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. 2018 Aug 22;39(12):5074–5084. doi: 10.1002/hbm.24346

Cerebellar development and its mediation role in cognitive planning in childhood

Judy A Kipping 1, Yingyao Xie 1, Anqi Qiu 1,2,3,✉
PMCID: PMC6866433  PMID: 30133063

Abstract

Recent evidence suggests that the cerebellum contributes not only to the planning and execution of movement but also to the high‐order cognitive planning. Childhood is a critical period for development of the cerebellum and cognitive planning. This study aimed (a) to examine the development of cerebellar morphology and microstructure and (b) to examine the cerebellar mediation roles in the relationship between age and cognitive planning in 6‐ to 10‐year‐old children (n = 126). We used an anatomical parcellation to quantify cerebellar regional gray matter (GM) and white matter (WM) volumes, and WM microstructure, including fractional anisotropy (FA) and mean diffusivity (MD). We assessed planning ability using the Stockings of Cambridge (SOC) task in all children. We revealed (a) a measure‐specific anterior‐to‐posterior gradient of the cerebellar development in childhood, that is, smaller GM volumes and greater WM FA of the anterior segment of the cerebellum but larger GM volumes and lower WM FA in the posterior segment of the cerebellum in older children; (b) an age‐related improvement of the SOC performance at the most demanding level of five‐move problems; and (c) a mediation role of the lateral cerebellar WM volumes in age‐related improvement in the SOC performance in childhood. These results highlight the differential development of the cerebellum during childhood and provide evidence that brain adaptation to the acquisition of planning ability during childhood could partially be achieved through the engagement of the lateral cerebellum.

Keywords: cerebellum, cognitive planning, diffusion tensor imaging, Stockings of Cambridge, structural MRI, Tower of London

1. INTRODUCTION

The cerebellum is one of the first brain structures to differentiate during embryogenesis (Larsell, 1947) and has great development in childhood (Yakovlev & Lecours, 1967). Clinical studies indicate that damage to the cerebellum influences visuospatial, language, and executive function (Steinlin, 2008). Especially, converging evidence from neuroimaging and lesion studies supports the involvement of the cerebellum in one aspect of executive function, cognitive planning. Cognitive planning involves the evaluation and selection of multiple sequences of thoughts and actions to achieve a desired goal (McCormack & Hoerl, 2008; McCormack, 2011). It has been shown that cognitive planning skills in early childhood can predict later academic achievement (Gerstle, Beebe, Drotar, Cassedy, & Marino, 2016) and social maturity (Hughes, 1998; Zorza, Marino, & Acosta, 2016). However, cerebellar mechanisms associated with cognitive planning are less studied compared to those with working memory and attention (Chen & Desmond, 2005; Courchesne & Allen, 1997; Marvel & Desmond, 2010), especially in children. While mid‐childhood is a crucial period for development of the cerebellum and cognitive planning (Kipping, Margulies, Eickhoff, Lee, & Qiu, 2018; Kipping, Tuan, Fortier, & Qiu, 2017; Luciana & Nelson, 2002), there is a need to investigate and understand the normative development of the cerebellum and its relationship with cognitive planning in early life.

Anatomically, the anterior cerebellar (I‐V) and inferior posterior cerebellar regions (VIIIa/VIIIb) are connected to the primary cortical regions, which form the sensorimotor system (Buckner, Krienen, Castellanos, Diaz, & Yeo, 2011; Grodd, Hulsmann, Lotze, Wildgruber, & Erb, 2001; Kipping et al., 2013; Kipping et al., 2017; Wang, Kipping, Bao, Ji, & Qiu, 2016). In contrast, the lateral posterior cerebellum is most connected to the prefrontal cortex and is expected to be involved in higher‐order cognitive functions (Moore, D'Mello, McGrath, & Stoodley, 2017; Stoodley, Valera, & Schmahmann, 2012). It has been shown that the cortical maturation occurs earlier in the somatosensory, vision, audition, motor cortices than in the frontal cortex (Gogtay et al., 2004). Guillery (2005) suggested that this asynchrony in the maturation of cortical regions may be relevant to the hierarchy of connections between cortical areas: the early maturation of receptive sensory areas (responsible for low‐level processing) would enable a stabilization of the information used by integrative areas (involved in high level processing) which develop later on. To extend this idea, one would expect that the development of the anterior and posterior cerebellar regions might follow the same pattern as shown in the cortex. Hence, we hypothesized that the regional cerebellar development may follow the sequence of the cortical maturation based on their anatomical topographic mapping. However, a substantial body of literature reports quadratic or linear relationship of age with the total cerebellar gray matter (GM) (Brain Development Cooperative Group, 2012; Tiemeier et al., 2010) and white matter (WM) volumes (Taki et al., 2013; Tiemeier et al., 2010). Only Tiemeier et al. (2010) quantified the anterior, superior, and inferior posterior cerebellar volumes. However, it had a limited sample of children aged from 6 to 8 years.

