RESEARCH ARTICLE

Sources of Heterogeneity in Functional
Connectivity During English Word Processing
in Bilingual and Monolingual Children

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

j o u r n a l

Nia Nickerson1

, Valeria Caruso1, Tai-Li Chou4
, Adriene M. Beltz1

James R. Booth5

, Xiao-Su Hu1
, and Ioulia Kovelman1

Xin Sun1,2

, Rebecca A. Marks3

, Rachel L. Eggleston1, Kehui Zhang1

, Chi-Lin Yu1
,

, Twila Tardif1

,

1Department of Psychology, University of Michigan, Ann Arbor, MI, USA
2Department of Psychology, University of British Columbia, Vancouver, Canada
3Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA
4Department of Psychology, National Taiwan University, Taipei, Taiwan
5Department of Psychology and Human Development, Vanderbilt University, Nashville, TN, USA

Keywords: brain development, functional connectivity, bilingualism, children, fNIRS, individual
differences

ABSTRACT

Diversity and variation in language experiences, such as bilingualism, contribute to
heterogeneity in children’s neural organization for language and brain development. To
uncover sources of such heterogeneity in children’s neural language networks, the present
study examined the effects of bilingual proficiency on children’s neural organization for
language function. To do so, we took an innovative person-specific analytical approach
to investigate young Chinese-English and Spanish-English bilingual learners of structurally
distinct languages. Bilingual and English monolingual children (N = 152, M(SD)age =
7.71(1.32)) completed an English word recognition task during functional near-infrared
spectroscopy neuroimaging, along with language and literacy tasks in each of their languages.
Two key findings emerged. First, bilinguals’ heritage language proficiency (Chinese or Spanish)
made a unique contribution to children’s language network density. Second, the findings
reveal common and unique patterns in children’s patterns of task-related functional
connectivity. Common across all participants were short-distance neural connections within
left hemisphere regions associated with semantic processes (within middle temporal and
frontal regions). Unique to more proficient language users were additional long-distance
connections between frontal, temporal, and bilateral regions within the broader language
network. The study informs neurodevelopmental theories of language by revealing the effects
of heterogeneity in language proficiency and experiences on the structure and quality of
emerging language neural networks in linguistically diverse learners.

INTRODUCTION
Early language experiences shape a child’s mind and brain while also laying foundations for
reading (Werker & Hensch, 2015). Bilingualism offers enriched linguistic experiences that add
to the heterogeneity in children’s neural organization for language and reading acquisition
(Hernandez et al., 2019). To better capture the developing neural heterogeneity for language
processing, the present study utilized a person-specific network mapping approach to

Citation: Sun, X., Marks, R. A.,
Eggleston, R. L., Zhang, K., Yu, C.-L.,
Nickerson, N., Caruso, V., Chou, T.-L.,
Hu, X.-S., Tardif, T., Booth, J. R., Beltz,
A. M., & Kovelman, I. (2023). Sources
of heterogeneity in functional
connectivity during English word
processing in bilingual and
monolingual children. Neurobiology of
Language, 4(2), 198–220. https://doi.org
/10.1162/nol_a_00092

DOI:
https://doi.org/10.1162/nol_a_00092

Supporting Information:
https://doi.org/10.1162/nol_a_00092

Received: 1 June 2022
Accepted: 10 November 2022

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

Corresponding Author:
Xin Sun
sunxin@umich.edu

Handling Editor:
Marcela Peña Garay

Copyright: © 2023
Massachusetts Institute of Technology
Published under a Creative Commons
Attribution 4.0 International
(CC BY 4.0) license

The MIT Press

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
.

e
d
u
n
o

/

l
/

l

a
r
t
i
c
e
–
p
d

f
/

/

/

/

4
2
1
9
8
2
0
7
9
0
1
9
n
o
_
a
_
0
0
0
9
2
p
d

/

.

l

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

Neural network heterogeneity in children

characterize sources of heterogeneity in children’s emerging neural pathways for English and
to identify how these language neural networks are influenced by early bilingual experiences
with Spanish or Chinese.

According to the Neuroemergentist Framework, complex neurocognitive processes
develop out of interactions between an individual’s expertise and the environmental ecosys-
tem (Claussenius-Kalman et al., 2021). Bilingual development thus stems from dynamic com-
munications between individuals’ developing cognitive skills (e.g., attention and working
memory) as well as their bilingual experiences and contexts (e.g., linguistic and orthographic
features and contexts of use). As a result, bilinguals may form different patterns of neural orga-
nization for language processing in relation to monolinguals and/or bilinguals of different lan-
guage groups. For instance, a meta-analysis found that bilinguals speaking a language with
more predictable sound-to-print mapping (e.g., French, as compared to English) rely on
enhanced phonological networks (i.e., bilateral temporal regions). In contrast, those speaking
a language with less predictable associations between language sounds and printed form (e.g.,
Chinese, as compared to English) rely on enhanced networks for lexical integrations (i.e., left
middle/inferior frontal regions; Liu & Cao, 2016). Moreover, connectivity studies have found
that, due to the increased cognitive demands of bilingual coordination, compared with mono-
linguals, bilinguals form enhanced connectivity between bilateral inferior frontal regions, as
well as between the basal ganglia and the frontal cortex, which guides language perception,
comprehension, and cognitive control (Berken et al., 2016; Marian et al., 2017).

Most prior research has approached bilingual brain development with group averages.
However, variations in bilingual experiences may yield meaningful variability in the neural
networks within groups. To advance the understanding of heterogeneity in neural mechanisms
of spoken language processing, and how they are influenced by bilingualism, we examined
the functional connectivity of cortical networks for spoken word recognition. Prior work has
shown that children’s language and reading proficiency are positively associated with
strengthened neural connectivity along key neural pathways of language processing (Skeide
et al., 2016; Yeatman et al., 2011). Moreover, these neural networks develop as a function of
language experience, including bilingualism (Ip et al., 2017; Kovelman et al., 2008; Marian
et al., 2017). In other words, bilingual experiences contribute to the neural network heteroge-
neity of language development (Claussenius-Kalman et al., 2021; Hernandez et al., 2019).
Using a person-specific approach, we aimed to uncover sources of individual variation in
the development of neural networks that support language and literacy development.

The Developing Neural Basis for Spoken Word Processing

Spoken words are comprised of sound (phonological units) and meaning (semantic units). Pro-
ficient adult speakers typically engage two parallel processing streams that allow them to
simultaneously consider the multifaceted nature of phonological and lexico-semantic repre-
sentations during word recognition. In the adult brain, these are commonly represented as
dorsal and ventral neural streams (Hickok, 2022; Hickok & Poeppel, 2007). The dorsal or pho-
nological stream includes the dorsal aspect of the left inferior frontal gyrus (IFG) and superior
temporal gyrus (STG), as well as the arcuate fasciculus (AF) fiber tract that connects those
regions. The ventral or semantic stream includes the ventral aspect of the left IFG, the middle
temporal gyrus (MTG), and the inferior fronto-occipital fasciculus that connects them (Su et al.,
2018). These two parallel processing streams improve in their functionality over the course of
children’s language development, as children learn to efficiently access both lexical and sub-
lexical information. Children’s spoken language skills are linked to functional and anatomical

Neurobiology of Language

199

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
.

e
d
u
n
o

/

l
/

l

a
r
t
i
c
e
–
p
d

f
/

/

/

/

4
2
1
9
8
2
0
7
9
0
1
9
n
o
_
a
_
0
0
0
9
2
p
d

.

/

l

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

Neural network heterogeneity in children

strengths within and between these networks (Cao et al., 2008; Friederici et al., 2011; Skeide
et al., 2016; Yeatman et al., 2011; Yu et al., 2018).

Functional connectivity studies reveal how brain regions work together during a language
task and how these brain connections relate to developmental outcomes in language profi-
ciency (Friederici et al., 2011; Jasińska et al., 2020; Qi et al., 2021; Xiao et al., 2016; Yu
et al., 2018, 2021). This research generally suggests a gradual shift in the development of
inter- (between) and then intra- (within) hemisphere associations. For instance, Friederici
et al. (2011) examined functional connectivity in 6-year-old children and adults who were
performing an auditory sentence comprehension task during functional magnetic resonance
imaging (fMRI). Findings revealed that younger children formed stronger functional connec-
tions between the left frontal regions and their right hemisphere homologs than adults. In con-
trast, adults showed stronger connectivity between the left frontotemporal regions. This and
similar findings (Enge et al., 2020; Weiss-Croft & Baldeweg, 2015) exemplify the merits of
functional connectivity research in revealing changes in language development, paving the
way for more nuanced inquiries into sources of heterogeneity of such change.

Connecting Spoken Language Networks to Reading

Learning to read requires children to connect their understanding of spoken words to orthog-
raphy, or written symbols. Therefore, neural networks for spoken language are essential for
children’s behavioral outcomes such as emergent literacy (Jasińska et al., 2020; Yu et al.,
2018, 2021). For instance, Jasińska et al. (2020) examined the longitudinal effects of functional
connectivity in 4-year-old children who passively listened to words during functional near-
infrared spectroscopy (fNIRS). Findings revealed that children who exhibited stronger func-
tional connectivity during this auditory task between the left IFG and right STG regions at
age 4 years had better reading proficiency a year later. Building upon this and similar prior
findings (Qi et al., 2021), we aimed to advance beyond the traditional functional correlation
methods that average across diverse speakers. Here we estimate individualized neural net-
works to better capture sources of heterogeneity in children’s emerging neural architecture
for language and how the neural networks speak to children’s developing language and liter-
acy skills (Arredondo et al., 2022; Beltz et al., 2016).

Individual Differences in the Neural Connectivity for Language in Bilingual Children

Neuroimaging research on bilingualism often finds connectivity differences between bilingual
and monolingual populations. These examinations include both anatomical connectivity as
studied through white matter tracts (García-Pentón et al., 2014; Mohades et al., 2012) and
resting-state functional connectivity (Berken et al., 2016; Sun et al., 2019; Thieba et al.,
2019). For example, in an anatomical diffusion tensor imaging study, Gao et al. (2022) exam-
ined the relation between bilingual proficiency and white matter tracts in Chinese-English
bilingual children raised in China. Findings revealed that children with thicker AF tracts
around left STG regions had better word reading proficiency in both English and Chinese.
The AF is a tract that connects frontal and temporal language regions and generally increases
in its thickness over the course of language development (Skeide & Friederici, 2016). Such
neuroanatomical findings support the idea that there is a relation between bilingualism factors
and neural connections critical for language processing (Bialystok et al., 2012).

