Trajectories of imaging markers in brain aging: the Rotterdam Study

Vinke, Elisabeth J.; Groot, Marius de; Venkatraghavan, Vikram; Klein, Stefan; Niessen, Wiro J.; Ikram, M. Arfan; Vernooij, Meike W.

doi:10.1016/j.neurobiolaging.2018.07.001

Cited by 134 publications

(140 citation statements)

References 54 publications

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“…On the other hand, our meta-GWAS of cortical thickness, carried out in the same individuals, yielded only two loci. This negative finding is consistent with a very low number of loci associated with both global and regional values of cortical thickness reported in the two reports from the CHARGE 16 and ENIGMA 17 Consortia; it may reflect substantial dynamics of cortical thickness during puberty 18 and aging 19 . For surface area PC1 (PC2), we replicated 695/ 807 (PC1) and 952/1,155 (PC2) GWAS-significant SNPs (Table E2A and E2B, in Extended Data).…”

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confidence: 90%

Planar cell polarity pathway and development of the human visual cortex

Shin

Hofer

et al. 2018

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The radial unit hypothesis provides a framework for global (proliferation) and regional (distribution) expansion of the primate cerebral cortex 1 . Using principal component analysis (PCA), we have identified cortical regions with shared variance in their surface area and cortical thickness, respectively, segmented from magnetic resonance images obtained in 23,800 participants. We then carried out meta-analyses of genome-wide association studies of the first two principal components for each phenotype. For surface area (but not cortical thickness), we have detected strong associations between each of the components and single nucleotide polymorphisms in a number of gene loci. The first (global) component was associated mainly with loci on chromosome 17 (9.5e-32 ≤ p ≤ 2.8e-10), including those detected previously as linked with intracranial volume 2,3 and/or general cognitive function 4 . The second (regional) component captured shared variation in the surface area of the primary and adjacent secondary visual cortices and showed a robust association with polymorphisms in a locus on chromosome 14 containing Disheveled Associated Activator of Morphogenesis 1 (DAAM1; p=2.4e-34). DAAM1 is a key component in the planar-cell-polarity signaling pathway 5,6 . In follow-up studies, we have focused on the latter finding and established that: (1) DAAM1 is highly expressed between 12 th and 22 nd post-conception weeks in the human cerebral cortex; (2) genes co-expressed with DAAM1 in the primary visual cortex are enriched in mitochondriarelated pathways; and (3) volume of the lateral geniculate nucleus, which projects to regions of the visual cortex staining for cytochrome oxidase (a mitochondrial enzyme), correlates with the surface area of the visual cortex in major-allele homozygotes but not in carriers of the minor allele. Altogether, we speculate that, in concert with thalamocortical input to cortical subplate, DAAM1 enables migration of neurons to cytochrome-oxidase rich regions of the visual cortex, and, in turn, facilitates regional expansion of this set of cortical regions during development.Using magnetic resonance imaging, one can derive a number of metrics informative with regard to development and aging of the human cerebral cortex, including cortical surface area and cortical thickness. The two measures provide insights into different developmental processes, each with a different timeline. Cortical surface area reflects primarily the tangential growth of the cerebral cortex during prenatal development; the phase of symmetric division of progenitor cells in the proliferative zones during the first trimester is particularly important for the tangential growth through additions of ontogenetic columns 1 . The subsequent phase of asymmetric division continues to increase the number of ontogenetic columns (and thus surface area) but it also begins to contribute to the thickness of cerebral cortex formed by post-mitotic neurons migrating from the proliferative zones to the cortical plate in the inside-out manner 1 . I...

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confidence: 90%

Planar cell polarity pathway and development of the human visual cortex

Shin

Hofer

et al. 2018

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“…The features were then grouped into different subsets by hand, and the age dependence of each subset (and also of many of the features on their own) was studied. Similarly, [Vinke et al, 2018] included data from several modalities, and studied aging trajectories in different measures from different modalities, but did not go as far as brain age (or brain-age delta) modelling, or attempt to identify latent modes of aging. Finally, [Kaufmann et al, 2019] used a single imaging modality (T1-weighted structural images) from 45,000 subjects pooled from 40 studies, to investigate the relationship between brain aging and several diseases.…”

Section: Discussionmentioning

confidence: 99%

“…The imaging feature set can be derived from more than one imaging modality, in which case it can contain information not just about the structural geometric layout of the brain, but also, for example, structural connectivity, white matter microstructure, functional connectivity, iron deposition, and cognitive task activation [Groves et al, 2012, Brown et al, 2012, Liem et al, 2017, Vinke et al, 2018. Such "multimodal" data allows for brain age modelling to take advantage of a richer range of structural and functional measures of change in the brain, but it is still the case that most brain-age modelling only estimates a single overall brain age per individual.…”

Section: Introductionmentioning

confidence: 99%

“…The algorithm then learns to predict the subjects' ages from their brain imaging features. Finally, the true age is typically subtracted from the estimated brain age, to create a delta, potentially with corrections for biases such as systematic mis-estimation of brain age [Le et al, 2018.The imaging feature set can be derived from more than one imaging modality, in which case it can contain information not just about the structural geometric layout of the brain, but also, for example, structural connectivity, white matter microstructure, functional connectivity, iron deposition, and cognitive task activation [Groves et al, 2012, Brown et al, 2012, Liem et al, 2017, Vinke et al, 2018. Such "multimodal" data allows for brain age modelling to take advantage of a richer range of structural and functional measures of change in the brain, but it is still the case that most brain-age modelling only estimates a single overall brain age per individual.Hence, while the explicit goal of much brain-age research is to obtain a single estimate of brain age (and brain-age delta) per subject, one could nevertheless expect that multiple distinct biological processes contribute to the changes seen in the brain with aging.…”

