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An Update on EEG Connectivity Metrics For Neurotherapists Thomas F. Collura, Ph.D., QEEGT November 6, 2009 (c) 2009 Thomas F. Collura, Ph.D., QEEGT.

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Presentation on theme: "An Update on EEG Connectivity Metrics For Neurotherapists Thomas F. Collura, Ph.D., QEEGT November 6, 2009 (c) 2009 Thomas F. Collura, Ph.D., QEEGT."— Presentation transcript:

1 An Update on EEG Connectivity Metrics For Neurotherapists Thomas F. Collura, Ph.D., QEEGT November 6, 2009 (c) 2009 Thomas F. Collura, Ph.D., QEEGT

2 Current Issues Resolve definition of metrics Associate metrics with function Use for assessment & diagnosis Use for training Use for evaluation of training results New insights re. function, disorders (c) 2009 Thomas F. Collura, Ph.D., QEEGT

3 The Purpose of Connectivity Training To reflect whole brain function Show relationship between two sites Reflect amount of information shared Reflect speed of information sharing Real-time recording or postprocessed Useful for assessing brain function Useful for training brain connectivity Takes us beyond amplitude training (c) 2009 Thomas F. Collura, Ph.D., QEEGT

4 Generalized Connectivity Model (c) 2009 Thomas F. Collura, Ph.D., QEEGT

5 System Identification and Parameter Estimation (c) 2009 Thomas F. Collura, Ph.D., QEEGT

6 Measurement Example - Temperature We never measure “temperature” We do observe: Position of a column of mercury (“thermometer”) Deflection of a bimetal strip (dial indicator) Electrical potential (thermocouple) Electrical resistance (thermistor) Distribution of light energy (infrared spectrometry) Color of a substance (“mood ring”) Interpret in terms of a model & theory (c) 2009 Thomas F. Collura, Ph.D., QEEGT

7 Similarity Example How similar are two people? Do they speak the same language? Can they wear the same clothing? Can they eat the same food? Can they use the same medicine? Can they play the same instruments? Do they enjoy the same music? Do they practice the same religion? (c) 2009 Thomas F. Collura, Ph.D., QEEGT

8 Cortical Layers (c) 2009 Thomas F. Collura, Ph.D., QEEGT

9 Engineering Diagram of the Brain From interstitiality.net (c) 2009 Thomas F. Collura, Ph.D., QEEGT

10 Thalamo-Cortical Cycles (c) 2009 Thomas F. Collura, Ph.D., QEEGT

11 Concentration/Relaxation Cycle (c) 2009 Thomas F. Collura, Ph.D., QEEGT

12 Connectivity Measures Many ways to measure connectivity Always asking “how similar are the signals?” Relative Phase sensitive or insensitive Absolute phase sensitive or insensitive Amplitude sensitive or insensitive Measurement across time or across frequency Source of raw data –Waveform –FFT –Digital Filter (IIR or FIR) or JTFA (c) 2009 Thomas F. Collura, Ph.D., QEEGT

13 Connectivity (coherence & phase) Coherence: Amount of shared information Phase: Speed of shared information Thalamocortical –Theta, Alpha, SMR Corticortical –Beta, Gamma Intrahemispheric – e.g. language Interhemispheric Fronto-frontal – attention, control occipito-parietal – sensory integration, aging (c) 2008 Thomas F. Collura, Ph.D.(c) 2009 Thomas F. Collura, Ph.D., QEEGT

14 Connectivity Measures - Summary Pure Coherence (is relative phase stable?) –joint energy / product of self-energies Synchrony Metric (do phase and amplitude match?) –Joint energy (real parts)/ sum of self-energies Spectral Correlation Coefficient (FFT amplitudes same?) –Correlation (across frequency) between amplitude spectra Comodulation (do components wax & wane together?) –Correlation (across time) between amplitude time-series Asymmetry –Relative amplitude between two sites Phase (is relative timing stable or same?) –Arctan of ratio of sin & cosine components Sum & Difference Channels (arithmetic comparison) –Simply add or subtract raw waveforms (c) 2009 Thomas F. Collura, Ph.D., QEEGT

