Asynchronous Development of Cerebellar, Cerebello-Cortical, and Cortico-Cortical Functional Networks in Infancy, Childhood, and Adulthood

10.1093/cercor/bhw298Cerebal CortexGUSTO (Growing up towards Healthy Outcomes

Trade-off of cerebello-cortical and cortico-cortical functional networks for planning in 6-year-old children

Childhood is a critical period for the development of cognitive planning. There is a lack of knowledge on its neural mechanisms in children. This study aimed to examine cerebello-cortical and cortico-cortical functional connectivity in association with planning skills in 6-year-olds (n = 76). We identified the cerebello-cortical and cortico-cortical functional networks related to cognitive planning using activation likelihood estimation (ALE) meta-analysis on existing functional imaging studies on spatial planning, and data-driven independent component analysis (ICA) of children's resting-state functional MRI (rs-fMRI). We investigated associations of cerebello-cortical and cortico-cortical functional connectivity with planning ability in 6-year-olds, as assessed using the Stockings of Cambridge task. Long-range functional connectivity of two cerebellar networks (lobules VI and lateral VIIa) with the prefrontal and premotor cortex were greater in children with poorer planning ability. In contrast, cortico-cortical association networks were not associated with the performance of planning in children. These results highlighted the key contribution of the lateral cerebello-frontal functional connectivity, but not cortico-cortical association functional connectivity, for planning ability in 6-year-olds. Our results suggested that brain adaptation to the acquisition of planning ability during childhood is partially achieved through the engagement of the cerebello-cortical functional connectivity

Overlapping and parallel cerebello-cerebral networks contributing to sensorimotor control: An intrinsic functional connectivity study

In concert with sensorimotor control areas of the cerebrum, the cerebellum shows differential activation patterns during a variety of sensorimotor-related tasks. However, the spatial details and extent of the complex and heterogeneous cerebello-cerebral systems involved in action control remain uncertain. In this study, we use intrinsic functional connectivity (iFC) to examine cerebello-cerebral networks of five cerebellar lobules (I–IV, V, VI and VIIIa/b) that have been empirically identified to form the functional basis of sensorimotor processes. A refined cerebellar seed-region selection allowed us to identify a network of primary sensorimotor and supplementary motor areas (I–V), a network of prefrontal, premotor, occipito-temporal and inferior-parietal regions (VI), and two largely overlapping networks involving premotor and superior parietal regions, the temporo-parietal junction as well as occipito-temporal regions (VIIIa/b). All networks involved the medial prefrontal/cingulate cortex. These cerebral clusters were used in a partial correlation analysis to systematically map cerebral connectivity throughout the entire cerebellum. We discuss these findings in the framework of affective and cognitive control, sensorimotor, multisensory systems, and executive/language systems. Within the cerebellum we found that cerebro-cerebellar systems seem to run in parallel, as indicated by distinct sublobular functional topography of prefrontal, parietal, sensorimotor, cingulate and occipito-temporal regions. However, all areas showed overlapping connectivity to various degrees in both hemispheres. The results of both analyses demonstrate that different sublobular parts of the cerebellar lobules may dominate in different aspects of primary or higher-order sensorimotor processing. This systems-level cerebellar organization provides a more detailed structure for cerebello-cerebral interaction which contributes to our understanding of complex motor behavior

Location and statistical parameters of additional clusters obtained using the liberal threshold the more liberal threshold not corrected for the number of derivatives.

<p>Cluster labels correspond to <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0097176#pone-0097176-g007" target="_blank">Figures 7</a>, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0097176#pone-0097176-g008" target="_blank">8</a> and <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0097176#pone-0097176-g009" target="_blank">9</a>.</p

Degree Centrality cluster found using the more liberal threshold not corrected for the number of derivatives.

<p>From left to right: cluster location, scatterplot showing relation between dependent variables (mean Degree Centrality values) and contrast scores (questionnaire factors), and the network obtained by seeding with the cluster. All derivatives have been z scored. All scatterplots represents the whole population (n = 121).</p

Partial (lower diagonal) and full (upper diagonal) Pearson correlations between age, head motion and estimated factors of the NYC-Q.

<p>Numbers and colours represent r values. Significant correlations (two-tailed p = 0.025) are marked with an asterisk.</p

Spatial distribution of fALFF, ReHo, and DC measures across subjects.

<p>Each map was obtained from a one sample t test, converted to z values and thresholded ad Z = 10 (for visualisation purposes). The bottom row features the data inferred mask used in the group analysis. pCC and mPFC show high ReHo values and mPFC show high fALFF values. Those are the major hubs of DMN which suggests that even without relating the measures to the questionnaire results DMN plays an important role in brain activation at rest.</p

Additional ReHo clusters found using the more liberal threshold not corrected for the number of derivatives.

<p>From left to right: location of the clusters (A–B), scatterplots showing relation between dependent variables (mean ReHo values) and contrast scores (questionnaire factors), and networks obtained by seeding with the corresponding cluster. All derivatives have been z scored. All scatterplots represents the whole population (n = 121).</p

Factor loadings on the questions recovered from the second section of the NYC-Q describing the form of self-generated thoughts.

<p>Questions (rows) were decomposed into factors (columns) using Exploratory Factor analysis. Factors were named based on subjective interpretation of the loadings. Weights (how much each question contributes to each factor) are represented both numerically as well as on a colour scale.</p

Factor loadings on the questions from the first section of the NYC-Q describing the content of self-generated thoughts.

<p>Questions (rows) were decomposed into factors (columns) using Exploratory Factor analysis. Factors were named based on subjective interpretation of the loadings. Weights (how much each question contributes to each factor) are represented both numerically as well as on a colour scale. Questions adapted from DSSQ are marked with an asterisk.</p