Linking Changes in Sulcal Morphology to Cognitive Development from Childhood to Adolescence

Linking Changes in Sulcal Morphology to Cognitive Development from Childhood to Adolescence | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Linking Changes in Sulcal Morphology to Cognitive Development from Childhood to Adolescence Shuyu Li, Yijin Shan, Huiting Qiao, Yirong He, Lei Chu, Debin Zeng, and 17 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5561682/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Throughout childhood and adolescence, the development of motor skills and cognitive functions is accompanied by concurrent changes in the morphology of cortical sulci. In this study, we analyzed structural MRI (sMRI) data from a longitudinal cohort of 312 children (145 females) aged 6 to 14 years to reconstruct and label the brain's sulci, followed by mixed-effects modeling to assess age-related morphological changes. We further investigated the relationship between morphological changes and cognitive development during this period via the least absolute shrinkage and selection operator (LASSO) method. Our results revealed a significant increase in the width of secondary/tertiary sulci compared with primary sulci. An increased width of the secondary intermediate ramus of the intraparietal sulcus in children predicted improvements in Attention Network Test performance, whereas an increased width of the posterior intralingual sulcus was most strongly associated with improvements in working memory performance. Through gene enrichment analysis, we discovered that the age-related changes in sulcal morphology are linked to underlying biological processes, including synaptic reorganization and myelination. This study enhances our understanding of the relationship between sulcal morphology and cognitive function, highlighting mechanisms that may influence brain development from childhood to adolescence. Biological sciences/Neuroscience/Learning and memory/Working memory Biological sciences/Neuroscience/Cognitive neuroscience/Attention Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction In gyrencephalic mammals such as humans, the surface of the cerebral cortex folds into intricate grooves known as sulci and ridges called gyri 1 . These complex patterns are not random but rather arise from sophisticated biological and biomechanical mechanisms and are intrinsically dictated by a precise program of gene expression 2 – 8 . Consequently, the appearance of the sulcus follows a relatively stable time sequence across individuals. Around the 26th gestational week (GW), primary sulci begin to form, followed by the emergence of secondary sulci around the 32nd GW. Tertiary sulci appear later, typically around full term or shortly thereafter 9 – 11 . The morphology of cerebral sulci continues to develop over the first two years postnatally and undergoes dynamic changes throughout the lifespan in conjunction with overall brain maturation 12 . Notably, the macroscopic morphology of the cerebral cortex is shaped to some extent by microstructural changes occurring concurrently within the brain. Various macroscopic morphological features of the brain have distinct microscopic underpinnings. For instance, the surface area is driven by early radial glia proliferation and relates to the number of minicolumns in the adult brain. Additionally, it has been linked to myelination processes. Cortical thickness is determined by the number of intermediate progenitor cells within each ontogenetic column and is associated with synaptic pruning and myelination 13 . Moreover, complex regional differences in cellular proliferation, coupled with axonal tension and other biomechanical mechanisms, collectively influence sulcal patterns, including the degree of folding, sulcal depth, length, and width 14 . During the initial stages of neurogenesis, neuroepithelial cells proliferate and differentiate into neurons. From 12 to 20 GW, these neurons migrate along radial glial cells to their final destinations, forming the cortical structure 15 . Subsequently, the microstructure of the brain undergoes continuous changes throughout the lifespan. From childhood to adolescence, the processes of axonal myelination and synaptic reorganization in brain regions persist, with the synaptic pruning process resulting in the elimination of more than 40% of excitatory and inhibitory synapses 16 . In terms of macrostructural features, cortical volume exhibits an inverted U-shaped developmental trajectory 17 . Cortical thickness generally decreases, with the parietal lobe experiencing the most substantial reduction and the occipital lobe experiencing the least substantial reduction 18 . The cortical surface area peaks in childhood/adolescence, followed by a slight decrease 19 . Sulcus depth decreases while sulcus width increases, a phenomenon referred to as "cortical flattening" that is most prominently observed in the occipital and frontal lobes 20 . The overall degree of cortical folding increases during early childhood and subsequently decreases 21 . Changes in macroscopic sulcal morphology are concurrent with improvements in children's cognitive and behavioral capabilities 22 . Fine motor skills and hand‒eye coordination improve dramatically in children aged 6–8 years. Moreover, children between the ages of 10 and 12 years experience rapid development in reasoning and memory skills 23 . Several studies have shown that age-related reductions in localized cerebral gyrification are associated with cognitive development 24 . Research has demonstrated that the sulcal pattern of the left intraparietal sulcus is correlated with numerical ability in children 25 . Notably, tertiary sulci, which form later in development, are more susceptible to morphological changes in response to environmental perturbations 26 , 27 . Research has demonstrated that the depth of the tertiary sulcus in the prefrontal lobe is related to an individual's reasoning performance 28 . Additionally, the presence or absence of the ventral component of the para-intermediate frontal sulcus has been shown to influence reasoning ability 29 . However, these studies have focused predominantly on specific brain regions, leaving a significant gap in whole-brain investigations. A recent study suggests that early-developing sulci located in unimodal cortices are associated with sensorimotor abilities, while later-developing complex sulci are linked to higher-order cognitive functions 8 . Thus, we speculate that the morphological developmental changes in various sulci across the whole brain from childhood to adolescence may reflect specific developmental patterns and are associated with different cognitive functions. In this study, we utilized a well-established methodology to extract whole-brain sulci. The age-related development of extracted sulci from childhood to adolescence was established via a mixed-effects model (MEM). To further investigate the relationship between whole-brain sulcus development and cognitive function, we employed the data-driven least absolute shrinkage and selection operator (LASSO) method 30 . We then conducted gene enrichment analysis using whole-brain gene expression data to explore the potential biological processes associated with morphological changes occurring from childhood to adolescence. Methods Participants Structural MR images were collected from the first batch of Children School Functions and Brain Development Project in China (CBD, Beijing Cohort), which includes a longitudinal dataset of 360 participants (163 females). After performing comprehensive data quality control procedures( exclude participants with cognitively abnormal, history of neurological disorders, mental disorders, head injuries, or physical illnesses ) 47 , the final sample comprised 312 typically developing children aged between 6.1 and 13.9 years (145 females, 167 males). Specifically, three MRI scans were available for 47 children (31 females and 16 males), two MRI scans were available for 97 children (49 females and 48 males), and one MRI scan was available for 168 children (65 females and 103 males), resulting in a total of 490 MRI scans (Supplementary Fig. 1). All the children were recruited from primary schools in Beijing on the basis of their performance on a standardized cognitive ability test administered in Chinese. Written informed consent was obtained from each child's parent or guardian, and the study protocol was approved by the Ethics Committee of Beijing Normal University 48 . Data acquisition Image acquisition High-resolution structural MR data were acquired with 3T Siemens Prisma scanners at Peking University, Beijing, China 49 . For each subject, T1-weighted structural images were acquired using the following parameters: repetition time (TR) = 2530 ms; echo time (TE) = 2.98 ms; inversion time (TI) = 1100 ms; flip angle (FA) = 7°; field of view (FOV) = 256 × 224 mm²; number of slices = 192; slice thickness = 1 mm; bandwidth (BW) = 240 Hz/Px; and scan time = 5 min and 58 s. Cognitive Assessment The participants' cognitive performance was assessed via the classic numerical N-back working memory (WM) task and a child-friendly version of the Attention Network Test (ANT) 50 . In the WM task, participants completed 12 blocks of tasks under three different workload conditions: 0-back, 1-back, and 2-back. In the 0-back condition, participants were instructed to identify whether the current digit was the number 1. In the 1-back and 2-back conditions, participants had to determine whether the current digit matched the one presented one or two steps earlier in the sequence, respectively. The final WM dataset included scans from 409 participants (219 females, 190 males). The metric rx2-back was computed by regressing 2-back hits on 0-back hits. Additionally, ANT performance was measured across 411 scans (213 females, 198 males) as assessed by the response times for alerting, orienting, and executive control tasks. Alerting was assessed by comparing response times between double-cue and no-cue conditions; orienting was determined by comparing spatial-cue and center-cue conditions; and executive control was measured by contrasting response times between incongruent and congruent target conditions. MRI Processing As illustrated in Fig. 1 a, all T1-weighted images were preprocessed using the HCP pipeline 31 , which is primarily based on FreeSurfer (version 6.0, http://surfer.nmr.mgh.harvard.edu/ ) 32 . This preprocessing included brain extraction, gray and white matter segmentation, and cortical surface reconstruction. Subsequently, the HCP pipeline resampled and registered the reconstructed cortical surfaces to a template. The resulting segmented cortical structures and deformation field information were then directly imported into the Morphologist toolbox integrated within BrainVISA ( http://brainvisa.info ) 33,34 . This toolbox utilizes the Statistical Parametric Anatomy (SPAM) model to automatically identify cortical sulci, employs supervised learning to label the sulci, and quantitatively measures the morphology of the sulci. 