mapping neuroplastic potential in brain-damaged patients

Mapping neuroplastic potential inbrain-damaged patients

Guillaume Herbet,1,2 Maxime Maheu,3,4 Emanuele Costi,5 Gilles Lafargue6 andHugues 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, France2 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, France3 Departements d’Etudes Cognitives, Ecole Normale Superieure, F-75005 Paris, France4 Faculte des Sciences Fondamentales et Biomedicales, Universite Paris Descartes, F-75006 Paris, France5 Department of Neuroscience, Division of Neurosurgery, University of Brescia, Brescia, Italy6 Univ. Lille, EA 4072 – PSITEC – Psychologie: Interactions, Temps, Emotions, 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 longitu-dinal fasciculus; SLF = superior longitudinal fasciculus

IntroductionProgressive or sudden damage to the brain poses substan-

tial 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 circum-

stances, and can successfully circumvent (at least in part)

the expected functional consequences of structural damage

(Duffau et al., 2005a). These highly adaptive processes ex-

emplify the brain’s ability to re-establish normal function

(Rudrauf, 2014). The mechanisms of active functional

doi:10.1093/brain/awv394 BRAIN 2016: 139; 829–844 | 829

Received July 10, 2015. Revised November 6, 2015. Accepted November 24, 2015. Advance Access publication February 8, 2016

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compensation are enabled by the nature of the brain’s in-

trinsic 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 neuro-

logical 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 astonish-

ing, lesion-induced plasticity comes from the field of neuro-

surgery 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 large-

scale 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 founda-

tion 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 low-

grade 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 demon-

strated 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 investiga-

tion 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 num-

bers of patients included in these projects limited the stat-

istical 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 intra-

operative mapping in a homogeneous cohort of 231 pa-

tients with diffuse low-grade glioma. We focused on

understanding: (i) the anatomic factors that constrain

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 pa-tients [98 females; mean � standard deviation (SD) (range)age: 39.68 � 10.17 (18–66) years] were collected retrospect-ively (n = 386 before applying exclusion criteria; see below).All patients had been operated on for histopathologically con-firmed diffuse low-grade glioma by the same, highly experi-enced neurosurgeon over a 5-year period (2009–14) and thushad undergone multifunctional cortical and subcortical intrao-perative mapping with direct electrical stimulation. Patientspresenting with a high-grade glioma or having received radio-therapy (i.e. with a possible impact on neurological functionsand brain plasticity) were excluded from our analysis at theoutset (n = 109). Patients having undergone supratotal resec-tion (i.e. complete resection plus removal of a wide marginaround the tumour) were also excluded (n = 22), so that onlypatients with a complete or partial resection were included.Patients with abnormalities on MRI (e.g. tumour-related de-formation, hygroma, abnormal ventricle size, etc.) were alsoexcluded (n = 8) to avoid normalization problems(Supplementary material). All subjects gave informed consentfor the retrospective extraction of their clinical data. Figure 1shows the study flow chart and the clinical data specificallyextracted for the present analysis.

Cortical and subcorticalintraoperative mapping

Diffuse low-grade glioma is a rare neurological tumour thatprogressively invades the brain parenchyma. It preferentiallymigrates along white matter fibre pathways. Its typicallyslow rate of spreading is a crucial pathophysiological featurebecause it allows progressive, functional compensation fordamaged structures, as recently modelled (Keidel et al.,2010) and formally demonstrated with functional MRI(Krainik et al., 2004). Although this marked, lesion-inducedplasticity is a prerequisite for neurosurgery in patients withdiffuse low-grade glioma, between-area variability in the neu-roplastic potential (Duffau et al., 2005a; Mandonnet et al.,2007; Ius et al., 2011) and between-subject variability in ana-tomic and functional organization (Tate et al., 2014) necessi-tate the use of a intraoperative cognitive and sensorimotormonitoring with direct electrostimulation. Stimulation inducesa transitory functional inactivation and enables anatomic-functional correlations to be established in real-time. Hence,functional structures can be detected and spared. The systema-tical use of intraoperative functional mapping in brain tumoursurgery has almost fully abrogated the risk of sensorimotorimpairments and has considerably reduced the occurrence ofcognitive comorbidities—at least for the functions assessedduring the procedure. Although direct electrostimulation hasgenerally been used to provide a functional map of the cortical

830 | BRAIN 2016: 139; 829–844 G. Herbet et al.


http://brain.oxfordjournals.org/lookup/suppl/doi:10.1093/brain/awv394/-/DC1

surface, our group has developed great expertise in subcorticalmapping over the past two decades (Duffau, 2015). Our abil-ity to perform both cortical and subcortical mapping was acrucial aspect of the present work.

In the present study, we used a bipolar electrode (tip-to-tipdistance: 5 mm) to deliver biphasic current (using the Nimbussystem 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); stimulationduration: no more than 4 s. The surgical technique has beendescribed in detail elsewhere (Duffau et al., 1999, 2002,2005b).

