Finding related functional neuroimaging volumes
Finn Årup Nielsen and Lars Kai Hansen
Running title: Finding related volumes
Keywords: Functional neuroimaging, information retrieval,
content-based image retrieval, multivariate analysis.
Address for correspondence:
Finn Årup Nielsen
Informatics and Mathematical Modelling, Building 321,
Technical University of Denmark, Lyngby, Denmark
Tel: +45 4525 3921
Fax: +45 4587 2599
We describe a content-based image retrieval technique for finding
related functional neuroimaging experiments by voxelization of sets of
stereotactic coordinates in Talairach space, comparing the volumes and
reporting related volumes in a sorted list.
Voxelization is accomplished by convolving each coordinate with a
The scheme allows us to compare experiments represented as either
lists of coordinates or volumes, and we introduce alternative
entrances to databases by image-based indices constructed via novelty
measures and singular value decomposition.
Identification of related research in functional neuroimaging can be
carried out, e.g., by searching in bibliographic databases such as PubMed,
browsing ``table of contents'' of scientific journals or searching
BrainMap [Fox and Lancaster, 1994] with, e.g., behavioral or location
Here we describe a content-based image retrieval method based on
activation information in 3-dimensional (3D) Talairach space
[Talairach and Tournoux, 1988].
The information might either come in the form of a list of points
representing activation hot spots or it might come as a statistical
parametric map, e.g., a volume of -statistics from a statistical
analysis of a functional neuroimaging data set.
Our first goal is to establish a service comparable to ``Related
Articles'' of PubMed.
Retrieval systems for digital text have existed for several decades
and are often based on the vector space model [Salton, 1971],
where a document is represented in a vector with each element
associated with a word or phrase.
Retrieval systems for other digital objects than text
have also been proposed, e.g, on images and sounds
[Feiten and Günzel, 1994].
Some image retrieval systems are based on text description of images,
but others have included features, e.g.,
color, texture, shape and keywords.
This allows for image query by example, i.e., ``Show me images similar
to this image'' as in the IBM QBIC (Query by Image Content) system
[Flickner et al., 1995,Faloutsos et al., 1994,Niblack et al., 1993].
A number of other systems exists, see [Eakin, 2000] for
Web-based image retrieval systems have also been suggested
[Sclaroff, 1995] and implemented in, e.g., WebSEEk
[Smith and Chang, 1996] and ImageRover
[Sclaroff et al., 1997] as well as AltaVista
Neuroimaging retrieval systems have also been constructed, e.g.,
[Liu and Dellaert, 1998]
describe image retrieval for 3D medical images specifically CT brain
scans containing normal, stroke and ``blood cases''.
The basic ``object'' is a half 2D slice where features are extracted
from, such as mean, standard deviation and asymmetry measures.
Other medical image retrieval systems and methods have been described,
[Petrakis and Faloutsos, 1997], [Chu et al., 1998],
Inet [Orphanoudakis et al., 1996],
MIMS [Chbeir et al., 1999] and
a system for decision support in clinical pathology
[Comaniciu et al., 1999].
In research-oriented functional neuroimaging retrieval systems the
BrainMap stands out:
BrainMap is a database holding functional neuroimaging
studies [Fox and Lancaster, 1994] both accessible through a
web-interface and a stand-alone program
[Lancaster et al., 1997].
It allows for search via ``reference'', ``behavioral'', ``location''
and ``protocol'' criteria.
A location criterion can consist of a bounding box in Talairach space.
Finding related volumes was also considered in
[Van Horn et al., 2001] in connection with the Functional Magnetic
Resonance Data Center,
though this database at the time of writing implements search via
the bibliographic information only,
and [Ford et al., 2001] describe briefly an ``inter- and
intra-study data mining'' tool for functional magnetic resonance
imaging (fMRI) activation maps
based on ``activation signatures'' such as size, shape, number of
foci, location and orientation.