It is known that cognitive planning is considered as a function subserved by the dorsolateral frontal cortex (Beauchamp, Dagher, Aston, & Doyon, 2003; Newman, Carpenter, Varma, & Just, 2003; Nitschke, Kostering, Finkel, Weiller, & Kaller, 2017; Schall et al., 2003; Wagner, Koch, Reichenbach, Sauer, & Schlosser, 2006). The Tower of London task (Shallice, 1982) is widely used to investigate the cognitive planning ability, which requires participants to imagine a complex sequence of steps to move blocks from one position to another under specific constraints. Functional MRI studies on the Tower of London task have found both the prefrontal cortex (Beauchamp et al., 2003; Newman et al., 2003; Nitschke et al., 2017; Schall et al., 2003; Wagner et al., 2006) and the lateral posterior cerebellum to be involved (Beauchamp et al., 2003; Schall et al., 2003; Stoodley & Schmahmann, 2009). Notably, the lateral posterior cerebellum— including lobules VI, Crus I, II, and VIIb—has direct anatomical connections with the dorsolateral prefrontal cortex (Kelly & Strick, 2003; Middleton & Strick, 2000; Schmahmann & Pandya, 1997), supporting the notion of a cerebello‐cortical functional system underlying planning ability (Diamond, 2000). Moreover, lesion studies have observed that focal damage in the lateral posterior cerebellum results in planning deficits in both adults (Grafman et al., 1992; Schmahmann & Sherman, 1998) and children (Cantelmi, Schweizer, & Cusimano, 2008; Levisohn, Cronin‐Golomb, & Schmahmann, 2000), further supporting the cerebellar role in planning. In particular, the cerebellar role for planning has been highlighted in the context of cognitive and behavioral optimization (Koziol, Budding, & Chidekel, 2010). Hence, we hypothesize that the lateral posterior cerebellum plays a role in mediating age‐related improvement of cognitive planning in children.

This study aimed (a) to investigate age associations with the volumes and microstructure of the cerebellar regions and their possible anterior‐to‐posterior gradient pattern and (b) to examine whether regional cerebellar volumes and microstructure, especially the lateral posterior cerebellum, would play a mediation role in the relationship between age and cognitive planning in a group of 6‐to‐10‐year‐old children. Planning ability was assessed using the Stockings of Cambridge task (SOC) (Luciana & Nelson, 2002; Robbins et al., 1998), which is a computerized version of the Tower of London task. We parcellated cerebellar gray and WM on structural T1‐weighted MRI and cerebellar WM microstructure on diffusion‐weighted MRI using an atlas‐based segmentation technique (Bazin et al., 2014). Microstructural measures included two common diffusion tensor imaging (DTI) measures—fractional anisotropy (FA) and mean diffusivity (MD) in cerebellar regions. Our results revealed (a) age associations with cerebellar morphology and microstructure in an anterior‐to‐posterior gradient pattern and (b) a mediation role of the lateral posterior cerebellar morphological development in the improvement of planning ability during mid‐ and late childhood.

2. MATERIALS AND METHODS

2.1. Subjects

Written consent was obtained from participants' parents under the approval of the Institutional Review Board of the National University of Singapore.

Children aged 6–10 years were recruited from an existing children brain and cognitive development study (Kipping et al., 2017; Phua, Rifkin‐Graboi, Saw, Meaney, & Qiu, 2012; Qiu et al., 2012b; Qiu, Rifkin‐Graboi, Tuan, Zhong, & Meaney, 2012a; Zhong et al., 2014). Subjects with an existing diagnosis of chronic medical conditions (e.g., cancer, congenital abnormalities) and/or mental illnesses (e.g., ADHD, Autism) were excluded. This study included 126 subjects who had both T1‐weighted MRI and cognitive task data (mean = 7.21 years; standard deviation [SD] = 1.19 years). Among them, 87 subjects also had DTI data (mean = 7.45 years, SD = 1.35 years). The age distributions in both samples are shown in Figure S1 in the Supporting Information.

2.2. Stockings of Cambridge

We examined the SOC task using the Cambridge Neuropsychological Test of Automated Battery (CANTAB). CANTAB includes language‐independent cognitive tests (Luciana & Nelson, 2002) administered on a computer fitted with a touch‐sensitive screen and a two‐button response pad. Participants were first screened on two motor and learning tasks to verify the ability to follow simple instructions. Subsequently, participants performed.

