The aim of this study was to evaluate excitatory/inhibitory intracortical circuit changes in patients with vascular depression, and whether there are any interhemispheric differences of motor cortical excitability. Fifteen vascular depressed elderly (VD), ten nondepressed subcortical vascular disease patients (SVD) and ten age-matched controls underwent bilateral motor threshold and paired-pulse studies. They were also assessed for their brain vascular burden at MRI and neuropsychological profile. Executive dysfunction and apathy were significantly higher in VD; we were unable to find significant differences in resting motor threshold, cortical silent period and paired-pulse curves between VD, SVD and controls, and between the two hemispheres in the VD group. Our findings might suggest that neurophysiological mechanisms underlying VD differ from those previously reported in Major Depression (reduced excitability in the left hemisphere) and seem to be similar to those of patients with SVD. Our findings also, support the "vascular depression" hypothesis, suggesting that in VD patients the depressive syndrome is not the primary disease but can be considered as one of the clinical manifestations in the wide symptom spectrum of the cerebral small vessel disease.
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http://dx.doi.org/10.1016/j.ijpsycho.2011.09.006 | DOI Listing |
ERJ Open Res
January 2025
South Wales Clinical Psychology Training Programme, Cardiff and Vale University Health Board and Cardiff University, Cardiff, UK.
https://bit.ly/3YLHIHm.
View Article and Find Full Text PDFNat Ment Health
November 2024
Department of Behavioral Sciences and Social Medicine, Florida State University College of Medicine, Tallahassee, FL, USA.
Loneliness is one critical risk factor for cognitive health. Combining data from ongoing aging studies and the published literature, we provided the largest meta-analysis on the association between loneliness and dementia ( = 21 samples, = 608,561) and cognitive impairment ( = 16, = 103,387). Loneliness increased risk for all-cause dementia (HR = 1.
View Article and Find Full Text PDFBJS Open
December 2024
Department of Cardiology, Thorax Centre, Cardiovascular Institute, Erasmus MC, Rotterdam, The Netherlands.
Background: Contrary to the impact of screening, the effect of long-term surveillance on the quality of life of patients with an abdominal aortic aneurysm is not well known. Therefore, the aim of this study was to describe patient-reported outcomes of patients with an abdominal aortic aneurysm approaching the surgical threshold.
Methods: This multicentre, observational cohort study included patients with an abdominal aortic aneurysm with a maximum aneurysm diameter of greater than or equal to 40 mm.
Health Data Sci
January 2024
Department of Global Health, School of Public Health, Peking University, Beijing, China.
Digital exclusion is a global issue that disproportionately affects older individuals especially in low- and middle-income nations. However, there is a wide gap in current research regarding the impact of digital exclusion on the mental health of older adults in both high-income and low- and middle-income countries. We analyzed data from 5 longitudinal cohorts: the Health and Retirement Study (HRS), the English Longitudinal Study of Aging (ELSA), the Survey of Health, Ageing and Retirement in Europe (SHARE), the China Health and Retirement Longitudinal Study (CHARLS), and the Mexican Health and Aging Study (MHAS).
View Article and Find Full Text PDFBr J Anaesth
January 2025
Department of Anesthesiology, Center of Anesthesiology and Intensive Care Medicine, University Medical Center Hamburg-Eppendorf, Hamburg, Germany; Outcomes Research Consortium, Cleveland, OH, USA. Electronic address:
Background: Hypotension is associated with organ injury and death in surgical and critically ill patients. In clinical practice, treating hypotension remains challenging because it can be caused by various underlying haemodynamic alterations. We aimed to identify and independently validate endotypes of hypotension in big datasets of surgical and critically ill patients using unsupervised deep learning.
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