Resting-state connectivity studies ask participants to stay awake while they are not engaged
in any given task to reveal a presumed default state of brain operations. A resting-state fMRI
study found that adults with early bilingual exposure (before age 5) showed stronger intrinsic

Neurobiology of Language

200

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
.

e
d
u
n
o

/

l
/

l

a
r
t
i
c
e
–
p
d

f
/

/

/

/

4
2
1
9
8
2
0
7
9
0
1
9
n
o
_
a
_
0
0
0
9
2
p
d

/

.

l

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

Neural network heterogeneity in children

functional connectivity between the left and right IFG regions and between left IFG and pre-
frontal regions than later-exposed bilinguals (Berken et al., 2016). The findings suggest that
early bilingual exposure influences the neural organization of the frontal lobe network essen-
tial for language control (Berken et al., 2016; Bialystok et al., 2012). The advantage of resting-
state neuroimaging studies is that they can capture spontaneous signals that do not tie to a
specific mental state (i.e., a task). Nevertheless, non-task resting-state paradigms may lack
empirical benefits such as sensitivity to brain-behavior associations (Finn, 2021). To the best
of our knowledge, no prior study has examined bilingual children’s functional connectivity
networks while participants engage in a language task, which is a knowledge gap we aim
to fill in the present work.

Another important but understudied issue is how to best depict the neural networks of
language processing for bilingual children. Much current knowledge about bilingualism
stems from analytical approaches that have dichotomized bilinguals versus monolinguals or
otherwise categorized different groups of bilinguals, such as splitting by age of exposure or
proficiency (e.g., Liu & Cao, 2016; Sulpizio et al., 2020). However, bilinguals can differ in
many ways. Newly emerging research thus advocates for approaches that leverage the hetero-
geneity of bilingual profiles to better understand bilingualism (Luk & Bialystok, 2013; Marian &
Hayakawa, 2021). The present work thus adopts a person-specific approach to examine
such heterogeneity of functional connectivity for language in relation to children’s bilingual
language and reading development.

Examining Person-Specific Neural Network With GIMME

Person-specific analytical approaches, such as group iterative multiple model estimation
(GIMME; Gates & Molenaar, 2012), advance upon conventional data analysis methods that
average across heterogenous individuals by instead identifying connections among a priori
regions of interest (ROIs) that are shared across participants (group level), across a subgroup
of participants (subgroup level), as well as connections that are unique to one or some individ-
uals (individual level). In this way, GIMME networks capture both the broad homogeneity of the
group and the heterogeneity of individuals. Specifically, GIMME uses a data-driven approach to
yield person-specific directed connectivity maps; GIMME begins with a null network and then
adds connections among ROIs that are meaningful (i.e., significant) for at least 75% of partic-
ipants to all participants’ networks followed by adding connections that are meaningful for a
subgroup of participants. Finally, GIMME adds connections that are meaningful just to an indi-
vidual. Connections are added until each person’s network represents their observed data well
and has person-specific weights. Simulation studies suggest that GIMME shows exceptional
robustness in modeling heterogeneous data compared to nearly 40 alternative methods, and
it has been applied to a wide range of psychological studies with neuroimaging data (Dotterer
et al., 2020; Gates & Molenaar, 2012; Goetschius et al., 2020; Lane et al., 2019; Price et al.,
2020). Altogether, as a data-driven network mapping approach, GIMME addresses the limita-
tions of traditional group-oriented approaches that rely on averages while also allowing for both
group-level inferences and accurate reflections of individual-level heterogeneity.

GIMME has been used to examine the functional connectivity of the attention networks in bilin-
gual children. Arredondo et al. (2022) used GIMME to estimate bilingual and monolingual chil-
dren’s neural connectivity with the attention network task among six pre-specified left superior,
middle frontal, as well as parietal brain channels with fNIRS. GIMME identified two subgroups,
one that consisted of almost all monolinguals (92%) and half of the bilinguals (54%), and another
that consisted of a small portion of monolinguals (8%) and the other half of bilinguals (46%).

GIMME:
Group iterative multiple model
estimation; a network-mapping
method that identifies connections
of variables (i.e., brain signals)
collected among multiple time points
for each individual.

Neurobiology of Language

201

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
.

e
d
u
n
o

/

l
/

l

a
r
t
i
c
e
–
p
d

f
/

/

/

/

4
2
1
9
8
2
0
7
9
0
1
9
n
o
_
a
_
0
0
0
9
2
p
d

/

.

l

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

Neural network heterogeneity in children

Heritage language:
Language learned by its speakers at
home as children, while they are
often exposed to a different language
outside home environments.

Notably, the bilinguals in the first group were more English-dominant (i.e., “monolingual-like”),
whereas the bilinguals in the second group had more balanced proficiency across their two lan-
guages. Importantly, the second group also had significantly higher network density (i.e., number
of connections) centered around the left frontal regions compared to the first group, which also
corresponded to higher attention task accuracy. These results suggest more complex attentional
neural networks for early bilingual children with more balanced dual language proficiency. In
sum, GIMME has been shown to be an effective approach for understanding sources of heteroge-
neity in the neural organization of cognitive functions in bilingual children, but many questions
remain unanswered, particularly regarding neural networks during a language task.

The Present Study

The current study employed GIMME analysis of fNIRS data to examine the effects of early and
systematic bilingual experiences on children’s emerging neural architecture for language pro-
cesses and their relation to literacy development. The participant groups included children
(ages 5–10 years) who were English monolinguals, Chinese-English bilinguals, or Spanish-
English bilinguals, all experiencing English-dominant education in the US. The bilinguals were
exposed to a heritage language (Chinese or Spanish) at home from birth, to English around age
2, and were capable of reading words/characters in their heritage languages. The study spe-
cifically asked participants to complete an auditory word-processing task during fNIRS neuro-
imaging. Children heard three words and were asked to identify the two words that shared a
unit of meaning (morpheme) while ignoring a phonological distractor (e.g., bedroom, class-
room, mushroom). The task probed children’s ability to analyze words’ lexico-semantic and
phonological constituents necessary for successful word processing. The ability to operate
upon words’ sound and meaning units is thought to support children’s emergent literacy
(Kuo & Anderson, 2006; Sun, Zhang, Marks, Nickerson, et al., 2022).

Functional connectivity analyses were performed with a priori brain regions of language
processing, including bilateral frontal and left temporal areas. These regions have been iden-
tified as essential to spoken word recognition by previous research (Enge et al., 2020; Friederici
et al., 2011; Jasińska et al., 2020) as well as for the current sample (see Sun et al., 2023, for the
functional activation patterns). We used GIMME to ask two experimental questions. First, we
asked: What is the relation between individual differences in functional connectivity for word
processing and children’s literacy skills in English? To answer this question, we applied
GIMME to identify potentially different groups of learners. We then examined the relationship
between children’s English proficiency and their network characteristics, focusing on network
density within the identified language regions, which is thought to reflect the quality of the
language network (Jasińska et al., 2020). Second, we asked: How do bilinguals’ heritage lan-
guage skills contribute to the neural network quality of English word processing? We predicted
significant associations between children’s neural networks and behavioral profiles, and there-
fore examined the brain-behavioral associations between children’s connectivity network pat-
terns and their proficiency in English and their heritage language. Together, the goal of the
study was to inform our understanding of the effects of bilingualism and sources of heteroge-
neity in children’s emergent language networks.

MATERIALS AND METHODS

Participants
Participants were 152 children (75 girls, Mage = 7.71 years, SDage = 1.32, age range = 5.12–
10.19) recruited from southeast Michigan, USA. Participants were all typically developing

Neurobiology of Language

202

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
.

e
d
u
n
o

/

l
/

l

a
r
t
i
c
e
–
p
d

f
/

/

/

/

4
2
1
9
8
2
0
7
9
0
1
9
n
o
_
a
_
0
0
0
9
2
p
d

.

/

l

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

Neural network heterogeneity in children

without a history of developmental delays in language or literacy, deficits in hearing, or other
neurological or physical disorders. All children grew up in the United States, attended English-
only schools, and were proficient English users, as determined by standard vocabulary scores
over 85 on the Peabody Picture Vocabulary Test 5 (PPVT-5; Dunn, 2019). All three groups
were matched on age, gender, grade distribution, maternal education, and non-verbal working
memory (see Table 1). Parents and children provided appropriate informed consent or assent
and received $40 for their participation. The study was approved by the Institutional Review
Board for research with human subjects.

Participants had diverse language experiences: 35.5% were English monolinguals (N = 54),
while the remaining 64.5% were either bilingual English-Chinese (N = 48) or English-Spanish
(N = 50) speakers. According to the parental reports, the bilinguals were exposed to their
heritage language (Chinese or Spanish) from birth at home and with at least one parent

Demographics

Age

Gradea

Table 1. Demographics and English task performance by participant group

English monolingual N = 54
M(SD) or n

Spanish bilingual N = 50
M(SD) or n

Chinese bilingual N = 48
M(SD) or n

7.66 (1.32)

7.84 (1.22)

7.63 (1.44)

K

1

2

3

4

14

9

16

8

7

6

18

7

12

5

16

9

7

11

7

Working Memoryb

Maternal Educationc

7.15 (2.43)

88.9%

7.36 (2.15)

84.0%

7.76 (2.64)

95.8%

English task performance

Vocabulary

158.40 (26.84)

144.30 (28.14)

145.09 (32.95)

Phonological awareness

Morphological awareness

Word reading

Reading comprehension

Sentence reading fluency

21.42 (7.79)

25.31 (11.32)

46.75 (16.79)

26.12 (9.18)

37.55 (17.68)

23.62 (7.23)

24.42 (10.01)

48.54 (15.27)

24.64 (6.88)

34.78 (18.49)

22.63 (7.48)

24.54 (11.18)

50.47 (14.36)

27.22 (8.35)

40.71 (21.39)

p

0.921

0.051

0.443

–

0.022

0.401

0.712

0.234

0.540

0.335

fNIRS task accuracy (%)

79.6 (9.3)

77.2 (10.2)

81.1 (9.8)

0.336

a Grade distribution used a χ2 test with df = 8.

b Measured by a backward digit span task (Wechsler, 2014).

c % bachelor’s degree or above.

Neurobiology of Language

203

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
.

e
d
u
n
o

/

l
/

l

a
r
t
i
c
e
–
p
d

f
/

/

/

/

4
2
1
9
8
2
0
7
9
0
1
9
n
o
_
a
_
0
0
0
9
2
p
d

.

/

l

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

Neural network heterogeneity in children

considering themselves to be a native speaker of the language. Bilingual children were also
systematically exposed to English before or beginning at age two (i.e., used English regularly in
contexts such as daycare or preschool). Heritage language vocabulary was used to identify
children’s heritage language proficiency. Of note is that although they provide some informa-
tion about language proficiency, both standard scores of Chinese and Spanish should be inter-
preted cautiously, as the norm of the Chinese vocabulary task was based on children growing
up in Taiwan in 1988 (PPVT–Revised; Dunn & Dunn, 1998), and the Spanish norm was based
on children growing up in Mexico and Puerto Rico in 1986 (Test de Vocabulario en Imágenes
Peabody [TVIP]; Dunn et al., 1986). To account for the limitation from the norm and to capture
variations in the bilingual heritage speakers, no participants were excluded on account of low
heritage language vocabulary. Nonetheless, all Spanish bilingual participants had a Spanish
receptive vocabulary standard score above 70, and 93% of Chinese bilingual participants
passed this threshold in Chinese receptive vocabulary.

