White Matter Abnormalities and Correlation With Severity in Normal Tension Glaucoma: A Whole Brain Atlas-Based Diffusion Tensor Study

Wang, Rong; Tang, Zuohua; Sun, Xinghuai; Wu, Lingjie; Wang, Jie; Zhong, Yuting; Xiao, Zhang

doi:10.1167/iovs.17-23597

Cited by 33 publications

(27 citation statements)

References 53 publications

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“…5. Mode 53 62 involves changes in white matter fibre organisation in the posterior thalamic radiation (also known as the optic radiation, and connecting to visual cortex), and was associated with glaucoma, as previously reported [Wang et al, 2018]. Mode 50 62 involves changes in white matter di usivity in the superior cerebellar peduncle, and was associated with IQ and several markers of socio-economic status.…”

Section: Discussionmentioning

confidence: 68%

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

Smith

Elliott

Alfaro-Almagro

et al. 2020

eLife

146

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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 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.

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Section: Discussionmentioning

confidence: 68%

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

Smith

Elliott

Alfaro-Almagro

et al. 2020

eLife

146

View full text Add to dashboard Cite

show abstract

“…5. Mode 53 62 involves changes in white matter fibre organisation in the posterior thalamic radiation (also known as the optic radiation, and connecting to visual cortex), and was associated with glaucoma, as previously reported [Wang et al, 2018]. Mode 50 62 involves changes in white matter diffusivity in the superior cerebellar peduncle, and was associated with IQ and several markers of socio-economic status.…”

Section: Discussionmentioning

confidence: 68%

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

Smith

Elliott

Alfaro-Almagro

et al. 2019

Preprint

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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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“…Similar findings were reported in normal-tension glaucoma (NTG), as DTI detected white matter damage (WM) in the four regions associated with visual and visual-related functions, specifically in bilateral posterior thalamus, bilateral sagittal striatum, bilateral cingulum hippocampus and bilateral fornix/stria terminalis. Moreover, DTI parameters in those brain areas turned out to correlate with such specific glaucoma indices, such as the mean deviation of the visual field and retinal nerve fiber layer thickness, and occurred before detectable visual field loss [61]. The primary visual cortex also exhibited more severe functional deficits than higher-order visual brain areas in glaucoma [62].…”

Section: Glaucomamentioning

confidence: 96%

Radiation-Induced Cerebro-Ophthalmic Effects in Humans

Loganovsky

Marazziti

Fedirko

et al. 2020

Life

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Exposure to ionizing radiation (IR) could affect the human brain and eyes leading to both cognitive and visual impairments. The aim of this paper was to review and analyze the current literature, and to comment on the ensuing findings in the light of our personal contributions in this field. The review was carried out according to the PRISMA guidelines by searching PubMed, Scopus, Embase, PsycINFO and Google Scholar English papers published from January 2000 to January 2020. The results showed that prenatally or childhood-exposed individuals are a particular target group with a higher risk for possible radiation effects and neurodegenerative diseases. In adulthood and medical/interventional radiologists, the most frequent IR-induced ophthalmic effects include cataracts, glaucoma, optic neuropathy, retinopathy and angiopathy, sometimes associated with specific neurocognitive deficits. According to available information that eye alterations may induce or may be associated with brain dysfunctions and vice versa, we propose to label this relationship “eye-brain axis”, as well as to deepen the diagnosis of eye pathologies as early and easily obtainable markers of possible low dose IR-induced brain damage.

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White Matter Abnormalities and Correlation With Severity in Normal Tension Glaucoma: A Whole Brain Atlas-Based Diffusion Tensor Study

Cited by 33 publications

References 53 publications

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

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

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

Radiation-Induced Cerebro-Ophthalmic Effects in Humans

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