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Research

Core language brain network for fMRI language
task used in clinical applications

Qiongge Li

1,2,∗

, Gino Del Ferraro1,3,∗

, Luca Pasquini3,4, Kyung K. Peck3,5,

Hernán A. Makse1, and Andrei I. Holodny3,6,7

1Levich Institute and Physics Department, City College of New York, New York, USA
2Physics Department, The Graduate Center of City University of New York, New York, USA
3Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, USA
4Neuroradiology Unit, NESMOS Department Sant’Andrea Hospital La Sapienza University, Rome, Italy
5Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, USA
6New York University School of Medicine, New York, USA
7Neuroscience, Weill Medical College of Cornell University, New York, USA
* Equal contributions as ﬁrst authors.

Keywords: Task-based fMRI, Functional
Presurgical langugage mapping, k-core

language networks, Healthy controls, Graph theory,

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ABSTRACT

Functional magnetic resonance imaging (fMRI) is widely used in clinical applications to
highlight brain areas involved in speciﬁc cognitive processes. Brain impairments, ad esempio
tumors, suppress the fMRI activation of the anatomical areas they invade and, così,
brain-damaged functional networks present missing links/areas of activation. IL
identiﬁcation of the missing circuitry components is of crucial importance to estimate the
damage extent. The study of functional networks associated with clinical tasks but performed
by healthy individuals becomes, Perciò, of paramount concern. These “healthy” networks
can, Infatti, be used as control networks for clinical studies. In this work we investigate the
functional architecture of 20 healthy individuals performing a language task designed for
clinical purposes. We unveil a common architecture persistent across all subjects under
study, that we call “core” network, which involves Broca’s area, Wernicke’s area, IL
premotor area, and the pre-supplementary motor area. We study the connectivity of this
circuitry by using the k-core centrality measure, and we ﬁnd that three of these areas belong
to the most robust structure of the functional language network for the speciﬁc task under
study. Our results provide useful insights on primarily important functional connections.

AUTHOR SUMMARY

Neurosurgeons employ language fMRI to localize important language areas for patients
with brain impairment. Yet, brain pathologies (per esempio., brain tumors, strokes, epilepsy) affect
functional connectivity by disrupting functional links and suppressing the activation of
brain areas. Così, although clinical tasks are designed to guarantee robust activation, IL
functional connectivity of patients with brain pathologies is ultimately damaged by brain
impairments. To better quantify the damage produced by the brain pathology on the
functional connectivity, it is paramount to have, as a benchmark, functional networks of
healthy individuals who perform a task for clinical cases. Our ﬁndings identify a group of
functional regions of interest linked together in a functional circuitry that have a decisive role
for the language task used in clinical applications.

a n o p e n a c c e s s

j o u r n a l

Citation: Li, Q., Del Ferraro, G.,
Pasquini, L., Peck, K. K., Makse, H. A.,
& Holodny, UN. IO. (2020). Core language
brain network for fMRI language task
used in clinical applications. Network
Neuroscience, 4(1), 134–154. https://
doi.org/10.1162/netn_a_00112

DOI:
https://doi.org/10.1162/netn_a_00112

Supporting Information:
https://doi.org/10.1162/netn_a_00112
http://www-levich.engr.ccny.cuny.edu/
webpage/hmakse/brain/

Received: 12 Giugno 2019
Accepted: 28 ottobre 2019

Competing Interests: The authors have
declared that no competing interests
exist.

Corresponding Author:
Andrei I. Holodny
holodnya@mskcc.org

Handling Editor:
Alex Arenas

Copyright: © 2019
Istituto di Tecnologia del Massachussetts
Pubblicato sotto Creative Commons
Attribuzione 4.0 Internazionale
(CC BY 4.0) licenza

The MIT Press

Core language brain network

Aphasia:
Language impairment affecting the
production or comprehension of
speech and the ability to read or
write. Aphasia is caused by brain
injuries such as strokes, head
traumas, brain tumors, or brain
infections.

INTRODUCTION

Broca’s area (BA) and Wernicke’s area (WA) have long been recognized as essential language
centers. Studies of aphasic patients have shown that damage to BA and WA causes loss of
ability to produce speech (expressive aphasia) and difﬁculty understanding language (recep-
tive aphasia), rispettivamente (Dronkers, Plaisant, Iba-Zizen, & Cabanis, 2007; Wernicke, 1970).
Further evidence has shown that other secondary and tertiary anatomical brain areas are also
involved in language Friederici (2011), including the pre-supplementary motor area (pre-SMA;
Hertrich, Dietrich, & Ackermann, 2016), the premotor area (preMA; Duffau et al., 2003), E
the basal ganglia (Booth, Legna, Lu, Houk, & Bitan, 2007). Despite this evidence, a full char-
acterization of the language network is still debated (Fedorenko & Kanwisher, 2009; Friederici,
Chomsky, Berwick, Moro, & Bolhuis, 2017).

Functional MRI (fMRI) has been largely used to investigate the blood-oxygen-level depen-
dent (BOLD) activation of the human brain, for both clinical and research purposes. Although
it cannot fully resolve the issue of “functional specialization” of brain regions by itself, it sheds
light on which regions are engaged in certain cognitive processes. Therefore fMRI allows us to
constrain hypotheses on the structure of the language network.

Language task fMRI:
Functional MRI scan where
participants perform speciﬁc
language tasks while the fMRI brain
activity is measured. It is used as a
radiology clinical routine to
determine the localization of
language areas in patients with brain
impairments.

Language has been investigated using both resting-state fMRI (rs-fMRI) and tasked-based
fMRI (tb-fMRI). The former studies brain activation of subjects at rest (Lee, Smyser, & Shimony,
2013), whereas tb-fMRI delineates brain areas functionally involved in the performance of a
speciﬁc task (Bookheimer, 2002). Task-based fMRI is task-dependent, questo è, different language
tasks may activate different areas involved in language function (Xiong et al., 2000). Conse-
quently, clinical studies employ a speciﬁc class of language tasks that have been shown to
produce robust activation in individual participants and thus facilitate the localization of the
language-sensitive cortex (Brennan et al., 2007; Ramsey, Sommer, Rutten, & Kahn, 2001).