Measures and Procedure

All participants completed the full battery of behavioral and neuroimaging tests during a
single laboratory visit. Participants completed language and literacy measurements in each
of their languages including vocabulary, phonological awareness, word reading, reading
comprehension and fluency, and morphological awareness. Across languages, these tasks
were maximally matched by either using similar standardized assessments that are already
available (e.g., vocabulary across languages) or building measures that were maximally similar
across language assessments (e.g., an experimental elision task in Chinese to match the Span-
ish and English versions). In selecting language measures, we took into account the need to
make these measures maximally comparable and the fact that the measures need to capture
specific features of each language. We therefore acknowledge that the tests are maximally
comparable in capturing respective skills, but not identical across languages. All self-
developed tasks are openly available and can be found in Sun, Zhang, Marks, Karas, et al.
(2022). For the current study, data and codes can be found at https://osf.io/uv3t6/?view_only
=46569a15ebd241808a01d51f550c65dd.

Vocabulary

Vocabulary was tested with the Peabody Picture Vocabulary Test in English (PPVT-5; Dunn,
2019); in Chinese (PPVT-Revised; Dunn & Dunn, 1998); and in Spanish (TVIP; Dunn et al.,
1986). Children saw four pictures, heard a word, and selected the picture that best describes
the word.

Phonological awareness

Phonological awareness was measured with a sound elision task in which children heard a
word and were asked to omit a phonetic unit from the word (e.g., “Cat without /k/ is ___.”
[at]). The English task used the Elision subtest from the Comprehensive Test of Phonological
Processing (Wagner et al., 1999), the Spanish task used the Test of Phonological Processing in
Spanish (Francis et al., 2001), and the Chinese task was adapted from Newman et al.’s (2011)
measure with the same paradigm.

Morphological awareness

For this task, we aimed to tap into lexical morphological awareness across languages and cap-
ture morphological features of each language (i.e., compound structures in Chinese and both
compound and derivational structures in English/Spanish). In English, we used the Early

Neurobiology of Language

204

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
.

e
d
u
n
o

/

l
/

l

a
r
t
i
c
e
–
p
d

f
/

/

/

/

4
2
1
9
8
2
0
7
9
0
1
9
n
o
_
a
_
0
0
0
9
2
p
d

.

/

l

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

Neural network heterogeneity in children

Lexical Morphology Measure (Marks, Labotka, et al., 2022), which includes compound and
derivational words. Children were asked to complete a sentence with part of a given word
(e.g., “Football. Ouch! You stepped on my ____.” [foot]; “Friendly. She is my best ___.”
[friend]). A parallel task was used in Spanish (Marks, Sun, et al., 2022). In Chinese, a morpho-
logical construction measure was used (modified from Song et al., 2015). Children were asked
to create a new word with a given word, for example, “Apple trees grow apples. What trees
might grow bread? [bread trees].”

Word/character reading

Word/Character reading was measured by presenting a list of words/characters and asking
children to read them aloud. The English task was the Letter-Word Identification subtest from
Woodcock-Johnson IV (Schrank et al., 2014); the Spanish task was the Word Identification
subtest from Batería III Woodcock-Muñoz (Muñoz-Sandoval et al., 2005); and the Chinese task
was a self-developed measure (Sun, Zhang, Marks, Karas, et al., 2022).

Sentence reading fluency

Sentence reading fluency was measured using a 3-min timed task in which children read short
sentences and indicate whether each sentence is true or false (e.g., “The sky is blue” is “True”;
“The milk is black” is “False”). English and Spanish tasks used the Sentence Reading Fluency
subtest from the Woodcock-Johnson IV (Schrank et al., 2014) and Woodcock-Muñoz (Muñoz-
Sandoval et al., 2005), respectively, and the Chinese task was a self-developed measure (Sun,
Zhang, Marks, Karas, et al., 2022).

Passage reading comprehension

Passage reading comprehension was tested in English and Spanish. They both used the Pas-
sage Comprehension Woodcock-Johnson IV (Schrank et al., 2014) and Woodcock-Muñoz
(Muñoz-Sandoval et al., 2005), respectively. Passage-level reading comprehension was not
measured in Chinese because the Chinese-speaking children were generally not able to read
and comprehend passage-long texts in Chinese.

Table 1 displays children’s English task performance by bilingual group, and the three
groups were maximally matched in these tasks except for English vocabulary (Monolinguals >
Bilinguals, and the two bilingual groups did not differ). Table 2 displays all children’s task per-
formance on the behavioral tasks by language. Note that the current sample included early
exposed, simultaneous dual-language learners with relatively balanced bilingual proficiency,
and it is typical for these children to show positive associations between skills of their two
languages (Chung et al., 2019; Wagley et al., 2022).

Neuroimaging Word Processing Task

The neuroimaging word processing task assessed children’s morpho-semantic word knowl-
edge using a lexical decision task. During each task item, children heard three words, one
target word followed by two words of choice. Children were asked to select the word that
shared either a root or derivational morpheme with the target word. Example items are bed-
room, classroom, mushroom (shared root morpheme -room); disagree, dishonest, distance
(shared derivational morpheme dis-). In the control condition, one of the choice words
matched the target in its entirety (whole word match: country, country, dentist). The task
followed a block design with 12 four-trial blocks (48 items in total). During each trial, partici-
pants heard three words and were instructed to select which of the last two matches the first with

Neurobiology of Language

205

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
.

e
d
u
n
o

/

l
/

l

a
r
t
i
c
e
–
p
d

f
/

/

/

/

4
2
1
9
8
2
0
7
9
0
1
9
n
o
_
a
_
0
0
0
9
2
p
d

.

/

l

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

Neural network heterogeneity in children

Table 2.

Behavioral and neuroimaging task performance by language (Ms and SDs)

English task
Score

Chinese task

Score

r

Partial r c

Score

Spanish task
r

Partial r c

Oral Language Measures

Vocabulary

149.6 (29.9)

54.8 (29.2)

0.15

0.03

67.7 (19.4)

0.70***

0.46***

Phonological awareness

22.6 (7.5)

22.0 (9.6)

0.84***

0.79***

13.6 (6.2)

0.80***

0.79***

Morphological awareness

24.2 (10.8)

13.5 (6.3)

0.52***

0.27****

27.9 (13.3)

0.66***

0.59***

Literacy Measures

Single word reading

48.6 (15.6)

17.44 (13.8)

0.44***

0.14

42.8 (20.1)

0.67***

0.47***

Reading comprehension

26.0 (8.3)

/

/

/

19.9 (7.9)

0.69***

0.44***

Sentence reading fluency

37.2 (18.6)

11.93 (7.7)

0.54***

0.36***

26.1 (15.7)

