical innovations permit the macroscopic observation of fascicles
through the in vivo and cranially-intact post mortem brain. Even greater strides can be
taken by using extremely high field instruments in the smaller primate species, using
microscopic MRI.
Microscopic MRI. The notion of using MRI at microscopic resolutions arose early in
the development of this technique (Lauterbur 1973). The spatial resolution in biological
samples is typically limited by line-width broadening (T2 effects), diffusion, signal-tonoise ratio (S/N), factors whose physical limits have been discussed in detail by
Callaghan (Callaghan 1991) and others (House 1984; Cho et al. 1988; Kuhn 1990;
Blumich and Kuhn 1992; Zhou and Lauterbur 1992). Estimates of the theoretical limits of
resolution in the MR image range from 2 to 0.5 micron (Cho et al. 1988; Kuhn 1990;
Callaghan 1991). By judicious choice of experimental conditions (e.g. bandwidth and
gradient strength), deterioration in resolution due to the combination of these effects can
be compensated to a degree, resulting in a practical spatial resolution, currently limited
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by the amount of time available to acquire the image. The quality and usable resolution of
these MRI images can be enhanced in several ways, such as: increasing the main
magnetic field strength to 12T ; optimizing the radio frequency (RF) coil for small
samples; employing 3D volume imaging rather than slice imaging; and using fast
imaging pulse sequences (e.g. DEFT, FLASH, EPI). Indeed, several groups have
achieved spatial resolutions of 10 micron or less (Blumich and Kuhn 1992), and MRI of
rodent eyes and xenographs correlates well with subsequent histological examinations of
the same tissues. Aguayo and coworkers (1987) resolved cell clusters and structures as
small as basement membranes.
Features and Benefits
The creation of a comprehensive framework capable of encompassing diverse
information about the mouse holds tremendous promise for integrating the genotype and
phenotype of this animal. Genetic information is expressed in complex and everchanging patterns throughout the development of the animal. A comprehensive
description of these patterns and how they relate to the emerging morphology is crucial to
our understanding of the interactions that underlie the processes of development, normal
structure and function, disease and evolution.
Studies of gene expression are rapidly producing a vast amount of information relating to
these complex patterns. It is currently impossible to adequately compare results from
different animals, investigators and laboratories. It is also difficult to make comparisons
between the expression of different genes in order to assess the possibility of complex
networks of genetic interaction. These problems cannot be addressed by conventional
means of publication, but require the development of an electronic database, together
with tools for cross-modality correlation. Moreover, text descriptions of gene expression
are of limited value due the spatial complexity of the patterns and partly because domains
of gene expression do not necessarily correspond to named anatomical structures. The
proposed multimodality, multidimensional atlas will address these limitations.
Preliminary Results and Expertise
Instrumentation
MRI. In this section we outline preliminary results obtained using our current 11.7T
vertical bore MRI instrument. We focus on four topics demonstrating the feasibility of
using µMRI as an anatomical survey methodology for obtaining 3D high resolution data
of intact mice in vivo:
• µMRI of fixed mouse embryos at specific gestational ages
• In vivo in utero MRI of a day 12 pregnant mouse
• RF & gradient coil optimization
• Diffusion tensor MR imaging of a transgenic MS mouse model system
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We begin with a discussion of 3D MR imaging of fixed mouse specimens which
demonstrates the excellent image quality, resolution, and contrast that can be obtained on
immobile samples in a several hour experiment (Jacobs et al., 1999). Preliminary
diffusion tensor imaging of a transgenic mouse model for multiple sclerosis illustrates the
wealth of information inherent in this imaging modality and its ability to delineate nerve
tracts and abnormalities in the mouse spinal cord. We have also been able to collect
multislice images of an anesthesized pregnant mouse indicating that it is feasible to
obtain reasonable MR images in a 15 minute experiment with a live mouse. Finally, we
comment on some of our experience building MRI probes (specifically RF & gradient
coils) for our 11.7T systems which affirms that customizing this portion of the hardware
can provide significant benefit at relatively modest cost in time and effort.
Figure 1. Volume rendering of MR images
of fixed mouse embryos. Gestation days are
noted. The Day 6.5 image (left panel) has
been sliced in half to show internal details.
The Day 14 embryo is rendered semitransparent to show internal anatomy. In
vivo, in utero MR images of Day 12 mouse
embryos. The mother and RF coil are
shown to the upper right. A single slice
showing several adjacent embryos is shown
to the upper left. Lower panels show serial
longitudinal slices through a single live
embryo. (right panel). A higher resolution
version of this figure appears in the
Appendix.
MRI of Fixed Mouse Embryos at Different Gestational Days. Figure 1 shows
renderings of 3D MRI data of fixed mouse embryos. Each specimen was excised and
fixed at the gestation day noted. A single sagittal slice from the 6.5day and the 11.5day
specimen are displayed, while volume renderings of the other datasets are shown. The
datasets were all acquired with a 3D spin echo protocol with TR=880~1000ms. Excellent
contrast is seen in the developing nervous system (see slice data day 11.5 and
semitransparent day14). In this study, good contrast was obtained with short TE's
(~30ms) for the young samples, but longer TE's (~100ms) were necessary to obtain good
contrast in the older samples. Spatial resolution is 15microns in the 6.5-10.5day samples,
30microns for the 11-13day samples, and 60microns for the older samples. Imaging time
ranged from 3-12hours in most cases, but was 24hours for the larger 16day specimen.
Ventricles are always easily distinguished from the surrounding tissue. The developing
heart is seen as early as day 8.5 (image not shown) and the rudiments of the skeletal
system are apparent in the day14 image. The RF coil used was matched to the sample
size to ensure maximum filling factor.
These images demonstrate that:
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• spatial resolution ~50microns (isotropic??????) affords detection of many internal
features;
• contrast changes with gestation day and may be enhanced by tuning experimental
parameters;
• good signal to noise can be obtained in a reasonable time, even for the 30micron
resolution images;
• imaging times are long, but not excessive for large 3D datasets.
MRI of Aged Microcebus. We compared brains of young and aged Microcebus
using several 2D and 3D MRI algorithms at 11.7T and standard histological examination
of the same tissue. The small size and short life span of Microcebus make it an attractive
model of primate systems. A representative image (see Figure 2) of a 12-year old
formalin fixed mouse lemur brain shows features consistent with iron deposits localized
mainly in nuclei of the hypothalamus and subthalamus. This demonstrates the feasibility
of imaging live animals under anesthesia.
Figure 2. A 12 yr Microcebus brain imaged with MRI
(inset) then sectioned and stained for non-heme iron with
Prussian Blue stain. There is excellent correspondence
between the Prussian Blue stain and MR image intensity.
In agreement with MR results, no Prussian Blue stain was
noted in the young brains.
In vivo in utero MRI of a day-12 pregnant mouse. Artifacts arising from sample
motion are an ever present problem in MR imaging. We investigated this problem by in
vivo imaging pregnant mice. A pregnant mouse at day-12 gestation was put into deep
anesthesia with an IP injection of phenobarbitol, placed in an Alderman-Grant RF coil,
then into our 11.7T MRI instrument. A number of MR datasets were recorded over the
next 4 hours. A multislice spin echo protocol taking 10min per experiment was
employed. Most of the datasets had significant motion artifacts due, presumably, to
diaphragm movement transmitted to the uterus. Some datasets were of high fidelity. One
such dataset with no apparent artifacts is shown in Figure 1. In the upper left, a slice
through the uterus shows five tightly packed mouse pups. The 15 smaller panels show
serial slices through a single pup. Slice position and orientation are shown in the cartoon
to the lower right. Ventricles are apparent in panels c-e; convolutions in the snout are
seen in f-i; and the digits of the right paw can be seen in panels i and j. Subsequent to this
imaging experiment, gestation proceeded normally to term with no obvious ill effects on
the mother or pups.
RF & gradient coil optimization. Over the past several years we have explored the
characteristics of a number of different types of RF coils with special emphasis on
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signal/noise performance at 500 MHz, the frequency at which magnetic resonance occurs
at 12T. Birdcage resonators (Joseph, 1989; Hayes, 1985; Watkins, 1988), modified saddle
coils (Vaslow, 1991), implanted coils (Hollett, 1987), and more novel designs
particularly suited to high frequencies (van Vaals, 1990) have been considered. Sadly, at
500MHz coils with diameters larger than ~3cm become difficult to tune due to
distributed capacitance effects (Bowtell, 1992). Because RF coil sensitivity is a key
parameter in improving the poor NMR signal, it is a key feature of the hardware. For
imaging the smaller embryos shown in Figure 2, we used simple solenoid coils and a
tune/match network positioned as close as possible to the coil. For samples in the 510mm size we used a loop-gap resonator composed of a series of single turn solenoids
each tuned to 500MHz. This coil has excellent B1 homogeneity & a high Q. For larger
samples, we have made both Alderman-Grant and birdcage resonators. Both have good
sensitivity and are straightforward to design and construct. Prototypes of sizes
appropriate to imaging both in vivo and in vitro mice brains have been built and function
adequately (see Figure 2). Recent work by Dodderll and colleagues has been especially
helpful (Eccles et al., 1994; Crozier et al., 1994; Hsu et al., 1995; Mahony et al., 1995;
Roffmann et al., 1996).
High resolution macroscopic in vivo imaging. By acquiring multiple individual scans
on standard 1.5T clinical scanners, and averaging the MR signal post hoc (see Appendix
for details) we have produced an extremely high quality image of a single human subject
(Figure 3). This approach will be extended using the high field machine to acquire finer
initial resolution and thereby push the limits of in vivo macroscopic MRI in the mouse.
Figure 3. Details from N=27 T1 average
volume. By combining seven 0.78mm3 and
twenty 1.0mm3 volumes, the signal/noise
ratio was enhanced beyond that of a single
scan, resulting in previously unobserved
anatomic detail.
