2D Neural Maps for Exploring Brain Connectivity

INTERNATIONAL JOURNAL OF COMPUTER ENGINEERING &
Proceedings of the 2nd International Conference on Current Trends in Engineering and Management ICCTEM -2014
17 – 19, July 2014, Mysore, Karnataka, India
TECHNOLOGY (IJCET)
ISSN 0976 – 6367(Print)
ISSN 0976 – 6375(Online)
Volume 5, Issue 9, September (2014), pp. 01-10
© IAEME: www.iaeme.com/IJCET.asp
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IJCET
© I A E M E
A NOVEL BASED APPROACH TO INVESTIGATE DISTINCTIVE
REGION OF BRAIN CONNECTIVITY USING NEURAL MAPS
Jenish Lavji1, Prof. Bhumika Shah2
1Department of Computer Engineering, Sarvajanik College of Engineering and Technology,
Surat, Gujarat, India
2Department of Computer Engineering, Sarvajanik College of Engineering and Technology, Surat,
Gujarat, India
ABSTRACT
Introduce two-dimensional neural maps for exploring connectivity in the brain. Main goal,
which are primarily in the field of tractography visualization. First Objectives is a 2D path
representation of tractography data sets that, in contrast to the previously used 2D point
representation. Main objective is to achieve abstraction and filtration, can help users overcome the
difficulties of visual complexity. While abstraction involves simplification and generalization,
filtration here entails clustering and hierarchization. To obtain a hierarchy of 2D neural diagrams
from a whole-brain tractogram, these can be considered immersions of neural paths in the plane and
link the 2D neural maps with the 3D stream tube representations.
Keywords: Abstraction, Brain Connectivity, Clustering, Human Brain, Visualization.
I. INTRODUCTION
The human brain is massive interconnected organ, consisting tens of millions of nerve fibers
grouped into hundreds of major tracts. While the ultimate goal of neuroscience is to understand how
it works, you may not be aware that understanding the brain’s structure is also an important goal in
itself. The brain's structure is very unpredictable, with nerve fiber clusters much of the time weaving
together in hard-to-predict pathways. A basic test that neuroscientists face is the manner by which to
scan and break down the unpredictable pathway. The central core part of the brain known as the
white matter, includes moderately huge fiber tracts that intervene correspondence between neurons at
broadly differentiated areas. Information about these white matter associations may as well upgrade

the comprehension of typical brain capacity. Such learning might as well additionally help diagnose
certain neurotic disarrange in patients.
2

