Multiple Factors Involved in the Pathogenesis of White Matter Lesions

Lin, Jing; Wang, Dilong; Lan, Linfang; Fan, Yuhua

doi:10.1155/2017/9372050

Cited by 106 publications

(98 citation statements)

References 84 publications

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“…Given the link between the retina and the brain, our findings echoes the association between the retina and the brain in WMH as previously reported in the other cerebral small vessel diseases such as Alzheimer's disease (Debette & Markus, ; Rhodius‐Meester et al, ). Although microstructural changes in the white matter have been noted for its liability to microvascular damage (Connor et al, ; Lin, Wang, Lan, & Fan, ), our study highlighted the role of neurodegeneration in the white matter damage on MRI images using the Fazekas scale; further studies on the association between the retinal vasculature and the cerebral microstructure are needed to validate this hypothesis and also evaluation of the optic nerve could be investigated. With regard to aging, we observed that increase in age was associated with thinning of the retinal layers and susceptibility to lesions in the white matter which is congruent with previous reports (Harwerth, Wheat, & Rangaswamy, ; Mutlu et al, ).…”

Section: Discussionmentioning

confidence: 57%

Retinal sublayer defect is independently associated with the severity of hypertensive white matter hyperintensity

Kwapong

Peng

et al. 2019

Brain and Behavior

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Purpose:To investigate the association of specific retinal sublayer thicknesses on optical coherence tomography (OCT) imaging with brain magnetic resonance imaging (MRI) markers using the Fazekas scale in hypertensive white matter hyperintensity (WMH) subjects.Methods: Eighty-eight participants (32 healthy controls and 56 hypertensive white matter hyperintensity subjects) underwent retinal imaging using the OCT and MRI.A custom-built algorithm was used to measure the thicknesses of the retinal nerve fiber layer (RNFL) and ganglion cell layer and inner plexiform layer (GCIP). Focal markers for white matter hyperintensities were assessed on MRI and graded using the Fazekas visual rating. Results:Hypertensive WMH showed significantly reduced (p < .05) RNFL and GCIP layers when compared to healthy controls, respectively. A significant correlation was found between the RNFL (ρ = −.246, p < .001) and GCIP (ρ = −.338, p < .001) of the total participants and the Fazekas score, respectively. Statistical differences were still significant (p < .05) when correlations were adjusted for intereye correlation, age, hypertension, smoking, body mass index, and diabetes. Among the cases of hypertensive WMH, higher Fazekas scores were significantly associated (p < .05) with the thinning of both the RNFL and GCIP layers after adjustment of age and other risk factors. Conclusions:Retinal degeneration in the RNFL and GCIP was independently associated with focal lesions in the white matter of the brain and deteriorates with the severity of the lesions. We suggest that imaging and measurement of the retinal sublayers using the OCT may provide evidence on neurodegeneration in WMH. K E Y W O R D SFazekas scale, retina, spectral-domain optical coherence tomography, white matter hyperintensity How to cite this article: Qu M, Kwapong WR, Peng C, et al.Retinal sublayer defect is independently associated with the severity of hypertensive white matter hyperintensity. Brain

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Section: Discussionmentioning

confidence: 57%

Retinal sublayer defect is independently associated with the severity of hypertensive white matter hyperintensity

Kwapong

Peng

et al. 2019

Brain and Behavior

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“…That is, regardless of whether nocturnal BP increased or decreased too much, SCI patients suffered more hazards of moderate-severe leukoaraiosis. The relationship between hypotension and leukoaraiosis might be interrelated with the damage to cerebral selfregulation function [41,42], which requires special attention. Fortunately, the population with ED BP rhythm only comprises a small proportion of hypertensive patients.…”