Subtleties of nuclear
boundaries and fascicles within the thalamus
and hippocampus became evident (a) as did
brainstem divisions (b). Marked intensity
differences within the basal ganglia
highlighted their components (c) and
interconnections, such as the gray bridges
between the caudate and putamen. These
gray-gray differences were also clarified
within the brainstem (d), and even the small
penetrating vessels were resolved (in for
instance, the perforated substance (d) or
embedded in the insular cortex).
PET (Molecular Mapping)
Positron Emission Tomography (PET) is a non-invasive imaging technique where
positron-emitting isotopes such as carbon-11, nitrogen-13, oxygen-15 or fluorine-18 are
attached to biologically relevant molecules and injected in trace amounts into the subject.
When the isotope decays, the emitted positron annihilates with an electron in tissue to
produce two 511 keV gamma rays which are emitted 180° apart. A PET scanner detects
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these pairs of gamma rays externally and reconstructs tomographic cross-sections. The
intensity of the image at any point reflects the concentration of the isotope (and therefore
the tracer of interest). The main advantages of PET are that it is an extremely sensitive
technique (picomolar concentrations of tracer can be detected) and that the available
isotopes allow just about any molecule of biological interest to be labeled with a positron
emitter with little or no modification to the molecule's biochemical activity.
PET is essentially an in vivo analog of autoradiography - indeed it is often referred to
as such. The potential advantage of PET however is clear. PET can study the same
animal repeatedly, giving investigators the ability to acquire longitudinal studies. While
PET may never approach the resolution of autoradiography, it does have sufficient
resolution to address certain applications and it has the critical advantage that the animal
remains alive to be studied at another time.
PET is a very high resolution positron emission tomography (PET) scanner
developed at UCLA over the past four years and designed specifically for animal imaging
(Cherry et al, 199x). The motivation for PET was to permit repeat, non-invasive studies
of biological parameters in vivo. PET is able to resolve volumes as small as 6 µL (1.8
mm spatial resolution in all directions) and has an absolute sensitivity of up to 200
cps/µCi. Images from PET are fully quantitative. PET has been used in a range of
animal models, including mice, rats, rabbits and small non-human primates. Over 1000
studies have now been successfully completed on this prototype system using a variety of
tracers such as [F-18]fluorodeoxyglucose (FDG), [F-18]fluoroethylspiperone (FESP) and
[C-11]WIN35,428 (WIN). Limitations of the current system include the small axial field
of view and the limited sensitivity, both of which can be improved by adding more
detectors to the system. PET is a fully 3-D scanner acquiring volumetric data from this
imaging field of which can be reconstructed into any arbitrary voxel dimensions and
matrix size. Typically, data are reconstructed into 128x128x25 volumes (usually
displayed as 25 transverse slices covering the 1.8 cm axial field of view) with cubic
voxels measuring 0.7 mm. Each time point produces a raw dataset size of 1.54 Mbytes
Compounds currently used with PET:
FDG (glucose utilization)
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N-ammonia (myocardial perfusion)
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C-raclopride (dopaminergic neurons - presynaptic)
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C-WIN 35,428 (dopamine transporter)
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F-fluorodopa (dopamine synthesis capacity)
64
Cu- labeled antibody fragments (tumor targeting)
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F-fluoroethylspiperone (D2 receptor – postsynaptic; imaging of reporter gene expression) (REF!!!!)
F-fluoroganciclovir; 18F-fluoropenciclovir (imaging of reporter gene expression) (REF!!!!)
Cryosectioning.
Our preliminary studies have concentrated on development and validation of the
technology to collect high spatial and densitometric imagery of whole brain (Toga et al.,
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1994). During these studies we have sectioned and collected data from human, monkey,
rat and mouse brains, sometimes with the entire cranium intact. To accomplish this, we
have systematically developed a battery of histological protocols for optimal preparation
of frozen specimens. We made a series of engineering modifications to the
cryomacrotome to permit accurate image capture directly from the blockface. An
automated specimen placement feature, camera, and illumination system were engineered
and optimized for consistent and reliable collection of in-register serial image data
(Figure 4).
Figure 4. A cryosectioning apparatus (left) comprises a –
20o C freezer in which a powered sled carries a frozen
specimen under a microtome knife.
One of either a
commercial RGB CCD camera (shown) or a grayscale CCD
camera equipped with a tunable liquid crystal filter (not
shown) is installed with the plane of focus held constant
with respect to the block face. As each section is cut from
the block, either a single RGB image or a sequence of
grayscale images at varying frequencies (~200 to 1000 nm at
as low as 5nm intervals) is captured. This data forms a
"spectral cube" for each slice imaged.
CHANGE FIGS TO MOUSE SLICES
CryoImage acquisition. We have built and tested a sectioning and imaging system able
to section through the entire neuroaxis of brain and retain accurate spatial dimensions.
We have performed preliminary validations using a dial caliper to determine y axis and z
axis drift of the cryomicrotome and reconstructed and resampled data volumes to visually
inspect any variations in alignment or magnification. These tests resulted in a measured
y axis and z axis variation of 0.04% and 0.08%, respectively. Overall illumination was
measured using a plain grayfield background during image capture and found to be
symmetric, although not completely uniform. Our current illumination system is
comprised of fiber optics driven by a Cuda voltage regulated power supply and color
temperature matched to the filters of the current camera system. The variations that do
remain are corrected using a 3D histogram equalization process developed in the
laboratory.
Specimen preparation. Fixed tissue, necessary for some of the histological treatments,
may be preferred over fresh frozen material as the intact, fresh frozen brain tends to lose
its integrity and shred during sectioning, which we believe is an interaction of the friable
or delicate unfixed sections and the intrinsic or a priori matrix destruction caused by
freezing artifact in fresh, noncryoprotected large specimens (Duvernoy, 1988).
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Infiltration of the fixed specimen with appropriate cryoprotectants such as glycerol
greatly reduces or eliminates freezing artifact and markedly improves sectioning
characteristics (see Rosene and Rhodes, 1990). We have examined the changes in tissue
with different cryoprotectant and fixative processing (refs). These can influence the
spatial integrity of the reconstructed data.
Acquisition of histological data. We investigated a number of protocols for section
retrieval, including adhesive tape (Holmbom et al., 1991; van Leeuwen et al., 1990;
Jossan et al., 1991), high molecular weight polymers including polyvinylpyrrolidone
(PVP) and polyvinyl alcohol (PVA) (Aaron and Carter, 1987; Hill and Elde, 1990; Fink,
1992), and gelatin (Heinsen and Heinsen 1991; Hine and Rodriguez, 1992). We have
used gelatin on the blockface to obtain sections of cryosectioned brain over a broad range
of thickness from 50 up to 900 (Quinn et al., 1993). We have made appropriate
modifications to an engineered roll plate provide reliable collection of quality tissue
sections.
Figure 5. Digitized stained cryosection image. This figure demonstrates a portion of a flatbed-scanner digitization of a
50-micron section (left), collected after cryosectioning (right, showing sagittal reconstruction of coronal sections) and
stained for cell bodies.
Spatial Normalization. Two aspects of spatial normalization require particular
attention. First, serially sectioned tissue must be realigned to reconstruct the volume
from which it was sampled. Second, reconstructed volumes of histologic, cryosection, or
histochemical data must be repositioned and/or warped to make them coincident with
either an atlas coordinate system or other volumetric data. The ability to compare
histologic and molecular maps with in vivo metabolic, gene expression or growth rate
data is critically dependent on the accurate alignment of serial tissue sections with a
digitally reconstructed specimen (Mega et al., 1997, 1998).
At UCLA, over 15 years of research have been directed at developing tools to transform,
deform, and correlate multimodality datasets. These algorithms have been used to
integrate 2D, 3D and 4D brain data in a variety of species, acquired from multiple
subjects and imaging devices (Toga and Arnicar, 1985; Banerjee and Toga, 1993;
Cannestra et al., 1997; Thompson and Toga, 1996, 1997a,b; 1998a,b, 1999a,b,c,d; Mega
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et al., 1997, 1998; Toga, 1998; Woods et al., 1998a,b, 1999). We have also validated a
range of tools to map 2D and 3D histologic, neurochemical and quantitative protein
expression data into precise structural register with in vivo MRI and metabolic PET data
acquired from the same subject in vivo (Mega et al., 1997, 1998; Cannestra et al., 1998).
Additional studies have correlated high-resolution MRI, cryosection and
myeloarchitectonic data (Holmes et al., 1996, 1997). In on-going projects, we have
synthesized multi-subject brain atlases from a variety of modalities (Toga and Thompson,
1998, 1999; Mega et al., 1999). These include atlases that are dynamic (Thompson et al.,
1998, 1999), probabilistic (Mazziotta et al., 1995; Toga and Thompson, 1998, 1999), or
specific to a particular diseased subpopulation (Thompson et al., 1998, 1999; Mega et al.,
1999; Zoumalan et al., 1999).
Alignment of Histologic Sectioned Material. For histologic data, serial section
alignment is the first step in single subject reconstruction. Physical sectioning procedures,
required for histological staining, require a superpositioning scheme to re-register the
series, prior to comparing it with data from other modalities or with the atlas. Section
alignment may also require local deformation or warping transformations (Toga, 1998),
to correct for complex patterns of tissue deformation during processing. Two situations
fall within the scope of this proposal: the first occurs when a full cryosection volume is
available as a reference for histologic alignment. This is the case during atlas
construction, as histologic and genetic maps are assembled from subjects that populate
the atlas. The second occurs when tissue sections are acquired without a corresponding
blockface image. This situation requires multiaxial atlas navigation and computational
search tools to identify the optimal alignment of the sections into the atlas.
2D-to-2D Blockface Alignment.
Use of blockface imagery greatly eases the problem
of registering stained tissue (Mega et al., 1997; Thompson and Toga, 1999). Data
collected on the cryomacrotome blockface is in precise spatial register, due to the
tomographic nature of the image acquisition (see above). Blockface imagery provides a
reference against which other data (from the same specimen) can be compared.
