# On the typical development of the central sulcus in infancy: A longitudinal evaluation of its morphology and link to behaviour

Amaia Dornier, Alexia Gérard, Yann Leprince, Lucie Hertz-Pannier, Jean-François Mangin, Marianne Barbu-Roth, Jessica Dubois, Dollyane Muret

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Amaia Dornier, Alexia Gérard, Yann Leprince, Lucie Hertz-Pannier, Jean-François Mangin, et al. On the typical development of the central sulcus in infancy: A longitudinal evaluation of its morphology and link to behaviour. Developmental Neuroscience, 2025, pp.1-20. 10.1159/000546958. hal-05283047

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# On the Typical Development of the Central Sulcus in Infancy: A Longitudinal Evaluation of Its Morphology and Link to Behaviour

Amaia Dornier<sup>a, b</sup> Alexia Gérard<sup>a, b</sup> Yann Leprince<sup>b</sup> Lucie Hertz-Pannier<sup>a, b</sup>
Jean-François Mangin<sup>c</sup> Marianne Barbu-Roth<sup>d</sup> Jessica Dubois<sup>a, b</sup>
Dollyane Muret<sup>a, b</sup>

<sup>a</sup>Université Paris Cité, INSERM, NeuroDiderot, Paris, France; <sup>b</sup>Université Paris Saclay, CEA, NeuroSpin, UNIACT, Gif-sur-Yvette, France; <sup>c</sup>Université Paris Saclay, CEA, NeuroSpin, BAOBAB, Gif-sur-Yvette, France; <sup>d</sup>Université Paris Cité, CNRS, Integrative Neuroscience and Cognition Center, Paris, France

## Keywords

Cortical folding · Typical development · Sensorimotor system · Anatomical magnetic resonance imaging · Infants

## Abstract

**Introduction:** The progressive folding of the cortex is an important feature of neurodevelopment starting around the 14th week of gestation. The central sulcus (CS) is one of the first to fold. Since it represents the anatomical boundary between primary somatosensory and motor functional regions, its developing morphology may inform on the acquisition of sensorimotor skills. We aimed to identify potential asynchronous morphological changes along the CS during infancy, with the hypothesis that this may reflect the differential onset in the emergence of motor milestones across body parts. **Method:** Based on 3T anatomical magnetic resonance imaging and dedicated post-processing, we characterized the evolution in CS depth and curvature, along with their respective interhemispheric asymmetries in 33 typical infants (aged 1 and 3 months, 22 with longitudinal data) in relation to 23 young adults as a reference. Four regions of interest (ROIs) along the CS, supposed to correspond to different parts of the body

and one centred on the hand knob (HK), were reproducibly examined and compared across groups. We also explored the relationship between the age-related changes in morphological features and the global motor scaled scores evaluated at 3 months of age with the Bayley Scales of Infant and Toddler Development. **Results:** No interhemispheric asymmetry in CS depth and curvature was observed. While all ROIs showed significant increases in CS depth and curvature between 3-month-olds and adults, the results were more variable between 1 and 3 months of age depending on cross-sectional and longitudinal analyses. The central-medial and central-lateral regions showed the most consistent increase in depth. Besides, motor development at 3 months of age was not significantly related to CS morphological changes, but a positive trend was observed for depth changes in the (HK-related) central-medial ROI. **Conclusion:** The rapid evolution of CS folding during infancy may reflect the intense but asynchronous maturation of the brain sensorimotor system, with the differential growth of cortical areas related to body parts and underlying white matter connections. Although it will

Jessica Dubois and Dollyane Muret are co-last authors.
have to be replicated on larger groups and at other ages, this longitudinal and multimodal study highlights the potential of characterizing CS features as key markers of early sensorimotor development, both at the cerebral and behavioural levels. Combining anatomical and functional neuroimaging could provide deeper insights into the relationship between CS morphology and somatotopic organization in typical infants, but also in infants at risk of developing motor disorders.

© 2025 The Author(s).

Published by S. Karger AG, Basel

## Introduction

The development of the human brain is an extraordinarily intricate and complex process that relies on a series of key cellular mechanisms beginning during gestation (see [1] for a review). At the macroscopic level, an important feature of brain development lies in its progressive folding. Brain folding begins around the 14th week of gestation with the formation of primary sulci, followed by secondary folds appearing around the 32nd week, and tertiary folds beginning to form from the 38th week [2] (see [3] for a review), resulting in the well-defined sulci and gyri typical of the mature brain.

Among early developing sulci, the central sulcus (CS) is one of the first to form since its first dimples are visible from the 18th week, with an earlier onset in the right hemisphere than in the left [2, 4]. The CS is a critical marker of brain organization as it delineates the frontal and parietal lobes and marks the boundary between the primary motor (M1) and somatosensory (S1) cortices hosting sensorimotor functions. Interestingly, its shape features in adults have recently been linked to its topographical organization [5] that characterizes the well-known *Homunculus* [6]. In particular, the “hand-knob” (HK) is a key marker of hand sensorimotor function: its prominent asymmetrical morphology and central position in the CS are thought to reflect mature hand motor function and lateralization to some extent, notably with regard to handedness [7]. While such anatomo-functional link has not yet been demonstrated in developmental populations, recent evidence showed that this homunculus is already present from a very young age in preterm infants [8], and that the main shape characteristics of the CS were found to be already encoded at 30 weeks of post-menstrual age (PMA) in premature infants imaged close to birth [9]. In addition, these early features observed by term-equivalent age in preterms were suggested to partially predict their later motor abilities and manual laterality at 5 years of age [9].

Taken together, this evidence makes the CS the ideal candidate for an early anatomical marker of sensorimotor development. Nevertheless, the development of the CS and the evolution of its morphological features in the first months after birth remain poorly understood.

Considering the CS as a whole, its folding becomes elaborate in the third gestational trimester, as observed in preterm infants imaged at both 30 weeks PMA and term-equivalent age, and some asymmetries in shape were shown between the left and right hemispheres [9]. Interestingly, a quantitative analysis of the CS depth in early childhood revealed different variations with age depending on the location on the CS: while the sulcus depth showed a rightward asymmetry at 1 year of age in the lateral part of the CS, this disappeared before 2 years of age [10]. Besides, while the CS demonstrated an overall increase in depth between 1 and 3 years of age, this was more pronounced in the central regions of the sulcus [10]. One of the hypotheses put forward to explain these heterogeneous changes in depth along the CS involved the formation of a “pli de passage” (i.e., a cortical bridge that connects two gyri by partially crossing a sulcus) in the central region [11], consequently to the development and myelination of underlying white matter connections [10]. However, while unexplored so far, the first post-natal months are an important period not only for brain folding [12] but also for the intense sensorimotor experiences and skill acquisitions emerging at these ages [13–16].

In addition to genetic factors, these behaviours and experiences are thought to impact but also to rely on the development of underlying brain networks (see [1, 3] for reviews). In particular, the development of both behaviours and underlying brain systems is supposed to proceed asynchronously along the body axis, from proximal to distal regions and from superior to inferior regions [17]. Such asynchrony in maturation is a well-known phenomenon across cerebral networks [18, 19] and has been previously highlighted using magnetic resonance imaging (MRI) in typical infants, both at the white matter and cortical levels [20, 21]. As such, one could expect different developmental trajectories of maturation along the CS as sensorimotor acquisitions and body usage emerge, but this has never been explored during infancy.

Leveraging the proven advantages of MRI to map the folding process noninvasively and quantitatively in early development (see [3] for a review), we aimed to fill this gap by imaging typical infants at 1 and 3 months of age, in association with behavioural assessment at the latter age. We examined the evolution of key morphological
markers of the CS (depth, curvature, and their respective asymmetries) with reference to a group of young adults as these morphological features also vary along the CS in the mature brain. As we assumed the development of folding to proceed asynchronously along the CS, we considered different regions supposed to be associated with different body parts, in particular the HK that was identified at the individual level. We hypothesize that the depth and curvature of the CS, along with their respective asymmetries, increase with age, with significant changes already evident between 1-month (1 M)-old and 3-month (3 M)-old infants, particularly in the HK region. We further related sensorimotor acquisitions with the evolution of CS morphological markers in order to contribute to our understanding of how early brain structure supports behavioural development in infants. We hypothesize that more advanced development at 3 months is associated with more pronounced anatomical changes in the characteristics of the CS.

