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Longitudinal changes in resting-state fMRI from age 5 to age 6 years covary with language development
Yaqiong Xiao a, Angela D. Friedericia, Daniel S. Margulies b, Jens Brauer a'*
a Department of Neuropsychology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig 04103, Germany
b Max Planck Research Group for Neuroanatomy & Connectivity, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig 04103, Germany
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ABSTRACT
Article history: Received 13 August 2015 Accepted 6 December 2015 Available online xxxx
Keywords: Preschool children Language development Resting-state fMRI Intrinsic connectivity Frontal-to-temporal connection
Resting-state functional magnetic resonance imaging is a powerful technique to study the whole-brain neural connectivity that underlies cognitive systems. The present study aimed to define the changes in neural connectivity in their relation to language development. Longitudinal resting-state functional data were acquired from a cohort of preschool children at age 5 and one year later, and changes in functional connectivity were correlated with language performance in sentence comprehension. For this, degree centrality, a voxel-based network measure, was used to assess age-related differences in connectivity at the whole-brain level. Increases in connectivity with age were found selectively in a cluster within the left posterior superior temporal gyrus and sulcus (STG/STS). In order to further specify the connection changes, a secondary seed-based functional connectivity analysis on this very cluster was performed. The correlations between resting-state functional connectivity (RSFC) and language performance revealed developmental effects with age and, importantly, also dependent on the advancement in sentence comprehension ability over time. In children with greater advancement in language abilities, the behavioral improvement was positively correlated with RSFC increase between left posterior STG/STS and other regions of the language network, i.e., left and right inferior frontal cortex. The age-related changes observed in this study provide evidence for alterations in the language network as language develops and demonstrates the viability of this approach for the investigation of normal and aberrant language development.
© 2015 Published by Elsevier Inc.
Introduction
Given the importance of language development during childhood, an increasing number of studies have investigated the neural basis of language acquisition. In recent years, functional magnetic resonance imaging (fMRI) has been widely used to detect the brain mechanisms underlying language processing in adults (Friederici, 2006, 2011; Kinno et al., 2008; Makuuchi et al., 2009), as well as during childhood (Balsamo et al., 2006; Brauer and Friederici, 2007; Brauer et al., 2008; Knoll et al., 2012; Perani et al., 2011; Redcay et al., 2008; Szaflarski et al., 2006a, 2006b).
Studies on language processing using fMRI in adults have consistently reported activation in the left posterior superior temporal gyrus and sulcus (STG/STS) and the left inferior frontal cortex (IFC) as crucial regions for language comprehension (for a review, see Friederici, 2011). Specifically, the left IFC has been constantly reported as being involved in processing syntactically complex sentence structures (Ben-Shachar et al., 2004; Bornkessel et al., 2005; Friederici et al., 2006b; Grewe et al., 2005; Obleser et al., 2011; Santi and Grodzinsky, 2010). Moreover,
* Corresponding author at: Max Planck Institute for Human Cognitive and Brain Sciences, Stephanstr. 1a, 04103 Leipzig, Germany. Fax: +49 341 9940 2260. E-mail address: brauer@cbs.mpg.de (J. Brauer).
enhanced activation in the left posterior STG/STS has been reported for the processing of syntactic information in syntactically complex sentences in adults (Friederici et al., 2006a; Kinno et al., 2008; Roder et al., 2002). Developmental research has reported that the superior temporal cortex is required for rapid language acquisition during the second year of life (Redcay et al., 2008). A 10-year longitudinal study reported that bilateral superior temporal cortical activation played an increasing role in narrative comprehension from young children to adolescents (Szaflarski et al., 2012). In addition, the recruitment of left superior temporal cortex was shown for both semantic and syntactic processing in children aged 5 and 6 years (Brauer and Friederici, 2007) and for syntax-semantics interaction effects in 3-4- and 6-7-year-old children (Skeide et al., 2014).
It was furthermore shown that the maturation of structural connectivity correlates with the performance on processing complex sentences (Skeide et al., 2015), and that the structural connectivity is still not adult-like around the age of 7 years when children still have problems with processing such sentences (Brauer et al., 2011). Studies exploring the functional connectivity between the language-related areas so far have mostly been conducted with adults. They indicate a functional connectivity between the IFC and the STG/STS suggesting that these brain regions are functionally connected during sentence comprehension (den Ouden et al., 2012; Makuuchi and Friederici, 2013).
http://dx.doi.org/mi 016/j.neuroimage.2015.12.008 1053-8119/© 2015 Published by Elsevier Inc.
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Spontaneous low frequency (<0.1 Hz) fluctuations (LFFs) in the human brain at rest has been observed to be related to intrinsic brain activities (Biswal et al., 1995). During the past two decades, a large number of studies have used resting-state functional MRI data to map the brain organization underlying human cognition (e.g., Dosenbach et al., 2010; Fox et al., 2005; Fransson et al., 2011).
Functional connectivity of language regions was observed in LFFs factoring out task dependent activity when seeding in the respective brain regions (Friederici et al., 2011; Lohmann et al., 2010). Data from newborns using the same analysis method reveal that such a functional connectivity is not yet present early in life when infants start to acquire language (Perani et al., 2011). These findings suggest that the analysis of LFFs can serve the investigation of language development. And indeed a number of novel findings have expanded our knowledge on the development of functional and structural connectivity in infants and young children (de Bie et al., 2012; Fransson et al., 2011, 2007; Gao et al., 2009; Lee et al., 2013; Power et al., 2010; van den Heuvel et al., 2014).
The development of the language network from childhood to adulthood was shown to be characterized by a development from inter- to intra-hemispheric connectivities (Friederici et al., 2011). So far, however, research using resting-state fMRI data to identify and explore the language related networks is still very limited (Antonenko et al., 2012; Muller and Meyer, 2014; Tomasi and Volkow, 2012; Turken and Dronkers, 2011; Xiang et al., 2010), and resting-state fMRI data to explore the development of language in children is even more sparse. Recent studies have shown that RSFC-behavior correlations are advantageous to reveal the neural basis of individual variation in cognitive performance (e.g., Hampson et al., 2006; Kelly et al., 2008; Koyama et al., 2011; Seeley et al., 2007; Stevens and Spreng, 2014; Wang et al., 2010; Zou et al., 2013). Given the interesting relation between task-dependent fMRI and seed based task-independent resting-state fMRI data on language processing in adults, we expect to elucidate the development of the functional connectivity networks related to language development by combining behavioral and resting-state fMRI data.
Here, we present an exploratory examination of developmental changes in intrinsic connectivity patterns of children from age 5 to age 6 by using a voxel-wise network measure, which allows an unbiased comparison at a voxel-wise level. This age range was chosen since at this age the structural and functional development of the brain is in full progress (Gogtay et al., 2004; Knoll et al., 2012; Skeide et al., 2014) while at the same time performance in language functions increases steadily (Guasti, 2002; Sakai, 2005; Skeide et al., 2014). Combining resting-state functional connectivity (RSFC) with behavioral data on the development of sentence comprehension carries the potential to open new perspectives on the relation between brain maturation and the ontogeny of language in children. In order to explore the developmental changes in intrinsic connectivity patterns, longitudinal resting-state fMRI data were acquired from a cohort of typically developing children aged 5 years and one year later, and subjected to degree centrality analysis. As a measure of graph theory, degree centrality is among the most fundamental and most common centrality measures, and has been widely used to identify hubs in the human brain (e.g., Buckner et al., 2009; Cole et al., 2010; Tomasi and Volkow, 2011). Degree centrality has been found to be physiologically meaningful (Liang et al., 2013; Tomasi et al., 2015, 2013) and has been applied to investigate the changes in network connectivity associated with healthy aging (Hampson et al., 2012) and cognitive functions (van den Heuvel et al., 2009). Hubs, as highly connected central nodes in a network, are thought to play pivotal roles in the coordination of information flow (Sporns et al., 2007) and may also help to minimize wiring and metabolism costs by providing a limited number of long-distance connections that integrate local networks (Bassett and Bullmore, 2006). The approach used here is similar to that shown by Buckner et al. (2009) and Zuo et al. (2012). Binary network measure of degree centrality
was computed in a voxel-wise manner and used in order to identify developmental changes in intrinsic connectivity over one year. Subsequently, the result of this analysis was used as a seed to further detect how the connections change with age and, moreover, to what extent the functional resting-state network is related to language abilities. We expected to find growing involvement of core regions of the language network with age, in particular the posterior STG/STS and IFC. The importance of these regions in functional networks supporting language functions should be reflected in a relation between the growing network architecture and language development.