1 , 33 , 34 , 51 . Sulcal selecting and quality control To ensure the reliability of the statistical results, the following quality control steps were implemented: (1) Each extracted sulcus was manually inspected to confirm the absence of apparent segmentation or labeling errors. (2) Sulci with an extraction success rate below 75% were excluded to maintain an adequate sample size. (3) Certain sulcal branches were merged, categorizing the sulci into two main groups based on their emergence time: primary sulci and secondary/tertiary sulci (details provided in Supplementary Table 1 and Supplementary Method 1). Ultimately, we selected 16 primary sulci and 16 secondary/tertiary sulci from each hemisphere, all of which were reliably extracted from individual participants(Fig. 1 b). For the 64 brain sulci retained for further analysis in standardized Talairach space, six metrics of sulcal morphology were considered: cortical thickness (CT), sulcus width (SW), sulcus area (SA), maximum depth (maxD), mean depth (meanD), and sulcus length (SL). To capture the overall developmental patterns at the lobar level, we also computed lobar characteristics of sulcal morphology and overall characteristics for both primary sulci and secondary/tertiary sulci before Step (2). For all metrics, outlier identification (unusually large or small values) was performed prior to modeling (see Supplementary Method 2). Modeling the development of sulcal morphology All morphometric analyses were conducted using MATLAB ( https://www.mathworks.com/ ). We employed an MEM to analyze the extracted morphological features, a method that is particularly well suited for longitudinal data analysis. In this approach, the effects influencing the dependent variable are categorized into "fixed effects," such as age and sex, and "random effects," such as measurements from the same individual observed at different time points. The specific random and fixed effects utilized in this experiment are detailed in Table 1 . An example of a combination is as follows: Table 1 Candidate effects of mixed-effects models Fixed Effect Random Effect 1 + Sex + Age 1|subj_unique 1 + Sex + Age + Age² Age|subj_unique 1 + Sex + Age + Sex × Age Age + Sex|subj_unique 1 + Sex + Age² + Sex × Age Age² + Sex|subj_unique $$\:\text{S}\text{u}\text{l}\text{c}\text{a}\text{l}\:\text{M}\text{o}\text{r}\text{p}\text{h}\text{o}\text{l}\text{o}\text{g}\text{y}\sim1+\text{S}\text{e}\text{x}+\text{A}\text{g}\text{e}+(1│\text{s}\text{u}\text{b}\text{j}\_\text{u}\text{n}\text{i}\text{q}\text{u}\text{e})$$ 1 After model fitting, model selection was conducted via the Bayesian information criterion (BIC), which computes the likelihood function and incorporates a penalty term according to the number of parameters in the model to prevent overfitting or model underperformance. Using the MEM approach, we modeled the changes across six metrics within each cerebral lobe and within 32 sulci. To explore the distinct trajectories of primary and secondary/tertiary sulci, we aggregated and performed mixed-effects modeling on their respective six morphological features. This approach allowed us to compare the evolving characteristics between the two categories. Correlation of Morphological Changes and Cognitive Performance Scores LASSO Method Cognitive-related analyses were performed using Python ( https://www.python.org/ ). In this study, we investigated the relationship between changes in sulcal morphology metrics(ΔSM, including ΔSA, ΔmaxD, ΔmeanD, ΔSL, ΔCT, and ΔSW) and changes in cognitive performance (ΔCP, including Δ2-BACK, ΔAlerting, ΔOrienting, and ΔExecutive_Control) utilizing a dataset of 132 children who underwent WM tasks and 145 children who participated in ANT across multiple time points. The calculation of ΔSM and ΔCP was performed by subtracting the values obtained from the previous scan from those of the subsequent scan within the same subject. All the data were normalized via z scores for consistency. We employed a data-driven approach utilizing LASSO regression 30 . LASSO regression implements L1 regularization by imposing a penalty proportional to the sum of the absolute values of the coefficients, thereby effectively shrinking the parameter α. This method has been employed to identify a specific association between matrix reasoning ability and the depth of the prefrontal tertiary sulcus 28 . Model Selection Six ΔSM values for 32 sulci were included to predict four ΔCP values. Since changes in the four cognitive performance metrics were not associated with baseline age (r ≤ 0.17), baseline age was not included as a predictor variable in our analysis. The values of α, ranging from 0.00001 to 1, were optimized through cross-validation via the GridSearchCV function from the scikit-learn package in Python (Pedregosa et al. 2011; https://scikit-learn.org/stable/index.html ). The model with the smallest cross-validated root mean squared error (RMSEcv) was subsequently selected: $$\:\widehat{{y}_{i}}={{\beta\:}}_{0}+{{\beta\:}}_{1}{sulcus}_{1}+{{\beta\:}}_{2}{sulcus}_{2}+\dots\:+{{\beta\:}}_{n}{sulcus}_{n}+\text{ϵ}I$$ 2 In this model, the coefficients of the less important features were reduced to zero, leaving only the features of interest. Model Comparison To evaluate the performance of the model, we performed a Spearman correlation between the cognitive scores predicted by the model and the actual cognitive scores to assess the predictive power of the model pairs. We also compared the selected model with its nested full model: $$\:\widehat{{y}_{i}}={{\beta\:}}_{0}+{{\beta\:}}_{1}{x}_{1}+{{\beta\:}}_{2}{x}_{2}+\dots\:+{{\beta\:}}_{32}{x}_{32}+\text{ϵ}I$$ 3 where \(\:{\:x}_{1}\) to \(\:{\:x}_{32}\) represent the ΔSM of all sulci and \(\:{y}_{i}^{*}\) represents a specific ΔCP. Because the models are nested, their performance can be compared by selecting the model with the smallest RMSEcv. All the models were validated via the leave-one-out cross-validation (LOOCV) method. Gene Enrichment Analysis The genomic expression data were sourced from the Allen Human Brain Atlas (AHBA, https://human.brain-map.org , Hawrylycz, et al. 2012), a comprehensive whole-brain transcriptome atlas derived from microarray expression data. This dataset includes information on over 20,000 genes obtained from 3,702 tissue samples taken from distinct locations across the brains of six neurotypical adults (three white males, two African American males, and one white female; mean age = 42.5 years). The Morphologist toolbox includes a statistical probabilistic atlas ( https://brainvisa.info/web/morphologist.html ). The fourth dimension of this atlas represents the probability of each voxel belonging to a specific sulcal label, allowing for the manual allocation of each voxel in the whole brain to the layer (sulcal label) with the highest probability. Subcortical structures were excluded, and regions from the same sulcus with different segments were not merged, ultimately yielding 91 whole-brain regions with their Montreal Neurological Institute (MNI) coordinates. The resulting 3D NIFTI file with a 91×15633 (region × gene) matrix was imported into the abagen toolbox 36 (version 0.1.3; https://github.com/rmarkello/abagen ) for preprocessing of the microarray data. This process automatically generated gene expression profiles for each gene in different regions of the atlas. For each gene, the Pearson correlation coefficient and its corresponding p value were calculated between the gene expression data and the feature set T value map. A distance matrix was generated on the basis of the Euclidean coordinates of each brain region with the BrainSmash Python tool 53 (version 0.11.0, https://github.com/murraylab/brainsmash ) to create surrogate maps for permutation testing and correct for spatial autocorrelation 53 . Ultimately, we identified a set of genes that exhibited significant correlations between expression levels and morphological changes across regions (p < 0.05). These genes were then subjected to enrichment analysis via Metascape 38 . Gene Ontology (GO) analysis was conducted using Metascape to identify specific terms related to molecular functions, biological processes, and cellular components 54 . Results Age-related Development of Sulcal Morphology from Childhood to Adolescence The T1-weighted images obtained from 490 acquisitions across 312 subjects (aged 6–14 years, Supplementary Fig. 1) were surface-reconstructed using the HCP pipeline 31 developed with FreeSurfer ( http://surfer.nmr.mgh.harvard.edu/ ) 32 . This process aimed to achieve consistent resolution and register the brain surfaces in a common space. The reconstructed brain surfaces were imported into the Morphologist toolbox integrated within BrainVISA ( http://brainvisa.info ) 33,34 to extract sulcal structures, which were then labeled according to a predefined nomenclature. After excluding sulci with extraction success rates below 75% and merging related sulcal branches, we ultimately selected a total of 64 sulci across the whole brain (32 per hemisphere, including 16 primary and 16 secondary/tertiary sulci) (Fig. 1 b and Supplementary Table 1). The extracted sulci were used to compute cortical thickness (CT), sulcus width (SW), sulcus area (SA), maximum depth (maxD), mean depth (meanD), and sulcus length (SL) (Fig. 1 a). To systematically investigate the age-related development of sulcal morphology in children, we first analyzed the morphological changes in the sulci in each cerebral lobe. We subsequently modeled the development of each sulcus, with a specific focus on identifying any significant developmental differences between the primary and secondary/tertiary sulci. Morphological Changes of Each Sulcus Throughout the Brain Consistent with previous studies, we observed a systematic decrease in SA, maxD, meanD, and SL, along with overall cortical thinning across the brain, accompanied by an increase in SW(take central sulcus for example, Fig. 2 a) 20 , 35 . To represent the age-related changes in whole-brain sulcal morphology, we plotted a T value map specifically for the age variable (Fig. 2 b). SA decreased significantly with age across all sulci, with the most pronounced changes in the intraparietal sulcus. The reduction in the intraparietal sulcus was primarily due to a significant decrease in its length, whereas other cerebral sulci in the left hemisphere did not exhibit significant SL changes between ages 6 and 14 years. With respect to maxD and meanD, the greatest decreases occurred in the calloso-marginal fissure. CT decreased significantly in all sulci throughout the brain, except for the precentral sulcus in the left hemisphere and the occipito-temporal lateral sulcus in the right hemisphere. Most of the sulci tended to increase in width, particularly with the temporal and frontal lobes exhibiting notably significant widening. Overall, significant cortical flattening was observed in the sulci on the medial sides and in the central sulcus. Although the majority of significantly altered sulci were primary sulci, some secondary/tertiary sulci, such as the sub-parietal sulcus, also exhibited notable changes. Age-related Morphological Changes in Sulci: Comparative Analysis When the sulcal morphology across different lobes was examined, distinct patterns of change were observed. Notably, both the frontal and temporal lobes presented significant reductions in SA and meanD, in contrast, maxD remained stable compared to the parietal and occipital lobes. Moreover, a remarkable increase in SW was specifically noted in the occipital lobe. Overall, the changes observed in the right hemisphere mirrored those of the left hemisphere, suggesting a consistent bilateral pattern of sulcal development. The detailed changes in sulcal morphology from the ages 6 to 14 years between lobes are detailed in Table 2 and Fig. 3 a. Table 2 Comparative statistical analysis of brain region metrics hemisphere Measures Frontal Lobe Temporal Lobe Parietal Lobe Occipital Lobe t p t p t p t p left SA -7.26 1.63E-12 -6.67 7.32E-11 -4.54 7.30E-06 -3.38 7.86E-04 maxD > 0.05 > 0.05 -3.95 8.84E-05 -3.67 2.73E-04 meanD -7.09 5.00E-12 -4.7 3.44E-06 -5.31 1.64E-07 -5.72 1.83E-08 SL -2.19 2.90E-02 -2.41 1.60E-02 > 0.05 > 0.05 CT -4.99 8.46E-07 -4.64 4.35E-06 -7.85 2.89E-14 -6.37 4.63E-10 SW > 0.05 3.2 1.50E-03 > 0.05 4.42 1.23E-05 right SA -7.29 1.31E-12 -5.08 5.44E-07 5.72 1.88E-08 -3.83 1.42E-04 maxD > 0.05 > 0.05 -3.16 1.60E-03 -2.89 4.02E-03 