Age Differentiation within Gray Matter, White Matter, and between Memory and White Matter in an Adult Life Span Cohort

Mooij, Susanne M.M. de; Henson, Richard N.; Waldorp, Lourens J.; Kievit, Rogier A.

doi:10.1523/jneurosci.1627-17.2018

Cited by 70 publications

(61 citation statements)

References 60 publications

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“…In addition, NAdependent and NA-independent functions, as represented by their respective observed variables, were separable into two distinct factors in younger but not older adults in our sample. One potential explanation for this could be that older adults show greater "age dedifferentiation", whereby correlations among distinct measures of cognitive function become more intercorrelated with age [40][41][42] , possibly related to neuropathological changes 43 ; however, this is not a consistent finding as other studies have also found no evidence for age-related dedifferentiation among cognitive factors 38,44 . A second possible explanation is that although specific cognitive and behavioral functions relying on the LC-NA system have been described, it is conceivable that putatively NAindependent measures such as fluid intelligence, sentence comprehension, and facial recognition might also be reliant on this system due to the widespread distribution of noradrenergic neurons and the role of NA in attention and arousal that underlies diverse tests of cognitive functions.…”

Section: Discussionmentioning

confidence: 77%

“…In this exploratory analysis, older adults still showed a significant positive relationship between LC CR and the single latent factor, driven by the rostral region (standardized regression coefficient β = 0.157, z = 2.169, p(>|z|) = 0.030 for whole LC, β = 0.192, z = 2.465, p(>|z|) = 0.014 for rostral, and β = 0.114, z = 1.712, p(>|z|) = 0.087 for caudal LC), and the LCsingle latent factor regression paths between older and younger groups remained significantly different. Normalized total brain volume showed a significant negative correlation with age in both groups, and interestingly, was significantly related to the single latent factor in younger (β = 0.157, z = 2.169, p(>|z|) = 0.030) but not older adults (β = −0.051, z = −0.491, p(>|z|) = 0.623), possibly reflecting age-related differentiation within gray and white matter 38 . The main findings also remained unchanged after the addition of the repetition time (TR) group (30 ms or 50 ms) as a covariate on LC CR.…”

Section: Resultsmentioning

confidence: 89%

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Noradrenergic-dependent functions are associated with age-related locus coeruleus signal intensity differences

et al. 2020

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The locus coeruleus (LC), the origin of noradrenergic modulation of cognitive and behavioral function, may play an important role healthy ageing and in neurodegenerative conditions. We investigated the functional significance of age-related differences in mean normalized LC signal intensity values (LC-CR) in magnetization-transfer (MT) images from the Cambridge Centre for Ageing and Neuroscience (Cam-CAN) cohort -an open-access, population-based dataset. Using structural equation modelling, we tested the pre-registered hypothesis that putatively noradrenergic (NA)-dependent functions would be more strongly associated with LC-CR in older versus younger adults. A unidimensional model (within which LC-CR related to a single factor representing all cognitive and behavioral measures) was a better fit with the data than the a priori two-factor model (within which LC-CR related to separate NAdependent and NA-independent factors). Our findings support the concept that age-related reduction of LC structural integrity is associated with impaired cognitive and behavioral function.

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Section: Discussionmentioning

confidence: 77%

Section: Resultsmentioning

confidence: 89%

Noradrenergic-dependent functions are associated with age-related locus coeruleus signal intensity differences

et al. 2020

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“…To approximate white matter contributions to fluid and crystallized ability, we analyzed fractional anisotropy (FA; see Wandell, 2016). We based our choice of FA on previous studies of white matter in developmental samples (de Mooij et al, 2018;Kievit et al, 2016). We used FA as a general summary metric of white matter microstructure as it cannot directly discern between specific cellular components (e.g.…”

Section: Neural Measures: White Matter and Fractional Anisotropymentioning

confidence: 99%

“…In this case, gc and gf covariance would increase between childhood and adolescence, potentially indicating a strengthening of the g-factor across age. However, despite its increased attention in the literature, the debate remains unsolved as evidence in support of both hypotheses has been found (Bickley et al, 1995;de Mooij et al, 2018;Gignac, 2014;Hülür et al, 2011;Juan-Espinosa et al, 2000;Tideman and Gustafsson, 2004). Together, this literature highlights the importance of a lifespan perspective on theories of cognitive development, as neither age differentiation nor dedifferentiation may be solely able to capture the dynamic changes that occur from childhood to adolescence and (late) adulthood (Hartung et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Neurocognitive reorganization between crystallized intelligence, fluid intelligence and white matter microstructure in two age-heterogeneous developmental cohorts