## Methods

### Participants and Data Collection
Infant Group

Thirty-seven typically developing infants (19 girls) were included in this study. They were born of single full-term pregnancies (i.e., between 37.43 and 41.86 weeks of gestational age) and had no neurological risk or background (i.e., incident-free pregnancy and birth, no postnatal complications, and no immediate family members with neurological or genetic abnormalities). To ensure infants met these criteria, we relied on the guidelines established by the French "Haute Autorité de Santé" on the risk factors of neurodevelopmental disorders [22], and we sought the expertise of habilitated medical professionals from the team in case of uncertainty. Out of the 37 infants, 30 were recruited early enough to be included in a first session at approximately 1 M of age (PMA between 44 and 46 weeks +/- 5 days) and 36 of them came for a second session at 3 months (3 M) of age (PMA between 52 and 54 weeks +/- 5 days). MRI acquisition was performed on these infants and successfully collected in 33 of them, with 26 (13 girls) tested at 1 M (PMA at MRI: mean ± standard deviation: 45.20 ± 0.65 weeks, range: 44.14-46.43 weeks), and 29 (14 girls) at 3 M (PMA at MRI: 53.21 ± 0.82 weeks, 51.43-54.71 weeks). MRI acquisition could not be completed in 4 infants at 1 M and in 7 infants at 3 M

(83.33% of success rate) due to difficulties to make the infant fall or stay asleep (see below). Out of the 33 infants with collected data, 22 (11 girls) had longitudinal data with MRI successfully collected at the two ages.

Prior to inclusions, an attempt at a power analysis was performed for CS depth. In the absence of studies on this period of development, an effect size of differences between 1 M and 3 M of age was estimated based on data from the study by [23], who examined changes in CS depth in children aged 2-3 years. Since brain size and morphological changes are expected to be much higher over the 1-3 M period than over the 2-3-year period due to the higher increase in head circumference (8.5% vs. 2% according to Adikia's curves), we considered an effect size of 1.06 as the minimal effect we would observe in infants in CS regions with significant deepening. A power analysis with G\*Power then revealed that a sample size of 12 subjects was required to detect this effect size (with α = 0.05) in a matched-pair (longitudinal) design, and a sample size of 50 subjects for independent groups.

MRI examinations were performed using a 3 Tesla magnetic field scanner (Siemens Prisma) and a 20-channel head coil for most infants (except for 4 infants at 1 M for whom a 64-channel head coil was used). Acquisitions were carried out during the infant's spontaneous sleep, after nursing, swaddling and putting earplugs and headphones to protect from the scanner noise. If the infant woke up or moved during acquisition, the scan was interrupted and attempts were made to recollect the sequence to obtain artifact-free data. If attempts failed, the data/session were excluded from the analyses. For anatomical explorations with a good contrast between the developing grey and white matter, T2-weighted (T2w) images were acquired with a 2D Turbo Spin Echo sequence with the following parameters: TR = 4,200 ms, TE = 149 ms, flip angle = 140°, parallel imaging with GRAPPA acceleration factor = 2, spatial resolution = 0.8 × 0.8 × 2 mm, 60 slices (acquisition time = 1 min 55 s). Images were acquired in axial plane for all infants, and an additional scan was acquired in sagittal plane for 20 infants (9 at 1 M, 16 at 3 M).

Alongside the MRI acquisition, a behavioural evaluation of the infants was carried out on the same day, but in this study, we focused on the data collected at the age of 3 M. The gross and fine motor (GM/FM) skills were assessed with the Bayley Scales of Infant and Toddler Development 3rd edition (BSID-III [24]). At 3 M, up to the first 26 GM items and 21 FM items are evaluated [25], stopping after 5 consecutive failed items (i.e., child
developmental ceiling). GM and FM scaled scores, ranging from 1 to 19, were obtained, providing information on the infant's motor development in relation to other infants of the same age (calibration on a North American population). The global motor scaled score was computed as the sum of GM and FM scaled scores.

## Adult Group

This study also considered data from 23 healthy young adults from the Human Connectome Project (HCP), born full term (12 women, range of ages: 22-35 years) who were selected from the Human Connectome Project (HCP) database [26]. None showed brain abnormality or had a history of diabetes, hypertension, excessive alcohol consumption, or substance abuse, nor any sibling with severe neurodevelopmental, neuropsychiatric, or neurological disorders. MRI data acquisition was performed using a 3 Tesla magnetic resonance scanner (Siemens Connectome Skyra) equipped with a 32-channel head coil. For anatomical explorations, T1-weighted (T1w) images were collected using a 3D gradient-echo sequence (TR = 2,400 ms, TE = 3.16 ms, TI = 1,000 ms, parallel imaging with GRAPPA acceleration factor = 2, isotropic resolution of 1 mm) [27]. The high quality of MRI data was one of the criteria to select these subjects, as well as data availability and preprocessing in our institution.

## Processing of MRI Data

### Infant Group

For each infant at each age, axial T2w images were first Fourier-resampled to an isotropic spatial resolution of 0.8 mm. Segmentation processing was performed using iBEAT2 (version 120 [28]). After bias correction, a first segmentation was carried out to generate the brain mask, which was manually corrected if needed (for 5 participants). Within this adjusted brain mask, brain tissues (grey and white matter, cortico-spinal fluid) were segmented (iBEAT2 version 205). The resulting segmentations were further processed using BrainVISA software [29] through the “Morphologist” processing pipeline [30] to identify cortical sulci. After correcting the segmentations in the central regions when needed, the inner and outer cortical surfaces were reconstructed for both hemispheres. Objects representing cortical sulci were identified between the brain hull and pial surfaces, and automatically labelled [31]. Global brain volume was estimated as the sum of hemispheric volumes contained within the closed outer cortical surfaces [12, 32].

## Adult Group

T1w images were fully processed using BrainVISA and Morphologist pipeline [30], to obtain brain tissue segmentation, cortical surface reconstruction, and identification of cortical sulci as done for infants data.

## Analysis of CS Morphology

### Parametrization of CS

Based on the whole brain labelled set of cortical sulci, the CS of each participant was visually checked to ensure a proper and consistent reconstruction and labelling over infants and adults, particularly for longitudinal data. The length of left and right CS was estimated for each subject. The BrainVISA “Cortical Surface” pipeline, along with the sulcus parameterization [33-35] was further used to extract and parameterize the CS through the definition of a curvilinear coordinate (from 0 at its upper medial edge to 100 at its lower lateral edge, see Fig. 1).

### Measure of CS Depth and Curvature

Using BrainVISA toolboxes, each CS coordinate was associated with a depth value (i.e., geodesic distance between the crest and the bottom of the sulcus) and a sulcal profile value (i.e., signed distance from the CS plane of inertia that captures its main orientation). Zero sulcal profile values at coordinates 0 and 100 were replaced by neighbouring values at coordinates 1 and 99, and a one-dimensional Gaussian filter (sigma = 2) was applied to the sulcal profile values at all coordinates. The CS local curvature was then computed from the neighbouring sulcal profile values according to the following formula:

$$c(i) = \frac{p''(i)}{(1 + p'^2(i))^{3/2}}$$

$$\text{with } p'(i) = \frac{p(i+1) - p(i-1)}{2} \text{ and}$$

$$p''(i) = \frac{p(i+2) + p(i-2) - 2p(i)}{4}$$

c the curvature, p the sulcal profile, and i the position along CS ($2 \le i \le 98$).

In order to inform on the local sulcal curviness regardless of its direction, we further considered the absolute curvature values.

