Complex graph theory; Disorders of Consciousness; Global efficiency; Resting state networks; Brain; Diagnosis; Efficiency; Graph theory; Graphic methods; Altered states of consciousness; Clustering coefficient; Communication quality; Information efficiency; Loss of consciousness; Resting state; Complex networks
Abstract :
[en] Disorders of consciousness (DOC) is a consequence of severe brain injuries. DOC diagnosis is quite challenging because it may require patient collaboration. Investigations of brain activity in resting conditions propose that healthy brain is organized into large-scale resting state networks (RSNs) of sensory/cognitive relevance. The complete set of RSN together with their corresponding interaction induce a functional network of brain connectivity (FNC). Recently, the connectivity pattern between pairs of RSNs have been explored as biomarker of loss of consciousness. The role of this FNC in the DOC conditions remains poorly understood. In this work, we propose to use a network analysis method to explore complex properties of the functional brain network induced by the connectivity among RSNs. In particular, we aim to characterize the communication quality among network nodes, which have been suggested to be linked to altered states of consciousness. The proposed approach was evaluated on a population of 27 healthy controls and 49 subjects with DOC conditions. fMRI data was obtained and processed for each subject to built a FNC at individual level. The communication quality among network nodes was quantified by using global efficiency, average characteristic path, diameter, radius, average strength and average clustering coefficient. Our results suggests that the information efficiency transfer at the global level decrease with the level the severity of the loss of consciousness condition. These results highlight the importance of graph based features to characterize brain complexity, and in particular, complex phenomena as consciousness emergence. In addition, our results can be potentially used in the development of novel methods to support diagnosis of patients with DOC conditions.
Disciplines :
Neurology
Author, co-author :
Martinez, D. E.; Computer Science Department, Universidad Central, Bogotá, Colombia, Universidad Nacional de Colombia, Bogotá, Colombia
Martinez, J. H.; Universidad Politécnica de Madrid, Madrid, Spain, Universidad Del Rosario, Bogotá, Colombia
Rudas, J.; Universidad Nacional de Colombia, Bogotá, Colombia
C. Schnakers and S. Laureys, Coma and Disorders of Consciousness. London: Springer, 2012.
S. Laureys and N. Schiff, "Coma and consciousness: Paradigms (re)framed by neuroimaging, " NeuroImage, vol. 61, no. 2, pp. 478-491, 2012.
J. H. Adams, D. I. Graham, and B. Jennett, "The neuropathology of the vegetative state after an acute brain insult, " Brain, vol. 123, no. 7, pp. 1327-1338, 2000.
D. Graham, J. Adams, L. Murray, and B. Jennett, "Neuropathology of the vegetative state after head injury, " Neuropsychological Rehabilitation, vol. 15, no. 3-4, pp. 198-213, 2005.
J. Giacino, J. Fins, S. Laureys, and N. Schiff, "Disorders of consciousness after acquired brain injury: The state of the science, " Nature Reviews Neurology, vol. 10, no. 2, pp. 99-114, 2014.
J. T. Giacino, K. Kalmar, and J. Whyte, "The fJFKg coma recovery scale-revised: Measurement characteristics and diagnostic utility1, " Archives of Physical Medicine and Rehabilitation, vol. 85, no. 12, pp. 2020-2029, 2004.
C. Schnakers, C. Chatelle, S. Majerus, O. Gosseries, M. De Val, and S. Laureys, "Assessment and detection of pain in noncommunicative severely brain-injured patients, " Expert Review of Neurotherapeutics, vol. 10, no. 11, pp. 1725-1731, 2010.
J. Whyte, "Rancho los amigos scale, " in Encyclopedia of Clinical Neuropsychology, J. Kreutzer, J. DeLuca, and B. Caplan, Eds. Springer New York, 2011, pp. 2110-2110.
C. Schnakers, A. Vanhaudenhuyse, J. Giacino, M. Ventura, M. Boly, S. Majerus, G. Moonen, and S. Laureys, "Diagnostic accuracy of the vegetative and minimally conscious state: Clinical consensus versus standardized neurobehavioral assessment, " BMC Neurology, vol. 9, p. 35, 2009.
D. Cruse, M. Monti, and A. Owen, "Neuroimaging in disorders of consciousness: Contributions to diagnosis and prognosis, " Future Neurology, vol. 6, no. 2, pp. 291-299, 2011, cited By 1.
J. Gruzelier, "Eeg-neurofeedback for optimising performance. iii: A review of methodological and theoretical considerations, " Neuroscience and Biobehavioral Reviews, vol. 44, pp. 159-182, 2014.
E. Bullmore, "The future of functional mri in clinical medicine, " NeuroImage, vol. 62, no. 2, pp. 1267-1271, 2012, cited By 13.
D. Fernández-Espejo, T. Bekinschtein, M. M. Monti, J. D. Pickard, C. Junque, M. R. Coleman, and A. M. Owen, "Diffusion weighted imaging distinguishes the vegetative state from the minimally conscious state, " NeuroImage, vol. 54, no. 1, pp. 103-112, 2011.