Understanding brain connectivity can shed light on the brain’s cognitive functioning that
occurs via the connections and interaction between neurons. The term brain connectivity refers to
different aspects of brain organization including anatomical connectivity consisting of axonal fibers
across cortical regions and functional connectivity defined as the observed statistical correlations
between regions of interest (ROIs) [1]. Functional connectivity data is derived from functional
magnetic resonance imaging (fMRI). Based on the blood oxygen consumption level, Functional
connectivity, therefore, can be seen as relatedness among specific region of interests (ROIs) in the
brain which are highly correlated functionally. White matter fiber tracts, the so called anatomical
connectivity structures, are derived through applying tractography algorithms to diffusion tensor
imaging (DTI) data which represents anisotropic diffusion of water through bundles of neural axons.
The resulting fiber tracts (representing axon bundles) are often clustered based on their trajectory
similarities and regions [1].
Motivated by new technology called Diffusion Tensor Imaging (DTI) has emerged, providing
a non-invasive way to measure properties of white matter pathways [8]. The inherent complexity of
the diffusion data has motivated, a One class of techniques known as MR Tractography use to trace
the principal direction of diffusion through the tensor field, connecting points together into pathways
[8]. Limitation of point representations is that coordinate axes in the low-dimensional space lack an
anatomical interpretation. Motivated by this, two-dimensional neural maps representations
preserving meaningful and familiar coordinates [3].
Main objectives, which are primarily in the field of tractography visualization. First
objectives is a two-dimensional path representation of tractography data sets that, in contrast to the
previously used 2D point representation. Main objective is to achieve abstraction and filtration, can
help users overcome the difficulties of visual complexity. While abstraction involves simplification
and generalization, filtration here entails clustering and hierarchization. To obtain a hierarchy of two-dimensional
neural diagrams from a whole-brain tractogram, these can be considered immersions of
neural paths in the plane and link the two-dimensional neural maps with the three dimensional
stream tube representations.
The rest of the paper is organized as follows: Related work in Section 2, Proposed approach in
Section 3, Visualization Result in Section 4, and Conclusion in Section 5.
II. RELATED WORK
Diffusion-weighted magnetic resonance imaging (DWI) empowers neural pathways in vivo
cerebrum to be evaluated as a gathering of integral curve, called a tractogram. The investigation of
tractograms (i.e., tractography) has imperative requisitions in both clinical and essential neuroscience
inquire about on the cerebrum. Two-Dimensional point representations have been used for good
interaction with fiber tracts obtained from DWI data sets reputed as embedding methods [14]; these
representations provide an intriguing window into the manifold space of neural connectivity and help
in fine determination of tracts. Diffusion-weighted magnetic resonance imaging measures the
dissemination rate of water atoms in natural tissues in vivo [3], [11]. Since tissue qualities, geometric,
or overall, at a given point influence the dissemination rate, measured dispersion rate data is an
indicator of the tissue aspects at the point.
Specifically, water in fibrous tissues, for example cerebrum white matter (i.e., a gathering of
myelinated axons) diffuses speedier along fibers than orthogonal to them. It is conceivable to
evaluate fiber trajectories computationally utilizing dispersion models, for example the tensor model
that quantify anisotropic dispersion. Dispersion imaging dependent upon fitting second-order tensors
to DWI arrangements is known as Diffusion Tensor Imaging (DTI) [3]. DTI measures how water

diffuses within biological tissue, allowing us to find places in the brain where water diffuses faster in
some directions than others. It is widely believed that water diffuses faster along the length of a
neuron than across its boundary, which gives us hope that the direction of greatest diffusion aligns
with the local orientation of nerve fiber bundles.
3