SOC is an executive function task involving planning and execution of a series of actions. During the task, participants are shown two displays containing colored balls that can be perceived as stacks of colored balls in stockings or socks suspended from a beam. Participants are required to copy the pattern shown in the upper display by moving the balls in the lower display. Prior to the execution of ball moves, participants need to plan their moves. Participants should complete a trial with the minimum number of moves at increasing difficulty levels of two‐, three‐, four‐ and five‐move problems. The balls can be moved one at a time by touching the required ball and then by touching the target position. The number of trials solved with the minimum number of moves measures optimal adult‐like task performance. The mean number of moves across all the trials at each difficulty level estimates planning accuracy. This study used the mean number of moves instead of the minimum number of trials solved with the minimum number of moves to assess the accuracy of the task performance (Luciana, Collins, Olson, & Schissel, 2009; Luciana & Nelson, 2002).

2.3. MRI acquisition

Children underwent MRI scans using a 3 T Siemens Magnetom Trio Tim scanner with a 32‐channel head coil at the National University of Singapore. The image protocols were as follows: (a) high‐resolution isotropic T1‐weighted Magnetization Prepared Rapid Gradient Recalled Echo (MPRAGE; 190 slices with 1 mm slice thickness, in‐plane resolution = 1 mm, no inter‐slice gap, sagittal acquisition, field of view = 190 × 190 mm, matrix = 190 × 190, repetition time = 2,000 ms, echo time = 2.08 ms, inversion time = 850 ms, flip angle = 9°, GRAPPA = 2, acquisition time = 3 min 36 s per scan); (b) isotropic T2‐weighted imaging protocol (dual spin echo sequence; 42 slices with 3 mm slice thickness, no inter‐slice gaps, matrix = 192 × 192, field of view = 220 × 220 mm, repetition time = 3,040 ms, a first echo time = 11 ms, a second echo time = 123 ms, flip angle = 120°, GRAPPA = 3, acquisition time = 2 min 14 s per scan); (c) isotropic diffusion weighted imaging protocol (single‐shot echo‐planar sequence; 55 slices of 2.3 mm slice thickness, with no inter‐slice gaps, matrix = 96 × 96, field of view = 220 × 220 mm, repetition time = 6,800 ms, echo time = 89 ms, flip angle = 90°, 30 diffusion weighted images with b = 900 s/mm2, 5 baseline images without diffusion weighting, acquisition time = 4 min 56 s per scan). In the DTI acquisition, GRAPPA was chosen as 3 for the purpose of reducing the acquisition time and geometric distortion. T1‐weighted MRI and DTI were repeated twice to improve signal‐to‐noise ratio.

The image quality was verified immediately after the acquisition through visual inspection when the child was still in the scanner. A scan was repeated when ring artifact on T1‐weighted images and signal loss on DTI were large (see an example in Figure S2 in the Supporting Information). The image was removed from the study if no acceptable image was acquired after three repetitions.

2.4. MRI data analysis

2.4.1. Atlas‐based cerebellar parcellation

FreeSurfer was used to label each voxel in the T1‐weighted image as GM, WM, CSF, cerebellum, or subcortical structures (e.g., hippocampus, amygdala, thalamus, caudate, putamen, globus pallidus) (Fischl et al., 2002). In the Markov Random Field model of FreeSurfer, the prior probability of each structure was computed based on the manual segmentation of 20 subjects randomly selected from the sample of this study. The cerebellum was extracted from the T1‐weighted MRI and was then mapped to the ChroMa cerebellar atlas with the cerebellar anatomical labels (https://www.nitrc.org/frs/shownotes.php?release_id=2748) (Bazin et al., 2014) via large deformation diffeomorphic metric mapping (LDDMM) (Du et al., 2014; Du, Younes, & Qiu, 2011; Tan & Qiu, 2016; Tan & Qiu, 2018; Zhong, Phua, & Qiu, 2010). This ChroMa cerebellar atlas provides detailed cerebellar anatomical labels and cerebellar surfaces for the inner, mid‐cerebellar, and outer surfaces for data visualization. To evaluate the mapping accuracy, the cerebellum of 20 T1‐weighted anatomical images was manually segmented into the cerebellar gray and WM. The mapping accuracy was quantified based on the volume overlap ratio between the manual and automatic segmentation of the cerebellar white (mean Dice coefficient = 0.709) and gray (mean Dice coefficient = 0.791) matter. Figure S3 in the Supporting Information illustrates the manual and automatic segmentation of the cerebellar gray and WM. Cerebellar anatomical lobules in the atlas were defined based on the knowledge established by Schmahmann et al. (1999). The cerebellum of individual subjects was divided into anterior regions (bilateral lobule I‐II, III, IV, V), and posterior regions (bilateral VI, Crus I, Crus II, VIIB, VIIIA, VIIIB, IX and X, and vermal regions of VI, Crus I, Crus II, VIIb, VIIIa, VIIIb, IX, and X). As illustrated in Figure 1, these cerebellar regions, except vermal Crus I and II, were further divided into GM and WM. Only GM regions of vermal Crus I and II were defined. As a result, this study included 32 GM regions and 30 WM regions.