In this paper we analyze fMRI scans of 20 healthy individuals who perform the same lan-
guage task designed for clinical purposes. From the correlation of the BOLD signal we construct
the functional connectivity network for each subject, which is standardly employed to inves-
tigate statistical interdependencies among brain regions (Bullmore & Sporns, 2009; Gallos,
Makse, & Sigman, 2012; Hermundstad et al., 2013). We then employ graph theory to study
the networks’ properties as successfully done in Del Ferraro et al. (2018) to investigate mem-
ory formation. The motivation for this study is to use the resulting functional connectivity of
these healthy individuals as a benchmark for clinical study, as we explain next. We employ
a language clinical task because we are interested in studying the fMRI activation associated
with this speciﬁc type of task. We employ healthy subjects and not patients with brain impair-
ments because we want to study the fMRI activation without any interference that might arise
because of the brain impairment. Brain pathologies (per esempio., brain tumors, Wang et al., 2013;
strokes, Tombari et al., 2004; epilepsy, Rosenberger et al., 2009) indeed affect functional con-
nectivity by disrupting functional links and reducing the fMRI activation of brain areas (per esempio.,
the neurovascular decoupling effect due to brain tumor; Aubert, Costalat, Duffau, & Benali,
2002). The reconstruction of the functional connectivity in clinical cases, Perciò, is inﬂu-
enced by the presence of brain pathology (Wang et al., 2013). In other words, the functional
connectivity of a patient with a pathology such as a brain tumor, for instance, presents miss-
ing links and missing fMRI active areas compared with the healthy case, for the same speciﬁc
task. To better understand what functional damage was produced by the brain impairment, Esso
is important to have, as a benchmark, functional networks of healthy individuals performing
the same language task normally used for clinical cases. In this way, using clinical language
tasks performed by healthy subjects, we can study functional networks associated with clinical

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Core language brain network

fROI:
Functional region of interest. Group
of fMRI active voxels located in the
same anatomical brain region that
are identiﬁed as part of the same
active brain area.

tasks without perturbations that arise from brain impairments. These functional networks can
be used as benchmarks for other studies that use the same type of task but employ patients
with brain damage. The comparison between a healthy control and a patient’s functional net-
work relative to the same task could in principle establish what is the damage produced by the
brain impairment on the functional network and might, among others, guide tumor resection
to preserve functional links.

Motivated by these considerations, we investigated which is the language functional archi-
tecture shared among healthy subjects, questo è, the functional subnetwork that persists in each
analyzed individual beyond the intersubject variability. This architecture is indicative of a core
structure for the language task under study shared across individuals. Core architectures have
been identiﬁed in other contexts (Bassett et al., 2013), but very little is known about the core
for language tasks (Chai, Mattar, Blank, Fedorenko, & Bassett, 2016) and its investigation is
one of the main goals of our study.

Inoltre, we aim to uncover the functional connectivity of the subdivisions of the Broca’s
area (pars-opercularis, op-BA, and pars-triangularis, tri-BA, cioè., Brodmann area 44 E 45 Rif-
spectively), which plays a pivotal role in language function (Dronkers et al., 2007; Friederici,
2011). Previous studies based on fMRI showed that BA’s subdivisions perform different func-
tions in language processing. Newman, Just, Keller, Roth, and Carpenter (2003) showed that
tri-BA is more implicated in thematic processing whereas op-BA is more involved in syntac-
tic processing. Studies based on transcranial magnetic stimulation have shown that op-BA is
more specialized in phonological tasks and tri-BA more in semantic tasks (Devlin, Matthews,
& Rushworth, 2003; Gough, Nobre, & Devlin, 2005; Nixon, Lazarova, Hodinott-Hill, Gough,
& Passingham, 2004). Patients who show speech impairment often have direct damage to the
Broca’s area. Così, understanding how BA subdivisions are functionally wired to other brain
regions in healthy controls may be used for comparison in some clinical cases and could poten-
tially be of help to better clarify the effect of brain pathologies on this decisive language area.

From our analysis we ﬁnd that the functional architecture shared by most of the subjects un-
der study wires together Broca’s area (op-BA and tri-BA), Wernicke’s area, the pre-supplementary
motor area, and the premotor area. By investigating network properties at the subject level
we ﬁnd that, in each individual functional network, these areas belong to an innermost core,
more speciﬁcally the maximum k-core of the functional connectivity, which is a robust and
highly connected substructure of the functional architecture. The k-core measure has received
vast attention in network analysis since it provides a topological notion of the structural skele-
ton of a circuitry (Dorogovtsev, Goltsev, & Mendes, 2006; Kitsak et al., 2010; Pittel, Spencer,
& Wormald, 1996; Rubinov & Sporns, 2010). More recently, the maximum k-core has been
related to the stability of complex biological systems (Morone, Del Ferraro, & Makse, 2019)
and of resilient functional structures in the brain (Lucini, Del Ferraro, Sigman, & Makse, 2019).
Our results demonstrate that the functional architecture that persists beyond intersubject vari-
ability is part of the maximum k-core structure, an innermost highly connected subnetwork,
associated with a system’s resilience and stability (Morone et al., 2019).

Overall, our ﬁndings identify a group of functional regions of interest (fROIs) linked to-
gether in a functional circuitry that play a decisive role for the language task used in clinical
applications.

MATERIALS AND METHODS

The study was approved by the Institutional Review Board and an informed consent was ob-
tained from each subject. The study was carried out according to the declaration of Helsinki.

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Core language brain network

Twenty healthy right-handed adult subjects (13 males and 7 females; age range 36 years,
mean = 36.6; SD = 11.56) without any neurological history were included.

Functional Task

For the fMRI task, all subjects performed a verbal ﬂuency task using verb generation in response
to auditory nouns. During the verb generation task, subjects were presented with a noun (for
esempio, baby) by oral instruction and then asked to generate action words (Per esempio,
cry, crawl) associated with the noun. Four nouns were displayed over six stimulation epochs,
with each epoch lasting 20 S, which allowed for a total of 24 distinct nouns to be read over
the entire duration. Each epoch consisted of a resting period and a task period (see BOLD
activation in Figures 1A and 1B). In order to avoid artifacts from jaw movements, subjects
were asked to silently generate the words. Brain activity and head motion were monitored
using Brainwave software (GE, Brainwave RT, Medical Numerics, Germantown, MD), allowing
real-time observation.

Data Acquisition

A GE 3T scanner (General Electric, Milwaukee, Wisconsin, USA) and a standard quadrature
head coil was employed to acquire the MR images. Functional images covering the whole
brain were acquired using a T2*-weighted gradient echo planar imaging sequence (repeti-
tion time, TR/echo time, TE = 4, 000/40 ms; slice thickness = 4.5 mm; matrix = 128 × 128;
FOV = 240 mm). Functional matching axial T1-weighted images (TR/TE = 600/8 ms; slice
thickness = 4.5 mm) were acquired for anatomical coregistration purposes. Additionally,

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Figura 1. Activation map for a representative subject. BOLD signal for a nonactive and active
voxel are shown respectively in panels A and B together with the smoothed boxcar language model,
which depicts the auditory stimulus. The black curve represents BOLD signal, while the red curve
represents the smoothed boxcar task model design. The red curve’s peaks represent the continuous
stimuli (verb task), presented for a certain period of time (10 S), the “task” or “on” period. The red
curve’s troughs represent the rest period (no task) for the participant, which lasted 10 S, (C) 3D
visualization of the brain with fMRI active areas and corresponding p values.

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Core language brain network

3D T1-weighted SPGR (spoiled gradient recalled) sequences (TR/TE = 22/4 ms; slice thick-
ness = 1.5 mm; matrix = 256 × 256) covering the entire brain were acquired.

