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Abstract

Functional interactions among regions are largely shaped by the underlying structural architecture of the human brain. However, how structural networks develop during the prenatal period to support functional activity and cognitive development later in life remains poorly understood. In this study, we employed multi-modal MRI data from 40 neonates, aged 31 to 42 postmenstrual weeks, to characterize the maturation of structure-function coupling during the third trimester. We further examined whether this coupling could predict neurocognitive outcomes at two years of age. During this period, higher structure-function coupling was observed in the frontal and occipital lobes, whereas lower coupling was evident in the temporal lobe and limbic system. As development progressed, coupling in the frontal and parietal regions increased significantly, suggesting enhanced structural support for functional networks in these high-order cognitive related areas. Support vector regression analysis further revealed that structure-function coupling at birth significantly predicted the cognitive and language scores at two years of age. Together, these findings highlight the critical role of early structural organization in shaping subsequent cognitive development.

Keywords

Brain connectomics multi-modal MRI brain structural and functional relationships cognitive development prediction neonate

Introduction

The prenatal period represents a critical window for human brain development, during which rapid cellular and molecular processes establish the neural circuits of cerebral cortex that support future sensory, motor and higher-order cognitive functions (Dehaene-Lambertz & Spelke, 2015; Kostovic et al., 2019; Tau & Peterson, 2010). Recent advances in multimodal magnetic resonance imaging (MRI) and connectome-based framework have enabled the non-invasive mapping of brain connectivity across development, providing unprecedented opportunities to investigate brain maturation from a network perspective (Cao et al., 2017b; Huang et al., 2015). The developmental sequence of the structural and functional connectomes are generally aligned, with primary sensory regions maturing earlier and higher-order association areas later (Cao et al., 2017b; Zhao et al., 2019b). Notably, structural maturation typically precedes functional maturation, potentially providing the anatomical foundation for subsequent functional specialization. For example, diffusion MRI studies have revealed that the structural hubs supporting higher cognitive process such as dorsal medial frontal, parietal regions, precuneus, and posterior cingulate cortex emerge around the time of normal birth (Ball et al., 2014; Zhao et al., 2019a). In contrast, resting-state fMRI studies indicate that only primary networks, including the visual, auditory, and sensorimotor systems reach adult-like patterns at birth, whereas higher-order functional networks, such as the default mode and frontoparietal networks, require prolonged postnatal development (Cao et al., 2017a; Gao et al., 2015). Prior research has also demonstrated significant correlation between whole brain structural and functional networks (Hagmann et al., 2008), with these associations strengthening considerably across development (Hagmann et al., 2010; van den Heuvel et al., 2015). Understanding how structural networks develop to support functional activity is therefore critical for elucidating the organizational principles of early brain development and its long-term implications for cognition.

The architecture of the structural networks constrains and shapes the functional interactions among brain regions, which in turn supports the emergence of increasingly complex cognitive and behavioral functions (Fotiadis et al., 2024; Wang et al., 2015b). Structural connectivity has been shown to predict resting-state functional connectivity (Honey et al., 2007), whereas the strength of resting-state and task-based functional correlations can be used to infer structural characteristics such as the length, number, and spatial distribution of white matter fiber tracts (Hermundstad et al., 2013). More recent studies have shifted from global to regional analyses of structure-function coupling. In adults, the structure-function coupling is consistently stronger in unimodal sensory cortices and weaker in transmodal association cortices, reflecting the hierarchical constraints of cortical organization (Vázquez-Rodríguez et al., 2019). Regional coupling strength is also partly genetically determined (Gu et al., 2021) and has been associated with individual differences in executive function (Baum et al., 2020) and general intelligence (Feng et al., 2024). A growing number of studies have begun to explore the developmental changes in structure-function coupling across infancy (Tooley et al., 2025), childhood (Hong et al., 2023) and adolescence (Baum et al., 2020; Feng et al., 2024). In one-month-old infants, the coupling exhibits a heterogeneous spatial pattern, with higher values observed in the auditory, lateral prefrontal, and inferior parietal cortices (Tooley et al., 2025). By early childhood, relatively strong coupling emerges in the frontal, medial parietal, and occipital regions, whereas the lateral temporal and parietal cortices display weaker coupling (Hong et al., 2023). From childhood to adolescence, age-related increases are evident in the frontoparietal, dorsal attention, and default mode networks (Feng et al., 2024), as well as in the temporoparietal junction and prefrontal cortex (Baum et al., 2020), while decreases are primarily found in the visual, motor, and insular cortices (Baum et al., 2020; Feng et al., 2024). However, it remains unclear how structure-function coupling develops before birth and whether such early organization influences later neurocognitive outcomes.

To address these questions, we analyzed multimodal MRI data from 40 preterm and full-term infants (scanned between 32 and 42 postmenstrual weeks) to examine the emergence and development of structure-function coupling during the third trimester. Using a widely adopted approach based on the Pearson's or Spearman correlations between regional structural and functional connectivity profiles, we quantified coupling strength across the brain regions. We further tested whether structure-function coupling at birth could predict neurocognitive outcomes at two years of age.