Additionally, harvested tissue sections may be cut into small tissue quadrants for regional
radioimmunoassay, Western blotting, or ELISA. In recent studies we have linked
quantitative results of these assays can then be linked with their original blockface
locations for correlation with co-registered structural or metabolic data obtained in vivo
(Mega et al., 1997).
In one approach (Mega et al., 1997, 1998, 1999; Thompson and Toga, 1998; Shah et al.,
1999; Sanchez et al., 1999), we elastically warped stained sections back into their exact
blockface configuration with an algorithm based on the principles of continuum
mechanics (Thompson and Toga, 1998). Homologous landmark curves and points were
first identified in the distorted tissue and blockface images. A complex 2D deformation
vector field is applied to reconfigure the tissue section into the exact shape of the
blockface.
13
Figure 6: Recovery of Change in Brain Tissue due to Post Mortem Effects and Histologic Processing. Warping
algorithms based on continuum-mechanical models can recover and compensate for patterns of tissue change which
occur in post mortem histologic experiments. A brain section (left), gridded to produce tissue elements for biochemical
assays, is reconfigured (middle) into its original position in the cryosection blockface (Mega et al., 1997; Thompson
and Toga, 1996, 1998). Note the complexity of the required deformation vector field in a small tissue region
(magnified vector map, right) (Thompson and Toga, 1996; Schormann et al., 1996). These data can also be projected,
using additional warping algorithms, onto in vivo MRI and co-registered PET data from the same subject for digital
correlation and analysis (Mega et al., 1997).
Mathematical Considerations.
In (Bookstein, 1989; Joshi et al., 1995; Thompson
and Toga, 1996, 1997, 1998; Davatzikos, 1996; Thompson et al.,1998), a general
approach is developed for landmark-driven warping of 2D and 3D images, deforming
them into register with target images. Landmark points present in one image are forced to
match up with their counterparts in the other image. The image to be deformed is
considered to be embedded in an elastic block, which is subjected to internal forces that
deform it into the exact shape of a target image. In this mapping, internal neuroanatomic
boundaries are warped into the configuration of their counterparts in the target brain. The
algorithm finds the 2D displacement vector field u(x):R2 -> R2 , mapping one anatomy
onto the other, such that the following system of partial differential equations is satisfied:
L(u(x)) + F(x-u(x)) = 0, x in R,
u(x) = u0(x) , x in M0 or M1,
In these equations, L is a differential operator controlling the way in which one anatomy
is deformed into the other, and its properties can be used to make the deformation reflect
the mechanical properties of deformable elastic or fluid media (Fig. **). Common
choices of the differential operator L are the Laplacian Ñ2, biharmonic Ñ4 (Bookstein,
1989; Meyer et al., 1997; Kim et al., 1997) and Cauchy-Navier operator (l+m)Ñ(Ñ·) +
mÑ2 (Miller et al., 1993; Christensen et al., 1996; Davis et al., 1997; Thompson and
Toga, 1998). The term F(x-u(x)) corresponds to a continuum-mechanical body force
distributed though the deforming medium (see Davatzikos, 1996; Schormann et al., 1996;
Thompson and Toga, 1998, 1999 for details), and M0, M1 represent sets of anatomical
points and curves where vectors u(x) = u0(x) matching tissue regions with their
counterparts in the blockface image are known. Prior to making more local adjustments,
the algorithm calculates an initial global alignment of the section from the landmark data.
Recent studies have favored the use of Procrustes registration over principal axis
14
techniques for automated global alignment (Schormann et al., 1997). When some
landmarks are detected automatically, outliers which may adversely affect the global
alignment of the tissue section may be eliminated (Black and Rangarajan, 1996). This can
be achieved by using iterative closest point or robust point matching (Besl and McKay,
1992; Rangarajan et al., 1996, 1998; Feldmar and Ayache, 1996; Gold et al., 1997;
Subsol, 1998), or by using a Rayleigh-Bessel distribution (Schormann et al., 1996), or a
Hotelling's T2 statistic (Bookstein, 1989; Thompson et al., 1997; Zhou et al., 1999) on the
deformation vectors to reject incorrect landmark pairings.
We have performed extensive cross-validations of automated approaches for section
alignment, which include cross-correlative and least-square intensity difference
techniques in the spatial and frequency domains (Banerjee and Toga, 1993). In series of
coronally sectioned autoradiograms, fiducials inserted orthogonal to the cutting plane
(Toga and Arnicar, 1985; Goldszal et al., 1994) were used to evaluate the automated
methods for
global alignment.
Local Tissue Transformations.
To correct for more local tissue deformations,
excellent cross-modality alignment can be achieved by maximizing a measure of the
mutual information between the deformed tissue and the blockface as the deformation
field parameters are adjusted (Meyer et al., 1997; Kim et al., 1997; Viola and Wells,
1995). For each modality, the performance of intensity-based functionals for automated
section alignment will be cross-validated against manual methods using large sets of
anatomical landmark curves and surfaces, using methods developed in Thompson et al.
(1999). We have recently used such an approach to systematically investigate the
performance of the widely-used Automated Image Registration package on data from
many subjects and sources (Woods et al., 1993, 1998a,b, 1999; Thompson et al., 1999a,b;
Thompson and Toga, 1999).
As the complexity of the alignment transformation increases, automated approaches for
deformation recovery may become unstable as the optimization functional becomes nonconvex on the deformation field parameter space (Woods et al., 1998). To avoid this, we
developed an approach in which anatomical models are created in each dataset.
Anatomical knowledge on how landmark features match up is then leveraged to constrain
the mapping of one anatomy onto the other. With proper (Dirichlet or infinite) boundary
conditions, the matching vector field equation can be solved numerically by finite
difference (Christensen et al., 1993; Davatzikos, 1996; Thompson and Toga, 1998), finite
element (Gee et al., 1998) or spectral (Christensen et al., 1996) methods.
Although this requires the solution of a system of millions of coupled elliptic partial
differential equations, we implemented a fast multigrid algorithm, combined with
Chebyshev over-relaxation, which greatly accelerates the computation (Davatzikos, 1996;
Schormann and Zilles, 1996; Thompson and Toga, 1998, 1999). In our approach,
landmark curve tagging is carried out in a graphical Java Interface that runs on any
platform (e.g., UNIX or PC). The software requires only a few seconds of interaction
and computation per tissue section (Thompson et al., 1998). The continuum-mechanical
15
model is based on the Cauchy-Navier operator of linear elasticity, and models how real
physical tissues deform. Similar tissue alignment procedures have been developed by
other groups (Schormann et al., 1995; Davatzikos, 1996; Grenander and Miller, 1998),
based on thin-plate splines (Bookstein, 1989; Kim et al., 1997; Meyer et al., 1997),
Laplacian or volumetric splines (Davis et al., 1997), Fourier space regularization
(Gabrani and Tretiak, 1999), and fluid models based on space-time velocity
regularization (Grenander and Miller, 1998; Joshi et al., 1999). For a comparative review
of these approaches, discussing the benefits and limitations of each, see Thompson and
Toga (1998, 1999).
3D-to-3D Alignment of Cryosection, Metabolic and In Vivo Imaging Data. Each
complete cryosection volume, with its realigned tissue and gene expression data, will be
subsequently realigned with a high-resolution micro-MRI volume. This volume will be
acquired from the same subject in vivo at the final time-point in the MR imaging series.
The reconfigured blockface and histologic data therefore represent the specimen's
anatomy in its in vivo anatomical configuration, at a resolution obtainable only post
mortem. To align cryosection volumes with in vivo anatomic data, a 3D elastic
transformation is required, to compensate for post mortem anatomic distortion. First,
model surfaces are automatically extracted from each dataset. These model surfaces
include cortical, ventricular, hippocampal, callosal surfaces and the internal surfaces of
the basal ganglia, many of which can be extracted automatically, in parameterized
format, from both MRI and cryosection data (Thompson and Toga, 1996; Holmes et al.,
1996, 1997; Zhou et al., 1998, 1999a,b; Pitiot et al., 1999). Internal anatomical
correspondences are enforced in the surface-to-surface mappings using a covariant
regularization approach specialized for handling cortical and deep subnuclear data
(Thompson et al., 1998, 1999a,b,c). Surface-to-surface mappings are extended to the full
volume using a 3D continuum-mechanical transformation that matches functionally
important interfaces exactly (Thompson and Toga, 1996). Again, where possible,
automated image registration approaches (Woods et al., 1998b, 1999) are used first, to
recover polynomial deformations between gray-scale cryosection and MRI data. This
accelerates the automated extraction of structures that drive the residual local elastic
deformation (Thompson et al., 1999; Pitiot et al., 1999; Zhou et al., 1999). In vivo
metabolic (PET) data are then aligned to the mutually registered MR, cryo and histologic
volumes (Mega et al., 1997, 1998). Whole-brain PET volumes are aligned to the
volumetric MR data by optimizing the coefficients of a 6 parameter rigid transformation,
based on the ratio image uniformity of the MRI and aligned PET signal fields (Woods et
al., 1993). Recent validations have shown highly accurate cross-modality alignment, so
long as the optimal algorithm is used for each submodality transformation (Mega et al.,
1997).
16
Figure 6. Common space facilitates
multi-scale viewing.
This figure
demonstrates the use of a common
coordinate space to permit the
viewing of superimposed, multi-scale
data from different subjects and
modalities. In this case, a low
resolution (1.0mm3) in vivo MRI
(grayscale) image is overlaid by a
much higher resolution (100 micron)
cryosection image (hot metal
colorscale, see Appendix) from a
different post mortem study.
Reconstructions and Visualizations. Anatomic segmentations, descriptions, reconstructions
and visualizations have been part of our preliminary efforts also. We can generate 3D models
from the data using two different techniques. In the first technique, the anatomic boundaries are
defined by the user with contours. Although this is relatively tedious, it does allow the
investigator to interpret the image and differentiate the sometimes subtle textures that help
separate adjacent structures. The image below illustrates the results of these efforts.
