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Quantitative analysis of electroencephalographic (EEG) signals

Dept. of Epileptology University of Bonn. Quantitative analysis of electroencephalographic (EEG) signals. www.epileptologie-bonn.de. Quantitative EEG analysis. Quantitative EEG-methods: why? Example: Wavelet-based event-related potential (ERP)-analysis

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Quantitative analysis of electroencephalographic (EEG) signals

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  1. Dept. of Epileptology University of Bonn Quantitative analysis ofelectroencephalographic (EEG) signals www.epileptologie-bonn.de

  2. Quantitative EEG analysis • Quantitative EEG-methods: why? • Example: Wavelet-based event-related potential (ERP)-analysis • Phase-locking analysis of mediotemporal lobe (MTL) depth ERPs • Declarative memory formation: MTL connectivity • Summary

  3. Quantitative EEG methods: why? Example: sleep-EEG (qualitative) d 2 Hz q 5 Hz a 10 Hz b 20 Hz g > 30 Hz Rechtschaffen and Kales, 1968

  4. Quantitative EEG methods: why? Example: sleep-EEG (qualitative) Hypnogram Time

  5. Quantitative EEG methods: why? Example: sleep-EEG (quantitative) Electroencephalogr Clin Neurophysiol 1996; 98: 401-410

  6. Quantitative EEG methods: why? EEG = superposition of oscillations Visual analysis: only low-frequency oscillations b/g  perception, cognitive processes! 1/f amplitude- characteristic 

  7. Quantitative EEG methods: why? Theta-gamma interaction within hippocampus Interactions (hippocampus): Theta (5Hz)   Gamma (>30Hz) Chrobak u. Buzsáki, J. Neurosci. 1998

  8. Quantitative EEG methods: why? Event-related EEG: averaging Average event-related potential (ERP) Reduction of background „noise“: 1/n

  9. Quantitative EEG methods: why? Event-related EEG • Averaged ERP-response • ?? • Amplitude-Changes Phase-Locking evoked induced ( cognition)

  10. Wavelet-based ERP analysis Traditional approach: Fourier-transform Fourier-transform F () =  f(t)  eit dt Discrete: Fast-Fourier-transform (FFT)  f = 1 /  T ! Power density P () = F ()  F* () Spectral Coherence Cxy() = |Pxy()|2 Pxx()  Pyy()

  11. Wavelet-based ERP analysis Phase-locking vs. amplitude-changes

  12. Wavelet-based ERP analysis Phase-locking vs. amplitude-changes * Amplitude/Power (t,) Phase (t,) 

  13. Wavelet-based ERP analysis Phase-locking vs. amplitude-changes WT ERP-responses: t,3 ... t,2 t,1 Phases:  Histogram P(): Variance? 180° 0° -180° Circular variance: |  e i| Shannon entropy: 1 +  P log P Phase-locking index e.g. Lachaux et al., Hum. Brain Mapp. 1999; Tass et al., Phys. Rev. Lett. 1998

  14. Wavelet-based ERP analysis Phase-synchronisation Brain region A t,3 ? t,1 ? t,2 ? t,4 ? Brain region B Variance of phase differences t  Synchronisation index

  15. Phase-locking analysis of MTL depth ERPs Epilepsy (prevalence  1%) Seizures: • Unfamilar sensations • Unvoluntary body movements • Loss of consciousness

  16. Hippocampus Rhinal cortex Amygdala Parahippocampal cortex Phase-locking analysis of MTL depth ERPs MTL depth-recordings in epilepsy patients MTL-epilepsy:  45% pharmaco-resistant Presurgical evaluation: seizure focus? Memory processes

  17. Phase-locking analysis of MTL depth ERPs Hippocampal P3 (neue Wörter) „Oddball experiment“: X ... X ... X ... O ... X ... X ... O ... X ... X Target Target Hippocampus sclerosis Non-pathological side Neuroimage 2005; 24: 980-989

  18. Phase-locking analysis of MTL depth ERPs Hippocampal P3: low-frequency range Hippocampus sclerosis Non-pathological side Phase-locking Power Neuroimage 2005; 24: 980-989

  19. Phase-locking analysis of MTL depth ERPs Hippocampal P3: gamma range Hippocampus sclerosis Non-pathological side Phase-locking Power Neuroimage 2005; 24: 980-989

  20. Phase-locking analysis of MTL depth ERPs Anterior mediotemporal lobe (AMTL)-N4 „Continuous recognition experiment“: Haus ... Schiff ... Pferd ... Schiff ... Baum ... Haus ... Tisch ... (neue Wörter) New New New New Old Old New J. Cogn. Neurosci. 2004; 16:1595-1604

  21. Phase-locking analysis of MTL depth ERPs AMTL-N4 (new words) (old words) (neue Wörter) ERPs Phase-locking Power ( fMRI) J. Cogn. Neurosci. 2004; 16:1595-1604

  22. Declarative memory formation: MTL connectivity MTL depth electrodes Declarative long-term memory: Consciously accessible information, e.g. events and facts Rhinal Cortex Convergence of sensory data, semantic preprocessing Hippocampus Synaptic plasticity, long term potentiation (LTP) Interaction?