Materials and Methods

Participants and Neurocognitive Assessments

All study protocols were approved by the Institutional Review Board of the University of Texas Southwestern Medical Center and the Children's Hospital of Philadelphia. A total of 52 preterm and term infants (gestational ages at birth ranged from 25.1 to 41.0 weeks) were recruited and scanned at the Children's Medical Center at Dallas. All neonates were clinically healthy and with no medical reasons for MRI scanning. After removing 12 infants with excessive motion artifacts in scans (please see Data preprocessing), the remaining dataset comprised 40 infants scanned age between 31.9–41.7 postmenstrual weeks. In a follow-up, 26 of these infants underwent neurodevelopmental assessment at around two years of age (age range 20.3–26.9 months, corrected for prematurity), using the Bayley Scales of Infant and Toddler Development III (Bayley, 2006). Written informed consent was obtained from parents for each participating infant. The demographic information, exclusion criteria, and other details can be found in the Supplementary Materials and Table S1. This dataset has been used in our previous research for studying prenatal and perinatal human brain development (Cao et al., 2017a; Feng et al., 2019; Ouyang et al., 2019, 2020; Xia et al., 2024; Xu et al., 2019; Xu et al., 2024; Zhao et al., 2019a)

Image Acquisition

All MRI data were acquired on a Philips 3 T Achieve scanner with an 8-channel SENSE head coil at the Children's Medical Center in Dallas. Neonates were well-fed and naturally asleep before scanning. To minimize scanner noise, infants were equipped with the earplugs, earphones, and extra foam padding. The imaging protocol included T2-weighted structural MRI (sMRI), diffusion MRI (dMRI) and resting-state functional MRI (rs-fMRI) sequences. Detailed data acquisition is provided in Supplementary Materials.

Data Preprocessing and Network Construction

Data Preprocessing of dMRI Images

The diffusion MRI data were preprocessed using DTI Studio (Jiang et al., 2006). To minimize head motion and eddy current artifacts, all diffusion weighted images for each infant were registered to its corresponding b0 image through a 12-parameter affine automated image registration algorithm. After that, the six parameters of diffusion tensors were obtained by multivariate least-square fitting of the DWIs (Basser & Pierpaoli, 1996). The estimated diffusion tensor was diagonalized to obtain three eigenvalues (λ₁-λ₃) and their associated eigenvectors (ν₁-ν₃). The diffusion metrics, including fractional anisotropy (FA) and apparent diffusion coefficient (ADC) images were then calculated for each voxel.

Structural Network Construction

Network nodes were defined by transferring the 58 cortical regions from the Johns Hopkins University (JHU) neonate atlas (Oishi et al., 2011) into each infant's native dMRI space. Registration was performed using SPM8 software (http://www.fil.ion.ucl.ac.uk/spm/). Network edges were defined by whole brain deterministic tractography using Brute-force tracking algorithm performed with Diffusion Toolkit (http://trackvis.org/). Given the low FA in preterm brains, the FA threshold was set to 0.1 and angle threshold was 35 for tractography. The weight of each edge was defined as the product of the number of streamlines and their mean FA (FN × FA) between two regions. Finally, a symmetric weighted 58 × 58 structural connectivity matrix was constructed for each infant.

Data Preprocessing of rs-fMRI Images

The rs-fMRI images were preprocessed using Statistical Parametric Mapping (SPM12, http://www.fil.ion.ucl.ac.uk/spm), GRETNA (Wang et al., 2015a), and DPARSFA (Yan Chao-Gan, 2010). The first 15 volumes were discarded for reaching steady state, remaining 195 time points for each infant. The functional data were then corrected for slice timing and headmotion. The mean framewise displacement (FD) across volumes was calculated to evaluate the extent of head motion for the whole rs-fMRI scan. Data from 12 infants were removed due to excessive motion (displacement > 5 mm, rotation > 5°, or mean FD > 1 mm). Each infant's functional data were aligned and co-registered to the corresponding T2-weighted anatomical images using a linear transformation, then spatially normalized and resampled to 3-mm isotropic voxels in the JHU neonate template. Deep gray matter, white matter, and cerebrospinal fluid tissue templates at 37 weeks (Serag et al., 2012) were also registered to the same template to generate the corresponding tissue masks. Next, the normalized functional images underwent spatially smoothed with a Gaussian kernel (full width at half-maximum of 4 mm), linear detrend, and further processed using nuisance regression. The nuisance regression model included Friston's 24 head motion parameters (Friston et al., 1996), white matter, cerebrospinal fluid signals, and the global signals. Finally, temporally bandpass filtering (0.01–0.08 Hz) was applied.

Functional Network Construction

For each infant, the blood-oxygen-level-dependent (BOLD) time series were first extracted and averaged within each of the 58 cortical regions defined by the JHU parcellation. The functional connectivity was then calculated as the Pearson's correlation coefficient between the mean time series of any two pairs of brain regions, resulting in a 58 × 58 weighted adjacency matrix. Only significant positive connections (p < 0.05) were retained for subsequent analyses due to the ambiguous physiological interpretation of negative values (Murphy et al., 2009; Murphy & Fox, 2017).

Structure and Function Coupling

To quantify the strength of structure-function coupling, we calculated the absolute Pearson's correlation coefficients between regional structural and functional connectivity (Figure 1A). For each infant, the connectivity profile of each network node was extracted from the corresponding column of the structural and functional connectivity matrices, representing vectors of connectivity strength from a single network node to all other nodes in the network.

Figure 1.

Flowchart of structure-function coupling estimation and behavior prediction analysis. We used the SC-FC coupling at birth to predict neurocognitive outcomes at 2 years of age. The prediction workflow includes the following steps: (A) The structure-function coupling was quantified as the Pearson's correlation values between structure connectivity and functional connectivity. (B) Prediction models were established and tested with support vector regression model and cross-validation. (C) Prediction accuracy was evaluated by correlation between predicted and actual scores. Feature contributions from brain regions in the model were quantified by normalized feature contribution weights.

Statistical Analysis

To examine the age-related effects on nodal structure-function coupling, we employed a general linear model (GLM) that included postnatal age at scan, gender, mean frame-wise displacement, and interval time between birth and scan as covariates: $Y = β_{0} + β_{1} \times a g e + β_{2} \times s e x + β_{3} \times m F D + β_{4} \times a g e_{s c a n - b i r t h}$ (1)

Age effects were considered significant at p < 0.05, and the multiple comparisons were corrected using the false discovery rate (FDR) method (Benjamini & Hochberg, 1995).