Figure 1.

Figure 1

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Cerebellar anatomical parcellation in the coronal view. The first column displays the cerebellum on T1‐weighted MRI in a single individual. The second and third columns show the parcellation of the cerebellar lobular and vermal GM, and the cerebellar lobular and vermal parcellation in both GM and WM, respectively

2.4.2. DTI analysis

DTI was processed based on the procedure detailed in the study by Huang et al. (2008) to correct geometric distortion of the DTI due to B0‐susceptibility differences over the brain. In short, the T2‐weighted image was considered as the anatomical reference. Within a subject, the deformation that transformed its DTI to the T2‐weighted image characterized the geometric distortion of the DTI. Intrasubject registration was first performed using affine registration to remove linear transformation (rotation and translation) between the 35 diffusion‐weighted images and T2‐weighted image. Then, LDDMM sought the optimal nonlinear transformation that deformed the B0 image to the T2 weighted image (Huang et al., 2008). Such diffeomorphic transformation was applied to every diffusion‐weighted image to correct the DTI nonlinear geometric distortion. Finally, the diffusion‐weighted images were transformed to the corresponding T1‐weighted image based on the affine transformation between the T1 and T2 weighted images of each subject. The diffusion tensor was determined by multivariate least‐square fitting. FA and MD were computed based on the three eigenvalues of the tensor for quantifying the anisotropy and water diffusivity of each cerebellar WM region.

2.5. Statistical analysis

We first employed robust regression analysis to examine the associations between age as a continuous variable with cerebellar morphology and microstructure. Here, the cerebellar GM and WM volumes were normalized by the total cerebellar volume to assess age prediction to specific cerebellar morphology. Multiple comparisons were corrected for the number of the cerebellar measures (126 tests, specifically 33 cerebellar [32 regions‐specific + total] GM measures and 31 cerebellar [30 region‐specific + total] WM, FA, and MD measures, respectively) using Benjamini–Hochberg False Discovery Rate procedure at a significant level of p < .05 (Benjamini & Hochberg, 1995).

We further examined whether the coefficients associated with age in the aforementioned models gradually change from the anterior to the posterior cerebellum. The anterior and posterior direction of the cerebellum was denoted based on the anatomical definition established by Schmahmann et al. (1999) (Figure S4 in the Supporting Information ). We ordered the cerebellar regions as 1–12 (I‐II, III, IV, V, VI, Crus I, Crus II, VIIb, VIIIa, VIIIb, IX, and X), where 1 represents the most anterior region and 12 represents the most posterior region (Figure S4 in the Supporting Information). We then used correlation analysis between the standardized coefficients (listed in Table 1) and the spatial location (i.e., 1–12). This approach was previously introduced by Davis et al. (2009).

Table 1.

Relationships between age and cerebellar volumetric and microstructural measures

Lobules LH RH Vermis LH RH Vermis
GM volume WM volume
Anterior I‐II −0.090 −0.024 0.087 0.297
III 0.102 0.015 0.130 0.320
IV −0.165 −0.163 −0.031 −0.073
V −0.124 −0.192 −0.128 −0.253
Posterior VI −0.285 −0.237 0.207 0.150 0.002 −0.167
Crus I −0.064 0.037 0.071 0.446 0.301
Crus II −0.013 −0.101 −0.071 0.337 0.133
VIIb 0.045 −0.032 −0.038 0.166 0.048 −0.104
VIIIa 0.231 0.200 0.037 0.499 0.267 −0.046
VIIIb 0.052 0.235 0.021 0.452 0.385 −0.136
IX 0.053 0.081 0.109 0.105 0.212 −0.061
X −0.144 −0.131 0.264 0.152 0.221 0.085
FA MD
Anterior I‐II −0.060 0.135 0.123 −0.057
III 0.411 0.452 −0.108 −0.215
IV 0.335 0.556 −0.010 −0.108
V 0.234 0.402 −0.004 0.104
Posterior VI 0.084 0.257 0.024 −0.024 −0.031 0.088
Crus I 0.029 0.083 0.038 −0.062
Crus II −0.313 −0.157 −0.009 −0.095
VIIb −0.113 −0.136 −0.239 −0.205 −0.171 0.074
VIIIa −0.257 −0.211 −0.136 −0.123 −0.154 0.011
VIIIb −0.240 −0.028 −0.224 0.032 −0.116 0.052
IX 0.075 0.185 −0.161 0.010 −0.153 −0.008
X −0.283 −0.231 0.017 0.251 0.187 0.065
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Note. FA, fractional anisotropy; GM, gray matter; LH, left hemisphere; MD, mean diffusivity; RH, right hemisphere; WM, white matter.