15 Connectivity Measures - Analogs Pure Coherence –How much is information being shared? Synchrony Metric –Are the sites locked together in time? Spectral Correlation Coefficient –Are the “walkie-talkies” on the same wavelength? Comodulation –Are the sites’ C/R cycles related to each other? Asymmetry –Is there a balance/imbalance of activation? Phase –How quickly is information being exchanged? Sum & Difference Channels –How similar or different are the sites in exact activity? (c) 2009 Thomas F. Collura, Ph.D., QEEGT

16 Typical Ranges (c) 2009 Thomas F. Collura, Ph.D., QEEGT

17 Fz Cz (c) 2009 Thomas F. Collura, Ph.D., QEEGT

18 T3 T4 (c) 2009 Thomas F. Collura, Ph.D., QEEGT

19 F3 F4 (c) 2009 Thomas F. Collura, Ph.D., QEEGT

20 C3 C4 (c) 2009 Thomas F. Collura, Ph.D., QEEGT

21 O1 O2 (c) 2009 Thomas F. Collura, Ph.D., QEEGT

22 P3 P4 (c) 2009 Thomas F. Collura, Ph.D., QEEGT

23 Classical or “pure” Coherence Measure of phase stability between two signals – gets “inside” signals Wants them to be at the same frequency Doesn’t care about absolute phase separation Doesn’t care about relative amplitude Measures of amount of shared information Useful when sites have different timing Can use FFT or JTFA to calculate (c) 2009 Thomas F. Collura, Ph.D., QEEGT

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25 “Pure” Coherence How stable is the phase relationship between the two? (c) 2009 Thomas F. Collura, Ph.D., QEEGT

26 Pure Coherence: BMr-NG Concordance (c) 2009 Thomas F. Collura, Ph.D., QEEGT

27 “Training” Coherence/Similarity (BrainMaster) Similarity measure using Quad filters (JTFA) Measure of phase and amplitude match between two signals – gets “inside” signals Wants them to have zero phase separation Wants them to have same amplitude Useful for synchrony training Random signals will have low similarity Special case of coherence (“0 is a constant”) (c) 2009 Thomas F. Collura, Ph.D., QEEGT

28 Training Coherence (Similarity) Are the two channels consistently in phase and of the same size? (c) 2009 Thomas F. Collura, Ph.D., QEEGT

29 Spectral Correlation Coefficient (Lexicor) Measure of amplitude similarity in spectral energy – uses FFT amplitude data Wants two signals to have similar power spectral shape Completely ignores phase relationship Meaningful for a single epoch Random signals may have large correlation if spectra are similar (c) 2009 Thomas F. Collura, Ph.D., QEEGT

30 Spectral Correlation Coefficient (SCC/”Lexicor”) How similar (symmetrical) is the shape of the spectral amplitude of the two channels in a particular band? (c) 2009 Thomas F. Collura, Ph.D., QEEGT

31 SCC: BMr – Lexicor Concordance (G, B, A, T, D; as of 1/12/07) (c) 2009 Thomas F. Collura, Ph.D., QEEGT

32 Comodulation (Sterman/Kaiser) Measures similarity in amplitudes across time – classically uses FFT amplitude data Correlation between envelopes of two signals Completely ignores phase relationship Must be considered across time epoch Reflects how similarly signals wax and wane together Can be computed using digital filters Random signals will have low comodulation (c) 2009 Thomas F. Collura, Ph.D., QEEGT

33 Comodulation (SKIL) How similar is the waxing and waning of the amplitudes? (c) 2009 Thomas F. Collura, Ph.D., QEEGT

34 Phase measurement Various methods to compute Attempts to extract phase relationship using mathematical technique Stability and “wraparound” issues FFT or Quad Digital Filters Reflects how well signals line up in time Measure of speed of information sharing Useful for synchrony training (c) 2009 Thomas F. Collura, Ph.D., QEEGT

35 Phase Exactly how well do the peaks and valleys line up? (c) 2009 Thomas F. Collura, Ph.D., QEEGT

36 Sum-channel Adds two signals together in time domain Gets “inside” signals Peaks and valleys reinforce in time Very sensitive to phase relationship Wants signals to be in phase Largest when both signals are large Useful for synchrony training Can uptrain coherence with sum-channel mode Random signals: sum & difference will look the same (c) 2009 Thomas F. Collura, Ph.D., QEEGT

