doi:10.1093/brain/awv394
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Mapping neuroplastic potential in
brain-damaged patients
Guillaume Herbet,1,2 Maxime Maheu,3,4 Emanuele Costi,5 Gilles Lafargue6 and
Hugues Duffau1,2
It is increasingly acknowledged that the brain is highly plastic. However, the anatomic factors governing the potential for
neuroplasticity have hardly been investigated. To bridge this knowledge gap, we generated a probabilistic atlas of functional
plasticity derived from both anatomic magnetic resonance imaging results and intraoperative mapping data on 231 patients having
undergone surgery for diffuse, low-grade glioma. The atlas includes detailed level of confidence information and is supplemented
with a series of comprehensive, connectivity-based cluster analyses. Our results show that cortical plasticity is generally high in the
cortex (except in primary unimodal areas and in a small set of neural hubs) and rather low in connective tracts (especially
associative and projection tracts). The atlas sheds new light on the topological organization of critical neural systems and may
also be useful in predicting the likelihood of recovery (as a function of lesion topology) in various neuropathological conditions—a
crucial factor in improving the care of brain-damaged patients.
1 Department of Neurosurgery, Gui de Chauliac Hospital, Montpellier University Medical Center, F-34295 Montpellier, France
2 Institute for Neuroscience of Montpellier, INSERM U1051 (Plasticity of Central Nervous System, Human Stem Cells and Glial
Tumors research group), Saint Eloi Hospital, Montpellier University Medical Center, F-34091 Montpellier, France
3 Départements d’Etudes Cognitives, Ecole Normale Supérieure, F-75005 Paris, France
4 Faculté des Sciences Fondamentales et Biomédicales, Université Paris Descartes, F-75006 Paris, France
5 Department of Neuroscience, Division of Neurosurgery, University of Brescia, Brescia, Italy
6 Univ. Lille, EA 4072 – PSITEC – Psychologie: Interactions, Temps, Émotions, Cognition, F-59000 Lille, France
Correspondence to: Guillaume Herbet
Gui de Chauliac Hospital,
Montpellier University Medical Center 80,
avenue Augustin Fliche F-34296 Montpellier,
France
E-mail: [email protected]
Keywords: neuroplasticity; white matter connectivity; electrostimulation mapping; glioma; brain injury
Abbreviations: FLAIR = fluid-attenuated inversion recovery; IFOF = inferior-fronto-occipital fasciculus; ILF = inferior longitudinal fasciculus; SLF = superior longitudinal fasciculus
Introduction
Progressive or sudden damage to the brain poses substantial functional problems. Faced with a dramatic loss of
neural tissue, the brain must reallocate the remaining
physiological resources to maintain a satisfactory level of
function in a cognitively and socially demanding
environment. In fact, the brain is capable of meeting this
type of challenge under many neuropathological circumstances, and can successfully circumvent (at least in part)
the expected functional consequences of structural damage
(Duffau et al., 2005a). These highly adaptive processes exemplify the brain’s ability to re-establish normal function
(Rudrauf, 2014). The mechanisms of active functional
Received July 10, 2015. Revised November 6, 2015. Accepted November 24, 2015. Advance Access publication February 8, 2016
ß The Author (2016). Published by Oxford University Press on behalf of the Guarantors of Brain. All rights reserved.
For Permissions, please email: [email protected]
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| BRAIN 2016: 139; 829–844
compensation are enabled by the nature of the brain’s intrinsic anatomy, which is dynamically organized into highly
distributed, parallel neural networks (van den Heuvel and
Sporns, 2013) (some of which may be latent or redundant)
(Duffau, 2000).
Although there are a few literature reports on exceptional
cases of functional recovery or adaptation in various neurological contexts (Schumacher et al., 1987; Feuillet et al.,
2007; Philippi et al., 2012; Feinstein et al., 2015), the
most persuasive body of evidence for the brain’s astonishing, lesion-induced plasticity comes from the field of neurosurgery in general and the resection of diffuse, low-grade
glioma in particular. Indeed, it has been shown that the
surgical removal of large brain areas (including cortical
areas thought to act as critical cornerstones within largescale neurocognitive networks) during diffuse low-grade
glioma resection does not necessarily induce major, lasting
cognitive or sensorimotor impairments (Duffau et al.,
2005a; Desmurget et al., 2007)—shaking the very foundation of behavioural neurology. It is now thought that the
massive removal of brain tissues is made possible by the
progressive, functional reshaping induced by the slow
growth of this type of tumour (Desmurget et al., 2007).
This may partly explain why patients with diffuse lowgrade glioma typically present with mild, relatively
non-specific cognitive disturbances before surgery
(Heimans and Reijneveld, 2012). However, recent but
sparse evidence suggests that plasticity is limited for some
brain areas—most notably those with a key position in
long-range neural networks (such as those providing
axonal connectivity). The lack of functional compensation
for white matter tract damage has been variously demonstrated in patients with diffuse low-grade glioma (Ius et al.,
2011; Herbet et al., 2014a), stroke (He et al., 2007) and in
patients with other pathologies (such as traumatic brain
injury) (Genova et al., 2014; Sharp et al., 2014;
Cristofori et al., 2015; Herbet et al., 2015b; Fagerholm
et al., 2015).
