Simultaneously Probing Functional and Structural Brain

Connectivity in Real-time

Fibernavigator: An Interactive Tool for Brain Visualization

Maxime Chamberland

1,4

, Maxime Descoteaux

2,4

, Kevin Whittingstall

1,4

and David Fortin

3,4

Department of Nuclear Medicine and Radiobioloy, Faculty of Medicine and Health Science,

Université de Sherbrooke, Sherbrooke, Canada

Computer Science Departement, Faculty of Science, Université de Sherbrooke, Sherbrooke, Canada

Division of Neurosurgery and Neuro-Oncology, Faculty of Medicine and Health Science,

Université de Sherbrooke, Sherbrooke, Canada

Centre d’Imagerie Moléculaire de Sherbrooke (CIMS), Centre de Recherche Clinique CHUS, Sherbrooke, Canada

1 OBJECTIVES

The human brain can be viewed as a collection of

parallel networks. Those highly specialized

networks can be conceptualized to as a set of nodes

(gray matter functional areas) linked together by

edges (for example white matter axonal structure).

Functional MRI (fMRI) can provide 4D whole-brain

images that indicates changes in cortical blood flow,

volume and oxygen ratio as well (Blood-

Oxygenation-Level-Dependant or BOLD signal)

caused by cerebral activity across time (Bandettini et

al. 1993; Kwong et al. 1992; Turner 1992). The

spontaneous low fluctuations (< 0.1 Hz) present in

the BOLD signal allow the detection of temporally

correlated spatial patterns, also known as Resting

State Networks (RSNs) when the brain is at rest

(Biswal et al. 1995; Damoiseaux et al. 2006). A

common method of obtaining those networks is to

extract the BOLD time course from an a priori

region of interest (ROI) and perform the temporal

correlation with all other voxels of the brain. The

result is a correlation map or a functional

connectivity map based on the location of the seed

ROI.

Some have proposed a tool for voxel-wise brain

connectivity visualization but it often requires the

pre-calculation of a correlation matrix to be held in

memory (Dixhoorn 2012). Great effort was also

made towards GPU implementation of functional

connectivity exploration (Eklund et al. 2011)

However, the proposed software restrict the user

from placing their reference ROI directly into the 3D

space which greatly reduces the level of

interactivity. Another tool was proposed for

neurosurgical application which quickly allows the

user to interrogate data for pre-surgical planning

(Böttger et al. 2011). Here, the user is forced to

move the ROI solely on 2D anatomical slices, thus

only revealing activations present on those selected

slices.

In this work, we propose an interactive tool for

the exploration of functional connectivity in a fully

3D fashion, which can be coupled with our existing

real-time fiber tracking module inside the

Fibernavigator (Chamberland et al. 2014). Using a

healthy volunteer dataset, we qualitatively

demonstrate how both functional and structural

modules can be merged together for efficient brain

mapping exploration.

2 METHODS

Our new real-time functional exploration tool is

implemented on CPU and runs on a single core

computer, which does not require any specific

hardware. It works on any functional data (e.g

resting-state) that is preferably pre-processed. For

anatomical reference, the user has to first load a

subject-specific T1-weighted image. By placing a

ROI in the 3D environment, one can instantaneously

activate the functional correlation module while

dragging the ROI everywhere in the brain. The mean

signal is first extracted from the voxels encompassed

by the ROI, and then statistically compared to the

rest of the brain. The correlation coefficient (CC)

between voxels x and y is denoted as:

CC = cov(x,y)/σ

(1)

where cov(x, y) is the covariance of any two signals

and σ

are the standard deviation of the time

series. CCs are then converted to z-scores and

Chamberland M., Descoteaux M., Whittingstall K. and Fortin D..

Simultaneously Probing Functional and Structural Brain Connectivity in Real-time - Fibernavigator: An Interactive Tool for Brain Visualization.

 2014 SCITEPRESS (Science and Technology Publications, Lda.)

rendered at each voxel as small particles like in the

following OpenGL example:

glEnable(GL_BLEND) //Activate blending

glBlendFunc(...) //Blending function

glEnable(GL_POINT_SPRITE) //Render ON

glPointSize(size * z_Score) //Pt size

glColor4f(r, g, b, alpha * z_Score)

glBegin(GL_POINTS)

glVertex3f(x, y, z) //3D points

glEnd();

glDisable(GL_POINT_SPRITE) //Render OFF

glDisable(GL_BLEND) //Deactivate blend

To reduce cluttering, the opacity (alpha) of each

particle is weighted by its z-score value. A transfer

function is also responsible for the mapping between

z-scores and color display (using a hot colormap).