Methods
Participants
Fifty-three typically developing preschool children at age 5 years (27 males; mean age 5.5 years, range 5.0 to 6.0 years) participated in the study, and longitudinal data were obtained in a one-year follow-up measurement (mean age 6.5 years, range 6.0 to 7.1 years). Prior to participation, children's parents gave written, informed consent, and children gave verbal assent for attendance. All participants were right-handed, monolingual German speakers with no history of neurological, medical, or psychological disorders. The study was approved by the Ethical Review Board of the University of Leipzig (Germany).
Behavioral testing
Sentence comprehension was assessed by the standardized German test of sentence-comprehension (Test zum Satzverstehen von Kindern (TSVK); Siegmüller et al., 2011). The test employs a picture matching task with three possible pictures in response to verbally presented sentences at varying syntactic difficulty. Participants were then instructed to listen to stories, followed by pictures, and to select the picture that best fits the story. The number of correct responses was summed (in percent) and converted to standard scores (Tvalues).
MRI scanning
All data were obtained at a 3T magnetic resonance scanner (Siemens Tim Trio, Germany) with a 12-channel head coil. During resting-state data acquisition, children were instructed to lie as still as possible, keep their eyes open and watch the visual presentation of a screensaver featuring a lava lamp. Resting-state fMRI whole-brain volumes were acquired with a T2*-weighted gradient-echo echo-planar imaging (EPI) sequence using the following parameters: TR 2000 ms, TE 30 ms, flip angle = 90°, slice thickness 3 mm, gap = 1 mm, FOV 19.2 cm, matrix = 64 x 64, 28 slices, 100 volumes. High-resolution 3-D structural images were acquired with a T1 -weighted, magnetization prepared rapid gradient echo (MPRAGE) sequence using the following parameters: TR 1480 ms, TE 3.46 ms, flip angle = 10°; slice thickness 1.5 mm, gap 0 mm; matrix 250 x 250; spatial resolution 1 x 1 x 1.5 mm.
Preprocessing
Data preprocessing was carried out using the Data Processing Assistant for Resting-State fMRI (DPARSF) (Chao-Gan and Yu-Feng, 2010, http://www.restfmri.net) which is based on Statistical Parametric Mapping (SPM8) (http://www.fil.ion.ucl.ac.uk/spm) and Resting-State fMRI Data Analysis Toolkit (REST) (Song et al., 2011, http://www. restfmri.net). Before preprocessing, the first three EPI volumes were discarded to avoid possible effects of scanner instability and allow for signal equilibration. Preprocessing steps included: i) slice timing by shifting the signal measured in each slice relative to the acquisition of the slice at the mid-point of each TR; ii) 3D motion correction using a least squares approach and a 6 parameter (rigid body) spatial
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transformation; iii) reorienting both functional and MPRAGE images and then co-registering MPRAGE image to the mean functional image of each subject; iv) MPRAGE images were segmented into gray matter, white matter (WM), and cerebrospinal fluid (CSF) tissue based on the NIH pediatric atlas (NIHPD) (Fonovetal., 2011, http://www.bic.mni. mcgill.ca/ServicesAtlases/NIHPD-obj1), using the asymmetric T1 version of the NIHPD atlas, age range 4.5-8.5 years old (prepuberty), based on 82 subjects; v) spatial normalization by using the parameters from the segmentation procedure in each subject and resampling voxel size to 3 x 3 x 3 mm3; vi) spatial smoothing with a 6-mm full-width-at-half-maximum (FWHM) Gaussian kernel; vii) nuisance regression, including principal components (PCs) extracted from subject-specific WM and CSF mask (5 PC parameters) using a component based noise correction method (CompCor) (Behzadi et al., 2007), as well as Friston 24-parameter model (6 head motion parameters, 6 head motion parameters one time point before, and the 12 corresponding squared items) (Friston et al., 1996). The CompCor procedure comprised detrending, variance (i.e., WM and CSF) normalization and PC analysis according to Behzadi et al. (2007); viii) band-pass temporal filtering (0.01-0.1 Hz). For degree centrality calculation, spatial smoothing was not included in the preprocessing but performed after Z-normalization in order to prevent artefactual local correlations between voxels (Zuo et al., 2012).
CompCor was proposed to correct for physiological noise by regressing out PCs from noise regions of interest (ROIs) (Behzadi et al., 2007). Compared with mean signal regression, where average signal were extracted from WM and CSF mask, signals captured by PCs derived from these noise ROIs can better account for voxel-specific phase differences in physiological noise due to the potential of principle component analysis to identify temporal pattern of physiological noise (Thomas et al., 2002).
Given concerns regarding a possible confounding influence of micromovements in intrinsic functional connectivity analyses (Power et al., 2012, 2014; Satterthwaite et al., 2012; Van Dijk et al., 2012), the framewise displacement (FD) of time series (Jenkinson et al., 2002) was calculated as it is preferable for its consideration of voxel-wise differences in its derivation (Yan et al., 2013a). Seven subjects with motion (mean FD) greater than mean + 2 * SD (Yan et al., 2013b) were excluded, with threshold of 0.229 mm for children at age 5 and 0.221 mm at age 6, separately. For the remaining 46 data sets, the average of mean FD at age 5 was 0.101 mm (SD = 0.04 mm, range = 0.0370.206 mm), and at age 6 was 0.092 mm (SD = 0.039 mm, range =
0.025-0.184 mm). Differences of mean FD were calculated by using paired t-test and showed no significant differences (t(45) = 1.349, p = .184). Nevertheless, the mean FD was controlled as a covariate of no interest in subsequent group-level statistical analyses to reduce any remaining potential effect of head motion.
Calculation of degree centrality maps
Degree centrality maps were computed by using the REST toolbox that employs an approach similar to that shown by Buckner et al. (2009) and Zuo et al. (2012). Specifically, for each voxel i the connectivity between the time course of this given voxel i and the time course of every other voxel within the mask of gray matter of the brain was computed. Then the correlation map of voxel i was converted to a binary map of connectivity thresholded at r = 0.25, setting all connections below the threshold to zero while setting all remaining connections to
1. The sum of all non-zero connections in this binary map was calculated to yield the degree centrality of the voxel i. This process was repeated for each voxel in the brain to produce a whole-brain map of the network degree.
