Age-related resting-state functional connectivity of the Vigilant Attention network in children and adolescents

Morandini, Hugo A.E.; Rao, Pradeep; Hood, Sean; Zepf, Florian Daniel; Silk, Tim; Griffiths, Kristi R.

doi:10.1016/j.bandc.2021.105791

Cited by 4 publications

(2 citation statements)

References 39 publications

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“…In the current study, beta and gamma activity increased with increasing DHEA in posterior parietal and occipital regions of the brain, which are in line with roles in attention and early sensory processing. Thus, these relationships may reflect that these processing capabilities are fine-tuned behaviorally and neurally during this period of development ( Morandini et al, 2021 ).…”

Section: Discussionmentioning

confidence: 99%

Impacts of adrenarcheal DHEA levels on spontaneous cortical activity during development

Penhale

Picci

Ott

et al. 2022

Developmental Cognitive Neuroscience

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Section: Discussionmentioning

confidence: 99%

Impacts of adrenarcheal DHEA levels on spontaneous cortical activity during development

Penhale

Picci

Ott

et al. 2022

Developmental Cognitive Neuroscience

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“…Studies have examined the consistency of age-related changes in brain structure and function across neurotypical cohorts (Cao et al, 2017; Herting et al, 2018; Madan & Kensinger, 2017; Mills et al, 2016; Morandini et al, 2021; Tamnes et al, 2017; Wang et al, 2021). For example, annual percentage changes in cortical thickness and surface area across childhood and adulthood have been shown to differ between four samples of typically developing individuals, with the precise patterns varying across lobes of the brain (Tamnes et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Dataset factors influencing age-related changes in brain structure and function in neurodevelopmental conditions

Vandewouw,

Ye,

Crosbie

et al. 2024

Preprint

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With brain structure and function undergoing complex changes throughout childhood and adolescence, age is a critical consideration in neuroimaging studies, particularly for those of individuals with neurodevelopmental conditions. However, despite the increasing use of large, consortium-based datasets to examine brain structure and function in neurotypical and neurodivergent populations, it is unclear whether age-related changes are consistent between datasets, and whether inconsistencies related to differences in sample characteristics, such as demographics and phenotypic features, exist. To address this, we built models of age-related changes of brain structure (cortical thickness and surface area; N=1,579) and function (resting-state functional connectivity strength; N=1,792) in two neurodiverse datasets: the Province of Ontario Neurodevelopmental network (POND) and the Healthy Brain Network (HBN). We examined whether deviations from these models differed between the datasets, and explored whether these deviations were associated with demographic and clinical variables. We found significant differences between the two datasets for measures of cortical surface area and functional connectivity strength throughout the brain. For cortical surface area, the patterns of differences were associated with intelligence, while for functional connectivity strength, positive associations were observed with head motion. Our findings highlight that patterns of age-related changes in the brain may be influenced by demographic and phenotypic characteristics, and thus future studies should consider these when examining or controlling forage effects in analyses.

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Dataset factors associated with age‐related changes in brain structure and function in neurodevelopmental conditions

Vandewouw,

Ye,

Crosbie

et al. 2024

Human Brain Mapping

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With brain structure and function undergoing complex changes throughout childhood and adolescence, age is a critical consideration in neuroimaging studies, particularly for those of individuals with neurodevelopmental conditions. However, despite the increasing use of large, consortium‐based datasets to examine brain structure and function in neurotypical and neurodivergent populations, it is unclear whether age‐related changes are consistent between datasets and whether inconsistencies related to differences in sample characteristics, such as demographics and phenotypic features, exist. To address this, we built models of age‐related changes of brain structure (regional cortical thickness and regional surface area; N = 1218) and function (resting‐state functional connectivity strength; N = 1254) in two neurodiverse datasets: the Province of Ontario Neurodevelopmental Network and the Healthy Brain Network. We examined whether deviations from these models differed between the datasets, and explored whether these deviations were associated with demographic and clinical variables. We found significant differences between the two datasets for measures of cortical surface area and functional connectivity strength throughout the brain. For regional measures of cortical surface area, the patterns of differences were associated with race/ethnicity, while for functional connectivity strength, positive associations were observed with head motion. Our findings highlight that patterns of age‐related changes in the brain may be influenced by demographic and phenotypic characteristics, and thus future studies should consider these when examining or controlling for age effects in analyses.

show abstract

Age-related resting-state functional connectivity of the Vigilant Attention network in children and adolescents

Cited by 4 publications

References 39 publications

Impacts of adrenarcheal DHEA levels on spontaneous cortical activity during development

Impacts of adrenarcheal DHEA levels on spontaneous cortical activity during development

Dataset factors influencing age-related changes in brain structure and function in neurodevelopmental conditions

Dataset factors associated with age‐related changes in brain structure and function in neurodevelopmental conditions

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