Eurographics Workshop on Visual Computing for Biology and Medicine (2010)
D. Bartz, C. P. Botha, J. Hornegger, R. Machiraju, A. Wiebel, and B. Preim (Editors)
Visual analysis of integrated resting state functional brain
connectivity and anatomy
A.F. van Dixhoorn1, B.H. Vissers1, L. Ferrarini2, J. Milles2 and C.P. Botha1,2
1 Department of Mediamatics, Delft University of Technology, Delft
2 Department of Radiology, Leiden University Medical Center, Leiden
The Netherlands
Abstract
Resting state functional magnetic resonance imaging (rs-fMRI) is an important modality in the study of the func-
tional architecture of the human brain. The correlation between the resting state fMRI activity traces of different
brain regions indicates to what extent they are functionally connected. rs-fMRI data typically consists of a matrix
of correlations, also denoted as functional correlations, between regions in the brain. Visualization is required
for a good understanding of the data. Several well-known representations have been used to visualize this type
of data, including multi-dimensional scaling, spring embedding, scatter plots and network visualization. None of
these methods provide the ability to show the functional correlation in relation to the anatomical distance and
position of the regions, while preserving the ability to quickly identify outliers in the data. In this paper, a visual
analysis application is presented that overcomes this limitation by combining the strengths of the two-dimensional
representations with three dimensional network and iso-surfacing visualizations. We show how the application
facilitates rs-fMRI connectivity research by means of a case study evaluation.
Categories and Subject Descriptors (according to ACM CCS): Information Visualization [I.3.3]: brain mapping,
interactive, scatter plot, network visualization, functional brain connectivity—
1. Introduction
Recent developments in medical imaging techniques have
accelerated research in brain mapping and brain functional
connectivity. A number of studies have investigated the rela-
tionship between brain activity and functional connectivity.
Methods used include resting state functional magnetic reso-
nance imaging (rs-fMRI) connectivity of healthy persons in
resting state [SSC∗05] as well as diffusion spectrum or ten-
sor imaging (DSI/DTI) to identify structural connections in
the human brain.
Resting state fMRI is based on the observation that low-
frequency (0.01Hz−0.1Hz) signal fluctuations in grey mat-
ter regions are perceivable in a resting brain. These sig-
nals seem to relate to spontaneous neuronal activity. Cor-
relations between resting state signals from different parts
of the brain indicate the functional connectivity between
those regions [BYHH95]. Research by Hagmann et al. re-
vealed a strong relationship between structural and func-
tional connectivity [HCG∗08]. More recently, Salvador et
al. [SSC∗05] studied the organization of the human brain in
a resting state by investigating pairwise functional connec-
tions between ninety anatomical regions of interest (ROIs).
Salvador and his group revealed that the amount of connec-
tivity between regions can be predicted by the anatomical
distance between the respective regions, generally satisfying
an inverse square law. Pairs of anatomical regions that signif-
icantly deviate from this relation were identified as being re-
gions that are anatomically symmetric (interhemispheric) or
local (intra-hemispheric, neighboring). Comparing the func-
tional brain architecture of healthy persons with that of a
patient affected by brain injury revealed a significant dif-
ference in the interhemispheric connectivity [SSC∗05]. The
output of this type of study usually are the individual matri-
ces containing the correlation between each pair of anatom-
ical regions for each subject. In our case this set of matrices
is supplemented with an average connectivity matrix repre-
senting the connectivity characteristics of the whole subject
group.
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Van Dixhoorn, Vissers, et al. / Visual analysis of integrated resting state functional brain connectivity and anatomy
The existing analysis pipeline is primarily hypothesis-
driven, and consists of compute-intensive offline analysis of
the correlation data. Up to now, our collaborators have not
been making use of visual analysis capabilities, only seldom
using non-interactive visual representations of the correla-
tion matrix.
In this paper we present a system which uses coupled
views in order to facilitate scientists’ understanding of func-
tional connectivity data. The contributions of this work can
be summarized as follows:
• We present a visual analysis approach for studying con-
nectivity in resting-state functional MRI data that couples
information and scientific visualization views.
• Our method improves on current work by coupling views
providing 3-D structural context with views that focus on
representing connectivity without spatial context.