In this context, diffuse low-grade glioma is undoubtedly
an outstanding pathophysiological model for the investigation of neuroplastic potential, and thus may help to reveal
critical brain systems whose loss can never be compensated
for. By taking advantage of observations in patients with
diffuse low-grade glioma, researchers have developed
atlases of functional resectability (Mandonnet et al.,
2007; Ius et al., 2011). However, the relatively low numbers of patients included in these projects limited the statistical power, the extent of the areas examined and thus
ability to draw inferences. Furthermore, the atlases were
solely descriptive. These considerations prompted us to
apply a more systematic, comprehensive approach to this
topic. Hence, the primary objective of the present study
was to build a probabilistic atlas of functional plasticity
by using both anatomic MRI data and the results of intraoperative mapping in a homogeneous cohort of 231 patients with diffuse low-grade glioma. We focused on
understanding: (i) the anatomic factors that constrain
G. Herbet et al.
lesion-induced plasticity; and (ii) the role of white matter
fibre connectivity in particular. A secondary objective was
to provide a tool that clinicians and researchers could use
easily and widely.
Materials and methods
Data collection and patients
Neuroanatomic and intraoperative mapping data on 231 patients [98 females; mean standard deviation (SD) (range)
age: 39.68 10.17 (18–66) years] were collected retrospectively (n = 386 before applying exclusion criteria; see below).
All patients had been operated on for histopathologically confirmed diffuse low-grade glioma by the same, highly experienced neurosurgeon over a 5-year period (2009–14) and thus
had undergone multifunctional cortical and subcortical intraoperative mapping with direct electrical stimulation. Patients
presenting with a high-grade glioma or having received radiotherapy (i.e. with a possible impact on neurological functions
and brain plasticity) were excluded from our analysis at the
outset (n = 109). Patients having undergone supratotal resection (i.e. complete resection plus removal of a wide margin
around the tumour) were also excluded (n = 22), so that only
patients with a complete or partial resection were included.
Patients with abnormalities on MRI (e.g. tumour-related deformation, hygroma, abnormal ventricle size, etc.) were also
excluded (n = 8) to avoid normalization problems
(Supplementary material). All subjects gave informed consent
for the retrospective extraction of their clinical data. Figure 1
shows the study flow chart and the clinical data specifically
extracted for the present analysis.
Cortical and subcortical
intraoperative mapping
Diffuse low-grade glioma is a rare neurological tumour that
progressively invades the brain parenchyma. It preferentially
migrates along white matter fibre pathways. Its typically
slow rate of spreading is a crucial pathophysiological feature
because it allows progressive, functional compensation for
damaged structures, as recently modelled (Keidel et al.,
2010) and formally demonstrated with functional MRI
(Krainik et al., 2004). Although this marked, lesion-induced
plasticity is a prerequisite for neurosurgery in patients with
diffuse low-grade glioma, between-area variability in the neuroplastic potential (Duffau et al., 2005a; Mandonnet et al.,
2007; Ius et al., 2011) and between-subject variability in anatomic and functional organization (Tate et al., 2014) necessitate the use of a intraoperative cognitive and sensorimotor
monitoring with direct electrostimulation. Stimulation induces
a transitory functional inactivation and enables anatomicfunctional correlations to be established in real-time. Hence,
functional structures can be detected and spared. The systematical use of intraoperative functional mapping in brain tumour
surgery has almost fully abrogated the risk of sensorimotor
impairments and has considerably reduced the occurrence of
cognitive comorbidities—at least for the functions assessed
during the procedure. Although direct electrostimulation has
generally been used to provide a functional map of the cortical
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Figure 1 Study design, showing the time course of the patients’ perioperative management and the data extraction. The purple
box indicates the clinical data extracted for the present study.
surface, our group has developed great expertise in subcortical
mapping over the past two decades (Duffau, 2015). Our ability to perform both cortical and subcortical mapping was a
crucial aspect of the present work.
In the present study, we used a bipolar electrode (tip-to-tip
distance: 5 mm) to deliver biphasic current (using the Nimbus
system from Newmedic) with the following characteristics:
pulse frequency: 60 Hz; single pulse phase duration: 1 ms;
amplitude: 1–5 mA (mean SD: 2.77 0.87 mA); stimulation
duration: no more than 4 s. The surgical technique has been
described in detail elsewhere (Duffau et al., 1999, 2002,
2005b).
During wide-awake surgery, we systematically assessed word
articulation and sensorimotor processes (Schucht et al., 2013;
Rech et al., 2014; Almairac et al., 2015) and language ability,
including semantic cognition (Duffau et al., 2002, 2005b;
Moritz-Gasser et al., 2013). Depending on the site of the
lesion, we also assessed visuospatial cognition (Thiebaut de
Schotten et al. 2005), social cognition (Herbet et al., 2015c),
visual processes (Gras-Combe et al., 2012) and reading aloud
(Zemmoura et al., 2015) (see Supplementary Table 1 for an
overview of the tasks used during surgery and the disturbances
induced).
Anatomic MRI
High-resolution 3D-T1 (resolution: 1 1 1 mm) images
and fluid-attenuated inversion recovery (FLAIR) images (resolution: 0.898 0.898 6 mm) were acquired 3 months after
surgery (i.e. once surgery-related oedema has usually receded)
with a 1.5 T Siemens Avento system or a 3 T Siemens Skyria
system (Siemens Medical Systems). The FLAIR sequence was
mostly used because it provides much higher contrast between normal brain tissue and infiltrated brain tissue than
the 3D-T1 sequence. The latter was only used to represent
the distribution of cavity resections with a high spatial
resolution.