Prediction of Neurocognitive Outcomes

Finally, we investigated whether the structure-function coupling at birth could predict later neurocognitive outcomes. A support vector regression (SVR) model (Chang & Lin, 2011) was trained using nodal structure-function coupling values at birth as input features to predict each infant's cognitive, language, and motor scores at two years old. The SVR model was conducted with a linear kernel and default parameters (C = 1, ε=0. 001) using the LIBSVM toolbox for MATLAB, (https://www.csie.ntu.edu.tw/∼cjlin/libsvm/). Predictive performance was evaluated using a leave-one-out cross-validation (LOOCV) strategy. During each iteration, the SVR model was trained on N-1 infants and then used to predict the Bayley score of the remaining test infant. Prediction accuracy was quantified by the partial correlation coefficient between the actual and predicted scores, controlling for postnatal age at the timing of scan, gender, mean FD, and the interval time between birth and scan. We also included a measure of mean absolute error (MAE) to address the population-level prediction errors between the predicted and actual scores. To assess statistical significance, permutation test (n = 10, 000) was performed. For each permutation, the Bayley scores of all infants were randomly shuffled before running the SVR analysis and the resulting partial correlations were used to construct a null distribution. Finally, a new SVR model was retrained using all infants to estimate the relative contribution of each brain node. For a linear SVR, the weight vector ω represents the contribution of each feature, and is computed as a weighted sum of the support vectors and their coefficients. The contribution weight for each brain region was defined as the absolute value of its corresponding element in ω (Cui & Gong, 2018).

Validation Analysis

To assess the robustness of our main findings, we performed additional analysis to examine the potential confounding effects, including stricter head motion control and non-parametric coupling estimation. (i) Stricter head motion control. To better control the motion-related artifacts, spike regressors were used in the nuisance regression model. The bad volumes were defined as FD above 0.5 mm and their adjacent volumes (one back and two forward). One infant with “bad” volumes more than 50% of the rs-fMRI data were excluded from subsequent analysis. (ii) Non-parametric coupling estimation. To verify that our results were not dependent on the choice of correlation metric, structure-function coupling strength was re-calculated used the absolute Spearman correlation coefficients between nodal structural connectivity and functional connectivity profiles.

Results

The Increased Structure-Function Coupling During Prenatal Development

We found that the structure-function coupling exhibited heterogeneous spatial patterns across the whole brain, characterized by stronger coupling in primary sensory and motor, visual regions and medial prefrontal and cingulate cortex and weaker coupling in higher-order association areas including lateral frontoparietal regions during prenatal period (Figure 2A). With development, age-related increases were observed in the bilateral middle fronto-orbital gyrus, bilateral gyrus rectus, right superior and middle frontal gyrus, bilateral precuneus, right fusiform gyrus, and right entorhinal area, whereas the right precentral gyrus exhibited a decreasing trend (Figure 2B, p < 0.05). After correction for multiple comparisons, only the left angular gyrus and right middle fronto-orbital gyrus remained significant (Figure 2C, p_FDR < 0.05).

Figure 2.

Developmental changes of structure-function coupling during the third trimester. (A) Fitted spatial patterns of nodal structure-function coupling corrected for gender, postnatal age at scan and the duration time between birth and scan. (B) Age effects on regional structure-function coupling (p < 0.05, uncorrected). (C) Structure-function couplings in left angular gyrus and right middle fronto-orbital gyrus significantly increased with age (p_FDR<0.05).

Prediction of Neurocognitive Outcomes Using Structure-Function Coupling

We employed SVR model with LOOCV strategy to investigate whether the structure-function coupling at birth could predict individualized neurocognitive outcomes at two years of age. The results revealed that neonatal structure-function coupling significantly predicted both cognitive (Figure 3A, left panel, r = 0.32, p_perm = 0.024; MAE = 5.88, p_perm <0.001) and language (Figure 3B, left panel, r = 0.44, p_perm = 0.002; MAE = 6.59, p_perm <0.001) scores at follow-up. Brain regions contributing strongly to prediction of cognitive scores were primarily located in the left precentral gyrus, left entorhinal cortex, right hippocampus, medial and lateral frontal regions, left superior parietal gyrus, left middle temporal gyrus, right precuneus and cuneus, and right cingulate gyrus (Figure 3A, right panel). Nodes contributing most strongly to the language score prediction were mainly distributed in the left inferior frontal gyrus, right superior temporal gyrus, left and right insular, right angular gyrus, left fusiform gyrus, right entorhinal cortex, right cuneus, and cingulate gyrus (Figure 3B, right panel). In contrast, nodal structure-function coupling at birth did not significantly predict motor scores (r = 0.167, p_perm = 0.075).

Figure 3.

Neurocognitive outcomes at 2 years of age prediction based on structure-function coupling at birth. (A) Prediction for individual's cognition ability. (B) Prediction for individual's language ability. Left panel, Pearson's correlation between the predicted scores and the real scores (r = 0.32, p = 0.024 for cognition; r = 0.44, p = 0.002 for language). Right panel, absolute weight of brain regions in the prediction model.

Validation Results

To assess the reliability of our main findings, we estimated the potential influences of different data analysis strategies, including stricter head motion control (Supplemental Fig. S1) and non-parametric coupling analysis (Supplemental Fig. S2). The results demonstrated that our main findings remained stable across these different analysis strategies. Notably, individual-level structure-function coupling maps at birth could reliably and significantly predict language scores under other conditions (all p_perms < 0.05) (Supplementary Fig. S1 and S2).

Discussion

In this study, we investigated the emergence and development of the relationship between brain structural and functional connectivity during the third trimester, as well as its predictive value for later neurocognitive outcomes. During this critical developmental period, structure-function coupling exhibited heterogeneous patterns across the entire brain, characterized by stronger coupling in the cingulate gyrus, frontal and occipital regions, and lower coupling in the temporal lobe and limbic system. Age-related increases were primarily observed in the left angular gyrus, bilateral middle fronto-orbital gyrus, superior and middle frontal gyrus, bilateral precuneus, right fusiform gyrus, and right entorhinal area. Notably, structure-function coupling at birth significantly predicted cognitive and language abilities at two years of age. Overall, these findings offer novel insights into the early establishment of structural and functional relationship in the human brain and highlight their foundational role in supporting subsequent cognitive and language development.