0.76***

0.69***

Note. r = the English-Spanish or English-Chinese bivariate correlation of the respective language and literacy measure. c = partial correlation controlling for age.
*p < 0.05, **p < 0.01, ***p < 0.001. a keypress. Each trial took 7.5 s and the whole task took about 7.2 min. An example item is shown in Figure 1. All task items are available in Table S1 in the Supporting Information available at https://doi.org/10.1162.nol_a_00092. fNIRS Data Acquisition fNIRS data were collected using the TechEN-CW6 system (NIRSOptix, 2018) with 690 and 830 nm wavelengths and a 50 Hz sampling frequency. The fNIRS cap had 12 near-infrared light sources and 24 detectors that were symmetrically located on both hemispheres, yielding 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 . e d u n o / l / l a r t i c e - p d f / / / / 4 2 1 9 8 2 0 7 9 0 1 9 n o _ a _ 0 0 0 9 2 p d . / l 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 1. An example trial of the fNIRS word-processing task. For each trial, participants first hear the target word (e.g., “bedroom”) and see a white box on the top of the screen, then they hear two words of choice (e.g., “classroom,” “mushroom”) and simultaneously see a blue and a yellow box, respectively. Neurobiology of Language 206 Neural network heterogeneity in children 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 . e d u n o / l / l a r t i c e - p d f / / / / 4 2 1 9 8 2 0 7 9 0 1 9 n o _ a _ 0 0 0 9 2 p d / . l 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 fNIRS probe setup and GIMME ROI location. Each ROI is formed by a source-detector Figure 2. pair. The 10 ROIs are bilateral C1, C2, C3, and left hemisphere C4, C5, C6, C7 (orange highlights). Red squares: light sources; blue circles: light detectors. IPL: inferior parietal lobe; TTG: transverse temporal gyri. 46 source-detector data channels (23 per hemisphere; see Figure 2). The fNIRS channels aimed to capture key regions of language and reading networks, including frontal, temporal, and pari- etal regions. Of important note is that fNIRS is a surface-based neuroimaging method that may not provide the same level of precision as fMRI. Therefore, all references to anatomical loca- tions are approximations of the neural regions maximally overlayed by specific channels. For the current investigation, brain region localizations captured by the fNIRS channels were co- registered using MRI as well as surface-based registration technologies. (For more information about the channel MNI localization, see Figure S1 in the Supporting Information and Hu et al., 2020). The depth of near-infrared light penetration was ∼3 cm, thus detecting cortical activities. fNIRS data for the current project are openly available on the Deep Blue Data repository and can be found in the data manuscript (Sun, Zhang, Marks, Karas, et al., 2022). To ensure consistency in fNIRS cap placements across participants, trained experimenters follow standardized study protocols as established in fNIRS and electroencephalography fields to take head measurements and place caps. Specifically, experimenters first located partici- pants’ nasion, inion, Fpz, and left and right pre-auricular points, and took the head circumfer- ences. Next, F7, F8, T3, and T4 were anchored to their respective sources or detectors on the fNIRS cap. Experimenters then attached the fNIRS cap to participants’ scalps and inserted the optodes to their respective source or detector positions. Finally, experimenters conducted data quality control by checking the participant’s cardiac signal components and the signal-to- noise ratio among key channels of interest. Neurobiology of Language 207 Neural network heterogeneity in children Data Analysis fNIRS data preprocessing fNIRS data were analyzed with the NIRS brain AnalyzIR, a MATLAB-based toolbox (Santosa et al., 2018), as well as self-developed scripts. Data were first downsampled from 50 Hz to 2 Hz to fit the standard analysis protocols of GIMME (as recommended by Beltz & Gates, 2017, and done in Arredondo et al., 2022). Specifically, because GIMME conducts network mapping based on data temporal dynamics, data series with high frequency may exclusively yield high autoregressions, making it harder to detect connections between ROIs, which are often of primary interest (i.e., relationships between frontal and temporal regions; Beltz & Molenaar, 2015). Next, applying the modified Beer-Lambert Law, the optical density data was converted to hemoglobin concentration data. The data analysis focused on HbO signal as it contributes to about 76% of the fNIRS signal and the TechEN CW6 system obtains the HbO signal more reliably than HbR (Gagnon et al., 2012). Regions of interest We selected 10 ROIs with two steps. First, generally, ROIs should tap into key auditory word and morpho-semantic processes according to prior literature (e.g., Bulut, 2022; Enge et al., 2020; Ip et al., 2017). Thus, ROIs should include three main hubs, namely, frontal, superior temporal, and middle temporal regions. Second, specifically, ROIs should stay engaged when participants are working on the current task (for specific brain activation map, see Figure S2). The final ROIs included bilateral C1 (ventral IFG [vIFG]), bilateral C2 (middle frontal gyrus [MFG], and IFG), bilateral C3 (vIFG), left C4, and C5 (STG), and left C6 and C7 (MTG). GIMME model fitting GIMME builds person-specific connectivity networks with group-level, subgroup-level, and individual-level connections based on time-series data among a set of pre-determined ROIs (Lane et al., 2019). The connections can be contemporaneous, which depicts directed asso- ciations between ROIs at the same time points; and the connections can be lagged, which shows directed associations from a time point to its next time point within the same ROI or from one ROI to another (Beltz & Gates, 2017). For the current fNIRS data set, we focused on contemporaneous associations to better describe the cross-ROI relationships (for similar appli- cations, see Goetschius et al., 2020). fNIRS data has high autocorrelations within a channel, which often yields lagged connections within each individual ROI and these connections typ- ically do not provide much meaningful information but are important to model statistically (Smith et al., 2011, 2012). The fNIRS HbO time-series data for each participant were extracted and submitted to the GIMME algorithm in R (Lane et al., 2017; https://cran.r-project.org/web/packages/gimme). GIMME first estimates a null model and gradually adds group-level connections that would significantly improve the model fit for 75% of the sample, according to Lagrange multiplier tests (criterion supported by simulations in Gates & Molenaar, 2012; Lane et al., 2019). After all group-level connections are added, GIMME then prunes connections that may no longer meet the 75% criterion. Next, GIMME ide”tifi’s subgroups using the Walktrap community detection algorithm and adds subgroup-level connections using a 50% criterion so that iden- tification of a subgroup connection means significantly improving model fit for 50% of the subgroup, according to Lagrange multiplier tests (criterion supported by simulations in Lane et al., 2019). The last stage adds significant individual-level connections for a participant, according to Lagrange multiplier tests, until the network fits well. According to Brown Neurobiology of Language 208 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 . e d u n o / l / l a r t i c e - p d f / / / / 4 2 1 9 8 2 0 7 9 0 1 9 n o _ a _ 0 0 0 9 2 p d / . l 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 Neural network heterogeneity in children (2014), models with excellent fit should have at least two out of four fit indices meet the following criteria: standardized root mean residual (SRMR) ≤ 0.05, comparative fit index (CFI) ≥ 0.95, root mean squared error of approximation (RMSEA) ≤ 0.05, and non-normed fit index (NNFI) ≥ 0.95. Group and subgroup neural connectivity Group-level connections and subgroup-level connectivity patterns were described and com- pared by the location of the connections and connection density by subgroup. To examine how subgroups differ from each other, we further compared participants’ in-scanner task accuracy as well as their language and literacy task performance across subgroups with one-way analysis of variance (ANOVA). Person-specific neural network density For each participant, network density was calculated by the number of connections within their neural network (Dotterer et al., 2020; Goetschius et al., 2020). To investigate how participants’ English language and literacy proficiency is associated with their brain networks, we ran bivar- iate and partial correlation analyses correlating network density with task performance, includ- ing neuroimaging task accuracy and individual standardized assessments of English (i.e., vocabulary, word reading, reading comprehension, sentence reading fluency, respectively), partial correlations controlling for age. To investigate how bilingual children’s heritage lan- guage proficiency is associated with their brain networks, for each bilingual group, we further conducted separate multiple regression analyses using heritage language vocabulary and word/character reading to predict children’s brain network density, controlling for age and English proficiency. We chose these two measures as indicators of heritage oral and reading proficiency, respectively. We excluded analyses with the sentence-level fluency reading task because many children were not able to read and comprehend full sentences in their heritage language (N = 15 Spanish and N = 22 Chinese children were not able to complete the task). RESULTS The current GIMME analysis yielded well-fitting models across participants, with an average SRMR at 0.027, CFI at 0.962, RMSEA at 0.103, and NNFI at 0.940. We next report group-, subgroup-, and person-specific results in greater detail. Group-Level Neural Connections GIMME identified two group-level connections that were shared by over 75% of participants. One was located between two left frontal channels: left C1 (IFG) and left C2 (MFG/IFG). The second group-level connection was located between the two left MTG channels (left C6 and C7; see Figure 3, black connections). Subgroup Neural Connectivity Three subgroups emerged from the data driven GIMME search. Subgroups 1, 2, and 3 had approximately equivalent numbers of participants, N = 44, 51, and 47, respectively. Partici- pants from the three language groups equally fell into the three subgroups, χ2(4) = 6.91, p = 0.141. Subgroup 1 had 9 monolinguals, 23 Spanish bilinguals, and 22 Chinese bilinguals. Sub- group 2 had 19 monolinguals, 14 Spanish bilinguals, and 17 Chinese bilinguals. Subgroup 3 had 16 monolinguals, 14 Spanish bilinguals, and 18 Chinese bilinguals (see Figure 4 for a pie chart display of the subgroup composition). Neurobiology of Language 209 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 . e d u n o / l / l a r t i c e - p d f / / / / 4 2 1 9 8 2 0 7 9 0 1 9 n o _ a _ 0 0 0 9 2 p d / . l 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 Neural network heterogeneity in children Figure 3. GIMME subgroup neural networks: The brain illustration (top row) and the map illustration (bottom row). Black lines indicate group-level connections; green lines indicate subgroup-level connections. Each circle represents a channel. IFG: inferior frontal gyrus; STG: superior temporal gyrus; MTG: middle temporal gyrus; d: dorsal; v: ventral. The subgroup-level connections are shown as green connections in Figure 4. Subgroup 1 had three subgroup-level connections: within left IFG (left C3–C1); within right IFG (right C3– C1); and between the two left STG channels (C4–C5). Subgroup 2 had five subgroup-level connections: within left IFG (left C1–C3); left and right contralateral IFG (bilateral C3); within right IFG (right C3–C1); right IFG and MFG (right C1–C2); and right IFG and contralateral left STG (right C3–left C4). Subgroup 3 had eight subgroup-level connections: left and right contralateral IFG (bilateral C1); left and right contralateral IFG (bilateral C3); within left IFG (left C3–C1); within right IFG (right C1–C3); right IFG and MFG (right C1–C2); left STG and IFG (left C5–C1); left IFG and STG (left C3–C5); the two left STG channels (left C4–C5). In sum, for subgroup 1, the subgroup-level connections were exclusively within the same brain hub (i.e., within IFG or left STG); for subgroup 2, there were additional cross-lateral connections, especially among the phonological areas, such as between IFG and STG; and subgroup 3 had additional left-lateralized connections across brain hubs, such as between left IFG and STG. One-way ANOVA showed that the three subgroups differed significantly in their network density (i.e., number of connections), F(149, 2) = 138.6, p < 0.001, η2 = 0.65. Pairwise comparisons revealed that the three groups all differed from one another: subgroup 3 had the densest network compared to subgroup 2, followed by subgroup 1 (all ps < 0.001, Tukey-corrected). Figure 4. GIMME subgroup composition by language group. Neurobiology of Language 210 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 . e d u n o / l / l a r t i c e - p d f / / / / 4 2 1 9 8 2 0 7 9 0 1 9 n o _ a _ 0 0 0 9 2 p d . / l 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 Neural network heterogeneity in children Table 3. Language and reading proficiency by GIMME subgroup Age Subgroup 1 M(SD) 7.35 (1.24) Subgroup 2 M(SD) 7.66 (1.25) Subgroup 3 M(SD) 8.04 (1.39) fNIRS task accuracy (%) 76.43 (8.03) 79.00 (9.77) 81.91 (10.59) Vocabulary 139.16 (27.98) 150.86 (28.85) 156.50 (30.52) Phonological awareness 20.73 (7.29) 22.61 (7.23) 23.95 (7.82) Morphological awareness 21.58 (10.94) 25.00 (10.46) 25.50 (10.96) Single word reading 44.44 (15.91) 47.49 (15.78) 52.70 (14.32) Reading comprehension 23.56 (8.86) 25.96 (7.99) 27.84 (7.78) Sentence reading fluency 30.68 (16.06) 36.63 (19.50) 42.83 (18.00) ANOVA or Age-controlled ANCOVA p 0.032 Pairwise comparison (Tukey-applied) G3 > G1*