We finally used Baron and Kenny's steps (Baron & Kenny, 1986) to examine whether the association between age and planning was mediated via region‐specific cerebellar morphological and microstructural variability. We illustrated our mediation models in Figure 2. We used robust regression analysis in each step of Baron and Kenny's method (Baron & Kenny, 1986) to overcome any violations of assumptions of traditional regression analysis. In the first step, we employed robust regression to examine the relationship between age and the SOC performance (path c in Figure 2), where age was an independent variable and the SOC performance was considered as a dependent variable. In the second step (path a in Figure 2), we followed the results of the age prediction to cerebellar morphology and microstructure stated above. In the third step, we examined the robust regression for path c’ in Figure 2, where cerebellar measures with significant age prediction obtained in the second step and age were considered independent variables and the SOC performance was a dependent variable. In the fourth step, we examined whether the age prediction of the SOC performance in path c’ was significantly less than that in path c via a bootstrapping algorithm in MATLAB (Preacher & Hayes, 2004). For this, we used unstandardized regression coefficients and computed the mediation effect for each of 10,000 bootstrapped resamples. We used the 95% confidence interval to estimate significance. If the confidence interval did not contain zero, an effect is considered significantly different from zero at p < .05 (two‐tailed).

Figure 2.

Figure 2

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Schematic of the mediation model. This model was used to examine the mediation role of the cerebellar morphology and microstructure (M: Mediator) in the relationship between age (X) and the stockings of Cambridge (Y: SOC) task performance

3. RESULTS

3.1. Associations of age with cerebellar morphological and microstructural measures

Figure 3 illustrates region‐specific volumetric measures of cerebellar gray and WM, and microstructural measures of cerebellar WM, namely FA and MD. We reported associations between age and cerebellar measures (path a) at a false discovery rate (FDR) corrected P‐value<.05 (uncorrected p < .004).

Figure 3.

Figure 3

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Region‐specific T1 (top row) and DTI (bottom row) measures in the cerebellum. Bar plots represent region‐specific group mean values, and error bars show standard deviations. DTI measures for vermal crus I and crus II are not displayed, as they were excluded from the statistical analysis due to their small sizes

3.1.1. Gray matter volumes

Our analysis revealed that the volumes of the left cerebellar VI and total cerebellar GM (standardized coefficient = −0.299, p < .001) were negatively associated with age (Table 1). In contrast, the volume in the posterior cerebellar X was positively associated with age (Table 1).

Our analysis further revealed that age associations with the cerebellar GM volumes gradually increased from the anterior to the posterior cerebellum (r = 0.386, p = .029). This finding suggested that the cerebellar GM development in childhood follows the anterior–posterior gradient pattern (Figure 4 ).

Figure 4.

Figure 4

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Scatter plots show the age associations with the cerebellar gray matter volumes (top panel) and with the cerebellar white matter FA (bottom panel) in terms of the cerebellar anatomical locations. The cerebellar anatomical locations are labeled from the anterior regions (I‐II, III, IV, V) to the posterior regions (VI, crus I, crus II, VIIb, VIIIa, VIIIb, IX, X)

3.1.2. White matter volumes

The WM volume of the right cerebellar V was negatively associated with age, whereas the WM volumes in the anterior (right I–II, III) and posterior (bilateral Crus I, left Crus II, bilateral VIIIa and VIIIb) cerebellum were positively associated with age (Table 1).

Our analysis did not reveal that age associations with the cerebellar WM volumes were a function of cerebellar anatomical location (r = .155, p = .414).

3.1.3. White matter FA

The FA values of the posterior left Crus II and left X were negatively associated with age, whereas the FA values of the anterior regions (bilateral III, IV, and right V) and right VI were positively associated with age (Table 1).

Our analysis further revealed that age associations with cerebellar WM FA decreased in the direction from the anterior to the posterior cerebellum (r = −.659, p < .001). This finding suggested an anterior‐to‐posterior gradient of age associations with the cerebellar WM FA (Figure 4 ).

3.1.4. White matter MD

The MD value of the anterior right III was negatively associated with age, whereas the MD value of the posterior left X was positively associated with age (Table 1 ).

