-
Categories
-
Pharmaceutical Intermediates
-
Active Pharmaceutical Ingredients
-
Food Additives
- Industrial Coatings
- Agrochemicals
- Dyes and Pigments
- Surfactant
- Flavors and Fragrances
- Chemical Reagents
- Catalyst and Auxiliary
- Natural Products
- Inorganic Chemistry
-
Organic Chemistry
-
Biochemical Engineering
- Analytical Chemistry
- Cosmetic Ingredient
-
Pharmaceutical Intermediates
Promotion
ECHEMI Mall
Wholesale
Weekly Price
Exhibition
News
-
Trade Service
2 type diabetes mellitus (T2DM) associated with cognitive impairment, and may progress to dementia
.
However, the brain function mechanism of T2DM-related dementia is still poorly understood
2 type diabetes mellitus (T2DM) associated with cognitive impairment, and may progress to dementia
Recently, Human Brain Mapping magazine published an article proposing to use high-level FC to reveal abnormal connection patterns in T2DM-CI, and adopt multiple, machine learning-based strategies
In order to better understand the cognitive impairment caused by T2DM, the study studied 23 T2DM-CI and 27 T2DM-noCI patients and 50 healthy controls (HCs), and studied T2DM-CI and T2DM no cognition Whether this pattern of functional impairment (T2DM-noCI) is different
.
First, establish a large-scale high-order brain network based on the time synchronization of dynamic FC time series between multiple brain regions, and then use this information to classify T2DM-CI (and T2DM-noCI) from matched HCs based on support vector machines
In order to better understand the cognitive impairment caused by T2DM, the study studied 23 T2DM-CI and 27 T2DM-noCI patients and 50 healthy controls (HCs), and studied T2DM-CI and T2DM no cognition Whether this pattern of functional impairment (T2DM-noCI) is different
Dynamic-based higher-order functional connectivity (dHOFC) network construction framework and network classification of type 2 diabetes with cognitive impairment (T2DM-CI) and healthy control group (HC)
.
.
Differentiating performance between T2DM-CI and HC and T2DM-noCI and HC
Differentiating performance between T2DM-CI and HC and T2DM-noCI and HCThe left panel shows the first two higher-order functional connections based on discriminative dynamics selected from the classification between type 2 diabetes with cognitive impairment (T2DM-CI) and healthy controls (HC) according to the frequency of selection (95.
83%) (DHOFC) node
.
83%) (DHOFC) node
.
The scatter plots of dynamic-based high-order functional connectivity (dHOFC) features [(a) and (b) represent the local clustering coefficients of dHOFC node 1 and node 2, respectively, and type 2 diabetes combined with cognitive impairment (T2DM- Comparison of Montreal Cognitive Assessment (MoCA) scores in CI) group
.
.
The left panel shows the first discriminative dynamic high-order functional connection (dHOFC) node selected according to the selection frequency (90.
38%) from the classification of type 2 diabetes (T2DM-noCI) and healthy controls (HC) without cognitive impairment
.
38%) from the classification of type 2 diabetes (T2DM-noCI) and healthy controls (HC) without cognitive impairment
.
The research model achieved an accuracy of 79.
17% in the differential diagnosis of T2DM-CI and HC , but only 59.
62% in the differential diagnosis of T2DM-noCI and HC
.
Compared with HC, T2DM-CI has an abnormal high-order FC mode, which is different from T2DM-NOCI
The research model achieved an accuracy of 79.
This study is the first classification study of T2DM-CI and HC and T2DM-noCI and HC based on the brain function network
This study shows that the cognitive impairment caused by T2DM T2DM may have a wide range of functional connectivity changes
Original source
Classification of type 2 diabetes mellitus with or without cognitive impairment from healthy controls using high-order functional connectivity.
https://doi.
org/10.
1002/hbm.
25575
org/10.
1002/hbm.
25575 Leave a message here
