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The pathological changes of cerebral small vascular disease, such as white matter hyperintensity (WMH) in individuals with cardiometabolic risk factors, can interfere with the movement trajectory of white matter and eventually lead to cognitive decline
.
However, there is no consensus on the exact basic topology mechanism
Vascular consensus
Laura WM Vergoossen et al.
examined whether WMH is related to cognitive function, and whether structural connectivity measures mediate or explain such associations in the adult population
.
In addition, by assessing the amount of channel-specific WMH and its association with cognitive function-specific white matter fiber bundles, potential local abnormalities in white matter were investigated
It was performed in a population based on prospective type 2 diabetes , and structural and diffusion tensor MRI scans were performed from December 2013 to February 2017
.
Collected WMH total volume and WMH volume of specific fiber bundles, brain network indicators, cognitive scores, demographic data, cardiovascular and life>
Diabetes Cardiovascular
Enrollment
EnrollmentA total of 5083 participants (mean age, 59 years ± 9 [standard deviation]; 2592 men; 1027 with diabetes) were evaluated
.
Automatic atlas guides the reconstruction of three orthogonal images of 13 pre-selected white matter tracts
.
A, MRI scan shows cingulate gyrus (CGC), cingulate hippocampus (CGH), fronto-occipital tract (IFO), inferior longitudinal tract (ILF), top of upper longitudinal tract (SLFPT), upper longitudinal tract temporal (SLFT) ), hook bundle (UNC) long combined bundle
Automatic atlas guides the reconstruction of three orthogonal images of 13 pre-selected white matter tracts
A, MRI scan showed that the white matter hyperintensity (WMH) volume of specific fiber tracts was significantly correlated with the information processing speed score (P, 0.
05), and adjusted according to age, gender, education level, diabetes status and cardiovascular risk factors (model 3)
.
B, WMH diagram of the research sample
A, MRI scan showed that the white matter hyperintensity (WMH) volume of specific fiber tracts was significantly correlated with the information processing speed score (P, 0.
Based on the Fazekas score, the connection map qualitatively describes the white matter tracts between the 100 automatic anatomical marker map regions, and the tract volume has the largest (absolute) difference between the groups
.
The red line represents the small fiber bundles in the white matter hyperintensity group (Fazekas score ≥ 1 vs 0), and the blue line represents the large fiber bundles
Based on the Fazekas score, the connection map qualitatively describes the white matter tracts between the 100 automatic anatomical marker map regions, and the tract volume has the largest (absolute) difference between the groups
The relationship between white matter hyperintensity (WMH) volume, local network efficiency and information processing speed (IPS) score, and the mediating effect of local efficiency
.
The solid line represents the direct effect (c9); the dashed line represents the indirect effect (a∙b) and percentage mediation (percent mediation [PM] = indirect/total = indirect/[indirect + total])
.
These associations are given by standardized regression coefficients (standardized b) and are based on age, gender, education level, intracranial volume, diabetes status, MRI patch update, body mass index, ratio of total cholesterol to high-density lipoprotein, systolic blood pressure, lipid Quality modification and antihypertensive drugs and adjustments to previous cardiovascular diseases
.
* = p, 0.
01
.
The relationship between white matter hyperintensity (WMH) volume, local network efficiency and information processing speed (IPS) score, and the mediating effect of local efficiency
.
The solid line represents the direct effect (c9); the dashed line represents the indirect effect (a∙b) and percentage mediation (percent mediation [PM] = indirect/total = indirect/[indirect + total])
.
These associations are given by standardized regression coefficients (standardized b) and are based on age, gender, education level, intracranial volume, diabetes status, MRI patch update, body mass index, ratio of total cholesterol to high-density lipoprotein, systolic blood pressure, lipid Qualitative modification and antihypertensive drugs and adjustments to previous cardiovascular diseases
.
* = p, 0.
01
.
cholesterol
WMH greater volume of more local network efficiency ( normalized coefficient β , 0.
065; P <.
001) , the lower the processing speed of the information ( normalized coefficient β , -0.
073; P <.
001) correlation
.
Also, a lower local efficiency (normalized coefficient β, - 0.
084; P <from 0.
001) with a lower processing speed related information
.
In particular, the relationship between WMHs and information processing speed is mediated by local network efficiency (mediation percentage, 7.
2% [95% CI: 3.
5, 10.
9]; P<0.
05)
.
Finally, in the white matter bundle, which has an important influence on the information processing speed, WMH has a heavier load
.
065; P <.
001) , 0.
065; P , lower information processing speed ( ( normalized coefficient β normalized coefficient β , -0.
073; P <.
001) , -0.
073; P Related
addition, the lower local efficiency (normalized coefficient β,.
- 0.
084; P <from 0.
001) and a lower information processing Speed is related
.
In particular, the relationship between WMHs and information processing speed is mediated by local network efficiency (mediation percentage, 7.
2% [95% CI: 3.
5, 10.
9]; P<0.
05)
.
Finally, it is important to the speed of information processing Among the affected white matter bundles, WMH load is larger
.
− 0.
084; P<0.
001) is related to lower information processing speed
.
In particular, the relationship between WMHs and information processing speed is mediated by local network efficiency (mediation percentage, 7.
2%) [95% CI: 3.
5, 10.
9]; P<0.
05)
.
Finally, in the white matter bundles that have an important influence on the information processing speed, WMH load is greater
.
In summary, the study found that white matter hyperintensity (WMH) lesion volume, local network efficiency, and information processing speed scores are interrelated
.
More specifically, the adverse effect of larger WMH volume on cognitive function is mediated by local network efficiency
.
Local studies have found that the larger the WMH volume in the white matter tract, which is very important for information processing, the slower the cognitive speed, which reflects the decline in cognitive ability due to the pathological characteristics of white matter in elderly individuals with cardiometabolic risk factors
.
.
More specifically, the adverse effect of larger WMH volume on cognitive function is mediated by local network efficiency
.
Local studies have found that the larger the WMH volume in the white matter tract, which is very important for information processing, the slower the cognitive speed.
This reflects the decline in cognitive ability due to the pathological characteristics of white matter in elderly individuals with cardiometabolic risk factors
.
The study found that white matter hyperintensity (WMH) lesion volume, local network efficiency, and information processing speed scores are interrelated
.
More specifically, the adverse effect of larger WMH volume on cognitive function is mediated by local network efficiency
.
Local studies have found that the larger the WMH volume in the white matter tract, which is very important for information processing, the slower the cognitive speed.
This reflects the decline in cognitive ability due to the pathological characteristics of white matter in elderly individuals with cardiometabolic risk factors
.
Original source
Interplay of White Matter Hyperintensities, Cerebral Networks, and Cognitive Function in an Adult Population: Diffusion-Tensor Imaging in the Maastricht Study
Interplay of White Matter Hyperintensities, Cerebral Networks , and Cognitive Function in an Adult Population: Diffusion-Tensor Imaging in the Maastricht Study in this message