### Definition of Regions of Interest

For each participant (infant/adult), the coordinate of the centre of the HK along the CS was identified based on anatomical (T2w/T1w) images and 3D surfaces, according to its shape and localization [36], checking for overall consistency across left and right hemispheres.
<figure><img src="image_2.png" /><figcaption>**Fig. 1.** Examples of individual central sulcus (CS) and CS parameterization (left hemisphere). **a** 1-M-old infant showing a typical CS. **b** 1-M-old infant showing a discontinuous CS. **c** Adult showing a typical CS. All CS are displayed (in red) on the cortical surface of the subject. CS are parameterized along the sulcus with</figcaption></figure>

Four regions of interest (ROIs) were then defined at the individual level: one was centred on the HK coordinate (hereafter, central-medial), two were placed, respectively, at the lowest (most medial) and highest (most lateral) coordinates of the CS (hereafter, medial and lateral), and a fourth ROI was placed at equidistance between the central-medial and lateral ROIs (hereafter, central-lateral). To minimize noise issues at CS extremities, the 5 lowest and highest coordinates of the CS were excluded before defining the ROIs. Based on these criteria and given the range of HK coordinates observed across both the infant and adult groups, a consistent ROI size of 14 coordinates was used for all subjects and for both hemispheres to obtain comparable ROIs across participants, though defined based on the individual's anatomy.

Depth and absolute curvature values were measured for each hemisphere along the CS coordinates, as well as averaged over its entire length and over each ROI. Averages over left and right hemispheres were also considered.

## Statistical Analyses

### Outlier Identification

Within each age group (1 M, 3 M, adults), subjects with an absolute z-score in brain volume higher than 2 were excluded from all analyses, as well as subjects with discontinuous (i.e., interrupted) CS. When analyzing morphological features (depth, absolute curvature), subjects displaying z-scores higher than 2 for averaged measures (over left and right CS) were considered as outliers. As for analyses targeting behavioural scores, we further excluded subjects with FM and GM scores lower than or equal to 7 since this threshold has been associated with a risk of atypical neurodevelopment [24].

coordinates ranging from 0 (most medial edge) to 100 (most lateral edge) and displayed from both a dorsal (top) and lateral (bottom) view to highlight the CS curvature and depth, respectively. CS coordinates are coded by the gradient of colour: blue = 0, red = 100.

## Evaluation of Interhemispheric Asymmetries

For each morphological feature $F$ (i.e., depth and absolute curvature averaged over CS) and each subject, an asymmetry index ($AI$) was computed between the left (L) and right (R) hemispheres following this equation [37]:

$$AI = \frac{F_L - F_R}{F_L + F_R}$$

A positive index indicates left asymmetry, while a negative index indicates right asymmetry. The presence or absence of asymmetry was evaluated for each age group independently using Wilcoxon test on AI. Correction for multiple comparisons (three groups) was performed using False Discovery Rate (FDR) approach, and a statistical threshold $p < 0.05$ was considered as significant. In case of non-significance, the average between right and left measures was considered for further analysis. Supplementary analyses on interhemispheric asymmetries (see online suppl. Information SI 1; for all online suppl. material, see https://doi.org/10.1159/000546958) were performed on the HK coordinates (see online suppl. Table 1.1) and on the morphological features obtained within each ROIs (see online suppl. Table 1.2).

## Evolution and Relationships between Brain Volume and CS Measures

To assess whether the differences between groups were of the same order of magnitude between brain features, the means (noted $\bar{F}$) were compared between the 1 M and 3 M groups using the formula of relative difference: $\frac{\bar{F}_3 - \bar{F}_1}{\bar{F}_3 + \bar{F}_1}$ (and similarly for the 3 M and adult groups).

For each CS morphological feature (i.e., depth and absolute curvature averaged over the left and right CS), we further investigated the relationship with brain
volume using Pearson correlations performed after logarithmic linearization to account for possible alometries [32]. This was done for each age group separately and when combining 1 M and 3 M infants. Correlations between averaged depth and absolute curvature were also tested for each group and combined infant groups.

## Impact of Age Group and ROI Localization on CS Features

We then aimed to evaluate whether each CS morphological feature (i.e., depth, absolute curvature averaged over left and right hemispheres) differed across the four ROIs in a distinct way across the three age groups (1 M, 3 M, adults). First, a linear mixed-effects model (LMM) was performed on the entire dataset (both longitudinal and cross-sectional age groups, but excluding outliers identified for the considered feature within each group) considering group and ROI as fixed-effect predictors, as well as their interaction (group × ROI), and participants as random-effect predictors. Post hoc tests were conducted using pairwise comparisons of estimated marginal means (EMMs), and adjustments for multiple comparisons were applied using the FDR approach.

Second, we focused on infants with longitudinal data to compute the relative change in each feature *F* of each ROI between 1 M and 3 M (in %) using the following equation:

$$ \Delta F = \frac{F_{3M} - F_{1M}}{F_{3M} + F_{1M}} $$

We tested whether this relative change was different from 0 using Student *t* tests (four ROIs). The effect of ROIs was then assessed using an analysis of variance with the factor ROI. Post hoc tests consisted of paired *t* tests across ROI pairs (6 comparisons). For each analysis, correction for multiple comparisons was performed with the FDR approach. For both depth and absolute curvature, supplementary analyses along the CS were performed (see online suppl. Information SI.2; Fig. 2.1).

## Relationship between the Evolution of CS Morphology and Sensorimotor Development

To evaluate whether sensorimotor acquisitions at 3 M were related to the developmental evolution of CS regions, we considered the global motor scaled score of infants and related it with longitudinal MRI measures (1 M, 3 M). Since the inter-individual variability in the age gap between 1 M and 3 M might impact the age-related difference in MRI features, we considered the variables

$\frac{F_{3M}-F_{1M}}{age_{3M}-age_{1M}}$ in linear models all describing the global motor scaled score but testing different hypotheses successively. The first model evaluated the relationship with a global brain feature not specific to CS, so the brain volume was considered. With the second model, we tested the relationship with global CS morphological characteristics, considering depth and absolute curvature averaged over the sulcus. The third model questioned a variable relationship depending on location along the CS: we considered the morphological characteristics averaged over the two central ROIs independently, as the medial and lateral ROIs showed less robust changes with age. For the last two models, we had checked beforehand that depth and absolute curvature were not correlated, and that they did not depend on brain volume changes. Correction for multiple comparisons was performed across models with FDR approach.

## Additional Validation Analyses

Supplementary analyses on the intra-subject variability related to the pipeline used for sulcus identification (see online suppl. Information SI.3; Fig. 3.1; Tables 3.1, 3.2) and to the T2w reconstruction approach (super resolution vs. resampling; see online suppl. Information SI.3; Fig. 3.2, 3.3; Tables 3.3, 3.4) were performed.

## Results

### Global CS Morphology
#### CS Identification

Reliable CS reconstructions were obtained for all subjects (examples in Fig. 1a, c) thanks to the careful verification of brain segmentations and manual corrections whenever needed. Among all subjects, three infants showed a discontinuous CS (example in Fig. 1b): two at both ages (longitudinal data) in the left hemisphere, and one at 3 M in the right hemisphere. These infants were excluded from all analyses. As expected from the increasing brain size with age (Fig. 2a), CS length also qualitatively increased from 1 M to 3 M and from 3 M to adults (Fig. 2b).