Atlas construction
The primary objective of the present study was to establish a
probabilistic atlas of functional plasticity by using intraoperative direct electrostimulation responses as an indicator of the
presence of sensorimotor and mental functions within the lesioned brain structures. The basic premise is that damaged
tissues that still show some degree of function are more
refractory to functional compensation (Mandonnet et al.,
2007). Importantly, all the resections in our centre are performed according to the individual’s functional limits (this is
a key strength of our patient cohort and enabled us to apply
the methodology described here). Hence, lesioned tissues are
only spared by the surgeon if the area is responsive to direct
electrostimulation (i.e. with a transitory sensorimotor/cognitive
impairment). Accordingly, it follows that the postoperative residual lesions (i.e. lesioned tissues not resected, viewed with
anatomic FLAIR MRI) contain voxel sites at which a functional response was elicited. Accordingly, it is easy to compute
the probability () of observing a stimulation-induced functional disturbance by simply generating the ratio between the
cumulative number of postoperative residual lesions
[npost(x,y,z)] and the cumulative number of preoperative
observed lesions [npre(x,y,z)] for each voxel (x,y,z):
ðx; y; zÞ ¼
npost ðx; y; zÞ
npre ðx; y; zÞ
The resulting probabilistic map (with a 0 to 1 scale) approximates to a functional compensation index for each voxel,
where 0 corresponds to the total absence of a functional response for a lesioned voxel (i.e. a high functional compensation index) and 1 corresponds to the omnipresence of a
functional response (i.e. a low functional compensation
index). For example, if a given voxel contained 20 residual
lesions after surgery and 24 before surgery (i.e. the voxel in
question was resected four times), the probability of observing
a functional response in this voxel is 20/24, i.e. 0.83. This
rather high probability can be interpreted as a low functional
compensation index.
To generate this type of map, we reconstructed the preoperative lesions and postoperative residual lesions for each patient.
The procedure for normalizing MRI data in MNI space and
defining the lesions is detailed in the Supplementary material.
We then created a ‘preoperative lesion overlap map’ and a
‘postoperative residual lesion overlap’ using MRIcron software
(http://www.mccauslandcenter.sc.edu/mricro/mricron/).
The
number of lesions in each voxel in each map
[npre ðx; y; zÞ and npost ðx; y; zÞ] and the ratio between these respective numbers (ðx; y; zÞ) were automatically computed
using an in-house software routine developed in MATLAB
(release 2014b, The MathWorks, Inc., Natick, MA, USA).
A schematic illustration of the experimental procedure is provided in Fig. 2.
| BRAIN 2016: 139; 829–844
G. Herbet et al.
The confidence map
feature enables one to perform additional analyses and makes
the Rojkova et al. atlas particularly interesting to work with.
For the purposes of the present study, we selected all the associative fasciculi: the inferior fronto-occipital fasciculus (IFOF),
the inferior longitudinal fasciculus (ILF), the uncinate fasciculus,
the three branches of the superior longitudinal fasciculus (SLF I,
II and III), the anterior, posterior and long segments of the
arcuate fasciculus (SLF III), and the cingulum. We also extracted
the corticospinal tract and the major frontal tracts (notably the
frontal aslant tract, the frontostriatal tract, the superior frontal
tract (SFT) and the orbitopolar tract). These tracts were then
thresholded at a value of 0.5 (i.e. a 50% probability that a
given voxel belongs to a specific tract).
The functional plasticity map was then projected onto each
pair of fasciculi. We used the Duda-Hart test to decide
whether the functional compensation index () should be
split into two or more clusters (Duda and Hart, 1973). This
test is not a clustering test per se but helps to determine
whether a given data set is homogeneous (the null hypothesis)
or inhomogeneous (the alternative hypothesis). If the null hypothesis is rejected (P 5 0.001, in the present study), a formal
cluster analysis is performed (see Tate et al., 2014 for this type
of use of the Duda-Hart statistic). Given that statistical tests
applied to normally distributed data are strongly biased by the
number of observations (Lin et al., 2013) and that the size of
white matter tracts (i.e. the number of voxels) can vary considerably, we took certain precautions to minimize these
sources of bias in our statistical analysis. First, the DudaHart test was iteratively performed on a few samples of
voxels, rather directly on the entire dataset for each pair of
fasciculi. More specifically, each white matter tract was separated into n samples of 450 voxels (Supplementary material).
The voxels in each sample were randomly distributed, using a
permutation procedure. The Duda-Hart test was then applied
independently to each voxel sample.
A tract was only considered to be inhomogeneous (and
therefore eligible for cluster analysis) if all the Duda-Hart
tests were significant. For instance, if a given tract contained
9534 voxels, this tract was separated into 21 samples of 454
voxels and was considered as inhomogeneous if all the 21
Duda-Hart test results achieved statistical significance.
Once a particular bundle was categorized as being heterogeneous, the cluster analysis per se was carried out on the functional compensation index () by using the unsupervised k-means
algorithm (with k going from 2 to 7; we did not set the number
of clusters in advance). The Bayesian information criterion (BIC,
Schwarz, 1978) was then used to determine the optimal number
of clusters. As the k-mean algorithm assumes sphericity, we
derived a Gaussian mixture model based on the clusters’ respective sizes, means and dispersions. We then computed the model
evidence (i.e. the probability of actually observing the data with
that particular model) and used it in the BIC as the model’s
likelihood, which corresponds to a goodness-of-fit criterion.
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To better gauge the scope of the results, detailed information on
confidence [i.e. the extent to which we can say that () is reliable for a given voxel] is required. The robustness of the functional plasticity map is directly related to the number of
preoperative observations in each voxel (npre). Indeed, the
more often a given voxel is damaged prior to surgery (npre),
the greater the degree of confidence in the functional compensation index computed for this voxel (npost)—irrespective of the
number of postoperative lesions. However, the number of preoperative lesions is not linearly related to the level of confidence.