ADD SWANSON and/or 3D model here
The second technique for visualizing reconstructions involves the use of a volume
rendering technique. This approach does not explicitly define the surface geometry but
can produce 3D models of structure. Its success depends on the degree of contrast
attainable with the white matter stains. The resulting models generated using either
technique can be resampled using cutaways to expose different planes of section.
Combination too……………..
17
Figure 7. Volume rendering of
downsampled cryosection data.
To demonstrate the usefulness of
even low-resolution cryotome
data for anatomic visualization.
This figure shows the anterior
commissure as it crosses the
midline. The figure was prepared
by downsampling a 10243
cryosection data set to 2563 then
rendering it (interactively) using
the hardware texture mapping
and an intensity based opacity
scale. The anterior commissure
can be followed for its entire
extent, to the point where it
becomes distributed under the
globus pallidus. This technique
will be extended to allow the
interactive realtime viewing of
superpositions of complementary
information
from
multiple
modalities.
Atlasing
We have produced atlases of several species both in print form containing serial
sections and descriptive templates as well as truly 3D digital atlases that have interactive
capabilities. The essential element to most atlases is the spatial correspondence between
the description of the anatomy (or functional anatomy) and a numeric index. This
enables coordinate ↔ nomenclature ↔ anatomy to be equated.
18
Figure 8. Atlas of the rat. This figure illustrates the product of this digital rodent atlas and the steps required
to construct it (right)
Rat. An electronic atlas of the rat was produced using data from the Paxinos and
Watson templates reconstructed into 3D (refs). This early prototype included data from
multiple animals ‘stitched’ together to form a complete survey of the whole brain. From
that set of experiments we recognized the need for sufficient sampling frequency, the
need to use either a single animal or a population with appropriate warping for spatial
normalization. In a subsequent project we collected a single animal for the creation of a
digital web based rat atlas (www.loni.ucla.edu/data/rat/; Toga et al., 1995). Figure 8
shows a horizontal example from this database.
Monkey. We also have created an atlas of the Nemistrina monkey (Cannestra et
al., 199x) that included multiple modalities, specifically MRI, PET, CT and
cryosectioned data. These were then spatially normalized relative to one another, placed
within a coordinate system, labeled and made available electronically. Animation
sequences describing systems of the brain were created as well and have been used for
teaching graduate and medical students (Cannestra eta l.,199x). We have also produced
atlases in the more traditional print format, including the Rhesus monkey (Paxinos,
Huang and Toga, 1999) and the rat (Swanson, 1997) and combination atlases that include
both print and electronic components (Swanson, 1999)
Human. Human atlases that describe anatomic detail from multiple modalities
have been created too. While these have a different focus from the current project, their
construction required that we deal with many of the technical issues necessary for success
in creating a useable multimodality, multidimensional atlas of the mouse. ADD HERE
Although this is not an exhaustive list of the essential elements for this project, we
have worked on and published in these relevant areas;
1. imaging technology and acquisition
2. imaging gene expression
3. registration,
refs
4. reconstruction,
refs
5. spatial normalization,
6. coordinate systems,
7. multiple modality correlation,
8. mathematical image analysis and statistics
9. visualization,
10. interaction
11. measurement.
12. Population based image databasing
13. Multispecies atlas dissemination
Mathematics and Computer Science
Reconstruction
Visualization
19
Intensity and feature (point, curve and surface) based deformation
Imaging
Anatomy
Informatics
Genetics
Genetic (or Molecular or Biochemical) Methodologies
In Situ Hybridization Tissue is frozen, cryosectioned, and gently fixed. Cellular
membranes are premeablized, and subsequently probed with a radiolabelled antisense
cDNA. This process yields excellent resolution, down to intracellular localization, but it
is dificult to quantitate. Northern blot RNA is isolated from a tissue sample,
electrophoresed, and immobilized on a nylon membrane. The blot is then probed with a
radiolabelled cDNA, exposed on a phosphor storage screen, and quantified on a
phosphorimager. This method is considered highly quantitative, but requires a lot of
tissue, and therefore has low resolution.
RT-PCR RNA is isolated from a tissue sample, Reverse Transcribed, and amplified by
PCR. The cDNA sample is then electrophoresed, immobilized on a nylon membrane,
and probed with a radiolabelled cDNA. The hybridized blot is exposed on a phosphor
storage screen and quantified on a phosphorimager. This method is also considered fairly
quantitative, and requires less tissue than a Northern blot. It is, however, subject to
non-linear amplification due to PCR.
Immunohistochemistry Tissue is frozen, cryosectioned, and gently fixed. Cellular
membranes are premeablized, and subsequently probed with an antibody specific to the
protien of interest. The protein is visualized with the use of a flourochrome-labeled
secondary antibody and flourescence microscopy. This process yields excellent
resolution, down to intracellular localization, but it is dificult to quantitate.
Western blot Protein is isolated from a tissue sample, electrophoresed, and immobilized
on a nitrocellulose membrane. The blot is then probed with an antibody specific for
protein of interest and visualized . The blot is exposed on film, the exposure scanned into
a computer by a laser densitomiter, and relative signal strengths compared. This method
is considered quantitative, requires little tissue, but must have an antibody to the protein.
20
Relationship with other Grants
The present application complements other funded projects from our group. The
experience gained from the research activities of these grants provides considerable
advantage to this project. In particular, there are five funded projects in which the
principal investigators participate. These are titled International Consortium for Brain
Mapping (ICBM), Digital Representation of Human Brain, Adaptive Algorithms and
Quantitative Transformations. The funding agencies for these are shown in Figure 9.
The present proposal will benefit from these projects by leveraging algorithm development and
technique development with other species and data. Algorithms: Computer science efforts that
can be built upon can be found in funded projects from the National Science Foundation and from
the NCRR. The NSF grant is focused on the development of warping strategies to compare one
brain with another. While the morphology of these data sets is static, subroutines have been
developed which can be utilized in the proposed work. The computer science expertise and
mathematical background necessary for the development of these warping strategies will be of
great assistance to the proposed project. The NCRR grant has set the stage and created the
framework for the current application and provides a large battery of algorithm development and
software development activities to help ensure the success of this mouse atlas program.
21
Research Design and Methodology
Design
The overall design of this proposal has the following components;
22
Acquisition, five modalities will be collected including MR, PET, Cryoblockface images, histology and the anatomic templates
Subject populations, adult mice of the C57BL/6J strain at 3 different weight
categories will be collected in all the above listed modalities. Animals at 9 different
maturational stages also will be collected corresponding to the Theiler stages xyz. The
number of subjects for each of the 12 groups (3 weights and 9 developmental stages) will
be determined based upon observed within group variability (PAUL FIX THIS; WHAT
IS NEEDED POWER??). In addition, we will collect example subject 1-3 subjects each
for the strain XYZ in all modalities.
Volume reconstruction. Volumes for each of the above will be processed
(including registration, image processing and JHKJH) and reconstructed.
Spatial normalization. Spatial normalization to equate all subjects and all
modalities into a common stereotaxic coordinate system. Procedures for placement of
individuals into this system will be defined and tested. Feature based (bony and soft
tissue landmarks) and intensity based automatic approaches will be employed.
Anatomic delineations. Anatomic delineations of adult (weight category xx)
will create templates identifying individual structures within the coordinate system.
Template transformation. Templates from histology will be transformed to the
other modalities and correspondance determined after various types of deformation
correction.
Deformation strategies. Deformation strategies to equate different subjects will
be developed and tested for their efficacy transforming different weight categories,
developmental stages and strains.
Software. Interactive software for the mapping of new datasets, examination of
the multimodality, multidimensional atlas will be developed.
Atlas Database. An indexable archive containing the multiple modalities and
subjects will be created. Queries of location by coordinate or anatomic name will
reference a comprehensive volume and pointing to a place within the volume will
produce the coordinate or anatomic name. In addition, the display will be selectable as a
single or multiple modalities.
Genetic Expression Maps (GEMS). GEMS will be collected to test the utility of
the atlas and associated software.
Acceptance testing. Beta and community testing will be employed to improve
the atlas and software throughout and via outreach meetings with the gene mapping and
brain mapping communitities.
23
Development
Multiple Subjects
Additional strain
Multiple modalities (single subjects; in vivo – in vitro)
create figure that shows input to the atlas; MR, Cryo, Histo, template, PET
Acquisition
MRI. The overall goal of component of the project is to implement
MR imaging as a facile means of obtaining 3D anatomical information
in mice. Toward this end, much of the necessary MRI hardware is
already in place. We plan modest MRI probe design changes
optimized for imaging the mouse and a significant MRI console
upgrade. We will investigate and implement imaging protocols that
provide the maximal relevant information in the minimal time. During the all phases of
this project we will ensure that the instrumentation is appropriate for all phases of this
project. In this Section we discuss:
• probe design approach,
• imaging protocol development for in vivo mouse MRI at high fields,
• mouse model systems we will use as initial test cases.
Probe Design for Magnetic Resonance Imaging Microscopy. The MRI probe contains
the RF coil with ancillary circuitry, magnetic field gradient coils, life support and
monitoring, and plumbing to the outside world. To acquire an image the specimen is
placed inside the probe and the assembly is placed inside the large static magnetic field.
Thus, optimization of all probe characteristics is essential to secure good images and
stable preparations. Design considerations involve ensuring the viability of the in vivo
samples being imaged, as well as, achieving the necessary signal-to-noise ratio (SNR)
and resolution. Due care will be taken to minimize the potential for vibration when
24
integrating all the pieces of the probe. The trial-and-error method works well here
because these RF circuits are conceptually straightforward, relatively simple to build, and
easy to test. We will concentrate on adjusting the following sections:
• Specimen environment and monitoring,
• RF coils,
• Gradient coils.
