Cargando…
The correlation between changes in gray matter microstructure and cerebral blood flow in Alzheimer’s disease
OBJECTIVE: To investigate the relationship between changes in cerebral blood flow (CBF) and gray matter (GM) microstructure in Alzheimer’s disease (AD) and mild cognitive impairment (MCI). METHODS: A recruited cohort of 23 AD patients, 40 MCI patients, and 37 normal controls (NCs) underwent diffusio...
Autores principales: | , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10272452/ https://www.ncbi.nlm.nih.gov/pubmed/37333456 http://dx.doi.org/10.3389/fnagi.2023.1205838 |
_version_ | 1785059497919643648 |
---|---|
author | Niu, Xiaoxi Guo, Ying Chang, Zhongyu Li, Tongtong Chen, Yuanyuan Zhang, Xianchang Ni, Hongyan |
author_facet | Niu, Xiaoxi Guo, Ying Chang, Zhongyu Li, Tongtong Chen, Yuanyuan Zhang, Xianchang Ni, Hongyan |
author_sort | Niu, Xiaoxi |
collection | PubMed |
description | OBJECTIVE: To investigate the relationship between changes in cerebral blood flow (CBF) and gray matter (GM) microstructure in Alzheimer’s disease (AD) and mild cognitive impairment (MCI). METHODS: A recruited cohort of 23 AD patients, 40 MCI patients, and 37 normal controls (NCs) underwent diffusional kurtosis imaging (DKI) for microstructure evaluation and pseudo-continuous arterial spin labeling (pCASL) for CBF assessment. We investigated the differences in diffusion- and perfusion-related parameters across the three groups, including CBF, mean diffusivity (MD), mean kurtosis (MK), and fractional anisotropy (FA). These quantitative parameters were compared using volume-based analyses for the deep GM and surface-based analyses for the cortical GM. The correlation between CBF, diffusion parameters, and cognitive scores was assessed using Spearman coefficients, respectively. The diagnostic performance of different parameters was investigated with k-nearest neighbor (KNN) analysis, using fivefold cross-validation to generate the mean accuracy (mAcc), mean precision (mPre), and mean area under the curve (mAuc). RESULTS: In the cortical GM, CBF reduction primarily occurred in the parietal and temporal lobes. Microstructural abnormalities were predominantly noted in the parietal, temporal, and frontal lobes. In the deep GM, more regions showed DKI and CBF parametric changes at the MCI stage. MD showed most of the significant abnormalities among all the DKI metrics. The MD, FA, MK, and CBF values of many GM regions were significantly correlated with cognitive scores. In the whole sample, the MD, FA, and MK were associated with CBF in most evaluated regions, with lower CBF values associated with higher MD, lower FA, or lower MK values in the left occipital lobe, left frontal lobe, and right parietal lobe. CBF values performed best (mAuc = 0.876) for distinguishing the MCI from the NC group. Last, MD values performed best (mAuc = 0.939) for distinguishing the AD from the NC group. CONCLUSION: Gray matter microstructure and CBF are closely related in AD. Increased MD, decreased FA, and MK are accompanied by decreased blood perfusion throughout the AD course. Furthermore, CBF values are valuable for the predictive diagnosis of MCI and AD. GM microstructural changes are promising as novel neuroimaging biomarkers of AD. |
format | Online Article Text |
id | pubmed-10272452 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-102724522023-06-17 The correlation between changes in gray matter microstructure and cerebral blood flow in Alzheimer’s disease Niu, Xiaoxi Guo, Ying Chang, Zhongyu Li, Tongtong Chen, Yuanyuan Zhang, Xianchang Ni, Hongyan Front Aging Neurosci Neuroscience OBJECTIVE: To investigate the relationship between changes in cerebral blood flow (CBF) and gray matter (GM) microstructure in Alzheimer’s disease (AD) and mild cognitive impairment (MCI). METHODS: A recruited cohort of 23 AD patients, 40 MCI patients, and 37 normal controls (NCs) underwent diffusional kurtosis imaging (DKI) for microstructure evaluation and pseudo-continuous arterial spin labeling (pCASL) for CBF assessment. We investigated the differences in diffusion- and perfusion-related parameters across the three groups, including CBF, mean diffusivity (MD), mean kurtosis (MK), and fractional anisotropy (FA). These quantitative parameters were compared using volume-based analyses for the deep GM and surface-based analyses for the cortical GM. The correlation between CBF, diffusion