Prior studies in healthy adults have demonstrated that brain regions with stronger structural connectivity also tend to exhibit more stable and robust functional interactions. However, this relationship is not a simple one-to-one correspondence (Liao et al., 2015; Wang et al., 2015b). In adults and adolescents, regional differences in structure–function coupling have been well characterized (Baum et al., 2020; Feng et al., 2024; Vázquez-Rodríguez et al., 2019). The unimodal cortices such as primary sensory and motor areas, visual, auditory and somatomotor areas generally exhibit strong coupling. In contrast, heteromodal association cortices, paralimbic region and limbic regions, including the lateral temporal cortex, frontoparietal regions, and anterior cingulate typically display weak coupling (Baum et al., 2020; Feng et al., 2024; Vázquez-Rodríguez et al., 2019). This gradient of coupling strength is thought to underlie the hierarchical organization of the cortex, in which unimodal regions are tightly linked to their structural substrates, whereas transmodal cortices support more adaptive and integrative processing (Fotiadis et al., 2024). Our finding, together with prior work in preterm and one month old infants, extend this hierarchical coupling to the earliest stages of human brain development (Tooley et al., 2025; Wang et al., 2025). These results suggest that the fundamental architecture linking structural and functional networks begins to emerge before birth, providing an anatomical and functional foundation upon which postnatal experience can sculpt domain-specific cognitive abilities. Furthermore, we observed age-related increases in coupling strength within frontal and parietal regions and a decreasing trend in the precentral gyrus, consistent with recent studies in perinatal brain development (Tooley et al., 2025; Wang et al., 2025). Taken together, these patterns likely reflect the asynchronous maturation of neural systems: primary sensory and motor areas, along with occipital visual regions, undergo rapid structural growth and early functional specialization, whereas association cortices and limbic circuits mature more slowly, continuing to refine throughout infancy and childhood (Cao et al., 2017b; Zhao et al., 2019b).

Explosive brain growth occurred in infancy period is thought to establish the neural foundations that supports motor, language, and cognitive functions throughout later development (Gao, 2025; Gilmore et al., 2018). Emerging research utilizing machine-learning based algorithm have demonstrated that early brain features could serve as biomarkers for predicting later behavior outcomes (Li et al., 2024; Ouyang et al., 2020, 2024; Rosenberg et al., 2018; Xia et al., 2024; Xu et al., 2024). Building on this line of work, we found that neonatal brain structure-function coupling predicts cognitive and language abilities at two years of age. The cognitive scale in Bayley-III assesses memory, simple problem-solving, and object relatedness (Bayley, 2006). Brain regions with strong contributions to cognitive predictions were primarily located in the medial and lateral prefrontal cortex, left superior parietal gyrus, left middle temporal gyrus, left entorhinal cortex, right hippocampus, right precuneus and cuneus, and right cingulate gyrus. The medial and lateral prefrontal cortex, together with the cingulate gyrus, are known to involved in attention, decision-making and working memory (Leech & Sharp, 2014; Miller & Cohen, 2001). The right precuneus, a pivotal hub of default mode network, is with higher-order cognitive functions such as self-referential processing, visuo-spatial imagery and episodic memory retrieval (Buckner & DiNicola, 2019; Cavanna & Trimble, 2006). The left middle temporal gyrus, right hippocampus and left entorhinal cortex are core regions of memory system involved in episodic memory formation and retrieval (Dickerson & Eichenbaum, 2009). The language scale of Bayley-III test includes receptive and expressive communication, which measure the child's ability to understand and use spoken language to follow instructions and recognize or label objects and people based on verbal descriptions (Bayley, 2006). Brain regions that contribute significantly to language ability predictions include the left inferior frontal gyrus, the right superior temporal gyrus, the right angular gyrus, the inferior occipital gyrus, and the bilateral insula left fusiform gyrus, right entorhinal cortex, right cuneus and cingulate gyrus. Specifically, the left inferior frontal gyrus (Broca's area) is critically involved in speech production, syntactic processing, and verbal working memory, supporting expressive language development (Hickok & Poeppel, 2007). The right superior temporal gyrus participates in auditory perception and prosodic processing (Bhaya-Grossman & Chang, 2022; Kriegstein & Giraud, 2004). The left fusiform gyrus is implicated in visual word and letter-form recognition, linking orthographic input to phonological and semantic representations (Starrfelt & Gerlach, 2007). The right entorhinal cortex, part of the medial temporal memory system, contributes to associative memory formation that supports vocabulary learning and lexical retrieval (Liuzzi et al., 2019). Finally, the cingulate gyrus is involved in cognitive control and performance monitoring during speech and language tasks, ensuring fluent and context-appropriate language production (Hickok & Poeppel, 2007). Interestingly, the left superior parietal gyrus and right cuneus showed strong predictive contribution to both cognitive and language abilities. The left superior parietal gyrus contributes to spatial attention, sensory integration and the manipulation of information in working memory (Husain & Nachev, 2007). The right cuneus is primarily involved in visuospatial processing, motion perception and visual attention (Collignon et al., 2011; Palejwala et al., 2021). These regions play pivotal roles in nonverbal cognitive functions, such as visual exploration and perceptual reasoning, which are assessed in the Bayley-III cognitive scale through tasks like object tracking, shape matching, and spatial manipulation (Bayley, 2006). Moreover, their involvement in integrating visual and spatial information likely facilitates early language acquisition, as infants depend on visual cues (such as gaze direction, gestures, and mouth movements) to map words onto objects and actions (Çetinçelik et al., 2021). Taken together, these findings suggest that although brain regions involved in higher-order cognition are still immature at birth, the brain foundations for cognitive and behavioral development may already be established early in life.