37 Difference-channel Same as bipolar montage Similar signals will cancel Emphasizes differences Useful for coherence downtraining Cannot uptrain coherence with bipolar Random (uncorrelated) signals: sum & difference signals will look the same (c) 2009 Thomas F. Collura, Ph.D., QEEGT

38 Channel Sum & Difference The following animation shows the relationship between the phase of two signals and the amplitude of their sum and difference: sumphase4.avi (c) 2009 Thomas F. Collura, Ph.D., QEEGT

39 Channel Sum & Difference (c) 2009 Thomas F. Collura, Ph.D., QEEGT

40 Ratio of Sum / Difference (c) 2009 Thomas F. Collura, Ph.D., QEEGT

41 Channel Recombination – BrainScape JTFA F3 and F4 (c) 2009 Thomas F. Collura, Ph.D., QEEGT

42 Channel Recombination – BrainScape JTFA C3 and C4 (c) 2009 Thomas F. Collura, Ph.D., QEEGT

43 Channel Recombination – BrainScape JTFA T3 and T4 (c) 2009 Thomas F. Collura, Ph.D., QEEGT

44 Channel Recombination – BrainScape JTFA O1 and O2 (c) 2009 Thomas F. Collura, Ph.D., QEEGT

45 (c) 2008 Thomas F. Collura, Ph.D. Normal Distribution males vs. females Photo by Gregory S. Pryor, Francis Marion University, Florence, SC. From: (C. Starr and R. Taggart The Unity and Diversity of Life. 10th Ed. Page 189.) (c) 2009 Thomas F. Collura, Ph.D., QEEGT

46 Live Z-Scores Absolute Power (8 bands per channel) Relative Power (8 bands per channel) Power Ratios (10 ratios per channel) Asymmetry (8 bands per path) Coherence (8 bands per path) Phase (8 bands per path) Based on database of >600 subjects Based on age, eyes open/closed (c) 2009 Thomas F. Collura, Ph.D., QEEGT

47 Live Z Scores – 2 channels (76 targets) 26 x = 76 (52 power, 24 connectivity) (c) 2009 Thomas F. Collura, Ph.D., QEEGT

48 Live Z Scores – 4 channels (248 targets) 26 x x 6 = 248 (104 power, 144 connectivity) (c) 2009 Thomas F. Collura, Ph.D., QEEGT

49 Z-Score Targeting Options Train Z Score(s) up or down –Simple directional training Train Z Score(s) using Rng() –Set size and location of target(s) Train Z Score(s) using PercentZOK() –Set Width of Z Window via. PercentZOK(range) –Set Percent Floor as a threshold Combine the above with other, e.g. power training (c) 2009 Thomas F. Collura, Ph.D., QEEGT

50 Choice of sites Modular / Functional Approach (Walker et. al.) Functional Hubs (Demos / Thatcher) Symptom-based (Demos / Thatcher / Soutar / Brownback) Choice of “boxes” (Stark / Lambos / Rutter) (c) 2009 Thomas F. Collura, Ph.D., QEEGT

51 MINI-Q II Quads (c) 2008 Thomas F. Collura, Ph.D. (c) 2009 Thomas F. Collura, Ph.D., QEEGT

52 (c) T. F. Collura, Ph.D. Other Common Quads 1. F3 F4 P3 P4 – “Little Box” - General 2. F3 F4 C3 C4 – “Front Box” - Attention 3. C3 C4 P3 P4 – “Rear Box” - Perception 4. F7 F8 T5 T6 – “Big Box” - Assessment 5. C3 C4 Fz Pz – “Cross” – Motor strip (c) 2009 Thomas F. Collura, Ph.D., QEEGT

53 Observations with LZT Cyclic normalization of power and connectivity Typical individual signatures Trainees respond to variations in challenge Brain capable of choosing which parameters to normalize Brain must explore functional boundaries Excessive freedom can produce abreaction (c) 2009 Thomas F. Collura, Ph.D., QEEGT

54 Cyclic normalization Initial buildup of amplitudes Reflects change in activation Normalization of connectivity follows (c) 2009 Thomas F. Collura, Ph.D., QEEGT