• Our method also gives visual feedback on the degree of
connectivity between functional regions. Most existing
techniques cater only for binary connectivity.
• By means of a case study evaluation, we demonstrate how
our technique improves on the existing pipeline for rs-
fMRI connectivity analysis.
The rest of this paper is structured as follows; in section
2 related work is examined, followed by our proposed so-
lution presented in section 3. Section 4 briefly summarizes
the used software packages. The evaluation of the software
can be found in section 5, it includes expert user feedback,
case study propositions and general examples. Finally, the
conclusion and future work are addressed in section 6.
2. Related work
In this section we discuss visualization techniques that are
used specifically for functional connectivity, broadly divided
into techniques that either do or do not explicitly represent
the spatial layout of the data. We also briefly discuss relevant
techniques that are used for network visualization in general.
This type of connectivity data is typically defined, for a
network with N nodes, as an NxN matrix, where each cell
(i, j) in the matrix contains the correlation between the re-
gions denoted by i and j. In essence, the correlation ma-
trix defines a network where the nodes represent the regions
and links represent functional connectivity.The regions are
mostly defined by anatomical templates, such as the Ta-
lairach atlas [TT88] and the standard brain templates from
the Montreal Neurological Institute [ECM92] (also known
as the AAL template).
Non-spatial visualization techniques that have been
used to study rs-fMRI connectivity data include multi-
dimensional scaling, spring embedding, matrix bitmaps and
scatterplots. These methods are generally used to identify
structural clusters in the data, but do not represent its spatial
layout. Multi-dimensional scaling results in a spatial config-
uration that emphasizes functional connectivity: regions that
are similar in terms of function (highly correlated), will be
plotted in the same neighborhood in space. MDS has been
used by Salvador et al. to visualize the output of a clus-
ter analysis on the partial correlation matrix [SSC∗05]. In
their study on the maturing of the brain, Fair et al. use 2-
D spring embedding to visualize the brain network. This
technique seems to be especially useful when investigating
change over time [FCP∗09], but has also been used by Hag-
mann et al. to visualize structural patterns in the correlation
matrix [HCG∗08]. A natural way of visualizing the output of
rs-fMRI connectivity research is by representing it as a ma-
trix bitmap (or pixmap). This is a pixel-based representation
that results in a matrix of size NxN for a network of N items,
where each cell (i, j) is color coded according to the con-
nection strength between region i and j [FCD∗08, BEW95].
Ordering the matrix enhances the ability to detect patterns
of relations. A typical ordering that has been used in rs-
fMRI connectivity research is based on anatomical hierar-
chy [HCG∗08].
The relation between anatomical distance, as extracted
from an anatomical template, and functional distance can be
visualized using a scatterplot [SSC∗05]. For the dataset used
in this paper, this is illustrated in figure 5.
When representation of the spatial layout of the data
is required, the most common visualization is a spatially
embedded node-link diagram. In this network visualization
method, the regions and connections between them are ren-
dered as a network in three dimensions, where each node,
representing a ROI, is rendered at the center of mass of the
corresponding region. Connections between the ROIs are vi-
sualized as lines between the nodes, and the link strength can
be encoded by line thickness or color. A two-dimensional
pseudo-anatomical variation has been used by Fair et al.
[FCP∗09], Dosenbach et al. [DFM∗07] and Hagmann et
al. [HCG∗08]. Visualizations in three dimensions in a cor-
rect anatomical context have been used by Worsley et al.
[WCLE05], and to a lesser degree by Bezgin et al. and Cao
and Worshley [BRSK09, CW99].
As can be seen in [WCLE05] combining the contours of
the regions with the links in one view quickly starts clutter-
ing the view. Ghoniem et al. argue that when graphs are big-
ger than twenty vertices, the matrix-based visualization out-
performs node-link representations on most tasks [GFC05].
Another way to deal with the cluttering problem is to use the
node-link visualization in an interactive fashion, where the
user is able to threshold the edges based on their strength.
There are tools available for the analysis and visualization of
fMRI correlation data, such as the BrainMiner visualization
tool [MWZ∗00], CoCoMac Paxinos 3-D Viewer [BRSK09],
and the commercial software BrainVoyager QX [GEF06],
focusing mostly on the basic 3-D representation of connec-
tions. Network visualizations are also used in the research
domains of communication networks, social networks and
biological networks [BEW95, PWS08, HFM07].
c© The Eurographics Association 2010.

