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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

Francesco Riganello1, 2, Stephen K. Larroque1*, Mohamed Ali Bahri3, Lizette Heine4, Charlotte Martial1, Manon Carrière1, Vanessa Charland-Verville1, Charlène Aubinet1, Audrey Vanhaudenhuyse5, Camille Chatelle1, Steven Laureys1 and Carol Di Perri1, 6
  • 1 Coma Science Group, University of Liège, Belgium
  • 2 Ricerca in Advanced NeuroRehabilitation, Istituto S. Anna, Italy
  • 3 GIGA-Cyclotron Research Center In Vivo Imaging, University of Liege, Belgium
  • 4 INSERM U1028 Centre de Recherche en Neurosciences de Lyon, France
  • 5 Sensation & Perception Research Group, GIGA-Consciousness, University hospital of Liege, Belgium
  • 6 Centre for Clinical Brain Sciences (CCBS), University of Edinburgh, United Kingdom

Motivation: Heart rate variability (HRV) reflects the heart-brain two-way dynamic interactions[1-5]. HRV entropy analysis quantifies the unpredictability and complexity of the heart rate beats intervals and over multiple time scales using multiscale entropy (MSE)[6-8]. The complexity index (CI) provides a score of a system’s complexity by aggregating the MSE measures over a range of time scales[8]. Most HRV entropy studies have focused on acute traumatic patients using task-based designs[9]. We here investigate the CI and its discriminative power in chronic patients with unresponsive wakefulness syndrome (UWS) and minimally conscious state (MCS) at rest, and its relation to brain functional connectivity. Methods: We investigated the CI in short (CIs) and long (CIl) time scales in 16 UWS and 17 MCS sedated. CI for MCS and UWS groups were compared using a Mann-Whitney exact test. Spearman’s correlation tests were conducted between the Coma Recovery Scale-revised (CRS-R) and both CI. Discriminative power of both CI was assessed with One-R machine learning model. Correlation between CI and brain connectivity (detected with functional magnetic resonance imagery using seed-based and hypothesis-free intrinsic connectivity) was investigated using a linear regression in a subgroup of 12 UWS and 12 MCS patients with sufficient image quality. Results and Discussion: Significant differences were found between MCS and UWS for CIs and CIl (0.0001≤p≤0.006). Significant correlations were found between CRS-R and CIs and CIl (0.0001≤p≤0.026). The One-R classifier selected CIl as the best discriminator between UWS and MCS with 85% accuracy, 19% false positive rate and 12% false negative rate after a 10-fold cross-validation test. Positive correlations were observed between CI and brain areas belonging to the autonomic system. CI was found to be significantly higher in MCS compared to UWS patients, with high discriminative power and lower false negative rate than the reported misdiagnosis rate of human assessors, providing an easy, inexpensive and non-invasive diagnosis tool. CI is correlated to functional connectivity changes in brain regions belonging to the autonomic nervous system, suggesting that CI can provide an indirect way to screen and monitor connectivity changes in this neural system. Future studies should investigate further the extent of CI’s predictive power for other pathologies in the disorders of consciousness spectrum.

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Acknowledgements

This research was supported by: University and Hospital of Liège, F.R.S.-F.N.R.S., French Speaking Community Concerted Research Action (ARC 12-17/01), Center-TBI (FP7-HEALTH- 602150), Human Brain Project (EU-H2020-fetflagship-hbp-sga1-ga720270), Luminous project (EU-H2020-fetopen-ga686764), Mind Science Foundation, IAP research network P7/06 of the Belgian Government (Belgian Science Policy), Belgian National Plan Cancer (139), European Space Agency, Belspo and European Commission.

References

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Keywords: disorders of consciousness (DOC), fMRI — functional magnetic resonance imaging, ECG, heart rate variability (HRV) analysis, machine learning (artificial intelligence), unresponsive wakefulness syndrome (UWS), minimally conscious state (MCS), Central autonomic network, coma recovery scale-revised (CRS-R)

Conference: Belgian Brain Congress 2018 — Belgian Brain Council, LIEGE, Belgium, 19 Oct - 19 Oct, 2018.

Presentation Type: e-posters

Topic: NOVEL STRATEGIES FOR NEUROLOGICAL AND MENTAL DISORDERS: SCIENTIFIC BASIS AND VALUE FOR PATIENT-CENTERED CARE

Citation: Riganello F, Larroque SK, Bahri M, Heine L, Martial C, Carrière M, Charland-Verville V, Aubinet C, Vanhaudenhuyse A, Chatelle C, Laureys S and Di Perri C (2019). 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. Front. Neurosci. Conference Abstract: Belgian Brain Congress 2018 — Belgian Brain Council. doi: 10.3389/conf.fnins.2018.95.00018

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Received: 30 Jul 2018; Published Online: 17 Jan 2019.

* Correspondence: Mr. Stephen K Larroque, Coma Science Group, University of Liège, Liège, Liège, 4000, Belgium, lrq3000@gmail.com