White Matter Degeneration with Aging: Longitudinal Diffusion MR Imaging Analysis

Groot, Marius de; Cremers, Lotte G.M.; Ikram, M. Arfan; Hofman, Albert; Krestin, Gabriël P.; Lugt, Aad van der; Niessen, Wiro J.; Vernooij, Meike W.

doi:10.1148/radiol.2015150103

Cited by 96 publications

(85 citation statements)

References 32 publications

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“…Past findings have been inconsistent in their characterization of the trajectory and spatial distribution of age effects across brain white matter tracts and of associations with hemisphere and biological sex27891011121314151617181920212223. A statistically well-powered study of age associations with white matter microstructure, in particular in middle-aged and older age groups (45+ years), would address these gaps in our understanding.…”

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confidence: 96%

Ageing and brain white matter structure in 3,513 UK Biobank participants

et al. 2016

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Quantifying the microstructural properties of the human brain's connections is necessary for understanding normal ageing and disease. Here we examine brain white matter magnetic resonance imaging (MRI) data in 3,513 generally healthy people aged 44.64-77.12 years from the UK Biobank. Using conventional water diffusion measures and newer, rarely studied indices from neurite orientation dispersion and density imaging, we document large age associations with white matter microstructure. Mean diffusivity is the most age-sensitive measure, with negative age associations strongest in the thalamic radiation and association fibres. White matter microstructure across brain tracts becomes increasingly correlated in older age. This may reflect an age-related aggregation of systemic detrimental effects. We report several other novel results, including age associations with hemisphere and sex, and comparative volumetric MRI analyses. Results from this unusually large, single-scanner sample provide one of the most extensive characterizations of age associations with major white matter tracts in the human brain.

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confidence: 96%

Ageing and brain white matter structure in 3,513 UK Biobank participants

et al. 2016

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“…While doing so, it is essential to avoid any asymmetry bias [16]. We therefore extended the approach in [5] to support three scans. An overview is presented in Figure 2.…”

Section: Coregistration Of Dwismentioning

confidence: 99%

“…Longitudinal studies in DW-MRI have for instance been used to quantify small changes in the white matter after ischemic stroke [3], during development [4], and during normal ageing [5].…”

Section: Introductionmentioning

confidence: 99%

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Longitudinal analysis of diffusion-weighted MRI with a ball-and-sticks model

Arkesteijn

Poot

Niestijl

et al. 2017

2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017)

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Purpose: To increase the sensitivity in longitudinal analysis of DW-MRI data with the ball-and-sticks model. Methods: Longitudinal DW-MRI data (baseline and two follow-up scans) of 25 middle-aged subjects (47 to 61 years at base line) were acquired. After coregistering all the diffusion-weighted images (DWIs) from the baseline and follow-up scans to a subject-specific intermediate space, an extended ball-and-sticks model was fitted. Stick orientations were constrained such that they did not change over time. The stick fractions were warped and projected onto the TBSS (tract-based spatial statistics) skeleton, and were compared to a reference framework in which all scans were processed independently. Results: Compared to the reference framework, the standard deviation of the apparent noise on the primary stick fractions on the TBSS skeleton was reduced with approximately a factor two. Conclusion: The use of the proposed longitudinal DW-MRI pipeline may significantly increase the precision compared to a default cross-sectional image processing pipeline.

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“…In line with macrostructural MRI correlates of brain ageing, modern techniques such as diffusion tensor imaging (DTI) have facilitated the in-vivo inspection of age-related microstructural changes of the brain 22,23 , supporting histological findings and revealing regional patterns and dynamics of structural connectivity (SC) degeneration, a phenomenon postulated to lead to cortical disconnection with loss of functional integration of neurocognitive networks 7,24 . Several DTI studies in normal ageing support that white matter atrophy is associated with a widespread microstructural degeneration of white matter fibers, with changes predominantly affecting frontal tracts 24 , and gradually extending to posterior tracts 25 , a pattern that inverts the sequence of myelination during early development and supports the "last-in-first-out principle" for white matter deterioration along the lifespan.…”

Section: Introductionmentioning

confidence: 99%

What the structural-functional connectome reveals about brain aging: The key role of the fronto-striatal-thalamic circuit and the rejuvenating impact of physical activity

Bonifazi

Erramuzpe

Díez

et al. 2017

Preprint

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Physiological ageing affects brain structure and function impacting its morphology, connectivity and performance. However, at which extent brain-connectivity metrics reflect the age of an individual and whether treatments or lifestyle factors such as physical activity influence the age-connectivity match is still unclear. Here, we assessed the level of physical activity and collected brain images from healthy participants (N=155) ranging from 10 to 80 years to build functional (resting-state) and structural (tractography) connectivity matrices that were combined as connectivity descriptors. Connectivity descriptors were used to compute a maximum likelihood age estimator that was optimized by minimizing the mean absolute error. The connectivity-based estimated age, i.e. the brain-connectome age (BCA), was compared to the chronological age (ChA). Our results were threefold. First, we showed that ageing widely affects the structural-functional connectivity of multiple structures, such as the anterior part of the default mode network, basal ganglia, thalamus, insula, cingulum, hippocampus, parahippocampus, occipital cortex, fusiform, precuneus and temporal pole. Second, our analysis showed that the structure-function connectivity between basal ganglia and thalamus to orbitofrontal and frontal areas make a major contribution to age estimation. Third, we found that high levels of physical activity reduce BCA as compared to ChA, and vice versa, low levels increment it. In conclusion, the BCA model results highlight the impact of physical activity and the key role played by the connectivity between basal ganglia and thalamus to frontal areas on the process of healthy aging. Notably, the same methodology can be generally applied both to evaluate the impact of other factors and therapies on brain ageing, and to identify the structural-functional brain connectivity correlate of other biomarkers than ChA.

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White Matter Degeneration with Aging: Longitudinal Diffusion MR Imaging Analysis

Cited by 96 publications

References 32 publications

Ageing and brain white matter structure in 3,513 UK Biobank participants

Ageing and brain white matter structure in 3,513 UK Biobank participants

Longitudinal analysis of diffusion-weighted MRI with a ball-and-sticks model

What the structural-functional connectome reveals about brain aging: The key role of the fronto-striatal-thalamic circuit and the rejuvenating impact of physical activity

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