Van Dixhoorn, Vissers, et al. / Visual analysis of integrated resting state functional brain connectivity and anatomy
Figure 1: The application’s main window with the three main components. (A) - The Anatomical Views component. Contains
the Anatomical Region View (left) and Anatomical Network View (right). (B) - The Abstract Views component. From left to right
the Scatterplot View, Matrix Bitmap and Hierarchical Edge Bundling View. (C) - The Filtering and Selection View with the
Selection Info View (top) and Filter View (bottom).
We present a visual analysis application that incorporates
several of the aforementioned techniques, combining them
in various linked views of the same data. The idea of query-
ing in a query friendly view and providing insight in a (3-
D) visualization view is proven to be useful in [KPM∗08].
Using this approach, disadvantages of one view can be com-
pensated for by using the other views. Our solution improves
on the state of the art in the following ways: In contrast to
the systems in [MWZ∗00, BRSK09, GEF06], our tool inter-
actively couples views for quick outlier and pattern detection
with 3-D spatial representations as well as techniques for in-
teractive selection and filtering. In addition, we introduce the
application of hierarchical edge bundling [Hol06] to visual-
ize hierarchy and adjacency relations in the brain.
3. Method
The methods that are currently used for studying resting-
state functional connectivity MRI data work well in studying
basic questions concerning the data, but they do not cope
well when both the anatomical information and functional
connectivity data are part of the research question. In this
section, we present our visual analysis approach that cou-
ples the anatomical information and the connectivity data
(consisting of data from 53 subjects) by combining exist-
ing techniques from information visualization and scientific
visualization to improve on the existing pipeline for rs-fMRI
connectivity analysis. In the rest of this section we describe
our method, starting by giving a general overview of the sys-
tem and then following with the details of the system’s com-
ponents.
3.1. Application Overview
We implemented our method as a software tool that loads
the anatomical data (from an AAL template) and functional
connectivity data from a file and displays this data in sev-
eral different linked views. A selection in any of the views
is reflected in the other views, where possible. The views
are sub-windows in the application’s main window (see fig-
ure 1) and can be categorized into three main components:
the anatomical (figure 1A), the abstract (figure 1B) and the
filtering and selection views (figure 1C).
3.2. Anatomical Views (figure 1A)
The Anatomical Regions and Anatomical Network views
use a 3-D window to render their information in anatomical
context. The views are linked: Mouse interaction in one view
has its effect on both views. The anatomical data is loaded
from an AAL brain template with 90 anatomical regions.
Anatomical Regions Using the AAL brain template, iso
surfaces are extracted for each of the 90 anatomical regions.
A default color map is used to distinguish different regions.
The default color map also visualizes brain lobes by en-
coding the lobe regions in a similar color. The Anatomical
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Van Dixhoorn, Vissers, et al. / Visual analysis of integrated resting state functional brain connectivity and anatomy
Regions View offers two main modes of interaction: region
mode and link mode.
The region mode is activated when a single region is selected
(as opposed to a selection of links, in which always two or
more regions are selected). In this mode, the Anatomical Re-
gion View will render this region in its own color, and all
other regions according to a colormap that is based on either
correlation or deviation from 1D2 . Additionally, the opacity
of the region surfaces is based on this number, emphasizing
highly correlated or highly unexpected linked regions. This
mode gives the analyst the ability to quickly see the connec-
tion properties for a single region.
The link mode is activated when multiple regions are se-
lected (one link or more selected). So, if a selection is made
in any of the other views, this mode is automatically en-
abled. In this case the Anatomical Regions View highlights
all anatomical regions that are part of the selection in their
default color. Furthermore, this mode has two options. Either
the non-selected regions can be completely removed from
the view, or they can be rendered in dark gray in order to
provide context.
The main role of this view is to visualize regions that are
selected (either in the view itself or in other views, see sec-
tion 3.4) in their anatomical context (spatial location and
size).
Anatomical Network In the Anatomical Network view
each selected region is represented as a node with its diame-
ter being based upon the total correlation strength of all the
links this region participates in. The links are represented as
tubes with their diameter being based upon the (absolute)
correlation strength of the link it represents.