The individual-level degree centrality maps were then standardized by converting to z-scores and maps were averaged across participants and compared (Buckner et al., 2009; Van Dijk et al., 2012; Zuo et al., 2012). The z-score transformation is achieved by subtracting mean
degree and dividing standard deviation of degree across all voxels as de- 267 scribed in previous studies (Buckner et al., 2009; Zuo et al., 2012). 268 Group-level degree centrality map for each age group was obtained by 269 implementing one-sample t-test. Multiple comparisons were corrected 270 at the cluster-level using Gaussian random field theory (| Z| >3.5, 271 cluster-wisep < .001, GRF corrected). 272
The threshold used to compute degree centrality in this study was 273 chosen to be consistent with previous studies (Buckner et al., 2009; 274 Hampson et al., 2012; Van Dijk et al., 2012), and different threshold se- 275 lections did not qualitatively change the results for the cortex (Buckner 276 et al., 2009; Hampson et al., 2012). For an analysis with alternative 277 thresholds, see Supplementary Fig. S1. Furthermore, the weighted ver- 278 sion of degree centrality was also computed, assuring the robustness 279 of the findings with nearly identical results as shown in Supplementary 280 Fig. S2. 281
Developmental changes in degree centrality 282
The primary analysis of this study examined intrinsic connectivity 283 differences in the longitudinal data identifying clusters that change 284 their degree centrality with development. Paired t-test was performed 285 to detect the developmental changes in voxel-wise connectivity maps 286 from age 5 to age 6 years, controlled for head motion (mean FD). 287
Seed-based connectivity changes and relation to advances in language 288 performance 289
However, the aforementioned primary analysis does not provide in- 290 formation about which connections are changing or the relation be- 291 tween the connections and language performance. To explore this, a 292 secondary seed-based analysis was implemented. The resulting clusters 293 from the primary analysis were subjected to a seed-based analysis on 294 functional connectivity. 295
RSFC analyses were performed at both measurement time points 296 using REST software. For RSFC calculation, the mean time series of the 297 seed were first computed for each participant by averaging the time se- 298 ries of all the voxels in the seed (6-mm-radius sphere), and then an in- 299 dividual level RSFC correlation map (r-map) was produced within the 300 whole brain. Next, r-maps were converted into z-maps with application 301 of Fisher's r-to-z transformation to get approximately normally distrib- 302 uted values for further statistical analysis. 303
Average functional connectivity maps for both time points (age 5 304 and age 6) were computed based on z-transformed maps to illustrate 305 the connectivity patterns of the cluster. In addition, the comparison of 306 connectivity maps between the two time points was obtained by 307 performing paired t-test, controlling for mean FD of each participant, 308 and corrected at the cluster-level using Gaussian random field theory 309 (Z > 2.3, cluster-wise p < .05, GRF corrected). 310
In order to model the relationship between changes in functional 311 connectivity and changes in behavioral performance, the absolute 312 changes of both connectivity strength and language comprehension 313 (TSVK) performance were calculated for age 6 subtracting age 5, and re- 314 sults were then entered into a model of RSFC-behavior correlation. For 315 further exploration of behavioral effects, the whole group data were di- 316 vided into two subgroups by the median of changes in TSVK perfor- 317 mance from age 5 to age 6. Participants with change value greater 318 than median were considered to show greater advancement in lan- 319 guage abilities (18 participants) whereas participants with change 320 value smaller than median were considered to show less advancement 321 in language abilities (20 participants). Subsequently, RSFC-behavioral 322 correlation was obtained for each of the two subgroups. Finally, all sta- 323 tistical r-maps were transformed to z-maps and corrected at the cluster- 324 level using Gaussian random field theory (Z > 2.3, cluster-wise p < .05, 325 GRF corrected). 326
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Results
Behavioral results
Mean accuracy for the sentence comprehension task was 67.69% (SD 11.38) at age 5 years and 78.8% (SD 8.37) at age 6 years. Performance was above chance at both time points (age 5: t(45) = 10.55, p < .001; age 6: t(45) = 23.34, p < .001), and there was a significant performance difference between the two measurement time points (t(45) = — 7.53, p < .001) (Fig. 1).
Group-level degree centrality and changes with age
Degree centrality maps indicate that hubs at age 5 and age 6 years covered regions of the default mode network (DMN), including posterior cingulate cortex/precuneus (PCC), lateral temporal cortex, lateral parietal cortex, and medial/lateral prefrontal cortices (Fig. 2) as known from adult data (Buckner et al., 2009). Interestingly, the comparison between the two measurement time points yielded one cluster centered on the left posterior STG/STS (MNI coordinates: — 45, — 51, 21; peak z: 3.95; 170 voxels) with increased connectivity at age 6 compared to age 5 years (Fig. 3).
Seed-based connectivity changes and relation to advances in language performance
In a next step, the resulting cluster from the degree centrality analysis was used as a seed in order to examine functional connectivity of this cluster. This seeding in the left posterior STG/STS revealed a number of correlated regions at both ages, including middle frontal gyrus, bilateral PCC, dorsomedial prefrontal cortex, bilateral STG/STS and angular gyrus bilaterally (Figs. 4A and B). At age 6 years, the IFC was additionally involved (Fig. 4B). Direct comparison of functional connectivity between the two measurement time points showed developmental changes in the left inferior frontal sulcus (IFS) of the IFC and left angular gyrus from age 5 to age 6 years (Fig. 4C). Individual variations in correlations between left posterior STG/STS and left IFS as well as left angular gyrus are shown in Figs. 4D and E, respectively.
In order to further evaluate behavioral relevance of these functional networks, changes in RSFC were correlated with changes in language comprehension performance from age 5 to age 6 years. Participants were allocated to two subgroups with either greater or less change in language performance based on a median split. RSFC-behavior correlation for each subgroup showed distinct patterns. Specifically, correlations in the left and right IFC were observed in children with greater
s_ IS u
*** P< .001
Fig. 1. Mean accuracy of sentence comprehension performance (TSVK) at age 5 and 6 years. Error bars represent standard error of the mean.
advancement in language abilities, whereas correlations in bilateral PCC, ventromedial prefrontal cortex and anterior cingulate cortex were observed in children with less advancement in language abilities (Figs. 5A and B; Table 1). All maps are displayed with the BrainNet Viewer (Xiang et al., 2010, http://www.nitrc.org/projects/bnv/).
Discussion
The current study investigated the neural basis of language development in longitudinal resting-state functional MRI data in a cohort of typically developing children at age 5 and age 6. Using a data-driven approach to investigate degree centrality, we found at both ages a similar pattern of hubs covering regions of the DMN. A significant cluster of stronger intrinsic connectivity at age 6 compared to age 5 years was observed in the left posterior STG/STS. The RSFC-behavior correlation revealed connections from this cluster to language-relevant regions in bilateral IFC for children with greater advancement in language abilities, whereas for children with less advancement in language abilities stronger connectivity of DMN regions was observed. These findings demonstrate the development of functional resting-state networks during a one-year period between age 5 and age 6 and its relation to concurrent development of language abilities.
Increased degree centrality in left posterior STG/STS with age
Importantly, we found increased connectivity between ages 5 to 6 years in the left posterior STG/STS. There was no other region that showed connectivity increase above threshold and there was no regions with concurrent decreased connectivity change. Accumulated evidence supports the role of the posterior STG/STS in language comprehension (for a review, see Friederici, 2011). Task-related activation in this region has been reported for processing syntactic information in word list (Humphries et al., 2005; Snijders et al., 2009), complex sentences (Cooke et al., 2002; Friederici et al., 2006a, 2009; Kinno et al., 2008; Roder et al., 2002), and combined syntactic and semantic sentential information (Friederici et al., 2010; 2003) as well as argument processing (Grewe et al., 2007, 2006). Taken together, evidence suggests this region as central component for the integration of linguistic information at different levels.
Note that the specific functional role of increased degree centrality in the left posterior STG/STS from age 5 to age 6 cannot be concluded directly from resting-state functional brain data alone. These changes can potentially be related to a variety of developmental changes in brain maturation and human development. However, based on the specific location of this increase in connectivity in the posterior STG/STS, we hypothesize that it is related to the central involvement of this region in the language network where changes in the functional network are manifested at that age when language abilities increase prominently (e.g., Guasti, 2002; Sakai, 2005). The posterior STG/STS had been shown a central part of the language network in studies with adults (Friederici, 2011; Hickok and Poeppel, 2007; Vigneau et al., 2006) and with children (Berl et al., 2010; Brauer et al., 2008; Knoll et al., 2012; Skeide et al., 2015). Therefore, a secondary analysis exploring changes in RSFC based on this region was performed to further examine which network connections terminating in this region are changing from age 5 to age 6, whether they are part of the language network, and whether there is a relation to behavioral changes in language abilities.