Section: Discussionmentioning

confidence: 99%

Abnormal blood pressure circadian rhythms are relevant to cerebral infarction and Leukoaraiosis in hypertensive patients

et al. 2020

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Background: To investigate the relationships between blood pressure (BP) circadian rhythms and acute cerebral infarction (ACI), silent cerebral infarction (SCI) and the severity of leukoaraiosis in hypertensive patients. Methods: A retrospective case-control study was performed among hypertensive patients with 24-h ambulatory blood pressure monitoring (ABPM) and cranial magnetic resonance imaging (MRI). Results: A total of 1267 patients were enrolled. Lower nocturnal blood pressure (BP) decreases were observed in ACI patients than in controls (3.3% vs 8.2%, P<0.001). Reverse-dipper pattern (RD) and non-dipper pattern (ND) were found to be independent risk factors for ACI. In ACI patients, both RD and ND BP circadian rhythms were revealed to be independent risk factors for moderate-severe leukoaraiosis. In addition, in SCI patients, RD (OR = 1.7, 95% CI, 0.9-3.0; P = 0.047) or extreme-dipper pattern (ED) (OR = 2.9, 95% CI, 1.2-7.0; P = 0.015) were found to be independent risk factors for moderate-severe leukoaraiosis. Moreover, the greater the severity of leukoaraiosis was, the higher the ratio of abnormal BP circadian rhythms. Conclusion: RD and ND BP circadian rhythms might not only be relevant to the onset of ACI but also correlate with the severity of leukoaraiosis. Thus, when modulating BP with antihypertensive drugs, the BP circadian rhythms, and not merely the BP level, should warrant more attention.

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“…Some correlations were found between leukoaraiosis and cognitive decline (Grueter et Schulz 2012). Its physiopathology is multifactorial, with role of inflammation, blood brain barrier disruption, genetic factors, ischemic factors (for a review see (Lin et al 2017)).…”

Section: Why Brain Age Prediction ?mentioning

confidence: 99%

Brain age prediction of healthy subjects on anatomic MRI with deep learning: going beyond with an "explainable AI" mindset

Hérent¹,

Jégou²,

Wainrib³

et al. 2018

Preprint

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Objectives: Define a clinically usable preprocessing pipeline for MRI data. Predict brain age using various machine learning and deep learning algorithms. Define Caveat against common machine learning traps. Data and Methods: We used 1597 open-access T1 weighted MRI from 24 hospitals. Preprocessing consisted in applying: N4 bias field correction, registration to MNI152 space, white and grey stripe intensity normalization, skull stripping and brain tissue segmentation. Prediction of brain age was done with growing complexity of data input (histograms, grey matter from segmented MRI, raw data) and models for training (linear models, non linear model such as gradient boosting over decision trees, and 2D and 3D convolutional neural networks). Work on interpretability consisted in (i) proceeding on basic data visualization like correlations maps between age and voxels value, and generating (ii) weights maps of simpler models, (iii) heatmap from CNNs model with occlusion method. Results: Processing time seemed feasible in a radiological workflow: 5 min for one 3D T1 MRI. We found a significant correlation between age and gray matter volume with a correlation r = -0.74. Our best model obtained a mean absolute error of 3.60 years, with fine tuned convolution neural network (CNN) pretrained on ImageNet. We carefully analyzed and interpreted the center effect. Our work on interpretability on simpler models permitted to observe heterogeneity of prediction depending on brain regions known for being involved in ageing (grey matter, ventricles). Occlusion method of CNN showed the importance of Insula and deep grey matter (thalami, caudate nuclei) in predictions. Conclusions: Predicting the brain age using deep learning could be a standardized metric usable in daily neuroradiological reports. An explainable algorithm gives more confidence and acceptability for its use in practice. More clinical studies using this new quantitative biomarker in neurological diseases will show how to use it at its best.

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Multiple Factors Involved in the Pathogenesis of White Matter Lesions

Cited by 106 publications

References 84 publications

Retinal sublayer defect is independently associated with the severity of hypertensive white matter hyperintensity

Retinal sublayer defect is independently associated with the severity of hypertensive white matter hyperintensity

Abnormal blood pressure circadian rhythms are relevant to cerebral infarction and Leukoaraiosis in hypertensive patients

Brain age prediction of healthy subjects on anatomic MRI with deep learning: going beyond with an "explainable AI" mindset

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