3.3. Abstract Views (figure 1B)
This component consists of three 2-D views that focus on the
connectivity information outside of its anatomical context.
Scatterplot Having the Euclidean anatomical distance on
the x-axis and the correlation strength on the y-axis, the
scatterplot shows the relation between distance and correla-
tion. A curve is plotted in the scatterplot showing the rule of
thumb ( 1D2 ). The general spread of the points is expected to
be around this curve. Points far away from the curve, may be
considered outliers. The main purpose of this view is spot-
ting and selecting outliers in the data.
Matrix Bitmap The matrix bitmap is a direct visualization
of the correlation matrix, although the rows and columns
are re-ordered. The left half of the columns (and top half
of the rows) reflect regions of the left hemisphere, the right
(and bottom) half reflects the right hemisphere. This way,
the horizontal and vertical center lines are mirroring left and
right symmetrical regions. The minor (secondary) diagonal
defines the line of full symmetry. See figure 2 for a visual
explanation.
Figure 2: The matrix bitmap with several links highlighted.
The highlighted link in the left top is the symmetrical equiv-
alent of the highlighted link in the right bottom. The same
holds for the pair of highlighted links in the right top of
the bitmap. Additionally, the link in the left top is the half-
symmetrical equivalent of the link in the right top (on the
same row). The same applies to the two links in the right top
and right bottom of the matrix (in the same column).
Each pixel in the bitmap represents a link and the color
encodes some statistic for that link. Three statistics are avail-
able for each link, resulting in three different matrix bitmaps.
The first matrix bitmap directly visualizes the correlation
matrix of the subject, giving each pixel a color value directly
based upon the correlation of that link. The second one sub-
tracts the average correlation matrix from the correlation ma-
trix of the current subject, giving each pixel a color based
upon the correlation of the link minus the average correla-
tion for that link using a perceptually linear colormap. The
third matrix bitmap subtracts the matrix containing the rule
of thumb values from the correlation matrix of the current
subject, effectively showing for each link the difference with
the expected value for that link, thus emphasizing links that
deviate significantly from the expected value.
The selected links in the bitmap are currently displayed
by placing a black dot in the top and left edge of the bitmap,
its place corresponding to the column and row the selected
link is in. This way, when all selected links are in one area
of the brain the user will quickly notice this as all the black
dots are close together.
Hierarchical Edge Bundles This view visualizes hierarchy
of the brain, based on the Brodmann regions, in a circular
layout. The root of the hierarchy is the whole brain, repre-
sented by a node in the center of the circle. The next level
in the hierarchy is the distinction between the left and right
brain half (with nodes left and right from the root node). The
next level are the seven lobes in the human brain for each
c© The Eurographics Association 2010.

Van Dixhoorn, Vissers, et al. / Visual analysis of integrated resting state functional brain connectivity and anatomy
Figure 3: Hierarchical Edge Bundles view with multiple
links selected. Edges loosely follow the brain’s hierarchy.
For printing purposes, the image has been post-processed
not to show all links, and to let the selected links stand out
more.
brain half and finally, the lowest level in the hierarchy are
the 45 regions for each brain half.
Each link is represented as an (elastic) edge between two
region nodes. The edge is attracted by the nodes from higher
hierarchy levels by the bundling strength which can be var-
ied by the user. Increasing the bundling strength results in
all edges going through their anatomical parents in the hi-
erarchy, bundling similar links together as long as they take
the same route through the brain hierarchy. Decreasing this
value results in smooth edges which pass loosely by their
anatomical parents. The main purpose of the hierarchical
edge bundles view is to show the course of a link from one
region through the hierarchy of the brain to the other region.
See also figure 3.
3.4. Filtering and Selection (figure 1C)
The last component consists of two views. The info view
shows textual, statistical information of the current selection,
the filter view enables the user to occlude data from the other
views.
Info view The info view (top view in the component) shows
information on the current selection. It shows general and
statistical information such as the name and ID of a single
link, or number of selected links otherwise, distance, corre-
lation, mean, and median for the selected link(s) as well as
the total set.
The main purpose of this view is to enable the user to
quickly gain insight in the current selection values, and how
those relate to the total dataset.