### Outlier Identification

In terms of brain volume, we identified one outlier infant at both 1 M and 3 M, which was excluded from all analyses, and no adult outlier (Fig. 2a). Regarding depth measures (averaged over the whole left and right CS), two outlier infants were identified at both ages, as well as one adult (Fig. 2c left column). Regarding absolute
<figure><img src="image_3.png" /><figcaption>**Fig. 2.** Quantification of brain features, outlier identification, and evaluation of interhemispheric asymmetry within each group. **a** Boxplot of the brain volume (in cm³) observed for each group (infants at 1 M and 3 M of age and adults). Individual data points are represented on top of the boxplots. One infant was identified as an outlier at both ages (bigger points). **b** Boxplot of the length (in mm) averaged over the left and right CS and observed for each group. **c** Boxplots of the depth (left column) and absolute curvature (right column) averaged over the left and right CS and observed for each group (outliers shown with bigger points). **d** Boxplots of the interhemispheric asymmetry index computed for each group for the average depth of the CS (left column) and the average absolute curvature of the CS (right column). A positive asymmetry indicates higher values in the left hemisphere relative to the right hemisphere (and vice versa). No significant asymmetries were observed.</figcaption></figure>
<table><caption>**Table 1.** Summary statistics on interhemispheric asymmetries</caption><thead><tr><th>Group</th><th>N</th><th>Median</th><th>W</th><th>p value</th><th>BF10</th></tr></thead><tbody><tr><td colspan="6"><em>a Depth</em></td></tr><tr><td>1 M</td><td>21</td><td>-0.001</td><td>110</td><td>0.865</td><td>0.23</td></tr><tr><td>3 M</td><td>23</td><td>0.01</td><td>95</td><td>0.20</td><td>0.42</td></tr><tr><td>AD</td><td>22</td><td>0.016</td><td>88</td><td>0.222</td><td>0.26</td></tr><tr><td colspan="6"><em>b Absolute curvature</em></td></tr><tr><td>1 M</td><td>21</td><td>0.025</td><td>93</td><td>0.452</td><td>0.27</td></tr><tr><td>3 M</td><td>23</td><td>0.048</td><td>76</td><td>0.06</td><td>0.83</td></tr><tr><td>AD</td><td>21</td><td>0.012</td><td>77</td><td>0.191</td><td>0.79</td></tr></tbody></table>

Results of the Wilcoxon tests on asymmetry indices for depth (a) and absolute curvature (b) averaged over the CS, for the 1 month (1 M), 3 months (3 M), and adult (AD) groups.

curvature measures, one outlier infant at both 1 M and 3 M, one at 1 M and one at 3 M, along with two adults, were identified (Fig. 2c right column). The outlier infants for depth were not the same as those for curvature, but the adult identified as outlier for depth was also an outlier for curvature. These subjects were excluded from the analyses of the corresponding feature, in addition to subjects with discontinuous CS or outlier brain volume, leading to the following numbers of subjects: depth: *n* = 21 (1 M), 23 (3 M), 22 (AD); curvature: *n* = 21 (1 M), 23 (3 M), 21 (AD).

## Interhemispheric Asymmetries

Regarding interhemispheric asymmetries, the Wilcoxon tests performed on asymmetry indices for depth and absolute curvature averaged over the whole CS showed no significant differences for any group of subjects (Fig. 2d; Table 1). This suggested that overall CS morphological features were not different between the left and right hemispheres. Averaged left and right measures were thus considered in the following analyses.

## Relationships between Features

Based on the computation of the feature relative difference comparing mean values over groups, we observed larger increase in brain volume (1 M–3 M: 10.48%, 3 M–AD: 36.53%) than in CS depth (1 M–3 M: 3.96%, 3 M–AD: 10.20%) and absolute curvature (1 M–3 M: 5.16%, 3 M–AD: 15.03%). Increases were also qualitatively larger between infants at 3 M and adults than between both infant groups.

We then evaluated whether CS morphological features (depth and absolute curvature) were related to brain volume using Pearson correlations between measures on a logarithmic scale and over each group. No significant result was found for infants at 1 M (depth: *N* = 21, *r* = 0.27; curvature: *N* = 21, *r* = 0.23; all *p* values >0.30) or at 3 M (depth: *N* = 23, *r* = 0.36; curvature: *N* = 23, *r* = 0.20; all *p* values >0.30), but a significant correlation was observed when considering both groups together (depth: *N* = 44, *r* = 0.70, *p* = 2.08.10⁻⁷; curvature: *N* = 44, *r* = 0.45, *p* = 2.7.10⁻³). In adults, these correlations were also significant (depth: *N* = 22, *r* = 0.69, *p* = 3.75.10⁻⁴; curvature: *N* = 21, *r* = 0.62, *p* = 1.55.10⁻³). This suggests that greater brain volume may be associated with deeper and curvier CS across different stages of development.

Besides, no significant correlation was observed between CS depth and absolute curvature in each group (1 M: *N* = 19, *r* = 0.29; 3 M: *N* = 21, *r* = 0.34; AD: *N* = 21, *r* = 0.33; all *p* values >0.10), except when considering both infant groups together (*N* = 40, *r* = 0.45, *p* = 0.003), suggesting a low association between CS depth and curvature.

## Regional CS Morphological Features

### Hand Knob Identification

Hand knobs (HK) were visually identified in all subjects, whatever their age (Fig. 3). The average position of the HK remained stable across ages, with minor variations between the left (coordinates: 41.8 ± 7.4, range: 27–59 [1 M]; 41.6 ± 7.7, range: 27–60 [3 M]; 43.7 ± 4.1, range: 30–49 [adults]) and right hemisphere (coordinates: 39.9 ± 5.5, range: 29–52 [1 M]; 38.9 ± 5.0, range: 30–47 [3 M]; 41.1 ± 4.8, range: 32–49 [adults]; see online suppl. Information SI.1; Table 1.1 for additional analyses on interhemispheric asymmetries in HK location). On average over each group, HK were localized at the level of a local plateau in depth (the highest depth was observed at higher coordinates) and high absolute curvature (Fig. 3).

## Feature Dependence on Age Group and ROIs Localization

A qualitative comparison of CS parameterized curves first revealed some differences across age groups, with an increased depth and curvature from 1 M to 3 M infants and to adults in most coordinates of the sulcus but more particularly in the central part (Figure 4b; see online suppl. Information SI.2; Fig. 2.1 for additional analyses along the CS). The HK identification allowed us to consider four ROIs along the CS (Fig. 4a), with consistent averaged localization and specificities across
<figure><img src="image_4.png" /><figcaption>**Fig. 3.** Identification of the HK along the CS parametrization for each age group. **a** Example of hand knob (HK) identification for an infant at 1 month (1 M) and 3 months (3 M), and an adult (AD). For each group, HK localizations (average shown with the vertical continuous line and standard deviation with dotted lines) are superimposed to CS parametrization (average shown with the continuous curves and standard deviations with the shaded area) for depth (b) and absolute curvature (c) for the left (dark colours) and right (light colours) hemispheres (average metrics over each group, with standard deviations in shaded colours).</figcaption></figure>
<figure><img src="image_5.png" /><figcaption>**Fig. 4.** Localization of CS ROIs and feature variability across age groups and ROIs. **a** Schema illustrating the ROIs localization along the CS. **b** CS parametrization for depth (left column) and absolute curvature (right column), averaged over left and right sulci over all subjects of each group (1 M in blue, 3 M in orange, AD in green: mean and standard deviation in shaded colours). The averaged position of the 4 ROIs (medial, central-medial, central-lateral, lateral) is highlighted in grey. **c** Boxplots of CS depth (left column) and absolute curvature (right column) averaged over each ROI, for the three age groups (same colour code as in **b**). **d** Boxplots of CS feature relative changes (in %) between 1 M and 3 M for infants with longitudinal data.</figcaption></figure>
<table><caption>**Table 2.** Summary of post hoc statistics in the comparison of depth between groups and ROIs</caption><thead><tr><th>Effect - Contrast</th><th>Estimate</th><th>SE</th><th>t ratio</th><th>p value adjusted</th></tr></thead><tbody><tr><td><strong>Effect of Age Group</strong></td><td></td><td></td><td></td><td></td></tr><tr><td>1 M-3 M</td><td>-1.08</td><td>0.19</td><td>-5.76</td><td>&lt;10<sup>-4</sup></td></tr><tr><td>3 M-AD</td><td>-3.28</td><td>0.24</td><td>-13.71</td><td>&lt;10<sup>-4</sup></td></tr><tr><td><strong>Effect of ROI</strong></td><td></td><td></td><td></td><td></td></tr><tr><td>medial ROI - central-medial ROI</td><td>-5.42</td><td>0.20</td><td>-26.62</td><td>&lt;10<sup>-4</sup></td></tr><tr><td>medial ROI - central-lateral ROI</td><td>-7.44</td><td>0.20</td><td>-36.49</td><td>&lt;10<sup>-4</sup></td></tr><tr><td>medial ROI - lateral ROI</td><td>0.86</td><td>0.20</td><td>4.21</td><td>0.0002</td></tr><tr><td>central-medial ROI - central-lateral ROI</td><td>-2.01</td><td>0.20</td><td>-9.86</td><td>&lt;10<sup>-4</sup></td></tr><tr><td>central-medial ROI - lateral ROI</td><td>6.28</td><td>0.20</td><td>30.83</td><td>&lt;10<sup>-4</sup></td></tr><tr><td>central-lateral ROI - lateral ROI</td><td>8.30</td><td>0.20</td><td>40.7</td><td>&lt;10<sup>-4</sup></td></tr><tr><td><strong>Effect of Age Group × ROI</strong></td><td></td><td></td><td></td><td></td></tr><tr><td>1 M-3 M</td><td></td><td></td><td></td><td></td></tr><tr><td>medial ROI</td><td>-0.72</td><td>0.36</td><td>-2.06</td><td>0.052</td></tr><tr><td>central-medial ROI</td><td>-1.36</td><td>0.36</td><td>-3.78</td><td>0.0002</td></tr><tr><td>central-lateral ROI</td><td>-1.64</td><td>0.36</td><td>-4.58</td><td>&lt;10<sup>-4</sup></td></tr><tr><td>lateral ROI</td><td>-0.68</td><td>0.36</td><td>-1.68</td><td>0.09</td></tr><tr><td>3 M-AD</td><td></td><td></td><td></td><td></td></tr><tr><td>medial ROI</td><td>-2.66</td><td>0.39</td><td>-6.90</td><td>&lt;10<sup>-4</sup></td></tr><tr><td>central-medial ROI</td><td>-3.42</td><td>0.39</td><td>-8.87</td><td>&lt;10<sup>-4</sup></td></tr><tr><td>central-lateral ROI</td><td>-4.13</td><td>0.39</td><td>-10.7</td><td>&lt;10<sup>-4</sup></td></tr><tr><td>lateral ROI</td><td>-2.93</td><td>0.39</td><td>-7.61</td><td>&lt;10<sup>-4</sup></td></tr></tbody></table>