Planar focus representations have been proposed for enhancing association with DTI fiber
tracts. The projection of fiber tracts into a plane represent in planar curve as opposed to points [3],
[14]. The complex structure of tractography data sets has motivated earlier work to apply clustering
techniques to tractograms [10], [11], [13].
The success of a tract clustering is often determined by the degree to which the similarity
measure used in the process can capture anatomical features that are of interest to a specific user.
Clustering is an important tool for creating abstractions and filtrations in general and is central
representation in particular [3].
A clustering method that propagates cluster labels from fiber to neighboring fiber. It assigns
each unlabeled fiber to the cluster of its closest neighbor, if the closest neighbor is below a threshold.
A partition of the data with a specific number of clusters can be acquired by setting a threshold on the
maximal accepted distance [15], [16].
Use a spectral embedding technique called Laplacian eigenmaps in which the high
dimensional fibers are reduced to points in a low dimensional Euclidean space and these positions are
mapped to a continuous RGB color space, such a way that similar fibers are assigned similar colors
[17]. A clustering method based on normalized cuts is used to group fibers [10].
Basak Alper, Benjamin Bach, Nathalie Henry Riche, Tobias Isenberg and Jean-Daniel Fekete
[1] proposed that the analysis of brain connectivity tasks is a vast field in recent study; for two
techniques: node-links and matrices. For visualization, matrices perform this tasks well and node link
performs outperforms.
Maria Giulia Preti, Nikos Makris, Maria Marcella Lagana, George Papadiamitiou, Francesca
Bagilo, Ludovica Griffanti, Raffaello Nemni, Pietro Cecconi, Carl-Fredrik Westin and Giuseppe
Baselli [2] proposed that Diffusion tensor imaging (DTI) and functional magnetic resonance imaging
(fMRI) investigate two different aspects of brain networks: white matter anatomical connectivity and
gray matter functional connectivity. Individual fMRI driven tractography is usually applied and only
few studies address on group analysis. fMRI driven tractography gives more effective result by apply
to the group study and creation of tractography atlas on gray matter areas. fMRI guided tractography
atlas gives more accurate and precise result compared to probabilistic atlas.
Radu Jianu, Cagatay Demiralp and David Laidlaw [3] proposed that Create a standard stream
tube model that display the diffusion weighted cerebrum imaging information plus neural path
representation into the plane. To visualize planer neural maps gives more visual clarity and simplicity
tract of interest. Colin Studholme [4] proposed that, advances in fast 2D MRI have led to its growing
clinical use in un-sedated fetal brain studies, as a tool for challenging neurodevelopmental cases.
David Akers [7] proposed that Disentangle and analyze neural pathway estimate from
magnetic resonance imaging data, scientist need an interface three dimensional pathways. For that
use of pen and mice that gives only two degree of opportunity. For that CINCH solve that problem to
provide bimanual interface employ pen and trackball and marking language to select three
dimensional pathways. Anthony Sherbondy, David Akers, Rachel Mackenzie, Robert Dougherty and
Brian Wandell [8] proposed that Diffusion tensor imaging (DTI) plus Magneto resonance (MR)
tractography techniques used for desirable properties of white matter pathways. For that precomputed
pathways and its statistical properties (length, fractional anisotropy, average curvature path) to extract
the desirable feature of white matter. It saves some time by precomputing pathways. For selecting
pathway use of query language and Boolean operation like AND OR.
Song Zhang, Cagatay Demiralp, and David Laidlaw [11] proposed that a new method for
visualizing three dimensional volumetric diffusion tensor magneto resonances images; distinguish

between linear anisotropy (stream tube model) and planar anisotropy (stream surface model). An
expert studying the white matter after gamma capsulotomy and preoperative making arrangement for
cerebrum tumour surgery indicates that stream tube associate well suite with major neural structures,
the two dimensional area and geometric historic points are vital in comprehension the visualization.
4