Data Processing

Functional MRI data were processed and analyzed using the software program Analysis of
Functional NeuroImages (AFNI; Cox, 1996). Head motion correction was performed using 3D
rigid-body registration. The ﬁrst volume was selected to register all other volumes. The ﬁrst
volume was chosen because it was acquired right next to the anatomical scan. During the
registration, the motion proﬁle was saved and during the statistical analysis any voxels highly
correlated with the proﬁle were regressed out. Spatial smoothing was applied to improve the
signal-to-noise ratio using a Gaussian ﬁlter with 4 mm full width of half maximum. Corrections
for linear trend and high-frequency noise were also applied. To obtain the activation map, IL
rectangular train representing the single-task (verb generation) block design is convolved with a
canonical hemodynamic response function (HRF; see the red curve in Figures 1A and 1B). Questo
time series is one regressor in the general linear model where the preprocessed BOLD response
is to ﬁt to this as well as a baseline (intercept). The BOLD signal is represented by the black
curve in Figures 1A and 1B). The test statistics for the activation maps are determined by cross-
correlation analysis within AFNI software. They were generated in the individual native space
at a minimum threshold of p < 0.0001 to identify activated voxels set by a neuroradiologist (see Figure 1C, for a representative subject). The individual voxel threshold level (uncorrected p < 0.0001) was set to minimize the con- tribution of false positives that could be caused by stimulus-correlated head motion and/or random noise ﬂuctuation. The level was set for all subjects. We looked at FDR (q value cal- culated in AFNI software) to control false positives among the detected voxels, and the FDR corrected is q < 0.001. We veriﬁed that the activated areas identiﬁed and considered in our graph theory analysis are signiﬁcant. Network Construction The following sections describe the functional network construction. In the ﬁrst subsection, we ﬁrst describe how to create, from the fMRI signal of the active voxels, a brain network for each individual separately. The second subsection discusses the group analysis or how we obtain, from the individual brain networks, a common architecture that unveils a persistent circuitry across all the single-subject brain networks. For each subject we construct a functional network. This Individual brain network construction. network can be seen at two different scales or levels: (a) at the voxel level and (b) at the fROI level, as we explain in more detail below. At the voxel level, active voxels in the individual activation map (p < 0.0001) deﬁne the nodes of our functional network, where a voxel is the lowest resolution measured by fMRI. Functional links are inferred by thresholding pairwise Pearson correlations (see Equation 1) between a pair of voxels, as standard in the literature (Bullmore & Sporns, 2009; Gallos et al., 2012; Hermundstad et al., 2013). The pairwise correlation is deﬁned as follows: l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . / / t e d u n e n a r t i c e - p d l f / / / / / 4 1 1 3 4 1 8 6 6 6 9 2 n e n _ a _ 0 0 1 1 2 p d . t f b y g u e s t t o n 0 7 S e p e m b e r 2 0 2 3 Cij = (cid:2) Network Neuroscience (cid:3)xixj(cid:4) − (cid:3)xi(cid:4)(cid:3)xj(cid:4) i (cid:4) − (cid:3)xi(cid:4))2((cid:3)x2 ((cid:3)x2 j (cid:4) − (cid:3)xj(cid:4))2 , (1) 138 Core language brain network Voxel-level functional network: Functional connectivity network where each node represents a voxel and a link depicts the functional interdependency between a pair of voxels. where xi is a vector encoding the fMRI time response of voxel i and (cid:3)·(cid:4) indicates a temporal average. Accordingly, pairs of voxels with correlation above a ﬁxed threshold are connected by a link (Bullmore & Sporns, 2009; Del Ferraro et al., 2018; Lucini et al., 2019). The threshold is an absolute threshold, which means we pick a reference value of the correlation as the smallest and assign a zero to any value below. The link weight is given by the correlation strength as deﬁned above in Equation 1. Nearby active voxels are grouped together based on each subject’s individual anatomy and are considered part of the same fROI. Figure 2, upper panel, shows a realization of the voxel-level functional network for a representative subject, where voxels that are part of the same fROI are colored equally. l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . / t / e d u n e n a r t i c e - p d l f / / / / / 4 1 1 3 4 1 8 6 6 6 9 2 n e n _ a _ 0 0 1 1 2 p d t . f b y g u e s t t o n 0 7 S e p e m b e r 2 0 2 3 Figure 2. Two visualizations of the individual functional network. The ﬁgure shows functional networks for a representative subject relative to the fMRI active brain areas during the language task. Upper panel: voxel-level network. Each node in the network represents a voxel, each link connects a pair of voxels in different brain modules, and it is indicative of functional interdependency. Links connecting voxels within the same brain module are not visible but exist. Lower panel: fROI-level network for the same voxel-level architecture shown in the upper panel. Voxels belonging to the same anatomical region are grouped into an fROI, represented as a node in the network. Node’s size is proportional to the number of voxels in the fROI. Colored borders have no meaning and are used only for illustrative purpose. Each link’s thickness connecting two fROIs is proportional to the sum of link’s weight connecting all the voxels in the two fROI (exact deﬁnition given in Equation 2). Network Neuroscience 139 Core language brain network We deﬁne fROIs within each subject individually, based on the activation and anatomy of the speciﬁc subject (Del Ferraro et al., 2018; Fedorenko, Hsieh, Nieto-Castañón, Whitﬁeld- Gabrieli, & Kanwisher, 2010; Lucini et al., 2019). For instance, all the active voxels in Brodmann area 22 of the superior temporal lobe deﬁne the Wernicke’s area fROI. The rea- son for choosing individual-based fROIs is that group-based ROI-level analysis suffers from intersubject variability in the location of activation. In contrast, individual-subject-based fROI analysis can reveal greater functional speciﬁcity (Fedorenko et al., 2010). Furthermore, working in individual native space prevents the propagation of errors due to coregistration to universal ATLAS. fROI-level functional network: Course-grained representation of a voxel-level functional network. Each node represents an fROI and a link depicts the functional interdependency between a pair of fROIs. At the fROI level or module level a node represents an entire fROI, that is, a group (col- lection) of nearby active voxels in the spatially (anatomically) proximate area. Hereafter, we might use the word “module” or brain “region” as substitute of the word fROI. At this level, a functional link connects two fROIs if and only if there exists at least one link, at the voxel level, between a pair of voxels in the two fROIs. The functional link’s weight between two fROIs i and j (Wij) is deﬁned as the sum of the number of links connecting pairs of voxels between the two fROIs, normalized by the sum of the two fROIs’ size: Wij = #links connecting i ↔ j size(fROIi) + size(fROIj) . (2) For each individual, we then normalize each Wij by the value of the largest W for that individual (Wmax): ˜Wij = Wij Wmax , for all i and j fROIs. (3) In this way the link’s weight scale is the same across subjects (see Supplementary Table S1) and the maximum weight is ˜W = 1 in each individual. Figure 2, lower panel, illustrates the functional network at the fROI level for a representative subject. For each individual, we are interested in uncovering the functional architecture of the subdivision of the Broca’s area, that is, tri-BA and op-BA, which correspond to all active voxels in Brodmann area 44 and 45, respectively. Each of these subareas has been associ- ated with different language processes in previous studies (Devlin et al., 2003; Gough et al., 2005; Newman et al., 2003; Nixon et al., 2004). Through