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Nonlinear dynamics and generalized synchronization: clinical applications in epilepsy and dementia C.J. Stam Department of clinical neurophysiology VU University Medical Center Amsterdam Oscillations and Instability; control, near and far from equilibrium in biology Leiden, 23-5-2005

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Nonlinear dynamics and generalized synchronization: clinical applications in epilepsy and dementia I.Introduction Functional connectivity Synchronization likelihood II.Applications Seizure detection Cognition Normal disturbed III.Small-world networks in Alzheimers disease

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Mechanisms of higher brain functions (cognition) 1.The brain shows local specialization 2.Complex tasks require cooperation between multiple brain areas 3.Synchronization is a key mechanism for functional integration 4.Synchronization results in the formation of functional networks with temporal and spatial structure

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Functional integration in the brain: - synchronous networks (binding) - dynamic changes tijd Cognitive dysfunction: breakdown of binding

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AB Dynamics of Synchronization: Functional connectivity Excessive: seizures Normal: fragile binding Diminished: Dysconnection / Cognitive dysfunction How do distributed systems in the brain integrate their activity under normal and pathological conditions? ?

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Christiaan Huygens 14-4-1629 / 8-7-1695 Synchronization of oscillators

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Synchronization: Adjustment of rhythms of (self sustained) oscillating objects through weak interactions

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Synchronization of chaotic oscillators Synchronization of chaos refers to a process wherein two (or many) systems (either equivalent or nonequivalent) adjust a given property of their motion to a common behavior due to a coupling or to a forcing (periodical or noisy) S. Boccaletti e.a. Physics reports 2002; 366: 1-101. Complete / identical synchronization (intermittent) lag synchronization (intermittent) phase synchronization Generalized synchronization

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Characterization of interdependencies between time series

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Synchronization likelihood: an unbiased measure of generalized synchronization in multivariate data sets C.J. Stam 1, B.W. van Dyk 2 Physica D, 2002; 163: 236-251 1 department of clinical neurophysiology, VU University Medical Centre 2 MEG Centre, VU University Medical Centre

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x(t)x(t+L)x(t+2*L) L x(t) x(t+L) x(t+2*L) time-delay embedding Trajectory in state space L Time series

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Generalized synchronization X Y State of the response system Is a (non linear) function of the state of the driver system Y=F(X)

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Synchronization likelihood X Y Measure of the synchronization between two signals Y=F(X)

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Synchronization likelihood X Y SL between X and Y at time i is the likelihood that Y a,b resembles Y i, given that X a,b resembles X i XiXi XaXa XbXb YiYi YaYa t=i YbYb

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YiYi XiXi ryry rxrx Synchronization likelihood X Y P ref = SL =

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Nonlinearly coupled non-identical Henon systems

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Linear and nonlinear components of coupling: multichannel surrogate data testing

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The influence of different noise levels on synchronization estimate

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5 Hz low passunfiltered Bias in synchronization estimates due to filtering

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Nonlinear dynamics and generalized synchronization: clinical applications in epilepsy and dementia I.Introduction Functional connectivity Synchronization likelihood II.Applications Seizure detection Cognition Normal disturbed III.Small-world networks in Alzheimers disease

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Seizure detection in the neonatal intensive care unit Seizure occur frequently in neurologically compromized neonates Up to 85% of the seizures are subclinical Current methods for seizure detection have limitations: Gotman CFM

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Seizure detection in neonates with synchronization likelihood Altenburg et al., Clin Neurophysiol. 2003;114: 50-5. Smit et al., Neuropediatrics 2004; 35: 1-7.

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Towne et al., Neurology 2000 236 coma patients no clinical symptoms of seizures EEG: 8% of these patients is in non convulsive status epilepticus (NCSE) NCSE: silent epidemic in intensive care patients

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oogknipperen

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propofol

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Visual Working Memory Task Response: items remembered

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synchronization likelihood during retention interval: increase in 2-6 Hz synchronization decrease of 6-10 Hz synchronization 2-6 Hz: theta working memory 6-10 Hz: lower alpha attention

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Changes in synchronization entropy during working memory task

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Nonlinear synchronization in EEG and whole-head MEG recordings of healthy subjects Stam CJ, Breakspear M, van Cappellen van Walsum AM, van Dijk BW. Human Brain Mapping 2003; 19: 63-78.

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Alzheimers disease: a dysconnection syndrome? ?

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Generalized synchronization in Alzheimers disease Subjects: 20 AD patients MMSE: 21.3 20 healthy controls Recording: 151 channel MEG Condition: eyes closed, no task

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Control gamma band (20-50 Hz) synchronous neural networks

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Alzheimer gamma band (20-50 Hz)

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Dynamics of functional connectivity in Alzheimers disease Alzheimer patients (N = 24) Control subjects (N = 19) 21 channel EEG, no-task, eyes-closed Synchronization likelihood: mean level of synchronization Synchronization rate: rate of change of synchronization ** **

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Alzheimer patientControl subject Dynamics of functional connectivity

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Are fluctuations of global synchronization levels scale-free?