  23. Declarative memory formation MTL connectivity Subsequent memory paradigm . . . „Mutter“ „72“ „Leistung“ „75“ „Ende“ „78“ „Sahne“ „81“ „Appetit“ „84“ „Uhr“ ? Sahne 87 Free recall Learning Distraction • 9 TLE patients with unilateral focus • “Dm-effect” (difference due to memory): remembered vs. forgotten words

  24. remembered forgotten – 20 µV ms I I I I I I I I I I I 20 400 800 1200 1600 2000 Declarative memory formation: MTL connectivity MTL-ERPs: “difference due to memory” Rhinalcortex • Dm-effects correlated (r = 0.92)  rhinal-hippocampal interaction • Direct evidence? g-sync.  coupling of assemblies Hippocampus Fernández et al., Science 1999

  25. 48 46 44 42 40 Frequency [Hz] 38 36 34 32 0.0 0.5 1.0 1.5 time [s] Declarative memory formation: MTL connectivity Rhinal-hippocampal gamma synchronisation Phase-synch. index: remembered - forgotten  Desynchronisation Synchronisation - 30 - 20 - 10 0 + 10 + 20 + 30 Change [%]: remembered - forgotten 0° -180° 180° Nat. Neurosci. 2001; 4: 1259-1264

  26. Declarative memory formation: MTL connectivity Changes of gamma power Nat. Neurosci. 2001; 4: 1259-1264

  27. Declarative memory formation: MTL connectivity Interpretation • Rhinal-hippocampal phase coupling initiates information transfer ( 100 ms poststim.) • Information transfer after onset of rhinal dm-effect (ERPs,  300 ms poststim.) • Phase decoupling terminates information transfer ( 1000 ms poststim.) • Reduced gamma power: specific assembly activation, suppression of gamma “noise”

  28. Declarative memory formation: MTL connectivity Memory-related theta-gamma cooperation • Non-specific increase of theta-coherence •  r = 0.80, p = 0.018 •  • Specific theta-gamma interaction Eur. J. Neurosci. 2003; 17: 1082-1088

  29. Declarative memory formation: MTL connectivity Gamma activity: interactions with theta and action potentials Interactions (hippocampus): Theta (5Hz)   Gamma (>30Hz)   Spikes Chrobak u. Buzsáki, J. Neurosci. 1998

  30. Declarative memory formation: MTL connectivity Hebbian assembly formation • Correlated firing of pre- and postsynaptic neuron  Increase of synaptic efficacy (1949) Experimental validation: • Long-term potentiation and depression (LTP, LDP) • Spike timing dependent synaptic plasticity (STDP) • Synchronized gamma activity: precise spike timing (t < 10 ms)(z.B. Engel u. Singer, Trends Cogn. Sci. 2001; Fries et al., Nat. Neurosci. 2001) Abbott u. Nelson, Nat.. Neurosci. 2000

  31. Declarative memory formation: MTL connectivity Rhinal-hippocampal coupling during sleep • Dreams are difficult to remember • Unrecognized scene shifts • Duration severely misestimated  • Memory formation during (REM-) sleep reduced(e.g. Hobson et al., Behav. Brain Sci. 2000) •  Sleep recordings in 8 unilateral MTLE patients • (Indirect) electrophysiological correlate?

  32. Declarative memory formation: MTL connectivity Rhinal-hippocampal coupling during sleep Eur. J. Neurosci. 2003; 18: 1711-1716

  33. Declarative memory formation: MTL connectivity Rhinal-hippocampal 40 Hz coherence 0.5 0. Awake Stage 1 REM Stage 2 SWS 0 2 4 6 Time (hours) Eur. J. Neurosci. 2003; 18: 1711-1716

  34. Declarative memory formation: MTL connectivity Memory formation during sleep • Direct correlate? •  Awakenings from REM sleep: dream recall in 6 patients (good, 79.2%) vs. 6 patients (poor, 6.7%) • No group differences in daytime memory performance • Sleep: “spontaneous memory formation”,  attention, volition, semantic processing  Core factor of declarative memory formation

  35. Declarative memory formation: MTL connectivity EEG power within hippocampus

  36. Declarative memory formation: MTL connectivity Rhinal-hippocampal EEG coherence

  37. Declarative memory formation: MTL connectivity Conclusion Rhinal-hippocampal connectivity = core factor of declarative memory formation

  38. Summary • Quantitative EEG-analysis • EEG = superposition of functionally specific oscillations • Averaged ERPs = phase locking + amplitude changes • Connectivity may be more relevant than amplitudes of local activations

  39. Guillén Fernández Peter Klaver Christoph Helmstädter Thomas Dietl Rüdiger Köhling Edgar Kockelmann Martin Lutz Wieland Burr Hakim Elfadil Mario Städtgen Carlo Schaller Christian E. Elger Dept. of Epileptology University of Bonn Kontakt: juergen.fell@ukb.uni-bonn.de www.epileptologie-bonn.de

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