Brain mapping in stereotactic surgery: a brief overview from the probabilistic targeting to the patient-based anatomic mapping.
ABSTRACT In this article, we briefly review the concept of brain mapping in stereotactic surgery taking into account recent advances in stereotactic imaging. The gold standard continues to rely on probabilistic and indirect targeting, relative to a stereotactic reference, i.e., mostly the anterior (AC) and the posterior (PC) commissures. The theoretical position of a target defined on an atlas is transposed into the stereotactic space of a patient's brain; final positioning depends on electrophysiological analysis. The method is also used to analyze final electrode or lesion position for a patient or group of patients, by projection on an atlas. Limitations are precision of definition of the AC-PC line, probabilistic location and reliability of the electrophysiological guidance. Advances in MR imaging, as from 1.5-T machines, make stereotactic references no longer mandatory and allow an anatomic mapping based on an individual patient's brain. Direct targeting is enabled by high-quality images, an advanced anatomic knowledge and dedicated surgical software. Labeling associated with manual segmentation can help for the position analysis along non-conventional, interpolated planes. Analysis of final electrode or lesion position, for a patient or group of patients, could benefit from the concept of membership, the attribution of a weighted membership degree to a contact or a structure according to its level of involvement. In the future, more powerful MRI machines, diffusion tensor imaging, tractography and computational modeling will further the understanding of anatomy and deep brain stimulation effects.
- SourceAvailable from: Juergen Ralf Schlaier[Show abstract] [Hide abstract]
ABSTRACT: This study aims to evaluate the improvements of cardinal motor symptoms depending on the stimulation site relative to a standardized, reconstructed three-dimensional MRI-defined subthalamic nucleus (STN.) This retrospective, clinical study includes 22 patients with idiopathic Parkinson's disease, who consecutively underwent bilateral subthalamic nucleus stimulation. Intraoperative microelectrode recording and clinical testing were performed. The location of the best stimulation site, found intraoperatively, and the positions of the active electrode contacts 12 months after the operation were correlated to a standardized, reconstructed three-dimensional MRI-defined STN. Further, the impact of the stimulation site on rigidity, tremor and akinesia was analysed. Significant improvement of the contralateral akinesia was observed if the intraoperative stimulation site was located more lateral and superior in the MRI-STN. Furthermore, active electrode contacts located superior to or in the superior part of the MRI-STN had a significantly better effect on the tremor of the contralateral hand than in other locations inside the STN. For rigidity and akinesia, these correlations were statistically not significant. Although we found significantly better results for tremor suppression in superior and lateral aspects of the STN, for overall clinical improvement, several patients fared better with randomly distributed stimulation sites in medial, posterior or inferior parts of the MRI-defined STN. Locations of stimulation sites with the best improvements of motor symptoms were distributed randomly throughout the whole MRI-defined STN, indicating that MRI-based targeting alone is not sufficient, but intraoperative clinical testing is necessary to determine the optimal stimulation site for each individual patient.Neurosurgical Review 02/2014; · 1.86 Impact Factor
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ABSTRACT: To assess stable anatomical features of the human thalamus, an unbiased diffusion tensor parcellation approach was used to segment thalamic substructures with similar spatial orientation. We determined localization, size and individual variations of 21 thalamic clusters in a group of 63 healthy human subjects (32 males/31 females). The laterality differences accounted for ±6 % and gender differences for ±4 % of the thalamic volume. Consecutively, five stable clusters in the anterior, medial, lateral and posterior thalamus were selected, which were common to 90 % of all subjects and contained at least 10 voxels. These clusters could be assigned to the anteroventral nucleus (AN) group, the mediodorsal (MD) nucleus, the medial pulvinar (PuM), and the lateral nuclei group. The subcortical and cortical connectivity of these