A Heartbeat Away From Consciousness: Heart Rate Variability Entropy can discriminate disorders of consciousness and is correlated with resting-state fMRI brain connectivity of the Central Autonomic Network

ObjectiveWe investigated longitudinal trajectories of resting-state fMRI (rsfMRI), autonomic function, and graded symptoms after sport-related concussion (SRC).BackgroundLimbic circuitry may be particularly vulnerable to traumatic brain injury, which could explain the affective and autonomic dysfunction that some patients develop. Relatively few studies have performed longitudinal rsfMRI analyses in concussion and fewer have combined imaging with autonomic and symptom data. We leveraged published limbic rsfMRI networks centered on the amygdala that include core affective and autonomic structures to test whether athletes with SRC would have altered connectivity, and that network recovery would be related to measures of autonomic function and symptom persistence.Design/MethodsWe compared rsfMRI connectivity of amygdala networks in college athletes with SRC (N = 31, female = 14) at three time points after concussion (T1 = 4 days, T2 = 10–14 days, T3 = 2–3 months) and matched controls with no concussion (in-sport control [ISC] N = 36, female = 17).ResultsSRCs show greater amygdala network connectivity as compared to ISCs (T1 p = 0.003, T2 p = 0.014) that normalizes over time (T3 p = 0.182). However, SRCs with higher versus lower heart rate variability (HRV), as measured by pNN50 at T1, have opposing trajectories of connectivity. That is, SRCs with higher HRV have connectivity that starts high and normalizes over time (T1 p = 0.001, T2 p = 0.055, T3 p = 0.576) whereas SRCs with lower HRV have connectivity that increases over time (T1 p = 0.429, T2 p = 0.050, T3 p = 0.002). Furthermore, SRCs with greatest connectivity at T3, presumably the least recovered, have the most symptoms on the Graded Symptom Checklist at ∼3 months (r = 0.635, p = 0.001).ConclusionsHeightened connectivity of amygdala circuitry acutely after a concussion and its normalization over time may be protective, and with HRV, may be a biomarker of symptom persistence.

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The Influence of Heart Rate Variability Biofeedback on Cardiac Regulation and Functional Brain Connectivity

Frontiers in Neuroscience ◽

10.3389/fnins.2021.691988 ◽

2021 ◽

Vol 15 ◽

Author(s):

Andy Schumann ◽

Feliberto de la Cruz ◽

Stefanie Köhler ◽

Lisa Brotte ◽

Karl-Jürgen Bär

Keyword(s):

Heart Rate ◽

Heart Rate Variability ◽

Functional Connectivity ◽

Resting State ◽

Brain Connectivity ◽

Cingulate Cortex ◽

Brain Regions ◽

Control Group ◽

Functional Brain ◽

Middle Cingulate Cortex

BackgroundHeart rate variability (HRV) biofeedback has a beneficial impact on perceived stress and emotion regulation. However, its impact on brain function is still unclear. In this study, we aimed to investigate the effect of an 8-week HRV-biofeedback intervention on functional brain connectivity in healthy subjects.MethodsHRV biofeedback was carried out in five sessions per week, including four at home and one in our lab. A control group played jump‘n’run games instead of the training. Functional magnetic resonance imaging was conducted before and after the intervention in both groups. To compute resting state functional connectivity (RSFC), we defined regions of interest in the ventral medial prefrontal cortex (VMPFC) and a total of 260 independent anatomical regions for network-based analysis. Changes of RSFC of the VMPFC to other brain regions were compared between groups. Temporal changes of HRV during the resting state recording were correlated to dynamic functional connectivity of the VMPFC.ResultsFirst, we corroborated the role of the VMPFC in cardiac autonomic regulation. We found that temporal changes of HRV were correlated to dynamic changes of prefrontal connectivity, especially to the middle cingulate cortex, the left insula, supplementary motor area, dorsal and ventral lateral prefrontal regions. The biofeedback group showed a drop in heart rate by 5.2 beats/min and an increased SDNN as a measure of HRV by 8.6 ms (18%) after the intervention. Functional connectivity of the VMPFC increased mainly to the insula, the amygdala, the middle cingulate cortex, and lateral prefrontal regions after biofeedback intervention when compared to changes in the control group. Network-based statistic showed that biofeedback had an influence on a broad functional network of brain regions.ConclusionOur results show that increased heart rate variability induced by HRV-biofeedback is accompanied by changes in functional brain connectivity during resting state.

