New insights into the dynamic development of the cerebral cortex in childhood and adolescence: Integrating macro- and microstructural MRI findings

Norbom, Linn B.; Ferschmann, Lia; Parker, Nadine; Agartz, Ingrid; Andreassen, Ole A.; Paus, Tomáš; Westlye, Lars T.; Tamnes, Christian K.

doi:10.1016/j.pneurobio.2021.102109

Cited by 89 publications

(65 citation statements)

References 249 publications

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“…Indeed, the number of papers that include both the term "aging" and "brain volume" (N=2715 in a PubMed search as of 01/06/2021) or "cortical thickness" (N=597) far exceeds those investigating other aspects of morphology, such as "aging" combined with "surface area" (N=125) or "curvature" (N=23). Even though several authors have pointed out that volume is a product of cortical thickness and surface area (Norbom et al, 2021;Storsve et al, 2014;Walhovd et al, 2016;Winkler et al, 2018), which in turn are two genetically independent aspects of brain structure (Hofer et al, 2020;McKay et al, 2014;Panizzon et al, 2009;van der Meer et al, 2020), the implication that thickness and area may have dissociable causes (e.g., in ageing) and consequences (e.g., for cognition) have rarely been discussed, especially in adult samples. Moreover, additional detailed morphometric shape measures (such as curvature or sulcal depth) may provide further insight into brain development across the adult lifespan and its relationship with cognitive performance.…”

Section: Introductionmentioning

confidence: 99%

A morphometric double dissociation: cortical thickness is more related to aging; surface area is more related to cognition

Borgeest

Henson

Kietzmann

et al. 2021

Preprint

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The thickness and surface area of cortex are genetically distinct aspects of brain structure, and may be affected differently by age. However, their potential to differentially predict age and cognitive abilities has been largely overlooked, likely because they are typically aggregated into the commonly used measure of volume. In a large sample of healthy adults (N=647, aged 18-88), we investigated the brain-age and brain-cognition relationships of thickness, surface area, and volume, plus five additional morphological shape metrics. Cortical thickness was the metric most strongly associated with age cross-sectionally, as well as exhibiting the steepest longitudinal change over time (subsample N=261, aged 25-84). In contrast, surface area was the best single predictor of age-residualized cognitive abilities (fluid intelligence), and changes in surface area were most strongly associated with cognitive change over time. These findings were replicated in an independent dataset (N=1345, aged 18-93). Our results suggest that cortical thickness and surface area make complementary contributions the age-brain-cognition triangle, and highlight the importance of considering these volumetric components separately.

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Section: Introductionmentioning

confidence: 99%

A morphometric double dissociation: cortical thickness is more related to aging; surface area is more related to cognition

Borgeest

Henson

Kietzmann

et al. 2021

Preprint

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“…The visualization helped identify that cluster D has high expression levels for many proteins. That cluster represents older children and the transition to adolescence (mean cluster age = 10.3 years, CI 9.6–11.1 years) when rapid changes in cortical microstructure have been found ( Norbom et al, 2021 ). In addition, groups of proteins with either high or low expression can be identified in a cluster, such as the higher expression of Golli-MBP, GFAP, CB1, and NR2B in cluster B.…”

Section: Application Of Robust Sparse K -Means Clustering Clusters To Study Human Visual Cortex Developmentmentioning

confidence: 99%

A Practical Guide to Sparse k-Means Clustering for Studying Molecular Development of the Human Brain

Balsor

Arbabi²,

Singh³

et al. 2021

Front. Neurosci.