Frontal-to-temporal connections in children with greater advancement in language comprehension
Previous task-dependent fMRI experiments in adults and children have consistently reported enhanced activation in both left IFC and left posterior STG/STS when processing syntactically complex sentences (Kinno et al., 2008; Knoll et al., 2012; Obleser et al., 2011; Thompson et al., 2010). The left frontal-to-temporal network connection between
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Z value
Fig. 2. Voxel-wise degree centrality maps at age 5 (2A) and age 6 (2B). Red-yellow colors indicates positive connectivity, whereas blue colors indicates negative connectivity. Z value is the scale of degree centrality. Multiple comparisons were corrected at the cluster-level using Gaussian random field theory (|Z| >3.5, cluster-wise p < .001, GRF corrected). L, left hemisphere; R, right hemisphere.
426 language-relevant brain regions develops as the brain matures and is
427 still structurally immature at the age of 5 to 6 years (Brauer et al.,
428 2011). Furthermore, the common activity of left IFC and posterior
429 STG/STS in the sense of a "default language network" has been observed
430 in LFFs (Lohmann et al., 2010), which was, moreover, shown not yet
431 fully developed at age 6 years (Friederici et al., 2011). Consistent with
432 these findings, we found that RSFC between bilateral IFC (left inferior
433 frontal gyrus and right IFS) and left posterior STG/STS was positively
434 correlated with greater advancement in language comprehension, sug-
435 gesting that this long-range connection is relevant for the progress in
436 the language abilities.
437 It has been widely acknowledged that the activation of left IFC is
438 crucial for language comprehension (Friederici, 2011; Friederici et al.,
439 2006a; Makuuchi et al., 2009; Santi and Grodzinsky, 2010). Other stud-
440 ies have shown increasing BOLD responses in the left IFC as task difficul-
441 ty increases and have related this to increased working memory and
442 phonological processing demands (Binder et al., 2005; Desai et al.,
443 2006; Lehmann et al., 2006; Tregellas et al., 2006). A developmental
444 study found that children with better syntactic processing skills showed
445 more prominent activation in the left IFC compared to children with
446 poorer syntactic processing skills (Nunez et al., 2011). Particularly,
447 mounting evidence from fMRI or behavioral studies has revealed that
448 language performance is closely related with working memory
449 (e.g., Baddeley, 2003; Gathercole and Susan, 2000; Määttä et al., 2014;
Montgomery and Evans, 2009; van Daal et al., 2008). It was shown Q4 that for both, syntactic processes as well as working memory demands, 451 the IFC is recruited (Makuuchi et al., 2009). The current findings of 452 stronger functional connectivity between IFC and posterior STG/STS 453 could be helpful for syntactic comprehension in a narrow sense but 454 also in a more general sense for working memory related processes. 455
The involvement ofDMN in children with less advancement in language 456 comprehension 457
The DMN was originally defined as a set of brain areas that consis- 458 tently show task-induced deactivation in functional imaging studies 459 (Binderetal., 1999; Raichle et al., 2001; Shulman et al., 1997). Recent 460 studies have found that the DMN was widely engaged in internal men- 461
tation (e.g., self-referential processing, mentalizing, affective cognition, theory of mind, episodic retrieval, autobiographical thought, mnemonic or prospective processes) (Andrews-Hanna et al., 2010, 2014a, 2014b; Buckner and Carroll, 2007; D'Argembeau et al., 2005; Gusnard et al., 2001; Gusnard and Raichle, 2001; Johnson, 2003; Northoff et al., 2006; Whitfield-Gabrieli et al., 2011).
Mounting evidence has confirmed the opposite relationship between behavioral performance and the suppression of the DMN (Anticevic et al., 2010; Daselaar et al., 2004; Shulman et al., 2007; White et al., 2013). For instance, successful performance on cognitive 471
posterior STQ/STS
Fig. 3. Comparison of degree centrality maps between age 5 and age 6 years (3A). Red-yellow colors indicates stronger degree centrality at age 6 compared to age 5 in the left posterior STG/STS. Multiple comparisons were corrected at the cluster level using Gaussian random field theory (Z> 2.3, cluster-wise p < .05, GRF corrected). Figure B illustrates individual variation in degree centrality of left posterior STG/STS and also includes the mean values of the cluster in posterior STG/STS at age 5 and age 6 years, as well as error bars representing standard error of the mean (3B). L, left hemisphere; R, right hemisphere. STG/STS, superior temporal gyrus and sulcus.
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rior STG/STS
rior STG/STS
age 6 > age 5
IFS aneular evrus
Corr. posterior STG/STS & IFS
Corr. posterior STG/STS & angular gyrus
Fig. 4. Average functional connectivity maps seeded in the left posterior STG/STS shown for children at age 5 (4A) and age 6 (4B). Significant correlations to left inferior frontal cortex are only found for age 6 (Z = 0.3 with minimal cluster size of 60 voxels). Fig. C depicts the direct contrast between the two time points (4C), with red-yellow colors indicating stronger connections at age 6 (Z> 2.3, cluster-wise p < .05, GRF corrected). In addition, the individual variation in correlations between left posterior STG/STS and left IFS (4D), as well as between posterior STG/STS and left angular gyrus (4E) are depicted including the mean correlation coefficients at age 5 and age 6. Error bars represent standard error of the mean. L, left hemisphere; R, right hemisphere. IFS, inferior frontal sulcus.
tasks has been related to a specific recruitment of task-relevant networks while deactivating resting-state networks such as the DMN (Anticevic et al., 2012, 2010; Hampson et al., 2010; Kelly et al., 2008). Similarly, less DMN suppression was associated with less efficient stimulus processing during attention lapses (Weissman et al., 2006). These findings support the view of a direct competition between exogenous and endogenous components for attentional and executive resources, and suggest that lower involvement of the DMN activity on a trial-by-trial basis is associated with better cognitive performance, indicating that the ability of DMN suppression is functionally important (for a review, see Anticevic et al., 2012). Therefore, it might be plausible to infer that the involvement of the DMN in functional connectivity for children with less advancement in language abilities is due to their insufficient suppression of the DMN.
Limitations
It is important to note that the interpretation of the current results should be limited to resting-state fMRI context, especially for the involvement of regions within the DMN, because the data presented
here are not from a task-based fMRI experiment. Although, consistent 490 activation of PCC was found in semantic processing, and it has been 491 proposed that the involvement of PCC might have to do with the nature 492 of episodic memory and PCC probably acts as an interface between the 493 semantic retrieval and episodic encoding systems based on the fact of 494
strong connections of PCC and hippocampus (Binder et al., 2009). Moreover, a model of involvement of regions within DMN was proposed when the task itself engages the semantic system (e.g., semantic tasks) (Binder et al., 2009), but it still requires more evidence with regarding to the role of the DMN in language processing. Therefore, in future studies, it would be necessary to identify to what extent the DMN is involved in language processing and the interactions between DMN and the language specific network by using language-related fMRI data. Another limitation of the present study is the relatively short acquisition time for the resting-state fMRI data. Considering the difficulties of data acquisition from typically developing young children during waking state, a total of 100 volumes resting-state fMRI data were collected, which is relatively short for intrinsic functional connectivity analysis. But studies with comparably short acquisition of resting-state fMRI data observed stable correlation strengths with acquisition times as
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Children with greater language development
Children with less language development
Z value
Fig. 5. Correlations between changes in functional connectivity seeded in the left posterior STG/STS cluster (green circle) and changes in language comprehension performance from age 5 to age 6 in children with greater advancement in language abilities (5A) and children with less advancement in language abilities (5B). While for the former, significant correlations to the bilateral inferior frontal cortex were found, for the latter, no such correlations to other parts of the language network were observed and rather correlations to regions within the DMN exist. Multiple comparisons were corrected at the cluster level using Gaussian random field theory (Z > 2.3, cluster-wise p < .05, GRF corrected). L, left hemisphere; R, right hemisphere. IFG, inferior frontal gyrus; IFS, inferior frontal sulcus; PCC, posterior cingulate cortex/precuneus; VMPFC/ACC, ventromedial prefrontal cortex/anterior cingulate cortex.
510 brief as 5 min (Van Dijk et al., 2010). Moreover, recent studies found
511 good inter-session reliability for functional homogeneity analyses with
512 scan durations as brief as 3 min (Zuo et al., 2013) and high reliability
513 of resting-state fMRI measures available with data length of 3 min
514 (Yan et al., 2013a). Hence, the present findings can be considered reli-
515 able and valid.