0.005

<0.001 0.064 0.045 0.002 0.003 <0.001 G3 > G1**

G3 > G1***

G2 > G1*

G3 > G1+

G3 > G1*

G3 > G1**

G3 > G2*

G3 > G1**

G3 > G1***

G3 > G2*

Note. The analysis of covariance (ANCOVA) tests were age controlled except for the Age test, which used an ANOVA. +p < 0.10, *p < 0.05, **p < 0.01, ***p < 0.001. To examine how GIMME subgroups may differ in the behavioral English tasks, we com- pared the English behavioral task proficiency among the three groups of participants con- trolled for age (see Table 3). Group 3 outperformed group 1 in the raw performances for all tasks except for phonological awareness (marginal insignificance, p = 0.050). Person-Specific Neural Network Density Neural network density and English proficiency Across all participants, children’s performance on all English measures, as estimated in raw scores, was significantly associated with children’s neural network density (rs = 0.21–0.32, ps < 0.011; Table 4). Notably, controlled for age, network density was still significantly Table 4. Correlation of neural network density with English language and reading proficiency English vocabulary English word reading Passage comprehension Sentence reading fluency Neuroimaging task accuracy Neural network density Bivariate r 0.25 0.21 0.23 0.32 0.28 p 0.004 0.011 0.005 <0.001 <0.001 Age-controlled r 0.08 0.01 0.04 0.16 0.14 p 0.322 0.875 0.632 0.045 0.086 211 Neurobiology of Language 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 . e d u n o / l / l a r t i c e - p d f / / / / 4 2 1 9 8 2 0 7 9 0 1 9 n o _ a _ 0 0 0 9 2 p d . / l 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 Neural network heterogeneity in children Table 5. Multiple regression predicting neural network density with Chinese/Spanish language and reading proficiency Neural network density Chinese bilingual B t p R2 0.161 Spanish bilingual B t p Model 1 Vocabulary as the predictor Age English vocabulary Heritage language vocabulary 0.67 −0.35 0.08 2.95 −1.54 0.56 0.005 0.132 0.577 Model 2 Word reading as the predictor 0.183 Age English word reading 0.33 −0.20 1.54 −0.96 0.131 0.340 Heritage language word reading 0.36 2.30 0.026 R2 0.125 0.030 −0.10 −0.51 0.08 0.43 0.34 2.20 0.09 −0.04 0.44 −0.17 0.28 1.36 0.611 0.735 0.033 0.664 0.866 0.181 associated with the score of reading fluency (r = 0.16, p = 0.045); while the associations with vocabulary, word reading, and reading comprehension did not reach significance (rs = 0.01– 0.14, ps = 0.086–0.875; Table 4). Note that due to the highly correlated nature of the behav- ioral tasks, it may not be appropriate to apply a multiple comparison correction. However, if applied, the bivariate associations will generally survive multiple comparison corrections, while age-controlled associations may not. Neural network density and heritage language proficiency For Chinese bilingual children, Chinese word reading was significantly associated with chil- dren’s neural network density, controlling for age and English reading (B = 0.36, p = 0.026), whereas Chinese vocabulary was not a significant predictor of neural network density (B = 0.08, p = 0.577; Table 5). In contrast, as for Spanish bilingual children, Spanish vocabulary significantly predicted children’s neural network density controlling for age and English vocab- ulary (B = 0.43, p = 0.033), whereas Spanish reading was not a significant predictor (B = 0.28, p = 0.181; Table 5). DISCUSSION Children’s unique language experiences lead to heterogeneous behavioral and neural profiles of language. Such individual variation makes it difficult to interpret group-level neuroimaging findings in child language, literacy, and bilingual development (Luk & Bialystok, 2013; Marian & Hayakawa, 2021). To advance our understanding of such heterogeneity, we used an inno- vative person-specific approach, GIMME, to identify variation in children’s neural networks for spoken word processing. The findings revealed that all participants, bilingual and monolingual children, formed short-distance neural connections within the left frontal and temporal regions, which are traditionally associated with word meaning retrieval and processing. Children who were older and more proficient in spoken and written English showed more long-distance connections within the broader language network and across the two hemi- spheres, suggesting that advancements in language skills are supported by more integrated neural networks (Hwang et al., 2013). Among bilinguals, those with stronger bilingual Neurobiology of Language 212 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 . e d u n o / l / l a r t i c e - p d f / / / / 4 2 1 9 8 2 0 7 9 0 1 9 n o _ a _ 0 0 0 9 2 p d / . l 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 Neural network heterogeneity in children proficiency showed greater neural network density along the key regions of language process- ing, as a neurodevelopmental index of greater efficiency in cognitive processing (Schedlbauer et al., 2014). The findings inform theoretical perspectives aiming to link children’s cognitive and brain development by contextualizing the effects of heterogeneity in language experi- ences and proficiency on their emerging neural architecture for language and literacy. Shared Effects in the Neurobiology of Word Meaning Processes Auditory word recognition builds upon the successful recognition of word sound and meaning constituents (Gwilliams, 2020; Perfetti & Hart, 2002). The present study employed a morpho- semantic word processing task that required children to dissect polysyllabic words into lexical morphemes and analyze the meanings of the morphemic units (i.e., bedroom, classroom, and mushroom). We acknowledge that here and henceforth our discussion of the observed results refers to maximal anatomical overlays of the fNIRS channels (Hu et al., 2020). The findings revealed that >75% of all participants showed common short-distance connections linking left
MTG subregions as well as IFG/MFG regions. MTG and IFG regions are commonly associated
with semantic analysis and lexical retrieval (Binder, 2017; Fiorentino & Poeppel, 2007),
whereas MFG is often associated with verbal working memory (Fegen et al., 2015; Gwilliams,
2020; Hagoort, 2019). Our findings thus support the idea that short-distance connections
within left frontal and middle temporal regions play key roles in successful word processing
by supporting morpho-semantic analyses that underlie spoken and written language develop-
ment (Arredondo et al., 2015; Ip et al., 2017; Sun et al., 2023). These shared connections have
implications for understanding the universality of language processing in children growing up
in diverse linguistic contexts.

Developmental Effects in Age and Proficiency Subgroups

GIMME identified three subgroups of participants with shared subgroup-level connections.
Subgroup 1 exhibited the simplest network with three additional short-distance connections:
one within right IFG, one within left IFG, and one within left STG regions. Subgroups 2 and 3
exhibited progressively more complex patterns with short- and long-distance connections.
They were located between the right frontal and left temporal or between the left frontal
and temporal regions. GIMME subgrouping was not related to children’s bilingual status, likely
due to the fact that all participants in the current study were proficient English language users
and attended English-only schools.

The subgrouping divisions correspond to children’s language and reading proficiency:
Controlling for age, Subgroup 1 had the least advanced English language and reading ability
and Subgroup 3 had the strongest competence. These findings suggest that language develop-
ment is supported by both short- and long-distance connectivity in a child’s brain (Ouyang
et al., 2017). Moreover, long-distance connections are likely critical in integrating different
aspects of language processes such as phonological and morpho-semantic analyses (Li
et al., 2014; Qi et al., 2019). Of special note, the left frontotemporal connection only existed
in the most proficient Subgroup 3. This left vIFG–STG connection links regions of morpho-
semantic and phonological analyses, likely reflecting the lexically abstract derivational mor-
phemes in the current task (e.g., singer, dancer, and finger; Gwilliams, 2020; Sun et al., 2023).
In sum, our findings suggest that children’s progress in word processing is supported by
improvements in how the language network nodes integrate to support different elements of
language subprocessing.

Neurobiology of Language

213

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
.

e
d
u
n
o

/

l
/

l

a
r
t
i
c
e
–
p
d

f
/

/

/

/

4
2
1
9
8
2
0
7
9
0
1
9
n
o
_
a
_
0
0
0
9
2
p
d

.