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Studying the molecular development of the human brain presents unique challenges for selecting a data analysis approach. The rare and valuable nature of human postmortem brain tissue, especially for developmental studies, means the sample sizes are small (n), but the use of high throughput genomic and proteomic methods measure the expression levels for hundreds or thousands of variables [e.g., genes or proteins (p)] for each sample. This leads to a data structure that is high dimensional (p ≫ n) and introduces the curse of dimensionality, which poses a challenge for traditional statistical approaches. In contrast, high dimensional analyses, especially cluster analyses developed for sparse data, have worked well for analyzing genomic datasets where p ≫ n. Here we explore applying a lasso-based clustering method developed for high dimensional genomic data with small sample sizes. Using protein and gene data from the developing human visual cortex, we compared clustering methods. We identified an application of sparse k-means clustering [robust sparse k-means clustering (RSKC)] that partitioned samples into age-related clusters that reflect lifespan stages from birth to aging. RSKC adaptively selects a subset of the genes or proteins contributing to partitioning samples into age-related clusters that progress across the lifespan. This approach addresses a problem in current studies that could not identify multiple postnatal clusters. Moreover, clusters encompassed a range of ages like a series of overlapping waves illustrating that chronological- and brain-age have a complex relationship. In addition, a recently developed workflow to create plasticity phenotypes (Balsor et al., 2020) was applied to the clusters and revealed neurobiologically relevant features that identified how the human visual cortex changes across the lifespan. These methods can help address the growing demand for multimodal integration, from molecular machinery to brain imaging signals, to understand the human brain’s development.

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“…29,72,73 Literature investigating age-related changes in cortical gray matter report initial cortical thickening followed by cortical thinning of frontotemporal regions with age, 74–76 which may be due to increasing synaptic pruning 75,77 as well as age-related increases in myelin content. 78 As pruning and associated reductions in dendritic arborization would be expected to decrease ND, the age-related increases in ND observed in cortical and subcortical gray matter in this study are likely driven by this increased myelin content. 29 The current study also detected a main effect of sex in superficial white matter indicating that females have higher ND than males, consistent with reports that female children show earlier diffusion changes compared to males.…”

Section: Discussionmentioning

confidence: 65%

Premature white matter microstructure in female children with a history of concussion

Nishat

Stojanovski

Scratch

et al. 2021

Preprint

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As maturation of the brain continues throughout development, there is a risk of interference from concussions which are common in childhood. A concussion can cause widespread disruption to axons and inflammation in the brain and may influence emerging cognitive abilities. Females are more likely to experience persistent problems after a concussion, yet the sex-specific impact of concussions on brain microstructure in childhood is not well understood.In children from a large population sample, this study (1) investigated differences in white matter and cortical microstructure between children with and without a history of concussion, and (2) examined relationships between altered brain microstructure and cognitive performance.Neurite density measures from diffusion weighted magnetic resonance imaging were examined in 9-to 10-year-old children in the Adolescent Brain Cognitive Development Study with (n = 336) and without (n = 7368) a history of concussion. (1) Multivariate regression models were used to investigate the relationships between concussion history, sex, and age in the deep white matter, superficial white matter, subcortical structures, and cortex. (2) Principal component analysis was performed on neurite density, and components were examined in relation to performance on the Flanker Inhibitory Control and Attention Task and the Pattern Comparison Processing Speed Task to investigate the relationship between altered neurite density and cognitive performance.Neurite density in all tissue types demonstrated robust positive relationships with age reflecting maturation of brain microstructure. (1) Comparisons between children with and without a history of concussion revealed higher neurite density in deep and superficial white matter in females with concussion. No group differences were observed in subcortical or cortical neurite density. (2) Higher neurite density in superficial white matter beneath the frontal and temporal cortices was associated with lower scores on the processing speed test in females with concussion, and higher scores on the processing speed test in males with concussion.These findings suggest that concussion in childhood leads to premature white matter maturation in females and that this may be associated with slower processing speed. These sex-specific effects on the developing brain may contribute to the enhanced vulnerability to persistent symptoms after concussion in females.

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New insights into the dynamic development of the cerebral cortex in childhood and adolescence: Integrating macro- and microstructural MRI findings

Cited by 89 publications

References 249 publications

A morphometric double dissociation: cortical thickness is more related to aging; surface area is more related to cognition

A morphometric double dissociation: cortical thickness is more related to aging; surface area is more related to cognition

A Practical Guide to Sparse k-Means Clustering for Studying Molecular Development of the Human Brain

Premature white matter microstructure in female children with a history of concussion

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