[This corrects the article DOI: 10.3389/fnagi.2023.1270226.].
[This corrects the article DOI: 10.3389/fnagi.2023.1270226.].
Introduction: Previous studies have shown that stroke patients exhibit greater neuroimaging-derived biological "brain age" than control subjects. This difference, known as the brain age gap (BAG), is calculated by comparing the chronological age with predicted brain age and is used as an indicator of brain health and aging. However, whether stroke accelerates the process of brain aging in patients with small-volume infarcts has not been established. By utilizing longitudinal data, we aimed to investigate whether small-volume infarctions can significantly increase the BAG, indicating accelerated brain aging.
Methods: A total of 123 stroke patients presenting with small-volume infarcts were included in this retrospective study. The brain age model was trained via established protocols within the field of machine learning and the structural features of the brain from our previous study. We used t-tests and regression analyses to assess longitudinal brain age changes after stroke and the associations between brain age, acute stroke severity, and poststroke outcome factors.
Results: Significant brain aging occurred between the initial and 6-month follow-ups, with a mean increase in brain age of 1.04 years (t = 3.066, p < 0.05). Patients under 50 years of age experienced less aging after stroke than those over 50 years of age (p = 0.245). Additionally, patients with a National Institute of Health Stroke Scale score >3 at admission presented more pronounced adverse effects on brain aging, even after adjusting for confounders such as chronological age, sex, and total intracranial volume (F 1,117 = 7.339, p = 0.008, η 2 = 0.059). There were significant differences in the proportional brain age difference at 6 months among the different functional outcome groups defined by the Barthel Index (F 2,118 = 4.637, p = 0.012, η 2 = 0.073).
Conclusion: Stroke accelerates the brain aging process, even in patients with relatively small-volume infarcts. This phenomenon is particularly accentuated in elderly patients, and both stroke severity and poststroke functional outcomes are closely associated with accelerated brain aging. Further studies are needed to explore the mechanisms underlying the accelerated brain aging observed in stroke patients, with a particular focus on the structural alterations and plasticity of the brain following minor strokes.
[This corrects the article DOI: 10.3389/fnagi.2024.1411031.].
Primary cilia (PC) are microtubules-based, independent antennal-like sensory organelles, that are seen in most vertebrate cells of different types, including astrocytes and neurons. They send signals to cells to control many physiological and cellular processes by detecting changes in the extracellular environment. Parkinson's disease (PD), a neurodegenerative disease that progresses over time, is primarily caused by a gradual degradation of the dopaminergic pathway in the striatum nigra, which results in a large loss of neurons in the substantia nigra compact (SNpc) and a depletion of dopamine (DA). PD samples have abnormalities in the structure and function of PC. The alterations contribute to the cause, development, and recovery of PD via influencing signaling pathways (SHH, Wnt, Notch-1, α-syn, and TGFβ), genes (MYH10 and LRRK2), defective mitochondrial function, and substantia nigra dopaminergic neurons. Thus, restoring the normal structure and physiological function of PC and neurons in the brain are effective treatment for PD. This review summarizes the function of PC in neurodegenerative diseases and explores the pathological mechanisms caused by PC alterations in PD, in order to provide references and ideas for future research.
Objectives: Sleep is an indispensable part of human health, which can help us to restore physical strength, enhance immunity and maintain nervous system stability. The relationship between sleep quality and cognitive dysfunction is unclear, especially at the community population level. This study aims to explore the association between sleep quality and cognitive dysfunction.
Methods: A total of 5,224 community residents were enrolled in this cross-sectional study. Cognitive function was assessed by the Mini-Mental State Examination (MMSE). Sleep quality was assessed by the multidimensional sleep questionnaire. Multivariate logistic regression was used to analyze the association between sleep quality and cognitive dysfunction. The adjusted models took into account relevant demographic, clinical, and sleep variables.
Results: A total of 3,106 participants were enrolled in this study, of whom 463 (15%) had cognitive dysfunction. Total sleep duration, staying up, sleep latency, number of awakenings, and history of sleep medications were associated with cognitive dysfunction in unadjusted models, and these effects were consistent after adjustment. First, those who slept 6-7.9 h per day (OR = 0.57, 95% CI 0.40 to 0.80, p = 0.001) had a lower risk for cognitive dysfunction compared to those who slept less than 6 h per day. Second, participants who stayed up more than 10 times over the 3 months (OR = 1.90, 95% CI 1.20 to 3.00, p = 0.006) were more likely to suffer cognitive dysfunction than those who never stayed up. Third, we also found that participants with sleep latencies of 16-30 min were less likely to experience cognitive dysfunction than those with sleep latencies of less than 16 min after adjusting confounders (OR = 0.33, 95% CI 0.23 to 0.47, p < 0.001). Fourth, participants who woke up once (OR = 1.65, 95% CI 1.19 to 2.30, p = 0.003) and three or more times (OR = 2.34, 95% CI 1.25 to 4.36, p = 0.008) after falling asleep had a higher risk than those who did not wake up at night. Last, participants taking sleep medication (OR = 2.97, 95% CI 1.19 to 7.45, p = 0.020) were more vulnerable to cognitive dysfunction, relative to participants without taking any medications.
Conclusion: Our results suggest that after adjustment for potential confounding variables, poor sleep quality is associated with cognitive dysfunction.
Objective: White matter hyperintensities (WMH) are the most common neuroimaging manifestation of cerebral small vessel disease, and is frequently observed in Alzheimer's disease (AD). This study aimed to investigate the relationship between WMH and cognition and to verify the mediation of grey matter atrophy in this relationship.
Methods: The diffusion tensor imaging (DTI) technique analyses white matter fiber tract to assess white matter integrity. Voxel-based morphometry was applied to measure the grey matter volume (GMV). A linear regression model was applied to examine the associations between WMH and GMV, and mediation analyses was performed to determine the mediating role of regional GMV in the effect of WMH on cognitive function.
Results: Compared to the HC group, AD group have 8 fiber tract fractional anisotropy (FA) decreased and 16 fiber tract mean diffusivity (MD) increased. Compared to AD without WMH, AD with high WMH had 9 fiber tracts FA decreased and 13 fiber tracts MD increased. High WMH volume was negatively correlated with GMV in the frontal-parietal region. Low WMH volume was also negatively correlated with GMV except for the three regions (right angular gyrus, right superior frontal gyrus and right middle/inferior parietal gyrus), where GMV was positively correlated. Mediation analysis showed that the association between WMH and executive function or episodic memory were mediated by GMV in the frontal-parietal region.
Conclusion: Damage to white matter integrity was more severe in AD with WMH. Differential changes in DTI metrics may be caused by progressive myelin and axonal damage. There was a negative correlation between WMH and grey matter atrophy in frontal-parietal regions in a volume-dependent manner. This study indicates the correspondence between WMH volume and GMV in cognition, and GMV being a key modulator between WMH and cognition in AD. This result will contribute to understanding the progression of the disease process and applying targeted therapeutic intervention in the earlier stage to delay neurodegenerative changes in frontal-parietal regions to achieve better treatment outcomes and affordability.
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