Abstract
Purpose: Individuals with depression have an increased risk of cardiovascular disease, and more often have a poor prognosis with cardiovascular disease. This study aimed to investigate the impact of depression on Left Ventricular (LV) alterations using Cardiovascular Magnetic Resonance Featuretracking (CMR-FT).
Methods: Seven anesthetized, healthy Chinese miniature swine were included in the study. Basic data, including CMR scans at baseline and after 14 days of depression modeling, were collected. Behavioral tests, including the Open-field Test (OFT), Sucrose Preference Test (SPT), and measurements of the time taken to consume a specific amount of food and sugar, were conducted to assess the success of the depression models. CMR cine images were acquired and CVI software was employed to analyze Global Longitudinal Strain (GLS), Global Circumferential Strain (GCS), and Global Radial Strain (GRS). Late Gadolinium Enhancement (LGE) imaging was used to detect myocardial infarction and/or scar.
Results: The outcomes demonstrated successful depression modeling, indicated by reduced scores in the OFT and SPT, as well as an extended time to intake food and sugar compared to baseline. However, no significant differences were observed in LV End-diastolic Volume (LVEDV), LV Endsystolic Volume (LVESV), LV Ejection Fraction (LVEF), LV End-diastolic Myocardial Mass (LVMASSED), and Cardiac Output (CO) before and after modeling. Regarding LV global strain parameters, there was a downward trend in GRS (25.35% ± 6.9% vs. 22.86% ± 6.4%, P=0.021), GCS (-16.71% ± 4.2% vs. -14.78% ± 2.3%, P=0.043), and GLS (-17.66% ± 2.9% vs. -14.53% ± 2.5%, P=0.056), respectively, after modeling. GRS and GCS were significantly reduced after modeling compared to baseline.
Conclusion: The study suggests that depression may contribute to early LV systolic dysfunction, particularly affecting LV GCS and GRS.
Keywords: Depression, Cardiovascular magnetic resonance, Feature-tracking, Swine, Heart failure, Arrhythmia, Myocardial infarction.
Call for Papers in Thematic Issues
Deep Learning Approaches for Image Enhancement in Medical Diagnostics
The medical and healthcare sectors are very different from one another. Consumers in these sectors need more affordable, superior offerings. Health or medical specialists are typically the ones who analyse medical data. In particular, medical imaging evaluation heavily relies on machine learning and deep learning. They not only yield outstanding ...read more
Emerging Biomedical Imaging Informatic for Smart Healthcare
Biomedical imaging informatics is a field that deals with the acquisition, processing, and interpretation of images in biomedicine using computational approaches. Biomedical imaging has progressed from early, simple X-rays for fracture diagnosis and foreign body identification to various techniques used to research biological structure and function and answer fundamental biomedical ...read more
Emerging Trends & Techniques in Medical Imaging using Machine Learning and Deep Learning Techniques
This special issue will aim to discuss different approaches, initiatives and applications in Medical Imaging fields (including Machine Intelligence, Mining Engineering, Modeling and Simulation, Computer, Communication, Networking and Information Engineering, Systems Engineering, Innovative Computing Systems, Adaptive Technologies for Sustainable Growth, and Theoretical and Applied Sciences). This collection should inspire various ...read more
Multimodal Imaging Using Spectroscopic Techniques for Improved Surgical Guidance
The majority of patients with solid malignancies have surgical excision as their primary and most successful method of medical care. On the other hand, patients experience recurrence and subsequent recurrence. Their objective was to provide a simpler and more concise explanation of the obstacles faced by physicians and the ways ...read more
10