Our analysis did not reveal an anterior‐to‐posterior gradient of age associations with the cerebellar WM MD (r = .207, p = .273).

When considering age as a categorical variable, regression analysis confirmed the above age associations (Table S1 in the Supporting Information).

3.2. Association of age with the SOC performance

All children (n = 126) completed at least half of the trials of two‐move problems with the minimum number of moves, which reached ceiling effects and was not considered in this study. A positive association was found between age and the performance in SOC five‐move problems (standardized coefficient [s.c.] = −0.216, standard error [SE] = 0.091, p = .019), but not between age and the performance in SOC three‐move (s.c. = −0.147, SE. = 0.081, p = .071) and four‐move problems (s.c. = −0.159, SE = 0.093, p = .090). These results suggested that (a) children aged from 6 to 10 years had a certain level of cognitive planning and (b) the high‐level planning skill was improved as age increased in childhood.

When considering age as a categorical variable, regression analysis confirms the above age association (Supporting Information).

3.3. Mediation role of the cerebellum in the relationship between age and SOC performance

3.3.1. Gray matter volumes

None of the cerebellar GM volumes (total cerebellum, left VI, right X) predicted SOC task performance. No mediation analysis was further performed with cerebellar GM volumes as mediators.

3.3.2. White matter volumes

Cerebellar WM volumes of the anterior (right I‐II, III, and V) and posterior (bilateral Crus I, left Crus II, bilateral VIIIa and VIIIb) cerebellum were significantly associated with age (path a) and were each used as an independent variable together with age to predict the SOC task performance (path b). WM volumes of bilateral Crus I and left VIIIa were significantly associated with the SOC task performance (FDR corrected p < .05) (Table 2). Age predictions on the SOC task performance, while controlling for each of the cerebellar WM volumes of right Crus I (CI: [−0.016–0.001]), left Crus I (CI: [−0.019–0.001]), and left VIIIa (CI: [−0.028–0.003]) (path c’) were smaller than that in path c (Table 2), suggesting that these three cerebellar regions mediated the relationship between age and the SOC task performance (Figure 5 ).

Table 2.

Relationships between age and cerebellar regional white matter volumes (path a), between cerebellar regional white matter volumes and the SOC task performance while controlling for age (path b), and between age and the SOC task performance (five‐move problems) while controlling for cerebellar regional white matter volumes (path c’)

Path a Path b Path c’
WM volumes Standardized coefficient (SE) p Standardized coefficient (SE) p Standardized coefficient (SE) p
Right I‐II 0.297 (0.082) <.001 0.058 (0.098) .556 −0.23 (0.094) .016
Right III 0.32 (0.083) <.001 −0.142 (0.098) .147 −0.175 (0.095) .067
Right V −0.253 (0.082) .003 −0.071 (0.097) .464 −0.232 (0.095) .016
Right crus Ia 0.301 (0.09) .001 −0.207 (0.092) .027 −0.152 (0.092) .102
Left crus Ia 0.446 (0.081) <.001 −0.215 (0.098) .031 −0.132 (0.098) .18
Left crus II 0.337 (0.082) <.001 −0.180 (0.093) .056 −0.174 (0.094) .065
Right VIIIa 0.267 (0.091) .004 −0.044 (0.096) .65 −0.206 (0.095) .032
Left VIIIaa 0.499 (0.079) <.001 −0.282 (0.1) .007 −0.089 (0.102) .386
Right VIIIb 0.385 (0.084) <.001 −0.091 (0.1) .366 −0.183 (0.099) .066
Left VIIIb 0.452 (0.076) <.001 −0.145 (0.101) .153 −0.160 (0.1) .115
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SE, standard error; WM, white matter.

a

indicates a significant mediation effect via region‐specific cerebellar white matter.

Figure 5.

Figure 5

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The white matter volumes of three cerebellar regions, including left crus I (violet) and right crus I (orange), and left VIIIa (blue) mediated the relationship between age and planning ability (five‐move problems). Scatter plots illustrate significant relationships among the paths (a, b, c) in Figure 2

When considering age as a categorical variable, the mediation analysis confirmed the mediation effects of bilateral Crus I but not that of left VIIIa (Table S2 in the Supporting Information).

3.3.3. White matter FA and MD

Cerebellar WM FA of anterior (bilateral III and IV, right V) and posterior (right VI, left Crus II and X) cerebellum did not predict the SOC task performance. No mediation analysis was further performed with cerebellar WM FA values as mediators.

Cerebellar WM MD values in the anterior (right III) and posterior (left X) cerebellum did not predict the SOC task performance. No mediation analysis was further performed with cerebellar WM MD values as mediators.