our analysis we aim to ﬁnd out the speciﬁcity of their functional connectivity, to unveil whether their different engagement in language processing may be associated with a different functional wiring with the rest of the brain. Thus, when building the individual functional network, we group the active voxels of the BA into two different and separate fROIs: op-BA and tri-BA (see Figure 2). We named the fROIs according to their main anatomical boundaries as follows. We re- tained the classical designations of BA (Brodmann area 44–45, inferior frontal gyrus) and WA (Brodmann area 22, superior temporal gyrus), as these designations still predominate in neuro- surgery, which dominates clinical practice (Friederici, 2011). We deﬁned the ventral premotor area (v-preMA) as the ventral portion of the premotor cortex, which includes the inferior part of Brodmann area 6, centered on the posteriormost portion of the middle frontal gyrus (MFG; Friederici, 2011). The superior portion of Brodmann area 6 was considered dorsal premotor area (d-preMA). The anteriormost part of the middle frontal gyrus was identiﬁed as anterior middle frontal gyrus (aMFG). The pre-SMA was deﬁned within the medial frontal cortex, at the level of Brodmann area 6 (Nachev, Kennard, & Husain, 2008). The precentral gyrus was iden- tiﬁed with Brodmann area 4, the supramarginal gyrus was identiﬁed with Brodmann area 40 Network Neuroscience 140 l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . t / / e d u n e n a r t i c e - p d l f / / / / / 4 1 1 3 4 1 8 6 6 6 9 2 n e n _ a _ 0 0 1 1 2 p d t . f b y g u e s t t o n 0 7 S e p e m b e r 2 0 2 3 Core language brain network and angular gyrus with Brodmann area 39 (Friederici, 2011). The deep opercular cortex (DOC) included the innermost portion of the frontal operculum (Friederici, 2011). The visual and the auditory cortex, which are active areas that support nonlinguistic pro- cessing, were excluded from the analysis (Fedorenko et al., 2010; Fedorenko & Kanwisher, 2009). These areas are indeed activated because the subject is presented with auditory stimuli and may keep the eyes open. The same functional network construction as described above is carried over for all 20 subjects individually, both at the voxel and the fROI level. Next, we carry out a group analysis to identify the common functional network shared across individuals, beyond the intersubject variability, as described in following section. Common network construction across subjects. Our interest in studying functional networks for single individuals performing language tasks is aimed at uncovering functional architectures that are persistent across healthy subjects and could be useful and informative when dealing with clinical cases. Individual functional networks have innate subject variability (e.g., one subject activates in one speciﬁc area or has a functional link while another does not). Therefore, after we reconstructed the individual functional networks, we performed a group analysis at the fROI level by investigating which set of links and brain areas is persistent across subjects or, in other words, which functional subarchitecture is common among all the individuals. This functional architecture is informative of which areas and functional links persist beyond the intersubject variability, and therefore it represents a language core structure for the speciﬁc language task under study. Accordingly, surgical intervention, as for instance tumor resection, should operate by preserving such core structure existing across healthy controls. In addition, functional damage to this structure due to brain pathologies—and observed from the functional connectivity of the patient—may be informative of the damage extent (e.g., a missing functional link in the core may signify a larger harm than a missing connection between more peripheral areas not in the core). We name this most persistent functional architecture across subjects, at the fROI level, common network. This common architecture is deﬁned retaining a pair of fROIs and a functional link connecting them only if these areas and link are present across subjects. The weight of the functional link connecting two fROIs i and j in the common network (WC ij ) is deﬁned as the average of the ˜Wij connecting those fROIs across subjects: WC ij = 1 N N ∑ l=1 ˜W(l) ij , (4) where N is the total number of individuals. We report and discuss the results of this quantitative analysis in the following sections. RESULTS Individual Networks For each individual we observe fMRI activation in both hemispheres, however, left dominance is clearly observed, as expected since all the subjects are right-handed (Isaacs, Barr, Nelson, & Devinsky, 2006; Knecht et al., 2000). The number of left hemisphere areas of activation is greater and in most cases their frequency of activation is greater as well. Network Neuroscience 141 l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . / t / e d u n e n a r t i c e - p d l f / / / / / 4 1 1 3 4 1 8 6 6 6 9 2 n e n _ a _ 0 0 1 1 2 p d t . f b y g u e s t t o n 0 7 S e p e m b e r 2 0 2 3 Core language brain network Active fROIs across subjects include the following, in alphabetic order: angular gyrus (L), Broca’s area (L; op-BA and tri-BA), Broca’s area (R), caudate (L and R), deep opercular cortex (L and R; Friederici, 2011), aMFG (L and R), precentral gyrus (L and R), ventral and dorsal preMA (L), ventral preMA (R), pre-SMA, supramarginal gyrus (L and R), and Wernicke’s area (L and R). Detailed information on the frequency of activation of each area across subjects is summarized in Supporting Information Table S2. In the following, for brevity, we will refer to left hemisphere brain areas simply with the name of the areas, omitting the speciﬁcation (L). The functional network for a representative subject at both the voxel and the fROI level is shown in Figure 2. All the single subjects’ functional connectivity at the fROI level for each of the 20 healthy individuals considered in our study are shown in Supporting Information Figure S1, and all the connectivity values between pairs of fROIs are reported in Supporting Information Table S1. We observe that, overall, the preMA is the most connected area across subjects, in terms of connectivity weight. In 8 over 20 individuals the strongest functional connection is between preMA–op-BA and in 7 over 20 cases it is between preMA–pre-SMA. In total, the preMA turns out to have the strongest connection with one of the other areas in 17 out of 20 subjects; in 3 cases the strongest functional connection is between op-BA and tri-BA. For further details on the connectivity of each area, see Supporting Information Table S1. Wernicke’s area is known to structurally connect to BA through the arcuate fasciculus, a bundle of axons linking the inferior frontal gyrus with the superior temporal gyrus. We inves- tigated the functional connections of the BA subdivisions with the rest of the brain and, with focus on WA, at the fROI level, we compared how frequent op-BA connects to WA versus how frequent tri-BA connects to WA. We ﬁnd that op-BA connects to WA in 18 out of 20 subjects (90% of the cases), while tri-BA connects to WA in 15 out of 20 individuals (75% of the cases). In terms of connectivity weight, in 10 out of 20 subjects (50%) WA connects more strongly to op-BA than to tri-BA, whereas in 7 subjects (35%) we have the opposite ﬁnding, tri-BA con- nects more to WA than the opercular counterpart. In 2 individuals the functional connectivity of op-BA and tri-BA to WA is, instead, approximately the same. One subject does not show WA activation at all. Regarding other relevant areas such as preMA and pre-SMA, we ﬁnd that the connectivity frequency of these areas with op-BA and tri-BA is about the same. Indeed the preMA connects to op-BA in 18 subjects and to tri-BA in 17 out of 20. The pre-SMA connects to op-BA in 19 subjects and to tri-BA in 18 individuals. So, overall, the connectivity frequency of the BA subdivisions with preMA and pre-SMA is similar. In terms of connectivity weight, op-BA con- nects more strongly to both preMA and pre-SMA compared with tri-BA. Thus, although the BA