The difference in confidence between 1 and 10 observations in a
voxel is much greater than the difference in confidence between
25 and 34 observations, although the absolute difference remains the same: the confidence in () is null when npre is 1,
generally acceptable when npre is 10, and excellent when npre is
between 25 and 34. In this context, we therefore generated a
power map in which the degree of confidence in each voxel
!(x,y,z) was defined according to the following equation:
!ðx; y; zÞ ¼
logðnpre ðx; y; zÞ þ 1Þ
logðN þ 1Þ
where N is the total number of patients (231). The log-scaled
power index ð!Þ ranges from 0 (areas not covered in the map)
to 1 (the maximum theoretical level of confidence, which
would be achieved if a given voxel was damaged in all
patients).
Region of interest-based analyses of
the cortex
Because intra- and interregion potentials for functional compensation in the cortex can vary markedly, we used a region of
interest-based approach to classify the neuroplastic potential of
areas of the cerebral cortex. To this end, we simply computed
the mean SD and the mean SD ! for each anatomic
region (except for less well covered areas, i.e. the occipital
cortex and structures close to the brainstem—particularly the
amygdala and the hippocampal complex).
Individual cortical regions of interest (78) were generated
using the Wake Forest University PickAtlas toolbox (Maldjian
et al., 2003). All but one of the region of interest masks
[Brodmann area (BA)10, from the digital Brodmann atlas]
were derived from the Automated Anatomical Labeling atlas
(Tzourio-Mazoyer et al., 2002). For each mask, we computed
the mean SD and thus classified the regions of interest according to their respective functional compensation indices.
Cluster analyses of white matter
tracts
First, we selected white matter tracts of interest from the tractography-based atlas recently published by Rojkova et al. (2015).
In contrast to the atlas built by the same group a few years ago
(Thiebaut de Schotten et al., 2011), the new atlas is derived
from MRI data on 48 healthy, right-handed subjects of much
the same age (mean SD: 45.45 14.79 years) as our study
population; this should limit any age-related bias. Furthermore,
the new atlas incorporates the major frontal intralobar tracts as
well as the classical associative and projection pathways; this
Results
The spatial topography of the lesions
Given that our cohort of patients with diffuse low-grade
glioma was the largest and most homogenous studied to
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Figure 2 Schematic illustration of the procedure for generating the functional plasticity map and the confidence map.
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date, we focused on the spatial topography of the tumour
resections and the residual lesions. As shown in Fig. 3A, the
resection cavities’ overlap indicated that resections in the
temporal lobe, the ventroposterior part of the right and
left prefrontal cortices, and the left pre-supplementary
motor area were most frequent. The maximum overlap
(n = 34) occurred in the right temporal pole. Figure 3B
shows that the voluntarily spared residual tumour infiltrations were distributed all along the course of the white
matter tracts and around the perforated substance. The
maximum overlap was observed in the right ventral white
matter circuitry (n = 25).
Functional plasticity, confidence and
combined maps
Figure 4A shows the probabilistic map of functional plasticity generated by the method described above. It contained 340 levels of probability ranging from 0 to 1. As
shown in the Figure, the probability of observing a functional response in damaged tissues (i.e. low functional compensation) was highest ( 4 0.90) in areas of the
sensorimotor cortex (including the dorsal part of the precentral and the postcentral gyrus, as well as the paracentral
lobule), a small part of the superior temporal cortex
(including Heschl’s gyrus and part of Wernicke’s area),
the posterior inferior temporal cortex, and the basal ganglia
(such as the caudate and the putamen). However, most of
the voxels associated with such a high probability were
located in deep white matter fibres. In contrast, voxels
with the lowest probability levels ( 5 0.10) were observed
throughout the prefrontal cortex, the anterior and inferior
temporal cortices, the anterior insula, and most parts of the
inferior and superior parietal cortices.
The log-scaled index ð!Þ of the confidence map was very
high in the prefrontal and anterior temporo-insular cortices,
the supplementary motor area, the middle temporal pole
and in the deep white matter (Fig. 4B).
To facilitate interpretation, we generated an additive,
hybrid map that combined the functional compensation
map and the confidence map (Fig. 4C); this made it much
simpler to visually pinpoint structures with high (or low)
functional compensation indices with a sufficient degree of
confidence. Inspection of the combined map confirmed that
most of the white matter tracts (shown in purple) couldn’t
be compensated for after damage. In the same way, visual
inspection clearly showed that the prefrontal cortex, the
anterior insula, the anterior and inferior temporal cortices
(in blue) can be compensated for—at least when considering the functions assessed during surgery.
Cortical-based region of interest
analyses
The most representative regions of interest are shown in
Fig. 5. It was clear that some regions [such as BA10 (left:
G. Herbet et al.
= 0.017 0.024; right: = 0.0015 0.020) and the
middle temporal pole (left: = 0.040 0.05; right,
= 0.057 0.029)] had a very high functional compensation index on both sides of the brain, whereas others [such
as the precentral gyrus (left: = 0.433 0.330; right:
= 0.691 0.391),
the
postcentral
gyrus
(left:
= 0.456 0.367; right: = 0.529 0.415), and the
insula (left: = 0.527 0.302; right: = 0.343 0.321)]
had a low functional compensation index. Interestingly,
the standard deviations for these latter areas were large—
suggesting that some subparts (but not others) of these
areas can be compensated for. For instance, the ventral
part of the precentral gyrus (in the premotor cortex) can
be compensated for, whereas the dorsal part (in the motor
cortex) cannot. An exhaustive description of our results is
given in Supplementary Table 2. It is noteworthy that the
posterior cingulate exhibited the lowest functional compensation index.
