Objectives: In this cross-sectional study, we aimed to explore the mechanisms of early cognitive impairment in a post stroke non-dementia cerebral small vessel disease (SVD) cohort by comparing the SVD score with the structural brain network measures.
Method: 127 SVD patients were recruited consecutively from a stroke clinic, comprising 76 individuals with mild cognitive impairment (MCI) and 51 with no cognitive impairment (NCI). Detailed neuropsychological assessments and multimodal MRI were performed. SVD scores were calculated on a standard scale, and structural brain network measures were analyzed by diffusion tensor imaging (DTI). Between-group differences were analyzed, and logistic regression was applied to determine the predictive value of SVD and network measures for cognitive status. Mediation analysis with structural equation modeling (SEM) was used to better understand the interactions of SVD burden, brain networks and cognitive deficits.
Results: Group difference was found on all global brain network measures. After adjustment for age, gender, education and depression, significant correlations were found between global brain network measures and diverse neuropsychological tests, including TMT-B (r = -0.209, p < .05), DSST (r = 0.206, p < .05), AVLT short term free recall (r = 0.233, p < .05), AVLT long term free recall (r = 0.264, p < .05) and Rey-O copy (r = 0.272, p < .05). SVD score showed no group difference and was not correlated with cognition tests. Network global efficiency (E) was significantly related to cognitive state (p < .01) but not the SVD score. Mediation analysis showed that the standardized total effect (p = .013) and the standardized indirect effect (p = .016) of SVD score on cognition was significant, but the direct effect was not.
Conclusions: Brain network measures, but not the SVD score, are significantly correlated with cognition in post-stroke SVD patients. Mediation analysis showed that the cerebral vascular lesions produce cognitive dysfunction by interfering with the structural brain network in SVD patients. The brain network measures may be regarded as direct and independent surrogate markers of cognitive impairment in SVD.
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http://dx.doi.org/10.1016/j.nicl.2019.101712 | DOI Listing |
Brain
January 2025
Translational Neuroimaging Laboratory, Montreal Neurological Institute, H3A 2B4, Montreal, Canada.
Plasma phosphorylated tau biomarkers open unprecedented opportunities for identifying carriers of Alzheimer's disease pathophysiology in early disease stages using minimally invasive techniques. Plasma p-tau biomarkers are believed to reflect tau phosphorylation and secretion. However, it remains unclear to what extent the magnitude of plasma p-tau abnormalities reflects neuronal network disturbance in the form of cognitive impairment.
View Article and Find Full Text PDFJ Med Internet Res
January 2025
Department of Computer Science and Software Engineering, United Arab Emirates University, Al Ain, United Arab Emirates.
Background: Neuroimaging segmentation is increasingly important for diagnosing and planning treatments for neurological diseases. Manual segmentation is time-consuming, apart from being prone to human error and variability. Transformers are a promising deep learning approach for automated medical image segmentation.
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March 2025
Department of Neurology and Experimental Neurology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt- Universität zu Berlin.
Background And Objectives: Cognitive deficits represent a major long-term complication of anti-leucine-rich, glioma-inactivated 1 encephalitis (LGI1-E). Although severely affecting patient outcomes, the structural brain changes underlying these deficits remain poorly understood. In this study, we hypothesized a link between white matter (WM) networks and cognitive outcomes in LGI1-E.
View Article and Find Full Text PDFPLoS Comput Biol
January 2025
Edmond and Lily Safra Center for Brain Sciences, The Hebrew University of Jerusalem, Jerusalem, Israel.
Theoretical neuroscientists and machine learning researchers have proposed a variety of learning rules to enable artificial neural networks to effectively perform both supervised and unsupervised learning tasks. It is not always clear, however, how these theoretically-derived rules relate to biological mechanisms of plasticity in the brain, or how these different rules might be mechanistically implemented in different contexts and brain regions. This study shows that the calcium control hypothesis, which relates synaptic plasticity in the brain to the calcium concentration ([Ca2+]) in dendritic spines, can produce a diverse array of learning rules.
View Article and Find Full Text PDFProc Natl Acad Sci U S A
February 2025
Princess Margaret Cancer Centre, University Health Network, Toronto, ON M5G 1L7, Canada.
ClpXP is a two-component mitochondrial matrix protease. The caseinolytic mitochondrial matrix peptidase chaperone subunit X (ClpX) recognizes and translocates protein substrates into the degradation chamber of the caseinolytic protease P (ClpP) for proteolysis. ClpXP degrades damaged respiratory chain proteins and is necessary for cancer cell survival.
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