Specimen Environment and Monitoring. The primary concerns here is to provide
temperature control, maintain humidity, and control oxygen and gaseous anesthesia
(typically vaporized isoflurane) levels for the in vivo specimens. We have successfully
accomplished this in our 89mm vertical bore system and will use this instrumentation in
the present project. An all plastic animal constraint and positioning setup has been
specially built for our small bore horizontal magnet. It allows for the precise
repositioning of the specimen in the instrument. This is essential for repeated imaging of
the same specimen (development) and for similar positioning of different specimens.
Radio Frequency Coils. It is through the RF coil that we excite the proton spins in
the sample and detect the ensuing MR signal. Good sensitivity, a homogeneous B1 field,
and efficient coupling with the sample translate directly into high fidelity images. We
have a number of solenoidal, loop-gap, surface, Alderman-Grant type, and birdcage coils
of different sizes on hand. When necessary (e.g. to maximize filling factor) we will
construct additional coils. Typically, we calculate coil characteristics (e.g. B1 field
uniformity) using MATHEMATICA, make a mask using a standard CAD package, etch
the coil pattern on a copper-teflon laminate, then construct the coil with the etched
copper-teflon laminate and appropriate nonmagnetic capacitors. We have found this a
convenient and relatively inexpensive way of making prototype coils. An HP 8752A
Network Analyzer will be used to test the performance of the isolated coils, coils plus
matching/tuning circuits, and the whole probe. This will allow is to conveniently and
efficiently evaluate the individual elements as well as the combined assembly.
Gradient Coils. ???????????????????
Acustar gradient set – ID, max strength & rise time, max sample size
Micro2.5 gradient set - ID, max strength & rise time, max sample size
No additional gradients planed as these fit mice just fine.
Imaging Protocols. The are basically three types of software in MR imaging: i) pulse
sequences, ii) image reconstruction programs, and iii) image presentation & analysis
routines. When implementing imaging pulse sequences at high fields, one must keep in
mind that many phenomena (e.g. magnetic susceptibility effects, chemical shift
dispersion, relaxation times) are quite different at 11.7T than at typical clinical field
strengths of 0.5-1.5T (Sharp, 1993; Bowtell, 1992; Bowtell, 1990). To gain as much
structural and anatomical information as possible from our specimens, we will employ
several different pulse sequences aimed at obtaining images where contrast arises from
different physical aspects of the sample (e.g. proton density, relaxation times, magnetic
susceptibility, diffusion). For our in vivo specimens the images must be acquired in a
relatively short period of time. Short recycle time spin echo (SE) sequences with multiple
echo acquisition will allow us to obtain both T1 and T2 weighted images simultaneously.
25
Diffusion weighted and in selected cases diffusion tensor imaging will be used to gain
information about myelination. We will implement these algorithms and will assess the
image characteristics versus time needed to acquire the data in order to arrive at the most
appropriate sequence(s) for collecting the information needed in this project.
Pet.
The goal is to use a reporter gene based method which permits the location, magnitude
and persistence of gene expression to be measured quantitatively in the intact mouse.
The basis of the method is to use a reporter gene whose product, rather than being
fluorescent, can trap a positron-labeled probe (PET reporter probe). Possible systems
under investigation include a receptor based system, where the gene for the dopamine
receptor is used as the reporter gene, and an enzyme based system where the herpes
simplex virus type 1 thymidine kinase (HSV1-tk) gene is the reporter gene. In the
receptor mediated system, [18F]fluoroethylspiperone is used as a PET reporter probe, and
is specifically bound only to cells expressing the dopamine receptor. Outside of the
striatum, only cells expressing the reporter gene will bind this compound, and therefore
its uptake as measured by PET can be related to expression of the reporter gene
(MacLaren et al, 1999). The enzyme mediated system uses [18F]-fluoroganciclovir
(FCGV) as the PET reporter probe. In the presence of HSV1-TK enzyme, FCGV is
phosphorylated and trapped in cells. Thus, the uptake of FGCV can also be related to
expression of the reporter gene (Gambhir et al, 1999). To facilitate quantitative and
meaningful studies in the mouse, a new high resolution PET system, microPET, has been
developed which provides isotropic 1.8 mm resolution images (Cherry et al, 1997).
Initial validation experiments have been carried out by adenoviral delivery of the reporter
gene into mice via tail vein injection. In both PET reporter gene/reporter probe systems, it
has been possible to demonstrate a quantitative correlation between message levels of the
reporter gene and uptake of the PET reporter probe in the microPET scans (Gambhir et
al, 1999; Maclaren et al, 1999). An example of a study with the HSV1-tk PET reporter
gene, correlated with digital whole body autoradiography, is shown in figure X.
The plan now is to measure the espression of genes of interest using a single promotor to
drive both the PET reporter gene and the gene of interest as a bicistronic message, with
an internal ribosomal entry site to facilitate translation of both proteins from a common
message.
FIG HERE
Figure X caption: MicroPET and digital whole body autoradiography (DWBA) images of mice after adenoviral
mediated delivery of the PET reporter gene. Swiss-Webster mice were injected via the tail vein with 1.5 x 109 pfu of
control virus (a) or 1.5x109 pfu of AdCMV-HSV1-tk virus (b). For each mouse, a whole-body mean coronal projection
image is displayed on the left. The liver outline in white was determined from both the FCGV signal and cryostat
slices. The second images from the left are coronal sections, approximately 2 mm thick, from the microPET. After
their PET scans, the mice were killed, frozen and sectioned. The next images are photographs of the tissue sections
(45 µm thickness) corresponding to approximately the midthickness of the microPET coronal section. The images on
the right are DWBA of these tissue sections. The color scale represents the FGCV %ID/g. Images are displayed on the
same quantitative color scale to allow signal intensity comparisons among them.
26
Cryosectioning of frozen specimens. Three dimensional volume
data sets of mouse brains will be reconstructed from high–resolution
digital serial images of cryosectioned specimens. Whole head and
brain specimens will be histologically prepared for sectioning by en
bloc fixation and cryoprotection against freezing artifact.
Specimens are sectioned on a large industrial cryomacrotome (PMV Stockholm, Sweden)
using a hardened steel knife. We have modified the cryomacrotome to enable quantitative
electronic image capture. It is now equipped with a digitally controlled camera and colorbalanced fiber optic lighting which is integral with the hydraulic descending knife
apparatus. The motorized sledge of the cryotome has been modified to include an
automated stop feature placing the specimen directly under the camera in a consistent
manner prior to each image capture. These modifications to the PMV cryomacrotome
provide blockface imaging technology, allowing capture of in–register serial images at
constant magnification throughout the whole–head sectioning process. Figure 12
describes this device.
Whole head and brain specimens are automatically cryosectioned (-20˚C) in 50–100
micro increments. Images of the blockface are captured every 100 microns for most
applications. This provides the appropriate relative match of pixel dimension in the
vertical and the planar axes. Digital images are transferred to the SGI supercomputer
system for multidimensional image processing and archival storage.
Image Processing. Digitization of the block face is accomplished using a software
program designed by our group. High resolution color images are captured directly from
the blockface and projected onto a color display monitor before being saved digitally.
Magnification, illumination, and color balance are held constant throughout the capture
and sectioning process. Registration of serial images (x,y values) is maintained by a
mechanical feature which automatically positions the specimen underneath the camera.
Distance between serial images (z value) is determined by the interval in microns
between captured blockface planes.
Histology. To construct an accurate 3-D computer graphics model of the brain from
histological sections, it will be necessary to (a) obtain serial sections from a single
brain, and (b) obtain these sections from one block-that is, by cutting a whole brain
from the rostral tips of the olfactory bulbs to upper segments of the cervical spinal
cord. Tissue sections will be taken every x microns. The specific
corresponding blockface image will be noted for subsequent 2D
deformation correction. As noted above, histological processing can induce significant
distortion relative to the relatively representative geometry of the frozen blockface.
Anatomic Description. Describing individual structures will be accomplished using one
or more of several strategies developed in our laboratory and adapted for this project. We
will do this by manual segmentation, semi-automatically using templates published or
27
developed by us using manual segmentation of other data, density gradients from stained
or MR contrast, or using purely probabilistic approaches that are based on location
(Mazziotta et al., 1995).
Anatomy-delineation: Neural structures (including cell groups, fiber tracts, and gross
anatomical features such as the ventricles) are determined under the microscope using
bright- and darkfield illumination of serial, Nissl-stained sections. Different features in
the brain have been identified with varying levels of accuracy in the primary literature,
and the best delineations rely on the widest array of neuroanatomical information,
including cytoarchitecture, chemoarchitecture, and connections (neural outputs of
particular cell types within an area, and neural inputs)-information that can be related to
the features observed in Nissl-stained sections (Swanson, 1998--second edition of the
atlas). Because very little experimental neuroanatomical research has been carried out in
the mouse, the vast majority of its structural parcellation must rely on information
obtained in the rat. Exactly how similar the architecture of rat and mouse brains are
remains to be determined.
Anatomy-nomenclature: Neuroanatomical nomenclature has evolved over more than two
thousand years, and for historical reasons it is not logical and internally consistent-like
any language. And because there is still a great deal to learn about the structural
organization of the brain, neuroanatomical nomenclature must remain flexible. Having
said this, there are nevertheless sophisticated and complex conventions that are associated
with the refinement of biological nomenclature and taxonomy that involve historical
precidence, internal consistency, and so on (Swanson, 1998). We have developed a
neuroanatomical nomenclature for the rat that is based almost entirely on references to
the primary neuroanatomical literature, is internally consistent for both the adult and the
developing central nervous system, and is applicable in so far as possible to mammals in
general (Alvarez-Bolado and Swanson, 1996 [development atlas]; Swanson, 1998).