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The influence of heart rate variability biofeedback on cardiac regulation and functional brain connectivity

10.1101/2020.08.10.243998 ◽

2020 ◽

Author(s):

Andy Schumann ◽

Feliberto de la Cruz ◽

Stefanie Köhler ◽

Lisa Brotte ◽

Karl-Jürgen Bär

Keyword(s):

Heart Rate ◽

Heart Rate Variability ◽

Prefrontal Cortex ◽

Functional Connectivity ◽

Resting State ◽

Brain Connectivity ◽

Brain Regions ◽

Anterior Insula ◽

Control Group ◽

Functional Brain

AbstractBackgroundHeart rate variability (HRV) biofeedback has a beneficial impact on perceived stress and emotion regulation. However, its impact on brain function is still unclear. In this study, we aimed to investigate the effect of an 8-week HRV-biofeedback intervention on functional brain connectivity in healthy subjects.MethodsHRV biofeedback was carried out in five sessions per week, including four at home and one in our lab. A control group played jump‘n’run games instead of the training. Functional magnetic resonance imaging was conducted before and after the intervention in both groups. To compute resting state functional connectivity (RSFC), we defined regions of interest in the ventral medial prefrontal cortex (VMPFC) and a total of 260 independent anatomical regions for network-based analysis. Changes of RSFC of the VMPFC to other brain regions were compared between groups. Temporal changes of HRV during the resting state recording were correlated to dynamic functional connectivity of the VMPFC.ResultsFirst, we corroborated the role of the VMPFC in cardiac autonomic regulation. We found that temporal changes of HRV were correlated to dynamic changes of prefrontal connectivity, especially to the middle cingulate cortex, left anterior insula, right amygdala, supplementary motor area, dorsal and ventral lateral prefrontal regions. The biofeedback group showed a drop in heart rate by 5.5 beats/min and an increased RMSSD as a measure of HRV by 10.1ms (33%) after the intervention. Functional connectivity of the VMPFC increased mainly to the right anterior insula, the dorsal anterior cingulate cortex and the dorsolateral prefrontal cortex after biofeedback intervention when compared to changes in the control group. Network-based statistic showed that biofeedback had an influence on a broad functional network of brain regions.ConclusionOur results show that increased vagal modulation induced by HRV-biofeedback is accompanied by changes in functional brain connectivity during resting state.

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Tracking whole-brain connectivity dynamics in the resting-state fMRI with post-facial paralysis synkinesis

Brain Research Bulletin ◽

10.1016/j.brainresbull.2021.04.025 ◽

2021 ◽

Author(s):

Zhen-Zhen Ma ◽

Jia-Jia Wu ◽

Xu-Yun Hua ◽

Mou-Xiong Zheng ◽

Xiang-Xin Xing ◽

...

Keyword(s):

Facial Paralysis ◽

Resting State ◽

Brain Connectivity ◽

Resting State Fmri ◽

Whole Brain

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Heart rate variability and the central autonomic network in the severe disorder of consciousness

Journal of Rehabilitation Medicine ◽

10.2340/16501977-0975 ◽

2012 ◽

Vol 44 (6) ◽

pp. 495-501 ◽

Cited By ~ 24

Author(s):

F Riganello ◽

G Dolce ◽

W Sannita

Keyword(s):

Heart Rate ◽

Heart Rate Variability ◽

Disorder Of Consciousness ◽

Central Autonomic Network ◽

Severe Disorder

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Resting-state brain activity can predict target-independent aptitude in fMRI-neurofeedback training

10.1101/2021.02.08.430334 ◽

2021 ◽

Author(s):

Takashi Nakano ◽

Masahiro Takamura ◽

Haruki Nishimura ◽

Maro Machizawa ◽

Naho Ichikawa ◽

...

Keyword(s):

Resting State ◽

Brain Connectivity ◽

Brain Activity ◽

Resting State Fmri ◽

Brain Regions ◽

Posterior Cingulate Cortex ◽

Fmri Data ◽

Independent Test ◽

The Individual ◽

The Brain

AbstractNeurofeedback (NF) aptitude, which refers to an individual’s ability to change its brain activity through NF training, has been reported to vary significantly from person to person. The prediction of individual NF aptitudes is critical in clinical NF applications. In the present study, we extracted the resting-state functional brain connectivity (FC) markers of NF aptitude independent of NF-targeting brain regions. We combined the data in fMRI-NF studies targeting four different brain regions at two independent sites (obtained from 59 healthy adults and six patients with major depressive disorder) to collect the resting-state fMRI data associated with aptitude scores in subsequent fMRI-NF training. We then trained the regression models to predict the individual NF aptitude scores from the resting-state fMRI data using a discovery dataset from one site and identified six resting-state FCs that predicted NF aptitude. Next we validated the prediction model using independent test data from another site. The result showed that the posterior cingulate cortex was the functional hub among the brain regions and formed predictive resting-state FCs, suggesting NF aptitude may be involved in the attentional mode-orientation modulation system’s characteristics in task-free resting-state brain activity.

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