Susumu Mori and Peter C.M. van Zijl [12] proposed that the state of the art of reconstruction
of the axonal tracts in the central nervous system (CNS) using diffusion tensor imaging (DTI) is
reviewed. While there is no doubt that DTI fiber tracking is providing exciting new opportunities to
study CNS anatomy. This technique can be used only for macroscopic analysis of white matter
architecture, but not to address connectivity questions at the cellular level. DTI tractography is
expected to be a powerful technique to investigate white matter anatomy and diseases [11].
Clustering is an important tool for creating abstractions and filtrations [3]. A clustering
method that propagates cluster labels from fiber to neighboring fiber. It assigns each unlabeled fiber
to the cluster of its closest neighbor, if the closest neighbor is below a threshold. A partition of the
data with a specific number of clusters can be acquired by setting a threshold on the maximal
accepted distance [15, 16]. Use a spectral embedding technique called Laplacian Eigen maps in
which the high dimensional fibers are reduced to points in a low dimensional Euclidean space and
these positions are mapped into a RGB color space, such a way that similar fibers are assigned
similar colors [17]. A clustering method based on normalized cuts is used to group fibers [10].
In the event that fibers are remade and envisioned independently through the complete white
matter, the showcase gets effortlessly jumbled making it troublesome to get understanding in the
information. Various clustering techniques have been proposed to automatically obtain bundles that
should represent anatomical structures. Found that the use of hierarchical clustering using single-link
and a fiber similarity measure based on the mean distance between fibers gave the best results [9].
The utilization of information driven bunching techniques for utilitarian connectivity
examination in fMRI. K-Means and Spectral Clustering calculations as plan to the ordinarily utilized
Seed-Based Analysis. K-Means (KM) Clustering Algorithm is based on minimizing the Euclidean
distance in such a way that to maximizing correlation. Spectral Clustering (SC) utilizes the Eigen –
deterioration of a couple shrewd partiality grid built from information focuses. SC can identify cluster
with complex signal geometries. [5].
A novel based approach for joint clustering and point by point correspondence. Knowledge of
point by point correspondence gives accurate and precise clustering and also gives tract-oriented
quantitative analysis. Employ an expectation maximization algorithm in gamma mixture model with
parameter i.e. spatial mean, variance and standard deviation. Point by point correspondence obtained
by constructing distance map and label map for each iteration of expectation maximization algorithm.
Result gives more effectiveness in terms of time using expectation maximization algorithm applies on
fiber bundles of white matter pathways of brain [6].
A structure for unsupervised division of white matter strand follow got from dissemination
weighted MRI information. Fiber follow are contrasted pair wise with make a weighted undirected
chart which is divided into rational sets utilizing the standardized cut basis. Determining Fiber
similarities: Split the computation of similarity into following steps: 1) Mapping fiber traces to the
Euclidean Feature space that preserve some information, but not information about fiber shape and
connectivity. 2) Gaussian kernel for comparison of points in the Euclidean feature space.
3) Combining mapping to a Euclidean feature space with Gaussian kernel [10].
Diffusion Tensor tracking provides line trajectory data representing neural fiber pathways
throughout the brain. It is difficult to display and analyze the thousands of resulting tracks. An
automated clustering approach was devised to segment the data into major component pathways. The
fuzzy c-means (FCM) clustering algorithm was used, which provides probabilistic values for cluster
membership based on distance measures between tracks [13].

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III. PROPOSED APPROACH

In proposed approach, both point and path representations are projections of fiber tracts onto
the plane: Each tract is represented with a 2D point in the former and with a 2D curve in the latter.
Generation of these two representations shares three common steps. First, acquire an entire-cerebrum
tractogram by fiber following in a dissemination-tensor volume fitted to a given DWI brain
succession. Second, compute similarities between all pairs of tracts within the tractogram, obtaining a
similarity (or affinity) matrix. Third, using the affinity matrix from the previous step, run an
agglomerative hierarchical clustering algorithm on the tractogram, obtain a hierarchy of cluster tree
(i.e., dendrogram).
To create the 2D point representation of the tractogram by embedding the tracts in the plane
with respect to the similarity matrix, using a simple iterative force directed method. For use of the
hierarchical clustering tree to create multiscale point representations.
In the case of the path representation, first pick a cut on the clustering tree and obtain a
clustering. Then, by treating cluster centroids as pivots, to create projections of tractograms onto the
major orthogonal planes as curves. For render these 2D integral curves elaborately utilizing heuristics
controlled by the topology and geometry of the relating tracts. Brief overview of this work shown in
figure 1.
Fig. 1: Workflow of Proposed Approach
Details of each step can be explain in following sections.
A. Fiber Tracking
Fiber trajectories are computed from DTI data by integrating bidirectional along the principal
eigenvector of the underlying tensor field. This method is known as fiber tracking, yields a thick
gathering of indispensable bends (i.e., a tractogram).
DWI brain datasets used here for acquired from healthy volunteers on a 1.5T Siemens
Symphony scanner with the following acquisition parameters in 12 bipolar diffusion-encoding
gradient directions: thickness = 1.7 mm, FOV = 21.7 cm × 21.7 cm, TR = 7200 ms, TE = 156 ms,

b = 1000, and NEX = 3. For every DWI succession, the corresponding DTI volume was then
acquired by fitting six free parameters of a single second-order tensor at every voxel to the twelve
estimations from the DWI grouping [3]. For quantify the similarity between two tracts using the
distance measure discussed in [3]. This measure tries to catch the amount any given two tracts take
after a comparative way, while giving more weight to the focuses closer to tract closes. To find
distance between each pair of integral curves as denoted and assemble them into the distance matrix.
6