/

l

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

Neural network heterogeneity in children

Language Proficiency and Person-Specific Network Density

To understand how neural heterogeneity speaks to children’s behavioral profiles, we exam-
ined associations between neural network density and English language and reading profi-
ciency across all participants. Prior work has linked low-density levels with early-life adversity
and disease (e.g., Goetschius et al., 2020) whereas higher network density has been associated
with greater efficiency in cognitive tasks (Arredondo et al., 2022; Schedlbauer et al., 2014).
Therefore, we had expected that children with stronger language and reading competencies
should exhibit greater network density along the key regions of language processing. This pre-
diction was generally supported by the findings, especially when we looked at children’s raw
score performance, including the in-scanner task accuracy (r = 0.30, p < 0.001) as well as the behavioral measures (rs = 0.21–0.32, ps ≤ 0.011). This brain–behavior association remained significant for sentence fluency controlling for age (although it should be noted that this may not survive multiple corrections due to the highly correlated nature among the behavioral tasks). This task requires a well-coordinated concert of word decoding, sentence comprehen- sion, and cognitive monitoring skills, thus corresponding to a need for a more holistic neuro- cognitive network that the current channels have covered (Norton & Wolf, 2012). The findings for age-controlled scores for other tasks did not reach significance, likely due to the tightly interrelated nature of age and raw performance (Qi et al., 2021). Nevertheless, their validity is supported by both the sentence fluency task and the prior findings of positive associations between functional connectivity and language/reading proficiency (Finn et al., 2014; Qi et al., 2021; Skeide et al., 2016; Yu et al., 2018). 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 . e d u n o / l / l a r t i c e - p d f / / / / 4 2 1 9 8 2 0 7 9 0 1 9 n o _ a _ 0 0 0 9 2 p d . / l 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 Bilingual Proficiency and Person-Specific Network Density To identify potential bilingual effects in children’s emerging neural networks for language, we examined the role of heritage language proficiency in their network density controlling for age and English proficiency. As heritage language measures differed across the two languages, the analyses were done for the Spanish- and Chinese-speaking groups separately. The analyses revealed significant contributions of heritage language proficiency to bilinguals’ neural net- work density, but in different aspects across the two bilingual groups. In Spanish bilinguals, the network density was associated with Spanish vocabulary, whereas in Chinese bilinguals, the network density was associated with Chinese character reading. There are several possible explanations for these findings. Our English word processing functional task used in this study involves recognizing multimorphemic word units and the ability to dissect and comprehend words is critical for literacy success (Ehri, 1998; Goodwin et al., 2012); for bilingual learners, the properties of their home language may interact differ- ently with English to influence this mechanism. The Spanish language contributes to English morpho-semantic skills through a cross-linguistic transfer at points of shared morphemic units including roots and affixes (Hernández et al., 2016). Prior behavioral data has shown that bilin- guals with better Spanish vocabulary knowledge have better morphological literacy skills than English monolinguals and bilinguals who are less proficient in Spanish (Kuo et al., 2017). Our new neuroimaging findings suggest that children’s proficiency with Spanish vocabulary may facilitate their neural efficiency for processing morphologically complex English words, poten- tially via cross-linguistic transfer of shared morpho-semantic competencies. In Chinese bilinguals, network density was positively associated with Chinese reading pro- ficiency. Unlike Spanish-English bilingualism where speakers can enjoy the knowledge of cross-linguistically shared morphemic units, there are very few shared words between Chinese and English, as manifested by the null-to-small associations of vocabulary skills across the two Neurobiology of Language 214 Neural network heterogeneity in children languages (r = 0.10 according to a meta-analysis by Yang et al., 2017). Nevertheless, a critical element of Chinese literacy is that it is monosyllabic and Chinese characters reflect mor- phemes at the lexical level (McBride et al., 2022). Prior work has shown that Chinese-English bilinguals place greater reliance on morpho-semantic literacy skills and show enhanced neu- ral activations of semantic processing during morpho-semantic tasks in English, relative to English monolinguals (Dong et al., 2020; Ip et al., 2017, 2019; Ruan et al., 2018; Sun et al., 2023; Sun, Zhang, Marks, Nickerson, et al., 2022). Structural neuroimaging research has found that Chinese-English bilinguals with better reading skills in both of their languages also had thicker left AF white matter tracts linking left IFG and STG regions (Gao et al., 2022). It is therefore possible that Chinese reading proficiency contributes to children’s neural effi- ciency for morphologically complex words in English, potentially via cross-linguistic transfer of morpho-syllabic literacy skills that are shared across bilinguals’ two languages. Theoretical Contributions and Inferences Successful word recognition builds upon neurocognitive processes and integrations of word sound and meaning constituents. Therefore, neurodevelopmental frameworks pose that advancements in language faculty are supported by the emergence of networks that serve both specific and integrative language functions (Hickok & Poeppel, 2007; Werker & Hensch, 2015). Our findings advance these theoretical perspectives by demonstrating that school- age children have developed short-range neural connections that are specific to the word task at hand. More specifically, for our meaning-based task, most children demonstrated short-distance functional connectivity within the left MTG regions known for their key role in lexico-semantic processes, as well as within left IFG/ MFG regions known to support analytical and cognitive demands for lexical tasks (Hagoort, 2019). Advancing beyond these short-distance connections, older and more proficient language learners built long-distance connections linking the critical regions of language functions, reflecting more integrated neural processes (Schedlbauer et al., 2014). In other words, our findings advance theories of language, cognition, and brain development by revealing the neurodevelopmental differences in language network quality and its association with literacy during elementary school years. Language experiences differ across individuals. Bilingualism adds to the variability as chil- dren grow up with dual language experiences. Variations in bilingual experiences have long puzzled researchers who aim to identify core features of the elusive “bilingual brain” and its development (Claussenius-Kalman et al., 2021; Marian & Hayakawa, 2021). The present work leveraged this variability to better understand how individual differences contribute to bilin- gual language development and processing. Remarkably, the findings converged across two linguistically different bilingual groups: Spanish-English and Chinese-English bilingual children. Both groups showed greater network density in English in relation to their heritage language skills. The findings demonstrate that heritage language skills, even in languages as distinct as Spanish and Chinese, are related to children’s neural integration for language pro- cessing, a core characteristic of efficient language processes. Limitations Several limitations should be considered when interpreting the current results. First, the sample included a wide age range, making it somewhat difficult to dissect the impacts of developmen- tal maturity and skill proficiency. However, our analysis was able to parse out age, and the results, in general, revealed that both age and bilingual proficiency play significant roles in children’s neural network connectivity for English word processing. Future studies could Neurobiology of Language 215 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 . e d u n o / l / l a r t i c e - p d f / / / / 4 2 1 9 8 2 0 7 9 0 1 9 n o _ a _ 0 0 0 9 2 p d / . l 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 Neural network heterogeneity in children recruit children at similar developmental stages to better obviate the effects of age. It is likely that for children of the same ages, those with higher language and reading proficiency also have a higher neural density within the broad language networks. Second, although the cur- rent study was able to recruit children with heterogeneous language experiences, the sample is still homogenous in many other aspects. For example, children were mostly from middle-class families and attended schools in southeast Michigan. Future studies could look to dissect neural network variation in groups that are diverse in these aspects such as socioeconomic backgrounds. Prior resting-state research has found that adolescents with childhood adverse experiences had sparser neural networks within the salience and default mode networks (Goetschius et al., 2020). It is therefore likely that lower-income socioeconomic backgrounds are associated with network sparsity within the brain regions for language. Third, the two bilingual groups were not fully equivalent in their heritage language reading proficiency, as the Spanish bilinguals on average had higher reading skills in Spanish than the Chinese bilinguals in Chinese. This is likely due to their English-dominant educational context, making it easier to transfer English literacy to Spanish than to Chinese. However, both groups were indeed competent in reading single words/characters in their heritage language, and their spoken language environments and proficiency were maximally equivalent. Conclusion The study investigated sources of heterogeneity in children’s neural organization for spoken language skills that underlie both spoken and written language development. The findings revealed that, across participants, children’s English language proficiency was associated with their neural network characteristics, as manifested by the connectivity density within key brain regions of language processes. A more focal examination of the bilingual participants in the study further revealed that children’s dual-language proficiency was associated with their neural network characteristics, a finding that advances our understanding of the benefits of heritage language exposure and literacy instruction for children who speak a home language that is different from the society’s dominant languages. The findings thus highlight the impor- tance of understanding not only group-level but also individual effects of language experience on the neural organization for cognitive function. ACKNOWLEDGMENTS We are grateful to the families in Ann Arbor, Michigan, and the surrounding neighborhoods for their participation in our study. We also thank the research assistant team for their help with data collection. FUNDING INFORMATION Ioulia Kovelman, National Institutes of Health (https://dx.doi.org/10.13039/100000002), Award ID: R01HD092498. AUTHOR CONTRIBUTIONS Xin Sun: Conceptualization; Formal analysis; Investigation; Visualization; Writing – original draft; Writing – review & editing. Rebecca A. Marks: Conceptualization; Writing – review & editing. Rachel L. Eggleston: Investigation; Writing – review & editing. Kehui Zhang: Investi- gation; Writing – review & editing. Chi-Lin Yu: Investigation; Writing – review & editing. Nia Neurobiology of Language 216 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 . e d u n o / l / l a r t i c e - p d f / / / / 4 2 1 9 8 2 0 7 9 0 1 9 n o _ a _ 0 0 0 9 2 p d / . l 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 Neural network heterogeneity in children Nickerson: Investigation; Writing – review & editing. Valeria Caruso: Investigation; Writing – review & editing. Tai-Li Chou: Writing – review & editing. Xiao-Su Hu: Investigation; Writing – review & editing. Twila Tardif: Writing – review & editing. James R. Booth: Writing – review & editing. Adriene M. Beltz: Methodology; Writing – review & editing. Ioulia Kovelman: Conceptualization; Funding acquisition; Methodology; Resources; Writing – review & editing. DATA AVAILABILITY STATEMENT Data for the broader project are available on Deep Blue; see Sun, Zhang, Marks, Karas, et al. (2022, https://doi.org/10.1016/j.dib.2022.108048). For the current study, data and codes can be found at https://osf.io/uv3t6/?view_only=46569a15ebd241808a01d51f550c65dd. REFERENCES Arredondo, M. M., Ip, K. I., Shih Ju Hsu, L., Tardif, T., & Kovelman, I. (2015). Brain bases of morphological processing in young chil- dren. Human Brain Mapping, 36(8), 2890–2900. https://doi.org /10.1002/hbm.22815, PubMed: 25930011 Arredondo, M. M., Kovelman, I., Satterfield, T., Hu, X., Stojanov, L., & Beltz, A. M. (2022). Person-specific connectivity mapping uncovers differences of bilingual language experience on brain bases of attention in children. Brain and Language, 227, Article 105084. https://doi.org/10.1016/j.bandl.2022.105084, PubMed: 35176615 Beltz, A. M., & Gates, K. M. (2017). Network mapping with GIMME. Multivariate Behavioral Research, 52(6), 789–804. https://doi.org /10.1080/00273171.2017.1373014, PubMed: 29161187 Beltz, A. M., & Molenaar, P. C. (2015). A posteriori model valida- tion for the temporal order of directed functional connectivity maps. Frontiers in Neuroscience, 9, Article 304. https://doi.org /10.3389/fnins.2015.00304, PubMed: 26379489 Beltz, A. M., Wright, A. G., Sprague, B. N., & Molenaar, P. C. (2016). Bridging the nomothetic and idiographic approaches to the analysis of clinical data. Assessment, 23(4), 447–458. https://doi.org/10.1177/1073191116648209, PubMed: 27165092 Berken, J. A., Chai, X., Chen, J. K., Gracco, V. L., & Klein, D. (2016). Effects of early and late bilingualism on resting-state functional connectivity. Journal of Neuroscience, 36(4), 1165–1172. https://doi.org/10.1523/ JNEUROSCI.1960-15.2016, PubMed: 26818505 Bialystok, E., Craik, F. I., & Luk, G. (2012). Bilingualism: Conse- quences for mind and brain. Trends in Cognitive Sciences, 16(4), 240–250. https://doi.org/10.1016/j.tics.2012.03.001, PubMed: 22464592 Binder, J. R. (2017). Current controversies on Wernicke’s area and its role in language. Current Neurology and Neuroscience Reports, 17, 58. https://doi.org/10.1007/s11910-017-0764-8, PubMed: 28656532 Brown, T. A. (2014). Confirmatory factor analysis for applied research. Guilford Press. Bulut, T. (2022). Neural correlates of morphological processing: An activation likelihood estimation meta-analysis. Cortex, 151, 49–69. https://doi.org/10.1016/j.cortex.2022.02.010, PubMed: 35397379 Cao, F., Bitan, T., & Booth, J. R. (2008). Effective brain connectivity in children with reading difficulties during phonological process- ing. Brain and Language, 107(2), 91–101. https://doi.org/10.1016 /j.bandl.2007.12.009, PubMed: 18226833 Chung, S. C., Chen, X., & Geva, E. (2019). Deconstructing and reconstructing cross-language transfer in bilingual reading development: An interactive framework. Journal of Neurolinguis- tics, 50, 149–161. https://doi.org/10.1016/j.jneuroling.2018.01 .003 Claussenius-Kalman, H., Hernandez, A. E., & Li, P. (2021). Expertise, ecosystem, and emergentism: Dynamic developmental bilingualism. Brain and Language, 222, Article 105013. https:// doi.org/10.1016/j.bandl.2021.105013, PubMed: 34520977 Dong, Y., Tang, Y., Chow, B. W.-Y., Wang, W., & Dong, W.-Y. (2020). Contribution of vocabulary knowledge to reading com- prehension among Chinese students: A meta-analysis. Frontiers in Psychology, 11, Article 525369. https://doi.org/10.3389/fpsyg .2020.525369, PubMed: 33132948 Dotterer, H. L., Hyde, L. W., Shaw, D. S., Rodgers, E. L., Forbes, E. E., & Beltz, A. M. (2020). Connections that characterize callousness: Affective features of psychopathy are associated with personalized patterns of resting-state network connectivity. NeuroImage: Clinical, 28, Article 102402. https://doi.org/10 .1016/j.nicl.2020.102402, PubMed: 32891038 Dunn, D. M. (2019). Peabody Picture Vocabulary Test (5th ed.). Pearson. Dunn, L. M., & Dunn, L. M. (1998). The Peabody Picture Vocabu- lary Test—Revised (L. Lu & H. Liu, Trans.; Chinese ed.). Taipei: Psychology Publisher. (Original work published in 1997) Dunn, L., Padilla, E., Lugo, D., & Dunn, L. (1986). TVIP: Test Voca- bolario Imágenes Peabody. Pearson. Ehri, L. C. (1998). Grapheme-phoneme knowledge is essential for learning to read words in English. In J. L. Metsala & L. C. Ehri (Eds.), Word recognition in beginning literacy (pp. 3–40). Routledge. https://doi.org/10.4324/9781410602718-6 Enge, A., Friederici, A. D., & Skeide, M. A. (2020). A meta-analysis of fMRI studies of language comprehension in children. NeuroImage, 215, Article 116858. https://doi.org/10.1016/j.neuroimage.2020 .116858, PubMed: 32304886 Fegen, D., Buchsbaum, B. R., & D’Esposito, M. (2015). The effect of rehearsal rate and memory load on verbal working memory. NeuroImage, 105, 120–131. https://doi.org/10.1016/j.neuroimage .2014.10.034, PubMed: 25467303 Finn, E. S. (2021). Is it time to put rest to rest? Trends in Cognitive Sciences, 25(12), 1021–1032. https://doi.org/10.1016/j.tics.2021 .09.005, PubMed: 34625348 Finn, E. S., Shen, X., Holahan, J. M., Scheinost, D., Lacadie, C., Papademetris, X., Shaywitz, S. S., Shaywitz, B. A., & Constable, R. T. (2014). Disruption of functional networks in dyslexia: A whole-brain, data-driven analysis of connectivity. Biological Psy- chiatry, 76(5), 397–404. https://doi.org/10.1016/j.biopsych.2013 .08.031, PubMed: 24124929 Neurobiology of Language 217 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 . e d u n o / l / l a r t i c e - p d f / / / / 4 2 1 9 8 2 0 7 9 0 1 9 n o _ a _ 0 0 0 9 2 p d / . l 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 Neural network heterogeneity in children Fiorentino, R., & Poeppel, D. (2007). Compound words and struc- ture in the lexicon. Language and Cognitive Processes, 22(7), 953–1000. https://doi.org/10.1080/01690960701190215 Francis, D., Carlo, M., August, D., Kenyon, D., Malabonga, V., Caglarcan, S., & Louguit, M. (2001). Test of Phonological Pro- cessing in Spanish. Center for Applied Linguistics. Friederici, A. D., Brauer, J., & Lohmann, G. (2011). Maturation of the language network: From inter- to intrahemispheric connectiv- ities. PLOS ONE, 6(6), Article e20726. https://doi.org/10.1371 /journal.pone.0020726, PubMed: 21695183 Gagnon, L., Yücel, M. A., Dehaes, M., Cooper, R. J., Perdue, K. L., Selb, J., Huppert, T. J., Hoge, R. D., & Boas, D. A. (2012). Quan- tification of the cortical contribution to the NIRS signal over the motor cortex using concurrent NIRS-fMRI measurements. Neuro- Image, 59(4), 3933–3940. https://doi.org/10.1016/j.neuroimage .2011.10.054, PubMed: 22036999 Gao, Y., Meng, X., Bai, Z., Liu, X., Zhang, M., Li, H., Ding, G., Liu, L., & Booth, J. R. (2022). Left and right arcuate fasciculi are uniquely related to word reading skills in Chinese-English bilin- gual children. Neurobiology of Language, 3(1), 109–131. https:// doi.org/10.1162/nol_a_00051 García-Pentón, L., Pérez Fernández, A., Iturria-Medina, Y., Gillon- Dowens, M., & Carreiras, M. (2014). Anatomical connectivity changes in the bilingual brain. NeuroImage, 84, 495–504. https://doi.org/10.1016/j.neuroimage.2013.08.064, PubMed: 24018306 Gates, K. M., & Molenaar, P. C. (2012). Group search algorithm recovers effective connectivity maps for individuals in homoge- neous and heterogeneous samples. NeuroImage, 63(1), 310–319. https://doi.org/10.1016/j.neuroimage.2012.06.026, PubMed: 22732562 Goetschius, L. G., Hein, T. C., McLanahan, S. S., Brooks-Gunn, J., McLoyd, V. C., Dotterer, H. L., Lopez-Duran, N., Mitchell, C., Hyde, L. W., Monk, C. S., & Beltz, A. M. (2020). Association of childhood violence exposure with adolescent neural network density. JAMA Network Open, 3(9), Article e2017850. https://doi .org/10.1001/jamanetworkopen.2020.17850, PubMed: 32965498 Goodwin, A., Lipsky, M., & Ahn, S. (2012). Word detectives: Using units of meaning to support literacy. The Reading Teacher, 65(7), 461–470. https://doi.org/10.1002/TRTR.01069 Gwilliams, L. (2020). How the brain composes morphemes into meaning. Philosophical Transactions of the Royal Society B, 375(1791), Article 20190311. https://doi.org/10.1098/rstb.2019 .0311, PubMed: 31840591 Hagoort, P. (2019). The neurobiology of language beyond single-word processing. Science, 366(6461), 55–58. https://doi .org/10.1126/science.aax0289, PubMed: 31604301 Hernández, A. C., Montelongo, J. A., & Herter, R. J. (2016). Using Spanish-English cognates in children’s choices picture books to develop Latino English learners’ linguistic knowledge. The Read- ing Teacher, 70(2), 233–239. https://doi.org/10.1002/trtr.1511 Hernandez, A. E., Claussenius-Kalman, H. L., Ronderos, J., Castilla- Earls, A. P., Sun, L., Weiss, S. D., & Young, D. R. (2019). Neuro- emergentism: A framework for studying cognition and the brain. Journal of Neurolinguistics, 49, 214–223. https://doi.org/10.1016 /j.jneuroling.2017.12.010, PubMed: 30636843 Hickok, G. (2022). The dual stream model of speech and language processing. Handbook of Clinical Neurology, 185, 57–69. https:// doi.org/10.1016/ B978-0-12-823384-9.00003-7, PubMed: 35078610 Hickok, G., & Poeppel, D. (2007). The cortical organization of speech processing. Nature Reviews Neuroscience, 8(5), 393–402. https:// doi.org/10.1038/nrn2113, PubMed: 17431404 Hu, X.-S., Wagley, N., Rioboo, A. T., DaSilva, A. F., & Kovelman, I. (2020). Photogrammetry-based stereoscopic optode registration method for functional near-infrared spectroscopy. Journal of Biomedical Optics, 25(9), Article 095001. https://doi.org/10 .1117/1.JBO.25.9.095001, PubMed: 32880124 Hwang, K., Hallquist, M. N., & Luna, B. (2013). The development of hub architecture in the human functional brain network. Cere- bral Cortex, 23(10), 2380–2393. https://doi.org/10.1093/cercor /bhs227, PubMed: 22875861 Ip, K. I., Hsu, L. S.-J., Arredondo, M. M., Tardif, T., & Kovelman, I. (2017). Brain bases of morphological processing in Chinese- English bilingual children. Developmental Science, 20(5), Article e12449. https://doi.org/10.1111/desc.12449, PubMed: 27523024 Ip, K. I., Marks, R. A., Hsu, L. S.-J., Desai, N., Kuan, J. L., & Tardif, T. (2019). Morphological processing in Chinese engages left tempo- ral regions. Brain and Language, 199, Article 104696. https://doi .org/10.1016/j.bandl.2019.104696, PubMed: 31655417 Jasińska, K. K., Shuai, L., Lau, A. N. L., Frost, S., Landi, N., & Pugh, K. R. (2020). Functional connectivity in the developing language network in 4-year-old children predicts future reading ability. Developmental Science, 24(2), Article e13041. https://doi.org /10.1111/desc.13041, PubMed: 33032375 Kovelman, I., Baker, S. A., & Petitto, L. A. (2008). Age of first bilingual language exposure as a new window into bilingual reading development. Bilingualism: Language and Cognition, 11(2), 203–223. https://doi.org/10.1017/S1366728908003386, PubMed: 19823598 Kuo, L.-J., & Anderson, R. C. (2006). Morphological awareness and learning to read: A cross-language perspective. Educational Psychologist, 41(3), 161–180. https://doi.org/10.1207 /s15326985ep4103_3 Kuo, L.-J., Ramirez, G., de Marin, S., Kim, T.