Dataset Information

Cerebral white matter lesions and regional blood flow are associated with reduced cognitive function in early-stage cognitive impairment.

ABSTRACT:

Background

Differences in the extent of cerebral white matter lesions (WML) and regional cerebral blood flow (rCBF) in early-stage cognitive impairment (ESCI) contribute to the prognosis of cognitive decline; however, it is unclear precisely how WML and rCBF affect cognitive decline in ESCI.

Objective

We examined the association between WML, rCBF, and cognitive impairment in the ESCI, using path analysis to clarify how these variables affect each other.

Methods

Eighty-three patients who consulted our memory clinic regarding memory loss were included in this study based on the Clinical Dementia Rating. Participants underwent the Mini-Mental State Examination (MMSE), brain magnetic resonance imaging (MRI) for voxel-based morphometry analysis, and brain perfusion single-photon emission computed tomography (SPECT) for rCBF evaluation in cortical regions, using 3D stereotactic surface projection (3D-SSP) analysis.

Results

Path analysis was performed on the MRI voxel-based morphometry and SPECT 3D-SSP data, showing a significant correlation between both and MMSE scores. In the most suitable model (GFI = 0.957), correlations were observed between lateral ventricular (LV-V) and periventricular WML (PvWML-V) volumes [standardized coefficient (SC) = 0.326, p = 0.005], LV-V and rCBF of the anterior cingulate gyrus (ACG-rCBF; SC = 0.395, p < 0.0001), and ACG-rCBF and PvWML-V (SC = 0.231, p = 0.041). Furthermore, a direct relationship between PvWML-V and MMSE scores was identified (SC = -0.238, p = 0.026).

Conclusion

Significant interrelationships were observed among the LV-V, PvWML-V, and ACG-rCBF that directly affected the MMSE score in the ESCI. The mechanisms behind these interactions and the impact of PvWML-V on cognitive function require further investigation.

SUBMITTER: Kuroda T

PROVIDER: S-EPMC9978183 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature

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Publications

Cerebral white matter lesions and regional blood flow are associated with reduced cognitive function in early-stage cognitive impairment.

Kuroda Takeshi T Ono Kenjiro K Honma Motoyasu M Asano Miki M Mori Yukiko Y Futamura Akinori A Yano Satoshi S Kanemoto Mizuki M Hieda Sotaro S Baba Yasuhiko Y Izumizaki Masahiko M Murakami Hidetomo H

Frontiers in aging neuroscience 20230216

<h4>Background</h4>Differences in the extent of cerebral white matter lesions (WML) and regional cerebral blood flow (rCBF) in early-stage cognitive impairment (ESCI) contribute to the prognosis of cognitive decline; however, it is unclear precisely how WML and rCBF affect cognitive decline in ESCI.<h4>Objective</h4>We examined the association between WML, rCBF, and cognitive impairment in the ESCI, using path analysis to clarify how these variables affect each other.<h4>Methods</h4>Eighty-three ...[more]

PMID: 36875693

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Project description:BackgroundDamage to brain white matter often occurs in individuals with chronic kidney disease, which might be related to their cognitive decline. This study aims to investigate tract-specific white matter damage in patients with end-stage kidney disease by using fixel-based analysis.MethodsImages of 31 end-stage kidney disease patients and 16 normal controls (aged: 61.1 ± 10.4 years; 11 men) were acquired from a 1.5T magnetic resonance scanner. The patients were subsequently divided into with normal cognition (N = 17, aged: 66.9 ± 7.2 years; 10 men) and cognitive impairment (N = 14, aged: 72.4 ± 9.4 years; 7 men). Cognitive assessment, and neurologic, hematologic and biochemical samples were collected. Fixel-based analysis was used to examine the tract-specific damage within white matter. Differences between groups were evaluated through connectivity-based fixel enhancement and non-parametric permutation testing. Correlation with biomarkers was conducted through general linear model. Significance was determined with family-wise error-corrected P-value <.05.ResultsReduced fixel-based metrics were observed in specific tract located the cerebral peduncle, internal capsule, corpus callosum, fornix and superior corona radiata in patients when compared with normal controls, indicating a reduction in fiber content. The fibers crossing the corpus callosum and the fornix/stria terminalis are particularly vulnerable sites, which can be associated with the decrease in both Mini-Mental State Examination (R2 ranged between 0.420 and 0.556) and Montreal Cognitive Assessment (R2 ranged between 0.425 and 0.509), as well as the plasma concentration of calcium (R2 ranged between 0.207 and 0.322). The plasma concentration of indoxyl sulfate was associated with the descending tracts from right posterior limb of internal capsule to cerebral peduncle (R2 ranged between 0.262 and 0.335).ConclusionsTract-specific white matter damage can be noticed in the patients with end-stage kidney disease, and could be associated with their cognitive decline.

Project description:BackgroundVascular risk factors like white matter lesions (WMLs) are increasingly recognized as risk factors for vascular dementia (VaD) and can predict Alzheimer's disease (AD) at least a decade before the clinical stage of the disease. This study aimed to predict cognitive decline and use machine learning techniques to classify older individuals (aged 50 years or older) with WMLs as having vascular mild cognitive impairment (VaMCI), VaD, or in good cognitive health (CH).MethodsA total of 79 individuals with WMLs were selected for this study and categorized into the following 3 groups: CH (n=25), VaMCI (n=33), and VaD (n=21). Data from the entire cohort was then divided into a training dataset (n=56) and testing dataset (n=23). The data were extracted from gray matter (GM) segmentations using voxel-based morphometry (VBM). A relevance vector regression (RVR) approach was used to test the relationship between the structural brain images and clinical scores. To predict the individual-level subtypes, we applied 2 different machine learning-based classifiers: support vector machine (SVM) and Gaussian process classification (GPC). All predictive models were trained on the training dataset and then validated on the testing dataset of age-matched participants.ResultsMulti-domain cognitive performance could be predicted based on the pattern of GM atrophy in older people with WMLs using a RVR approach. The classification of VaD versus CH (cross-validation accuracy =93.94%, test set accuracy =76.92%) and VaMCI versus CH (cross-validation accuracy =95.24%, test set accuracy =87.50%) could be successfully achieved using both SVM and GPC. However, SVM (cross-validation accuracy =67.57%, test set accuracy =70.59%) performed better than GPC in the classification of VaD versus VaMCI.ConclusionsBased on the patterns of gray matter and RVR-based model could achieve prediction of cognitive test scores, and SVM and GPC could classify the severity of cognitive impairment in older people with WMLs.

Dataset Information

Cerebral white matter lesions and regional blood flow are associated with reduced cognitive function in early-stage cognitive impairment.

Background

Objective

Methods

Results

Conclusion

Publications

Cerebral white matter lesions and regional blood flow are associated with reduced cognitive function in early-stage cognitive impairment.

Similar Datasets

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