Acknowledgments 531
We would like to thank Jeanine Auerswald, Kodjo Vissiennon, and 532 Riccardo Cafiero for their contributions to data acquisition. We also 533 thank Xiangyu Long and Seung-Goo Kim for their valuable comments 534 on data analysis. This research was supported by an ERC advanced 535 grant awarded to ADF (ERC-2010-AdG 20100407, NEUROSYNTAX). 536
516 Conclusion
517 Exploring the development of intrinsic brain connectivity, increases
518 in the left posterior STG/STS were identified as significant changes in the
519 degree centrality during a one-year period in typically developing chil-
520 dren between age 5 and age 6. The RSFC of left posterior STG/STS to
521 language-relevant perisylvian regions is significantly associated with
522 greater advancement in language abilities, whereas RSFC of left posteri-
523 or STG/STS to regions within DMN is significantly correlated with less
524 advancement in language abilities. These findings suggest that function-
525 al connectivity within the language network considerably develops
526 from age 5 to age 6 and becomes behaviorally relevant. The present
527 data provide evidence for alterations in functional networks with re-
528 spect to language development during the preschool age, and demon-
529 strate the viability of these methods for characterizing the brain basis
530 and ontogeny of language in children.
t1.1 Table 1
t1.2 Details of RSFC-behavior correlations in two subgroups of children with greater or less ad-t1.3 vancement in sentence comprehension over the one-year period from age 5 to 6 years.
Appendix A. Supplementary data
t1.4 Subgroup L/R Region BA PeakMNI coordinates Voxels Z value
t1.5 x y z
t1.6 Children with greater L IFG 44 -42 24 9 60 3.31
t1.7 advancement R IFS 46 51 39 30 100 3.38
t1.8 Children with less L/R PCC VMPFC, ACC 7 15 -30 9 705 4.10
t1.9 advancement L/R 32 -6 48 -9 148 4.01
tl.10 Note: L, left hemisphere; R, right hemisphere; BA, Brodmann's area. IFG, inferior frontal tl.ll gyrus; IFS, inferior frontal sulcus; PCC, posterior cingulate cortex/precuneus; VMPFC,ven-tl.12 tromedial prefrontal cortex; ACC, anterior cingulate cortex.
Supplementary data to this article can be found online at http://dx. 538 doi.org/10.1016/j.neuroimage.2015.12.008. 539
References
Andrews-Hanna, J.R, Reidler, J.S., Sepulcre, J., Poulin, R., Buckner, R.L., 2010. Functional- 541
anatomic fractionation of the brain's default network Neuron 65, 550-562. 542
Andrews-Hanna, J.R., Saxe, R., Yarkoni, T., 2014a. Contributions of episodic retrieval and 543
mentalizing to autobiographical thought: evidence from functional neuroimaging, 544
resting-state connectivity, and fMRI meta-analyses. Neurolmage 91, 324-335. 545
Andrews-Hanna, J.R, Smallwood, J., Spreng, R.N., 2014b. The default network and self- 546
generated thought: component processes, dynamic control, and clinical relevance. 547
Ann. N. Y. Acad. Sci. 1316,29-52. http://dx.doi.org/10.1111/nyas.12360. 548
Anticevic, A., Repovs, G., Shulman, G.L., Barch, D.M., 2010. When less is more: TPJ and de- 549
fault network deactivation during encoding predicts working memory performance. 550
Neurolmage 49, 2638-2648. 551
Anticevic, A., Cole, M.W., Murray, J.D., Corlett, P.R., Wang, X.J., Krystal, J.H., 2012. The role 552
of default network deactivation in cognition and disease. Trends Cogn. Sci. 16, 553
584-592. 554
Antonenko, D., Meinzer, M., Lindenberg, R., Witte, A.V., Floel, A., 2012. Grammar learning 555
in older adults is linked to white matter microstructure and functional connectivity. 556
Neurolmage 62,1667-1674. http://dx.doi.org/10.1016/j.neuroimage.2012.05.074. 557
Baddeley, A., 2003. Working memory and language: an overview. J. Commun. Disord. 36, 558
189-208. 559
Balsamo, L., Xu, B., Gaillard, W., 2006. Language lateralization and the role of the fusiform 560
gyrus in semantic processing in young children. Neurolmage 31,1306-1314. 561
Bassett, D.S., Bullmore, E., 2006. Small-world brain networks. Neuroscientist 12,512-523. 562
Behzadi, Y., Restom, K., Liau, J., Liu, T.T., 2007. A component based noise correction 563
method (CompCor) for BOLD and perfusion based fMRI. Neurolmage 37, 90-101. 564
Ben-Shachar, M., Palti, D., Grodzinsky, Y., 2004. Neural correlates of syntactic movement: 565
converging evidence from two fMRI experiments. Neurolmage 21,1320-1336. 566
Berl, M.M., Duke, E.S., Mayo, J., Rosenberger, L.R., Moore, E.N., VanMeter, J., Ratner, N.B., 567
Vaidya, C.J., Gaillard, W.D., 2010. Functional anatomy of listening and reading 568
comprehension during development. Brain Lang. 114,115-125. 569
Binder, J.R., Frost, J.A., Hammeke, T.A., Bellgowan, P., Rao, S.M., Cox, R.W., 1999. Conceptu- 570
al processing during the conscious resting state: a functional MRI study. J. Cogn. 571
Neurosci. 11, 80-93. 572
ARTICLE IN PRESS
Y. Xiao et al./ Neurolmage xxx (2015 ) xxx-xxx
Binder, J.R., Medler, D., Desai, R., Conant, L., Liebenthal, E., 2005. Some neurophysiological constraints on models of word naming. Neurolmage 27, 677-693.
Binder, J.R., Desai, R.H., Graves, W.W., Conant, L.L., 2009. Where is the semantic system? A critical review and meta-analysis of 120 functional neuroimaging studies. Cereb. Cortex 19, 2767-2796.
Biswal, B., Zerrin Yetkin, F., Haughton, V.M., Hyde, J.S., 1995. Functional connectivity in the motor cortex of resting human brain using echo-planar MRI. Magn. Reson. Med. 34, 537-541.
Bornkessel, I., Zysset, S., Friederici, A.D., von Cramon, D.Y., Schlesewsky, M., 2005. Who did what to whom? The neural basis of argument hierarchies during language comprehension. NeuroImage 26, 221 -233.
Brauer, J., Friederici, A.D., 2007. Functional neural networks of semantic and syntactic processes in the developing brain. J. Cogn. Neurosci. 19,1609-1623.
Brauer, J., Neumann, J., Friederici, A.D., 2008. Temporal dynamics of perisylvian activation during language processing in children and adults. Neurolmage 41,1484-1492.
Brauer, J., Anwander, A., Friederici, A.D., 2011. Neuroanatomical prerequisites for language functions in the maturing brain. Cereb. Cortex 21,459-466.
Buckner, R.L., Carroll, D.C., 2007. Self-projection and the brain. Trends Cogn. Sci. 11, 49-57.
Buckner, R.L., Sepulcre, J., Talukdar, T., Krienen, F.M., Liu, H., Hedden, T., Andrews-Hanna, J.R., Sperling, R.A., Johnson, K.A., 2009. Cortical hubs revealed by intrinsic functional connectivity: mapping, assessment of stability, and relation to Alzheimer's disease. J. Neurosci. 29,1860-1873.
Chao-Gan, Y., Yu-Feng, Z., 2010. DPARSF: a MATLAB toolbox for "pipeline" data analysis of resting-state fMRI. Front. Syst. Neurosci. 4.
Cole, M.W., Pathak, S., Schneider, W., 2010. Identifying the brain's most globally connected regions. Neurolmage 49,3132-3148.
