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Myocardial infarction (MI) induces neuroinflammation indirectly, chronic neuroinflammation may cause neurodegenerative diseases. Changes in the proteomics of heart and brain tissue after MI may shed new light on the mechanisms involved in neuroinflammation. This study explored brain and heart protein changes after MI with a data-independent acquisition (DIA) mode proteomics approach. Permanent ligation of the left anterior descending coronary artery (LAD) was performed in the heart of rats, and the immunofluorescence of microglia in the brain cortex was performed at 1d, 3d, 5d, and 7d after MI to detect the neuroinflammation. Then proteomics was accomplished to obtain the vital proteins in the heart and brain post-MI. The results show that the number of microglia was significantly increased in the Model-1d group, the Model-3d group, the Model-5d group, and the Model-7d group compared to the Sham group. Various proteins were obtained through DIA proteomics. Linking to key targets of brain disease, 14 proteins were obtained in the brain cortex. Among them, elongation of very long chain fatty acids protein 5 (ELOVL5) and ATP-binding cassette subfamily G member 4 (ABCG4) were verified through western blotting (WB). The results of WB were consistent with the proteomics results. Therefore, these proteins may be related to the pathogenesis of neuroinflammation after MI. [ABSTRACT FROM AUTHOR]

While for a long time in human history it was assumed that the heart was important for emotional and cognitive functions, in today's "rational" world the role of the heart is often reduced to its function as a pump that supplies the body with blood. However, more and more studies are showing that the heart actually contributes to daily cognitive and emotional functions. Evidence is accumulating that much of cognition and emotion - perhaps all - represent integrated (body) cardioneural states and as such are constantly dependent on an intact heart-brain interaction (HBI). I will show how even simple perceptual tasks are dependent on HBI and also on breathing. It follows that any disturbance of the HBI will alter our emotions and cognition. Such disturbances can be caused, for example, by medication and recreational drugs. Long-term and/or repeated psychosocial stress can also have a negative impact on HBI. In particular, I will outline how long-term changes in HBI could be of great importance for the development of cardiovascular diseases such as hypertension and cardiac arrhythmias, as well as mental disorders such as anxiety and depression. [ABSTRACT FROM AUTHOR]

Some of the most important integrative control centers for the autonomic nervous system are located in the brainstem and the hypothalamus. However, growing recent neuroimaging evidence support that a set of cortical regions, named the central autonomic network (CAN), is involved in autonomic control and seems to play a major role in continuous autonomic cardiac adjustments to high-level emotional, cognitive or sensorimotor cortical activities. Intracranial explorations during stereo-electroencephalography (SEEG) offer a unique opportunity to address the question of the brain regions involved in heart-brain interaction, by studying: (i) direct cardiac effects produced by the electrical stimulation of specific brain areas; (ii) epileptic seizures inducing cardiac modifications; (iii) cortical regions involved in cardiac interoception and source of cardiac evoked potentials. In this review, we detail the available data assessing cardiac central autonomic regulation using SEEG, address the strengths and also the limitations of this technique in this context, and discuss perspectives. The main cortical regions that emerge from SEEG studies as being involved in cardiac autonomic control are the insula and regions belonging to the limbic system: the amygdala, the hippocampus, and the anterior and mid-cingulate. Although many questions remain, SEEG studies have already demonstrated afferent and efferent interactions between the CAN and the heart. Future studies in SEEG should integrate these afferent and efferent dimensions as well as their interaction with other cortical networks to better understand the functional heart-brain interaction. [ABSTRACT FROM AUTHOR]

• Statistical modelling of brain-heart interaction by including prior cardiac physiological knowledge. • Biophysical modelling constrained by a model of joint heart-brain variability. • The model identifies physiologically plausible mechanisms and significant differences associated to clinical conditions. • We present the first study on patient-specific modelling relating cardiac function and brain damage. • This study is based on the largest presented cohort of personalised subjects to date (3445 individuals). [Display omitted] Initiatives such as the UK Biobank provide joint cardiac and brain imaging information for thousands of individuals, representing a unique opportunity to study the relationship between heart and brain. Most of research on large multimodal databases has been focusing on studying the associations among the available measurements by means of univariate and multivariate association models. However, these approaches do not provide insights about the underlying mechanisms and are often hampered by the lack of prior knowledge on the physiological relationships between measurements. For instance, important indices of the cardiovascular function, such as cardiac contractility, cannot be measured in-vivo. While these non-observable parameters can be estimated by means of biophysical models, their personalisation is generally an ill-posed problem, often lacking critical data and only applied to small datasets. Therefore, to jointly study brain and heart, we propose an approach in which the parameter personalisation of a lumped cardiovascular model is constrained by the statistical relationships observed between model parameters and brain-volumetric indices extracted from imaging, i.e. ventricles or white matter hyperintensities volumes, and clinical information such as age or body surface area. We explored the plausibility of the learnt relationships by inferring the model parameters conditioned on the absence of part of the target clinical features, applying this framework in a cohort of more than 3 000 subjects and in a pathological subgroup of 59 subjects diagnosed with atrial fibrillation. Our results demonstrate the impact of such external features in the cardiovascular model personalisation by learning more informative parameter-space constraints. Moreover, physiologically plausible mechanisms are captured through these personalised models as well as significant differences associated to specific clinical conditions. [ABSTRACT FROM AUTHOR]