Filter view The filter view enables the user to filter on a
min, max or range of correlation or distance. Another option
is to filter on a confidence interval around the curve 1D2 . In
case of the range and confidence interval filters outliers as
well as inliers can be filtered. Filtered links and regions will
no longer appear in the views.
The main purpose of this view is to occlude values which
are not interesting for the data analyst, this way also prevent-
ing occlusion of the important values.
4. Implementation
The cross-platform application is implemented in C++, us-
ing Qt4, Qt Designer, Qwt, VTK and finally Matlab for load-
ing the connectivity matrices.
All time consuming tasks are performed once, on startup
of the tool. This includes generating iso-surfaces of all re-
gions in the brain template image and building the iso-
surfaces (nodes and links) for the network representation.
Once loaded the complete application is real-time, without
any optimization as is.
Creating the fully connected network involves generating
90 spheres and 4005 tubes, which requires under ten sec-
onds on a system having an Intel 2 Core Duo T7300 (2Ghz),
NVIDIA GeForce 8600M GT, and running on Windows 7
64-bit Professional. To prevent blocking the user interface,
this task is performed in a separate thread and the user is
updated on the progress. The total amount of memory used
ranges from 170 to about 250 Megabytes.
5. Evaluation
We evaluated the proposed system following the guidelines
and terminology set forth for case study research [Yin09].
The main study question was defined as: How can the pro-
posed visualization tool assist neuroscientists in their re-
search on resting state fMRI connectivity? We defined the
case in this question as being the use of our software by the
third and fourth authors of this paper, respectively LF and
JM, both published neuroscientists and experts in rs-fMRI
connectivity.
For the purpose of this evaluation, two meetings were
held with the users. We first conducted an informal inter-
view during which users could give general feedback on the
system prototype. Two weeks later we conducted a focused
review, during which we used the tool to analyse a real-
world multi-subject dataset together with the domain scien-
tists, collecting and structuring their feedback according to
the case study propositions that we had formulated before
the session. Together the propositions and the accompany-
ing structured feedback function to answer the main study
question.
In the following subsections, we first illustrate the general
c© The Eurographics Association 2010.

Van Dixhoorn, Vissers, et al. / Visual analysis of integrated resting state functional brain connectivity and anatomy
use of the visualization tool with four examples, after which
we discuss user feedback structured according to the case
study propositions.
5.1. General examples
The dataset used in the following examples consists of the
90× 90 correlation matrices of 53 subjects, as well as the
average correlation matrix over all subjects.
Symmetric interhemispheric connections show an unusu-
ally high correlation Several long distance connections are
correlated unusually strongly and deviate significantly from
the expected relation ( 1D2 ). Using the presented tool, just
one action is required to see which connections these out-
liers represent. The user makes a selection in the scatter plot
using the mouse (see figure 4 (a)) and the Network View
immediately updates the network representation, now only
showing those regions that correspond to the selected points
in the scatter plot (see figure 4 (b)). At the same time, the
Regions View is updated and renders a three dimensional,
anatomically correct representation of the selected regions
(see figure 4(c)).
Another way to easily spot these outliers is to look at the
correlation bitmap matrix. These connections are visible as
pixels at the second diagonal (from the right top to the center
of the triangular matrix, see section 3.3) and indeed pop out
by their bright color. Clicking on any of these pixels will
render the corresponding connections in the Network and
Regions View. Taking a look at the ’deviation from expected’
bitmap confirms that the most prominent outliers indeed are
the symmetric interhemispheric connections.
Finally, the same observation could have been made by
using the Filter View. By adjusting the sliders, the user is
able create a filter that accepts only the connections that de-
viate significantly from the expected relation ( 1D2 ).
Figure 4: Identifying outliers. Identifying the outliers is just
a matter of selecting them in the scatter plot (A) and the cor-
responding connections (B) and regions (C) are immediately
rendered in the 3-D views.
Local intra-hemispheric connections show an unusually
high correlation. In a similar way, the user can identify the
links in the low distance region of the scatter plot. Selecting
these regions (distance between 1.5cm and 3cm, correlation
between 0.2 and 0.6) either in the Scatterplot View or using
the sliders in the Filter View identifies the points as connec-
tions between regions that are in proximity to each other. If
the user has defined a color map for the regions based on the
hierarchical structure of the brain (regions in the same lobe
get similar color), the regions view will identify the selected
regions as being either in the same lobe (correlation between
0.2 and 0.35) or symmetrical (correlation between 0.35 and
0.6).