55 Z-score Coherence Range Training (feedback when Z-score is in desired range) (c) 2009 Thomas F. Collura, Ph.D., QEEGT

56 Range Function Rng(VAR, RANGE, CENTER) = 1 if VAR is within RANGE of CENTER = 0 else Rng(BCOH, 10, 30) –1 if Beta coherence is within +/-10 of 30 Rng(ZCOB, 2, 0) –1 if Beta coherence z score is within +/-2 of 0 (c) 2009 Thomas F. Collura, Ph.D., QEEGT

57 Range training with multiple ranges X = Rng(ZCOD, 2,0) + Rng(ZCOT, 2, 0), + Rng(ZCOA, 2, 0) + Rng(ZCOB, 2, 0) = 0 if no coherences are in range = 1 if 1 coherence is in range = 2 if 2 coherences are in range = 3 if 3 coherences are in range = 4 if all 4 coherences are in range Creates new training variable, target = 4 (c) 2009 Thomas F. Collura, Ph.D., QEEGT

58 Coherence ranges training with Z Scores (4 coherences in range) (c) 2009 Thomas F. Collura, Ph.D., QEEGT

59 Combined Amplitude and Coherence-based protocol If (point awarded for amplitudes) AND (coherence is normal) THEN (play video for 1 second) (c) 2009 Thomas F. Collura, Ph.D., QEEGT

60 PercentZOK() function PercentZOK(RANGE) –Gives percent of Z Scores within RANGE of 0 –1 channel: 26 Z Scores total –2 channels: 76 Z Scores total –4 channels: 248 Z Scores total Value = 0 to 100 Measure of “How Normal?” (c) 2009 Thomas F. Collura, Ph.D., QEEGT

61 Z Score training using percent Z’s in target range Size of range window (UTHR - currently 1.4 standard deviations) Threshold % for Reward (CT: between 70% and 80%) %Z Scores in range (between 50 and 90%) % Time criterion is met (between 30% and 40%) (c) 2009 Thomas F. Collura, Ph.D., QEEGT

62 Effect of changing %Z threshold Reduce threshold -> percent time meeting criteria increases (c) 2009 Thomas F. Collura, Ph.D., QEEGT

63 Effect of widening Z target window Widen window -> higher % achievable, selects most deviant scores (c) 2009 Thomas F. Collura, Ph.D., QEEGT

64 MVP “PzOK” Targeting (c) 2009 Thomas F. Collura, Ph.D., QEEGT

65 Z-score based targeting Threshold replaced with target size Feedback contingency determined by target size and % hits required Eliminates need for “autothresholding” Integrates QEEG analysis with training in real time Protocol automatically and dynamically adapts to what is most needed Consistent with established QEEG-based procedures with demonstrated efficacy (c) 2009 Thomas F. Collura, Ph.D., QEEGT

66 Progress of Live Z-Score Training (c) 2008 Thomas F. Collura, Ph.D. (c) 2009 Thomas F. Collura, Ph.D., QEEGT

67 Progress of MVP Variable (c) 2008 Thomas F. Collura, Ph.D. (c) 2009 Thomas F. Collura, Ph.D., QEEGT

68 Live Z-Score Selection (c) 2008 Thomas F. Collura, Ph.D. (c) 2009 Thomas F. Collura, Ph.D., QEEGT

69 Live Z-Score Training Policy EEG deviation(s) should be consistent with clinical presentation(s) EEG normalization should be reasonable Consider coping, compensatory traits Consider “peak performance” traits Consider phenotypes & recommendations Monitor subjective and clinical changes (c) 2008 Thomas F. Collura, Ph.D.(c) 2009 Thomas F. Collura, Ph.D., QEEGT

70 Normalize using Live Z-Scores Excessive Frontal Slowing Excessive Beta or high beta Hypercoherence, not left hemisphere (F3- P3) Hypocoherence, not central (C3-C4) Localized (focal) excess or deficit (c) 2008 Thomas F. Collura, Ph.D.(c) 2009 Thomas F. Collura, Ph.D., QEEGT

71 Coping/Compensating Z-Scores Diffuse Low alpha – chronic pain (barrier) Diffuse high alpha –chronic anxiety coping mechanism Posterior asymmetries –PTSD, stress coping, cognitive dissonance Substance Abuse, Addiction –Effects of EEG normalization not well understood (c) 2008 Thomas F. Collura, Ph.D.(c) 2009 Thomas F. Collura, Ph.D., QEEGT