meanD -8.94 9.35E-18 -4.5 8.21E-06 -2.67 7.80E-03 -3.71 2.37E-04 SL > 0.05 -2.08 3.70E-02 -2.53 1.10E-02 > 0.05 CT -6.46 2.67E-10 -5.04 6.50E-07 -7.35 8.92E-13 -6.73 4.90E-11 SW > 0.05 2.61 9.40E-03 2.68 7.50E-03 3.34 8.79E-04 We summarized the characteristics of the two categories and separately constructed the MEM developmental models. Overall, the characteristics of primary sulci, including SA, meanD, and SL, show significantly greater variability than those of the secondary/tertiary sulci. Notably, while CT changes did not significantly differ between the two groups, the secondary/tertiary sulci displayed a marked increase in SW, whereas the primary sulci did not (see Table 3 and Fig. 3 b). Table 3 Comparative analysis of primary and secondary/tertiary sulci development hemisphere Measures Primary Secondary t p t p left SA -8.38 5.94E-16 -2.76 5.80E-03 maxD > 0.05 > 0.05 meanD -6.35 4.97E-10 -4.43 1.16E-05 SL -2.92 3.50E-03 > 0.05 CT -6.87 1.90E-11 -6.96 1.10E-11 SW > 0.05 1.99 4.70E-02 right SA -6.76 4.07E-11 -3.12 1.90E-03 maxD > 0.05 > 0.05 meanD -6.88 1.87E-11 -2.58 1.00E-02 SL -2.31 2.10E-02 > 0.05 CT -6.92 1.45E-11 -6.84 2.40E-11 SW > 0.05 2.88 4.10E-03 Changes in Sulcal Features from Ages 6 to 14 Are Associated with Cognitive Performance Using LASSO regression, we modeled the relationship between ΔSM values and ΔCP values. As the shrinkage coefficient α varied from 0.00001 to 1 (Fig. 4 a), the coefficients of the model also changed, as illustrated in the heatmap, with less important sulcal features diminishing to zero and the sulci of primary interest remaining. The model with the lowest RMSEcv was selected as the optimal model. By performing Spearman correlations between the optimal model's predicted ΔCP and the actual ΔCP, we found that only ΔSW significantly predicted ΔCP. Specifically, the Spearman correlation coefficient between the actual Δ2-BACK value and the value predicted by ΔSW was 0.42 (p = 0.029; Fig. 4 c). The correlation coefficient between the actual ΔExecutive_Control value and the value predicted by ΔSW was 0.49 (p = 0.0068; Fig. 4 c). Other features demonstrated some predictive power for ΔCP but were not statistically significant (p > 0.05). Consequently, we focused on identifying the specific sulcal characteristics that contributed to the predictive power of the ΔSW models. Notably, the width of the posterior intralingual sulcus was positively correlated with WM performance, whereas the ΔSW of the secondary intermediate ramus of the intraparietal sulcus in the frontal lobe was negatively correlated with ANT performance (Fig. 4 d). In the evaluation of model performance, the LASSO model showed overall improvement over its nested full model (without the addition of shrinkage coefficients), as indicated by a significant reduction in the RMSEcv (Fig. 4 b). All results pertain to the sulci of the left hemisphere, with no significant findings observed in the sulci of the right hemisphere. Sulcus Development in Children Aged 6–14 Years Is Associated with Gene Expression Profiles To elucidate the intrinsic connection between gene expression and morphometric changes in brain sulci, we converted the 4D BrainVISA atlas into 3D NIFTI format and imported it into the Abagen toolbox 36 (version 0.1.3, https://github.com/rmarkello/abagen), which utilizes genome expression data from the AHBA (https://human.brain-map.org, Hawrylycz, et al. 2012). Gene expression data from various sulcal regions across the entire brain formed a 91 × 15,633 matrix (region × gene) (Fig. 5a). Pearson’s correlation analysis was conducted between the T values of whole-brain sulcal morphology changes and this matrix to identify gene sets that were significantly correlated with changes in sulcal morphology. Gene enrichment analysis and visualization were subsequently performed using the Metascape web tool 38 (http://metascape.org). GO analysis revealed several neurodevelopment-related terms (Fig. 5b). For example, the differential development of SA was enriched for terms such as "dendrite" and "neuron projection development," which are associated with neuronal biological processes. Synapse-related terms, including "synaptic signaling" and "regulation of synapse organization," were correlated with the differential development of SL. These findings suggest that changes in SL may be influenced by synaptic reorganization. Given that our study focused on children, terms such as 'exploration behavior' and 'social behavior' also appeared in the enrichment analysis results, representing relevant developmental processes during childhood. Discussion In our study examining developmental patterns at the regional level in the brain, the frontal and temporal lobes presented fundamentally consistent developmental trajectories characterized by significant decreases in SA, SL, and meanD, whereas maxD did not significantly change. Conversely, maxD in the occipital and parietal lobes demonstrated a consistent decrease. These findings may reflect the divergent developmental patterns between the higher-order association cortex and sensorimotor cortex 39 , and a similar pattern is reflected in the results at the level of each sulcus. Cortical flattening, characterized by an increase in SW and a simultaneous decrease in meanD, was observed only in the occipital and temporal lobes. The developmental differences between primary and secondary/tertiary sulci may stem from the brain's prioritization of regions that support vital functions, while some sulci continue to develop over a more protracted period 40 . Research indicates that alterations in the morphology of tertiary sulci are associated with cognitive decline during the aging process 41 . In our study, the changes in SA, meanD, and SL were more pronounced in primary sulci than in secondary/tertiary sulci, and the only notable change in secondary/tertiary sulci was a significant increase in SW compared with primary sulci. This discrepancy suggests that variations in SW may be governed by distinct underlying principles compared with other structural characteristics. The reorganization of the brain in response to cognitive and experiential shifts leads to pronounced variability in the SW of secondary and tertiary sulci. The secondary intermediate ramus of the intraparietal sulcus is located within the posterior parietal cortex and is closely connected to other regions of the parietal lobe, including areas primarily involved in visual processing and executive function 42 . In this study, the secondary intermediate ramus of the intraparietal sulcus emerged as the sole sulcus significantly associated with executive control functions. Previous studies have established structural‒functional associations between the calcarine sulcus and the V1 region 43 ; however, no such relationship has been identified between the structure of the intralingual sulcus and WM function near the calcarine sulcus. Our findings indicate a strong predictive ability of the posterior intralingual sulcus with respect to children's WM. This ability may be attributed to the proximity of this sulcus to the inferior temporal cortex, which is associated with shape and object recognition 44 . With respect to the structure‒function relationship, SW serves as a predictor of cognitive decline in older adults. Our results indicated a positive correlation between WM metrics and specific SWs, whereas executive control metrics demonstrated a negative correlation with specific SWs, suggesting that wider sulci may reflect enhanced performance in both cognitive domains. This association may be explained by the reorganization of neural connections that occurs from childhood to adolescence, which represents the optimization of the brain structure for more efficient neuronal information transfer. Thus, wider sulci may indicate a greater extent of neural reorganization. However, the underlying cellular biology and mechanical principles governing these phenomena warrant further exploration. In genetic manipulation experiments during embryonic development, alterations in the size of the somatosensory‒motor area resulted in significant defects in tactile and motor behaviors 45 . These findings provide evidence that the morphology of cerebral sulci is not randomly distributed but is instead constructed by an underlying physiological organization that is closely associated with genetic factors. The expression of different genes at various developmental stages initiates specific biological processes of neural development 46 . The effects of genetics may be related to the spatial constraints of sulci and gyri 7 , as recent studies indicate that gene expression gradients during the fetal period correlate with the orientation of early-developing sulci and can be observed through adult brain studies 8 . Therefore, investigating gene expression differences across various sulcal and gyral regions is a meaningful endeavor. In this study, we conducted a correlation analysis between gene expression levels across various sulcal regions and the magnitudes of t values of sulcal morphological features derived from MEM analyses. The results indicated that SA was associated with dendritic development and neuron projection development, whereas SL was linked to synaptic development and restructuring. This relationship may also reflect the processes of neuronal pruning and synaptic reorganization during childhood, wherein a reduction in SL corresponds to a decrease in SA, consequently limiting the spatial capacity for dendritic extension and neuronal branching. However, since the gene expression data utilized in this study were derived from postmortem adult brains, we cannot ascertain whether these genes were expressed at levels equivalent to those in children between the ages of 6 and 14 years. Therefore, to elucidate the intricate associations between gene expression and sulcal morphological changes during this developmental period, more comprehensive studies and additional gene expression data are needed. Declarations Data availability The data that support the findings of this study are available from the authors upon reasonable request and with permission of the corresponding author. Code availability The material and code that support the findings of this study are available from the authors upon reasonable request and with permission of the corresponding author. Competing interests The authors declare no competing financial interests. Author contributions Conceptualization : Y.S.,Y.H.,S.L.; Investigation : Y.S.; Formal analysis : Y.S.; Methodology : Y.S. S.L.; Visualization : Y.S.; Writing- original draft : Y.S.; Writing-Review and Editing : H.Q., YR.H.,L.C.,S.L.; Data curation : D.Z., T.Z., X.L., X.C., Y.X., T.L., L.S., W.M., Y.W., D.W., M.H., Z.P., S.T., J.G. S.Q., S.T., Q.D.; Supervision : Y.H., S.L.; Funding Acquisition : Q.D., H.Y.; S.L. Acknowledgements This work was supported by the Scientific and Technological Innovation 2030 - Major Projects 2021ZD0200500 ( https://en.most.gov.cn/ ), the National Natural Science Foundation of China ( https://www.nsfc.gov.cn/english/site_1/index.html , 32271146, 82021004, 31521063), the Startup Funds for Top-notch Talents at Beijing Normal University and the Beijing Municipal Science & Technology Commission ( https://kw.beijing.gov.cn/ , Z15110000391512). The authors also would like to thank all the families and children for their support and participation. References DESTRIEUX C, FISCHL B, DALE A, HALGREN E (2010) Automatic parcellation of human cortical gyri and sulci using standard anatomical nomenclature. NeuroImage 53:1–15 Richman DP, Stewart RM, Hutchinson JW, Caviness VS (1975) Mechanical model of brain convolutional development. Sci (New York N Y) 189:18–21 Kriegstein A, Noctor S (2006) Martínez-Cerdeño, V. 