clusters revealed that: (1) the oblique cranio-caudal-oriented fibers of the AN cluster mainly connect to limbic structures, (2) the numerous dorso-frontal-oriented fibers of MD mainly project to the prefrontal cortex and the medial temporal lobe, (3) the fibers of the PuM running in parallel with the x-axis project to medio-occipital and medio-temporal areas and connect visual areas with the hippocampus and amygdala and via intrathalamic pathways with medio-frontal areas, and (4) the oblique caudo-cranial fibers of the two lateral clusters located anteriorly in the motor and posteriorly in the sensory thalamus are routing sensory-motor information from the brain stem via the internal capsule to pre- and peri-central regions of the cortex.Brain Structure and Function 03/2014; · 7.84 Impact Factor
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ABSTRACT: High quality segmentation of diffusion tensor images (DTI) is of key interest in biomedical research and clinical application. In previous studies, most efforts have been made to construct predefined metrics for different DTI segmentation tasks. These methods require adequate prior knowledge and tuning parameters. To overcome these disadvantages, we proposed to automatically learn an adaptive distance metric by a graph based semi-supervised learning model for DTI segmentation. An original discriminative distance vector was first formulated by combining both geometry and orientation distances derived from diffusion tensors. The kernel metric over the original distance and labels of all voxels were then simultaneously optimized in a graph based semi-supervised learning approach. Finally, the optimization task was efficiently solved with an iterative gradient descent method to achieve the optimal solution. With our approach, an adaptive distance metric could be available for each specific segmentation task. Experiments on synthetic and real brain DTI datasets were performed to demonstrate the effectiveness and robustness of the proposed distance metric learning approach. The performance of our approach was compared with three classical metrics in the graph based semi-supervised learning framework.PLoS ONE 03/2014; 9(3):e92069. · 3.53 Impact Factor
Brain mapping in stereotactic surgery: A brief overview from the
probabilistic targeting to the patient-based anatomic mapping
Jean-Jacques Lemaire,a,i,⁎Jérôme Coste,a,hLemlih Ouchchane,d,iFrançois Caire,e,i
Christophe Nuti,f,iPhilippe Derost,bVittorio Cristini,gJean Gabrillargues,c,iSimone Hemm,i
Franck Durif,band Jean Chazala
aCHU Clermont-Ferrand, Hôpital Gabriel Montpied, Service de Neurochirurgie A, Clermont-Ferrand, F-63003, France
bCHU Clermont-Ferrand, Hôpital Gabriel Montpied, Service de Neurologie A, Clermont-Ferrand, F-63003, France
cCHU Clermont-Ferrand, Hôpital Gabriel Montpied, Service de Radiologie A, Clermont-Ferrand, F-63003, France
dUniv Clermont 1, UFR Médecine, Unité de Bio statistiques, télématique et traitement d’image, Clermont-Ferrand, F-63001, France
eCHU Limoges, Hôpital Dupuytren, Service de Neurochirurgie, Limoges, F-87042, France
fCHU Saint-Etienne, Hôpital Bellevue, Service de Neurochirurgie, Saint-Etienne, F-42055, France
gUniversity of Texas Health Science Center, School of Health Information Sciences, Houston, TX, USA
hInserm, E216, Clermont-Ferrand, F-63001, France
iInserm, ERI 14, Clermont-Ferrand, F-63001, France
Received 10 February 2007; revised 18 May 2007; accepted 22 May 2007
Available online 14 June 2007
In this article, we briefly review the concept of brain mapping in
stereotactic surgery taking into account recent advances in stereotactic
imaging. Thegold standard continuesto rely on probabilistic andindirect
and the posterior (PC) commissures. The theoretical position of a target
defined on an atlas is transposed into the stereotactic space of a patient’s
brain; final positioning depends on electrophysiological analysis. The
definition of the AC–PC line, probabilistic location and reliability of the
electrophysiological guidance. Advances in MR imaging, as from 1.5-T
is enabled by high-quality images, an advanced anatomic knowledge and
dedicated surgical software. Labeling associated with manual segmenta-
of patients, could benefit from the concept of membership, the attribution
of a weighted membership degree to a contact or a structure according to
its level of involvement. In the future, more powerful MRI machines,
diffusion tensor imaging, tractography and computational modeling will
further the understanding of anatomy and deep brain stimulation effects.
© 2007 Elsevier Inc. All rights reserved.