Connectivity-based cluster analyses
Although visual inspection of the probability map suggested to us that structures with a low functional compensation index corresponded broadly to neural tissue that
provides axonal connectivity, it was also clear that the
index was not homogeneous along the entire course of a
given tract. This observation suggested the presence of specific patterns of neuroplasticity within the tracts (as seen in
cortical areas), with some subparts (but not others) exhibiting a high functional compensation index. By way of an
example, the middle and posterior parts of the inferior
fronto-occipital fasciculus had a low functional compensation index (Fig. 4 coloured in orange or red), whereas the
anterior portion, which projects onto a large part of the
prefrontal cortex (Catani et al., 2002; Thiebaut de Schotten
et al., 2012; Sarubbo et al., 2013), had a high functional
compensation index (Fig. 4 coloured in black and blue).
Similar conclusions were reached for other associative or
projection tracts (see below).
In view of the apparent complexity of these results, we
simplified the data patterns by performing a cluster analysis
(rather than merely projecting the functional plasticity map
onto white matter tracts). Given that we did not make any
specific assumptions about the data’s intrinsic structure, we
applied an unsupervised k-means algorithm (Hartigan,
1975).
Only 12 of 17 tracts taken into consideration were clustered (Fig. 6). For each clustered tract, the optimal number
of clusters never exceeded two and thus yielded models
with a relatively low level of complexity. It is noteworthy
that for each cluster, the mean squared error over the
voxels never exceeded 2 103 (Supplementary Fig. 1).
The between-cluster difference in size varied but was not
exceedingly disproportionate—indicating that each cluster
was meaningful.
The results obtained for the corticospinal tract emphasized the quality of this analysis (Fig. 6). Only the most
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Figure 3 Spatial topography of tumor resections and residual lesion infiltrations. (A) The resection cavity overlap map. The ‘rain
ramp’ colour scale represents the total number of subjects with a resection at each voxel, from 1 (in black) to 34 (the maximum overlap, in white).
(B) The residual lesion overlap map. As in A, the ‘rain ramp’ colour scale was chosen to represent the lesion distribution at each voxel.
dorsal-anterior portion of this pathway was judged to have
a high functional compensation index. The remainder of
the pathway had a very low functional compensation
index. This fits well with the observation that direct electrostimulation of most of this fasciculus always induces
movement disorders—even after tumour infiltration
(Schucht et al., 2013).
In general, our cluster analyses segregated some individual tracts into a section with a rather low functional compensation index and a section with a high functional
compensation index. When considering (for example) the
white matter pathways involved in ventral connectivity, the
anterior parts of the IFOF and the ILF had a high functional compensation index, whereas the middle and posterior parts had a very low functional compensation index
(Fig. 6). This corresponds well to clinical reality, as direct
electrostimulation of the middle and posterior parts of these
white matter bundles respectively disturbs semantics
(Duffau et al., 2005b; Moritz-Gasser et al., 2013) and the
ability to read aloud (Zemmoura et al., 2015), despite
lesion infiltration. These interpretations also applied to
the different branches of the SLF, the cingulum and certain
intralobar frontal tracts (such as the frontostriatal tract and
the frontal longitudinal tract).
Lastly, two layers of the arcuate fasciculus/SLFIII (the
anterior and long segments) were not clustered with a relatively high mean —indicating that the probability of finding a functional response over the entire course was
relatively high. This contrasted with other frontal tracts
(such as the fronto-polar tract), which were not clustered
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G. Herbet et al.
Figure 4 The components of the functional plasticity atlas. (A) The functional plasticity map. This probability map is plotted according the
‘actc’ colour code. Black (corresponding to a value of 0) means that the probability of detecting a functional response during direct electrostimulation is
null (i.e. a high functional compensation index). In contrast, red (corresponding to a value of 1) means that the probability of detecting a functional
response during direct electrostimulation is maximal (i.e. a low functional compensation index). The map is thresholded with 340 levels of probability.
(B) The confidence map. The ‘rain ramp’ colour code denotes the 376 sensitivity levels of the confidence map. Black corresponds to a null power (i.e.
areas not affected by the lesions). White corresponds to the theoretical maximum power. The tick lines on the colour bar indicate the range of the
confidence index (0.13, 0.68). (C) The combined map. This map was built by additively combining map A and map B. Red indicates a low functional
compensation index with a low level of confidence; purple indicates a low plasticity index with a high level of confidence; blue indicates a high functional
compensation index with a high level of confidence; and black indicates a high functional compensation index with a low level of confidence.
because of a homogeneous and very low mean (Supplementary Fig. 2).
Discussion
We have developed a powerful new tool for studying neuroplasticity potential. It has substantial advantages over
previously published tools. Specifically, the new atlas is
based on a large sample of patients (enabling high coverage) and is accompanied by detailed confidence information
and comprehensive anatomic cluster analyses. Our overall
findings suggest that whereas cortical plasticity is generally
high (except for areas around or belonging to the pre- and
postcentral gyrus, the posterior temporal cortex, and the
middle and posterior cingulate), functional compensation
of white matter connectivity is rather low (most notably
in the associative and projection tracts). These general
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Figure 5 Cortical region of interest-based analyses. The most representative cortical regions of interest are included in this Figure (see
also Supplementary Table 2). In each histogram, the red bar shows the mean SD and the blue bar shows the mean SD !.
observations favour the hypothesis whereby the brain harbours a small collection of functionally irreplaceable structures that are essential for the maintenance of a wideranging set of basic cerebral functions, as previously suggested (Ius et al., 2011).
