Skip to main content
Key Specialties
Cardiology Diabetology Endocrinology Gastroenterology Geriatrics and Gerontology Gynecology and Obstetrics Hematology Infectious Disease Internal Medicine Nephrology Neurology Oncology Pediatrics Respiratory Medicine Rheumatology Surgery
Congress Coverage
2024 ACC Congress 2023 ESMO Congress 2023 EASD Congress 2023 ERS Congress 2023 ESC Congress
Clinical Collections
Advances in Alzheimer’s

Keynote webinar | Spotlight on medication adherence

LIVE: Thursday 27th June 2024, 18:00-19:30 (CEST)
Join our expert panel to discover why you need to understand the drivers of non-adherence in your patients, and how you can optimize medication adherence in your clinics to drastically improve patient outcomes.
ADVANCED SEARCH
Log in
Top
BMC Neurology
Published in: BMC Neurology 1/2013

Open Access 01-12-2013 | Research article

Visual pursuit response in the severe disorder of consciousness: modulation by the central autonomic system and a predictive model

Authors: Francesco Riganello, Maria D Cortese, Giuliano Dolce, Walter G Sannita

Published in: BMC Neurology | Issue 1/2013

Login to get access

Abstract

Background

A visual pursuit response is reportedly observed in ~20-30% of subjects in vegetative state (VS/UWS) and predicts better outcome; it is a key marker of evolution into the minimally conscious state (MCS). The probability of observing a positive response, however, has proven variable during the day, with comparable timing of the minima and maxima in VS/UWS and MCS. We verified if measures of sympathetic/parasympathetic balance are possible independent variables on which the occurrence of a pursuit response could depend and be predicted.

Methods

Fourteen subjects in VS/UWS and sixteen in MCS for more than one year were studied. A mirror was used to test the pursuit response for a total 231 useful trials. Non-invasive measures of the sympathetic/parasympathetic functional state (Heart rate variability descriptors nuLF and peakLF) used in the study of responsiveness in VS/UWS and MCS subjects were recorded and processed by descriptive statistics and advanced Support Vector Machine (SVM).

Results

A pursuit response was observed in 33% and 78.2% of subjects in VS or MCS, respectively. Incidence was higher at HRV nuLF values in the 20–60 range and peakLF values at 0.06-0.12 Hz (76.6%) and at nuLF values in the 10–60 range and peakLF values at 0.05-0.10 Hz (80.7%) in the VS and MCS, respectively. The SVM generated model confirmed the results in the training leave one out and 10 fold cross validation tests (81% and 81.4%).