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Detrended fluctuation analysis (DFA) Time series integration Fluctuation at timescale t Plot of Log(fluctuation) / Log(timescale) Scaling (self similarity) exponent: slope of linear fit through Log(fluctuation) / Log(timescale)

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Detrended fluctuation analysis of synchronization likelihood SL 8-13 Hz SL 13-30 Hz DFA 8-13 Hz DFA 13-30 Hz

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Detrended fluctuation analysis

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Disturbed fluctuations of resting state EEG synchronization in Alzheimers disease C.J. Stam, T. Montez, B.F. Jones, S.A.R.B. Rombouts, Y. van der Made, Y.A.L. Pijnenburg, Ph. Scheltens Clin Neurophysiol, 2005; 116: 708-715

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Interim conclusions: Results so far: Synchronisation analysis can detect and characterize functional networks Networks change: Cognitive tasks Brain pathology Questions: What is an optimal network? How can we detect / characterize an optimal network?

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Nonlinear dynamics and generalized synchronization: clinical applications in epilepsy and dementia I.Introduction Functional connectivity Synchronization likelihood II.Applications Seizure detection Cognition Normal disturbed III.Small-world networks in Alzheimers disease

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How to analyze a complex system as the brain? Graph theory Information theory Self-organized criticality Chaos theory

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The Kevin Bacon game

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Fig. 1 A B C D E F : vertex: edge Graph Cp: Cluster coefficient Lp: Pathlength

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The enigma of the small-world phenomenon Most networks are sparsely connected Most connections are local (high Cluster coefficient) The distance between any two network elements is small: how is this possible? Example: 10 11 neurons 10 4 synapses / neuron Typically any two neurons are only 2 to 3 synapses away

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small-world networks: High cluster coefficient C Short path length L Realistic model real complex networks optimal configuration: Sparse connectivity Maximal communication between all parts of the network Balance local specialisation / global integration

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Experimental evidence for the existence of small-world networks in the brain: Neuro anatomical networks: C. Elegans (Watts and Strogatz, 1998) Visual cortex cat (Scannell et al., 1994) Animal model / database (Hilgetag et al., 2000) Functional neural networks: Animal model / strychnine (Stephan et al., 2000) fMRI (Dodel et al., 2002; Eguiluz et al., 2004) MEG (Stam, 2004)

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C/C random = 2.08 L/L random = 1.09

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Questions: Is it possible to detect functional networks with EEG ? Can these networks be characterized with graph theoretical measures? What changes occur in Alzheimers disease ? Loss of clustering (cluster coefficient C) ? Loss of integration (path length L) ? How does this correlate with cognitive dysfunction ?

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Small-world networks in Alzheimers disease Alzheimer N = 15 69.6 (7.9) MMSE = 21.4 (4.0) Controls (subjective complaints) N = 13 70.6 (7.7) MMSE = 28.4 (1.1) EEG 21 channels Beta band (13-30 Hz) Rest / eyes closed

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Application of graph analysis to EEG: C L threshold 12 3 4

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Alzheimer patients Synchronization matrix Control subjects

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Alzheimer patients Synchronization matrix converted to graph Control subjects

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T=0.029T=0.034T=0.045 Fully connectedSplitting offFragmentation Graph splitting and fragmentation A BC

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Problem: Mean synchronisation is lower in AD than controls Applying the same threshold means that AD networks will have less connections Increased path length in Ad might be a trivial consequence of the smaller number of supra threshold connections Solution: compute C and L as a function of K (edges / vertex)

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Alzheimer patientsControl subjects Networks Normalized for K (edges / vertex)

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small-world networks? C/C random L/L random Present studyAD1.930.97 * Controls2.130.89 Stam, 2004Controls1.891.19 Salvador, 2005Controls2.081.09 Hilgetag, 2000Macaque visual ctx 1.851.02 Cat whole ctx1.991.07 Watts & Strogatz, 1998 C. Elegans5.61.18

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Conclusions: Synchronization likelihood analysis can track fragile binding in EEG and MEG Healthy subjects: Frequency specific changes in synchronization in working memory task Scale-free fluctuations of SL Alzheimer patients: Lower synchronization Disturbed fluctuations of SL Disturbed spatial patterns

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Acknowledgements: Afdeling KNF R.L.M. Strijers E.M. Vriens H.E. Ronner W. de Rijke L.S. Smit laboranten Afdeling neurologie H.W. Berendse Y.A.L. Pijnenburg Ph. Scheltens M.C. Visser MEG centrum B.W. van Dijk T. Montez J.C. de Munck J. Verbunt K. Cover Kinderneurologie R.J. Vermeulen J. Altenburg Neonatale IC W.P.F. Fetter Intensive care A.R.J. Girbes J.J. Spijkstra Neurochirurgie W.P. VanderTop UMC F.S.S. Leijten W Spetgens Overige R. Ferri S. Micheloyannis M. Breakspear G. Nolte J. Terry