Keywords: Stereotaxy; DBS; MRI; Targeting; Anatomy; Computer-aided
Introduction . . . . . . . . . . . . . . . . . . . . . . . . 109
Background in classical stereotactic surgery with
indirect probabilistic targeting. . . . . . . . . . . . . . . 111
The indirect probabilistic targeting . . . . . . . . . . 111
Imaging for indirect targeting . . . . . . . . . . . . . 111
Indirect anatomic analysis of data. . . . . . . . . . . 111
The “direct” patient-based anatomic mapping in
stereotactic surgery . . . . . . . . . . . . . . . . . . . . 112
Future trends . . . . . . . . . . . . . . . . . . . . . . . 114
References. . . . . . . . . . . . . . . . . . . . . . . . . 114
Brain mapping in the classical meaning of stereotactic surgery
is based on intra operative electrophysiological recordings in order
to locate, for a given patient, the so-called invisible targets. Since
the pioneering days, the stereotactic targeting was indirect because
the targets were de facto arranged in areas relative to ventricular
baselines as imaging techniques failed to show the internal
anatomy of the brain (Talairach et al., 1957). For most of the
deep brain structures, basal ganglia, internal subdivision of
thalamus and main white bundles, a reference position in relation
to ventricular landmarks was proposed based on anatomic
specimen studies transcribed in stereotactic atlases (Talairach et
al., 1957; Schaltenbrand and Bailey, 1959). Nowadays in spite of
considerable progresses in magnetic resonance imaging (MRI), few
surgical teams have shifted to pure direct anatomic targeting with
NeuroImage 37 (2007) S109–S115
⁎Corresponding author. CHU Clermont-Ferrand, Hôpital Gabriel
Montpied, Service de Neurochirurgie A, rue Montalembert, F-63003
Clermont-Ferrand, France. Fax: +33 473 752 166.
E-mail address: email@example.com (J.-J. Lemaire).
Available online on ScienceDirect (www.sciencedirect.com).
1053-8119/$ - see front matter © 2007 Elsevier Inc. All rights reserved.
Fig. 1. Stereotactic location of the subthalamic nucleus (STN). STN projection area (pale grey surface) (Benabid et al., 2002); projections of STN boundaries
determined on axial (black dots and lines), sagittal (dark grey dots and lines) and frontal (light grey lines, white circles) slices (Schaltenbrand and Bailey, 1959).
Frontal (left; white circle, projection of ACPC) and lateral (right; doted line, ACPC line; white circles, AC and PC) views (TH, thalamus height; AC, PC see text
Fig. 2. 4.7-T MRI reference of the human thalamus and basal ganglia. Imaging on a Biospec 4.7-T MRI system (Bruker, GmbH, Ettlingen, Germany), anatomic
specimen, 3D spin-echo sequence T1-weighted; isotropic voxel=250 μm3. Isocentric images, reconstructed in the coronal (left) and axial (right) planes: Raw
data (top row): labelled and highlighted structures (bottom row).
S110J.-J. Lemaire et al. / NeuroImage 37 (2007) S109–S115
neither ventricular baselines and/or atlas matching nor neuronal
activity recordings (Lemaire et al., 1999; Coubes et al., 2002, Caire
et al., 2006; Plaha et al., 2006). This direct targeting concept
depends exclusively on the visualization of the detailed internal
anatomy of each patient’s brain providing a personal MRI map.
Here, we briefly review the concept of brain mapping in stereo-
tactic surgery by indirect and direct methods of targeting, in light
of recent advances in stereotactic MRI.
Background in classical stereotactic surgery with
indirect probabilistic targeting
The indirect probabilistic targeting
The gold standard in classical stereotactic surgery relies world-
wide on indirect probabilistic targeting, relative to a stereotactic
reference. Historically ventricle landmarks represented this refer-
ence because X-ray ventriculography (intraventricular injection of
an iodized contrast agent and/or the air) was the only technique
visualizing the gross internal brain morphology. With the advent of
slice (computerized tomography, CTand MR) imaging, the method
has shifted without redefinition of landmarks yet determined on
radiographic projections. Because slice imaging can often be
anatomically poorly informative in clinical routine, the ventricle
landmarks continue to be used widely: the anterior (AC) and the
posterior (PC) diencephalic white commissures around the third
ventricle, sometimes the floor of the body of the lateral ventricle
(corresponding to the superior border of the thalamus) and the width
of the third ventricle. The stereotactic coordinates of targets, in
relation to the stereotactic reference, are provided by classical
stereotactic atlases and stereotactic graphs (Talairach et al., 1957;
Schaltenbrand and Bailey, 1959). Thus the probabilistic targeting
relies on transposition of the theoretical position of a target, atlas- or
graph-based, into the stereotactic space of a given patient’s brain
through the ventricular stereotactic reference. The use of propor-
of individual variability (Talairach et al., 1957; Schaltenbrand and
Bailey, 1959; Velasco et al., 2001; Benabid et al., 2002) with
continuous effort to optimize this approach (Nowinski et al., 2005;
Yelnik et al., 2007). Although the method seems universally
applicable, basic points can lead to errors of location: (1) the
stereotactic locations provided by atlases and graphs can differ (e.g.