subdivisions connect to preMA and pre-SMA with about the same frequency across subjects, op-BA has, overall, a larger connectivity weight. To investigate the overall functional connectivity that each fROI has with the rest of the common network shown in Figure 3 we computed, for each individual, the sum of the func- tional links of each fROI with the rest of the network. In each subject, each fROI that appears in the common network can then be ranked according to this connectivity strength (most con- nected at the top and least connected at the bottom). We present these results in Supporting Information Figure S2. The results show that preMA is either the most connected or the second most connected area in 95% of the cases. On the contrary, WA is either the least connected or the second least connected area in 95% of the cases. Network Neuroscience 142 l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . / t / e d u n e n a r t i c e - p d l f / / / / / 4 1 1 3 4 1 8 6 6 6 9 2 n e n _ a _ 0 0 1 1 2 p d t . f b y g u e s t t o n 0 7 S e p e m b e r 2 0 2 3 Core language brain network l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . / / t e d u n e n a r t i c e - p d l f / / / / / 4 1 1 3 4 1 8 6 6 6 9 2 n e n _ a _ 0 0 1 1 2 p d . t f b y g u e s t t o n 0 7 S e p e m b e r 2 0 2 3 Figure 3. Common network across subjects for the language task under study. The ﬁgure illustrates the functional network, beyond intersubject variability, shared across individuals (17 out of 20). The weight of a link connecting two fROIs is proportional to the average of the functional links connecting those fROIs across subjects. Upper panel: fROIs are located on their anatomical loca- tion on the brain. Lower panel: pictorial illustration of the network in the upper panel, with the fROIs equally spaced on a plane. Common Network Across Subjects and Functional Subdivisions of Broca’s Area The common network at the fROI level, as described in the Common Network Construction section, is made by those fROIs and links present (persistent) across the majority of subjects. As a result of the left dominance at the individual level, no consistent overlap of right-hemisphere activation has been found across subjects. We ﬁnd that the persistent structure across individuals (17 over 20), beyond intersubject variability, is made by op-BA, tri-BA, WA, preMA, and pre-SMA connected together in a func- tional architecture (see Figure 3). This circuitry represents the core structure for the speciﬁc clinical language task under investigation since it is the functional architecture that prevails in nearly all subjects. We ﬁnd this network in 17 over 20 subjects and not in all of them be- cause three subjects show lack of activation for either the op-BA (1 case), the tri-BA (1 case), or neither WA nor tri-BA (1 case). The common network shown in Figure 3 is therefore the one prevailing in closely all the subjects and, thus, the functional structure that is persistent be- yond intersubject variability. We tested the robustness of these results by varying the threshold in each individual network (discussed in the Individual Brain Network Construction section) by 5−10% and by recomputing the common network analysis. We ﬁnd that within this range of variation the common network is made by the same brain modules (fROIs) and links shown in Figure 3. Network Neuroscience 143 Core language brain network Furthermore, this conclusion is additionally supported by additional ﬁndings we obtained on a study conducted on bilingual healthy subjects when they speak their native language (Li, Pasquini, et al., 2019). In Li, Pasquini, et al. (2019) we study the functional network of English monolingual subjects and of bilinguals (native in Spanish, bilingual in English). We ﬁnd that for both groups, when subjects speak their native language the persistent structure is made by BA, WA, preMA, and pre-SMA. Additionally, data in Li, Pasquini, et al. (2019) were acquired for a letter-generation language task, that is, a different task that we use in the present paper (verb generation). Thus, results of Li, Pasquini, et al. (2019) together with the present ﬁndings support evidence that the core language network is the most consistent functional architecture beyond intersubject variability and it is not limited to one speciﬁc language task. max max) is between op-BA and preMA (WC In terms of functional connectivity, the strongest connectivity weight in the common net- work (WC = 0.74 ± 0.31, where the average is made across all the subjects that have such link). The triangular BA also connects with the preMA but with about half of the magnitude (WC = 0.37 ± 0.29). Detailed information on the functional connection of the other areas in the common network is reported in Supporting Information Table S3. Broca’s area has been long recognized as a central language area; its strong connec- tivity with the preMA(L) is of particular interest since the preMA(L) has been more recently iden- tifiedas an area with dominant role for language (Duffau et al., 2003). We discuss this result further in the Discussion subsection: Functional and Structural Connectivity of the Common Network. When we look at the connectivity of the BA subdivisions with Wernicke’s areas, a primary area for language comprehension, we observe that, in the common network, WA only connects to op-BA. This reveals the existence of larger coactivation of the BOLD signal between these two areas that might also be driven by their spatial vicinity (WA is anatomically closer to op-BA than to tri-BA). In more detail, as discussed in Results section, at the individual level we ﬁnd that WA connects to tri-BA in 15 out of 20 cases whereas it connects to op-BA in 18 out of 20 cases. Therefore, overall, BA subdivisions both connect to WA in several different individuals with a slightly larger presence of WA–op-BA connectivity across subjects. In terms of connectivity weight, when we count only subjects where both op-BA and tri-BA connect to WA, we ﬁnd that op-BA connects slightly more strongly to WA compared with tri-BA (WC = 0.17 ± 0.23 versus WC = 0.15 ± 0.20, respectively). Furthermore, we observe that op-BA has a larger connectivity than tri-BA both on the number of connections with the rest of the areas in this network (4 versus 3 respectively, the extra one being WA–op-BA) and in terms of functional connectivity weight. Indeed, the av- erage connectivity of the op-BA, across subjects and across areas, in the common network is WC = 0.45 ± 0.25 (WC = 0.55 ± 0.20 without the link WA–op-BA), whereas the comprehen- sive connectivity weight of the tri-BA is WC = 0.32 ± 0.18. Finally, we observe that the average values for the common-network functional weights reported in Supporting Information Table S3 have large standard deviations (magnitude com- parable with the mean). This result signals a large intersubject variability for the weight of the single functional link across subjects. To investigate this further, we plot the empirical distri- bution of all the functional links’ weights across subjects and observe that it displays a long tail shape (see Supporting Information Figure S3), which is explicative of the large standard deviation values. The Common Network is Part of the Maximum k-core: The Most Resilient Architecture The notion of k-core in theoretical physics has been used as a fundamental measure of cen- trality and robustness within a network (Morone et al., 2019). Since it was ﬁrst introduced in Network Neuroscience 144 l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . t / / e d u n e n a r t i c e - p d l f / / / / / 4 1 1 3 4 1 8 6 6 6 9 2 n e n _ a _ 0 0 1 1 2 p d t . f b y g u e s t t o n 0 7 S e p e m b e r 2 0 2 3 Core language brain network social sciences (Seidman, 1983) it has been used in several contexts (Kitsak et al., 2010), as in random network theory (Pittel et al., 1996) or to describe large-scale structure in the brain (Hagmann et al., 2008). The k-core of a given architecture is deﬁned as the maximal subgraph, not necessarily glob- ally connected, made of all nodes having degree (number of connections) at least k. In practice, the k-core