Conclusion

The pursuit response incidence depends to a relevant extent on the sympathetic/parasympathetic balance and autonomic functional state. Extensive monitoring appears advisable.
Appendix
Available only for authorised users
Literature
1.
Laureys S, Celesia GG, Cohadon F, Lavrijsen J, Leòn-Carriòn J, Sannita WG, Sazbon L, Schmutzhard E, Von Wild KR, Zeman A, et al: Unresponsive wakefulness syndrome: a new name for the vegetative state or apallic syndrome. BMC Med. 2010, 8: 68-10.1186/1741-7015-8-68.CrossRefPubMedPubMedCentral
2.
Giacino JT, Kalmar K: Diagnostic and prognostic guidelines for the vegetative and minimally conscious states. Neuropsychol Rehabil. 2005, 15 (3–4): 166-174.CrossRefPubMed
3.
Bosco A, Lancioni GE, Olivetti Belardinelli M, Singh NN, O’Reilly MF, Sigafoos J: Vegetative state: efforts to curb misdiagnosis. Cogn Process. 2010, 11 (1): 87-90. 10.1007/s10339-009-0355-y.CrossRefPubMed
4.
Candelieri A, Cortese MD, Dolce G, Riganello F, Sannita WG: Visual Pursuit: Within-Day variability in the severe order of consciousness. J Neurotrauma. 2011, 28 (10): 2013-2017. 10.1089/neu.2011.1885.CrossRefPubMed
5.
Riganello F, Candelieri A, Quintieri M, Conforti D, Dolce G: Heart rate variability: An index of brain processing in vegetative state? An artificial intelligence, data mining study. Clin Neurophysiol. 2010, 121 (12): 2024-2034. 10.1016/j.clinph.2010.05.010.CrossRefPubMed
6.
Riganello F, Candelieri A, Dolce G, Sannita WG: Residual emotional processing in the vegetative state: A scientific issue?. Clin Neurophisiol. 2011, 122 (5): 1061-1062. 10.1016/j.clinph.2010.09.006.CrossRef
7.
Riganello F, Sannita WG: Residual Brain Processing in the Vegetative State. J Psychophysiol. 2009, 23 (1): 18-26. 10.1027/0269-8803.23.1.18.CrossRef
8.
Monti MM, Vanhaudenhuyse A, Coleman MR, Boly M: Willful Modulation of Brain Activity in Disorders of Consciousness. N Engl J Med. 2010, 18 (7): 579-589.CrossRef
9.
Owen AM, Coleman MR: Functional neuroimaging of the vegetative state. Nat Rev Neurosci. 2008, 9: 235-243. 10.1038/nrn2330.CrossRefPubMed
10.
Appelhans BM, Luecken LJ: Heart rate variability and pain: associations of two interrelated homeostatic processes. Biol Psychol. 2008, 77 (2): 174-182. 10.1016/j.biopsycho.2007.10.004.CrossRefPubMed
11.
Berntson GG, Cacioppo JT: Heart rate variability: A neuroscientific perspective for further studies. Card Electrophysiol Rev. 1999, 3: 279-282. 10.1023/A:1009920002142.CrossRef
12.
Lombardi F: Clinical implications of present physiological understanding of HRV components. Card Electrophysiol Rev. 2002, 6 (3): 245-249. 10.1023/A:1016329008921.CrossRefPubMed
13.
Riganello F, Dolce G, Sannita WG: Heart rate variability and the central autonomic network in the severe disorder of consciousness. J Rehabil Med. 2012, 44 (6): 495-501. 10.2340/16501977-0975.CrossRefPubMed
14.
Borer-Alafi N, Gil M, Sazbon L, Korn C: Loewenstein communication scale for the minimally responsive patient. Brain Inj. 2002, 16 (7): 593-609. 10.1080/02699050110119484.CrossRefPubMed
15.
Hagen C, Malkmus D, Durham P: Levels of cognitive functioning. Rehabilitation of the head injured adult: comprehensive physical management. Edited by: Professional Staff Association of Rancho Los Amigos H. 1987, Downey CA: Rancho Los Amigos Hospital Inc
16.
Rappaport M, Hall KM, Hopkins K, Belleza T, Cope DN: Disability rating scale for severe head trauma: coma to community. Arch Phys Med Rehabil. 1982, 63 (3): 118-123.PubMed
17.
Giacino JT, Kalmar K, Whyte J: The JFK Coma Recovery Scale-Revised: measurement characteristics and diagnostic utility. Arch Phys Med Rehabil. 2004, 85 (12): 2020-2029. 10.1016/j.apmr.2004.02.033.CrossRefPubMed
18.
Selvara N, Jaryal A, Santhosh J, Deepak KK, Anand S: Assessment of heart rate variability derived from finger-tip photoplethysmography as compared to electrocardiography. J med egin technol. 2008, 32 (6): 479-484. 10.1080/03091900701781317.CrossRef
19.
Candelieri A, Cortese MD, Riganello F, Sannita WG: Functional Status And The Eye-Tracking Response. A Data Mining Classification Study in the Vegetative and Minimaly Conscious States. 2011, Rome: In HeathInf Proceeding: 2011, 138-141.
20.
Task Force of the European Society of Cardiology the North American Society of Pacing E: Heart Rate Variability Standards of Measurement, Physiological Interpretation, and Clinical Use. Circulation. 1996, 93: 1043-1065. 10.1161/01.CIR.93.5.1043.CrossRef
21.
Niskanen JP, Tarvainen MP, Ranta-aho PO, Karjalainen PA: Software for Advanced HRV Analysis. Com Methods Prog Biomed. 2004, 76 (1): 73-81. 10.1016/j.cmpb.2004.03.004.CrossRef
22.
Han J, Kamber M: Data mining: concepts and techniques. 2006, San Francisco: Department of Computer Science, University of Illinois at Urbana-Champaign. Morgan Kaufmann
23.