Cooke, A., Zurif, E.B., DeVita, C., Alsop, D., Koenig, P., Detre,J., Gee, J., Pinango, M., Balogh, J., Grossman, M., 2002. Neural basis for sentence comprehension: grammatical and short-term memory components. Hum. Brain Mapp. 15, 80-94.
D'Argembeau, A., Collette, F., Van der Linden, M., Laureys, S., Del Fiore, G., Degueldre, C., Luxen, A., Salmon, E., 2005. Self-referential reflective activity and its relationship with rest: a PET study. Neurolmage 25, 616-624.
Daselaar, S., Prince, S., Cabeza, R., 2004. When less means more: deactivations during encoding that predict subsequent memory. Neurolmage 23, 921-927.
de Bie, H.M., Boersma, M., Adriaanse, S., Veltman, D.J., Wink, A.M., Roosendaal, S.D., Barkhof, F., Stam, C.J., Oostrom, K.J., Delemarre-van de Waal, H.A., Sanz-Arigita, E.J., 2012. Resting-state networks in awake five- to eight-year old children. Hum. Brain Mapp. 33,1189-1201.
den Ouden, D.-B., Saur, D., Mader, W., Schelter, B., Lukic, S., Wali, E., Timmer, J., Thompson, C.K., 2012. Network modulation during complex syntactic processing. Neurolmage 59, 815-823.
Desai, R., Conant, L.L., Waldron, E., Binder, J.R., 2006. FMRI of past tense processing: the effects of phonological complexity and task difficulty. J. Cogn. Neurosci. 18, 278-297.
Dosenbach, N.U., Nardos, B., Cohen, A.L., Fair, D.A., Power, J.D., Church, JA., Nelson, S.M., Wig, G.S., Vogel, A.C., Lessov-Schlaggar, C.N., Barnes, K.A., Dubis, J.W., Feczko, E., Coalson, R.S., Pruett Jr., J.R., Barch, D.M., Petersen, S.E., Schlaggar, B.L., 2010. Prediction of individual brain maturity using fMRI. Science 329,1358-1361.
Fonov, V., Evans, A.C., Botteron, K., Almli, C.R., McKinstry, R.C., Collins, D.L., 2011. Unbiased average age-appropriate atlases for pediatric studies. NeuroImage 54, 313-327.
Fox, M.D., Snyder, A.Z., Vincent, J.L., Corbetta, M., Van Essen, D.C., Raichle, M.E., 2005. The human brain is intrinsically organized into dynamic, anticorrelated functional networks. Proc. Natl. Acad. Sci. U. S. A. 102, 9673-9678.
Fransson, P., Skiöld, B., Horsch, S., Nordell, A., Blennow, M., Lagercrantz, H., Aden, U., 2007. Resting-state networks in the infant brain. Proc. Natl. Acad. Sci. U. S. A. 104, 15531-15536.
Fransson, P., Aden, U., Blennow, M., Lagercrantz, H., 2011. The functional architecture of the infant brain as revealed by resting-state fMRI. Cereb. Cortex 21, 145-154.
Friederici, A.D., 2006. The neural basis of language development and its impairment. Neuron 52, 941-952.
Friederici, A.D., 2011. The brain basis of language processing: from structure to function. Physiol. Rev. 91,1357-1392.
Friederici, A.D., Rueschemeyer, S.-A., Hahne, A., Fiebach, C.J., 2003. The role of left inferior frontal and superior temporal cortex in sentence comprehension: localizing syntactic and semantic processes. Cereb. Cortex 13,170-177.
Friederici, A.D., Bahlmann, J., Heim, S., Schubotz, R.I., Anwander, A., 2006a. The brain differentiates human and non-human grammars: functional localization and structural connectivity. Proc. Natl. Acad. Sci. U. S. A. 103, 2458-2463.
Friederici, A.D., Fiebach, C.J., Schlesewsky, M., Bornkessel, I.D., Von Cramon, D.Y., 2006b. Processing linguistic complexity and grammaticality in the left frontal cortex. Cereb. Cortex 16,1709-1717.
Friederici, A.D., Makuuchi, M., Bahlmann, J., 2009. The role of the posterior superior temporal cortex in sentence comprehension. Neuroreport 20, 563-568.
Friederici, A.D., Kotz, S.A., Scott, S.K., Obleser, J., 2010. Disentangling syntax and intelligibility in auditory language comprehension. Hum. Brain Mapp. 31,448-457.
Friederici, A.D., Brauer, J., Lohmann, G., 2011. Maturation of the language network: from inter-to intrahemispheric connectivities. PLoS One 6, e20726.
Friston, K.J., Williams, S., Howard, R., Frackowiak R.S., Turner, R., 1996. Movement-related effects in fMRI time-series. Magn. Reson. Med. 35, 346-355.
Gao, W., Zhu, H., Giovanello, K.S., Smith, J.K., Shen, D., Gilmore, J.H., Lin, W., 2009. Evidence on the emergence of the brain's default network from 2-week-old to 2-year-old healthy pediatric subjects. Proc. Natl. Acad. Sci. U. S. A. 106,6790-6795.
Gathercole, A.-M.A., Susan, E., 2000. Limitations in working memory: implications for language development. Int. J. Lang. Commun. Disord. 35,95-116.
Gogtay, N., Giedd, J.N., Lusk L., Hayashi, K.M., Greenstein, D., Vaituzis, A.C., Nugent, T.F., Herman, D.H., Clasen, L.S., Toga, A.W., 2004. Dynamic mapping of human cortical
development during childhood through early adulthood. Proc. Natl. Acad. Sci. U. S. 659 A. 101,8174-8179. 660
Grewe, T., Bornkessel, I., Zysset, S., Wiese, R., von Cramon, D.Y., Schlesewsky, M., 2005. The 661 emergence of the unmarked: a new perspective on the language-specific function of 662 Broca's area. Hum. Brain Mapp. 26,178-190. 663
Grewe, T., Bornkessel, I., Zysset, S., Wiese, R., von Cramon, D.Y., Schlesewsky, M., 2006. 664 Linguistic prominence and Broca's area: the influence of animacy as a linearization 665 principle. NeuroImage 32,1395-1402. 666
Grewe, T., Bornkessel-Schlesewsky, I., Zysset, S., Wiese, R., von Cramon, D.Y., Schlesewsky, 667 M., 2007. The role of the posterior superior temporal sulcus in the processing of 668 unmarked transitivity. NeuroImage 35, 343-352. 669
Guasti, M.T., 2002. Language acquisition: The growth of grammar. MIT Press, Camrbidge, 670 MA. 671
Gusnard, D.A., Raichle, M.E., 2001. Searching for a baseline: functional imaging and the 672 resting human brain. Nat. Rev. Neurosci. 2, 685-694. 673
Gusnard, D.A., Akbudak, E., Shulman, G.L., Raichle, M.E., 2001. Medial prefrontal cortex 674 and self-referential mental activity: relation to a default mode of brain function. 675 Proc. Natl. Acad. Sci. U. S. A. 98,4259-4264. 676
Hampson, M., Driesen, N.R., Skudlarski, P., Gore, J.C., Constable, R.T., 2006. Brain connec- 677 tivity related to working memory performance. J. Neurosci. 26,13338-13343. 678 Hampson, M., Driesen, N., Roth, J.K., Gore, J.C., Constable, R.T., 2010. Functional connectiv- 679 ity between task-positive and task-negative brain areas and its relation to working 680 memory performance. Magn. Reson. Imaging 28,1051-1057. 681
Hampson, M., Tokoglu, F., Shen, X., Scheinost, D., Papademetris, X., Constable, R.T., 2012. 682 Intrinsic brain connectivity related to age in young and middle aged adults. PLoS 683 One 7, e44067. 684
Hickok G., Poeppel, D., 2007. The cortical organization of speech processing. Nat. Rev. 685 Neurosci. 8, 393-402. 686
Humphries, C., Love, T., Swinney, D., Hickok, G., 2005. Response of anterior temporal 687 cortex to syntactic and prosodic manipulations during sentence processing. Hum. 688 Brain Mapp. 26,128-138. 689