Atlas Templates. In preliminary work for this project, we worked on the segmentation
of neuroanatomic structures within 3D data sets using anatomic templates from published
atlases like that of Talairach (Talairach and Tourneaux, 1988), Paxinos (Paxinos and
Watson, 19xx) and Swanson (Swanson, 19xx). These efforts define a nomenclature to
coordinate relationship. Once the model has been placed into the stereotactic coordinate
system, as described above, the data set will be resampled so that a given plate from the
reconstructed model is identical to a plate described in the atlas. The anatomic template
will then be overlaid upon the histological image, such that anatomic boundaries can be
seen. Fitting the anatomic templates provided by published atlases to data will greatly
increase the number of structures that can be labelled. Digital reconstructions of stained
tissue (using blockface images for registration and deformation) will be used to provide
additional structural information to more completely map the brain.
Anatomic templates will be prepared by tracing photographs of histological sections.
These maps are interpretations of the outlines of structures delineated in the brain, and
are named according to criteria discussed above. In our case, digitized photos of brain
28
sections (scanned at 300dpi) are placed in Adobe Illustrator 8, and maps or templates are
drawn as transparent overlays (layers). These drawings are vector-based, and as such are
essentially infinitely scalable, and can be printed at very high resolution. This approach
has the great advantage that an infinite number of perfectly aligned transparent overlays
can be created, each of which contains some type of neuroanatomical data (e.g., the
results of pathway tracing experiments, immunohistochemistry, or in situ hybridization).
These data overlays can be displayed in any order and combination, and they can form
the basis of a graphical database of neuroanatomical information (Dashti et al., 1997).
This approach to atlas production-computer graphics templates displayed over digital
photographs of brain sections-has the great advantage that other interpretations of
structure can also be superimposed over the brain sections. That is, alternative
interpretations are easy to accommodate. The basic principle is that a coordinate system
is constructed for the brain itself-that is, for the photographs of brain sections. Then,
different names (i.e., aliases) can be assigned to the same coordinate(s), and these names
are indexed by reference to the photograph itself (or, actually, to the set of coordinates
defining the border of structures delineated in the maps or templates. It is also important
to define how the coordinate system in the histological sections is related to a coordinate
system in the in vivo brain (i.e., from MR image). Based on the MR and histological
sections, it is possible to construct a 3-D cartesian coordinate system for the brain.
Genetic Expression
Anatomy
Delineation
Nomenclature
Template
Coordinate System/Stereotaxis
Maps and Models
Warping
Modality
Registration Warp Type
Rationale
Modality
1. Histo


Cryo
Fluid/intensity/feature
Cryo is in vitro spatial gold STD but
has insufficient anatomic detail; Histo
is distorted due to processing
2. Cryo

MR
Fluid/ intensity/ feature
MR is in vivo spatial gold STD
3. Template

Histo
Histo
Histo is anatomic gold STD
(3-4.) PET

MR/Cryo
Affine
All are tomographic so no local deform;
PET has lowest resolution
Warping Data Across Modalities, Across Subjects and Across Time
29
Elastic image registration, or warping algorithms, calculate a deformation field that
reconfigures one brain to match another. Depending on whether the datasets being
matched are (1) from different imaging modalities, (2) from different subjects or strains,
or (3) from different time-points, warping algorithms have a powerful range of
applications.
The 3 major objectives are:
1. Data Fusion across Modalities and Across Subjects (see Background and Significance,
page **). Histologic, gene expression and biochemical maps are reconfigured in 2D into
their blockface configuration, correcting tissue distortions due to staining. A subsequent
3D-to-3D warp reconfigures cryosection and associated histochemical data into register
with micro-MR and co-registered genetic and metabolic PET data from the same subject
in vivo. As appropriate (Thompson and Toga, 1999), landmark-driven, model-driven or
intensity-based warping will be applied to histologic, cryosection, MR and PET data from
additional subjects to register them with an atlas coordinate system or other volumetric
data.
2. Population-Based Atlasing: Mapping Within and Between Strain Variability. Warping
algorithms create detailed maps of anatomic differences between subjects, at a given
developmental stage. When applied in a probabilistic framework, warping algorithms can
measure anatomic variability within and between strains, generating quantitative maps of
the magnitude and principal directions of variation. Algorithms that average geometric
and intensity features across subjects will be used to generate well-defined average
templates of anatomy at each time-point in the atlas. Atlas templates will be constructed
in the form of volumetric MR and cryosection data, along with sets of 3D structural
models. Both models and image templates can be generated in their mean anatomical
configuration, using group-specific atlasing methods introduced in Thompson et al.,
1999a,b (see Appendix; cf. Grenander and Miller, 1998, for a similar approach).
In (Thompson et al., 1999) we introduced several computational methods for populationbased averaging of anatomy. These pattern-theoretic and shape-theoretic approaches
(Grenander, 1976; Bookstein, 1989; Miller and Grenander, 1994; Thompson and Toga,
1999) treat geometric and intensity variation separately, and encode inter-subject
differences in brain structure. A set of high-dimensional elastic mappings are calculated,
based on the principles of continuum mechanics, matching the anatomy of a large number
of subjects in an anatomic database (Thompson et al., 1996, 1997; cf. Haller et al., 1997;
Csernansky et al., 1998; Grenander and Miller, 1998). These mappings generate a local
encoding of anatomic variability, and are used to create a crisp anatomical image
template with highly-resolved structures in their mean anatomical configuration.
Probabilistic Anatomical Maps. Probabilistic maps of structure will be invoked to adjust
for the effects of anatomic variability when sampling functional imaging and genetic
attribute data across subjects (Dinov et al., 1999). They will also be leveraged to provide
anatomical prior information for automated structure labeling, parameterization and
30
modeling algorithms (Pitiot et al., 1999; Zhou et al., 1999). If warping approaches are
used to measure 3D anatomic differences across subjects, anatomic differences between
strains and population subgroups can be mapped. At each time-point, major anatomical
systems will be modeled in the micro-MR and co-registered cryosection data using
parametric surface meshes (Thompson et al., 1996a,b,c, 1997a,b,c, 1998a,b; Holmes et
al., 1996; Mega et al., 1997, 1998). This computational approach provides models that
can be compared, averaged or combined across subjects. Surface models can also be
measured to provide a variety of morphometric statistics (e.g., surface areas, volumes,
complexity, surface curvatures, and other descriptors; Thompson et al., 1996, 1998). The
resulting models can also be rendered, visualized graphically and animated (Thompson
and Toga, 1997), allowing between strain anatomic differences and structural variations
to beillustrated directly.
Genotype vs. Phenotype. Methods to compare probabilistic information on brain
structure from different subpopulations are under rapid development, and include
approaches based on random tensor fields (Thompson and Toga, 1997a,b, 1998; Thirion
et al., 1998; Gaser et al., 1998; Cao and Worsley, 1999), singular value decomposition
and ManCova (Ashburner et al., 1998), shape-theoretic approaches (Bookstein, 1997),
stochastic differential equations (Christensen et al., 1993) and pattern theory (Grenander
and Miller, 1998). By encoding patterns of anatomic variation across subjects, we will
determine the effects on brain structure of embryonic age, body weight, brain weight,
sex, strain and other knockout-specific genetic factors (Thompson et al., 1999).
Multivariate analysis of covariance will be used to identify the patterns of linkage
between shape variations in anatomy and other endogenous or genetic factors. At a gross
anatomical level, this will facilitate the exploration of relationships between genotype and
phenotype in C57BL/6J and other strains.
2. Warping across time to quantitate development, measure growth rates,
generate interpolated atlases, and compare data at different time-points.
Changing Morphology. Given the dynamically changing anatomy of the developing
mouse, powerful computational tools are required to compare data at different timepoints, measure growth rates, and generate interpolated atlases. Interpolated atlases will
be created to optimally represent brain structure and its associated genetic and functional
attributes at time-points in between those when data is acquired. Several years of research
have been directed towards the issues involved in representing a dynamically changing
morphology in rodent, Macaque, and human pediatric brain data (Toga et al., 1996;
Thompson et al., 1998, 1999; Sowell et al., 1999a,b; Blanton et al., 1998). More recently,
we developed tools that measure local growth rates during brain development. These
tools also enable the projection of brain maps from one time-point to the next.
A Dynamic Atlasing Framework.
The dynamic atlas will be developed in two stages.
First, high-resolution MR data from one subject acquired at multiple time-points will be
used to generate a sequence of non-linear deformation fields. Based on these fields, in
vivo growth rates will be quantified (Thompson et al., 1999). In vivo genetic and
metabolic PET data will be correlated with growth rates to clarify the relation between
31
gene expression and the underlying dynamics of brain growth. Additionally, metabolic
and in vivo genetic data will be projected forward and backward across time to enable
multiple time-point correlations.
Once a single subject has been mapped at all time-points, repetition of this procedure in
additional subjects will enable us to generate an average anatomical representation for the
group at every time-poimt (Thompson et al., 1999). By creating a series of average
anatomical templates for the group, the non-linear registration of these templates across
time will be used to identify the generic features of growth in the group, and their relation
to instantaneous gene expression patterns and metabolic data across subjects and strains.
Once registered, maps of growth rates are simply another form of attribute data linked to
the underlying anatomy. These signal fields can therefore be averaged or subjected to
principal component or principal deformation analyses (Bookstein, 1989; Thompson et
al., 1999). Additional multivariate approaches, linking deformation field variance with
exogenous variables (cf. Joshi et al., 1999; Thompson et al., 1999; Davatzikos and
Resnick, 1998; Bookstein, 1997) will be used to reveal growth patterns that are
characteristic of a particular strain or embryonic phase.