subgraph can be derived by removing from the network all nodes with degree less than k. The removal of these nodes reduces the degree of their neighbors, and if the degree of the latter drops below k then also these nodes should in turn be removed. The procedure iterates until there are no further nodes that can be pulled out from the network. The remain- ing graph is the k-core of the network. A k-core structure includes subnetworks with higher ks: k + 1, k + 2, and so forth. For instance, the 1-core includes the 2-core which, in turn, includes the 3-core and so forth (see Figure 4). In each k-core, nodes in the periphery (not included in the k + 1-core) are called k-shell (ks). Thus, in each network, k-core (and k-shell) structures are nested within each other with increasing k. The innermost structure of the network corresponds to the graph with the maximum k-core (Dorogovtsev et al., 2006). Thus, by deﬁnition, the max k-core is not nested into any other structure with higher k. As a consequence, by deﬁnition, the max k-shell always coincides with the max k-core. Figure 4A illustrates k-core and k-shell structures in a simple explanatory network. Recently, the maximum k-core (kmax core) has been linked to the most resilient structure of bio- logical systems with positive interactions (Morone et al., 2019) and, in an fMRI study of human brains, the kmax core of the functional connectivity for a visual-task-based experiment has been found to be the most robust structure, which remains active even during subliminal conscious states (subject not aware of seeing images; Lucini et al., 2019). Motivated by these recent ﬁndings, we pruned each voxel-level individual functional net- work until the maximum k-core structure, and we investigated to which k-core each node l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . / / t e d u n e n a r t i c e - p d l f / / / / / 4 1 1 3 4 1 8 6 6 6 9 2 n e n _ a _ 0 0 1 1 2 p d . t f b y g u e s t t o n 0 7 S e p e m b e r 2 0 2 3 k-core and k-shell of a network. Panel A illustrates pictorially a network. Nodes in Figure 4. the same disk have the same k-core. A k-core structure includes subnetworks with higher ks, so the 1-core includes the 2-core which, in turn, includes the 3-core, and so forth. Nodes that are in the k-core but not in the k + 1-core are called k-shell and are colored differently. The maximum k-core = 4 and depicted with brown nodes. coincides with the maximum k-shell, in this network is kmax core Panel B illustrates pictorially the construction of the k-core histogram shown in Figure 5. Note that here nodes in each k-shell are colored differently, whereas in Figure 5 different colors indicate nodes in different fROIs, piled up according to their k-shell as in this panel. Network Neuroscience 145 Core language brain network (voxel) belongs. We focused on the areas part of the common network (BA, WA, pre-SMA, and preMA) because these are the fROIs that form a persistent language structure across in- dividuals, as shown in Figure 3. We aim to explore whether these regions are part of some signiﬁcative k-core structure that might shed light on the architecture of the network. Our goal is to investigate, across subjects, which fROIs characterize the occupancy of each k-shell and, thus, we proceed as follows. For each individual network we compute the k-core and k-shell of all the nodes (voxels) as described above. Each subject has, in general, a different kmax core (k-shell), which is a integer number that can go up to the maximum degree of the subject’s network. Thus, in order to compare results across subjects, we normalize the k-core range- of-values to 1, in each subject. We do this by dividing all the k-core (k-shell) values in each individual network by the individual kmax core for that network. In Figure 5 we then plot the total k-shell occupancy for all the individuals, and we color differently the contribution of each fROI in order to visualize to which k-shell they belong. Results in Figure 5 show that the maximum k-shell (which is in turn the maximum k-core) is the most populated of all the k-shells of the common network. More importantly, if we look at each area individually, we observe that the largest concentration of pre-SMA, op-BA, tri-BA, and v-preMA is in the maximum k-shell. Among the areas of the common network, WA is the only area that does not have the largest portion in the kmax core but, rather, populates smaller k-shell values. These results are robust to 5−10% variation of the threshold used to build individual functional networks (discussed in the Individual Brain Network Construction section). In order to asses how much of our results are given by construction we compared them with randomized voxel assignation to fROI and recomputed the k-shell occupancy as a null model. Results are shown in Supporting Information Figure S4 and show that the relative occupancy of each fROI in the maximum k-shell is different from the random case. Furthermore, WA in the real model l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . / / t e d u n e n a r t i c e - p d l f / / / / / 4 1 1 3 4 1 8 6 6 6 9 2 n e n _ a _ 0 0 1 1 2 p d . t f b y g u e s t t o n 0 7 S e p e m b e r 2 0 2 3 k-shell occupancy. The histogram shows the k-shell occupancy for nodes in the four Figure 5. fROIs of the common network of Figure 3. Overall, the majority of the nodes of this structure are located in the maximum k-shell, which coincides with kmax core, a quantity linked to the robustness of a complex network (Morone et al., 2019). Of the four fROIs of the common network, the pre-SMA, op-BA, tri-BA, and ventral preMA are mostly part of the kmax core. Wernicke’s area (WA) is more an outlier; it is mostly located in lower k-shells and minimally located in the kmax core. Network Neuroscience 146 Core language brain network populates more the smaller shells compared with the null model case. We conclude that our results are not due to a random effect. In Morone et al. (2019) the authors have shown that, for complex networks with positive couplings, the kmax core of the network is the most resilient structure under decreasing of the cou- pling weight. In our functional networks, all the links are obtained through thresholding of pairwise correlations that, from our ﬁndings, turn out to be all positive. This is because the BOLD signal is extracted from a task-based fMRI experiment, stimulated by an external input. In this way, active voxels are those mostly correlated with the task model and, when comput- ing pairwise correlations among voxels correlated with the same external stimulus, most likely one ﬁnds positive correlations, as we observe from our data analysis. This result allows us to interpret the functional networks wired by positive interactions and, therefore, the theory of Morone et al. (2019) applies. Accordingly, we can interpret the maximum k-core structure of our network as the most resilient one under decreasing of the correlation weight. In other words, the circuitry made by the pre-SMA, BA, and preMA represents the most robust structure of the functional network. Wernicke’s area, although it is part of the common network, for the most part does not lay in the kmax core of the network, probably because of its more peripheral anatomical location compared with the other fROIs of this common architecture. Therefore, although it is one of the most important areas for language, it is not part of the most resilient core. Rich-Club Response to Changes in Threshold The rich-club coefﬁcient is a measure of the connectivity as a function of degree (Zhou & Mondragón, 2004). If subnetworks of nodes have a tendency towards a higher percentage of connectivity for larger degrees, then the network is said to be a “rich club.” It has been shown that the brain structural network typically conforms to this paradigm (van den Heuvel & Sporns, 2011). Since the number of connections will naturally increase with degree, it is standard to normalize the rich-club coefﬁcient by the coefﬁcient obtained for a random network with the same number of nodes and degree distribution. The rich-club coefﬁcient is deﬁned as φ(k) = 2E>k
N>k(N>k − 1) ,