A related method identifies global patterns and ``cluster''
experiments [Lloyd, 2000]: Multidimensional scaling was used
to map 35 positron emission tomography (PET) studies to a
3D space based on their activations represented in an
87-dimensional space redundantly
comprising Brodmann areas, gyri, sulci and lobes.
Earlier, brief descriptions of our work are available in
[Nielsen, 2001,Nielsen and Hansen, 2002a].
We downloaded the BrainMap database through its web-site and extracted
fields that were relevant for our purpose.
Since its activation data is in an ``experiment'' structure containing
a variable length list of activation foci (``locations'') we
convert the 3D locations to a voxel-volume representation by a voxelization step where each location in an
``experiment'' is convolved with a Gaussian
kernel in the same manner as a Parzen window/Specht
[Nielsen and Hansen, 2002b,Turkeltaub et al., 2002].
Some of the locations carry a sign and in the present application we
maintain this sign and negate the kernel for the negative locations.
For those voxels where the sign is not explicit we assume that it is
We normalize with the number of locations in each experiment, thus if
there are no negative locations the voxelized volume is a probability
The full voxelization equation determining the value at the voxel
is from locations
We fix the kernel width at mm corresponding to
approximately 24 mm full width half maximum.
This width should incorporate both the uncertainty of the location as
well as the spatial extent of the original activation
[Brett et al., 2002].
Due to memory constraints we use a coarse sampling with
Voxelization can be regarded as the inverse operation of finding a
local maxima or the identification of the
center of gravity/mass of a connected region in a thresholded
Once all volumes are constructed we vectorize each volume into a
and collect all vectors in a matrix
A similarity matrix is computed as a normalized inner product
between the vectors
This measure is related to the reproducibility index in the NPAIRS framework
[Strother et al., 2002].
The similarities are sorted and for each volume the most similar
and most dissimilar volumes are reported in two lists.
Static HTML web-pages are generated containing both lists as well as
summaries of the experiment, a simple Corner Cube visualization
[Rehm et al., 1998] and links to BrainMap and Pubmed.
We further included six volumes from a motor learning positron
emission tomography (PET) study
[Balslev et al., 2002].
These volumes represent the cluster centers of a K-means
clustering [MacQueen, 1967,Goutte et al., 1999].
They were resampled and converted from MNI to Talairach space with
Brett's transformation [Brett, 1999].
The complete pipeline for both the volume data and the BrainMap data
is displayed in figure 1.
Pipeline for finding related volumes for data from the
Apart from indices based on the bibliographic information associated
with an experiment we can produce image-based indices.
A simple ad hoc novelty/outlier measure is generated by
finding the mean volume
the average across all volumes and comparing this through the inner
product with all the volumes.
The novelty for the 'th experiment is returned as the absolute value
of the inverse normalized inner product
See [Nielsen and Hansen, 2002b] for more advanced outlier detection in
An other image-based index is generated through singular value
decomposition (SVD) of the experiment voxel matrix , --
related to principal component analysis (PCA) as used for PET
and fMRI [Friston et al., 1993,Hansen et al., 1999]
For this operation we only include entries from the BrainMap database
that have ``Peer Reviewed'' as publication type, excluding reviews and
unpublished studies that would otherwise contribute with a
considerable part of the variance in .
We compare the 20 first eigenimages in with each individual
volume in by a simple inner product and construct two lists
for each eigenimage:
One with the volumes that are most similar with the eigenimage and a
second with volumes that are most dissimilar (or similar to the
eigenimage with all signs reversed).
Both lists are equally important since the sign on an eigenimage
can be reversed if the sign of is also changed.
We expect that the eigenimages will correspond to global
patterns within the entire set of studies.
As a small test we compared two extra studies
[Hyder et al., 1997,Phelps et al., 1997] to the data set from the
These two studies are fMRI reproductions of PET studies investigating
a ``willed action'' component with a sensorimotor and a verbal task
[Frith et al., 1991].
We should expect the corresponding volumes to appear high in the list
of related volumes.