Intermediate Brain Atlases. To generate intermediate atlases, two approaches will be
evaluated. After appropriate intensity normalization and preprocessing, linear
interpolation of atlas geometry and its constituent maps will be used to generate
intermediate atlases. To do this we will use the maps acquired at adjacent time-points and
the appropriate percentage of the deformation field required to register them. We have
used this approach in the past to create animations of rodent development. These
animations were based on surface models of cortical anatomy acquired at multiple timepoints, and a distance field blending model (Absher et al., 1994; Payne and Toga, 1996;
Toga et al., 1996). A second, more adventurous approach recognizes that structure
growth is unlikely to be linear between sampled time-points, since this would create
discontinuities in the predicted growth rates at the time-points where data is acquired. By
using space-time regularization (Grenander and Miller, 1994; Joshi et al., 1997; Dupuis et
al., 1998; Thompson and Toga, 1999) a smoothly differentiable growth rate field can be
recovered which is consistent with all the anatomical models in the series. This velocity
field for structure growth is unique in the sense that it minimizes a measure of the
irregularity of growth rates over the space of all growth profiles that are consistent with
the imaging data. For predicting interpolated brain maps, the advantages of this approach
will be tested over the simpler, linear approach by leaving out (jack-knifing) sample data
sets. The accuracy of each model in interpolating growth or other brain maps can
therefore be determined, by assessing how each omitted brain map is predicted by each
model from the remaining data, using least-squares measures for each imaging device as
an approximation metric.
3D Models. While 2D parameterization supports the analysis of surface changes, fully 3D
volumetric changes will be captured using model-driven 3D image warping (Thompson and
Toga, 1996). Modeling morphometric changes also requires 3-D methods to quantitate and
visualize the rate and extent of the complex growth, pruning or atrophic processes taking place
throughout the brain. 3D reconstruction techniques for representing the internal and external
geometry of the brain will provide the foundation to model dynamic changes in its cellular
32
architecture. Manipulation of the morphometric models with image warping techniques will
provide a mathematical representation of the changing biological system. To guarantee biological
as well as computational validity, these warping strategies must be specifically designed to
handle neuroanatomic data in 3 and 4 dimensions. This work will be based on our recently
developed algorithm for elastic warping of brain image data (Thompson and Toga, 1996).
Anatomic constraints are used to calculate a deformation field that reconfigures the anatomy of
one subject into the shape of another. This algorithm provides detailed, quantitative 3D maps of
the anatomic differences between one subject and the other Automated warping approaches
(Woods et al., 1998b) will be developed in parallel, allowing cross-validation of independent
methods (Specific Aim 4).
Warping of one scan onto another acquired at a later time-point can be regarded as finding a
mathematical transformation which warps the 3D rectilinear lattice of the original scan into the
configuration of a curvilinear mesh which threads through the target scan. This curvilinear mesh
describes the complex profiles of dilation, contraction and shearing at a local level in the
anatomy, and can be regarded as a 3D parameterization of the later-stage anatomy.
4D Models. Concatenation of a series of warps representing structural change over time will
result in 4D parameterization of neuroanatomic change for a given subject in its full spatial and
temporal complexity. In practice, the 4D parameterization will consist of a variety of 4D maps of
neuroanatomic change. Different features of the local change will be represented by animating
scalar, vector or tensor maps of individual deformational characteristics, depending on which
attributes are most instructive to emphasize.
Tensor Maps. Tensor maps will be used to show multidimensional quantities characterized by a
large set of interdependent parameters at each point in space. Examples include diffusion tensor
maps, which encode the magnitude and principal directions of intracellular diffusion processes,
and covariance tensor maps (Thompson and Toga, 1997) which encode confidence limits and
principal directions of anatomic variation at each anatomic point. Deformation processes
recovered by our warping algorithms will be described by a variety of tensors, reflecting the
magnitude and principal directions of dilation or contraction, the rate of strain, and the local curl,
divergence and gradient of flow fields representing the transformation. In contrast with prior
approaches based on rigid registration, each of these tensor maps provides a local measure of
structural change.
33
Due to the tensorial nature of the maps of growth and atrophy, one approach to calculating these
maps will be to use continuum-mechanical models for matching 3D images, pioneered for brain
image registration by (Christensen et al., 1993; Miller et al., 1993). These continuum models will
be adapted so that they can be driven by structure models with a variety of rectilinear and
spherical parameterizations (Davatzikos, 1996; Thompson and Toga, 1996, 1997, 1998).
Continuum-mechanical registration algorithms inherently invoke tensor descriptions of structure
dilation, contraction, divergence and shearing. Fluid registration models will also be
investigated, which incorporate the concept of deformation velocity, rate of strain tensors, and
Figure 11: Inter-Modality Warping: Mapping 3D Digital
Cryosection Volumes onto 3D MRI Volumes. The result
of warping a 3D cryosection image (lower left) into the
shape of a target MRI anatomy is shown in (lower right),
with cortical landmarks of the target anatomy
superimposed. Note the reconfiguration of major
occipital lobe sulci into the shape of the target anatomy.
Registration of critical lobar, sulcal and cytoarchitectural
boundaries is only possible with a high-dimensional
warping technique (Thompson and Toga, 1996;
Christensen et al., 1996).
Figure 12: Connected Surface Systems used to Drive the 3D Warp. Deep
surfaces include: the anterior and posterior calcarine (CALCa/p), cingulate
(CING), parieto-occipital (PAOC) and callosal (CALL) sulci, the Sylvian
fissure (SYLV), the superior and inferior surfaces of the rostral (VTSs/i) and
inferior horns (VTIs/i) of the right lateral ventricle. Ventricles and deep sulci
are represented by connected systems of rectangularly-parameterized surface
meshes, while the external surface has a spherical parameterization which
satisfies the discretized system of Euler-Lagrange equations used to extract
it. Connections are introduced between elementary mesh surfaces at known
tissue-type and cytoarchitectural field boundaries, and at complex anatomical
junctions (such as the PAOC/CALCa/CALCp junction shown here). Colorcoded profiles show the magnitude of the 3D deformation maps warping
these surface components onto their counterparts in a scan from an agematched normal subject. Color reproductions of this figure can be found in
the appendix.
concatenation of deformation fields through the relationship of material differentiation
(Christensen et al., 1996). In addition, computation of warping fields will also be carried out
using differential operators which rely on small deformation assumptions (Christensen et al.,
1996), such as the bi-harmonic operator. Different regularization approaches may offer specific
statistical advantages in the analysis of the resulting deformation maps (Bookstein, 1997).
34
Probability Distribution
DataBase
Braintree
Multiple Subjects
Additional Strain
The additional strain used should be either a commonly used strain, such as BALB/c, or a
highly informative one, such as Spret/Ei (shows the greatest sequence variation from
C57BL/6).
Genetic Expression
Gene expression will be studied in a number of ways. Measurements of mRNA from
tissue will be done by cytochemical methods such as in situ hybridization, with
verification by more traditional electrophoretic methods such as Northern blot and RTPCR. Measurements of protein expression from tissue will be done by cytochemical
methods such as immunohistochemistry, with verification by Western blot.
Software Development
A critical product of this research will be the creation of software. Each of the participating
institutions has many years of experience in algorithm research, software development and
distribution. The software development that will form the basis of this research project will build
upon an extensive library of subroutines and programs. We have created programs that have
serviced a wide variety of laboratories, nationally and internationally. We have built code to
work on many hardware and software platforms and distributed source code and documentation
via the Web and on magnetic/optical media.
Platform Migration. Although the present plans hold for us to develop the programs that will
create and interact with the atlas on unix workstations. Many of the interfaces will be built using
Java applets (see preliminary results) so that other platforms can utilize the product of our
development. A trend that is important to the present application is platform migration. The
downward pressure of sophisticated hardware/software solutions towards cheaper, smaller and
faster platforms has been a rewarding force in the field, leading to increased use by scientists of
technologies and software earlier considered proprietary by military, movie industry and other
privileged groups.
To facilitate the porting of programs between platforms and to present a unified interface, we
have adopted several open standards for all programs. These standards are shown in the
appendix:
Computer Programs
Overall design
Interface
35
TIME LINE
Recognizing the need for a rapid prototype development, we will develop a single
animal complete atlas of the mouse within the first year. It will not be complete and will
not have the final level of anatomic detail in the delineations. But it will enable its use
and the overall design to be demonstrated. As noted in the Administration section, we
plan on several venues for the evaluation of our prototype atlas. These include an
advisory board, the presentation of the atlas at NIH sponsored multidisciplinary meetings
and via publication in traditional print as well as web based media. These activities will
continue throughout the life of this project. Thus the first year of effort will concentrate
on the creation of a framework and a single animal representative. Year 02 will focus on
methodological and procedural refinement along with more complete delineations and
software development. We will begin testing the system with our own gene expression
data. Year 03 will see acquisition of subjects at different developmental stages and the
addition of a larger n for all groups. We will also begin beta testing of the software for
insertion of data into and interaction with the atlas. In year 04 we will begin distribution
of the atlas and accompanying software. In year 05, the comparison of additional strains,
the completion of the developmental delineations and the calculations of the probabilities
will be achieved.
Alpha – beta dissemination
I. Administration
The administrative plan for orchestrating the different aspects of this project will require
monthly meetings, either in person or on the telephone, with each of the principal
investigators from each of the institutions. In addition, we will meet in person, going
over the results and development of technology every two or three months at 1 of the 3
sites. Finally, as described elsewhere in this proposal, we intend to exchange technical
personnel to implement the technologies in each of the other sites.
Administering multisite consortium efforts has been a strength of UCLA, as the
Human Brain Project grant entitled "International Consortium for Brain Mapping" is
dependent, in large part, upon the cooperative spirit developed among the participants, as
well as the administration of investigators at geographically distant sites. Given the fact
that the 3 institutions involved in the present proposal are in close proximity
geographically, we are confident in our ability, as already demonstrated, to work together
cooperatively.
(multisite subcontracts, outreach to genetics community, annual meetings)
Advisory board??
36
E. Human Subjects
There are no human subjects in this project.