Fig. 1) as an atlas relies on only one brain per plane (Schaltenbrand
and Bailey, 1959) and a geometric figure represents only the area
1957; Schaltenbrand and Bailey, 1959; Benabid et al., 2002); (2) the
commissural points are defined appreciably differently on their
center (Schaltenbrand and Bailey, 1959; Benabid et al., 2002) or
their periphery (Talairach et al., 1957) or according to different
planes for the same atlas (Schaltenbrand and Bailey, 1959).
Moreover the expression of stereotactic coordinates is different
even if the orientation is standardized (x represents the laterality
relative to the vertical midline plane going through ACPC; y
represents the anterior–posterior position along the ACPC line; z
represents the superior–inferior position relative to the ACPC axial
plane, perpendicular to the midline plane). The units are in
millimeters in case of absolute coordinates or in percentage of a
standard, the ACPC line (e.g., 1/12 of ACPC) or the height of the
thalamus (e.g. 1/4 of the thalamus height), in case of proportional
coordinates. Coordinates are only proportional for y or z, since there
is no reliable ventricle landmark to determine a standard in the
frontal plane; however, a lateral proportionality can be applied
(Velasco et al., 2001). Due to this probabilistic approach, it is
(electrophysiological neuronal recordings and/or clinical assess-
ments) and multiple-tract explorations. The strength of the indirect
targetingisabove all the simplicity of the coordinate calculation and
the important electrophysiological knowledge harvested.
Imaging for indirect targeting
Clinical MRI, at least on the first generations of generalist
machines, has non-negligible image distortion making delicate the
ACPC definition and/or the location of surgical fiducials, as well as
a low tissue contrast making reliable recognition of deep brain
structures difficult. Therefore, matching CT with MRI was
proposed (Duffner et al., 2002) in order to take the best of the
two techniques, the geometric accuracy of CT plus the better
ventricular anatomy on MRI. Nowadays because of the progresses
of MRI machines, teams used more and more MRI to determine
ACPC directly (Patel et al., 2003).
Indirect anatomic analysis of data
Even though it was originally designed to reach an invisible
target for a given patient, the method of indirect location is also
applied to analyze the positions of lesions (ablative surgery) or
electrode contacts (deep brain stimulation) for different patients
regardless of the technique of targeting (Benabid et al., 2002; Plaha
et al., 2006). The aim is to determine the anatomic structures
involved in the therapeutic process. The coordinates of lesions or
contacts, for each patient and/or hemisphere, are displayed on an
Fig. 3. Axial 1.5-T MRI slices. Imaging (deep brain stimulation surgery,
Parkinsonian) on a Sonata machine (Siemens, GmbH, Erlangen, Germany),
inversion-recovery sequence, stereotactic conditions; voxel size=0.52×
0.62×2 mm3. Diencephalo-mesencephalic subthalamic structures are out-
lined and labeled (horizontal white bar=10 mm): substantia nigra (deep
blue), red nucleus (orange), subthalamic nucleus (yellow), mammillary
bodies (blue-green), nucleus of ansa lenticularis (light orange), substantia Q
(green), zona incerta (red), peri peduncular nucleus (pink), lateral (light
purple) and medial (deep purple) geniculate bodies.
