An Update on EEG Connectivity Metrics For Neurotherapists

An Update on EEG Connectivity Metrics For

Neurotherapists

Thomas F. Collura, Ph.D., QEEGTNovember 6, 2009

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

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


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


Generalized Connectivity Model


System Identification and Parameter Estimation


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


Similarity ExampleHow 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?


Cortical Layers


Engineering Diagram of the Brain

From interstitiality.net(c) 2009 Thomas F. Collura, Ph.D., QEEGT

Thalamo-Cortical Cycles


Concentration/Relaxation Cycle


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


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.


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


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?


Typical Ranges


Fz Cz


T3 T4


F3 F4


C3 C4


O1 O2


P3 P4


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


“Pure” Coherence

• How stable is the phase relationship between the two?


Pure Coherence: BMr-NG ConcordanceComparison of BrainM aster "pure" coherence (squared)

with NeuroGuide Coherence (1 Subject, 1-minute epochs)5 pairs: F3-F4, C3-C4, P3-P4, T3-T4, O1-O2

4 bands: delta (1-3.5), the ta (4-7), alpha (8-12), be ta (12.5-25)

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BrainM aster "pure" coherence (squared)

Neu

roG

uide

Coh

eren

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Series1


“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”)


Training Coherence (Similarity)

• Are the two channels consistently in phase and of the same size?


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


Spectral Correlation Coefficient (SCC/”Lexicor”)

• How similar (symmetrical) is the shape of the spectral amplitude of the two channels in a particular band?


SCC: BMr – Lexicor Concordance(G, B, A, T, D; as of 1/12/07)

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1 2 3 4 5

BrainMaster

Lexicor

Difference

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-5

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1 2 3 4 5Series1


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


Comodulation (SKIL)

• How similar is the waxing and waning of the amplitudes?(c) 2009 Thomas F. Collura,

Ph.D., QEEGT

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


Phase

• Exactly how well do the peaks and valleys line up?(c) 2009 Thomas F. Collura,

Ph.D., QEEGT

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


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


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


Channel Sum & Difference


Ratio of Sum / Difference


Channel Recombination – BrainScape JTFAF3 and F4


Channel Recombination – BrainScape JTFAC3 and C4


Channel Recombination – BrainScape JTFAT3 and T4


Channel Recombination – BrainScape JTFAO1 and O2



Normal Distributionmales vs. females

Photo by Gregory S. Pryor, Francis Marion University, Florence, SC.

From: (C. Starr and R. Taggart. 2003. The Unity and Diversity of Life. 10th Ed. Page 189.)


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


Live Z Scores – 2 channels (76 targets)

26 x 2 + 24 = 76 (52 power, 24 connectivity)(c) 2009 Thomas F. Collura,

Ph.D., QEEGT

Live Z Scores – 4 channels (248 targets)

26 x 4 + 24 x 6 = 248 (104 power, 144 connectivity)(c) 2009 Thomas F. Collura,

Ph.D., QEEGT

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


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)


MINI-Q II Quads



(c) 2007-9 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


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


Cyclic normalization

• Initial buildup of amplitudes• Reflects change in activation• Normalization of connectivity follows


Z-score Coherence Range Training(feedback when Z-score is in desired range)


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


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


Coherence ranges training with Z Scores(4 coherences in range)


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

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?”


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%)


Effect of changing %Z thresholdReduce threshold -> percent time meeting criteria increases


Effect of widening Z target windowWiden window -> higher % achievable, selects most deviant scores


MVP “PzOK” Targeting


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


Progress of Live Z-Score Training



Progress of MVP Variable



Live Z-Score Selection



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



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



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



“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



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



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


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



SL - EO Pre and Post

(c) 2008 Thomas F. Collura, Ph.D. Data from Stark & Lambos(c) 2009 Thomas F. Collura, Ph.D., QEEGT

SL - EO Loreta Pre and Post


SL - EC Pre and Post


SL - EC Loreta Pre and Post


Summary of 3 Cases


Recent ASD Case


Recent LD/Impulsive Case


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


An Update on EEG Connectivity Metrics For Neurotherapists

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