Simpson-Kent

Fuhrmann

Bathelt

et al. 2019

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Despite the reliability of intelligence measures in predicting important life outcomes such as educational achievement and mortality, the exact configuration and neural correlates of cognitive abilities remain poorly understood, especially in childhood and adolescence. Therefore, we sought to elucidate the factorial structure and neural substrates of child and adolescent intelligence using two cross-sectional, developmental samples (CALM: N=551 (N=165 imaging), age range: 5-18 years, NKI-Rockland: N=337 (N=65 imaging), age range: 6-18 years). In a preregistered analysis, we used structural equation modelling (SEM) to examine the neurocognitive architecture of individual differences in childhood and adolescent cognitive ability. In both samples, we found that cognitive ability in lower and typical-ability cohorts is best understood as two separable constructs, crystallized and fluid intelligence, which became more distinct across development, in line with the age differentiation hypothesis. Further analyses revealed that white matter microstructure, most prominently the superior longitudinal fasciculus, was strongly associated with crystallized (gc) and fluid (gf)abilities. Finally, we used SEM trees to demonstrate evidence for developmental reorganization of gc and gf and their white matter substrates such that the relationships among these factors dropped between 7-8 years before increasing around age 10. Together, our results suggest that shortly before puberty marks a pivotal phase of change in the neurocognitive architecture of intelligence.

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“…Such techniques have been used more recently in emerging field such as cognitive neuroscience. High dimensional individual differences in brain structure or function (e.g., volume or activity measures across dozens of regions or even thousands of voxels) are reduced to a smaller number of factors, which are then used, for instance, to study morphological differences in schizophrenia (Tien et al, 1996), how cortical structure relates to behavioural measures (Colibazzi et al, 2008), and to examine age-related differences (de Mooij et al, 2018). However, one key challenge when reducing the dimensionality of such structural (and functional) brain data is that of symmetry: Much like other body parts, contralateral (left/right) brain regions are highly correlated due to developmental and genetic mechanisms which govern the gross morphology of the brain.…”

Section: Introductionmentioning

confidence: 99%

Exploratory Factor Analysis with Structured Residuals for Brain Imaging Data

Kesteren

Kievit²

2020

Preprint

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Dimension reduction is widely used and often necessary to reduce high dimensional data to a small number of underlying variables -factors or componentsto make data analyses and their interpretation tractable. One popular technique is Exploratory Factor Analysis (EFA), which extracts factors when the underlying factor structure is not known. However, we observe that datasets exist where researchers indeed do not know the factor structure, but do have other relevant a priori knowledge. For instance, cognitive neuroscientists may want to reduce individual differences in brain structure across a large number of regions to a tractable number of factors. In this field, it is well established that the brain displays contralateral symmetry, such that the same regions in the left and right half of the brain will be highly correlated. Here, a) we show the adverse consequences of ignoring such a priori structure in standard factor analysis, b) we propose a technique for Exploratory factor analysis with structured residuals (EFAST) which accommodates such a priori structure into an otherwise standard EFA, and c) we apply this technique to a large (N = 647, 68 brain regions) empirical dataset, demonstrating the superior fit and improved intepretability of our approach. We provide an R software package to allow researchers to apply this technique to other suitable datasets. Contents1 Introduction 2 2 Factor analysis with structured residuals 43 Simulations 9 4 EFAST in practice: Modeling brain morphology 14 5 Summary and discussion 22 6 Acknowledgements 23Appendices 27 1 extract factors when these factors are measured in different ways: when measuring personality through a self-report questionnare and behaviour ratings, there are factors that explain correlation among items corresponding to a specific trait such as 'extraversion', and there are factors that explain additional correlation between items because they are gathered using the same methods (self-report and behavioural ratings). Thus, MTMM techniques separate the correlation matrix into two distinct, summative parts: correlation due to the traits of central interest, and correlation due to the measurement methods. However, MTMM requires a priori knowledge of the trait structure (e.g., the OCEAN model of personality).Here we propose a manner in which to instead conduct purely exploratory factor analysis (e.g. across many brain regions), whilst incorporating prior structure knowledge (e.g. symmetry). Standard implementations of EFA, CFA, and MTMM are inadequate to estimate factor structure under these circumstances, as they do not simultaneously allow for exploration and the incorporation of residual structure. We improve upon these procedures by developing a method called Exploratory factor analysis with structured residuals (EFAST). We show that EFAST outperforms EFA in empirically plausible scenarios, and that ignoring the problem of structured residuals in these scenarios adversely affects inferences.Note that we are not the first to suggest using structured residua...

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Age Differentiation within Gray Matter, White Matter, and between Memory and White Matter in an Adult Life Span Cohort

Cited by 70 publications

References 60 publications

Noradrenergic-dependent functions are associated with age-related locus coeruleus signal intensity differences

Noradrenergic-dependent functions are associated with age-related locus coeruleus signal intensity differences

Neurocognitive reorganization between crystallized intelligence, fluid intelligence and white matter microstructure in two age-heterogeneous developmental cohorts

Exploratory Factor Analysis with Structured Residuals for Brain Imaging Data

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