Several important issues warrant further investigation in future research. First, although preterm birth is a known risk factor for the potential adverse development of neurodevelopmental outcomes, MRI examinations of preterm infants are widely used as a substitute model to study brain maturation during the third trimester (Cao et al., 2017a; Ouyang et al., 2019). While this approach provides valuable insights, the presented findings should be validated using high-quality fetal imaging, which enables the direct characterization of in utero brain developmental trajectories (van den Heuvel & Thomason, 2016). Second, the relatively small sample size of this study reflects the inherent challenges of acquiring high-quality neonatal MRI data without sedation. The integration of large-scale datasets, such as the Developing Human Connectome Project (Edwards et al., 2022) and the Baby Connectome Project (Howell et al., 2019), will be essential to replicate and generalize these findings. Third, while prior research has suggested the presence of sex differences in early brain development (Gilmore et al., 2018), our analysis did not identify a significant association between sex and structure-function coupling. Future studies with larger samples and a balanced gender distribution are needed to conclusively explore this potential relationship. Finally, the maturation of functional connectivity is associated with regional cerebral blood flow (Liang et al., 2013; Yu et al., 2023). However, how evolving patterns of cerebral perfusion contribute to the establishment of structural-functional relationships during this early developmental window remains unclear. Multi-modal imaging approach that combines perfusion, diffusion, structural and functional MRI could provide a more comprehensive understanding of how vascular and neural processes jointly shape the emergence of large-scale brain networks in early life.

Supplemental Material

sj-docx-1-pac-10.1177_18344909251414889 - Supplemental material for Structural and Functional Coupling in Neonates and its Prediction for Neurocognitive Outcomes

Supplemental material, sj-docx-1-pac-10.1177_18344909251414889 for Structural and Functional Coupling in Neonates and its Prediction for Neurocognitive Outcomes by Yuehua Xu, Tengda Zhao, Tina Jeon, Minhui Ouyang, Tianjia Zhu, Lina Chalak, Nancy Rollins and Hao Huang in Journal of Pacific Rim Psychology

Footnotes

Acknowledgements

None.

ORCID iD

Yuehua Xu

Funding

The authors disclosed receipt of the following financial support for the research authorship,and/or publication of this article: This work was supported by the National Natural Science Foundation of China (Grant No. 82102131),and National Institute of Health (Grant Nos. R01MH092535,R01MH125333,R01EB031284,R01MH129981,R21MH123930,and P50HD105354).

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Supplemental Material

Supplemental material for this article is available online.

References

Ball

Aljabar

Zebari

Tusor

Arichi

Merchant

Robinson

E. C.

Ogundipe

Rueckert

Edwards

A. D.

Counsell

S. J.

(2014). Rich-club organization of the newborn human brain. Proceedings of the National Academy of Sciences of the United States of America, 111(20), 7456–7461. https://doi.org/10.1073/pnas.1324118111

Basser

P. J.

Pierpaoli

(1996). Microstructural and physiological features of tissues elucidated by quantitative-diffusion-tensor MRI. Journal of Magnetic Resonance Series B, 111(3), 209–219. https://doi.org/10.1006/jmrb.1996.0086

Baum

G. L.

Cui

Z. X.

Roalf

D. R.

Ciric

Betzel

R. F.

Larsen

Cieslak

Cook

P. A.

Xia

C. H.

Moore

T. M.

Ruparel

Oathes

D. J.

Alexander-Bloch

A. F.

Shinohara

R. T.

Raznahan

Gur

R. E.

Gur

R. C.

Bassett

D. S.

Satterthwaite

T. D.

(2020). Development of structure-function coupling in human brain networks during youth. Proceedings of the National Academy of Sciences of the United States of America, 117(1), 771–778. https://doi.org/10.1073/pnas.1912034117

Bayley

(2006). Bayley scales of infant and toddler development (third edition). Harcourt Assessment.

Benjamini

Hochberg

(1995). Controlling the false discovery rate - a practical and powerful approach to multiple testing. Journal of the Royal Statistical Society Series B-Statistical Methodology, 57(1), 289–300. https://doi.org/10.1111/j.2517-6161.1995.tb02031.x

Bhaya-Grossman

Chang

E. F.

(2022). Speech computations of the human superior temporal gyrus. Annual Review of Psychology, 73(1), 79–102. https://doi.org/10.1146/annurev-psych-022321-035256

Buckner

R. L.

DiNicola

L. M.

(2019). The brain's default network: Updated anatomy, physiology and evolving insights. Nature Reviews Neuroscience, 20(10), 593–608. https://doi.org/10.1038/s41583-019-0212-7

Cao

Dai

Z. J.

Liao

X. H.

Jeon

T. N.

Ouyang

M. H.

Chalak

L. N.

Y. C.

Rollins

Dong

Huang

(2017a). Early development of functional network segregation revealed by connectomic analysis of the preterm human brain. Cerebral Cortex, 27(3), 1949–1963. https://doi.org/10.1093/cercor/bhw038

Cao

Huang

(2017b). Developmental connectomics from infancy through early childhood. Trends in Neurosciences, 40(8), 494–506. https://doi.org/10.1016/j.tins.2017.06.003

10.

Cavanna

A. E.

Trimble

M. R.

(2006). The precuneus: A review of its functional anatomy and behavioural correlates. Brain, 129(3), 564–583. https://doi.org/10.1093/brain/awl004

11.

Çetinçelik

Rowland

C. F.

Snijders

T. M.

(2021). Do the eyes have it? a systematic review on the role of eye gaze in infant language development. Frontiers in psychology, 11, 589096. https://doi.org/10.3389/fpsyg.2020.589096

12.

Chang

C. C.

Lin

C. J.

(2011). LIBSVM: A library for support vector machines. ACM Transactions on Intelligent Systems and Technology, 2(3), 1–27. https://doi.org/10.1145/1961189.1961199

13.