B. Clustering
Apply clustering techniques to complex structure of tractograms, A tract of clustering is often
determined by the degree to which the similarity measure used in the process can capture anatomical
features that are the area of interest in brain. For a given tractography dataset, Find hierarchy of a
clustering tree using an agglomerative hierarchical clustering algorithm on the basis of tract distance
matrix. To pick the average-linkage paradigm in light of the fact that it is less sensitive than the base
linkage to broken tracts because of following errors.
The output of the clustering algorithm is a hierarchical tree called dendrogram. The height of
the tree can be thought as the radius of the bounding ball of the dataset–in the units of the similarity
measure used. And any horizontal cut on this tree provides a clustering of the dataset.
C. Visualization
Tractograms are often visualized with stream tube or variations of streamlines. Reflecting the
many-sided quality of the connectivity in the cerebrum, these models are generally apparently thick.
Interaction tasks over tracts, for example, fine group choice, projection of fiber tracts into a plane,
yet as planar bends as opposed to points.
IV. VISUALIZATION RESULT
Implement proposed scheme as mention in previous section using Visual C++ with G3D [18]
and Qt libraries [19]. In this section, shows different implementation process steps of proposed
scheme.
Fig. 2: Process Flow of Implementation

Step 1: Generate large no. of fiber tracts and select the corpus callosum fiber tract shown in figure 3.
7

Fig. 3: Selection of Corpus callosum (red) Fiber tracts
Step 2: Apply clustering algorithm shown in figure 4.
Fig. 4: Selection of fiber tracts and dynamic clustering to generate hierarchy of tree

Step 3: Two Dimensional embedding points and hierarchical tree and brain viewer shown in figure 5.
8

Fig. 5: Brain viewer selection, 2-D Embedding point and hierarchical tree
Quantitative Evaluation
Evaluate the seed based region of brain that can be described in following section.
1) Task
Evaluate and compare the result of point and path representations by measuring user
performance on white matter bundle selection task. Users are ask to select three major bundles, the
corpus callosum (cc), internal capsule (ic), and arcuate fasciculus (af), in two different brain datasets.
For selection of these bundles because they represent the easy-to-hard, selection-difficulty range
well.
2) Factors and Measure
For every framework, illustrate to users the underlying visualization ideas and exhibited the
essential connections, basically including brushing on 2D and stream tube representations. After this
introduction, users are ask to select the bundles (cc, af and ic) on two different training datasets.
Following training, the users perform the task on two different test datasets and mean while collect
their task completion times. After each selection, providing subjective confidence estimate in the
range 1-5 (1: not confident, 5: very confident) for the selection of particular tracts.
The sole element acknowledged in the quantitative test is the sort of low dimensional
representation: 2D point and 2D path. All subjects uses both types of representation. Record the user
bundle-selection in times and subjective confidence values as measures of performance.
3) Results
Keeping in mind that the end goal to comprehend if the contrasts between user exhibitions on
the two apparatuses are noteworthy, for estimation reason use t-test for pairwise choice. Effects
indicate that user is fundamentally faster on the 2D path device than the 2D point apparatus (p
=0.02). User is likewise altogether more certain with utilizing the 2D path representation than the 2D
point representation (p = 0.01).