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confidence: 99%

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Brain aging comprises many modes of structural and functional change with distinct genetic and biophysical associations

Smith

Elliott

Alfaro-Almagro

et al. 2019

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Brain imaging can be used to study how individuals' brains are aging, compared against population norms. This can inform on aspects of brain health; for example, smoking and blood pressure can be seen to accelerate brain aging. Typically, a single "brain age" is estimated per subject, whereas here we we identified 62 modes of subject variability, from 21,407 subjects' multimodal brain imaging data in UK Biobank. The modes represent different aspects of brain aging, showing distinct patterns of functional and structural brain change, and distinct patterns of association with genetics, lifestyle, cognition, physical measures and disease. While conventional brain-age modelling found no genetic associations, 34 modes had genetic associations. We suggest that it is important not to treat brain aging as a single homogeneous process, and that modelling of distinct patterns of structural and functional change will reveal more biologically meaningful markers of brain aging in health and disease. KeywordsBrain aging, brain imaging, UK Biobank.Corresponding author: Stephen Smith, steve@fmrib.ox.ac.uk IntroductionBrain imaging can be used to predict "brain age" -the apparent age of individuals' brains -by comparing their imaging data against a normative population dataset. The difference between brain age and actual chronological age (the "delta", or "brain age gap") is often then computed, providing a measure of whether a subject's brain appears to have aged more (or less) than the average age-matched population data. For example, looking at structural magnetic resonance imaging (MRI) data, a high degree of atrophy would cause a subject's brain to appear older than a normal age-matched brain. Estimation of brain age and the delta is of value in studying both normal aging and disease, with some diseases, such as Alzheimer's disease, showing similar patterns of change to that of accelerated healthy aging [Franke et al., 2010, Cole and Franke, 2017.The typical approach uses one or more imaging modalities, most commonly using just a single structural image from each subject. The data is then preprocessed, and features identified, for use in the brain age prediction. For example, the structural images may be warped into a standard space, and grey matter segmentation carried out; the voxelwise segmentation values themselves can then be the features. Alternatively, a smaller number of more highly-condensed features may be derived, such as volumes of grey and white matter within multiple brain regions. The resulting dataset, of multiple subjects' feature sets, along with their true ages, is then passed into a supervised-learning algorithm (e.g., regression, support vector machine or deep learning). The algorithm then learns to predict the subjects' ages from their brain imaging features. Finally, the true age is typically subtracted from the estimated brain age, to create a delta, potentially with corrections for biases such as systematic mis-estimation of brain age [Le et al., 2018.The imaging feature set can be derived from more ...

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“…Parameters quantified by DTI such as Fractional Anisotropy (FA), Mean Diffusivity (MD) and Radial Diffusivity (RD) can be used to indirectly infer changes in axonal integrity and myelination (Madden et al, 2012). In aging population, DTI studies described a decrease in FA and an increase in MD and RD in major white matter tracts (de Groot et al, 2015;Madden et al, 2012;Vinke et al, 2018), and these changes are associated with cognitive impairment (Arvanitakis et al, 2016;Bendlin et al, 2010;Borghesani et al, 2013;Wiseman et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Diffusion MRI free water is a sensitive marker of age-related changes in the cingulum

Edde

Theaud

Rheault

et al. 2019

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Diffusion MRI is extensively used to investigate changes in white matter microstructure. However, diffusion measures within white matter tissue can be affected by partial volume effects due to cerebrospinal fluid and white matter hyperintensities, especially in the aging brain. In previous aging studies, the cingulum bundle that plays a central role in the architecture of the brain networks supporting cognitive functions has been associated with cognitive deficits. However, most of these studies did not consider the partial volume effects on diffusion measures. The aim of this study was to evaluate the effect of free water elimination on diffusion measures of the cingulum in a group of 68 healthy elderly individuals. We first determined the effect of free water elimination on conventional DTI measures and then examined the effect of free water elimination on verbal fluency performance over 12 years. The cingulum bundle was reconstructed with a tractography pipeline including a white matter hyperintensities mask to limit the negative impact of hyperintensities on fiber tracking algorithms. We observed that free water elimination improved the sensitivity of conventional DTI measures to detect associations between tissue-specific diffusion measures of the cingulum and changes in verbal fluency in older individuals. Moreover, free water content measured along the cingulum was independently strongly associated with changes in verbal fluency. These observations suggest the importance of using free water elimination when studying brain aging and indicate that free water itself could be a relevant marker for age-related cingulum white matter modifications and cognitive decline.

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Trajectories of imaging markers in brain aging: the Rotterdam Study

Cited by 134 publications

References 54 publications

Planar cell polarity pathway and development of the human visual cortex

Planar cell polarity pathway and development of the human visual cortex

Brain aging comprises many modes of structural and functional change with distinct genetic and biophysical associations

Diffusion MRI free water is a sensitive marker of age-related changes in the cingulum

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