Jenkinson, M., Bannister, P., Brady, M., Smith, S., 2002. Improved optimization for the ro- 690 bust and accurate linear registration and motion correction of brain images. 691 NeuroImage 17,825-841. 692
Johnson, S.P., 2003. The nature of cognitive development. Trends Cogn. Sci. 7,102-104. 693 Kelly, AC., Uddin, L.Q., Biswal, B.B., Castellanos, F.X., Milham, M.P., 2008. Competition be- 694 tween functional brain networks mediates behavioral variability. NeuroImage 39, 695 527-537. 696
Kinno, R., Kawamura, M., Shioda, S., Sakai, K.L., 2008. Neural correlates of noncanonical 697 syntactic processing revealed by a picture-sentence matching task. Hum. Brain 698 Mapp. 29,1015-1027. 699
Knoll, L.J., Obleser, J., Schipke, C.S., Friederici, A.D., Brauer, J., 2012. Left prefrontal cortex 700 activation during sentence comprehension covaries with grammatical knowledge 701 in children. NeuroImage 62, 207-216. 702
Koyama, M.S., Di Martino, A., Zuo, X.-N., Kelly, C., Mennes, M., Jutagir, D.R., Castellanos, 703 F.X., Milham, M.P., 2011. Resting-state functional connectivity indexes reading 704 competence in children and adults. J. Neurosci. 31, 8617-8624. 705
Lee, W., Morgan, B.R., Shroff, M.M., Sled, J.G., Taylor, M.J., 2013. The development of re- 706 gional functional connectivity in preterm infants into early childhood. Neuroradiolo- 707 gy 55,105-111. 708
Lehmann, C., Vannini, P., Wahlund, L.-O., Almkvist, O., Dierks, T., 2006. Increased sensitivity 709 in mapping task demand in visuospatial processing using reaction-time-dependent he- 710 modynamic response predictors in rapid event-related fMRI. NeuroImage 31,505-512. 711 Liang, X., Zou, Q., He, Y., Yang, Y., 2013. Coupling of functional connectivity and regional 712 cerebral blood flow reveals a physiological basis for network hubs of the human 713 brain. Proc. Natl. Acad. Sci. U. S. A. 110,1929-1934. 714
Lohmann, G., Hoehl, S., Brauer,J., Danielmeier, C., Bornkessel-Schlesewsky, I., Bahlmann, J., 715 Turner, R., Friederici, A., 2010. Setting the frame: the human brain activates a basic 716 low-frequency network for language processing. Cereb. Cortex 20,1286-1292. 717 Maatta, S., Laakso, M.-L., Tolvanen, A., Ahonen, T., Aro, T., 2014. Children with differing de- 718 velopmental trajectories of prelinguistic communication skills: language and working 719 memory at age 5. J. Speech Lang. Hear. Res. 57,1026-1039. 720
Makuuchi, M., Friederici, A.D., 2013. Hierarchical functional connectivity between the 721 core language system and the working memory system. Cortex 49, 2416-2423. 722 Makuuchi, M., Bahlmann, J., Anwander, A., Friederici, A.D., 2009. Segregating the core 723 computational faculty of human language from working memory. Proc. Natl. Acad. 724 Sci. U. S. A. 106, 8362-8367. 725
Montgomery, J.W., Evans, J.L., 2009. Complex sentence comprehension and working 726 memory in children with specific language impairment. J. Speech Lang. Hear. Res. 727 52, 269-288. 728
Muller, A.M., Meyer, M., 2014. Language in the brain at rest: new insights from resting 729 state data and graph theoretical analysis. Front. Hum. Neurosci. 8. http://dx.doi.org/ 730 10.3389/fnhum.2014.00228. 731
Northoff, G., Heinzel, A., de Greck, M., Bermpohl, F., Dobrowolny, H., Panksepp, J., 2006. 732 Self-referential processing in our brain—a meta-analysis of imaging studies on the 733 self. NeuroImage 31,440-457. 734
Nunez, S.C., Dapretto, M., Katzir, T., Starr, A., Bramen, J., Kan, E., Bookheimer, S., Sowell, 735 E.R., 2011. fMRI of syntactic processing in typically developing children: structural 736 correlates in the inferior frontal gyrus. Dev. Cogn. Neurosci. 1, 313-323. 737
Obleser, J., Meyer, L., Friederici, A.D., 2011. Dynamic assignment of neural resources in au- 738 ditory comprehension of complex sentences. NeuroImage 56, 2310-2320. 739
Perani, D., Saccuman, M.C., Scifo, P., Anwander, A., Spada, D., Baldoli, C., Poloniato, A., 740 Lohmann, G., Friederici, A.D., 2011. Neural language networks at birth. Proc. Natl. 741 Acad. Sci. U. S. A. 108,16056-16061. 742
Power, J.D., Fair, D.A., Schlaggar, B.L., Petersen, S.E., 2010. The development of human 743 functional brain networks. Neuron 67, 735-748. 744
ARTICLE IN PRESS
Y. Xiao et al. / Neurolmage xxx (2015) xxx-xxx
Power, J.D., Barnes, K.A., Snyder, A.Z., Schlaggar, B.L., Petersen, S.E., 2012. Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion. Neurolmage 59, 2142-2154.
Power, J.D., Mitra, A., Laumann, T.O., Snyder, A.Z., Schlaggar, B.L., Petersen, S.E., 2014. Methods to detect, characterize, and remove motion artifact in resting state fMRI. Neurolmage 84,320-341.
Raichle, M.E., MacLeod, A.M., Snyder, A.Z., Powers, W.J., Gusnard, D.A., Shulman, G.L., 2001. A default mode of brain function. Proc. Natl. Acad. Sci. U. S. A. 98,676-682.
Redcay, E., Haist, F., Courchesne, E., 2008. Functional neuroimaging of speech perception during a pivotal period in language acquisition. Dev. Sci. 11, 237-252.
Röder, B., Stock, O., Neville, H., Bien, S., Rösler, F., 2002. Brain activation modulated by the comprehension of normal and pseudo-word sentences of different processing demands: a functional magnetic resonance imaging study. Neurolmage 15,1003-1014.
Sakai, K.L., 2005. Language acquisition and brain development. Science 310,815-819.
Santi, A., Grodzinsky, Y., 2010. fMRI adaptation dissociates syntactic complexity dimensions. Neurolmage 51,1285-1293.
Satterthwaite, T.D., Wolf, D.H., Loughead, J., Ruparel, K., Elliott, M.A., Hakonarson, H., Gur, R.C., Gur, R.E., 2012. Impact of in-scanner head motion on multiple measures of functional connectivity: relevance for studies of neurodevelopment in youth. NeuroImage 60, 623-632.
Seeley, W.W., Menon, V., Schatzberg, A.F., Keller, J., Glover, G.H., Kenna, H., Reiss, A.L., Greicius, M.D., 2007. Dissociable intrinsic connectivity networks for salience processing and executive control. J. Neurosci. 27, 2349-2356.
Shulman, G.L., Fiez, J.A., Corbetta, M., Buckner, R.L., Miezin, F.M., Raichle, M.E., Petersen, S.E., 1997. Common blood flow changes across visual tasks: II. Decreases in cerebral cortex. J. Cogn. Neurosci. 9, 648-663.
Shulman, G.L., Astafiev, S.V., McAvoy, M.P., d'Avossa, G., Corbetta, M., 2007. Right TPJ deactivation during visual search: functional significance and support for a filter hypothesis. Cereb. Cortex 17,2625-2633.
Siegmüller, J., Kauschke, C., van Minnen, S., Bittner, D., 2011. Test zum Satzverstehnen von Kindern — Eine profilorientierte Diagnostik der Syntax. Elsevier GmbH, München.
Skeide, M.A., Brauer, J., Friederici, A.D., 2014. Syntax gradually segregates from semantics in the developing brain. NeuroImage 100,106-111.
Skeide, M.A., Brauer, J., Friederici, A.D., 2015. Brain functional and structural predictors of language performance. Cereb. Cortex. http://dx.doi.org/10.1093/cercor/bhv042.