Van Bemmel JH, Munsen MA: Handbook of medical informatics. 1997, Berlin: Springer-Verlag
24.
Burr RL: Interpretation of normalized spectral heart rate variability indices in sleep research: a critical review. Sleep. 2007, 30 (7): 913-919.PubMedPubMedCentral
25.
Nickel P, Nachreiner F: Sensitivity and diagnosticity of the 0.1-Hz component of heart rate variability as an indicator of mental workload. Hum Factors. 2003, 45 (4): 575-590. 10.1518/hfes.45.4.575.27094.CrossRefPubMed
26.
Peng HC, Long F, Ding C: Feature selection based on mutual information: criteria of max- dependency, max-relevance, and min-redundancy. EEE Transactions on Pattern Analysis and Machine Intelligence. 2005, 27 (8): 1226-1238.CrossRef
27.
Cristianini N, Shawe-Taylor J: An introduction to Support Vector Machines: And Other Kernel-Based Learning Methods. 2000, Cambridge, England: Cambridge University PressCrossRef
28.
Noble WS: What is a support vector machine?. Nat Biotechnol. 2006, 24 (12): 1565-1567. 10.1038/nbt1206-1565.CrossRefPubMed
29.
Kampouraki A, Nikou C, Manis G: Robustness of Support Vector Machine-based Classification of Heart Rate Signals. 2005, New York City, USA: In Proceedings of the 28th IEEE EMBS, Annual International Conference: 2005
30.
Kohavi R: A study of cross-validation and bootstrap for accuracy estimation and model selection Artificial Intelligence. Proceeding of the fourteenth International Joint Conference. 1995, 2 (12): 1137-1143.
31.
Buhmann MD: Radia Bass Function. Acta Numerica. 2000, 9: 1-38. 10.1017/S0962492900000015.CrossRef
32.
Crottaz-Herbette S, Menon V: Where and when the anterior cingulate cortex modulates attentional response: combined fMRI and ERP evidence. J Cogn Neurosci. 2006, 18 (5): 766-780. 10.1162/jocn.2006.18.5.766.CrossRefPubMed
33.
Thayer JF, Lane RD: Claude Bernard and the heart-brain connection: further elaboration of a model of neurovisceral integration. Neurosci Biobehav Rev. 2009, 33 (2): 81-88. 10.1016/j.neubiorev.2008.08.004.CrossRefPubMed
34.
Krauzlis RJ: Neuronal activity in the rostral superior colliculus related to the initiation of pursuit and saccadic eye movements. J Neurosci. 2003, 23 (10): 4333-4344.PubMed
35.
Bruno MA, Vanhaudenhuyse A, Schnakers C, Boly M, Gosseries O, Demertzi A, Majerus S, Moonen G, Hustinx R, Laureys S: Visual fixation in the vegetative state: an observational case series PET study. BMC Neurol. 2010, 10: 35-10.1186/1471-2377-10-35.CrossRefPubMedPubMedCentral
36.
Giacino JT: The vegetative and minimally conscious states: consensus-based criteria for establishing diagnosis and prognosis. NeuroRehabilitation. 2004, 19 (4): 293-298.PubMed
37.
Multi-Society Task Force on PVS: Statement on medical aspects of the persistent vegetative state. N Engl J Med. 1994, 330: 1499-1508. and 1572–79CrossRef
38.
Giacino JT, Hirsch J, Schiff N, Laureys S: Functional neuroimaging applications for assessment and rehabilitation planning in patients with disorders of consciousness. Arch Phys Med Rehabil. 2006, 87 (12 Suppl 2): S67-S76.CrossRefPubMed
39.
Sannita WG: Individual variability, end-point effects and possible biases in electrophysiological research. Clin Neurophysiol. 2006, 117 (12): 2569-2583. 10.1016/j.clinph.2006.04.026.CrossRefPubMed
40.
Racine E, Amaram R, Seidler M, Karczewska M, Illes J: Media coverage of the persistent vegetative state and end-of-life decision-making. Neurology. 2008, 71 (13): 1027-1032. 10.1212/01.wnl.0000320507.64683.ee.CrossRefPubMedPubMedCentral
Metadata
Title
Visual pursuit response in the severe disorder of consciousness: modulation by the central autonomic system and a predictive model
Authors
Francesco Riganello
Maria D Cortese
Giuliano Dolce
Walter G Sannita
Publication date
01-12-2013
Publisher
BioMed Central
Published in
BMC Neurology / Issue 1/2013
Electronic ISSN: 1471-2377
DOI
https://doi.org/10.1186/1471-2377-13-164

Other articles of this Issue 1/2013

BMC Neurology 1/2013 Go to the issue

Case report

Encephalopathic Susac’s Syndrome associated with livedo racemosa in a young woman before the completion of family planning

Research article

Plaque morphology detected with Duplex ultrasound before carotid angioplasty and stenting (CAS) is not a predictor of carotid artery in-stent restenosis, a case control study

Research article

Trigger medications and patient-related risk factors for Parkinson disease psychosis requiring anti-psychotic drugs: a retrospective cohort study

Research article

The role of IFITM3 in the growth and migration of human glioma cells

Research article

Jugular venous reflux and brain parenchyma volumes in elderly patients with mild cognitive impairment and Alzheimer’s disease

Research article

Mental comorbidity and multiple sclerosis: validating administrative data to support population-based surveillance