During wide-awake surgery, we systematically assessed wordarticulation 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 thelesion, we also assessed visuospatial cognition (Thiebaut deSchotten 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 anoverview of the tasks used during surgery and the disturbancesinduced).

Anatomic MRI

High-resolution 3D-T1 (resolution: 1 � 1 � 1 mm) imagesand fluid-attenuated inversion recovery (FLAIR) images (reso-lution: 0.898 � 0.898 � 6 mm) were acquired 3 months aftersurgery (i.e. once surgery-related oedema has usually receded)with a 1.5 T Siemens Avento system or a 3 T Siemens Skyriasystem (Siemens Medical Systems). The FLAIR sequence wasmostly used because it provides much higher contrast be-tween normal brain tissue and infiltrated brain tissue thanthe 3D-T1 sequence. The latter was only used to representthe distribution of cavity resections with a high spatialresolution.

Atlas construction

The primary objective of the present study was to establish aprobabilistic atlas of functional plasticity by using intraopera-tive direct electrostimulation responses as an indicator of thepresence of sensorimotor and mental functions within the le-sioned brain structures. The basic premise is that damagedtissues 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 per-formed 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 areonly 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 re-

sidual lesions (i.e. lesioned tissues not resected, viewed withanatomic FLAIR MRI) contain voxel sites at which a func-

tional response was elicited. Accordingly, it is easy to compute

the probability (�) of observing a stimulation-induced func-tional disturbance by simply generating the ratio between the

cumulative number of postoperative residual lesions

[npost(x,y,z)] and the cumulative number of preoperativeobserved lesions [npre(x,y,z)] for each voxel (x,y,z):

�ðx; y; zÞ ¼npostðx; y; zÞ

npreðx; y; zÞ

The resulting probabilistic map (with a 0 to 1 scale) approxi-

mates to a functional compensation index for each voxel,where 0 corresponds to the total absence of a functional re-

sponse for a lesioned voxel (i.e. a high functional compensa-

tion index) and 1 corresponds to the omnipresence of afunctional 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 inquestion 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 preopera-

tive 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

[npreðx; y; zÞ and npostðx; y; zÞ] and the ratio between these re-

spective 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 pro-

vided in Fig. 2.

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.

Mapping neuroplasticity potential BRAIN 2016: 139; 829–844 | 831




http://www.mccauslandcenter.sc.edu/mricro/mricron/

The confidence map

To better gauge the scope of the results, detailed information onconfidence [i.e. the extent to which we can say that (�) is reli-able for a given voxel] is required. The robustness of the func-tional plasticity map is directly related to the number ofpreoperative observations in each voxel (npre). Indeed, themore often a given voxel is damaged prior to surgery (npre),the greater the degree of confidence in the functional compen-sation index computed for this voxel (npost)—irrespective of thenumber of postoperative lesions. However, the number of pre-operative lesions is not linearly related to the level of confidence.The difference in confidence between 1 and 10 observations in avoxel is much greater than the difference in confidence between25 and 34 observations, although the absolute difference re-mains the same: the confidence in (�) is null when npre is 1,generally acceptable when npre is 10, and excellent when npre isbetween 25 and 34. In this context, we therefore generated apower map in which the degree of confidence in each voxel!(x,y,z) was defined according to the following equation:

!ðx; y; zÞ ¼logðnpreðx; y; zÞ þ 1Þ

logðN þ 1Þ

where N is the total number of patients (231). The log-scaledpower index ð!Þ ranges from 0 (areas not covered in the map)to 1 (the maximum theoretical level of confidence, whichwould be achieved if a given voxel was damaged in allpatients).

Region of interest-based analyses ofthe cortex

Because intra- and interregion potentials for functional com-pensation in the cortex can vary markedly, we used a region ofinterest-based approach to classify the neuroplastic potential ofareas of the cerebral cortex. To this end, we simply computedthe mean � SD � and the mean � SD ! for each anatomicregion (except for less well covered areas, i.e. the occipitalcortex and structures close to the brainstem—particularly theamygdala and the hippocampal complex).

Individual cortical regions of interest (78) were generatedusing the Wake Forest University PickAtlas toolbox (Maldjianet 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 computedthe mean � SD � and thus classified the regions of interest ac-cording to their respective functional compensation indices.