Cortical plasticity is high, other than
in primary sensorimotor and
unimodal association areas
The primary sensory cortex and the primary motor cortex
are both critical brain structure. The former is an obligatory point of passage for incoming sensory information,
whereas the latter provides a unique interface for behaviourally expressing the motor programs generated upstream
(Mesulam, 1998). These anatomical functional constraints
prompt a relatively simple prediction: the primary sensory
cortex, the primary motor cortex and their underlying connectivities (together with regions that solely receive input
from primary sensory areas, such as the unimodal association areas) will have a low neuroplastic potential because
of the absence of alternative neural circuits for processing
sensorimotor information. Our results are consistent with
this line of reasoning, as the lowest functional compensation indices were observed in primary areas such as the
dorsal part of the precentral gyrus (the motor cortex and
the underlying corticospinal tract), the postcentral gyrus
(the somatosensory cortex) and Heschel’s gyrus (the auditory cortex). This was also true for areas with a unimodal
mode of operation (such as the posterior part of the inferotemporal cortex, the posterior fusiform gyrus, and part of
the superior temporal) and other sensorimotor-related areas
(such as the paracentral lobule, the most posterior part of
the supplementary area and the midcingulate cortex).
To some extent, one would expect the multimodal associative areas to display a low functional compensation index.
These cortical areas are thought to act as gateways by integrating information from unimodal areas prior to distributed
processing (Mesulam, 1990). Accordingly, they have a
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G. Herbet et al.
Figure 6 The tract-based cluster analyses. For each clustered tract, the central figure corresponds to the projection of the tract in 3D. The upper
histograms indicate the proportion of voxels in each cluster, on the left and the right of the brain. The lower histograms represent the mean SD of
each cluster, on the left and the right of the brain. The ‘actc’ colour code used here is the same as in Fig. 3. UF = uncinate fasciculus; FAT = frontal aslant
tract; FST = frontostriatal fasciculus; SFL = superior frontal fasciculus; PFC = prefrontal cortex; TP = temporal pole; MPC = medial parietal cortex;
PreC = precuneus; PCC = posterior cingulate cortex; ACC = anterior cingulate cortex; IPL = inferior parietal lobule; SPL = superior parietal lobule;
PVPFC = posteroventral prefrontal cortex; SMA = supplementary motor area; d = dorsal; m = medial; v = ventral.
Mapping neuroplasticity potential
prominent position within functional networks and are generally considered to be critical neural hubs (Buckner et al.,
2009). It has been hypothesized that damage to these areas
will induce significant, multimodal impairments (Mesulam,
1994; Lambon Ralph, 2014); this hypothesis was recently
verified (at least in part) by the results of computational modelling (Honey and Sporns, 2008; Alstott et al., 2009; Crossley
et al., 2014) and connectomics-based neuropsychological studies (Gratton et al., 2012). Furthermore, it has been suggested
that certain cortical epicentres are early sites of neurodegeneration in Alzheimer’s disease (Buckner et al., 2008).
However, it is noteworthy that only a few of these multimodal hubs (such as Wernicke’s area and the mid-to-posterior
middle temporal gyrus, for example) displayed a low functional compensation index in the present study. The inferior
parietal lobule (other than its most rostral part), the superior
parietal lobule, the precuneus (other than its more rostral
sensorimotor part), the anterior-to-middle temporal cortex
(including the temporal pole) and the entire prefrontal
cortex all displayed a high functional compensation index.
Strikingly, the posterior cingulate cortex was the area
with the lowest mean functional compensation index. In
fact, this part of the cortex is considered to be a critical
hub because of its huge number of neural connections with
almost all of the rest of the cortex and with critical subcortical structures (such as the thalamus) (Leech et al.,
2013). In addition to the fact that the posterior cingulate
cortex constitutes a key nexus in the default mode network
(Fransson et al., 2008), certain researchers have suggested
that this area has a role in mediating functional interactions
and the level of integration between specialized neural networks underlying highly integrated functions (such as cognitive control) (Fornito et al., 2012; Cocchi et al., 2013).
Our recently demonstration (using direct electrostimulation) of the posterior cingulate cortex’s critical involvement
in maintaining awareness of the external environment
argues in favour of this hypothesis (Herbet et al., 2014b).
Our present results are therefore consistent with the posterior cingulate cortex’s central role in the anatomic and
functional organization of the brain, as notably suggested
by connectomics studies (Hagmann et al., 2008; van den
Heuvel and Sporns, 2011).
White matter pathways have a low
functional compensation index
Axonal connectivity takes on a pivotal role in brain dynamics by conveying neural information to a variety of
brain loci that are often very distant on the anatomic
scale. This long-range organization confers white matter
pathways with special physiological characteristics for regulating integration and cortical synchronization (Siegel
et al., 2012; Engel et al., 2013). This is especially true
for the white matter fibres that underlie associative connectivity (Bressler and Menon, 2010). Consistently, most
of the voxels with a low functional compensation index in
BRAIN 2016: 139; 829–844
| 839
the current atlas were located along the topographical courses of the main white matter tracts. These observations are exactly in line with brain mapping
studies in which most of the associative tracts elicit functional abnormalities, despite tumour infiltration (Duffau,
2015).
However, this overall picture was more complex in some
respects, as witnessed by the results of our cluster analyses.
