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[3] Schneider C, Nguan C, Longpre M, et al. Motion of the kidney
between preoperative and intra-operative positioning. IEEE
Trans Bio-Med Eng. 2013; 60: 1619–1627.
BrainX
3
: a virtual reality tool for neurosurgical intervention
in epilepsy
D. Pacheco
1
, R. Zucca
1
, X. Arsiwalla
1
, D. Dalmazzo
1
, A. Principe
2
,
R. Rocamora
2
, G. Conesa
2
, P. Verschure
1,3
1
Universitat Pompeu Fabra, Barcelona, Spain
2
Hospital del Mar, Barcelona, Spain
3
ICREA (Institucio
´Catalana de Recerca i Estudis Avanc¸ats),
Barcelona, Spain
Keywords BX3 !Neurosurgery !Virtual reality !EEG
Purpose
Localizing functional regions of the cortex and deep-brain areas in
epileptic patients is an important pre-surgical procedure prior to
resection. This is often achieved using intra-cortical electrodes for
both stimulation as well as recordings of Local Field Potentials
(LFPs), concurrent to behavioral tasks related to language or motor
function. To assist and improve accuracy in the identification of
relevant brain networks associated to core cognitive functions, we
present a novel interactive virtual reality system that aids in 3D
visualization of precise electrode placement within cortical and sub-
cortical regions of the patient and facilitates dynamical functional
connectivity analysis of the aforementioned networks. The system is
an extension of the BrainX
3
tool [1–3], developed at the Laboratory
for the Synthetic, Perceptive, Emotive and Cognitive Systems
(SPECS).
Methods
Data were collected from an epileptic patient implanted with 125
depth electrodes for monitoring purposes in the context of a study
about active/passive navigation within a virtual maze. Electrode
placement have been reconstructed in 3D within BrainX
3
and
superimposed to the cortical brain structural network of 998 nodes
obtained from [4], and a semi-transparent volumetric representations
of anatomical regions (Fig. 1). Recorded EEG time-series are
replayed on the reconstructed network and graph metrics analyzed
with the embedded BCT toolbox [5].
Fig. 1 The BrainX3 main interface and visualization. Electrode and
anatomical data specific to the patient are reconstructed on top of a
dense three dimensional representation of the human connectome.
The figure shows 125 recording points from 15 electrodes inserted
in a single patient. Each recording point records data from the LFP
at one specific location. The bottom panel denotes a timeline in
which the timings of stimulus presentation during the task are
indicated
Results
The visualization of anatomical structures and the superimposed EEG
time-series data on the reconstructed network enables the neurosur-
geon to easily quantify the temporal dynamics of the recorded signal
over different brain areas in an interactive way. Several functional
connectivity metrics have been used to assess the dynamical changes
of the reconstructed network during rest and while performing a
spatial navigation/recognition memory task. Results show larger
functional activation in a sub-network including middle frontal and
superior temporal areas during the task which shows up in the
reconstructed network (Fig. 1).
Conclusions
In this abstract, we have introduced a further extension of the inter-
active BrainX3 virtual reality application specifically targeting the
reconstruction and correct identification of specific brain networks
prior to neurosurgical intervention which is aimed to facilitate the
workflow of pre-epileptic procedures.
References
[1] Arsiwalla XD, Zucca R, Betella A, Martinez E, Dalmazzo D,
Omedas P, Deco G, Verschure PF (2015). Network dynamics with
BrainX3: a large-scale simulation of the human brain network with
real-time interaction. Frontiers in neuroinformatics, 9, 2.
[2] Arsiwalla XD, Dalmazzo D, Zucca R, Betella A, Brandi S,
Martinez E, Omedas P, Verschure, P (2015). Connectomics to
Semantomics: Addressing the Brain’s Big Data Challenge1.
Procedia Computer Science, 53, 48–55.
[3] Betella A, Bueno EM, Kongsantad W, Zucca R, Arsiwalla XD,
Omedas P, Verschure PF (2014, April). Understanding large
network datasets through embodied interaction in virtual reality.
In Proceedings of the 2014 Virtual Reality International
Conference (p. 23). ACM.
[4] Hagmann P, Cammoun L, Gigandet X, Meuli R, Honey CJ,
Wedeen VJ, Sporns O (2008). Mapping the structural core of
human cerebral cortex. PLoS Biol, 6(7), e159.
[5] Rubinov M, Sporns O (2010). Complex network measures of
brain connectivity: uses and interpretations. Neuroimage, 52(3),
1059–1069.
In vivo robustness analyses of intraoperative registration
with fluorescent markers
E. Stenau
1
, T. Simpfendo
¨rfer
2
, M. Azizian
3
, W. P. Liu
3
,
K. K. Anderson
3
, D. Bergman
3
, O. Mohareri
3
, J. M. Sorger
3
,
D. Teber
2
, L. Maier-Hein
1
1
German Cancer Research Center (DKFZ), Computer-assisted
medical interventions, Heidelberg, Germany
2
University of Heidelberg, Department of Urology, Heidelberg,
Germany
3
Intuitive Surgical Inc., Sunnyvale, United States
Keywords Fluorescence-guided surgery !Augmented reality !Intra-
operative registration !Laparoscopic surgery
Purpose
While laparoscopic surgery provides many advantages over open
surgery such as faster wound healing and reduced pain for patients,
the orientation during such procedures and the identification of
structures of interest remains difficult [1, 2]. Even though a lot of
research has been invested into intra-operative registration methods
[3], robust real-time Augmented Reality (AR) guidance is still an
unsolved challenge. Some marker-based and surface-based approa-
ches feature real-time capability but suffer from poor performance in
the presence of smoke, blood or tissue in the field of view (FoV) of
the endoscope [4, 5]. Based on a recently proposed concept for robust
AR guidance in laparoscopy [5], this paper presents the first in vivo
Int J CARS (2017) 12 (Suppl 1):S1–S286 S113
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