groups (Fig. 4b): the (HK-related) central-medial ROI showing pronounced curvature; the central-lateral ROI showing the highest depth; the medial and lateral ROIs at the CS edges showing lower depth and lower absolute curvature than central ROIs. In order to further explore the feature dependence on position along the CS and on age groups, CS morphological features were then averaged over each left and right ROIs defined at the individual level (see online suppl. Information SI.1; Table 1.2 for additional analyses on interhemispheric asymmetries in ROI depth and absolute curvature).

The LMM model performed on CS depth of the included subjects (1 M: N = 21, 3 M: N = 23, AD: N = 22, marginal R² = 0.91, conditional R² = 0.92) highlighted significant main effects for the factors Group (F(2, 131.50) = 161.66, *p* < 2.2.10⁻¹⁶) and ROI (F(3, 203.75) = 797.70, *p* < 2.2.10⁻¹⁶), as well as a significant interaction between both (F(6, 203.75) = 5.05, *p* = 3.36.10⁻⁵). EMMs post hoc tests (Table 2) first confirmed that depth globally increased with age: CS was significantly deeper at 3 M than at 1 M in infants, and even deeper in adults than in 3 M infants (Fig. 4c left column). Second, the four ROIs significantly differed in depth: the central-lateral ROI appeared to be the deepest, followed by the central-medial ROI, the medial ROI, and the lateral ROI appeared to be the shallowest. Third, the ROIs seemed to

differently increase in depth with age, as a significant increase between 1 M and 3 M was only observed for the central-medial and central-lateral ROIs, while a significant increase was observed in all ROIs between 3 M and adulthood. Based on the computation of the depth relative difference comparing mean values between 3 M and AD groups, we qualitatively observed larger increase in the lateral ROI (12.57%) than in the other ROIs (central-lateral ROI: 10.07%, medial ROI: 10.65%, central-medial ROI: 9.38%).

The LMM model performed on CS absolute curvature of the included subjects (1 M: N = 21, 3 M: N = 23, AD: N = 21, marginal R² = 0.55, conditional R² = 0.68) also revealed significant main effects of group (F(2,118.81) = 45.33, *p* = 3.47.10⁻³) and ROI (F(3,200.95) = 51.59, *p* = 2.40.10⁻⁷), as well as a significant interaction (F(6,200.95) = 6.69, *p* = 4.24.10⁻⁷). EMMs post hoc tests (Table 3) further revealed the directionality of these effects. As for depth, CS curvature first showed an increase with age: the CS appeared to be significantly curvier at 3 M than at 1 M, and even curvier in adults (Fig. 4c right column). Second, the four ROIs significantly differed in curvature: the central-medial ROI (HK-related) appeared to be the curviest, followed by the central-lateral ROI, the lateral ROI, and the medial ROI was the least curved. Finally, the ROIs displayed distinct
<table><caption>**Table 3.** Summary of post hoc statistics in the comparison of absolute curvature between Groups</caption><thead><tr><th>Effect - Contrast</th><th>Estimate</th><th>SE</th><th>t ratio</th><th>p value adjusted</th></tr></thead><tbody><tr><td><strong>Effect of Age Group</strong></td><td></td><td></td><td></td><td></td></tr><tr><td>1 M-3 M</td><td>-0.006</td><td>0.002</td><td>-3.02</td><td>0.008</td></tr><tr><td>3 M-AD</td><td>-0.02</td><td>0.003</td><td>-7.59</td><td>&lt;10<sup>-4</sup></td></tr><tr><td><strong>Effect of ROI</strong></td><td></td><td></td><td></td><td></td></tr><tr><td>medial ROI - central-medial ROI</td><td>-0.03</td><td>0.002</td><td>-11.74</td><td>&lt;10<sup>-4</sup></td></tr><tr><td>medial ROI - central-lateral ROI</td><td>-0.02</td><td>0.002</td><td>-7.45</td><td>&lt;10<sup>-4</sup></td></tr><tr><td>medial ROI - lateral ROI</td><td>-0.007</td><td>0.002</td><td>-3.39</td><td>0.005</td></tr><tr><td>central-medial ROI - central-lateral ROI</td><td>0.009</td><td>0.002</td><td>4.29</td><td>0.0002</td></tr><tr><td>central-medial ROI - lateral ROI</td><td>0.02</td><td>0.002</td><td>8.35</td><td>&lt;10<sup>-4</sup></td></tr><tr><td>central-lateral ROI - lateral ROI</td><td>0.009</td><td>0.002</td><td>4.06</td><td>0.0004</td></tr><tr><td><strong>Effect of Age Group × ROI</strong></td><td></td><td></td><td></td><td></td></tr><tr><td>1 M-3 M</td><td></td><td></td><td></td><td></td></tr><tr><td>medial ROI</td><td>-0.006</td><td>0.004</td><td>-1.56</td><td>0.14</td></tr><tr><td>central-medial ROI</td><td>-0.007</td><td>0.004</td><td>-1.91</td><td>0.08</td></tr><tr><td>central-lateral ROI</td><td>-0.008</td><td>0.004</td><td>-2.07</td><td>0.06</td></tr><tr><td>lateral ROI</td><td>-0.003</td><td>0.004</td><td>-0.86</td><td>0.40</td></tr><tr><td>3 M-AD</td><td></td><td></td><td></td><td></td></tr><tr><td>medial ROI</td><td>-0.009</td><td>0.004</td><td>-2.08</td><td>0.06</td></tr><tr><td>central-medial ROI</td><td>-0.02</td><td>0.004</td><td>-4.71</td><td>&lt;10<sup>-4</sup></td></tr><tr><td>central-lateral ROI</td><td>-0.04</td><td>0.004</td><td>-8.46</td><td>&lt;10<sup>-4</sup></td></tr><tr><td>lateral ROI</td><td>-0.02</td><td>0.004</td><td>-5.25</td><td>&lt;10<sup>-4</sup></td></tr></tbody></table>

patterns of curvature increase throughout development. Between 3 M and adulthood, all ROIs exhibited significant changes, except for the medial ROI, which only displayed a tendency towards an increase. No significant changes were observed between 1 M and 3 M for any of the ROIs, but a trend towards increased curvature was noticed in the central-lateral ROI. Based on the computation of the curvature relative difference comparing mean values between 3 M and AD groups, we qualitatively observed larger increase in the central-lateral ROI (23.95%) than in the other ROIs (lateral ROI: 17.91%, central-medial ROI: 12.50%, medial ROI: 8.50%).