Collignon

Vandewalle

Voss

Albouy

Charbonneau

Lassonde

Lepore

(2011). Functional specialization for auditory-spatial processing in the occipital cortex of congenitally blind humans. Proceedings of the National Academy of Sciences of the United States of America, 108(11), 4435–4440. https://doi.org/10.1073/pnas.1013928108

14.

Cui

Z. X.

Gong

G. L.

(2018). The effect of machine learning regression algorithms and sample size on individualized behavioral prediction with functional connectivity features. Neuroimage, 178, 622–637. https://doi.org/10.1016/j.neuroimage.2018.06.001

15.

Dehaene-Lambertz

Spelke

E. S.

(2015). The infancy of the human brain. Neuron, 88(1), 93–109. https://doi.org/10.1016/j.neuron.2015.09.026

16.

Dickerson

B. C.

Eichenbaum

(2009). The episodic memory system: Neurocircuitry and disorders. Neuropsychopharmacology, 35(1), 86–104. https://doi.org/10.1038/npp.2009.126

17.

Edwards

A. D.

Rueckert

Smith

S. M.

Abo Seada

Alansary

Almalbis

Allsop

Andersson

Arichi

Arulkumaran

Bastiani

Batalle

Baxter

Bozek

Braithwaite

Brandon

Carney

Chew

Christiaens

Chung

, … Hajnal

J. V.

(2022). The developing human connectome project neonatal data release. Frontiers in Neuroscience, 16, 886772. https://doi.org/10.3389/fnins.2022.886772

18.

Feng

G. Z.

Wang

Y. W.

Huang

W. J.

Chen

H. J.

Cheng

Shu

(2024). Spatial and temporal pattern of structure-function coupling of human brain connectome with development. eLife, 13, RP93325. https://doi.org/10.7554/eLife.93325

19.

Feng

Oishi

Mishra

Song

Peng

Ouyang

Wang

Slinger

Jeon

Lee

Heyne

Chalak

Peng

Liu

Huang

(2019). Age-specific gray and white matter DTI atlas for human brain at 33, 36 and 39 postmenstrual weeks. NeuroImage, 185, 685–698. https://doi.org/10.1016/j.neuroimage.2018.06.069

20.

Fotiadis

Parkes

Davis

K. A.

Satterthwaite

T. D.

Shinohara

R. T.

Bassett

D. S.

(2024). Structure-function coupling in macroscale human brain networks. Nature Reviews Neuroscience, 25(10), 688–704. https://doi.org/10.1038/s41583-024-00846-6

21.

Friston

K. J.

Williams

Howard

Frackowiak

R. S. J.

Turner

(1996). Movement-related effects in fMRI time-series. Magnetic Resonance in Medicine, 35(3), 346–355. https://doi.org/10.1002/mrm.1910350312

22.

Gao

(2025). A hierarchical model of early brain functional network development. Trends in Cognitive Sciences, 29(9), 855–868. https://doi.org/10.1016/j.tics.2025.04.001

23.

Gao

Alcauter

Elton

Hernandez-Castillo

C. R.

Smith

J. K.

Ramirez

Lin

W. L.

(2015). Functional network development during the first year: Relative sequence and socioeconomic correlations. Cerebral Cortex, 25(9), 2919–2928. https://doi.org/10.1093/cercor/bhu088

24.

Gilmore

J. H.

Knickmeyer

R. C.

Gao

(2018). Imaging structural and functional brain development in early childhood. Nature Reviews Neuroscience, 19(3), 123–137. https://doi.org/10.1038/nrn.2018.1

25.

Z. J.

Jamison

K. W.

Sabuncu

M. R.

Kuceyeski

(2021). Heritability and interindividual variability of regional structure-function coupling. Nature Communications, 12(1), 4894. https://doi.org/10.1038/s41467-021-25184-4

26.

Hagmann

Cammoun

Gigandet

Meuli

Honey

C. J.

Wedeen

Sporns

(2008). Mapping the structural core of human cerebral cortex. PLOS Biology, 6(7), 1479–1493. https://doi.org/10.1371/journal.pbio.0060159

27.

Hagmann

Sporns

Madan

Cammoun

Pienaar

Wedeen

V. J.

Meuli

Thiran

J. P.

Grant

P. E.

(2010). White matter maturation reshapes structural connectivity in the late developing human brain. Proceedings of the National Academy of Sciences of the United States of America, 107(44), 19067–19072. https://doi.org/10.1073/pnas.1009073107

28.

Hermundstad

A. M.

Bassett

D. S.

Brown

K. S.

Aminoff

E. M.

Clewett

Freeman

Frithsen

Johnson

Tipper

C. M.

Miller

M. B.

Grafton

S. T.

Carlson

J. M.

(2013). Structural foundations of resting-state and task-based functional connectivity in the human brain. Proceedings of the National Academy of Sciences of the United States of America, 110(15), 6169–6174. https://doi.org/10.1073/pnas.1219562110

29.

Hickok

Poeppel

(2007). The cortical organization of speech processing. Nature Reviews Neuroscience, 8(5), 393–402. https://doi.org/10.1038/nrn2113

30.

Honey

C. J.

Kötter

Breakspear

Sporns

(2007). Network structure of cerebral cortex shapes functional connectivity on multiple time scales. Proceedings of the National Academy of Sciences of the United States of America, 104(24), 10240–10245. https://doi.org/10.1073/pnas.0701519104

31.

Hong

Cornea

Girault

J. B.

Bagonis

Foster

Kim

S. H.

Prieto

J. C.

Chen

H. T.

Gao

Styner

M. A.

Gilmore

J. H.

(2023). Structural and functional connectome relationships in early childhood. Developmental Cognitive Neuroscience, 64, 101314. https://doi.org/10.1016/j.dcn.2023.101314

32.