Cluster analyses of white mattertracts

First, we selected white matter tracts of interest from the tracto-graphy-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 derivedfrom MRI data on 48 healthy, right-handed subjects of muchthe same age (mean � SD: 45.45 � 14.79 years) as our studypopulation; this should limit any age-related bias. Furthermore,the new atlas incorporates the major frontal intralobar tracts aswell as the classical associative and projection pathways; this

feature enables one to perform additional analyses and makesthe Rojkova et al. atlas particularly interesting to work with.For the purposes of the present study, we selected all the asso-ciative 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 thearcuate fasciculus (SLF III), and the cingulum. We also extractedthe corticospinal tract and the major frontal tracts (notably thefrontal aslant tract, the frontostriatal tract, the superior frontaltract (SFT) and the orbitopolar tract). These tracts were thenthresholded at a value of 0.5 (i.e. a 50% probability that agiven voxel belongs to a specific tract).

The functional plasticity map was then projected onto eachpair of fasciculi. We used the Duda-Hart test to decidewhether the functional compensation index (�) should besplit into two or more clusters (Duda and Hart, 1973). Thistest is not a clustering test per se but helps to determinewhether a given data set is homogeneous (the null hypothesis)or inhomogeneous (the alternative hypothesis). If the null hy-pothesis is rejected (P50.001, in the present study), a formalcluster analysis is performed (see Tate et al., 2014 for this typeof use of the Duda-Hart statistic). Given that statistical testsapplied to normally distributed data are strongly biased by thenumber of observations (Lin et al., 2013) and that the size ofwhite matter tracts (i.e. the number of voxels) can vary con-siderably, we took certain precautions to minimize thesesources of bias in our statistical analysis. First, the Duda-Hart test was iteratively performed on a few samples ofvoxels, rather directly on the entire dataset for each pair offasciculi. More specifically, each white matter tract was sepa-rated into n samples of �450 voxels (Supplementary material).The voxels in each sample were randomly distributed, using apermutation procedure. The Duda-Hart test was then appliedindependently to each voxel sample.

A tract was only considered to be inhomogeneous (andtherefore eligible for cluster analysis) if all the Duda-Harttests were significant. For instance, if a given tract contained9534 voxels, this tract was separated into 21 samples of 454voxels and was considered as inhomogeneous if all the 21Duda-Hart test results achieved statistical significance.

Once a particular bundle was categorized as being heteroge-neous, the cluster analysis per se was carried out on the func-tional compensation index (�) by using the unsupervised k-meansalgorithm (with k going from 2 to 7; we did not set the numberof clusters in advance). The Bayesian information criterion (BIC,Schwarz, 1978) was then used to determine the optimal numberof clusters. As the k-mean algorithm assumes sphericity, wederived a Gaussian mixture model based on the clusters’ respect-ive sizes, means and dispersions. We then computed the modelevidence (i.e. the probability of actually observing the data withthat particular model) and used it in the BIC as the model’slikelihood, which corresponds to a goodness-of-fit criterion.

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




Figure 2 Schematic illustration of the procedure for generating the functional plasticity map and the confidence map.



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 infiltra-

tions 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 andcombined maps

Figure 4A shows the probabilistic map of functional plas-

ticity generated by the method described above. It con-

tained 340 levels of probability ranging from 0 to 1. As

shown in the Figure, the probability of observing a func-

tional response in damaged tissues (i.e. low functional com-

pensation) was highest (�4 0.90) in areas of the

sensorimotor cortex (including the dorsal part of the pre-

central 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 consider-

ing the functions assessed during surgery.

Cortical-based region of interestanalyses

The most representative regions of interest are shown in

Fig. 5. It was clear that some regions [such as BA10 (left:

� = 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 compensa-

tion 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 compen-

sation index.

Connectivity-based cluster analyses

Although visual inspection of the probability map sug-

gested to us that structures with a low functional compen-

sation 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 spe-

cific patterns of neuroplasticity within the tracts (as seen in

cortical areas), with some subparts (but not others) exhibit-

ing 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 compensa-

tion 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 clus-

tered (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 � 10�3 (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 empha-

sized the quality of this analysis (Fig. 6). Only the most






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 elec-

trostimulation of most of this fasciculus always induces

movement disorders—even after tumour infiltration

(Schucht et al., 2013).

In general, our cluster analyses segregated some individ-

ual tracts into a section with a rather low functional com-

pensation 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 func-

tional compensation index, whereas the middle and poster-

ior 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 rela-

tively high mean �—indicating that the probability of find-

ing 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

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.



because of a homogeneous and very low mean �

(Supplementary Fig. 2).

DiscussionWe have developed a powerful new tool for studying neu-

roplasticity potential. It has substantial advantages over

previously published tools. Specifically, the new atlas is

based on a large sample of patients (enabling high cover-

age) 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

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.




observations favour the hypothesis whereby the brain har-

bours a small collection of functionally irreplaceable struc-

tures that are essential for the maintenance of a wide-

ranging set of basic cerebral functions, as previously sug-

gested (Ius et al., 2011).