9

In next section, explain user evaluation and measure the performance parameter in terms of
time and confidence in tabular form shown in table 1.
Table 1: Results of user performance on bundle selection task
Measure
Time Confidence
CC IC AF Mean CC IC AF Mean
2-D Point 216 230 280 242 4.25 4.1 3.75 4.03
2-D Path 165 188 205 186 4.75 4.6 4.0 4.45
Observe that some interaction patterns worth reporting and notice that two distinct selection
strategies use with the 2D point and path. First, user is reliably brush over vast territories of the
projection to guarantee that the target group is select and afterward depend on the 3D perspective to
clean up the determination. Second, user aim for fine selections in the 2D projections and then
inspect the 3D view to determine whether any fibers the selection. User can added the missing tracts
using short, targeted brush strokes and then remove tubes that are erroneously added during this
operation. This user appear to have a superior understanding of the mapping between the 3D
perspective and the 2D projections, maybe illustrating the distinction in systems.
All subjects utilize the 2D point representation generally seldom. The most common
operation is to remove points that user is completely confident are not part of the selection (e.g., half
of the brain, or peripheral U-shaped bundles). In any case, without an agreeable context oriented
mapping between the 2D point and stream tube views, subjects are reluctant to perform striking
operations in 2D, in any event in the short run.
V. CONCLUSION
Combining traditional 3D model viewing with intuitive low-dimensional representations with
anatomical context can ease navigation through the complex fiber tract models, improving
exploration of the connectivity in the brain. Here, presented two planar maps, point and path
representations of tractograms, that facilitate exploration and analysis of brain connectivity. Both
representations are effective applications for abstraction and filtration concepts to tractograms. To
achieve abstraction by simplifying and generalizing, both geometrically and topologically, fiber tracts
with points and schematic curves in the plane. To create filtrations of tractograms by computing
hierarchical clustering trees. These help create better abstractions as well as provide a multiscale view
of data, which is important in reducing visual complexity and noise. Results suggest that the 2D path
representation is more intuitive and easier to use and learn than 2D point representation.
VI. REFERENCES
[1] Basak Alper, Benjamin Bach, Nathalie Henry Riche, Tobias Isenberg and Jean-Daniel Fekete,
“Weighted Graph Comparison Techniques for Brain Connectivity Analysis” ,Community
Health Institute-CHI, 27 April –2 May , 2013, Paris, France ,ACM 2013, pp.483-492.
[2] Maria Giulia Preti, Nikos Makris, Maria Marcella Lagana, George Papadiamitiou, Francesca
Bagilo, Ludovica Griffanti, Raffaello Nemni, Pietro Cecconi, Carl-Fredrik Westin and
Giuseppe Baselli, “A novel approach of fMRI- guided tractography analysis within a group:
construction of an fMRI-guided tractography atlas”, 34th Annual International Conference of
the IEEE EMBS , 28 August - 1 September , San Diego, California USA 2012, pp. 2283-
2288.

10

[3] Radu Jianu, Cagatay Demiralp and David H.Laidlaw, “Exploring Brain Connectivity with
Two-Dimensional Neural Maps”, IEEE Transaction on Visualization and Computer Graphics,
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[4] Colin Studholme, “Fetal Brain Mapping”, International Society for Burn Injuries-ISBI, IEEE
2012, pp. 495-498.
[5] Archana Venkataraman, Koene R.A. Van Dijk, Randy L.Bucker and Polina Golland,
“Exploring Functional Connectivity in FMRI via Clustering”, Proceedings of the IEEE
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2009, Taipei, Taiwan, pp. 441-444.
[6] Mahnaz Maddah, W. Eric L. Grimson, Simon K. Warfield and William M. Wells ,“A Unified
Framework for Clustering and Quantitative Analysis of White Matter Fiber Tracts”,Elsevier
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[14] Radu Jianu, Cagatay Demiralp and D. Laidlaw, “Exploring 3D DTI Fiber Tracts with Linked
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6, November-December. 2009, pp. 1449-1456.
[15] I.Corouge, S. Gouttard, and G. Gerig, “Towards a shape model of white matter fiber bundles
using diffusion tensor MRI”,In International Symposium on Biomedical Imaging, Conference
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[17] Anders Brun, Hae-Jeong Park, Hans Knutson, and Carl-Fredrik Westin,“Coloring of DT-MRI
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[18] G3D,http://g3d-cpp.sourceforge.net
[19] Qt,http://www.qtsoftware.com

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