Howell

B. R.

Styner

M. A.

Gao

Yap

P. T.

Wang

Baluyot

Yacoub

Chen

Potts

Salzwedel

Gilmore

J. H.

Piven

Smith

J. K.

Shen

D. G.

Ugurbil

Zhu

H. T.

Lin

W. L.

Elison

J. T.

(2019). The UNC/UMN baby connectome project (BCP): An overview of the study design and protocol development. Neuroimage, 185, 891–905. https://doi.org/10.1016/j.neuroimage.2018.03.049

33.

Huang

Shu

Mishra

Jeon

Chalak

Wang

Z. Y. J.

Rollins

Gong

G. L.

Cheng

Peng

Dong

(2015). Development of human brain structural networks through infancy and childhood. Cerebral Cortex, 25(5), 1389–1404. https://doi.org/10.1093/cercor/bht335

34.

Husain

Nachev

(2007). Space and the parietal cortex. Trends in Cognitive Sciences, 11(1), 30–36. https://doi.org/10.1016/j.tics.2006.10.011

35.

Jiang

H. Y.

van Zijl

P. C. M.

Kim

Pearlson

G. D.

Mori

(2006). Dtistudio: Resource program for diffusion tensor computation and fiber bundle tracking. Computer Methods and Programs in Biomedicine, 81(2), 106–116. https://doi.org/10.1016/j.cmpb.2005.08.004

36.

Kostovic

Sedmak

Judas

(2019). Neural histology and neurogenesis of the human fetal and infant brain. Neuroimage, 188, 743–773. https://doi.org/10.1016/j.neuroimage.2018.12.043

37.

Kriegstein

K. V.

Giraud

A. L.

(2004). Distinct functional substrates along the right superior temporal sulcus for the processing of voices. Neuroimage, 22(2), 948–955. https://doi.org/10.1016/j.neuroimage.2004.02.020

38.

Leech

Sharp

D. J.

(2014). The role of the posterior cingulate cortex in cognition and disease. Brain, 137(1), 12–32. https://doi.org/10.1093/brain/awt162

39.

Q. L.

Xia

M. R.

Zeng

D. B.

Y. H.

Sun

L. L.

Liang

X. Y.

Z. L.

Zhao

T. D.

Liao

X. H.

Yuan

H. S.

Liu

Huo

S. Y.

(2024). Development of segregation and integration of functional connectomes during the first 1,000 days. Cell Reports, 43(5), 114168. https://doi.org/10.1016/j.celrep.2024.114168

40.

Liang

Zou

Q. H.

Yang

Y. H.

(2013). Coupling of functional connectivity and regional cerebral blood flow reveals a physiological basis for network hubs of the human brain. Proceedings of the National Academy of Sciences of the United States of America, 110(5), 1929–1934. https://doi.org/10.1073/pnas.1214900110

41.

Liao

X. H.

Yuan

Zhao

T. D.

Dai

Z. J.

Shu

Xia

M. R.

Yang

Y. H.

Evans

(2015). Spontaneous functional network dynamics and associated structural substrates in the human brain. Frontiers in human neuroscience, 9, 478. https://doi.org/10.3389/fnhum.7015.00478

42.

Liuzzi

A. G.

Bruffaerts

Vandenberghe

(2019). The medial temporal written word processing system. Cortex, 119, 287–300. https://doi.org/10.1016/j.cortex.2019.05.002

43.

Miller

E. K.

Cohen

J. D.

(2001). An integrative theory of prefrontal cortex function. Annual Review of Neuroscience, 24(1), 167–202. https://doi.org/10.1146/annurev.neuro.24.1.167

44.

Murphy

Birn

R. M.

Handwerker

D. A.

Jones

T. B.

Bandettini

P. A.

(2009). The impact of global signal regression on resting state correlations: Are anti-correlated networks introduced? Neuroimage, 44(3), 893–905. https://doi.org/10.1016/j.neuroimage.2008.09.036

45.

Murphy

Fox

M. D.

(2017). Towards a consensus regarding global signal regression for resting state functional connectivity MRI. Neuroimage, 154, 169–173. https://doi.org/10.1016/j.neuroimage.2016.11.052

46.

Oishi

Mori

Donohue

P. K.

Ernst

Anderson

Buchthal

Faria

Jiang

H. Y.

Miller

M. I.

van Zijl

P. C. M.

Chang

L. D.

(2011). Multi-contrast human neonatal brain atlas: Application to normal neonate development analysis. Neuroimage, 56(1), 8–20. https://doi.org/10.1016/j.neuroimage.2011.01.051

47.

Ouyang

M. H.

Jeon

Sotiras

Peng

Q. M.

Mishra

Halovanic

Chen

Chalak

Rollins

Roberts

T. P. L.

Davatzikos

Huang

(2019). Differential cortical microstructural maturation in the preterm human brain with diffusion kurtosis and tensor imaging. Proceedings of the National Academy of Sciences of the United States of America, 116(10), 4681–4688. https://doi.org/10.1073/pnas.1812156116

48.

Ouyang

M. H.

Peng

Q. M.

Jeon

Heyne

Chalak

Huang

(2020). Diffusion-MRI-based regional cortical microstructure at birth for predicting neurodevelopmental outcomes of 2-year-olds. eLife, 9, e58116. https://doi.org/10.7554/eLife.58116

49.

Ouyang

M. H.

Whitehead

M. T.

Mohapatra

Zhu

T. J.

Huang

(2024). Machine-learning based prediction of future outcome using multimodal MRI during early childhood. Seminars in Fetal & Neonatal Medicine, 29(2-3), 101561. https://doi.org/10.1016/j.siny.2024.101561

50.

Palejwala

A. H.

Dadario

N. B.

Young

I. M.

O'Connor

Briggs

R. G.

Conner

A. K.

O'Donoghue

D. L.

Sughrue

M. E.

(2021). Anatomy and white matter connections of the lingual gyrus and cuneus. World neurosurgery, 151, e426–e437. https://doi.org/10.1016/j.wneu.2021.04.050

51.