-J., & Unal-Gezer, M. (2017). Bilingualism and morphological awareness: A study with children from general education and Spanish-English dual lan- guage programs. Educational Psychology, 37(2), 94–111. https://doi.org/10.1080/01443410.2015.1049586 Lane, S. [T.], Gates, K. [M.], Molenaar, P., Hallquist, M., & Pike, H. (2017). GIMME: Group iterative multiple model estimation [Computer software manual]. https://cran.r-project.org/web /packages/gimme Lane, S. T., Gates, K. M., Pike, H. K., Beltz, A. M., & Wright, A. G. C. (2019). Uncovering general, shared, and unique temporal patterns in ambulatory assessment data. Psychological Methods, 24(1), 54–69. https://doi.org/10.1037/met0000192, PubMed: 30124300 Li, H., Xue, Z., Ellmore, T. M., Frye, R. E., & Wong, S. T. (2014). Network-based analysis reveals stronger local diffusion-based connectivity and different correlations with oral language skills in brains of children with high functioning autism spectrum dis- orders. Human Brain Mapping, 35(2), 396–413. https://doi.org /10.1002/hbm.22185, PubMed: 23008187 Liu, H., & Cao, F. (2016). L1 and L2 processing in the bilingual brain: A meta-analysis of neuroimaging studies. Brain and Lan- guage, 159, 60–73. https://doi.org/10.1016/j.bandl.2016.05 .013, PubMed: 27295606 Luk, G., & Bialystok, E. (2013). Bilingualism is not a categorical variable: Interaction between language proficiency and usage. Journal of Cognitive Psychology, 25(5), 605–621. https://doi.org /10.1080/20445911.2013.795574, PubMed: 24073327 Marian, V., Bartolotti, J., Rochanavibhata, S., Bradley, K., & Hernandez, A. E. (2017). Bilingual cortical control of between-and within- language competition. Scientific Reports, 7(1), Article 11763. https://doi.org/10.1038/s41598-017-12116-w, PubMed: 28924215 Neurobiology of Language 218 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 . e d u n o / l / l a r t i c e - p d f / / / / 4 2 1 9 8 2 0 7 9 0 1 9 n o _ a _ 0 0 0 9 2 p d / . l 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 Neural network heterogeneity in children Marian, V., & Hayakawa, S. (2021). Measuring bilingualism: The quest for a “bilingualism quotient.” Applied Psycholinguistics, 42(S2), 527–548. https://doi.org/10.1017/S0142716420000533, PubMed: 34054162 Marks, R. A., Labotka, D., Sun, X., Nickerson, N., Zhang, K., Eggleston, R. L., Yu, C.-L., Uchikoshi, Y., Hoeft, F., & Kovelman, I. (2022). Morphological awareness and its role in early word reading in English monolinguals, Spanish–English, and Chinese–English simultaneous bilinguals. Bilingualism: Language and Cognition, 26(2), 268–283. https://doi.org/10.1017/S1366728922000517 Marks, R. A., Sun, X., McAlister López, E., Nickerson, N., Hernandez, I., Caruso, V. C., Satterfiled, T., & Kovelman, I. (2022). Cross-linguistic differences in the associations between morphological awareness and reading in Spanish and English in young simultaneous bilinguals. International Journal of Bilin- gual Education and Bilingualism, 25(10), 3907–3923. https://doi .org/10.1080/13670050.2022.2090226 McBride, C., Pan, D. J., & Mohseni, F. (2022). Reading and writing words: A cross-linguistic perspective. Scientific Studies of Reading, 26(2), 125–138. https://doi.org/10.1080/10888438 .2021.1920595 Mohades, S. G., Struys, E., Van Schuerbeek, P., Mondt, K., Van De Craen, P., & Luypaert, R. (2012). DTI reveals structural differ- ences in white matter tracts between bilingual and monolingual children. Brain Research, 1435, 72–80. https://doi.org/10.1016/j .brainres.2011.12.005, PubMed: 22197702 Muñoz-Sandoval, A. F., Woodcock, R. W., McGrew, K. S., & Mather, N. (2005). Batería III Woodcock-Muñoz. Riverside Publishing. Newman, E. H., Tardif, T., Huang, J., & Shu, H. (2011). Phonemes matter: The role of phoneme-level awareness in emergent Chinese readers. Journal of Experimental Child Psychology, 108(2), 242–259. https://doi.org/10.1016/j.jecp.2010.09.001, PubMed: 20980019 NIRSOptix. 2018. CW6 system [Apparatus]. https://nirsoptix.com /CW6.html Norton, E. S., & Wolf, M. (2012). Rapid automatized naming (RAN) and reading fluency: Implications for understanding and treatment of reading disabilities. Annual Review of Psychology, 63(1), 427–452. https://doi.org/10.1146/annurev-psych-120710 -100431, PubMed: 21838545. Ouyang, M., Kang, H., Detre, J. A., Roberts, T. P., & Huang, H. (2017). Short-range connections in the developmental connec- tome during typical and atypical brain maturation. Neuroscience & Biobehavioral Reviews, 83, 109–122. https://doi.org/10.1016/j .neubiorev.2017.10.007, PubMed: 29024679 Perfetti, C. A., & Hart, L. (2002). The lexical quality hypothesis. In L. Verhoeven, C. Elbro, & P. Reitsma (Eds.), Precursors of func- tional literacy (pp. 189–213). John Benjamins. https://doi.org/10 .1075/swll.11.14per Price, R. B., Beltz, A. M., Woody, M. L., Cummings, L., Gilchrist, D., & Siegle, G. J. (2020). Neural connectivity subtypes predict discrete attentional-bias profiles among heterogeneous anxiety patients. Clinical Psychological Science, 8(3), 491–505. https:// doi.org/10.1177/2167702620906149, PubMed: 33758682 Qi, T., Schaadt, G., Cafiero, R., Brauer, J., Skeide, M. A., & Friederici, A. D. (2019). The emergence of long-range language network structural covariance and language abilities. NeuroImage, 191, 36–48. https://doi.org/10.1016/j.neuroimage.2019.02.014, PubMed: 30738206 Qi, T., Schaadt, G., & Friederici, A. D. (2021). Associated func- tional network development and language abilities in children. NeuroImage, 242, Article 118452. https://doi.org/10.1016/j .neuroimage.2021.118452, PubMed: 34358655 Ruan, Y., Georgiou, G. K., Song, S., Li, Y., & Shu, H. (2018). Does writing system influence the associations between phonological awareness, morphological awareness, and reading? A meta- analysis. Journal of Educational Psychology, 110(2), 180–202. https://doi.org/10.1037/edu0000216 Santosa, H., Zhai, X., Fishburn, F., & Huppert, T. (2018). The NIRS brain AnalyzIR toolbox. Algorithms, 11(5), 73. https://doi.org/10 .3390/a11050073 Schedlbauer, A. M., Copara, M. S., Watrous, A. J., & Ekstrom, A. D. (2014). Multiple interacting brain areas underlie successful spatiotemporal memory retrieval in humans. Scientific Reports, 4(1), Article 6431. https://doi.org/10.1038/srep06431, PubMed: 25234342 Schrank, F. A., McGrew, K. S., & Mather, N. (2014). Woodcock- Johnson IV Tests of Cognitive Abilities. Riverside. Skeide, M. A., Brauer, J., & Friederici, A. D. (2016). Brain functional and structural predictors of language performance. Cerebral Cortex, 26(5), 2127–2139. https://doi.org/10.1093/cercor /bhv042, PubMed: 25770126 Skeide, M. A., & Friederici, A. D. (2016). The ontogeny of the cortical language network. Nature Reviews Neuroscience, 17(5), 323–332. https://doi.org/10.1038/nrn.2016.23, PubMed: 27040907 Smith, S. M., Bandettini, P. A., Miller, K. L., Behrens, T. E. J., Friston, K. J., David, O., Liu, T., Woolrich, M. W., & Nichols, T. E. (2012). The danger of systematic bias in group-level FMRI-lag-based causality estimation. NeuroImage, 59(2), 1228–1229. https://doi .org/10.1016/j.neuroimage.2011.08.015, PubMed: 21867760 Smith, S. M., Miller, K. L., Salimi-Khorshidi, G., Webster, M., Beckmann, C. F., Nichols, T. E., Ramsey, J. D., & Woolrich, M. W. (2011). Network modelling methods for FMRI. Neuro- Image, 54(2), 875–891. https://doi.org/10.1016/j.neuroimage .2010.08.063, PubMed: 20817103 Song, S., Su, M., Kang, C., Liu, H., Zhang, Y., McBride-Chang, C., Tardif, T., Li, H., Liang, W., Zhang, Z., & Shu, H. (2015). Tracing children’s vocabulary development from preschool through the school-age years: An 8-year longitudinal study. Developmental Science, 18(1), 119–131. https://doi.org/10.1111/desc.12190, PubMed: 24962559 Su, M., Zhao, J., de Schotten, M. T., Zhou, W., Gong, G., Ramus, F., & Shu, H. (2018). Alterations in white matter pathways underly- ing phonological and morphological processing in Chinese developmental dyslexia. Developmental Cognitive Neurosci- ence, 31, 11–19. https://doi.org/10.1016/j.dcn.2018.04.002, PubMed: 29727819 Sulpizio, S., Del Maschio, N., Fedeli, D., & Abutalebi, J. (2020). Bilingual language processing: A meta-analysis of functional neuroimaging studies. Neuroscience & Biobehavioral Reviews, 108, 834–853. https://doi.org/10.1016/j.neubiorev.2019.12.014, PubMed: 31838193 Sun, X., Li, L., Ding, G., Wang, R., & Li, P. (2019). Effects of language proficiency on cognitive control: Evidence from resting-state functional connectivity. Neuropsychologia, 129, 263–275. https://doi.org/10.1016/j.neuropsychologia.2019.03 .020, PubMed: 30951741 Sun, X., Marks, R. A., Zhang, K., Yu, C.-L., Eggleston, R. L., Nickerson, N., Chou, T.-L., Hu, X.-S., Tardif, T., Satterfield, T., & Kovelman, I. (2023). Brain bases of English morphological p r o c e s s i n g : A c o m p a r i s o n b e t w e e n C h i n e s e - E n g l i s h , Spanish-English bilingual, and English monolingual children. Developmental Science, 26(1), Article e13251. https://doi.org /10.1111/desc.13251, PubMed: 35188687 Sun, X., Zhang, K., Marks, R. [A.], Karas, Z., Eggleston, R., Nickerson, N., Yu, C.-L., Wagley, N., Hu, X., Caruso, V., Chou, Neurobiology of Language 219 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 . e d u n o / l / l a r t i c e - p d f / / / / 4 2 1 9 8 2 0 7 9 0 1 9 n o _ a _ 0 0 0 9 2 p d / . l 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 Neural network heterogeneity in children T.-L., Satterfield, T., Tardif, T., & Kovelman, I. (2022). Morphological and phonological processing in English monolingual, Chinese– English bilingual, and Spanish–English bilingual children: An fNIRS neuroimaging dataset. Data in Brief, 42, Article 108048. https://doi .org/10.1016/j.dib.2022.108048, PubMed: 35313503 Sun, X., Zhang, K., Marks, R. A., Nickerson, N., Eggleston, R. L., Yu, C.-L., Chou, T.-L., Tardif, T., & Kovelman, I. (2022). What’s in a word? Cross-linguistic influences on Spanish-English and Chinese-English bilingual children’s word reading development. Child Development, 93(1), 84–100. https://doi.org/10.1111/cdev .13666, PubMed: 34570366 Thieba, C., Long, X., Dewey, D., & Lebel, C. (2019). Young children in different linguistic environments: A multimodal neuroimaging study of the inferior frontal gyrus. Brain and Cognition, 134, 71–79. https://doi.org/10.1016/j.bandc.2018.05.009, PubMed: 30007529 Wagley, N., Marks, R. A., Bedore, L. M., & Kovelman, I. (2022). Contributions of bilingual home environment and language pro- ficiency on children’s Spanish–English reading outcomes. Child Development, 93(4), 881–899. https://doi.org/10.1111/cdev .13748, PubMed: 35289947 Wagner, R. K., Torgesen, J. K., Rashotte, C. A., & Pearson, N. A. (1999). Comprehensive Test of Phonological Processing: CTOPP. Pro-ed. Wechsler, D. (2014). Wechsler Intelligence Scale for Children—Fifth Edition ( WISC-V). The Psychological Corporation. Weiss-Croft, L. J., & Baldeweg, T. (2015). Maturation of language networks in children: A systematic review of 22 years of func- tional MRI. NeuroImage, 123, 269–281. https://doi.org/10.1016 /j.neuroimage.2015.07.046, PubMed: 26213350 Werker, J. F., & Hensch, T. K. (2015). Critical periods in speech per- ception: New directions. Annual Review of Psychology, 66(1), 173–196. https://doi.org/10.1146/annurev-psych-010814 -015104, PubMed: 25251488 Xiao, Y., Friederici, A. D., Margulies, D. S., & Brauer, J. (2016). Lon- gitudinal changes in resting-state fMRI from age 5 to age 6 years covary with language development. NeuroImage, 128, 116–124. https://doi.org/10.1016/j.neuroimage.2015.12.008, PubMed: 26690809 Yang, M., Cooc, N., & Sheng, L. (2017). An investigation of cross-linguistic transfer between Chinese and English: A meta- analysis. Asian-Pacific Journal of Second and Foreign Language Education, 2(1), Article 15. https://doi.org/10.1186/s40862-017 -0036-9 Yeatman, J. D., Dougherty, R. F., Rykhlevskaia, E., Sherbondy, A. J., Deutsch, G. K., Wandell, B. A., & Ben-Shachar, M. (2011). Ana- tomical properties of the arcuate fasciculus predict phonological and reading skills in children. Journal of Cognitive Neuroscience, 23(11), 3304–3317. https://doi.org/10.1162/jocn_a_00061, PubMed: 21568636 Yu, X., Ferradal, S. L., Sliva, D. D., Dunstan, J., Carruthers, C., Sanfilippo, J., Zuk, J., Zöllei, L., Boyd, E., Gagoski, B., Ou, Y., Grant, P. E., & Gaab, N. (2021). Functional connectivity in infancy and toddlerhood predicts long-term language and preli- teracy outcomes. Cerebral Cortex, Article bhab230. https://doi .org/10.1093/cercor/bhab230, PubMed: 34347052 Yu, X., Raney, T., Perdue, M. V., Zuk, J., Ozernov-Palchik, O., Becker, B. L., Raschle, N. M., & Gaab, N. (2018). Emergence of the neural network underlying phonological processing from the prereading to the emergent reading stage: A longitudinal study. Human Brain Mapping, 39(5), 2047–2063. https://doi.org /10.1002/hbm.23985, PubMed: 29380469 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 . e d u n o / l / l a r t i c e - p d f / / / / 4 2 1 9 8 2 0 7 9 0 1 9 n o _ a _ 0 0 0 9 2 p d . / l 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 Neurobiology of Language 220

Download pdf