S111J.-J. Lemaire et al. / NeuroImage 37 (2007) S109–S115
atlas or a graph. It is assumed that locations of lesions or contacts
are effectively in the same structure identified during surgery; this
depends mainly on surgical technical constraints and on the level
of reliability of electrophysiological intra-operative guidance. The
surgical constraints are linked up, among other things, with the
capabilities of stereotactic instrumentation. The reliability of
electrophysiological guidance is not absolute (Schiff et al., 2002;
Israel and Burchiel, 2004; McClelland et al., 2005) in particular
because of our partial, and almost exclusively atlas-based,
knowledge of “electro-anatomic” processes; a better comprehen-
sion of these latter should be allowed by a direct, patient-based,
The “direct” patient-based anatomic mapping in
In order to improve the surgical stereotactic method, since
targets become more visible with new MRI modalities (hardware
and software), a stereotactic reference is not mandatory anymore;
the patient’s brain is its own reference, a direct patient-based
Fig. 4. 3D optimization of trajectory. Electrode implantation planned (same patient as Fig. 2) in the posterior part of the subthalamic nucleus (yellow) near the
zona incerta (red) and the Forel's fields (pale green), see Fig. 2 for the others labeled structures: 3D volume rendering of the subthalamic structures (frontal view;
top left); pseudo coronal, pseudo axial and pseudo sagittal planes reconstructed along the right trajectory (clockwise from top right to bottom left).
Fig. 5. Fiber tracking on stereotactic 1.5-T DTI. Anisotropic diffusion images (diffusion tensor imaging [DTI]; top row, left) matched (mutual information
algorithm) with anatomic images (inversion-recovery sequence, voxel size=0.52×0.62×2 mm3; top row, right) plus 3D anatomic structures (objects were
createdafter manual outlining).DTI acquisitionwas performed on a 1.5-Tmachine(Siemens Sonata,GmbH,Erlangen,Germany):stereotactic conditions,with a
Leksell G frame; 6 directions, b value=750 s/mm2, voxel size=1.8×1.8×3 mm3; image post processing with Iplan (BrainLab, Feldkirchen, Germany), color-
coded fiber direction (blue for superior–inferior, red for left–right, and green for anterior–posterior) plus color map matched with 3D anatomic structures
(intermediate row). Tractography (bottom row): (1) tracking with a fractional anisotropy threshold ≥0.30 and length of fibers ≥40 mm; (2) volume of interest
(blue box displayed on a coronal slice; insert bottom right) placed along the right trajectory on the interface between the subthalamic nucleus and the pre rubral
Forel's field (same patient as Fig. 2); (3) right lateral view of anatomicstructures (see Figs. 2 and 3 for labels; the substantianigra is transparent; left structures are
hidden) fused with color-coded fibers according to the direction: ansa lenticularis (Al), subthalamic occipito-parietal bundle (OP b), frontal bundle going trough
the anterior limb of the internal capsule (Fr b) and the dento-rubro-thalamic fascicle (Fx DRT).
S112J.-J. Lemaire et al. / NeuroImage 37 (2007) S109–S115
S113J.-J. Lemaire et al. / NeuroImage 37 (2007) S109–S115
anatomic mapping gets rational as from 1.5 T (Lemaire et al.,
1999; Coubes et al., 2002; Plaha et al., 2006; Derost et al., 2007).
Some teams use transitional methods, locating targets with
reference to easy recognizable structures, like the red nucleus,
coupled with a classic indirect atlas-based approach (Starr et al.,
1999; Bejjani et al., 2000; Patel et al., 2003; Rampini et al.,
2003; Andrade-Souza et al., 2005). Because of the wide range of
anatomical quality of MRI sequences and the variability of
surgical techniques, analysis of the literature concerning direct
targeting reliability can be confusing (Andrade-Souza et al., 2005;
Breit et al., 2006). However, pure direct targeting, allowed by
patient-based anatomic mapping, yields benefits by reducing the
number of exploration tracts and duration of intra operative tests
due to optimal primary positioning (Caire et al., 2006) and by
improving the analysis of relationships between a lesion or a
contact and the structures implied in the clinical effect because of
detailed anatomic analysis (Ulla et al., 2006). The increasing risk
of hemorrhage with numerous exploration tracts (Hariz, 2002)
argues for optimizing the primary positioning. A more wide-
spread application of the method depends mostly on two factors:
technical, the transfer of adequate MRI sequences; anatomic, the
spread of the anatomic knowledge of nuclei and bundles related
to the thalamus and basal ganglia, arranged in a complex manner
and poorly known in detail. In practice, the identification and
targeting of an anatomic structure rely on 3 key-points: namely,
an MRI anatomic knowledge, high-quality images and dedicated
surgical software. Even though anatomical knowledge is mostly
based on anatomy books and stereotactic atlases, the identifica-
tion of structures is facilitated by a very high field MRI anatomic
reference (Lemaire et al., 2004) with contrasts similar to those
used in clinical conditions and with 3D topographic analysis (Fig.