V. Calhoun, T. Adali, G. Pearlson, and J. Pekar, "Group ica of functional mri data: seperability, stationarity, and inference." in Proceedings of International Conference on ICA and BSS, 2001.
Q. Yu, E. Erhardt, J. C. Sui, Y. D. Du, H. E. He, D. F. Hjelm, M. F. Cetin, S. Rachakonda, R. Miller, G. H. I. Pearlson, and V. E. G. H. Calhoun, "Assessing dynamic brain graphs of time-varying connectivity in fmri data: Application to healthy controls and patients with schizophrenia, " NeuroImage, vol. 107, pp. 345-355, 2015.
L. Robinson, L. Atlas, and T. Wager, "Dynamic functional connectivity using state-based dynamic community structure: Method and application to opioid analgesia, " NeuroImage, vol. 108, pp. 274-291, 2015.
B. B. Biswal, "Resting state fmri: A personal history, " NeuroImage, vol. 62, no. 2, pp. 938-944, 2012.
O. Sporns, "Structure and function of complex brain networks, " Dialogues in Clinical Neuroscience, vol. 15, no. 3, pp. 247-262, 2013.
A. Demertzi, F. Gmez, J. S. Crone, A. Vanhaudenhuyse, L. Tshibanda, Q. Noirhomme, M. Thonnard, V. Charland-Verville, M. Kirsch, S. Laureys, and A. Soddu, "Multiple fmri system-level baseline connectivity is disrupted in patients with consciousness alterations, " Cortex, vol. 52, no. 0, pp. 35-46, 2014.
M. van den Heuvel and O. Sporns, "An anatomical substrate for integration among functional networks in human cortex, " Journal of Neuroscience, vol. 33, no. 36, pp. 14 489-14 500, 2013.
M. Fraschini, A. Hillebrand, M. Demuru, L. Didaci, and G. Marcialis, "An eeg-based biometric system using eigenvector centrality in resting state brain networks, " IEEE Signal Processing Letters, vol. 22, no. 6, pp. 666-670, 2014.
E. b. Santarnecchi, S. Rossi, and A. Rossi, "The smarter, the stronger: Intelligence level correlates with brain resilience to systematic insults, " Cortex, vol. 64, 2015.
M. D. Fox and M. E. Raichle, "Spontaneous fluctuations in brain activity observed with functional magnetic resonance imaging, " Nature Review Neuroscience, vol. 8, pp. 700-711, 2007.
C. Rosazza and L. Minati, "Resting-state brain networks: Literature review and clinical applications, " Neurological Sciences, vol. 32, pp. 773-785, 2011, cited by 68.
Q. Yu, J. G. H. Sui, K. B. Kiehl, G. D. E. Pearlson, and V. C. D. F. Calhoun, "State-related functional integration and functional segregation brain networks in schizophrenia, " Schizophrenia Research, vol. 150, no. 2-3, pp. 450-458, 2013.
P.-J. Toussaint, S. Maiz, D. Coynel, J. Doyon, A. Messé, L. de Souza, M. Sarazin, V. Perlbarg, M.-O. Habert, and H. Benali, "Characteristics of the default mode functional connectivity in normal ageing and alzheimer's disease using resting state fmri with a combined approach of entropy-based and graph theoretical measurements, " NeuroImage, vol. 101, pp. 778-786, 2014.
L. J. Larson-Prior, J. M. Zempel, T. S. Nolan, F. W. Prior, A. Z. Snyder, and M. E. Raichle, "Cortical network functional connectivity in the descent to sleep, " Proceedings of the National Academy of Sciences, vol. 106, no. 11, pp. 4489-4494, 2009.
K. J. Friston, C. D. Frith, P. F. Liddle, and R. S. J. Frackowiak, "Functional connectivity: The principal-component analysis of large (pet) data sets, " J Cereb Blood Flow Metab, vol. 13, no. 1, pp. 5-14, 1993.
M. van den Heuvel, R. Mandl, and H. Hulshoff Pol, "Normalized cut group clustering of resting-state fmri data, " PLoS ONE, vol. 3, no. 4, p. e2001, 04 2008.
A. Demertzi, A. Soddu, and S. Laureys, "Consciousness supporting networks, " Current Opinion in Neurobiology, vol. 23, no. 2, pp. 239-244, 2013, cited By 29.
L. Heine, A. Soddu, F. Gomez, A. Vanhaudenhuyse, L. Tshibanda, M. Thonnard, V. Charland-Verville, M. Kirsch, S. Laureys, and A. Demertzi, "Resting state networks and consciousness. alterations of multiple resting state network connectivity in physiological, pharmacological and pathological consciousness states." Frontiers in Psychology, vol. 3, no. 295, 2012.
M. Brier, J. Thomas, A. Fagan, J. Hassenstab, D. Holtzman, T. Benzinger, J. Morris, and B. Ances, "Functional connectivity and graph theory in preclinical alzheimer's disease, " Neurobiology of Aging, vol. 35, pp. 757-768, 2014.
Y. Hannawi, M. Lindquist, B. Caffo, H. Sair, and R. Stevens, "Resting brain activity in disorders of consciousness: A systematic review and meta-analysis, " Neurology, vol. 84, no. 12, pp. 1272-1280, 2015.