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Supplementary Files SupplementalInformation.docx Linking Changes in Sulcal Morphology to Cognitive Development from Childhood to Adolescence Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5561682","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":386833555,"identity":"4efaf70d-8223-4244-9653-038b08c09a5e","order_by":0,"name":"Shuyu 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University","correspondingAuthor":false,"prefix":"","firstName":"Sha","middleName":"","lastName":"Tao","suffix":""},{"id":386833576,"identity":"36f8b08a-ba2e-4373-bc50-15793d1c5c41","order_by":21,"name":"Qi Dong","email":"","orcid":"","institution":"Beijing Normal University","correspondingAuthor":false,"prefix":"","firstName":"Qi","middleName":"","lastName":"Dong","suffix":""},{"id":386833577,"identity":"3650b508-82ac-459b-93d1-32bbd5e726f7","order_by":22,"name":"Yong He","email":"","orcid":"https://orcid.org/0000-0002-7039-2850","institution":"Beijing Normal University","correspondingAuthor":false,"prefix":"","firstName":"Yong","middleName":"","lastName":"He","suffix":""}],"badges":[],"createdAt":"2024-12-02 06:05:55","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5561682/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5561682/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":71058050,"identity":"75a4e544-aa69-4359-9611-6696e9ff36e4","added_by":"auto","created_at":"2024-12-10 16:33:44","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":637226,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eWorkflow for sulcal morphology analysis. a \u003c/strong\u003ePreprocessing phase: T1-weighted structural MRI (sMRI) images are initially processed using the Human Connectome Project (HCP) pipeline. The resulting ribbon and T1-weighted images are then input into the BrainVISA Morphologist pipeline, which includes steps for brain segmentation, gray-white matter differentiation, and the generation of pial and white matter meshes. These processes facilitate the construction of the cortical folding graph, resulting in automatically labeled graphs. Key sulcal metrics are extracted during this process and further analyzed in relation to cognitive outcomes, developmental modeling, and genetic analysis.\u003cstrong\u003e b \u003c/strong\u003eSulcal selection: Based on extraction stability and prevalence, we selected 16 primary sulci and 16 secondary/tertiary sulci in each hemisphere.\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-5561682/v1/acd24dd66e1bf12a6cfc9f07.png"},{"id":71059258,"identity":"18bc72ae-7aee-4fb3-be8c-23102c0dd58e","added_by":"auto","created_at":"2024-12-10 16:49:44","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1363987,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eQuantitative analysis of age-related changes in global sulcal morphology. a \u003c/strong\u003eThe developmental characteristics of six features for each sulcus (using the central sulcus as an example) were modeled using a mixed-effects model (MEM). The figure displays the T values and p values from the model, with blue representing males and orange representing females.\u003cstrong\u003e b \u003c/strong\u003eT values associated with age-related changes in six features across various sulci. All sulci shown in the figures exhibit significant age-related developmental effects.\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-5561682/v1/75c2a883848a793f3b6c6a1f.png"},{"id":71058048,"identity":"cd71c382-3a4a-4ba9-936f-14e95fb95a6e","added_by":"auto","created_at":"2024-12-10 16:33:44","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":834784,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDevelopmental Differences in Sulcal Morphology Across Cerebral Lobes and Between Primary and Secondary/Tertiary Sulci. a \u003c/strong\u003eBar chart depicting T values for sulcal morphology changes across the cerebral lobes, where * indicates p \u0026lt; 0.05, ** indicates p \u0026lt; 0.005, and *** indicates p \u0026lt; 0.001. \u003cstrong\u003eb \u003c/strong\u003eUsing the sulcus SA as an example, the developmental effects of primary sulcus SA, meanD, and SL are significantly greater than those of secondary/tertiary sulci; however, the developmental effect of secondary/tertiary SW is more pronounced than that of primary sulci.\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-5561682/v1/4dc336e76b10a1f7503b7e5b.png"},{"id":71059107,"identity":"b28a9d85-4277-420c-8a09-7faef0192d5c","added_by":"auto","created_at":"2024-12-10 16:41:44","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":474493,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSulcal changes correlated with cognitive task performance: LASSO method. a \u003c/strong\u003eTop: heatmap representing the LASSO coefficient of each left hemisphere sulcus changing with decreasing parameter α; middle: the black lines indicate RMSEcv as a function of log(α), while the red dots represent the minimum RMSEcv.\u003cstrong\u003e b \u003c/strong\u003eGraphs showing the performance of the LASSO model and its fully nested model across tasks. The left side of the dashed line represents the predictive performance of the LASSO model, whereas the right side represents the predictive performance of the full model. The LASSO model has a lower overall RMSEcv, indicating better model performance. \u003cstrong\u003ec\u003c/strong\u003e Scatter plots illustrating the correlation between actual cognitive performance values and predicted values, with Spearman's R values provided. \u003cstrong\u003ed\u003c/strong\u003e Brain maps indicating locations of significant sulci related to WM (2-back task) and the ANT (executive control performance). The color scale represents the magnitude of the LASSO regression coefficient.\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-5561682/v1/fa35392d169ecd43f209f46f.png"},{"id":71059105,"identity":"7e00687e-48b2-42d3-b59c-02ef985cc2f5","added_by":"auto","created_at":"2024-12-10 16:41:44","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":986573,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGene Enrichment Analysis Workflow and Results. a \u003c/strong\u003eWhole-brain gene expression data were organized according to the BrainVISA atlas to obtain gene expression levels in different brain regions. Correlation analyses were conducted with the T values of various whole-brain features. After performing permutation test, a significant subset of genes correlating expression levels and T values was identified for enrichment analysis. \u003cstrong\u003eb \u003c/strong\u003eThe figure displays clusters of genes significantly enriched for surface area (SA) and sulcal length (SL), along with their associated biological processes.\u003cstrong\u003e c \u003c/strong\u003eThe bubble plot and bar chart illustrate the number and proportion of related genes, alongside their corresponding P values.\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-5561682/v1/591af6ceeda70c21480d5dcf.png"},{"id":76236359,"identity":"e77ceddb-4bbf-4e70-a345-5dd8b002f053","added_by":"auto","created_at":"2025-02-13 20:39:17","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5807302,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5561682/v1/c2b6683a-23d1-4117-a5ac-e0d187cf4253.pdf"},{"id":71058046,"identity":"1d0785e0-3933-438e-84c8-9d68d167cc53","added_by":"auto","created_at":"2024-12-10 16:33:44","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":66019,"visible":true,"origin":"","legend":"Linking Changes in Sulcal Morphology to Cognitive Development from Childhood to Adolescence","description":"","filename":"SupplementalInformation.docx","url":"https://assets-eu.researchsquare.com/files/rs-5561682/v1/e384e1210d9d7529d15219e9.docx"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Linking Changes in Sulcal Morphology to Cognitive Development from Childhood to Adolescence","fulltext":[{"header":"Introduction","content":"\u003cp\u003eIn gyrencephalic mammals such as humans, the surface of the cerebral cortex folds into intricate grooves known as sulci and ridges called gyri\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. These complex patterns are not random but rather arise from sophisticated biological and biomechanical mechanisms and are intrinsically dictated by a precise program of gene expression\u003csup\u003e\u003cspan additionalcitationids=\"CR3 CR4 CR5 CR6 CR7\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. Consequently, the appearance of the sulcus follows a relatively stable time sequence across individuals. Around the 26th gestational week (GW), primary sulci begin to form, followed by the emergence of secondary sulci around the 32nd GW. Tertiary sulci appear later, typically around full term or shortly thereafter\u003csup\u003e\u003cspan additionalcitationids=\"CR10\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. The morphology of cerebral sulci continues to develop over the first two years postnatally and undergoes dynamic changes throughout the lifespan in conjunction with overall brain maturation\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eNotably, the macroscopic morphology of the cerebral cortex is shaped to some extent by microstructural changes occurring concurrently within the brain. Various macroscopic morphological features of the brain have distinct microscopic underpinnings. For instance, the surface area is driven by early radial glia proliferation and relates to the number of minicolumns in the adult brain. Additionally, it has been linked to myelination processes. Cortical thickness is determined by the number of intermediate progenitor cells within each ontogenetic column and is associated with synaptic pruning and myelination\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. Moreover, complex regional differences in cellular proliferation, coupled with axonal tension and other biomechanical mechanisms, collectively influence sulcal patterns, including the degree of folding, sulcal depth, length, and width\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eDuring the initial stages of neurogenesis, neuroepithelial cells proliferate and differentiate into neurons. From 12 to 20 GW, these neurons migrate along radial glial cells to their final destinations, forming the cortical structure\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. Subsequently, the microstructure of the brain undergoes continuous changes throughout the lifespan. From childhood to adolescence, the processes of axonal myelination and synaptic reorganization in brain regions persist, with the synaptic pruning process resulting in the elimination of more than 40% of excitatory and inhibitory synapses\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. In terms of macrostructural features, cortical volume exhibits an inverted U-shaped developmental trajectory\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. Cortical thickness generally decreases, with the parietal lobe experiencing the most substantial reduction and the occipital lobe experiencing the least substantial reduction\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. The cortical surface area peaks in childhood/adolescence, followed by a slight decrease\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. Sulcus depth decreases while sulcus width increases, a phenomenon referred to as \"cortical flattening\" that is most prominently observed in the occipital and frontal lobes\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. The overall degree of cortical folding increases during early childhood and subsequently decreases\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. Changes in macroscopic sulcal morphology are concurrent with improvements in children's cognitive