4. DISCUSSION

This study investigated the relationships of age with the volumetric and microstructural measures of the regional cerebellar GM and WM and cognitive planning, and examined mediation roles of the cerebellar measures in age‐related improvement of the cognitive planning in children aged from 6 to 10 years. Our findings revealed (a) a measure‐specific anterior‐to‐posterior gradient (positive or negative gradient) of the cerebellar development in childhood, that is, smaller GM volumes and higher WM FA of the anterior cerebellum but greater GM volumes and lower WM FA in the posterior cerebellum in older children; (b) an age‐related improvement of the SOC performance in the most demanding level of five‐move problems; and (c) a mediation role of the lateral cerebellar WM volumes in age‐related improvement in the SOC performance in childhood. These results highlight a differential development of the cerebellum during childhood and provide evidence that brain adaptation to the acquisition of planning ability during childhood could partially be achieved through the engagement of the lateral cerebellum.

Our study suggested a measure‐specific anterior‐to‐posterior gradient of the cerebellar development in childhood. In older children, smaller GM volumes and higher FA values were shown in the anterior cerebellum but greater GM volumes and lower FA values were observed in the posterior cerebellum (Table 1). Moreover, this differential development of the cerebellum in childhood coincides with the cerebellar anterior and posterior organization. An anterior–posterior boundary is found to represent functionally distinct systems related to ataxia and maladaptation (Martin, Keating, Goodkin, Bastian, & Thach, 1996), cerebellar motor syndrome, and cerebellar cognitive affective syndrome (Stoodley, MacMore, Makris, Sherman, & Schmahmann, 2016). In animal studies, an anterior–posterior boundary crossing Crus I medio‐laterally is identified through mutant cerebellar cell malformations (Eisenman, 2000; Wassef & Joyner, 1997). Moreover, the cerebellum forms functionally distinct circuits with cerebral brain regions to support brain functions during development and aging (Lee, Tan, & Qiu, 2016; Schmahmann, Weilburg, & Sherman, 2007; Stoodley & Schmahmann, 2009; Zhang, Lee, & Qiu, 2017). Most of the posterior cerebellar regions form long‐range connections with the association cortex, while the anterior cerebellar regions form connections with the primary sensorimotor cortex. Based on the anatomical connections between the cerebral cortex and cerebellum, the cerebellar GM developmental pattern follows the same trend of the cortical GM development, that is, the primary cortex develops earlier than the association cortex during childhood (Giedd et al., 1999; Gogtay et al., 2004). Our findings provided a novel insight on the developmental pattern of the cerebellar GM in parallel with that of the cortical GM.

In contrast, the opposite age associations with the FA values of the cerebellar WM were shown in this study, that is, in older children, FA values were higher in the anterior cerebellum but lower in the posterior cerebellum. The cerebellar microstructural WM development in the anterior cerebellum is in line with an age‐related increase in the cerebral WM FA during childhood (Lebel & Beaulieu, 2011; Lebel, Walker, Leemans, Phillips, & Beaulieu, 2008). An age‐related increase in the cerebral WM volume and an aged‐related increase in the FA values of major WM tracts are also observed in children (Lebel & Beaulieu, 2011), suggesting their axonal growth and enhanced coherence of WM fiber organization during childhood. On the other hand, an age‐related decrease in FA of the posterior cerebellum coincides with its greater WM volumes in the older children in our study. An increase in the posterior cerebellar WM volume accompanied by a decrease in the posterior cerebellar FA suggest axonal growth but that axonal directions might need to be fine‐tuned to align coherently together. Hence, the lower age‐related FA value in the posterior cerebellum and the higher age‐related FA values in the anterior cerebellum might implicate the differential growth of cerebellar pathways (Re et al., 2017).

We observed the anterior‐to‐posterior gradient of the cerebellar development based on the FA value but not on the MD value. This is not surprising. During childhood, progressive fiber organization may be reflected by an increase in anisotropy. However, MD may remain unchanged as the water diffusion increases along the fiber but decreases perpendicular to the fiber (Qiu, Mori, & Miller, 2015).

