Age-related differences in the structural complexity of subcortical and ventricular structures

Madan, Christopher R.; Kensinger, Elizabeth A.

doi:10.1016/j.neurobiolaging.2016.10.023

Cited by 44 publications

(72 citation statements)

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“…The complexity of each structure was calculated using as the fractal dimensionality of the filled structure. Our work previously demonstrated that fractal dimensionality indexes age-related differences in cortical and subcortical structures better than extant measures (i.e., cortical thickness, cortical gyrification, subcortical volume), where older adults exhibit reductions in structural complexity relative to younger adults (Madan & Kensinger, 2016, 2017aMadan, in press). In fractal geometry, several approaches have been proposed to quantify the 'dimensionality' or complexity of a fractal; the approach here calculates the Minkowski-Bouligand or Hausdorff dimension (see Mandelbrot, 1967).…”

Section: Cortical Parcellationsmentioning

confidence: 87%

“…Scan parameters were: TR=9.7 ms; TE=4.0 ms; flip angle=10°; voxel size=1.25×1×1 mm. This sample was previously used in Madan and Kensinger (2017a) and Madan (in press Mennes et al, 2013) and hosted on the the Neuroimaging Informatics Tools and Resources Clearinghouse (NITRC; Kennedy et al, 2016) (http://fcon_1000.projects.nitrc.org/indi/retro/dlbs.html). Participants were screened for neurological and psychiatric issues.…”

Section: Datasetsmentioning

confidence: 99%

“…Scan parameters were: TR=2530 ms; TE=1.64, 3.50, 5.36, 7.22 ms; flip angle=7°; voxel size=1×1×1 mm. This sample was previously used in Madan and Kensinger (2017a). Predicting age from cortical morphology 17…”

Section: Predicting Age From Cortical Morphology 15mentioning

confidence: 99%

“…Briefly, the toolbox calculates the 'fractal dimensionality' of a 3D structure, and is designed to use intermediate files from the standard FreeSurfer analysis pipeline. calcFD was first applied to measure the complexity of the cortical ribbon and lobes (Madan & Kensinger, 2016), but has been extended to subcortical structures (Madan & Kensinger, 2017a;Madan, in press), and the DKT parcellation atlas (Madan & Kensinger, 2017b).…”

Section: Figure 5 Illustration Of How Fractal Dimensionality Is Measmentioning

confidence: 99%

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Predicting age from cortical structure across the lifespan

Madan

Kensinger

2018

Preprint

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Predicting age from cortical morphology 2 Graphical AbstractSeveral measures of cortical structure differ in relation to age. We examined the cortical granularity of these differences across seven parcellation approaches, from a 1 region (unparcellated cortical ribbon) to 1000 regions (patches with boundaries informed by anatomical landmarks), and three measures: thickness, gyrification, and fractal dimensionality. Rather than merely examining age-related relationships, we examined how these parcellations and measures can be used to predict age.. CC-BY 4.0 International license peer-reviewed) is the author/funder. It is made available under aThe copyright holder for this preprint (which was not . http://dx.doi.org/10.1101/248518 doi: bioRxiv preprint first posted online Jan. 16, 2018; Predicting age from cortical morphology 3 AbstractDespite inter-individual differences in cortical structure, cross-sectional and longitudinal studies have demonstrated a large degree of population-level consistency in age-related differences in brain morphology. The present study assessed how accurately an individual's age could be predicted by estimates of cortical morphology, comparing a variety of structural measures, including thickness, gyrification, and fractal dimensionality. Structural measures were calculated across up to seven different parcellation approaches, ranging from 1 region to 1000 regions. The age-prediction framework was trained using morphological measures obtained from T1-weighted MRI volumes collected from multiple sites, yielding a training dataset of 1056 healthy adults, aged 18-97. Age predictions were calculated using a machine-learning approach that incorporated non-linear differences over the lifespan. In two independent, held-out test samples, age predictions had a median error of 6-7 years. Age predictions were best when using a combination of cortical metrics, both thickness and fractal dimensionality. Overall, the results reveal that age-related differences in brain structure are systematic enough to enable reliable age prediction based on metrics of cortical morphology.

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Section: Cortical Parcellationsmentioning

confidence: 87%

Section: Datasetsmentioning

confidence: 99%

Section: Predicting Age From Cortical Morphology 15mentioning

confidence: 99%

Section: Figure 5 Illustration Of How Fractal Dimensionality Is Measmentioning

confidence: 99%

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Predicting age from cortical structure across the lifespan

Madan

Kensinger

2018

Preprint

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“…In one novel and exciting approach, recent studies by Madan and Kensinger 68, 69 found that the structural complexity of certain brain regions (including MTL and striatum) is a more sensitive measure of age-related differences than volume and cortical thickness. This approach leverages individual variability rather than controlling for its effects as a set of confounding variables.…”

Section: Individual Differences Are Informativementioning

confidence: 99%

Selective vulnerabilities and biomarkers in neurocognitive aging

Reagh

Yassa

2017

F1000Res

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As the world’s population continues to age, an understanding of the aging brain becomes increasingly crucial. This review focuses on several recent ideas and findings in the study of neurocognitive aging, specifically focusing on episodic memory, and discusses how they can be considered and used to guide us moving forward. Topics include dysfunction in neural circuits, the roles of neurogenesis and inhibitory signaling, vulnerability in the entorhinal cortex, individual differences, and comorbidities. These avenues of study provide a brief overview of promising themes in the field and together provide a snapshot of what we believe will be important emerging topics in selective vulnerabilities in the aging brain.

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Age‐associated sex and asymmetry differentiation in hemispheric and lobar cortical ribbon complexity across adulthood: A UK Biobank imaging study

Nazlee

Waiter

Sandu

2022

Human Brain Mapping

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Cortical morphology changes with ageing and age‐related neurodegenerative diseases. Previous studies suggest that the age effect is more pronounced in the frontal lobe. However, our knowledge of structural complexity changes in male and female brains is still limited. We measured cortical ribbon complexity through fractal dimension (FD) analysis at the hemisphere and lobe level in 7010 individuals from the UK Biobank imaging cohort to study age‐related sex differences (3332 males, age ranged 45–79 years). FD decreases significantly with age and sexual dimorphism exists. With correction for brain size, females showed higher complexity in the left hemisphere and left and right parietal lobes whereas males showed higher complexity in the right temporal and left and right occipital lobes. A nonlinear age effect was observed in the left and right frontal, and right temporal lobes. Differential patterns of age effects were observed in both sexes with relatively more age‐affected regions in males. Significantly higher rightward asymmetries at hemisphere, frontal, parietal, and occipital lobe level and higher leftward asymmetry in temporal lobe were observed. There was no age‐by‐sex‐by asymmetry interaction in any region. When controlling for brain size, the leftward hemispheric, and temporal lobe asymmetry decreased with age. Males had significantly lower asymmetry between hemispheres and higher asymmetry in the parietal and occipital lobes than females. This work provides distinct patterns of age‐related sex and asymmetry differences that can aid in the future development of sex‐specific models of the normal brain to ascribe cognitive functional significance of these patterns in ageing.

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Age-related differences in the structural complexity of subcortical and ventricular structures

Cited by 44 publications

References 83 publications

Predicting age from cortical structure across the lifespan

Predicting age from cortical structure across the lifespan

Selective vulnerabilities and biomarkers in neurocognitive aging

Age‐associated sex and asymmetry differentiation in hemispheric and lobar cortical ribbon complexity across adulthood: A UK Biobank imaging study

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