parameters, and cognitive scores was assessed using Spearman coefficients, respectively. The diagnostic performance of different parameters was investigated with k-nearest neighbor (KNN) analysis, using fivefold cross-validation to generate the mean accuracy (mAcc), mean precision (mPre), and mean area under the curve (mAuc). RESULTS: In the cortical GM, CBF reduction primarily occurred in the parietal and temporal lobes. Microstructural abnormalities were predominantly noted in the parietal, temporal, and frontal lobes. In the deep GM, more regions showed DKI and CBF parametric changes at the MCI stage. MD showed most of the significant abnormalities among all the DKI metrics. The MD, FA, MK, and CBF values of many GM regions were significantly correlated with cognitive scores. In the whole sample, the MD, FA, and MK were associated with CBF in most evaluated regions, with lower CBF values associated with higher MD, lower FA, or lower MK values in the left occipital lobe, left frontal lobe, and right parietal lobe. CBF values performed best (mAuc = 0.876) for distinguishing the MCI from the NC group. Last, MD values performed best (mAuc = 0.939) for distinguishing the AD from the NC group. CONCLUSION: Gray matter microstructure and CBF are closely related in AD. Increased MD, decreased FA, and MK are accompanied by decreased blood perfusion throughout the AD course. Furthermore, CBF values are valuable for the predictive diagnosis of MCI and AD. GM microstructural changes are promising as novel neuroimaging biomarkers of AD. Frontiers Media S.A. 2023-06-02 /pmc/articles/PMC10272452/ /pubmed/37333456 http://dx.doi.org/10.3389/fnagi.2023.1205838 Text en Copyright © 2023 Niu, Guo, Chang, Li, Chen, Zhang and Ni. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Neuroscience Niu, Xiaoxi Guo, Ying Chang, Zhongyu Li, Tongtong Chen, Yuanyuan Zhang, Xianchang Ni, Hongyan The correlation between changes in gray matter microstructure and cerebral blood flow in Alzheimer’s disease |
title | The correlation between changes in gray matter microstructure and cerebral blood flow in Alzheimer’s disease |
title_full | The correlation between changes in gray matter microstructure and cerebral blood flow in Alzheimer’s disease |
title_fullStr | The correlation between changes in gray matter microstructure and cerebral blood flow in Alzheimer’s disease |
title_full_unstemmed | The correlation between changes in gray matter microstructure and cerebral blood flow in Alzheimer’s disease |
title_short | The correlation between changes in gray matter microstructure and cerebral blood flow in Alzheimer’s disease |
title_sort | correlation between changes in gray matter microstructure and cerebral blood flow in alzheimer’s disease |
topic | Neuroscience |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10272452/ https://www.ncbi.nlm.nih.gov/pubmed/37333456 http://dx.doi.org/10.3389/fnagi.2023.1205838 |
work_keys_str_mv | AT niuxiaoxi thecorrelationbetweenchangesingraymattermicrostructureandcerebralbloodflowinalzheimersdisease AT guoying thecorrelationbetweenchangesingraymattermicrostructureandcerebralbloodflowinalzheimersdisease AT changzhongyu thecorrelationbetweenchangesingraymattermicrostructureandcerebralbloodflowinalzheimersdisease AT litongtong thecorrelationbetweenchangesingraymattermicrostructureandcerebralbloodflowinalzheimersdisease AT chenyuanyuan thecorrelationbetweenchangesingraymattermicrostructureandcerebralbloodflowinalzheimersdisease AT zhangxianchang thecorrelationbetweenchangesingraymattermicrostructureandcerebralbloodflowinalzheimersdisease AT nihongyan thecorrelationbetweenchangesingraymattermicrostructureandcerebralbloodflowinalzheimersdisease AT niuxiaoxi correlationbetweenchangesingraymattermicrostructureandcerebralbloodflowinalzheimersdisease AT guoying correlationbetweenchangesingraymattermicrostructureandcerebralbloodflowinalzheimersdisease AT changzhongyu correlationbetweenchangesingraymattermicrostructureandcerebralbloodflowinalzheimersdisease AT litongtong correlationbetweenchangesingraymattermicrostructureandcerebralbloodflowinalzheimersdisease AT chenyuanyuan correlationbetweenchangesingraymattermicrostructureandcerebralbloodflowinalzheimersdisease AT zhangxianchang correlationbetweenchangesingraymattermicrostructureandcerebralbloodflowinalzheimersdisease AT nihongyan correlationbetweenchangesingraymattermicrostructureandcerebralbloodflowinalzheimersdisease |