Other highly correlated regions. Depending on the re-
search, the unusually strong connections between symmetric
regions may or may not be interesting. The ’compensate for
symmetry’ mode enables users to treat symmetric regions as
being equal (see section 3). Enabling this option has a signif-
icant effect on the scatterplot view. Points that correspond to
symmetrical connections are now disabled, see figure 5(b).
This puts emphasis on highly correlated regions that are not
symmetric and can be used to verify the observation made
by Salvador et al., that non-symmetric regions in different
hemispheres are infrequent.
Differences in the population. One additional feature that
our method offers is the ability to see whether the relations
also hold for individual subjects. For instance, using the
subject-slider in the top of the application’s main window,
we were able to observe that the selected connections in fig-
ure 5(b) are not consistently highly correlated throughout the
set of individual subjects.
5.2. Case study propositions and user feedback
The scatterplot allows the rapid localization of interesting
correlations (outliers). This proposition was confirmed, al-
though the possibility of using absolute correlation would
make it more useful. This will be implemented in future ver-
sions of the software.
The matrix bitmap allows the rapid detection of interest-
ing correlations (high, deviating from expectation or from
average, etc.) and visual patterns of correlations. Patterns
could point in the direction of whole groups being corre-
lated. This proposition was confirmed, with the users adding
that high correlation in fact pops out. It was noted that the
ordering of the elements is very important.
The hierarchical edge bundles are better than the dendro-
gram for showing connectivity through grouping, as they are
able to represent connections between parcels in different
hemispheres through the hierarchy in-between. Dendrogram
can be double-sided, but packing is inefficient. Both users
claimed that due to this technique being new in the context of
this application, they would need to use it more extensively
before being able to comment. They agreed speculatively
that it could be useful and commented that the specific hi-
erarchy chosen becomes important and further that the color
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Van Dixhoorn, Vissers, et al. / Visual analysis of integrated resting state functional brain connectivity and anatomy
(a) Scatter plot with symmetry compensation disabled (b) Scatter plot with symmetry compensation enabled
Figure 5: The Scatterplot View with the symmetric intra-hemispheric connections visible (5(a)) and hidden (5(b)). The latter
view immediately reveals an unusually strong connection at D = 4.8 for this subject. Selecting this region shows the connection
in anatomical context (representing the connection between Frontal_Inf_Oper_R and Temporal_Pole_Mid_R).
map should be adapted so that high correlations should be
more visible, especially so due to the visual complexity of
this representation.
The 3-D spatial embedding of functional correlation al-
lows the study of these connections in their anatomical con-
text & Being able to query in the anatomical view (click on
region, see all connected regions) aids in the understand-
ing of brain architecture. This was considered useful espe-
cially in the comparison of cases to controls, where research
is able to focus on specific regions of the brain, based on
prior knowledge of the pathology under study. For example
in Alzheimer’s Disease patients, it is expected that the hip-
pocampus plays a role, so researchers can use spatial query-
ing to rapidly see how other regions are connected. Also, an
initial selection in 2-D views, based on obvious high corre-
lations, can be further specified in the spatial view in order
to determine the total connectivity of a region and hence lo-
calize communication hubs.
Visual filtering and zooming alleviates clutter problems in
the 3-D spatial view and scatterplot & Linked interaction
leads to the conceptually different views strengthening each
other. Users agreed with both these propositions, even claim-
ing that the linked interaction was an essential element of the
tool.
The tool is useful during the pre-processing of data, in or-
der to detect interesting aspects (outliers) for further analy-
sis using the traditional pipeline. Although the users thought
that the tool might be useful during the exploration phase,
they commented that current research focuses on local and
global efficiency of the neural architecture, in which none
of the connections can be discarded. In this case, the tool is
less useful in its role as data filtering method. However, in
the future data will be acquired on a much larger number of
connections, in which case the tool could be more useful in
determining which parts to focus on.