The analyses of infant's longitudinal data allowed us to precise these findings by computing the relative change between 1 M and 3 M at the individual level (Fig. 4d). Although longitudinal MRI data were acquired for 22 infants, this analysis focused on 17 of them, after excluding 2 infants with discontinuous CS, 1 outlier in brain volume, and 2 outliers per CS feature. One-sample Student's *t* tests versus zero performed on each ROI over this longitudinal infant group revealed a significant increase of both depth and absolute curvature for all ROIs except for the lateral ROI (Fig. 4d; Table 4), suggesting morphological changes of the CS in these three regions between 1 M and 3 M. However, an analysis of variance performed on these relative changes observed across

ROIs revealed no significant effect of ROI neither for depth ($F(3,64) = 1.93, p = 0.134$, Bayes factor BF10 = 0.56, $\eta^2 = 0.083$) nor for curvature ($F(3,64) = 0.34, p = 0.799$, BF10 = 0.11, $\eta^2 = 0.016$) suggesting that the evolution of CS features does not differ between regions.

## Link to Sensorimotor Development

For all 29 infants with MRI data at 3 M of age, GM, FM, and global motor scaled scores measured with BSID-III evaluations at this age were in the typical range (Fig. 5a). The global score was further analyzed according to the evolution of brain volume and CS features in infants with longitudinal measures for both CS depth and absolute curvature (N = 15). The first linear model with changes in brain volume showed no significant effect ($F(1, 13) = 0.30, r^2 = 0.02, p = 0.60$), nor did the models with the evolution of depth and absolute curvature over the entire CS ($F(2, 12) = 2.136, r^2 = 0.26, p = 0.14$; depth: $t = -1.54, p = 0.31$; curvature: $t = 1.14, p = 0.31$) and over the central-lateral ROI ($F(2, 12) = 0.08, r^2 = 0.01, p = 0.92$; depth: $t = -0.40, p = 0.85$; curvature: $t = -0.15, p = 0.97$). Although not significant, the model considering the evolution of CS features over the (HK-related) central-medial ROI suggested a trend for a positive relationship between global motor BSID-III scores and the CS depth relative changes between
<table><caption>**Table 4.** Summary of Student's *t* tests with the Bayes factor (BF10) statistics examining the relative change between 1 M and 3 M of age for each ROI in terms of depth (a) and absolute curvature (b)</caption><thead><tr><th>ROI</th><th>t</th><th>Relative change, %</th><th>p value</th><th>p value adjusted</th><th>BF10</th><th>Cohen's d</th></tr></thead><tbody><tr><td colspan="7"><em>a Depth</em></td></tr><tr><td>medial ROI</td><td>3.48</td><td>4.05</td><td>0.003</td><td>0.004</td><td>14.48</td><td>0.84</td></tr><tr><td>central-medial ROI</td><td>9.93</td><td>4.53</td><td>&lt;10<sup>-4</sup></td><td>&lt;10<sup>-4</sup></td><td>&gt; 10<sup>4</sup></td><td>2.41</td></tr><tr><td>central-lateral ROI</td><td>8.06</td><td>4.73</td><td>&lt;10<sup>-4</sup></td><td>&lt;10<sup>-4</sup></td><td>33,130</td><td>1.95</td></tr><tr><td>lateral ROI</td><td>1.69</td><td>1.98</td><td>0.11</td><td>0.11</td><td>0.80</td><td>0.41</td></tr><tr><td colspan="7"><em>b Absolute curvature</em></td></tr><tr><td>medial ROI</td><td>2.94</td><td>7.27</td><td>0.009</td><td>0.01</td><td>5.51</td><td>0.71</td></tr><tr><td>central-medial ROI</td><td>4.36</td><td>4.95</td><td>0.0004</td><td>0.002</td><td>70.57</td><td>1.06</td></tr><tr><td>central-lateral ROI</td><td>3.03</td><td>5.79</td><td>0.008</td><td>0.01</td><td>6.45</td><td>0.73</td></tr><tr><td>lateral ROI</td><td>1.77</td><td>4.51</td><td>0.10</td><td>0.10</td><td>0.90</td><td>0.43</td></tr></tbody></table>

**Fig. 5.** Motor scores at the age of 3 months and relationship with CS morphological changes between 1 M and 3 M. **a** BSID-III motor scaled scores on the 29 infants at 3 M of age: gross motor (GM), fine motor (FM) and global motor scaled scores. **b** Illustration of the linear model testing the relationship between global motor scaled score at 3 M and age-related changes in morphological features of the CS central-medial (HK-related) ROI between 1 M and 3 M (depth represented in abscissa, absolute curvature coded in colour).

<figure><img src="image_6.png" /></figure>

1 M and 3 M ($F(2, 12) = 3.36, r^2 = 0.36, p = 0.07$; depth: $t = 2.45, p = 0.12$; curvature: $t = 0.32, p = 0.72$) (Fig. 5b). Overall, these results suggest that sensorimotor skills at

3 M of age are only weakly related to the early evolution in brain volume and CS morphological features between 1 M and 3 M of age.
Discussion

This study aimed to assess the differential evolution of
morphological features along the CS in a unique cross-
sectional and longitudinal dataset of typical infants
compared to adults with advanced MRI methodologies.
We provided evidence for a gradual increase in CS depth
and curvature between 1 M, 3 M, and adulthood co-
herent with the expected increase in brain folding, as well
as for morphological differences along the CS across age
groups. Interestingly, our results further showed that the
increase in CS depth and curvature between 1 M and
3 M is more pronounced for the central regions of the
CS. In fact, the central ROIs exhibited the most con-
sistent significant changes observed in both longitudinal
and cross-sectional data. However, comparisons of the
relative changes (computed based on longitudinal data)
across regions did not show significant differences, in-
dicating that central regions do not necessarily change
faster than others. Finally, when evaluating the link to
sensorimotor skills, only a trend for a positive rela-
tionship between global motor scaled scores at 3 M and
the depth evolution of the CS central-medial (HK-
related) region between 1 M and 3 M of age was
found, suggesting a weak association. Overall, this study
provides one of the first quantitative assessments of the
evolution and behavioural relevance of CS morpho-
logical features in the first months after birth.

Morphological Features of the CS

Variations in Depth and Curvature along the CS

We first highlighted that the non-uniform morphological characteristics along the CS observed in adults are already present in typically developing 1 M and 3 M-old infants. This arises from a gradient of varying depth and curvature, with the central regions of the CS being the deepest and most curved across all age groups. Regarding CS depth, we observed that the central-lateral ROI was the deepest, consistently with previous studies performed in both adults [34] and 12 and 60 M children [10]. The most immediate explanation for a deeper sulcus in central regions arises from the intrinsic construction of the CS sulcal object and the fact that the most medial and lateral regions are by definition close to the anatomical “anchors” that the extremities of the sulcus represent. Although we tried to account for this “anchor” bias by excluding five points on each extremity, this anatomical constraint may limit the observation of depth changes in these regions. It has also been suggested that the depth difference between the central-lateral and central-medial ROIs might rely on the presence of the middle fronto-parietal “pli de passage”

(PPFM) [10, 11, 34], a privileged place for white matter
connections between frontal and parietal lobes, in par-
ticular between motor and sensory regions. Showing a
higher increase in depth in the central-lateral ROI, our
results in 1–3 M infants and previous findings in
12–60 M-olds [10] seem to be consistent with the de-
velopment of PPFM and underlying connections in more
medial central regions. Besides, we observed that the
central-medial ROI showed the most pronounced cur-
vature, likely arising from the omega-shaped HK [36] it
includes. While our study was the first to detail CS cur-
vature, we might assume that this higher curvature relies
on a more intense cortical development and neuronal
proliferation in this region that is further constrained
by anatomical anchors such as the PPFM. Overall, we
suggest that the consistent variations of CS depth and
curvature found across age groups may arise from the
combination of anatomical and connectivity con-
straints in the sulcus definition and development.

