Coordinated brain development: exploring the synchrony between changes in grey and white matter during childhood maturation.

Brain development during childhood and early adolescence is characterized by global changes in brain architecture. Neuroimaging studies have revealed overall decreases in cortical thickness (CT) and increases in fractional anisotropy (FA). Furthermore, previous studies have shown that certain cortical regions display coordinated growth during development. However, there is significant heterogeneity in the timing and speed of these developmental transformations, and it is still unclear whether white and grey matter changes are co-localized. In this multimodal neuroimaging study, we investigated the relationship between grey and white matter developmental changes and asynchronous maturation within brain regions in 249 normally developing children between the ages 7-14. We used structural magnetic resonance imaging (MRI) and diffusion tensor imaging (DTI) to analyze CT and FA, respectively, as well as their covariance across development. Consistent with previous studies, we observed overall cortical thinning with age, which was accompanied by increased FA. We then compared the coordinated development of grey and white matter as indexed by covariance measures. Covariance between grey matter regions and the microstructure of white matter tracts connecting those regions were highly similar, suggesting that coordinated changes in the cortex were mirrored by coordinated changes in their respective tracts. Examining within-brain divergent trajectories, we found significant structural decoupling (decreased covariance) between several brain regions and tracts in the 9- to 11-year-old group, particularly involving the forceps minor and the regions that it connects to. We argue that this decoupling could reflect a developmental pattern within the prefrontal region in 9- and 11-year-old children, possibly related to the significant changes in cognitive control observed at this age.

Structural covariance in schizophrenia and first-episode psychosis: An approach based on graph analysis.

Schizophrenia is a neurodevelopmental disorder that produces abnormalities across different brain regions. Measuring structural covariance with MRI is a well-established approach to investigate common changes in distinct systems. We investigated structural covariance in schizophrenia in a large Brazilian sample of individuals with chronic schizophrenia (n = 143), First Episode Psychosis (n = 32), and matched healthy controls (n = 82) using a combination of graph analysis and computational neuroanatomy. Firstly, we proposed the connectivity-closeness and integrity-closeness centrality measures and them compared healthy controls with chronic schizophrenia regarding these metrics. We then conducted a second analysis on the mapped regions comparing the pairwise difference between the three groups. Our results show that compared with controls, both patient groups (in pairwise comparisons) had a reduced integrity-closeness in pars orbitalis and insula, suggesting that the relationship between these areas and other brain regions is increased in schizophrenia. No differences were found between the First Episode Psychosis and Schizophrenia groups. Since in schizophrenia the brain is affected as a whole, this may mirror that these regions may be related to the generalized structural alteration seen in schizophrenia.

Decreased centrality of cortical volume covariance networks in autism spectrum disorders.

Autism spectrum disorders (ASD) are a group of neurodevelopmental conditions characterized by atypical structural and functional brain connectivity. Complex network analysis has been mainly used to describe altered network-level organization for functional systems and white matter tracts in ASD. However, atypical functional and structural connectivity are likely to be also linked to abnormal development of the correlated structure of cortical gray matter. Such covariations of gray matter are particularly well suited to the investigation of the complex cortical pathology of ASD, which is not confined to isolated brain regions but instead acts at the systems level. In this study, we examined network centrality properties of gray matter networks in adults with ASD (n = 84) and neurotypical controls (n = 84) using graph theoretical analysis. We derived a structural covariance network for each group using interregional correlation matrices of cortical volumes extracted from a surface-based parcellation scheme containing 68 cortical regions. Differences between groups in closeness network centrality measures were evaluated using permutation testing. We identified several brain regions in the medial frontal, parietal and temporo-occipital cortices with reductions in closeness centrality in ASD compared to controls. We also found an association between an increased number of autistic traits and reduced centrality of visual nodes in neurotypicals. Our study shows that ASD are accompanied by atypical organization of structural covariance networks by means of a decreased centrality of regions relevant for social and sensorimotor processing. These findings provide further evidence for the altered network-level connectivity model of ASD.