The vast majority of individual tracts were generally
divided into a subsection with a low and a subsection
with a high functional compensation index—suggesting
that groups of fibres within the same tract differ in their
neuroplastic potential. This confirms observations made in
clinical practice. A telling example is the ILF, which connects the temporal pole to the occipital cortex and (perhaps) the occipitotemporal junction. Although the
posterior part of the ILF is always functional for a set of
cognitive processes [including reading aloud (Zemmourra
et al., 2015) and visual recognition (Mandonnet et al.,
2009)], its anterior part appears to abandon its functional
role once infiltrated by a lesion. These gradients of plasticity in the basal inferotemporal system can be analysed
with regard to the connectivity patterns in the occipitotemporal area. In addition to projections from the ILF, this
region receives widespread neural connections from the
posterior segment of the SLF, the IFOF (Bouhali et al.,
2014) and (perhaps) the arcuate fasciculus (AF)
(Epelbaum et al., 2008) or even the vertical occipital fasciculus (Yeatman et al., 2013). Accordingly, one can speculate that the information broadcast by the ILF towards the
temporal pole under normal circumstances could be redistributed via other connectivities if functional compensation
is prompted by tumour growth or another type of damage
(Duffau et al., 2013).
In the present study, certain tracts with a low mean functional compensation index (such as the AF and the anterior
SLF, corresponding to the anterior segment of the AF) were
not clustered. Crucially, these tracts provide a relatively
limited range of cortical terminations from a topographical
standpoint. For example, the anterior SLF only connects
the supramarginal gyrus to the precentral gyrus. It was recently reported that although the ventral part or the precentral gyrus (which is involved in articulatory processes)
can usually be resected, the plasticity of this area is spatially constrained. Indeed, functional compensation was
only observed more dorsally within the precentral gyrus—
suggesting that the premotor cortex has to stay connected
to the anterior SLF via its dorsal cortical terminations. This
could explain why this tract manages to retain its primary
function after damage (van Geemen, et al., 2014). The
same interpretation might apply to the long segment of
the AF, which connects the posterior inferior frontal
gyrus to the posterior temporal areas. Although our
group has previously demonstrated that it was possible to
completely remove Broca’s areas without inducing language
impairments (Plaza et al., 2009), the AF per se probably
maintains functional communication between the temporal
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G. Herbet et al.
Figure 7 A schematic explanation of the lack of functional compensation of the middle-to-posterior part of the IFOF. (A–C)
These panels are described in the main text. Green arrow = superficial and ventral layer of the IFOF; blue arrow = deep and dorsal layer of the
IFOF. ‘Transparency’ indicates the IFOF subnetwork damaged by the tumour. dlPFC = dorso-lateral prefrontal cortex; MFG = middle frontal
gyrus; FPC = fronto-polar cortex; OFC = orbito-frontal cortex; IFG = inferior frontal gyrus; STG = superior temporal gyrus; ITG = inferior
temporal gyrus; SPL = superior parietal lobule.
cortex and the posterior dorsolateral prefrontal cortex
(within which the tract also has cortical terminations)
(Martino et al., 2013). In this respect, it is noteworthy
that language impairments are regularly observed during
direct electrostimulation of the posterior dorsolateral prefrontal cortex (Tate et al., 2014).
Interpretation of the results of cluster analysis is subject
to some limitation. Although this statistical approach generated convincing findings, it may sometimes result in oversimplification and excessive extrapolation. For example,
our cluster analyses indicated that the most anterior part
of the IFOF had a high functional compensation index
Mapping neuroplasticity potential
(Fig. 6). However, it should be borne in mind that not all
of the IFOF’s frontal connections are resectable; only the
infiltrated fibres should be resected. The IFOF has a multilayer structure, as revealed by anatomic dissection (Sarubbo
et al., 2013) and q-ball tractography (Caverzasi et al.,
2014). There are at least two main strata that differ in
terms of their frontal cortical terminations: the deep,
dorsal layer projects into the prefrontal dorsolateral
cortex and the anterior prefrontal cortex, whereas the
superficial, ventral layer projects mainly toward the pars
orbitalis, triangularis and opercularis of the inferior frontal
cortex. Importantly, tumours never affect all of the IFOF
subnetworks. Indeed, tumours that originate in the insula
generally damage only the inferior frontal gyrus; in such a
case, only the ventral and superficial branches will be resected (Fig. 7A). In contrast, tumours that originate in the
pre-SMA or the SMA generally damage structures connected with the dorsal layer, and so only the dorsal and
deep branches of the IFOF will be resected (Fig. 7B). Given
that the two layers share a route in the temporal and occipital cortices, the tract’s middle and posterior parts will
remain functional (corresponding to the ‘non-resectable’
cluster). Indeed, some groups of fibres may still serve certain parts of the prefrontal cortex. Likewise, the IFOF remains functional when its middle part is damaged because
the tract continues to provide a direct pathway between
undamaged frontal and occipital areas (Fig. 7C).
Importantly, the fact that a given tract maintains function
does not mean that it is functionally disrupted (at least to
some extent). Indeed, recent research has demonstrated that
behavioural disturbances before or after surgery are mainly
explained by the degree of damage to white matter tracts—
notably for language (Almairac et al., 2015) and social
cognition (Herbet et al., 2014a, 2015a). As a consequence,
it is possible that disconnection is the main obstacle to
functional recovery after surgery; this hypothesis must be
specifically investigated in future work.
Clinical implications
Although the present conclusions are based on data from
patients with diffuse low-grade glioma, our findings go
beyond the framework of this neuro-oncological condition.
It is increasingly thought that disruption of structural connectivity may be a key pathophysiological feature in patients with poor functional outcomes after neurological
insult with diverse aetiologies. For instance, Cristofori
et al. (2015) have recently shown that damage to the SLF
and frontal projection fibres was good predictor of lack of
long-term recovery of executive functions following a penetrating traumatic brain injury. In a similar vein, Karnath
et al. (2011) and Thiebaut de Schotten et al. (2014) respectively demonstrated that disruption of right ventral connectivity (including the IFOF and ILF) or the SFL II is a strong
predictor of persistent spatial neglect after right-side stroke.