Interhemispheric Asymmetry in CS Depth and
Curvature

Another common feature that we observed across age
groups is the absence of significant interhemispheric
asymmetries in CS morphological features, contradicting
our hypothesis. This result might be surprising con-
sidering that asymmetries in CS shape were previously
reported in adults [7] and in preterm infants imaged at
30-40 weeks PMA [9], with a more frequent "double-
knob" configuration in the left hemisphere compared to
the classical "single-knob" configuration. Similarly, the
right CS was found to be deeper than the left in children
of 12 M of age, particularly in the lateral regions, though
this asymmetry disappeared before 24 M of age [10]. This
discrepancy with our results may arise from the fact that
our analysis of asymmetry was performed over the whole
CS, which would be consistent with previous findings
[38] showing no asymmetry in global CS surface in the
same preterm cohort as studied in [9]. Importantly, the
adjusted position of individual ROIs based on the HK
location on each hemisphere may have prevented us
from detecting these asymmetries, as this alignment
may mask natural variation observed in previous
studies. In line with this, trends for significant dif-
ferences in HK location across hemispheres were
observed in the 3 M and adult groups (see online suppl.
Information SI.1; Table 1.1), and no significant
asymmetries in depth and absolute curvature were
observed in infants when considering the four ROIs
(see online suppl. Table 1.2). Overall, our results
suggest that while early focal asymmetries might be
present in the HK region, there is no difference in
overall depth and curvature between the left and right
CS in either typical infants or adults.

Developmental Changes in CS Morphological
Features

Global Increase in CS Depth and Curvature with Age

As anticipated, our results also revealed an increase in CS depth and curvature between 1 M and 3 M and between 3 M and adulthood, coherent with the expected increase in cortical folding. While cerebral development is the result of numerous cellular mechanisms that begin during gestation [1], some authors have proposed the existence of a “folding protomap” responsible for an early sketch of folding patterns [11, 39, 40] (see [3] for a review). According to this theory, areas of the subventricular zones with higher neuronal proliferation would be responsible for higher expansion of the cortex, giving rise to gyri after neuronal migration [41]. Such mechanism is probably coupled with others to generate the complex dynamics of the folding process, such as the differential growth of brain tissues with different viscoelastic properties, the growth of white matter connections, or the anatomical and physical constraints limiting brain size and connection length to ensure efficient communication between regions (see [3] for a review). Our results are in line with these theories since the CS may become progressively deeper and curvier to adapt to the cortical growth of motor and somatosensory regions in pre- and post-central gyri.

Based on qualitative comparisons of average values
over groups, we further observed that the relative dif-
ference in CS morphological features was less important
than the one in brain volume, both between 1 M and 3 M,
and between 3 M and adulthood. This may arise from the
fact that the folding mechanism proceeds earlier than
other mechanisms, such as white matter growth that may
have a greater impact on brain growth (see [42] for a
review). And this may be particularly true for the CS that
is known to fold early on [2], in parallel to the early
microstructural maturation of the primary sensory and
motor regions it delineates [21]. Nevertheless, finding
significant differences in CS depth and curvature in just
2 months of development between 1 M and 3 M suggests
that the first post-natal months still represent a critical
phase in the folding of early maturing regions, although a
deceleration in whole-brain folding was previously ob-
served over this age range compared to the earlier
preterm period [12]. Although we were unable to im-
plement non-linear models in our study due to the
limited number of time points studied (only three across

the lifespan), it could be valuable for future research to
incorporate such models. This would allow for a more
precise characterization of the developmental trajectory
of CS morphology, potentially helping to identify the
point at which maturation plateaus.

Non-Uniform Changes in Depth and Curvature
along the CS with Age

Besides, we observed that the evolution of morpho-
logical features with age varied across CS regions. In
particular, between 1 M and 3 M, significant increases in
depth were only observed for the central-medial and
central-lateral ROIs, as well as only a trend of curvature
increase in the central-lateral ROI. This suggests intense
changes in central regions of the CS. Several speculations
have been made regarding the different stages of CS
formation during gestation [43]. It would initially be
divided into two pieces, the so-called sulcal roots or pits
separated by a cortical bridge [44, 45]. As development
progresses, the two parts would expand and eventually
merge, creating a fold where the cortical bridge initially
separated them. Once this fusion is complete, develop-
ment appears to continue primarily at the centre of the
emerging sulcus, where the fold formed during fusion
further widens and deepens, gradually making the
cortical bridge disappear from the surface. This hy-
pothesis would also align with the gradual disappearance
of the transient subplate remnant and its integration into
cortex and white matter, a process that spans the period
of axonal and dendritic path finding and early myeli-
nation [46, 47]. A more pronounced morphological
change at the centre of the CS could reflect distinct
maturation trajectories and varying mechanical con-
straints imposed by the underlying white matter
development – such as U-fibres within the cortical
bridge, which already delineates the motor and sensory
cortices and is thought to later develop into the PPFM as
it progressively deepens within the sulcus. Our cross-
sectional results of increased depth in the 2 CS central
regions, but not in medial and lateral regions, support
this model, highlighting significant early changes oc-
curring specifically in central regions. These are also in
agreement with the most intense changes reported in CS
central regions in 12–60 M-old children [10]. Never-
theless, our analysis of longitudinal data did not confirm
the difference in depth increase across ROIs, perhaps
because of the limited sample size and/or to the higher
variability in depth measures in the medial and
lateral ROIs.

Besides, it is important to note that within our initial
group of 33 infants, 3 had a discontinuous CS. According
to a recent theory [11], the formation of a discontinuous
CS might result partly from a premature development of
the cortical bridge (or PPFM) connecting the pre- and
post-central gyri, in comparison to the development of
the two independent pieces of the sulcus. However, given
the rarity of these cases observed in adults – 6 cases
observed out of 2,174 hemispheres in a previous study
[48] – and the frequency of these cases observed within
our infant sample, we might assume that the CS dis-
continuities we detected are not necessarily permanent
but that CS folding, particularly at its centre with PPFM
development, is still ongoing during the first months
after birth. This assumption of an ongoing morpho-
logical development is further supported by the fact that
these infants do not appear to show any abnormalities in
sensorimotor abilities, with a global motor scaled score
that is well within the average range of the studied
population. Scanning those 3 infants at a later age would
allow us to test this hypothesis.

Aside from anatomical substrates, an intense devel-
opment of CS central regions during infancy may also be
related to differential functional evolution of sensori-
motor regions related to different body parts. The
central-medial and central-lateral ROIs are thought to
correspond to the upper limbs and face which may
develop more intensely at these ages since they are most
densely innervated [6] and are intensely co-stimulating
each other from in utero [49] (see [13] for a review). In
line with this, a recent functional MRI study on preterm
infants [8] provided evidence for the existence of a so-
matotopic organization at 32 weeks PMA, already dis-
playing a higher magnification for the hand region than
for the ankle or the mouth. However, it should be kept in
mind that the coupling between the morphology of the
CS and the functional organization of the sensorimotor
cortex remains underexplored and not established in
infants. It would be informative to confirm our hy-
potheses by linking CS morphological features with the
developing somatotopic organization in typical infants,
based on a functional mapping of different body parts.

Methodological Considerations

A first consideration arose from the fact that age-
related increases and differences across regions were
more systematically detected with CS depth than with
curvature. While curvature could be expected to develop
in parallel to depth, it may be a marker more difficult to
quantify or less sensitive to the cortical expansion process
over this developmental period due to more stable me-
chanical constraints, limiting its progression compared to
depth. In addition, averaging over rather large ROIs may

have prevented us from detecting small and more local
changes in curvature with age, though a point-to-point
analysis along the CS did not reveal stronger effects (see
online suppl. Information SI.2; Fig. 2.1). Finally, the
higher heterogeneity and inter-individual variability (see
online suppl. Information SI.3) of this feature may make it
more difficult to detect group differences.