In parallel to the cerebellar development in childhood, planning abilities improve with age. Cerebellar functions have been discussed in the concept of internal process optimization on motor or cognitive planning (Bellebaum & Daum, 2007; Kipping et al., 2018). The lateral cerebellum is involved in processing of a series of individual movements (Ito, 2008) and individual thoughts (Schmahmann et al., 2007). Functional activation of the lateral cerebellum is increased during the early stage of sequence learning (Doyon et al., 2002). Difficulties in the sequential ordering of actions and thoughts have been shown in children with neurodevelopmental problems (Bauer, Hanson, Pierson, Davidson, & Pollak, 2009; Stoodley, 2016; Stoodley & Stein, 2013). Likewise, adults with cerebellar damage, specifically in the lateral region, show deficiency in planning skills (Grafman et al., 1992; Schmahmann, 2004). In our study, the relationship between age and planning abilities was mediated by the WM volume of the lateral cerebellum. Specifically, greater WM (bilateral Crus I and left VIIIa) volumes were associated with better cognitive planning in older children. Similarly, improved cognitive skills in children are also associated with the volumetric change of the cerebral WM (Darki & Klingberg, 2015; Mabbott, Noseworthy, Bouffet, Laughlin, & Rockel, 2006). As age increases in childhood, a greater WM volume in the lateral cerebellum (Crus I and VIIIa) is in line with more integrated functional cerebello‐cerebral connections (Kipping et al., 2017), which are involved in executive control processes (Koziol & Lutz, 2013; Shaw et al., 2018). Together, these findings suggest that the lateral cerebellum contributes to the acquisition of rapid and accurate planning ability during childhood development.

We notice that the anterior‐to‐posterior gradient of the cerebellar development in childhood was only directly reflected by cerebellar GM volumes and WM FA values but not by cerebellar WM volumes and WM MD values. However, such measure‐specific developmental pattern of the cerebellum does not mediate the age‐related improvement in cognitive planning in childhood. This is not a surprise since individual cognitive domains such as executive functioning, working memory, and spatial processes are mapped to distinct cerebellar regions in the posterior cerebellum (Keren‐Happuch, Chen, Ho, & Desmond, 2014; Stoodley & Schmahmann, 2009); rather the global developmental pattern supports the age‐related improvement in a specific cognitive function. Our study showed that the WM volume of the lateral cerebellum had the greatest increase during childhood compared to its GM volume, WM FA and MD values and was also associated with age‐related improvement in cognitive planning. Hence, our mediation findings not only support the idea of specific‐cognitive organization of the cerebellum but also provide evidence on the developmental synchrony of cognition and brain development during childhood.

Our study had several limitations. The WM segmentation in this study was not anatomically driven but based on the expansion from the GM parcellation. An alternative approach could be using tractography (Moura et al., 2017). The lack of the relationship between FA values and SOC might be due to the lobular‐specific rather than tract‐specific FA measure in this study. Higher tract‐specific FA values in cerebral tracts are reported in association with advanced cognitive development in children and adolescents (Darki & Klingberg, 2015). Moreover, our study was based on a cross‐sectional design. Future longitudinal designs including adolescents and adults are needed to more fully understand developmental trajectories of the brain‐cognition relationship with planning ability. Finally, the test–retest stability of the number of SOC trials solved with the minimum number of moves has been found to be relatively low in previous studies in adults (Lowe & Rabbitt, 1998) and children (Syvaoja et al., 2015). This study did not employ the number of SOC trials solved with the minimum number of moves. Instead, the mean number of moves, assessed by averaging the number of moves across all the trials at each difficulty level, was used in this study. As our study showed an expected improvement of the task performance, particularly in the most difficult task level (Luciana & Nelson, 2002), we expect that this measure can well represent planning accuracy for understanding its age association and the cerebellar role in such an association.

5. CONCLUSION

Our study examined morphological and microstructural properties of the anatomically parcellated cerebellum and their relations with planning ability in typically developing children aged 6–10 years. Our findings provide evidence on understanding that the cerebellum follows a differential topological maturation, specifically along an anterior–posterior direction. Furthermore, brain adaptation to planning acquisition in early life is achieved partially through engagement of the lateral cerebellar regions during this specific developmental time window and might function as a reference for future investigation of typical and atypical cognition‐related cerebellar development.

Supporting information

Appendix S1: Supplementary Material

Click here for additional data file. (3.1MB, docx)

ACKNOWLEDGMENTS

This research is supported by the Singapore National Research Foundation under its Translational and Clinical Research (TCR) Flagship Program and administered by the Singapore Ministry of Health's National Medical Research Council (NMRC), Singapore‐ NMRC/TCR/004‐NUS/2008; NMRC/TCR/012‐NUHS/2014. Additional funding is provided by the Singapore Institute for Clinical Sciences, Agency for Science Technology and Research (A*STAR), Singapore.

Kipping JA, Xie Y, Qiu A. Cerebellar development and its mediation role in cognitive planning in childhood. Hum Brain Mapp. 2018;39:5074–5084. 10.1002/hbm.24346

Funding information Singapore Institute for Clinical Sciences; National Medical Research Council, Grant/Award Number: NMRC/TCR/004‐NUS/2008NMRC/TCR/012‐NUHS/2014; Ministry of Health; Singapore National Research Foundation

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