and behavioral capabilities\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. Fine motor skills and hand‒eye coordination improve dramatically in children aged 6\u0026ndash;8 years. Moreover, children between the ages of 10 and 12 years experience rapid development in reasoning and memory skills\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eSeveral studies have shown that age-related reductions in localized cerebral gyrification are associated with cognitive development \u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. Research has demonstrated that the sulcal pattern of the left intraparietal sulcus is correlated with numerical ability in children \u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. Notably, tertiary sulci, which form later in development, are more susceptible to morphological changes in response to environmental perturbations \u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e,\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. Research has demonstrated that the depth of the tertiary sulcus in the prefrontal lobe is related to an individual's reasoning performance\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. Additionally, the presence or absence of the ventral component of the para-intermediate frontal sulcus has been shown to influence reasoning ability \u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. However, these studies have focused predominantly on specific brain regions, leaving a significant gap in whole-brain investigations. A recent study suggests that early-developing sulci located in unimodal cortices are associated with sensorimotor abilities, while later-developing complex sulci are linked to higher-order cognitive functions\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. Thus, we speculate that the morphological developmental changes in various sulci across the whole brain from childhood to adolescence may reflect specific developmental patterns and are associated with different cognitive functions.\u003c/p\u003e \u003cp\u003eIn this study, we utilized a well-established methodology to extract whole-brain sulci. The age-related development of extracted sulci from childhood to adolescence was established via a mixed-effects model (MEM). To further investigate the relationship between whole-brain sulcus development and cognitive function, we employed the data-driven least absolute shrinkage and selection operator (LASSO) method\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. We then conducted gene enrichment analysis using whole-brain gene expression data to explore the potential biological processes associated with morphological changes occurring from childhood to adolescence.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eParticipants\u003c/h2\u003e \u003cp\u003eStructural MR images were collected from the first batch of Children School Functions and Brain Development Project in China (CBD, Beijing Cohort), which includes a longitudinal dataset of 360 participants (163 females). After performing comprehensive data quality control procedures( exclude participants with cognitively abnormal, history of neurological disorders, mental disorders, head injuries, or physical illnesses )\u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e, the final sample comprised 312 typically developing children aged between 6.1 and 13.9 years (145 females, 167 males). Specifically, three MRI scans were available for 47 children (31 females and 16 males), two MRI scans were available for 97 children (49 females and 48 males), and one MRI scan was available for 168 children (65 females and 103 males), resulting in a total of 490 MRI scans (Supplementary Fig.\u0026nbsp;1). All the children were recruited from primary schools in Beijing on the basis of their performance on a standardized cognitive ability test administered in Chinese. Written informed consent was obtained from each child's parent or guardian, and the study protocol was approved by the Ethics Committee of Beijing Normal University\u003csup\u003e\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eData acquisition\u003c/h2\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003ch2\u003eImage acquisition\u003c/h2\u003e \u003cp\u003eHigh-resolution structural MR data were acquired with 3T Siemens Prisma scanners at Peking University, Beijing, China\u003csup\u003e\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e. For each subject, T1-weighted structural images were acquired using the following parameters: repetition time (TR)\u0026thinsp;=\u0026thinsp;2530 ms; echo time (TE)\u0026thinsp;=\u0026thinsp;2.98 ms; inversion time (TI)\u0026thinsp;=\u0026thinsp;1100 ms; flip angle (FA)\u0026thinsp;=\u0026thinsp;7\u0026deg;; field of view (FOV)\u0026thinsp;=\u0026thinsp;256 \u0026times; 224 mm\u0026sup2;; number of slices\u0026thinsp;=\u0026thinsp;192; slice thickness\u0026thinsp;=\u0026thinsp;1 mm; bandwidth (BW)\u0026thinsp;=\u0026thinsp;240 Hz/Px; and scan time\u0026thinsp;=\u0026thinsp;5 min and 58 s.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eCognitive Assessment\u003c/h2\u003e \u003cp\u003eThe participants' cognitive performance was assessed via the classic numerical N-back working memory (WM) task and a child-friendly version of the Attention Network Test (ANT) \u003csup\u003e\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn the WM task, participants completed 12 blocks of tasks under three different workload conditions: 0-back, 1-back, and 2-back. In the 0-back condition, participants were instructed to identify whether the current digit was the number 1. In the 1-back and 2-back conditions, participants had to determine whether the current digit matched the one presented one or two steps earlier in the sequence, respectively. The final WM dataset included scans from 409 participants (219 females, 190 males). The metric rx2-back was computed by regressing 2-back hits on 0-back hits.\u003c/p\u003e \u003cp\u003eAdditionally, ANT performance was measured across 411 scans (213 females, 198 males) as assessed by the response times for alerting, orienting, and executive control tasks. Alerting was assessed by comparing response times between double-cue and no-cue conditions; orienting was determined by comparing spatial-cue and center-cue conditions; and executive control was measured by contrasting response times between incongruent and congruent target conditions.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eMRI Processing\u003c/h2\u003e \u003cp\u003eAs illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea, all T1-weighted images were preprocessed using the HCP pipeline\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e, which is primarily based on FreeSurfer (version 6.0, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://surfer.nmr.mgh.harvard.edu/\u003c/span\u003e\u003cspan address=\"http://surfer.nmr.mgh.harvard.edu/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e)\u003csup\u003e32\u003c/sup\u003e. This preprocessing included brain extraction, gray and white matter segmentation, and cortical surface reconstruction. Subsequently, the HCP pipeline resampled and registered the reconstructed cortical surfaces to a template. The resulting segmented cortical structures and deformation field information were then directly imported into the Morphologist toolbox integrated within BrainVISA (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://brainvisa.info\u003c/span\u003e\u003cspan address=\"http://brainvisa.info\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e)\u003csup\u003e33,34\u003c/sup\u003e. This toolbox utilizes the Statistical Parametric Anatomy (SPAM) model to automatically identify cortical sulci, employs supervised learning to label the sulci, and quantitatively measures the morphology of the sulci.\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e,\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e,\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eSulcal selecting and quality control\u003c/h2\u003e \u003cp\u003eTo ensure the reliability of the statistical results, the following quality control steps were implemented: (1) Each extracted sulcus was manually inspected to confirm the absence of apparent segmentation or labeling errors. (2) Sulci with an extraction success rate below 75% were excluded to maintain an adequate sample size. (3) Certain sulcal branches were merged, categorizing the sulci into two main groups based on their emergence time: primary sulci and secondary/tertiary sulci (details provided in Supplementary Table\u0026nbsp;1 and Supplementary Method 1). Ultimately, we selected 16 primary sulci and 16 secondary/tertiary sulci from each hemisphere, all of which were reliably extracted from individual participants(Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb). For the 64 brain sulci retained for further analysis in standardized Talairach space, six metrics of sulcal morphology were considered: cortical thickness (CT), sulcus width (SW), sulcus area (SA), maximum depth (maxD), mean depth (meanD), and sulcus length (SL).\u003c/p\u003e \u003cp\u003eTo capture the overall developmental patterns at the lobar level, we also computed lobar characteristics of sulcal morphology and overall characteristics for both primary sulci and secondary/tertiary sulci before Step (2). For all metrics, outlier identification (unusually large or small values) was performed prior to modeling (see Supplementary Method 2).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eModeling the development of sulcal morphology\u003c/h2\u003e \u003cp\u003eAll morphometric analyses were conducted using MATLAB (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.mathworks.com/\u003c/span\u003e\u003cspan address=\"https://www.mathworks.com/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). We employed an MEM to analyze the extracted morphological features, a method that is particularly well suited for longitudinal data analysis. In this approach, the effects influencing the dependent variable are categorized into \"fixed effects,\" such as age and sex, and \"random effects,\" such as measurements from the same individual observed at different time points. The specific random and fixed effects utilized in this experiment are detailed in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e1\u003c/span\u003e. An example of a combination is as follows:\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCandidate effects of mixed-effects models\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFixed Effect\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRandom Effect\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u0026thinsp;+\u0026thinsp;Sex\u0026thinsp;+\u0026thinsp;Age\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1|subj_unique\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u0026thinsp;+\u0026thinsp;Sex\u0026thinsp;+\u0026thinsp;Age\u0026thinsp;+\u0026thinsp;Age\u0026sup2;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAge|subj_unique\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u0026thinsp;+\u0026thinsp;Sex\u0026thinsp;+\u0026thinsp;Age\u0026thinsp;+\u0026thinsp;Sex \u0026times; Age\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAge\u0026thinsp;+\u0026thinsp;Sex|subj_unique\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u0026thinsp;+\u0026thinsp;Sex\u0026thinsp;+\u0026thinsp;Age\u0026sup2; + Sex \u0026times; Age\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAge\u0026sup2; + Sex|subj_unique\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003cdiv id=\"Equ1\" class=\"Equation\"\u003e \u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$\\:\\text{S}\\text{u}\\text{l}\\text{c}\\text{a}\\text{l}\\:\\text{M}\\text{o}\\text{r}\\text{p}\\text{h}\\text{o}\\text{l}\\text{o}\\text{g}\\text{y}\\sim1+\\text{S}\\text{e}\\text{x}+\\text{A}\\text{g}\\text{e}+(1│\\text{s}\\text{u}\\text{b}\\text{j}\\_\\text{u}\\text{n}\\text{i}\\text{q}\\text{u}\\text{e})$$\u003c/div\u003e \u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAfter model fitting, model selection was conducted via the Bayesian information criterion (BIC), which computes the likelihood function and incorporates a penalty term according to the number of parameters in the model to prevent overfitting or model underperformance. Using the MEM approach, we modeled the changes across six metrics within each cerebral lobe and within 32 sulci. To explore the distinct trajectories of primary and secondary/tertiary sulci, we aggregated and performed mixed-effects modeling on their respective six morphological features. This approach allowed us to compare the evolving characteristics between the two categories.