A recent large-scale neuropsychological study by Corbetta
et al. (2015) also found that post-stroke impairments were
BRAIN 2016: 139; 829–844
| 841
mainly explained by disconnection mechanisms—leading
the researchers to propose a complete revision of today’s
anatomic-functional models derived solely from neuropsychological data on stroke patients. Given the convergence
between these literature findings and the present results,
our new atlas may help to predict a patient’s likelihood
of recovery as a function of the lesion topology in other
brain conditions. The key hypothesis is that patients with
structural insult to certain anatomic connectivities (as
defined in the present study) will have a poor functional
outcome. It would therefore be useful to extend our present
study to other patient populations and validate (or not) the
new atlas in that context.
Study limitations
The present study had certain limitations. First, the current
atlas is based on the premise that the absence of a direct
electrostimulation effect in a damaged tissue constitutes evidence of plasticity. However, it should be borne in mind
that there is inter-individual variability in the site of functional epicentres (most notably in the cortex) and that the
absence of a functional response to direct electrostimulation
might correspond to a false negative in some rare cases.
Second, the atlas cannot be extrapolated to all brain functions, as it only concerns the functions assessed by direct
electrostimulation mapping during surgery (Supplementary
Table 1). Although a number of critical brain processes
were carefully scrutinized, technical constraints (related to
direct electrostimulation) and the clinical context (limited
time) prevented us from assessing the highest-level processes (such as executive functions); this may have caused
us to overestimate the degree of plasticity. However, almost
all the patients in our centre routinely undergo extensive
neuropsychological assessments before surgery and then a
few days and a few months thereafter; this enables us to
check whether a wide range of cognitive functions
[including some not mapped during surgery, such as working memory (Teixidor et al., 2007), high-level social cognition (Herbet et al., 2013) and executive function (Duffau,
2014)] are retained effectively. It should be noted that all
the study participants (except those aged over 60–65, i.e.
retirees) resumed normal socioprofessional activities 3
months after surgery.
Another limitation relates to certain biases that influence
appropriate use of the atlas. Specifically, a few infiltrated
areas of the brain were not stimulated. As a consequence,
the functional compensation index within these areas
should be considered as information on ‘non-resectability’
rather than on plasticity. This is notably the case for structures close to the brainstem or near to the anterior perforated substance or the dorsoposterior insula (see above).
Hence, we were not able to provide interpretations for
structures such as the hippocampal and para-hippocampal
complex, the amygdala, the posterior cingulate bundle, and
the deep part of the uncinate fasciculus. Furthermore, it is
worth emphasizing that the routine clinical MRI datasets
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| BRAIN 2016: 139; 829–844
acquired in the present study had a rather low axial resolution. The use of FLAIR sequences (with a higher resolution) might further improve the atlas.
It should also be borne in mind that methodological
issues inevitably arise when applying a cluster analysis: (i)
the decision to cluster a particular tract is partly constrained by some of the parameters we used (see
‘Materials and methods’ section); and (ii) the penalization
related to the number of clusters is arbitrarily defined in the
BIC (although the Akaike information criterion gave similar results—data not shown). Despite these limitations, the
patterns obtained were highly consistent. Importantly, the
cluster analyses were simply performed on the measure—
meaning that no prior information about the white matter
bundles’ anatomic structures was specified in the analyses
at all. Furthermore, all the clustered bundles were clearly
meaningful at the anatomic level (i.e. the voxels in each
cluster were not spread across the bundles but were
highly organized).
Last, all tissues infiltrated by the lesion (according to
FLAIR images) were considered to be damaged to the
same extent. In fact, there is probably a degree of variability
in the signal intensity within the tumor (notably in the
centre). Although this potential confounding factor was
not controlled for in our study (due to the use of different
scanners), there is currently no evidence to suggest that a
difference in FLAIR signal intensity/lesion density is related
to differentially affected brain functions. Infiltration of any
tissue can potentially lead to impairment—even for lesions at
the edge of the tumour. For instance, infiltrations within the
deep white matter are typically less dense that those located
in the centre of the lesion (which is usually located in the
cortical matter or at the junction between the grey matter
and white matter). Yet we know now that the infiltration of
white matter tracts can be correlated with functional disturbances (Almairac et al., 2014; Herbet et al., 2014a). It should
also be noted that the FLAIR signal intensity is relatively
homogeneous in diffuse low-grade glioma, relative to other
types of tumor (such as high-grade glioma).
Conclusion
Using a systematic, multimodal approach with special emphasis on long-range white matter fibres, we developed an
atlas of neuroplasticity. Our overall findings clearly demonstrate that long associative tracts are critical building
blocks within brain-wide neurocognitive networks. In addition to providing this fundamental insight, the new atlas is
a unique tool for surgical planning and may be useful for
predicting the likelihood of recovery (as a function of lesion
topology) in various neurological conditions. This may be
critical for identifying patients who require cognitive rehabilitation and for providing appropriate care. Our findings also contribute to the ongoing debate on the
importance of disconnection mechanisms when modelling
the anatomic and functional architecture with neuropsychological patients (Corbetta et al., 2015).
G. Herbet et al.
Acknowledgements
We thank all the study participants for their contribution.
Funding
The authors declare no conflicts of interest and no specific
funding sources.
Supplementary material
Supplementary material is available at Brain online.
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