[Hyder et al., 1997,Phelps et al., 1997] have restricted field of view
only covering the frontal part of the brain.
The tools for this analysis are implemented in the Brede
toolbox [Nielsen and Hansen, 2000] available from
http://hendrix.imm.dtu.dk/software/brede/ and the resulting web-pages
with volumes are presently available from
Example view of the related volumes for an experiment
reported in [Corbetta et al., 1993] and available in the
797 HTML pages were generated and the voxelized volumes consisted of
An example of one of the generated web-pages is displayed in figure 2
based on one of the 12 experiments/volumes reported in
[Corbetta et al., 1993].
It shows two clusters of activations: one in the parietal lobe and an
other in the frontal lobe.
This pattern is repeated for the five most related experiments.
Novelty index showing a list of the most novel
The top of the novelty index is shown in figure 3:
The highest novelty is recorded in one of the three experiments
of [Allison et al., 1994].
The paper is the only one recorded with the ``electrophysiological''
modality (through implanted electrodes and combined with MRI).
Only and Talairach coordinates are shown in the
article, and the -coordinate in the database have been
estimated during entry.
Its high novelty might be due to this estimation and the rare modality.
The second largest novelty for a ``Peer Reviewed'' experiment appears
for the 5th entry in the table: [Reiman et al., 1989]
finds activation in the temporal pole in connection with anticipatory
A correction to this article later appeared where it was found that
the activation might not be a brain activation but an extracranial
muscle ``activation'' from teeth-clenching [Drevets et al., 1992].
The third highest novelty for a peer reviewed experiment is our
cluster volume described in [Balslev et al., 2002] and it is
confined to the rim of the brain, and which we attributed to
a possible motion artifact.
The isosurfaces from the
positive and the negative end of the second eigenimage.
Since the mean of the images is not extracted our first eigenimage
from the SVD separates experiments with positive and
The top of the positive end contains among others two experiments by
[Parsons et al., 1995] each which contain 59 locations distributed
across large parts of the brain.
At the other end is an experiment [Silbersweig et al., 1993]
with 12 negative activations.
The subsequent eigenimages show high loadings on specific regions, e.g.,
the positive part of the second eigenimage covers the central sulcus
and nearby areas with a large weight on the left hemisphere, see
This implies that motion studies score high.
The other end of the eigenimage shows a loading in the occipital lobe
with the top experiments all involving visual stimulation, e.g., a
passive movement observation versus imaging grasping objects contrast
from [Decety et al., 1994] scores highest.
The next principal component distinguishes between cognitive and
Higher components relates, e.g., to auditory presentation of words or
Yet higher eigenimages show increasing spatial frequency and are harder
When the sensorimotor experiments of [Hyder et al., 1997] and
[Frith et al., 1991] are compared then [Hyder et al., 1997]
is found as the 4th most related of published studies in the list of
[Frith et al., 1991], and [Frith et al., 1991] as the
9th most related to [Hyder et al., 1997]
(The list order is not necessarily symmetric).
A total of 11 experiments from 9 different papers appear in the
interval between the two
[Buckner et al., 1995,Petrides et al., 1993b,Jahanshahi et al., 1995,Deiber et al., 1991,Petrides et al., 1993a,George et al., 1993,Ceballos-Baumann et al., 1995,Grasby et al., 1993,Kapur et al., 1994]
where the two latter are PET studies with apomorphine which both
``activate'' the anterior cingulate cortex.
The verbal experiments [Phelps et al., 1997,Frith et al., 1991]
show little similarity, and neither are listed among the top 25
most related volumes of the other.
The experiments of [Frith et al., 1991,Hyder et al., 1997] show
relatively good agreement.
Many of the related volumes for the two experiments resemble the
task: self-initiated/``willed action'' motor response where the
subjects have to choose direction, time of response or which
finger to move
[Jahanshahi et al., 1995,Deiber et al., 1991,Ceballos-Baumann et al., 1995,Petrides et al., 1993b].