Interactive z-threshold and cluster-level sliders are

also available for visualization purpose. Finally, the

user can save and export the generated activation

map into a 3D nifti file. As one can see, the

computation step is performed in the native space

(i.e fMRI space) while the rendering stage is done at

the anatomical level (T1-space) using the

transformation matrix coming from fMRI-T1

registration of the datasets.

2.1 Datasets

Continuous functional recording was carried out on

a Siemens 1.5 Tesla (T) imaging system using a

standard echo-planar imaging (EPI) sequence. 35

axial slices were obtained with a 64 x 64 matrix,

TR/TE 2730/40 msec, for a voxel size of 3.4 x 3.4 x

4.2 mm. Diffusion data was acquired using a single-

shot echo-planar (EPI) spin echo sequence (TR/TE =

11700/98 ms, GRAPPA factor 2), with b-value of

1000 s/mm² and 64 uniform directions (128 x 128, 2

mm isotropic spatial resolution, upsampled to 1

mm³).

3 RESULTS

Figure 1 illustrates the functional connectivity tool

on its own. It shows the well-known “Default Mode

Network” (Raichle et al. 2001) obtained by placing a

4 mm³ ROI at the junction of the posterior cingulate

cortex and precuneus (z-score > 2). As one can see,

the expected 4 nodes of the network are well

represented, namely the medial prefrontal cortex and

the bilateral inferior parietal lobes.

Figure 2 demonstrates how the functional

connectivity tool can be coupled with our existing

real-time fiber tracking module. A 20 x 10 x 10 mm

ROI was interactively placed in the left lateral motor

cortex, revealing associated right activations

(namely known as the motor RSN, z-score > 2) with

underlying corticospinal tract and corpus callosum.

Tractography parameters are shown in Table 1.

Figure 1: Default Mode Network. Left: Seed ROI (circled)

interactively placed in the posterior cingulate cortex.

Middle: 3D rendering of co-activated nodes. Right: 2D

view of the activation map generated.

Figure 2: Motor activation and associated fiber pathway

(corticospinal tract / corpus callosum). By lauching seeds

and CC from the ROI (blue), the user can observe a joint

functional/structural network, without the need of

precomputing CC matrices.

Table 1: Tractography parameters.

Step size 1.0 mm

Max. Angle (θ) 35°

Threshold 0.15 (Frac.Anis.)

Min. Length 10 mm

Max. Length 200 mm

# of seeds 500

Experimentation was done on a laptop with the

following specs: System: Linux Mint 32-bit, Video

card: Geforce GT 640M memory 2GB, NVIDIA

Driver: 306.97, CPU: Intel(R)Core(TM) i7-3632QM

@ 2,20GHz, 16GB RAM. Mean frame-per-second

(FPS) ratio remained above 20 when moving the

ROI in the 3D space which indicates no latency.

4 DISCUSSION

We present here a new real-time interactive feature

of the Fibernavigator that allows fast visualization

of functional and structural organization of the brain

in a 3D fashion. It gives convincing results on the fly

and is an important tool to better understand how

connections lies behind functional networks. It can

also serve as a quality assurance tool at the

individual level for close inspection of data prior

launching massive analysis.

Since the 2 modules work independently, it

allows the user to either look at structural,

functional, or as described here, combined brain

connectivity. If one is interested in visualizing brain

connectivity without performing real-time fiber

tracking, it is possible to load a set of precomputed

tracts. In this case, the ROI will serve as a selection

object, only displaying streamlines that pass through

it.

Finally, our real-time technique will shortly be

introduced in a clinical setup and is achievable

without complex GPU programming. One possible

extension would be to not only initiate tractography

from the draggable ROI but also launch seed from

the generated functional clusters to fully visualize

the total extent of the underlying network.

SUPPLEMENTARY MATERIAL

Supplementary video data showing the real-time

functional tool in action can be found online at:

www.youtube.com/watch?v=HmlxktmVSPA.

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