The tool is useful for checking the quality of the data, i.e.
whether it satisfies expectations & The tool could be useful
for rapidly finding subjects that deviate from the whole col-
lection. One of the users explained that in a previous study
there had been some errors during data acquisition result-
ing in zero correlation between a specific region and all oth-
ers, and that it had taken a while before this error was fi-
nally discovered. Such an error would have shown up as an
obvious black line in the correlation pixmap and hence the
error would have been detected at an early stage. Also be-
ing able to scroll through all the subjects in the study, visu-
ally inspecting a subset of the connections, could be help-
ful in rapidly locating outlier datasets and other errors in the
data acquisition. Users claimed that with this tool they would
more readily do visual checks of their data in the future in
order to help ensure its quality.
Detecting ‘outlier’ links with a specific region without ac-
tually selecting all the links is easy using the link view. In
the link view, comparing the diameter of the node to the di-
ameter of the tube indicates the relation between this link
compared to all other links this region participates in. When
the diameter of the node (ROI) is small compared to the di-
ameter of the tube (link) the link is an outlier in the set of
links the region participates in.
Generally, both users were very enthusiastic about pos-
sibilities the tool offered for future research. An important
point for future improvement was the addition of visual en-
codings of group statistics over connections, so that for ex-
ample mean and standard deviation could be visualized for
the whole group.
Based on this case study and its analysis, we conclude
that the proposed visual analysis tool can facilitate research
on rs-fMRI connectivity by offering new ways of looking at
study data that enable the rapid localisation and anatomical
contextualization of interesting characteristics, whether they
be the result of acquisition errors or genuinely interesting
phenomena. However, to fully answer the main case study
question, all the propositions need to be taken into account.
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Van Dixhoorn, Vissers, et al. / Visual analysis of integrated resting state functional brain connectivity and anatomy
As we are planning to release the tool as open source, we ex-
pect to analyze a wider range of user feedback in the future.
6. Conclusions and Future Work
In this paper we have presented a tool that strongly couples
a number of well-known visualization techniques in order
to enable the visual analysis of data acquired in rs-fMRI
connectivity research. An analyst is able, using the tool, to
quickly identify correlated brain regions, visualize the rela-
tion to their corresponding distances in the anatomical space
and spot connections that deviate from the general relation.
Currently, the proposed tool uses a rough approximation
for the anatomical distance, but even with this approximation
it is possible to replicate many observations made in a study
typical for rs-fMRI connectivity research, by Salvador et al.
[SSC∗05]. A distance measure based on the center of mass
of the regions, or even a shortest surface-to-surface distance,
may improve the reliability.
Several improvements and additions will be reviewed,
such as visualizing change over time to visualize differences
between different subjects. Another addition that will be
evaluated is combining rs-fMRI connectivity data with data
that is acquired from research on the structural connectivity
of the brain (DSI/DTI studies). This could give insight in the
relation between functional connectivity measured by brain
activity and structural connectivity.
In addition, we will collect case study data of the users
to perform more formal case studies and quantitatively com-
pare this to results found with other visualization methods.
When it appears that multiple users perform the same task
multiple times, the case studies will be extended with task
performance measurements on those specific tasks. Finally,
new visual representations for connectivity exploration, (es-
pecially to improve on the visualization of continuous con-
nectivity) will be studied.
References
[BEW95] BECKER R. A., EICK S. G., WILKS A. R.: Visual-
izing Network Data. IEEE Transactions on Visualization and
Computer Graphics 1, 1 (1995), 16–28.
[BRSK09] BEZGIN G., REID A., SCHUBERT D., KÖTTER R.:
Matching spatial with ontological brain regions using Java tools
for visualization, database access, and integrated data analysis.
Neuroinformatics 7, 1 (2009), 7–22.
[BYHH95] BISWAL B., YETKIN F. Z., HAUGHTON V. M.,
HYDE J. S.: Functional connectivity in the motor cortex of rest-
ing human brain using echo-planar MRI. Magnetic Resonance in
Medicine 34, 4 (1995), 537–541.
[CW99] CAO J., WORSLEY K.: The Geometry of Correlation
Fields with an Application to Functional Connectivity of the
Brain. The Annals of Applied Probability 9, 4 (1999), 1021 –
1057.
