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  Heart-brain coupling: Resting heart rate variability is associated with network architecture in the resting brain

Kumral, D., Beyer, F., Husser, D., Schroeter, M. L., Loeffler, M., Witte, A. V., et al. (2017). Heart-brain coupling: Resting heart rate variability is associated with network architecture in the resting brain. Journal of Cerebral Blood Flow and Metabolism, 37(Suppl. 1): PS05-070, 420-421. doi:10.1177/0271678X17695986.

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Item Permalink: http://hdl.handle.net/21.11116/0000-0004-DB6D-C Version Permalink: http://hdl.handle.net/21.11116/0000-0004-DB6E-B
Genre: Meeting Abstract

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 Creators:
Kumral, Deniz1, Author              
Beyer, Frauke1, Author              
Husser, D.2, Author
Schroeter, Matthias L.1, Author              
Loeffler, M.2, Author
Witte, A. Veronica1, Author              
Villringer, Arno1, Author              
Gaebler, Michael1, Author              
Affiliations:
1Department Neurology, MPI for Human Cognitive and Brain Sciences, Max Planck Society, ou_634549              
2External Organizations, ou_persistent22              

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 Abstract: Heart rate variability (HRV) is an index for parasympathetic cardioregulation. Resting HRV is an individual trait marker and has been associated with physiological and psychological well-being (Thayer et al., 2012). The aim of this study was to investigate the relationship between HRV as a proxy for parasympathetic cardioregulation and brain connectivity over the adult lifespan. While temporal fluctuations in HRV and RSFC have been associated in previous studies (Chang et al., NeuroImage 2013), we treated average values of these measures as trait markers. We used two data sets: Sample 1: 273 healthy subjects (young: 28 ± 4 y, middle: 47 ± 6 y, old: 67 ± 5 y) from a large cohort study in Leipzig (Löffler et al., BMC Pub Health 2015) and for confirmatory analysis (Sample 2) 53 young subjects (24 ± 3 y) from an independent dataset in whom ECG and resting state fMRI were available. We calculated the root mean square of successive differences (RMSSD) of inter beat intervals as an index of trait HRV. RSFC was analyzed using eigenvector centrality mapping that captures neural connectivity at a voxel-level (Lohmann et al., PLOS One 2010). In Sample 1, HRV strongly decreased with age. Higher HRV was associated with increased centrality in right posterior cingulate cortex (PCC) in all age groups and in bilateral ventromedial prefrontal cortex (vmPFC) in young subjects only. In sample 2, we confirmed the vmPFC finding in young subjects (r = 0.24, p < 0.05). These regions have previously been associated with fluctuations in HRV and largely overlap with the default-mode network (Beissner et al., J Neurosci 2013); specifically vmPFC has been shown to modulate the vagal efferent outflow to the heart (Wong et al., NeuroImage 2007). Our finding that vmPFC only is related to HRV in young subjects supports the view that the well-known decrease of HRV with aging is related to attenuated central cardiovagal control.

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Language(s): eng - English
 Dates: 2017-04-01
 Publication Status: Published online
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 Identifiers: DOI: 10.1177/0271678X17695986
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Title: Journal of Cerebral Blood Flow and Metabolism
Source Genre: Journal
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Publ. Info: New York : Lippincott Williams & Wilkins
Pages: - Volume / Issue: 37 (Suppl. 1) Sequence Number: PS05-070 Start / End Page: 420 - 421 Identifier: ISSN: 0271-678X
CoNE: https://pure.mpg.de/cone/journals/resource/954925503202