Rosenberg

M. D.

Casey

B. J.

Holmes

A. J.

(2018). Prediction complements explanation in understanding the developing brain. Nature Communications, 9(1), 589. https://doi.org/10.1038/s41467-018-02887-9

52.

Serag

Aljabar

Ball

Counsell

S. J.

Boardman

J. P.

Rutherford

M. A.

Edwards

A. D.

Hajnal

J. V.

Rueckert

(2012). Construction of a consistent high-definition spatio-temporal atlas of the developing brain using adaptive kernel regression. Neuroimage, 59(3), 2255–2265. https://doi.org/10.1016/j.neuroimage.2011.09.062

53.

Starrfelt

Gerlach

(2007). The visual what for area: Words and pictures in the left fusiform gyrus. Neuroimage, 35(1), 334–342. https://doi.org/10.1016/j.neuroimage.2006.12.003

54.

Tau

G. Z.

Peterson

B. S.

(2010). Normal development of brain circuits. Neuropsychopharmacology, 35(1), 147–168. https://doi.org/10.1038/npp.2009.115

55.

Tooley

U. A.

Kenley

J. K.

Camacho

M. C.

Latham

Alexopoulos

Nielsen

A. N.

Smyser

T. A.

Warner

B. B.

Shimony

J. S.

Neil

J. J.

Luby

J. L.

Barch

D. M.

Rogers

C. E.

Smyser

C. D.

(2025). Structure-function coupling in the first month of life: Associations with age and attention. Proceedings of the National Academy of Sciences of the United States of America, 122(23), e2412729122. https://doi.org/10.1073/pnas.2412729122

56.

van den Heuvel

M. I.

Thomason

M. E.

(2016). Functional connectivity of the human brain in utero. Trends in Cognitive Sciences, 20(12), 931–939. https://doi.org/10.1016/j.tics.2016.10.001

57.

van den Heuvel

M. P.

Kersbergen

K. J.

de Reus

M. A.

Keunen

Kahn

R. S.

Groenendaal

de Vries

L. S.

Benders

M. J. N. L.

(2015). The neonatal connectome during preterm brain development. Cerebral Cortex, 25(9), 3000–3013. https://doi.org/10.1093/cercor/bhu095

58.

Vázquez-Rodríguez

Suárez

L. E.

Markello

R. D.

Shafiei

Paquola

Hagmann

van den Heuvel

M. P.

Bernhardt

B. C.

Spreng

R. N.

Misic

(2019). Gradients of structure–function tethering across neocortex. Proceedings of the National Academy of Sciences, 116(42), 21219–21227. https://doi.org/10.1073/pnas.1903403116

59.

Wang

Xia

Liao

Evans

(2015a). GRETNA: A graph theoretical network analysis toolbox for imaging connectomics. Frontiers in human neuroscience, 9, 386. https://doi.org/10.3389/fnhum.2015.00386

60.

Wang

Fang

T. Y.

Zhang

Cheng

Wang

C. F.

Chen

Y. Y.

Fan

Q. Y.

Zhao

Ming

(2025). The overgrowth of structure-function coupling in premature brain during infancy. Developmental Cognitive Neuroscience, 72, 101535. https://doi.org/10.1016/j.dcn.2025.101535

61.

Wang

Z. J.

Dai

Z. J.

Gong

G. L.

Zhou

C. S.

(2015b). Understanding structural-functional relationships in the human brain: A large-scale network perspective. Neuroscientist, 21(3), 290–305. https://doi.org/10.1177/1073858414537560

62.

Xia

Y. M.

Zhao

J. L.

Y. H.

Duan

D. N.

Xia

M. R.

Jeon

Ouyang

M. H.

Chalak

Rollins

Huang

(2024). Development of sensorimotor-visual connectome gradient at birth predicts neurocognitive outcomes at 2 years of age. iScience, 27(2), 108981. https://doi.org/10.1016/j.isci.2024.108981

63.

Cao

Liao

Xia

Wang

Jeon

Ouyang

Chalak

Rollins

Huang

(2019). Development and emergence of individual variability in the functional connectivity architecture of the preterm human brain. Cerebral Cortex, 29(10), 4208–4222. https://doi.org/10.1093/cercor/bhy302

64.

Y. H.

Liao

X. H.

Lei

T. Y.

Cao

Zhao

J. L.

Zhang

J. Y.

Zhao

T. D.

Q. L.

Jeon

Ouyang

M. H.

Chalak

Rollins

Huang

(2024). Development of neonatal connectome dynamics and its prediction for cognitive and language outcomes at age 2. Cerebral Cortex, 34(5), bhae204. https://doi.org/10.1093/cercor/bhae204

65.

Yan Chao-Gan

Zang. Y.-F.

(2010). DPARSF: A MATLAB toolbox for “pipeline” data analysis of resting-state fMRI. Frontiers in system neuroscience, 4, 13. https://doi.org/10.3389/fnsys.2010.00013

66.

Q. L.

Ouyang

M. H.

Detre

Kang

H. Y.

Hong

Fang

Peng

Huang

(2023). Infant brain regional cerebral blood flow increases supporting emergence of the default-mode network. eLife, 12, e78397. https://doi.org/10.7554/eLife.78397

67.

Zhao

T. D.

Mishra

Jeon

Ouyang

M. H.

Peng

Q. M.

Chalak

Wisnowski

J. L.

Heyne

Rollins

Shu

Huang

(2019a). Structural network maturation of the preterm human brain. Neuroimage, 185, 699–710. https://doi.org/10.1016/j.neuroimage.2018.06.047

68.

Zhao

T. D.

Y. H.

(2019b). Graph theoretical modeling of baby brain networks. Neuroimage, 185, 711–727. https://doi.org/10.1016/j.neuroimage.2018.06.038

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