(5)

where E>k and N>k is the number of edges and nodes in the network where all nodes with
φ(k)
degree less than k have been removed. The normalized coefﬁcient is then deﬁned by
φrand(k)
where φrand(k) refers to the rich-club coefﬁcient calculated in the same network that has been
randomly rewired while preserving the degree distribution.

In the Individual Brain Network Construction section we deﬁned an absolute threshold for
the voxel-voxel correlation in order to deﬁne what represents a minimum edge weight that
deﬁnes links in the functional language networks. Tuttavia, the choice of threshold is some-
what arbitrary and may have an effect on the results. In order to probe how the connectivity
responds to changes in the choice of threshold, we show normalized rich-club coefﬁcients for
a representative subject in Figure 6.

The threshold was lowered by 5% and raised by 5% in each case, and we can see that
the rich-club structure is evident as shown by the rising curve above 1 as a function of k up
to the high-degree sector of
the network where there is high volatility. We also ob-
serve that a 5% change in threshold value does not change qualitatively the behavior of the
rich-club coefﬁcient. These results show that, in addition to the rich-club behavior found in

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Core language brain network

Figura 6. Rich clubs. Normalized rich-club coefﬁcients for a representative subject with 5%
(lowered/raised) variation of the current threshold. All other subjects have qualitatively the same
normalized rich-club coefﬁcient. The red curve corresponds to the original threshold (rete).
The blue curve corresponds to a 5% lower variation of the threshold, which leads to a denser net-
lavoro. The green curve corresponds to a 5% higher variation of the threshold, which leads to a
sparser network. The ﬁgure shows rich-club behavior of the functional brain network, showing an
increasing normalized rich-club coefﬁcient.

(van den Heuvel & Sporns, 2011), functional brain networks, such as those we studied in this
lavoro, also show the same feature.

DISCUSSION

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In this study, we reconstructed the functional language network of 20 healthy subjects from
tb-fMRI data providing information about the functional connectivity between active areas on
fMRI maps, with both a voxel- and an fROI-level resolution. The language task designed for the
experiment is customarily used in clinical cases and has shown to produce robust activation in
previous studies (Brennan et al., 2007; Li, Dong, et al., 2019; Ramsey et al., 2001; Xiong et al.,
2000). Functional activation is generally sensitive to the fMRI task employed; our interest in
reconstructing functional networks for this speciﬁc task aimed to create benchmark results for
healthy individuals that can be used as reference for functional networks affected by brain
pathologies. Infatti, brain impairments are known to create damage to the functional con-
nectivity. It is therefore paramount to have healthy functional architectures relative to clinical
language tasks in order to make the comparison between healthy individuals, and patients’
functional networks possible.

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Our main ﬁnding is the existence of a common persistent functional network across subjects
that wires together BA, WA, ventral preMA, and pre-SMA in the left dominant hemisphere
for 17 out of 20 right-handed healthy subjects (Guarda la figura 3). We interpret this circuitry as a
core structure for the language task under study since this network persists across nearly all
individuals.

Inoltre, we compute the k-core of each node (voxel) in the common network—the
maximum value of which has been recently linked to network resilience in ecosystems and

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Core language brain network

Direct cortical stimulation:
Electrical stimulation of the cerebral
cortex with the aim of identifying
brain areas that take part in a speciﬁc
system function (per esempio., lingua,
motor). It remains the gold standard
for presurgical mapping of the motor
cortex and language areas to prevent
unnecessary functional damage.

fMRI studies (Lucini et al., 2019; Morone et al., 2019). We ﬁnd that three out of four areas
of the common architecture (speciﬁcally pre-SMA, BA, and preMA) are mostly concentrated
in the maximum k-core of the network (Guarda la figura 5). This led us to conclude, following the
ﬁndings of Lucini et al. (2019) and Morone et al. (2019), that these areas are the most robust
of the language network in terms of fMRI-correlated signal.

Wernicke’s area is a crucial language area and indeed appears as part of the common net-
work across individuals, yet its type of connectivity with the rest of the fROIs in this architecture
is slightly different from the connectivity of the other areas. Infatti, overall, WA shares two con-
nections with other areas in this network, one with BA and one with the preMA, whereas each
of the other fROIs has at least three total functional connections. This might be a by-product
of the more peripheral location of the WA compared with the other fROIs, which being spa-
tially closer to each other are facilitated to coactivate because of white ﬁbers wiring them
together. Wernicke’s area is also the only area among the four in the common network that
is not largely part of the maximum k-core (Guarda la figura 5). This result is in agreement with the
discussion above about this area and, Ancora, might be due to the more perimetric location of
the WA in the common network.

Finalmente, we investigated the functional architecture of the BA anatomical subareas, revealing
a different connectivity between tri-BA, op-BA, and the other areas of the common network
for this speciﬁc language task. In the ﬁrst subsection, we discuss our ﬁndings regarding the
functional connectivity of the BA subdivisions, contextualizing them with known white matter
connections that these areas share with the rest of the brain, found in other studies.

Functional and Structural Connectivity of the Common Network

We observe that the left ventral preMA is the most connected area of the common network,
with four total connections and the strongest connectivity with op-BA (WC = 0.74 ± 31) E
with pre-SMA (WC = 0.64 ± 0.31). Come mostrato in figura 2 for a representative subject, the ven-
tral preMA is functionally connected to all the main cortical language areas of the dominant
hemisphere, suggesting that this area may play an important role in speech production (other
subjects show qualitatively the same feature; see Supporting Information Figure S1).

Tate, Herbet, Moritz-Gasser, Tate, and Duffau (2014) investigated the crucial cortical epi-
centers of human language function by means of intraoperative direct cortical stimulation in
165 consecutive patients affected by low-grade glioma. The study shows that speech arrest is
localized to the ventral preMA instead of the classical BA. Inoltre, the presence of gliomas
growing in the left ventral preMA has been related to a higher percentage of speech deﬁcits
than gliomas inﬁltrating the classical BA, providing a possible clinical correlate of the results
of Tate et al. (Bizzi et al., 2012; Tate et al., 2014). Inoltre, Duffau et al. (2003), through
intraoperative functional mapping in awake patients, have concluded that the left dominant
preMA seems to play a major role in language since its electrical stimulation causes speech
disturbances. All these results, together with the ﬁndings of our study, pinpoint a central role
of the preMA in language production.

Tuttavia, one must be careful not to overinterpret these results, as the highest connectivity
does not necessarily imply a central or essential role of that particular fROI in the network.
Using advanced graph theoretical analysis, Morone and Makse (2015) demonstrated that the
most connected nodes in a network often do not correspond to the most essential nodes,
the elimination of which would lead to collapse of that particular network. This idea has been

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recently tested on functional networks obtained from fMRI of rodent brains and veriﬁed through
in vivo pharmaco-genetic intervention (Del Ferraro et al., 2018).

Although the correspondence between structural and functional connectivity is not fully
understood yet (Honey et al., 2009), the arrangement displayed by our study is supported by
structural evidence. The existence of a physical connection between ventral preMA and BA
seems realistic, given their spatial contiguity. Besides representing a shared origin for the main
bundles of the dorsal pathway (Chang, Raygor, & Berger, 2015; Dick, Bernal, & Tremblay,
2014), the two areas may be directly connected by a speciﬁc opercular-premotor fascicle
(described in the next section; Lemaire et al., 2013).