M. J. Jafri, G. D. Pearlson, M. Stevens, and V. D. Calhoun, "A method for functional network connectivity among spatially independent resting-state components in schizophrenia." NeuroImage, vol. 39, pp. 1666-1681, Feb 2008.
G.-R. Wu, W. Liao, S. Stramaglia, J.-R. Ding, H. Chen, and D. Marinazzo, "A blind deconvolution approach to recover effective connectivity brain networks from resting state fmri data, " Medical Image Analysis, vol. 17, no. 3, pp. 365-374, 2013.
U. Sakoglu, G. Pearlson, K. Kiehl, Y. Wang, A. Michael, and V. Calhoun, "A method for evaluating dynamic functional network connectivity and task-modulation: application to schizophrenia, " MAGMA, vol. 23, no. 5-6, pp. 351-366, Dec 2010.
X. Xie, Z. Cao, and X. Weng, "Spatiotemporal nonlinearity in restingstate fmri of the human brain, " NeuroImage, vol. 40, no. 4, pp. 1672-1685, 2008.
J. Rudas, J. Guaje, A. Demertzi, L. Heine, L. Tshibanda, A. Soddu, S. Laureys, and F. Gomez, "A method for functional network connectivity using distance correlation, " in Engineering in Medicine and Biology Society (EMBC), 2014 36th Annual International Conference of the IEEE, Aug 2014, pp. 2793-2796.
M. van den Heuvel and O. Sporns, "Network hubs in the human brain, " Trends in Cognitive Sciences, vol. 17, no. 12, pp. 683-696, 2013.
O. Sporns, "Graph theory methods for the analysis of neural connectivity patterns, " in Neuroscience Databases, R. Ktter, Ed. Springer US, 2003, pp. 171-185.
E. B. C. Bullmore and O. Sporns, "The economy of brain network organization, " Nature Reviews Neuroscience, vol. 13, no. 5, pp. 336-349, 2012, cited By 263.
O. Sporns, "The non-random brain: Efficiency, economy, and complex dynamics, " Frontiers in Computational Neuroscience, no. FEBRUARY, 2011.
M. Rubinov and O. Sporns, "Complex network measures of brain connectivity: Uses and interpretations, " NeuroImage, vol. 52, no. 3, pp. 1059-1069, 2010.
J. Zhou and W. Seeley, "Network dysfunction in alzheimer's disease and frontotemporal dementia: Implications for psychiatry, " Biological Psychiatry, vol. 75, pp. 565-573, 2014.
M. B. C. Rubinov and E. B. D. Bullmore, "Fledgling pathoconnectomics of psychiatric disorders, " Trends in Cognitive Sciences, vol. 17, no. 12, pp. 641-647, 2013.
V. b. Vuksanović and P. b. Hövel, "Functional connectivity of distant cortical regions: Role of remote synchronization and symmetry in interactions, " NeuroImage, vol. 97, pp. 1-8, 2014.
C. Schnakers, S. Majerus, J. Giacino, A. Vanhaudenhuyse, M.-A. Bruno, M. Boly, G. Moonen, P. Damas, B. Lambermont, M. Lamy, F. Damas, M. Ventura, and S. Laureys, "A french validation study of the coma recovery scale-revised (crs-r), " Brain Injury, vol. 22, no. 10, pp. 786-792, 2008.
K. Friston, "Chapter 2-statistical parametric mapping, " in Statistical Parametric Mapping, K. Friston, J. Ashburner, S. Kiebel, T. Nichols, and W. Penny, Eds. London: Academic Press, 2007, pp. 10-31.
P. Mazaika, F. Hoeft, G. Glover, and A. Reiss, "Methods and software for fmri analysis of clinical subjects, " NeuroImage, vol. 47, no. Supplement 1, p. S58, 2009.
M. J. McKeown, S. Makeig, G. G. Brown, T. Jung, S. S. Kindermann, A. J. Bell, and T. J. Sejnowski, "Analysis of fmri data by blind separation into independent spatial components, " Human Brain Mapping, vol. 6, pp. 160-188, 1998.
G. J. Székely, M. L. Rizzo, and N. K. Bakirov, "Measuring and testing dependence by correlation of distances, " The Annals of Statistics, vol. 35, no. 6, pp. 2769-2794, 2007.
S. Boccaletti, V. Latora, Y. Moreno, M. Chavez, and D.-U. Hwang, "Complex networks: Structure and dynamics, " Physics Reports, vol. 424, no. 45, pp. 175-308, 2006.
E. Bullmore and O. Sporns, "Complex brain networks: graph theoretical analysis of structural and functional systems, " Nature Reviews Neuroscience, pp. 186-198, 2009.
D. J. Watts and S. H. Strogatz, "Collective dynamics of small-world networks, " Nature, vol. 393, pp. 440-442, 1998.
V. Latora and M. Marchiori, "Efficient behavior of small-world networks, " Physical Review Letters, vol. 87, p. 198701, Oct 2001.
B. L. Welch, "The generalisation of student's problems when several different population variances are involved." Biometrika, vol. 34, pp. 28-35, 1947.