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eCorrelation of Morphological Changes and Cognitive Performance Scores\u003c/h2\u003e \u003cdiv id=\"Sec18\" class=\"Section3\"\u003e \u003ch2\u003eLASSO Method\u003c/h2\u003e \u003cp\u003eCognitive-related analyses were performed using Python (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.python.org/\u003c/span\u003e\u003cspan address=\"https://www.python.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). In this study, we investigated the relationship between changes in sulcal morphology metrics(ΔSM, including ΔSA, ΔmaxD, ΔmeanD, ΔSL, ΔCT, and ΔSW) and changes in cognitive performance (ΔCP, including Δ2-BACK, ΔAlerting, ΔOrienting, and ΔExecutive_Control) utilizing a dataset of 132 children who underwent WM tasks and 145 children who participated in ANT across multiple time points. The calculation of ΔSM and ΔCP was performed by subtracting the values obtained from the previous scan from those of the subsequent scan within the same subject. All the data were normalized via z scores for consistency. We employed a data-driven approach utilizing LASSO regression \u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. LASSO regression implements L1 regularization by imposing a penalty proportional to the sum of the absolute values of the coefficients, thereby effectively shrinking the parameter α. This method has been employed to identify a specific association between matrix reasoning ability and the depth of the prefrontal tertiary sulcus\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eModel Selection\u003c/h2\u003e \u003cp\u003eSix ΔSM values for 32 sulci were included to predict four ΔCP values. Since changes in the four cognitive performance metrics were not associated with baseline age (r\u0026thinsp;\u0026le;\u0026thinsp;0.17), baseline age was not included as a predictor variable in our analysis.\u003c/p\u003e \u003cp\u003eThe values of α, ranging from 0.00001 to 1, were optimized through cross-validation via the GridSearchCV function from the scikit-learn package in Python (Pedregosa et al. 2011; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://scikit-learn.org/stable/index.html\u003c/span\u003e\u003cspan address=\"https://scikit-learn.org/stable/index.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The model with the smallest cross-validated root mean squared error (RMSEcv) was subsequently selected:\u003cdiv id=\"Equ2\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ2\" name=\"EquationSource\"\u003e\n$$\\:\\widehat{{y}_{i}}={{\\beta\\:}}_{0}+{{\\beta\\:}}_{1}{sulcus}_{1}+{{\\beta\\:}}_{2}{sulcus}_{2}+\\dots\\:+{{\\beta\\:}}_{n}{sulcus}_{n}+\\text{ϵ}I$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e2\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eIn this model, the coefficients of the less important features were reduced to zero, leaving only the features of interest.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eModel Comparison\u003c/h2\u003e \u003cp\u003eTo evaluate the performance of the model, we performed a Spearman correlation between the cognitive scores predicted by the model and the actual cognitive scores to assess the predictive power of the model pairs. We also compared the selected model with its nested full model:\u003cdiv id=\"Equ3\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ3\" name=\"EquationSource\"\u003e\n$$\\:\\widehat{{y}_{i}}={{\\beta\\:}}_{0}+{{\\beta\\:}}_{1}{x}_{1}+{{\\beta\\:}}_{2}{x}_{2}+\\dots\\:+{{\\beta\\:}}_{32}{x}_{32}+\\text{ϵ}I$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e3\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\:x}_{1}\\)\u003c/span\u003e\u003c/span\u003eto \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\:x}_{32}\\)\u003c/span\u003e\u003c/span\u003erepresent the ΔSM of all sulci and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{y}_{i}^{*}\\)\u003c/span\u003e\u003c/span\u003e represents a specific ΔCP. Because the models are nested, their performance can be compared by selecting the model with the smallest RMSEcv. All the models were validated via the leave-one-out cross-validation (LOOCV) method.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eGene Enrichment Analysis\u003c/h2\u003e \u003cp\u003eThe genomic expression data were sourced from the Allen Human Brain Atlas (AHBA, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://human.brain-map.org\u003c/span\u003e\u003cspan address=\"https://human.brain-map.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, Hawrylycz, et al. 2012), a comprehensive whole-brain transcriptome atlas derived from microarray expression data. This dataset includes information on over 20,000 genes obtained from 3,702 tissue samples taken from distinct locations across the brains of six neurotypical adults (three white males, two African American males, and one white female; mean age\u0026thinsp;=\u0026thinsp;42.5 years). The Morphologist toolbox includes a statistical probabilistic atlas (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://brainvisa.info/web/morphologist.html\u003c/span\u003e\u003cspan address=\"https://brainvisa.info/web/morphologist.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The fourth dimension of this atlas represents the probability of each voxel belonging to a specific sulcal label, allowing for the manual allocation of each voxel in the whole brain to the layer (sulcal label) with the highest probability. Subcortical structures were excluded, and regions from the same sulcus with different segments were not merged, ultimately yielding 91 whole-brain regions with their Montreal Neurological Institute (MNI) coordinates. The resulting 3D NIFTI file with a 91\u0026times;15633 (region \u0026times; gene) matrix was imported into the abagen toolbox\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e (version 0.1.3; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/rmarkello/abagen\u003c/span\u003e\u003cspan address=\"https://github.com/rmarkello/abagen\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) for preprocessing of the microarray data. This process automatically generated gene expression profiles for each gene in different regions of the atlas. For each gene, the Pearson correlation coefficient and its corresponding p value were calculated between the gene expression data and the feature set T value map. A distance matrix was generated on the basis of the Euclidean coordinates of each brain region with the BrainSmash Python tool\u003csup\u003e\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u003c/sup\u003e (version 0.11.0, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/murraylab/brainsmash\u003c/span\u003e\u003cspan address=\"https://github.com/murraylab/brainsmash\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e ) to create surrogate maps for permutation testing and correct for spatial autocorrelation \u003csup\u003e\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u003c/sup\u003e. Ultimately, we identified a set of genes that exhibited significant correlations between expression levels and morphological changes across regions (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). These genes were then subjected to enrichment analysis via Metascape\u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e. Gene Ontology (GO) analysis was conducted using Metascape to identify specific terms related to molecular functions, biological processes, and cellular components \u003csup\u003e\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eAge-related Development of Sulcal Morphology from Childhood to Adolescence\u003c/h2\u003e \u003cp\u003eThe T1-weighted images obtained from 490 acquisitions across 312 subjects (aged 6\u0026ndash;14 years, Supplementary Fig.\u0026nbsp;1) were surface-reconstructed using the HCP pipeline\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e developed with FreeSurfer (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://surfer.nmr.mgh.harvard.edu/\u003c/span\u003e\u003cspan address=\"http://surfer.nmr.mgh.harvard.edu/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e)\u003csup\u003e32\u003c/sup\u003e. This process aimed to achieve consistent resolution and register the brain surfaces in a common space. The reconstructed brain surfaces were imported into the Morphologist toolbox integrated within BrainVISA (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://brainvisa.info\u003c/span\u003e\u003cspan address=\"http://brainvisa.info\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e)\u003csup\u003e33,34\u003c/sup\u003e to extract sulcal structures, which were then labeled according to a predefined nomenclature. After excluding sulci with extraction success rates below 75% and merging related sulcal branches, we ultimately selected a total of 64 sulci across the whole brain (32 per hemisphere, including 16 primary and 16 secondary/tertiary sulci) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb and Supplementary Table\u0026nbsp;1). The extracted sulci were used to compute cortical thickness (CT), sulcus width (SW), sulcus area (SA), maximum depth (maxD), mean depth (meanD), and sulcus length (SL) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea). To systematically investigate the age-related development of sulcal morphology in children, we first analyzed the morphological changes in the sulci in each cerebral lobe. We subsequently modeled the development of each sulcus, with a specific focus on identifying any significant developmental differences between the primary and secondary/tertiary sulci.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eMorphological Changes of Each Sulcus Throughout the Brain\u003c/h3\u003e\n\u003cp\u003eConsistent with previous studies, we observed a systematic decrease in SA, maxD, meanD, and SL, along with overall cortical thinning across the brain, accompanied by an increase in SW(take central sulcus for example, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea) \u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e,\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. To represent the age-related changes in whole-brain sulcal morphology, we plotted a T value map specifically for the age variable (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb). SA decreased significantly with age across all sulci, with the most pronounced changes in the intraparietal sulcus. The reduction in the intraparietal sulcus was primarily due to a significant decrease in its length, whereas other cerebral sulci in the left hemisphere did not exhibit significant SL changes between ages 6 and 14 years. With respect to maxD and meanD, the greatest decreases occurred in the calloso-marginal fissure. CT decreased significantly in all sulci throughout the brain, except for the precentral sulcus in the left hemisphere and the occipito-temporal lateral sulcus in the right hemisphere. Most of the sulci tended to increase in width, particularly with the temporal and frontal lobes exhibiting notably significant widening. Overall, significant cortical flattening was observed in the sulci on the medial sides and in the central sulcus. Although the majority of significantly altered sulci were primary sulci, some secondary/tertiary sulci, such as the sub-parietal sulcus, also exhibited notable changes.