Snijders, T.M., Vosse, T., Kempen, G., Van Berkum, J.J., Petersson, K.M., Hagoort, P., 2009. Retrieval and unification of syntactic structure in sentence comprehension: an fMRI study using word-category ambiguity. Cereb. Cortex 19,1493-1503.
Song, X.W., Dong, Z.Y., Long, X.Y., Li, S.F., Zuo, X.N., Zhu, C.Z., He, Y., Yan, C.G., Zang, Y.F., 2011. REST: a toolkit for resting-state functional magnetic resonance imaging data processing. PLoS One 6, e25031. http://dx.doi.org/10.1371/journal.pone.0025031.
Sporns, O., Honey, C.J., Kötter, R., 2007. Identification and classification of hubs in brain networks. PLoS One 2, e1049.
Stevens, W.D., Spreng, R.N., 2014. Resting-state functional connectivity MRI reveals active processes central to cognition. Wiley Interdiscip. Rev. Cogn. Sci. 5, 233-245.
Szaflarski, J.P., Holland, S.K., Schmithorst, V.J., Byars, A.W., 2006a. fMRI study of language lateralization in children and adults. Hum. Brain Mapp. 27, 202-212.
Szaflarski, J.P., Schmithorst, V.J., Altaye, M., Byars, A.W., Ret, J., Plante, E., Holland, S.K., 2006b. A longitudinal functional magnetic resonance imaging study of language development in children 5 to 11 years old. Ann. Neurol. 59, 796-807.
Szaflarski, J.P., Altaye, M., Rajagopal, A., Eaton, K., Meng, X., Plante, E., Holland, S.K., 2012. A 10-year longitudinal fMRI study of narrative comprehension in children and adolescents. NeuroImage 63,1188-1195.
Thomas, C.G., Harshman, R.A., Menon, R.S., 2002. Noise reduction in BOLD-based fMRI using component analysis. NeuroImage 17,1521-1537.
Thompson, C.K., den Ouden, D.-B., Bonakdarpour, B., Garibaldi, K., Parrish, T.B., 2010. Neural plasticity and treatment-induced recovery of sentence processing in agrammatism. Neuropsychologia 48,3211-3227.
Tomasi, D., Volkow, N.D., 2011. Functional connectivity hubs in the human brain. NeuroImage 57,908-917.
Tomasi, D., Volkow, N.D., 2012. Resting functional connectivity of language networks: 805 characterization and reproducibility. Mol. Psychiatry 17, 841-854. 806
Tomasi, D., Wang, G.-J., Volkow, N.D., 2013. Energetic cost of brain functional connectivity. 807 Proc. Natl. Acad. Sci. U. S. A. 110,13642-13647. 808
Tomasi, D., Shokri-Kojori, E., Volkow, N.D., 2015. High-Resolution Functional Connectivity 809 Density: Hub Locations, Sensitivity, Specificity, Reproducibility, and Reliability. Cereb. 810 Cortex. http://dx.doi.org/10.1093/cercor/bhv171. 811
Tregellas, J.R., Davalos, D.B., Rojas, D.C., 2006. Effect of task difficulty on the functional 812 anatomy of temporal processing. Neurolmage 32,307-315. 813
Turken, U., Dronkers, N.F., 2011. The neural architecture of the language comprehension 814 network: converging evidence from lesion and connectivity analyses. Front. Syst. 815 Neurosci. 5. http://dx.doi.org/10.3389/fnsys.2011.00001. 816
van Daal, J., Verhoeven, L., van Leeuwe, J., van Balkom, H., 2008. Working memory limita- 817 tions in children with severe language impairment. J. Commun. Disord. 41, 85-107. 818 van den Heuvel, M.P., Stam, C.J., Kahn, R.S., Pol, H.E.H., 2009. Efficiency of functional brain 819 networks and intellectual performance. J. Neurosci. 29, 7619-7624. 820
van den Heuvel, M.P., Kersbergen, K.J., de Reus, M.A., Keunen, K., Kahn, R.S., Groenendaal, 821 F., de Vries, L.S., Benders, M.J., 2014. The neonatal connectome during preterm brain 822 development. Cereb. Cortex. http://dx.doi.org/10.1093/cercor/bhu095. 823
Van Dijk K.R., Hedden, T., Venkataraman, A., Evans, K.C., Lazar, S.W., Buckner, R.L., 2010. 824 Intrinsic functional connectivity as a tool for human connectomics: theory, proper- 825 ties, and optimization. J. Neurophysiol. 103, 297-321. 826
Van Dijk, K.R., Sabuncu, M.R., Buckner, R.L., 2012. The influence of head motion on intrin- 827 sic functional connectivity MRI. NeuroImage 59, 431 -438. 828
Vigneau, M., Beaucousin, V., Herve, P.-Y., Duffau, H., Crivello, F., Houde, O., Mazoyer, B., 829 Tzourio-Mazoyer, N., 2006. Meta-analyzing left hemisphere language areas: 830 phonology, semantics, and sentence processing. Neurolmage 30,1414-1432. 831
Wang, L., Negreira, A., LaViolette, P., Bakkour, A., Sperling, R.A., Dickerson, B.C., 2010. 832 Intrinsic interhemispheric hippocampal functional connectivity predicts individual 833 differences in memory performance ability. Hippocampus 20, 345-351. 834
Weissman, D., Roberts, K., Visscher, K., Woldorff, M., 2006. The neural bases of momentary 835 lapses in attention. Nat. Neurosci. 9,971 -978. 836
White, T.P., Jansen, M., Doege, K., Mullinger, K.J., Park, S.B., Liddle, E.B., Gowland, P.A., 837 Francis, S.T., Bowtell, R., Liddle, P., 2013. Theta power during encoding predicts 838 subsequent-memory performance and default mode network deactivation. Hum. 839 Brain Mapp. 34, 2929-2943. 840
Whitfield-Gabrieli, S., Moran, J.M., Nieto-Castanon, A., Triantafyllou, C., Saxe, R., Gabrieli, 841 J.D., 2011. Associations and dissociations between default and self-reference 842 networks in the human brain. NeuroImage 55, 225-232. 843
Xiang, H.-D., Fonteijn, H.M., Norris, D.G., Hagoort, P., 2010. Topographical functional con- 844 nectivity pattern in the perisylvian language networks. Cereb. Cortex 20, 549-560. 845 Yan, C.G., Cheung, B., Kelly, C., Colcombe, S., Craddock R.C., Di Martino, A., Li, Q., Zuo, X.N., 846 Castellanos, F.X., Milham, M.P., 2013a. A comprehensive assessment of regional vari- 847 ation in the impact of head micromovements on functional connectomics. 848 NeuroImage 76, 183-201. 849
Yan, C.G., Craddock R.C., Zuo, X.N., Zang, Y.F., Milham, M.P., 2013b. Standardizing the 850 intrinsic brain: towards robust measurement of inter-individual variation in 1000 851 functional connectomes. NeuroImage 80, 246-262. 852
Zou, Q., Ross, T.J., Gu, H., Geng, X., Zuo, X.N., Hong, L.E., Gao, J.H., Stein, E.A., Zang, Y.F., 853 Yang, Y., 2013. Intrinsic resting-state activity predicts working memory brain 854 activation and behavioral performance. Hum. Brain Mapp. 34, 3204-3215. 855
Zuo, X.N., Ehmke, R., Mennes, M., Imperati, D., Castellanos, F.X., Sporns, O., Milham, M.P., 856 2012. Network centrality in the human functional connectome. Cereb. Cortex 22, 857 1862-1875. 858
Zuo, X.N., Xu, T., Jiang, L., Yang, Z., Cao, X.Y., He, Y., Zang, Y.F., Castellanos, F.X., Milham, 859 M.P., 2013. Toward reliable characterization of functional homogeneity in the 860 human brain: preprocessing, scan duration, imaging resolution and computational 861 space. NeuroImage 65, 374-386. 862