72 “Peak Performance” Z-Scores Left Hemispheric Hypercoherence( F3-P3) Central Intrahemispheric Hypocoherence (C3-C4) “Excess” SMR C4 “Excess” posterior alpha “Fast” posterior alpha Note: normalization can be avoided by keeping EEG sensors away from affected sites (c) 2008 Thomas F. Collura, Ph.D.(c) 2009 Thomas F. Collura, Ph.D., QEEGT

73 Phenotypes and Live Z-Scores Most Phenotypes “map” to live z-scores –Diffuse Slow –Focal Abnormalities, not epileptiform –Mixed Fast & Slow –Frontal Lobe Disturbances – excess slow –Frontal Asymmetries –Excess Temporal Lobe Alpha –Spindling Excessive Beta –Generally Low Magnitudes –Persistent Alpha –+ Diffuse Alpha deficit Exceptions: –“Epileptiform” (requires visual inspection of EEG waveforms) –Faster Alpha Variants, not Low Voltage (requires live z-score for peak frequency) Many phenotypes can be addressed via. LZT Training –Inhibits, rewards referenced to normal population or biased for enhance/inhibit Phenotypes do not (currently) consider connectivity deviations –Hypocoherent Intrahemispheric (L or R) –Hypercoherent Interhemispheric (e.g. frontal) –Diffuse Coherence / Phase Abnormalities (c) 2008 Thomas F. Collura, Ph.D. (c) 2009 Thomas F. Collura, Ph.D., QEEGT

74 Summary Wide range of methods available Various perspectives on the concept of “similar” All have strengths and weaknesses Important to understand basis of each metric and its application to NF All have value Importance of normative data to interpret (c) 2009 Thomas F. Collura, Ph.D., QEEGT

75 Case of SL 7YO Male, discipline problem, AD/HD, easily excited, aggressive QEEG Pre and post z-score training 21 sessions between QEEG’s PercentZ training at 85% reward Begin F3 F4 P3 P4, later F3 F4 C3 C4 Begin at +/- 2.0 S.D. All scores except 1 within 1.5 S.D. after training Significant clinical improvement Data courtesy Drs. C. Stark & W. Lambos (c) 2008 Thomas F. Collura, Ph.D.(c) 2009 Thomas F. Collura, Ph.D., QEEGT

76 SL - EO Pre and Post (c) 2008 Thomas F. Collura, Ph.D. Data from Stark & Lambos (c) 2009 Thomas F. Collura, Ph.D., QEEGT

77 SL - EO Loreta Pre and Post (c) 2008 Thomas F. Collura, Ph.D. Data from Stark & Lambos (c) 2009 Thomas F. Collura, Ph.D., QEEGT

78 SL - EC Pre and Post (c) 2008 Thomas F. Collura, Ph.D. Data from Stark & Lambos (c) 2009 Thomas F. Collura, Ph.D., QEEGT

79 SL - EC Loreta Pre and Post (c) 2008 Thomas F. Collura, Ph.D. Data from Stark & Lambos (c) 2009 Thomas F. Collura, Ph.D., QEEGT

80 Summary of 3 Cases (c) 2009 Thomas F. Collura, Ph.D., QEEGT

81 Summary of 3 Cases (c) 2009 Thomas F. Collura, Ph.D., QEEGT

82 Summary of 3 Cases (c) 2009 Thomas F. Collura, Ph.D., QEEGT

83 Summary of 3 Cases (c) 2009 Thomas F. Collura, Ph.D., QEEGT

84 Recent ASD Case (c) 2009 Thomas F. Collura, Ph.D., QEEGT

85 Recent ASD Case (c) 2009 Thomas F. Collura, Ph.D., QEEGT

86 Recent ASD Case (c) 2009 Thomas F. Collura, Ph.D., QEEGT

87 Recent LD/Impulsive Case (c) 2009 Thomas F. Collura, Ph.D., QEEGT

88 Summary Approaches to Brain Connectivity are proliferating Blending of QEEG and NF techniques Increasing symptom-based approach Exploring Brain’s ability to decipher FB General model of cyclic excitability and modulation of connectivity Opportunity for Brain to design its own strategy for normalization (c) 2009 Thomas F. Collura, Ph.D., QEEGT


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