2). The high quality of MRI images is achieved with a small
voxel size, below 1.5 mm3, and a high contrast between white
and grey matters achieved with dedicated T2-weighted (Lemaire
et al., 2001; Patel et al., 2003; Slavin et al., 2006) and inversion-
recovery sequences (Magnotta et al., 2000; Siadoux et al., 2005).
In clinical routine, there is no evidence, because of the intricate
relationships between hardware and software, that 3-T magnetic
field offers higher anatomic definition than 1.5-T magnetic field,
at least with optimized sequences. If the anatomic images are
acquired in stereotactic conditions, i.e. with the stereotactic frame
locked in the head coil, then head motion is minimized during the
acquisition time of about 10 min per plane, increasing the signal/
noise ratio (unpublished data). Anatomic sub structures in the
area of interest, e.g. the thalamo-subthalamic region or the
lenticular nucleus, can be labeled and manually highlighted (Fig.
3) after identification based on the analysis of their known
relative positions. The outlines of the structures can be helpful to
interpret data on non-conventional interpolated planes recon-
structed along the trajectories. Dedicated surgical software
simplifies and improves the manipulation of image sets, allowing
determination of the detailed anatomy, optimization of the
trajectories (Fig. 4) and more detailed study of relationships
between the anatomy and electrophysiological and clinical data.
Anatomic location of a set of lesions or contacts in a group of
patients is still topical. The membership concept could incorpo-
rate the fact that a contact or a lesion can involve several
structures. The membership degree can be weighted by a fuzzy
logic method integrating the expert’s opinion concerning the level
of uncertainty of the membership (Caire et al., 2006). The
membership degree can also be weighted by the level of
involvement of a contact into a structure and vice versa (Lemaire
et al., 2005).
Patient-based anatomic mapping should benefit from higher
magnetic field, although beyond 3 T there are still important
technical constraints preventing a routine clinical use, in particular
in stereotactic conditions. But as from 1.5 T, diffusion tensor
imaging (DTI) and tractography already offer new possibilities.
These techniques enable the analysis of the anisotropy of brain
tissue as well as fibbers constituting white bundles (Mori et al.,
1999; Mori and Van Zijl, 2002; Le Bihan, 2003; Wakana et al.,
2004; Hermoye et al., 2006) and several clinical applications have
been published (Mukherjee, 2005; Nimsky et al., 2005; Arfanakis
et al., 2006; Wilde et al., 2006). As bundles seem to participate in
the DBS effects (Gabriëls et al., 2003; Hamel et al., 2003),
studying their implication becomes possible. Parallel to the
knowledge of the tissue anisotropy, also extracted from DTI,
might be of interest because it could influence the electric current
diffusion (McIntyre et al., 2004). Until now, it has been difficult to
perform such imaging in stereotactic conditions, i.e. with a
stereotactic frame in place, because of an important image
distortion. Recently, modified surgical software yielded this
approach at 1.5 T with routine stereotactic conditions. After
matching of DTI with T2-weighted anatomic images and 3D
tractography, it is possible to determine the main bundles of the sub
thalamic region potentially implied in the DBS process like the
ansa lenticularis (Fig. 5).
Beyond improving stereotactic targeting, the patient-based
anatomic mapping would enable new considerations for functional
treatments relying on the spatial location inside specific brain
areas, like radiosurgery, or the topographic diagnosis of lesions as
during degenerative diseases, at least if the anatomy is not sub-
stantially modified. Furthermore, advances in predictive computa-
tional modeling (Frieboes et al., 2006) might help by reproducing
in the computer the complexity and multi-dimensionality of a
particular patient’s brain structure.
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