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eAge-related Morphological Changes in Sulci: Comparative Analysis\u003c/h3\u003e\n\u003cp\u003eWhen the sulcal morphology across different lobes was examined, distinct patterns of change were observed. Notably, both the frontal and temporal lobes presented significant reductions in SA and meanD, in contrast, maxD remained stable compared to the parietal and occipital lobes. Moreover, a remarkable increase in SW was specifically noted in the occipital lobe. Overall, the changes observed in the right hemisphere mirrored those of the left hemisphere, suggesting a consistent bilateral pattern of sulcal development. The detailed changes in sulcal morphology from the ages 6 to 14 years between lobes are detailed in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparative statistical analysis of brain region metrics\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ehemisphere\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eMeasures\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e 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colname=\"c2\"\u003e \u003cp\u003eCT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-4.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.46E-07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-4.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.35E-06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-7.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.89E-14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-6.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e4.63E-10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.50E-03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e4.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.23E-05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003eright\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-7.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.31E-12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-5.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.44E-07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.88E-08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-3.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.42E-04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emaxD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-3.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.60E-03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-2.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e4.02E-03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emeanD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-8.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.35E-18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-4.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8.21E-06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-2.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e7.80E-03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-3.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e2.37E-04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-2.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.70E-02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-2.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.10E-02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-6.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.67E-10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-5.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.50E-07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-7.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e8.92E-13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-6.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e4.90E-11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9.40E-03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e7.50E-03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e8.79E-04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWe summarized the characteristics of the two categories and separately constructed the MEM developmental models. Overall, the characteristics of primary sulci, including SA, meanD, and SL, show significantly greater variability than those of the secondary/tertiary sulci. Notably, while CT changes did not significantly differ between the two groups, the secondary/tertiary sulci displayed a marked increase in SW, whereas the primary sulci did not (see Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e3\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparative analysis of primary and secondary/tertiary sulci development\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ehemisphere\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eMeasures\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003ePrimary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eSecondary\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003et\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003et\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003eleft\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-8.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.94E-16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-2.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.80E-03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emaxD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emeanD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-6.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.97E-10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-4.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.16E-05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-2.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.50E-03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-6.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.90E-11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-6.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.10E-11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.70E-02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003eright\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-6.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.07E-11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-3.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.90E-03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emaxD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emeanD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-6.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.87E-11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-2.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.00E-02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-2.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.10E-02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-6.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.45E-11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-6.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.40E-11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.10E-03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\n\u003ch3\u003eChanges in Sulcal Features from Ages 6 to 14 Are Associated with Cognitive Performance\u003c/h3\u003e\n\u003cp\u003eUsing LASSO regression, we modeled the relationship between ΔSM values and ΔCP values. As the shrinkage coefficient α varied from 0.00001 to 1 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea), the coefficients of the model also changed, as illustrated in the heatmap, with less important sulcal features diminishing to zero and the sulci of primary interest remaining. The model with the lowest RMSEcv was selected as the optimal model. By performing Spearman correlations between the optimal model's predicted ΔCP and the actual ΔCP, we found that only ΔSW significantly predicted ΔCP. Specifically, the Spearman correlation coefficient between the actual Δ2-BACK value and the value predicted by ΔSW was 0.42 (p\u0026thinsp;=\u0026thinsp;0.029; Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ec). The correlation coefficient between the actual ΔExecutive_Control value and the value predicted by ΔSW was 0.49 (p\u0026thinsp;=\u0026thinsp;0.0068; Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ec). Other features demonstrated some predictive power for ΔCP but were not statistically significant (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Consequently, we focused on identifying the specific sulcal characteristics that contributed to the predictive power of the ΔSW models. Notably, the width of the posterior intralingual sulcus was positively correlated with WM performance, whereas the ΔSW of the secondary intermediate ramus of the intraparietal sulcus in the frontal lobe was negatively correlated with ANT performance (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ed). In the evaluation of model performance, the LASSO model showed overall improvement over its nested full model (without the addition of shrinkage coefficients), as indicated by a significant reduction in the RMSEcv (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb). All results pertain to the sulci of the left hemisphere, with no significant findings observed in the sulci of the right hemisphere.\u003c/p\u003e \n\u003cp\u003e\u003cstrong\u003eSulcus Development in Children Aged 6\u0026ndash;14 Years Is Associated with Gene Expression Profiles\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo elucidate the intrinsic connection between gene expression and morphometric changes in brain sulci, we converted the 4D BrainVISA atlas into 3D NIFTI format and imported it into the Abagen toolbox\u003csup\u003e36\u003c/sup\u003e (version 0.1.3, https://github.com/rmarkello/abagen), which utilizes genome expression data from the AHBA (https://human.brain-map.org, Hawrylycz, et al. 2012). Gene expression data from various sulcal regions across the entire brain formed a 91 \u0026times; 15,633 matrix (region \u0026times; gene) (Fig. 5a). Pearson\u0026rsquo;s correlation analysis was conducted between the T values of whole-brain sulcal morphology changes and this matrix to identify gene sets that were significantly correlated with changes in sulcal morphology. Gene enrichment analysis and visualization were subsequently performed using the Metascape web tool\u003csup\u003e38\u003c/sup\u003e (http://metascape.org).\u003c/p\u003e\n\u003cp\u003eGO analysis revealed several neurodevelopment-related terms (Fig. 5b). For example, the differential development of SA was enriched for terms such as \u0026quot;dendrite\u0026quot; and \u0026quot;neuron projection development,\u0026quot; which are associated with neuronal biological processes. Synapse-related terms, including \u0026quot;synaptic signaling\u0026quot; and \u0026quot;regulation of synapse organization,\u0026quot; were correlated with the differential development of SL. These findings suggest that changes in SL may be influenced by synaptic reorganization. Given that our study focused on children, terms such as \u0026apos;exploration behavior\u0026apos; and \u0026apos;social behavior\u0026apos; also appeared in the enrichment analysis results, representing relevant developmental processes during childhood.