Another consideration comes from the fact that cross-
sectional and longitudinal analyses were not fully con-
sistent to detect differences across CS regions. Here
again, the high inter-individual variability present in
developmental populations (each infant following a
specific neurodevelopmental trajectory) may have con-
tributed to differences observed between cross-sectional
and longitudinal analyses. We tried to limit this vari-
ability by defining the ROIs at the individual level, re-
scaling them to each infant and age, as well as positioning
them relatively to the position of the HK and CS ex-
tremities. Nevertheless, we cannot confidently assert that
the different ROIs evolve in a homothetic manner be-
tween 1 M and 3 M, which may limit their anatomical
correspondence relative to the somatotopic organization
and thus impede their comparison between infants and
across ages. Furthermore, this difference between the
two analyses may also be due to insufficient statistical
power in the cross-sectional data (for analysis of CS
depth: n = 44 with data at 1 M or 3 M, whereas power
analysis suggested a minimum sample size of 50).

Another methodological limitation of our study arose
from the intrinsic variability of the pipeline used for the
sulcal object identification, particularly at CS extremities
where the lower depth led to a noisier and more variable
identification of CS object. While this variability ap-
peared to have a relatively minor impact on our results
(see online suppl. information SI.3), it is important to
emphasize its potential implications for comparisons
between subjects and age groups.

Relating the Development of CS Morphological
Features and Sensorimotor Skills

The last objective of our study was to relate the de-
velopmental changes in CS morphological features with
the acquisition of sensorimotor skills in typical infants.
Although all tested models revealed non-significant re-
sults, a trend of relationship was observed for the age-
related increase in the depth of the (HK-related) central-
medial ROI. This trend may be related to the increased
hand usage that was found between 1 and 3 M [50]. At
the beginning of life, infants have limited means to
explore the world, which lead them to adopt mostly self-
touch behaviours (see [13] for a review). They begin
touching their face during the foetal stage [49] and continue after birth, initially making contact with their head, chest, and hands, before gradually moving towards their hips and feet [14]. Previous studies seem to show an increasing usage of the hands between 1 M and 3 M, progressing from self-touch behaviour to the emergence of reaching and object manipulation around 3–5 months [50]. While impossible to detect with Bayley scales at such early ages, this increasing hand usage may relate to the CS depth maturation in the (HK-related) central-medial ROI.

The absence of observed relationship between CS anatomical and behavioural development could be explained by various limitations. First, the sample size was limited to 15 infants as analyses required longitudinal measures for both CS depth and curvature, requiring the exclusion of all outlier infants. Second, though it is more readily available in young infants (with a short MRI acquisition achievable during natural sleep), the CS morphology might be a too indirect marker of the development of sensorimotor regions. It would be interesting to test the shape analysis approach [7], which had shown certain relationships between the CS shape of preterm infants and motor development at 5 years [9]. Measuring the microstructural maturation of the pre- and post-central cortices [21] would be an interesting way of obtaining more specific markers.

Another limitation comes from the battery of tests we used to assess global motor skills in infants at 3 M (Bayley scale, BSID-III [24]). Although this scale is frequently used in young children to detect neurodevelopmental disorders [51, 52], it may lack sensitivity in young infants, in particular to assess FM skills. Indeed, assessment items at 3M focus more on postural control (e.g., holding the head in different positions) and on FM skills that involve gaze evaluations than on hand and mouth usage (e.g., grasping a ring or hand-to-mouth behaviour). This neurodevelopmental evaluation further depends on the infant state of alertness, readiness and fatigue, but we were particularly careful to carry out the experiments under similar conditions. For future studies, it could be valuable to complement the results obtained from the BSID-III with finer measurements such as motion capture [53], which may offer useful insights for the long-term monitoring of infant motor development. Importantly, previous studies relating early CS development and motor skills in preterm infants actually considered outcomes at later ages: at either 24 or 30 months of age (corrected for gestational age at birth) with BSID-III [38], or at 5 years of age with the Movement Assessment Battery for Children, 2nd edition

(MABC-2) [9]. Following typical infants at later ages may thus be important to get a more relevant picture of their neurodevelopmental trajectory. Nevertheless, the trend in the relationship observed with the CS region associated with the hand may make us optimistic to continue exploring this avenue of research.

## Conclusion

Relying on a unique cross-sectional, longitudinal and multimodal design to study typical infants, we could capture the progressive evolution of CS morphological features across regions supposed to relate to somatotopic organization. While no interhemispheric asymmetry was found, the most robust deepening and curving of the CS between 1 M and 3 M of age was observed in its central regions, including the HK which was the only region to show some trend of relationship with the acquisition of sensorimotor skills. Age-related regional differences were more consistently detected using CS depth than curvature, suggesting that curvature may be a less reliable indicator of maturation stage, likely due to its greater variability along the sulcus. Such a framework might help identify early alterations in neurodevelopmental trajectories and design early interventions to improve the outcome of infants at risk of developing sensorimotor disorders.

## Acknowledgments

The authors thank Marianne Barbu-Roth's team (Marie-Victorine Dumuids-Vernet, Léa Guéret, Viviane Huet and Joëlle Provasi) for their advices on the BSID-III scale, David Germanaud for his advice on infants inclusion criteria, Denis Rivière, Olivier Coulon, Guillaume Auzias and Héloïse de Vareilles for discussion on the CS analyses and parameterization, Laurie Devisscher for organizing HCP data, the UNIACT team for the experiment organization at NeuroSpin (including the nurses and MRI technologists, and Bernadette Martins for ethical questions), and for their valuable help in recruiting infants: Christine Doublé, Marie Palu and healthcare professionals, including the team of midwives and pediatricians (Jean Gaschignard and Sophie Gobet) at the Orsay maternity hospital. And, last but not least, the authors would like to sincerely thank all the infants and parents who participated in this study, as well as adult participants.

## Statement of Ethics

The study in infants was performed with the agreement of the National Ethical Committee (“Comité de Protection des Personnes,” CPP Ile de France 3), under the protocol DEVine (CEA
100 054; ID RCB 2020-A00106-33). The Human Connectome Project (HCP) obtained approval from the Ethics Committee, and adult data used in this study were shared under strict conditions aiming at protecting participants' privacy. Authors have agreed to the HCP Open Access Data Use Terms. Parents were informed about the study and gave written consent to participate with their babies. HCP adult participants also provided written informed consent.

## Conflict of Interest Statement

The authors have no conflicts of interest to declare.

## Funding Sources

This work was supported by the Fondation de France (FdF-20-00111908, BodyBrain project), the French National Agency for Research (ANR-22-CE37-0028, BabyTouch project), the Fondation Médisite (FdF-18-00092867), the IdEx Université de Paris (ANR-18-IDEX-0001), the Institute of Neuroscience and Cognition (2023) and the French government as part of the France 2030 programme (ANR-23-IAIIU-0010, IHU Robert-Debré du Cerveau de l'Enfant). The Human Connectome Project, WU-

Minn Consortium (PI: Van Essen, Ugurbil; 1U54MH091657) was funded by the 16 NIH Institutes and Centers that support the NIH Blueprint for Neuroscience Research; and by the McDonnell Center for Systems Neuroscience at Washington University.

## Author Contributions

Conceptualization and funding acquisition: J.D., D.M., M.B.-R., and J.-F.M.; data curation: A.D., D.M., and A.G.; formal analysis and visualization: A.D. and D.M.; investigation: D.M., A.G., and L.H.-P.; methodology: A.D., Y.L., D.M., and J.D.; project administration: J.D., D.M., and M.B.-R.; resources: Y.L. and J.-F.M.; supervision and validation: D.M. and J.D.; writing – original draft: A.D., D.M., and J.D.; writing – review and editing: all.

## Data Availability Statement

The data that support the findings of this study are not publicly available due to their containing information that could compromise the privacy of research participants but are available from J.D. upon reasonable request.

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