[DFM∗07] DOSENBACH N. U. F., FAIR D. A., MIEZIN F. M.,
COHEN A. L., WENGER K. K., DOSENBACH R. A. T., FOX
M. D., SNYDER A. Z., VINCENT J. L., RAICHLE M. E.,
SCHLAGGAR B. L., PETERSEN S. E.: Distinct brain networks
for adaptive and stable task control in humans. Proc Natl Acad
Sci U S A 104, 26 (2007), 11073–11078.
[ECM92] EVANS A. C., COLLINS D. L., MILNER B.: An MRI-
based stereotactic atlas from 250 young normal subjects. 408.
[FCD∗08] FAIR D. A., COHEN A. L., DOSENBACH N. U. F.,
CHURCH J. A., MIEZIN F. M., BARCH D. M., RAICHLE M. E.,
PETERSEN S. E., SCHLAGGAR B. L.: The maturing architec-
ture of the brain’s default network. Proceedings of the National
Academy of Sciences of the United States of America 105, 10
(2008), 4028–4032.
[FCP∗09] FAIR D. A., COHEN A. L., POWER J. D., DOSEN-
BACH N. U. F., CHURCH J. A., MIEZIN F. M., SCHLAGGAR
B. L., PETERSEN S. E.: Functional Brain Networks Develop
from a “Local to Distributed” Organization. PLoS Comput Biol
5, 5 (2009), e1000381+.
[GEF06] GOEBEL R., ESPOSITO F., FORMISANO E.: Analysis
of functional image analysis contest (FIAC) data with brainvoy-
ager QX: From single-subject to cortically aligned group gen-
eral linear model analysis and self-organizing group independent
component analysis. Human brain mapping 27, 5 (2006), 392–
401.
[GFC05] GHONIEM M., FEKETE J.-D., CASTAGLIOLA P.: On
the readability of graphs using node-link and matrix-based rep-
resentations: a controlled experiment and statistical analysis. In-
formation Visualization 4, 2 (2005).
[HCG∗08] HAGMANN P., CAMMOUN L., GIGANDET X.,
MEULI R., HONEY C. J., WEDEEN V. J., SPORNS O.: Map-
ping the Structural Core of Human Cerebral Cortex. PLoS Biol
6, 7 (2008), e159.
[HFM07] HENRY N., FEKETE J.-D., MCGUFFIN M. J.: Node-
Trix: A Hybrid Visualization of Social Networks. IEEE Transac-
tions on Visualization and Computer Graphics 13, 6 (2007).
[Hol06] HOLTEN D.: Hierarchical Edge Bundles: Visualization
of Adjacency Relations in Hierarchical Data. IEEE Transactions
on Visualization and Computer Graphics 12, 5 (2006).
[KPM∗08] KUSSA., PROHASKA S., MEYER B., RYBAK J.,
HEGE H.: Ontology-Based Visualization of Hierarchical
Neuroanatomical Structures. In Proc Visual Computing for
Biomedicine 2008 (2008), Botha C., Kindlmann G., Niessen W.,
Preim B., (Eds.), pp. 177–184.
[MWZ∗00] MUELLER K., WELSH T., ZHU W., MEADE J.,
VOLKOW N.: Brainminer: A visualization tool for ROI-based
discovery of functional relationships, 2000.
[PWS08] PAVLOPOULOS G. A., WEGENER A.-L., SCHNEIDER
R.: A survey of visualization tools for biological network analy-
sis. BioData Mining 1 (2008).
[SSC∗05] SALVADOR R., SUCKLING J., COLEMAN M. R.,
PICKARD J. D., MENON D., BULLMORE E.: Neurophysio-
logical Architecture of Functional Magnetic Resonance Images
of Human Brain. Cerebral Cortex 15 (September 2005), 1332–
1342.
[TT88] TALAIRACH J., TOURNOUX P.: Co-planar stereotaxic
atlas of the human brain. 3-Dimensional proportional system: an
approach to cerebral imaging. Thieme, New York, 1988.
[WCLE05] WORSLEY K. J., CHEN J.-I., LERCH J., EVANS
A. C.: Comparing functional connectivity via thresholding cor-
relations and singular value decomposition. Philosophical trans-
actions of the Royal Society of London. Series B, Biological sci-
ences 360, 1457 (2005), 913–20.
[Yin09] YIN R. K.: Case Study Research: Design and Methods,
fourth ed. Sage, 2009.
c© The Eurographics Association 2010.