F. Vertebrate Animals
We will use the C57/BL6/J mouse. IRB approvals for all protocols will be obtained from
the respective institutions, most notably, UCLA and Caltech. Animals are treated using
the veterinary care services of UCLA and Caltech, respectively, and approval notices for
the UCLA aspects of this project are under review, as noted on the face sheet. Euthanasia
will be accomplished by overdose of anaesthetic followed by perfusion fixation. These
methods are consistent with the recommendations of the Panel on Euthanasia of the
American Veterinary Medical Association.
37
H. Consortium/ Contractual Arrangements
The organizational structure of this project involves participation from 3 sites, UCLA,
CalTech and USC. Investigators of each institution, Drs. Toga, Jacobs and Swanson
respectively, already have conducted preliminary experiments together, confirming their
ability to efficiently work together towards a common cause. For example, animals from
UCLA have been imaged at CalTech and algorithms developed at UCLA have been
utilized by USC. Anatomical nomenclature prescribed by USC has been used by UCLA
and cooperative anatomic studies between these two institutions have been conducted in
several species. Thus, the overall cooperative spirit of this project has already been
proved and resulted in several abstracts attesting to the successes of these research
activities. Secondly, the three sites are in relative geographic proximity. While in the
Los Angeles Basin area, distance is usually measured in time, rather than miles, the
commute from site to site usually can be accomplished within half an hour. Thus,
students, staff as well as faculty, can be exchanged among sites on a day to day basis and
have already done so, as described above. In order to extend this cooperative venture, we
have made arrangements to exchange students and staff for longer periods of time, so that
they may enhance the cooperation, learn new aspects of the project and help implement a
particular technical component at another site. It should be noted that the lead
investigators of each site have a long standing personal friendship of too many years to
acknowledge.
Thus, this is an integrated research project involving 3 different sites and several
investigators. The product of this cooperative venture will be greater than the sum of the
parts, resulting in a comprehensive atlas of the C57/BL6/J mouse describing its
developing and adult nervous system in vivo and post mortem.
Budget
Subcontracts are included in this proposal, where the budgets for each site are clearly
described in the budget section, and their justifications found earlier in this proposal. The
lead site, UCLA, also includes overhead costs for the distribution of these funds.
38
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a high resolution PET scanner for imaging small animals. IEEE Trans Nucl Sci 1997; 44: 1161-1166.
Cherry SR, Chatziioannou A, Shao Y, Silverman RW, Meadors K, Phelps ME. Brain imaging in small
animals with MicroPET. In: Quantitative Functional Brain Imaging with Positron Emission Tomography:
Eds: Carson R, Daube-Witherspoon M, Herscovitch P. Academic Press, San Diego, CA, 1998 pp. 3-9.
Qi J, Leahy RM, Cherry SR, Chatziioannou A, Farquhar TH. High resolution 3D Bayesian image
reconstruction using the microPET small animal scanner. Phys Med Biol 1998; 43: 1001- 1013.
Chatziioannou AF, Cherry SR, Shao Y, Silverman RW, Meadors K, Farquhar TH, Pedarsani M, Phelps
ME. Performance evaluation of microPET: A high resolution LSO PET scanner for animal imaging. J
Nucl Med (in press) 1999.
Papers on Imaging Gene Expression with PET:
Gambhir SS, Barrio JR, Wu L, Iyer M, Namavari M, Satyamurthy N, Bauer E, Parrish C, MacLaren
DC, Borghei AR, Green LA, Sharfstein A, Berk AJ, Cherry SR, Phelps ME, Herschman HR. Imaging
of adenoviral-directed herpes simplex virus type 1 thymidine kinase reporter gene expression in mice
with radiolabled ganciclovir. J Nucl Med 1998; 39: 2003-2011.
Gambhir SS, Barrio JR, Phelps ME, Iyer M, Namavari M, Satyamurthy N, Wu L, Green LA, Bauer E,
MacLaren DC, Nguyen K, Berk AJ, Cherry SR, Herschman HR. Imaging adenoviral-directed reporter
gene expression in living animals with positron emission tomography. Proc Natl Acad Sci 1999; 96:
2333-2338.
MacLaren DC, Gambhir SS, Satyamurthy N, Barrio JR, Sharfstein S, Toyokuni T, Wu L, Berk AJ, Cherry
SR, Phelps ME, Herschman HR. Repetitive, non-invasive imaging of the dopamine D2 receptor as a
reporter gene in living animals. Gene Therapy (in press) 1999
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APPENDIX
To facilitate the porting of programs between platforms and to present a unified interface, we
have adopted several open standards for all programs. These standards are:
1) ANSI C: All code generated from the Resource is required to conform to the
ANSI C standard, and to compile on any ANSI C compiler. Where use is made of
platform specific functions or hardware, these functions or hardware calls must be
identified by #ifdef to enable compilation to other platforms. Where functions can
be easily implemented in software, optional code should be provided as workarounds
for platforms lacking the specific feature.
2) OpenGL: All graphics code will use OpenGL. This industry wide standard
provides a common API for graphics and will permit visually oriented programs to
run across many platforms, while taking advantage of hardware support where
available.
3) OpenInventor/VRML2.0: All polygon based file formats and viewing tools will
use OpenInventor compatible formats, which also conform to the VRML2.0
standard. This will facilitate inter-site exchange of data as well as web-based
dissemination of data and results.
4) MINC: The common image file format that has been adopted is the Medical
Image –NetCDF file format. Use of a common file format greatly facilitates the
interoperability of processing tools and the exchange of data between centers. All
extensions for multi-dimensional display and analysis will be relative to the MINC
file format, reducing the likelihood of redundancies
Quality Assurance
We evaluate the quality of software according to the following criteria: Accuracy,
Robustness, Efficiency and Usability.
Accuracy. With each change in software comes the possibility of introducing new errors into the
programs. We check the consistency and accuracy of computational tools by comparing their
output with previously calculated and validated results. If the variance between the program’s
results and a “gold standard” exceeds an error threshold, the program is deemed unready for use.
For complex tools, we test all significant subsystems and interim calculations, as well as final
results.
Robustness. Unstable software is, in our experience, unusable. Robust software gracefully
terminates when problems are encountered and takes pains to inform the user of the conditions
that caused the difficulty. Our programs do this through diligent attention to the details of
software engineering.
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Specific examples of this are: Always checking the status of system resource requests
(such as memory allocation), Testing for “reasonableness” of interim results (e.g. fail on
divides by zero), extensive use of “preflight check” software engineering techniques, and
writing error messages which are meaningful to the users.
Efficiency. The value of an program is strongly related to it’s computational efficiency. This is a
particular concern with our large image datasets. We address this by several means: First, we
strive to create fast algorithms. Extensive run-time performance profiling is done to identify and
eliminate processing bottlenecks. Where possible, we write code that will take advantage of
multiple processors. This is usually done by structuring the internal architecture of our programs
to sensibly map onto multi-threaded processes and by using batch queue systems to perform large
scale load balancing on the various machines here. The mixture of large (program) and small
(program thread) granularity enables us to easily and flexibly get the best from our computing
resources.
Usability. Useful software must be understandable and easily operated. We accomplish this by
involving representatives of our user community during the various phases of software design and
implementation. We prototype our programs with computer novices and with experts. Particular
importance is given to “no assistance” tests – software implementers passively observe and
record the efforts of novices when using their programs. Program documentation is required to be
intelligible to the average user, and should include “real world” examples of how the tools are
used.
Software Portability
It is difficult to write portable software for the UNIX family of computers. Our experience in
developing for several major vendors (SUN, SGI, IBM, DEC) has led us to software development
guidelines which allow our code to compile and run on a wide variety of architectures. We use
Imake and GNU Configure to compile and install our programs. Internal to the code, we make
extensive use of conditional compilation directives to isolate platform and vendor specific
features, include files, and library names.
Software Distribution
We use a simple alpha, beta and final release schedule. We try to make a public release once
every 6 months.
1. Approximately a month before the final release date, announce to all
internal developers a freeze on updates. This constitutes the alpha release.
From this point until final release, only critical bug fixes are allowed. No new
features are permitted.
2. Review the status of all tests. Run the tests on several different Unix
platforms. Resolve all discrepancies. Repair all source code to remove
compiler warnings.
3. Run quality assurance procedure. Repair all errors and warnings reported.
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4. Setup e-mail mediated discussion lists to co-ordinate public release and
ensure effective communications between the user community and the
developers.
5. Prepare a beta release. At this point, tag the program with the beta release
number, e.g. 2.0 beta. From this point until the final release, the release source
code directory will be updated manually. No general updates will be
performed.
6. Release the beta version to the user community. Ask for rapid feedback and
fix any errors as quickly as possible. Several betas may be required.
7. Prepare a final release.
8. Compare the current release with the previous release. Prepare a document
that describes the changes.
9. Package the release. Use gzip and tar for Unix releases.
10. Announce the final release to the user community.
Programmer Guidelines
We have created software guidelines that contain coding, naming and documentation standards.
Adherence to standards:
1. Makes it easier for developers to understand other developers’ code.
2. Makes it easier for users to learn a system.
3. Makes it possible to generate documentation, web pages and testing scripts.
User Community Support
We make extensive use of the Internet to provide software support to the user community.
Web Site. We maintain a web site on the external Internet that points users to software resources
such as releases and documentation. These are the main way of supporting our programs. Our
software web sites include lists of Frequently Asked Questions (FAQ’s), progress reports on the
software, developer notes, application notes, and examples of how and why the software is used.
Electronic Mailing Lists. We also maintain a mailing list for communications pertinent to the
software. These are administered with the popular “Majordomo” list server software. This
software automatically performs most of the tasks of running an e-mail discussion group.
HTML Man Pages
We automatically create html man pages by processing the embedded documentation that resides
in our C++ code. An index is automatically created.
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Application Notes. We generate html pages of application notes. These typically present a case
study of how to use the software to accomplish a specific class of problems. For each case study
we provide a brief explanation of the problem and its solution with the tools. We also provide
sample code (or scripts) and data that can be used to repeat the case study on the reader’s
computer.
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