The pre-SMA shows connectivity with both ventral preMA and BA (Guarda la figura 3, Supporting
Information Figure S1, and Supporting Information Table S3). These functional connections
are consistent with the organization of the structural language connectome to some extent:
the frontal aslant tract (FAT), an association motor pathway that underlies verbal ﬂuency and
connects pre-SMA and BA (Catani et al., 2013; Ford, McGregor, Case, Crosson, & White,
2010; Jenabi, Peck, Young, Brennan, & Holodny, 2014), likely includes projection to posterior
regions of the MFG, corresponding to the ventral preMA (Chang et al., 2015).

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The low connectivity weight between ventral preMA and WA (see Supporting Information
Table S3) may be explained by the increased distance between the two structures. Of note, we
ﬁnd that the functional connectivity weight between op-BA and WA is similar to that of the
ventral preMA and WA (Supporting Information Table S3), which is consistent with their struc-
tural connection through the same white matter tract, corresponding to the arcuate component
of the AF/SLF system (Chang et al., 2015; Dick et al., 2014).

Broca’s Area Subdivisions

Our ﬁndings show that the subdivisions of Broca’s area present different patterns of connectivity
within the language network, with the opercular portion appearing more connected to all the
signiﬁcant nodes of the common network compared with the triangular part. This evidence
appears in line with the structural architecture of the network.

The prominent interaction between ventral preMA and op-BA found in this study (WC =
0.74 ± 0.31, see Supporting Information Table S3) supports the evidence of a structural link
between op-BA and preMA, as suggested by Lemaire et al. (2013) using DTI analysis. IL
authors investigated the structural connectome of the extended BA, identifying the U-shaped
opercular-premotor fasciculus that connects the op-BA to the ipsilateral preMA (Lemaire
et al., 2013). On the contrary, tri-BA and ventral preMA showed lower functional connectivity
(WC = 0.37 ± 0.29), possibly suggesting indirect communication through the op-BA.

The second strongest functional connection between BA’s subareas and other fROIs of the
common network that we ﬁnd is the link between op-BA–pre-SMA (WC = 0.35 ± 0.23). These
two areas are connected by the FAT (Ford et al., 2010), which originates in the SMA/pre-SMA
and terminates into the posteriormost aspect of the inferior frontal gyrus (IFG) (Catani et al.,
2013). Triangular BA and pre-SMA share a lower functional connectivity weight (WC = 0.20 ±
0.21) compared with op-BA and pre-SMA, reﬂecting the anatomic boundaries of the FAT.

Finalmente, the functional link between op-BA and WA is in line with the evidence of a dorsal
pathway of language between op-BA and STG through the AF/SLF system (dorsal pathway II;
Friederici, 2011).

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Core language brain network

Limitations of the Study

Functional networks are task dependent and brain impairments, such as brain tumors, affect
their functional connectivity (per esempio., by destroying functional links or preventing the fMRI acti-
vation of entire brain regions). In our study, we focused on a clinical language task since we
wanted to enhance a functional network that emerges in clinical studies. We employed healthy
subjects because we wanted to ﬁnd out the network associated with the clinical language task
without possible functional damage induced by brain impairments. A comparison with func-
tional networks of patients with brain impairments is of great interest but goes beyond the
purpose of the present paper and it is, instead, presented in a follow-up (Del Ferraro, Pasquini,
Peck, Holodny, & Makse, 2019). In Del Ferraro et al. (2019) we compare the functional net-
work as well as the structural network (obtained through diffusion tractography imaging) Di
patients who present speech impairment under awake cortical electrostimulation and patients
who do not. As a control, we also compare these networks with the same architecture for
healthy subjects, and the results of the present paper are further used as a benchmark.

Our study is limited to a speciﬁc clinical language task (cioè., verb generation), and since
functional networks are task-dependent, in principle, our results could be limited to this par-
ticular language task. In a follow-up work (Li, Pasquini, et al., 2019) we investigate the func-
tional connectivity differences between healthy subjects who are monolinguals, native English
speakers, and subjects who are bilingual, native Spanish speakers who speak English as a sec-
ond language. We employ a different type of language task (cioè., letter generation), and in both
datasets we ﬁnd that the core language network is the same as the one we ﬁnd in the present
paper by using a verb-generation task and discussed in the Results section. This strengthens
our ﬁndings showing that the core common network is not limited to a speciﬁc language task
but that it might be a robust structure shared across several tasks. Further studies that make
use of other language tasks are needed to elucidate this point and the generality of the core
common network.

As a ﬁnal limitation of our study we remark that the sample size of our data is limited to 20
individuals, and it would be important, in following works, to test the core network analysis
on larger datasets.

ACKNOWLEDGMENTS

We thank Mehrnaz Jenabi for help with AFNI and FSL software and Medeleine Gene for help
with mining the data.

SUPPORTING INFORMATION

Supporting information for this article is available at https://doi.org/10.1162/netn_a_00112.
Data that support the ﬁndings of this study are publicly available and have been deposited in
http://www-levich.engr.ccny.cuny.edu/webpage/hmakse/brain/ (Li, Del Ferraro, et al., 2019).

AUTHOR CONTRIBUTIONS

Qiongge Li: Formal analysis; Methodology; Software; Visualization; Writing – Original Draft;
Writing – Review & Editing. Gino Del Ferraro: Methodology; Software; Supervision; Writing –
Original Draft; Writing – Review & Editing. Luca Pasquini: Methodology; Validation; Writing –
Review & Editing. Kyung K. Peck: Data curation; Writing – Review & Editing. Hernán A. Makse:

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Core language brain network

Funding acquisition; Investigation; Supervision; Writing – Review & Editing. Andrei I. Holodny:
Funding acquisition; Investigation; Supervision; Writing – Review & Editing.

FUNDING INFORMATION

Hernán A. Makse, National Institutes of Health (http://dx.doi.org/10.13039/100000002), Award
ID: 1R01EB022720. Craig Thompson, National Institutes of Health (http://dx.doi.org/10.13039/
100000002), Award ID: P30 CA008748. Hernán A. Makse, National Science Foundation
(http://dx.doi.org/10.13039/100000001), Award ID: 1515022. Tim Ahles, National Institutes
of Health (http://dx.doi.org/10.13039/100000002), Award ID: U54CA137788. Tim Ahles, Na-
tional Institutes of Health (http://dx.doi.org/10.13039/100000002), Award ID: U54CA132378.
Luca Pasquini, Italian Scientists and Scholars in North America Foundation (http://dx.doi.org/
10.13039/100009799), Award ID: Imaging chapter award 2018. Luca Pasquini, European
Society of Radiology, Award ID: Bracco clinical fellowship 2018. Qiongge Li, City University
of New York (http://dx.doi.org/10.13039/100006462), Award ID: Doctoral student research
grant.

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