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn our study examining developmental patterns at the regional level in the brain, the frontal and temporal lobes presented fundamentally consistent developmental trajectories characterized by significant decreases in SA, SL, and meanD, whereas maxD did not significantly change. Conversely, maxD in the occipital and parietal lobes demonstrated a consistent decrease. These findings may reflect the divergent developmental patterns between the higher-order association cortex and sensorimotor cortex \u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e, and a similar pattern is reflected in the results at the level of each sulcus. Cortical flattening, characterized by an increase in SW and a simultaneous decrease in meanD, was observed only in the occipital and temporal lobes.\u003c/p\u003e \u003cp\u003eThe developmental differences between primary and secondary/tertiary sulci may stem from the brain's prioritization of regions that support vital functions, while some sulci continue to develop over a more protracted period \u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e. Research indicates that alterations in the morphology of tertiary sulci are associated with cognitive decline during the aging process \u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e. In our study, the changes in SA, meanD, and SL were more pronounced in primary sulci than in secondary/tertiary sulci, and the only notable change in secondary/tertiary sulci was a significant increase in SW compared with primary sulci. This discrepancy suggests that variations in SW may be governed by distinct underlying principles compared with other structural characteristics. The reorganization of the brain in response to cognitive and experiential shifts leads to pronounced variability in the SW of secondary and tertiary sulci.\u003c/p\u003e \u003cp\u003eThe secondary intermediate ramus of the intraparietal sulcus is located within the posterior parietal cortex and is closely connected to other regions of the parietal lobe, including areas primarily involved in visual processing and executive function \u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e. In this study, the secondary intermediate ramus of the intraparietal sulcus emerged as the sole sulcus significantly associated with executive control functions.\u003c/p\u003e \u003cp\u003ePrevious studies have established structural‒functional associations between the calcarine sulcus and the V1 region \u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e; however, no such relationship has been identified between the structure of the intralingual sulcus and WM function near the calcarine sulcus. Our findings indicate a strong predictive ability of the posterior intralingual sulcus with respect to children's WM. This ability may be attributed to the proximity of this sulcus to the inferior temporal cortex, which is associated with shape and object recognition \u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eWith respect to the structure‒function relationship, SW serves as a predictor of cognitive decline in older adults. Our results indicated a positive correlation between WM metrics and specific SWs, whereas executive control metrics demonstrated a negative correlation with specific SWs, suggesting that wider sulci may reflect enhanced performance in both cognitive domains. This association may be explained by the reorganization of neural connections that occurs from childhood to adolescence, which represents the optimization of the brain structure for more efficient neuronal information transfer. Thus, wider sulci may indicate a greater extent of neural reorganization. However, the underlying cellular biology and mechanical principles governing these phenomena warrant further exploration.\u003c/p\u003e \u003cp\u003eIn genetic manipulation experiments during embryonic development, alterations in the size of the somatosensory‒motor area resulted in significant defects in tactile and motor behaviors \u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e. These findings provide evidence that the morphology of cerebral sulci is not randomly distributed but is instead constructed by an underlying physiological organization that is closely associated with genetic factors. The expression of different genes at various developmental stages initiates specific biological processes of neural development \u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e. The effects of genetics may be related to the spatial constraints of sulci and gyri\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e, as recent studies indicate that gene expression gradients during the fetal period correlate with the orientation of early-developing sulci and can be observed through adult brain studies\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. Therefore, investigating gene expression differences across various sulcal and gyral regions is a meaningful endeavor.\u003c/p\u003e \u003cp\u003eIn this study, we conducted a correlation analysis between gene expression levels across various sulcal regions and the magnitudes of t values of sulcal morphological features derived from MEM analyses. The results indicated that SA was associated with dendritic development and neuron projection development, whereas SL was linked to synaptic development and restructuring. This relationship may also reflect the processes of neuronal pruning and synaptic reorganization during childhood, wherein a reduction in SL corresponds to a decrease in SA, consequently limiting the spatial capacity for dendritic extension and neuronal branching. However, since the gene expression data utilized in this study were derived from postmortem adult brains, we cannot ascertain whether these genes were expressed at levels equivalent to those in children between the ages of 6 and 14 years. Therefore, to elucidate the intricate associations between gene expression and sulcal morphological changes during this developmental period, more comprehensive studies and additional gene expression data are needed.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eData availability\u003c/h2\u003e\n\u003cp\u003eThe data that support the findings of this study are available from the authors upon reasonable request and with permission of the corresponding author.\u003c/p\u003e\n\u003ch2\u003eCode availability\u003c/h2\u003e\n\u003cp\u003eThe material and code that support the findings of this study are available from the authors upon reasonable request and with permission of the corresponding author.\u003c/p\u003e\n\u003ch2\u003eCompeting interests\u003c/h2\u003e \u003cp\u003eThe authors declare no competing financial interests.\u003c/p\u003e\u003ch2\u003eAuthor contributions\u003c/h2\u003e \u003cp\u003e \u003cb\u003eConceptualization\u003c/b\u003e: Y.S.,Y.H.,S.L.; \u003cb\u003eInvestigation\u003c/b\u003e: Y.S.; \u003cb\u003eFormal analysis\u003c/b\u003e: Y.S.; \u003cb\u003eMethodology\u003c/b\u003e: Y.S. S.L.; \u003cb\u003eVisualization\u003c/b\u003e: Y.S.; \u003cb\u003eWriting- original draft\u003c/b\u003e: Y.S.; \u003cb\u003eWriting-Review and Editing\u003c/b\u003e: H.Q., YR.H.,L.C.,S.L.; \u003cb\u003eData curation\u003c/b\u003e: D.Z., T.Z., X.L., X.C., Y.X., T.L., L.S., W.M., Y.W., D.W., M.H., Z.P., S.T., J.G. S.Q., S.T., Q.D.; \u003cb\u003eSupervision\u003c/b\u003e: Y.H., S.L.; \u003cb\u003eFunding Acquisition\u003c/b\u003e: Q.D., H.Y.; S.L.\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e \u003cp\u003eThis work was supported by the Scientific and Technological Innovation 2030 - Major Projects 2021ZD0200500 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://en.most.gov.cn/\u003c/span\u003e\u003cspan address=\"https://en.most.gov.cn/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), the National Natural Science Foundation of China (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.nsfc.gov.cn/english/site_1/index.html\u003c/span\u003e\u003cspan address=\"https://www.nsfc.gov.cn/english/site_1/index.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, 32271146, 82021004, 31521063), the Startup Funds for Top-notch Talents at Beijing Normal University and the Beijing Municipal Science \u0026amp; Technology Commission (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://kw.beijing.gov.cn/\u003c/span\u003e\u003cspan address=\"https://kw.beijing.gov.cn/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, Z15110000391512). The authors also would like to thank all the families and children for their support and participation.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eDESTRIEUX C, FISCHL B, DALE A, HALGREN E (2010) Automatic parcellation of human cortical gyri and sulci using standard anatomical nomenclature. NeuroImage 53:1\u0026ndash;15\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRichman DP, Stewart RM, Hutchinson JW, Caviness VS (1975) Mechanical model of brain convolutional development. Sci (New York N Y) 189:18\u0026ndash;21\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKriegstein A, Noctor S (2006) Mart\u0026iacute;nez-Cerde\u0026ntilde;o, V. Patterns of neural stem and progenitor cell division may underlie evolutionary cortical expansion. 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Nat Genet 25:25\u0026ndash;29\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-5561682/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5561682/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThroughout childhood and adolescence, the development of motor skills and cognitive functions is accompanied by concurrent changes in the morphology of cortical sulci. In this study, we analyzed structural MRI (sMRI) data from a longitudinal cohort of 312 children (145 females) aged 6 to 14 years to reconstruct and label the brain's sulci, followed by mixed-effects modeling to assess age-related morphological changes. We further investigated the relationship between morphological changes and cognitive development during this period via the least absolute shrinkage and selection operator (LASSO) method. Our results revealed a significant increase in the width of secondary/tertiary sulci compared with primary sulci. An increased width of the secondary intermediate ramus of the intraparietal sulcus in children predicted improvements in Attention Network Test performance, whereas an increased width of the posterior intralingual sulcus was most strongly associated with improvements in working memory performance. Through gene enrichment analysis, we discovered that the age-related changes in sulcal morphology are linked to underlying biological processes, including synaptic reorganization and myelination. This study enhances our understanding of the relationship between sulcal morphology and cognitive function, highlighting mechanisms that may influence brain development from childhood to adolescence.\u003c/p\u003e","manuscriptTitle":"Linking Changes in Sulcal Morphology to Cognitive Development from Childhood to Adolescence","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-12-10 16:33:39","doi":"10.21203/rs.3.rs-5561682/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"6834da29-bee9-4beb-b0fc-6ba1d5c1b4c4","owner":[],"postedDate":"December 10th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":41223528,"name":"Biological sciences/Neuroscience/Learning and memory/Working memory"},{"id":41223529,"name":"Biological sciences/Neuroscience/Cognitive neuroscience/Attention"}],"tags":[],"updatedAt":"2025-02-13T20:31:07+00:00","versionOfRecord":[],"versionCreatedAt":"2024-12-10 16:33:39","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5561682","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5561682","identity":"rs-5561682","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}