However, the discrimination between other tasks is not complete since
other related experiments have remotely related tasks:
Recall word from stem [Buckner et al., 1995], emotional
recognition [George et al., 1993] and the apomorphine studies
[Grasby et al., 1993,Kapur et al., 1994].
[Phelps et al., 1997] write ``there is excellent
agreement between the present fMRI study and the PET study'' in
comparing their study with [Frith et al., 1991].
This statement is based on the small distance (6.2 mm) between two
corresponding locations in each study.
Our method, that is globally oriented, finds little relatedness
between the two partly due to field of view (FOV):
Brain scanners have restricted FOV and some of them do not scan the
Furthermore, some researchers may choose to focus attention to a
few slices, e.g., in fMRI for gaining faster acquisition time.
Potentially (and probably), there is activation outside the FOV.
In the present method we assume these potential activations to be
zero, while a more elaborate scheme would treat them as unknown.
This would require a more precise specification of the stereotactic
location of the FOV than is found in the typical article.
The comparison between
[Frith et al., 1991,Hyder et al., 1997,Phelps et al., 1997]
is influenced by the fact the fMRI studies had restricted FOV
compared to the PET study.
However, the method is not insensitive to detect similar patterns
across experiments with different FOV, e.g., the experiment by
[Phelps et al., 1997] and the ``generate use from auditory presented
nouns'' experiment by [Petersen et al., 1988] show high
similarity even though 2 out of 5 locations in the experiment by
[Petersen et al., 1988] appear outside the FOV of
[Phelps et al., 1997].
The BrainMap database records not only original ``Peer Reviewed''
research articles but also meta-analyses and unpublished studies,
where some carry little information and are irrelevant, see, e.g., the
2-4 and 8-9 entries in the novelty list in figure 3.
In the present application we let it up to the user to ignore these
A more flexible interactive search would allow the user to determine
which data to include.
Spaces for calculation of distance/similarity.
||The distance between voxel values in a (voxelized)
||The distance computed in a subspace
||The distance in 3D Talairach space between points
There are several ways in which we can compute a similarity or
distance measure between two experiments.
Table 1 shows some of the spaces we can work
For the present application we have relied on a conceptual simple
voxel representation which has the advantage that voxelized point data
directly can be compared to other voxel-volume data provided they are
resampled and in the same stereotactic space.
However, it requires large data structures and a large number of
computations for every comparison where vectors with several thousand
elements has to be constructed and compared.
Since the number of experiment
than the number of voxels
(in the present data set)
the experiments can be represented in the lower -dimensional space
with an orthogonal transformation.
Using PCA for the transformation we might even further restrict
the space regarding the highest principal components as noise.
It is also possible to compare the volumes using only the sets of
locations computing the distance measure in the 3D Talairach space.
The voxelization is avoided but it is not possible to produce an
Contrary to the voxelization based methods this procedure has no
A further reduction in the computational complexity can be obtained by
using more advanced data structures than simple lists of points
[Samet, 1990], such that not all
terms need to be
Regardless of the space of distance computation the optimal distance
metric is still an open issue: kernel type, kernel width, normalization
and how the sign and magnitude of locations should be treated.
If we had labeled data, e.g., from manual scoring, we could
optimize for best performance.
We have shown the possibility in performing volume searches where
related experiments are found.
Experiments that report activation as points can be compared to
volumes by voxelization, and compared with normal search our method
incorporates an incertitude aspect with fuzzy queries.
We showed that image-based indices can be generated and these produce
meaningful novel entrances to a database.
Extensions to the scheme can include combination with text-based
queries and ad hoc retrieval, where users supply a volume and related
volumes are returned.
The method opens up for a quantitative comparison of activation
volumes where the reproducibility of tasks and the cognitive
components under study is assessed.
Daniela Balslev for help and discussion.
Research Imaging Center in San Antonio for access to the
Funding where provided by European Union (MAPAWAMO), American Human
Brain Project (International
Neuroimaging Consortium) and the Danish
Research Councils (THOR Center for Neuroinformatics).
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