Cortical plasticity is high, other thanin primary sensorimotor andunimodal association areas

The primary sensory cortex and the primary motor cortex

are both critical brain structure. The former is an obliga-

tory point of passage for incoming sensory information,

whereas the latter provides a unique interface for behav-

iourally 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 con-

nectivities (together with regions that solely receive input

from primary sensory areas, such as the unimodal associ-

ation 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 compensa-

tion 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 audi-

tory cortex). This was also true for areas with a unimodal

mode of operation (such as the posterior part of the infer-

otemporal 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 associa-

tive areas to display a low functional compensation index.

These cortical areas are thought to act as gateways by inte-

grating information from unimodal areas prior to distributed

processing (Mesulam, 1990). Accordingly, they have a

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 !.




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.



prominent position within functional networks and are gen-

erally 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 mod-

elling (Honey and Sporns, 2008; Alstott et al., 2009; Crossley

et al., 2014) and connectomics-based neuropsychological stu-

dies (Gratton et al., 2012). Furthermore, it has been suggested

that certain cortical epicentres are early sites of neurodegen-

eration in Alzheimer’s disease (Buckner et al., 2008).

However, it is noteworthy that only a few of these multi-

modal hubs (such as Wernicke’s area and the mid-to-posterior

middle temporal gyrus, for example) displayed a low func-

tional 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 sub-

cortical 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 net-

works underlying highly integrated functions (such as cog-

nitive control) (Fornito et al., 2012; Cocchi et al., 2013).

Our recently demonstration (using direct electrostimula-

tion) 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 pos-

terior 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 lowfunctional compensation index

Axonal connectivity takes on a pivotal role in brain dy-

namics 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 reg-

ulating integration and cortical synchronization (Siegel

et al., 2012; Engel et al., 2013). This is especially true

for the white matter fibres that underlie associative con-

nectivity (Bressler and Menon, 2010). Consistently, most

of the voxels with a low functional compensation index in

the current atlas were located along the topograph-

ical courses of the main white matter tracts. These obser-

vations are exactly in line with brain mapping

studies in which most of the associative tracts elicit func-

tional 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 con-

nects the temporal pole to the occipital cortex and (per-

haps) 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 plasti-

city in the basal inferotemporal system can be analysed

with regard to the connectivity patterns in the occipitotem-

poral 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 fas-

ciculus (Yeatman et al., 2013). Accordingly, one can specu-

late that the information broadcast by the ILF towards the

temporal pole under normal circumstances could be redis-

tributed 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 func-

tional 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 re-

cently reported that although the ventral part or the pre-

central gyrus (which is involved in articulatory processes)

can usually be resected, the plasticity of this area is spa-

tially 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



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 pre-

frontal cortex (Tate et al., 2014).

Interpretation of the results of cluster analysis is subject

to some limitation. Although this statistical approach gen-

erated convincing findings, it may sometimes result in over-

simplification and excessive extrapolation. For example,

our cluster analyses indicated that the most anterior part

of the IFOF had a high functional compensation index

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.



(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 multi-

layer 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 re-

sected (Fig. 7A). In contrast, tumours that originate in the

pre-SMA or the SMA generally damage structures con-

nected 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 oc-

cipital 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 cer-

tain parts of the prefrontal cortex. Likewise, the IFOF re-

mains 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 con-

nectivity may be a key pathophysiological feature in pa-

tients 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 pene-

trating traumatic brain injury. In a similar vein, Karnath

et al. (2011) and Thiebaut de Schotten et al. (2014) respect-

ively demonstrated that disruption of right ventral connect-

ivity (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

mainly explained by disconnection mechanisms—leading

the researchers to propose a complete revision of today’s

anatomic-functional models derived solely from neuropsy-

chological 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 evi-

dence of plasticity. However, it should be borne in mind

that there is inter-individual variability in the site of func-

tional 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 func-

tions, 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 pro-

cesses (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 work-

ing memory (Teixidor et al., 2007), high-level social cogni-

tion (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 struc-

tures close to the brainstem or near to the anterior perfo-

rated 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





acquired in the present study had a rather low axial reso-

lution. The use of FLAIR sequences (with a higher reso-

lution) 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 con-

strained 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 simi-

lar 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 disturb-

ances (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).

ConclusionUsing a systematic, multimodal approach with special em-

phasis on long-range white matter fibres, we developed an

atlas of neuroplasticity. Our overall findings clearly dem-

onstrate that long associative tracts are critical building

blocks within brain-wide neurocognitive networks. In add-

ition 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 re-

habilitation and for providing appropriate care. Our find-

ings also contribute to the ongoing debate on the

importance of disconnection mechanisms when modelling

the anatomic and functional architecture with neuropsycho-

logical patients (Corbetta et al., 2015).

AcknowledgementsWe thank all the study participants for their contribution.

FundingThe authors declare no conflicts of interest and no specific

funding sources.

Supplementary materialSupplementary material is available at Brain online.

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mapping neuroplastic potential in brain-damaged patients

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