an update on eeg connectivity metrics for neurotherapists
DESCRIPTION
An Update on EEG Connectivity Metrics For Neurotherapists . Thomas F. Collura, Ph.D., QEEGT November 6, 2009. Current Issues. Resolve definition of metrics Associate metrics with function Use for assessment & diagnosis Use for training Use for evaluation of training results - PowerPoint PPT PresentationTRANSCRIPT
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An Update on EEG Connectivity Metrics For
Neurotherapists
Thomas F. Collura, Ph.D., QEEGTNovember 6, 2009
(c) 2009 Thomas F. Collura, Ph.D., QEEGT
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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
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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
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Generalized Connectivity Model
(c) 2009 Thomas F. Collura, Ph.D., QEEGT
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System Identification and Parameter Estimation
(c) 2009 Thomas F. Collura, Ph.D., QEEGT
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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
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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?
(c) 2009 Thomas F. Collura, Ph.D., QEEGT
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Cortical Layers
(c) 2009 Thomas F. Collura, Ph.D., QEEGT
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Engineering Diagram of the Brain
From interstitiality.net(c) 2009 Thomas F. Collura, Ph.D., QEEGT
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Thalamo-Cortical Cycles
(c) 2009 Thomas F. Collura, Ph.D., QEEGT
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Concentration/Relaxation Cycle
(c) 2009 Thomas F. Collura, Ph.D., QEEGT
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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
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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
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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
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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
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Typical Ranges
(c) 2009 Thomas F. Collura, Ph.D., QEEGT
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Fz Cz
(c) 2009 Thomas F. Collura, Ph.D., QEEGT
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T3 T4
(c) 2009 Thomas F. Collura, Ph.D., QEEGT
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F3 F4
(c) 2009 Thomas F. Collura, Ph.D., QEEGT
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C3 C4
(c) 2009 Thomas F. Collura, Ph.D., QEEGT
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O1 O2
(c) 2009 Thomas F. Collura, Ph.D., QEEGT
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P3 P4
(c) 2009 Thomas F. Collura, Ph.D., QEEGT
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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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(c) 2009 Thomas F. Collura, Ph.D., QEEGT
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“Pure” Coherence
• How stable is the phase relationship between the two?
(c) 2009 Thomas F. Collura, Ph.D., QEEGT
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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)
0
10
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0 20 40 60 80 100
BrainM aster "pure" coherence (squared)
Neu
roG
uide
Coh
eren
ce
Series1
(c) 2009 Thomas F. Collura, Ph.D., QEEGT
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“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
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Training Coherence (Similarity)
• Are the two channels consistently in phase and of the same size?
(c) 2009 Thomas F. Collura, Ph.D., QEEGT
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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
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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
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SCC: BMr – Lexicor Concordance(G, B, A, T, D; as of 1/12/07)
80
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1 2 3 4 5
BrainMaster
Lexicor
Difference
-10
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1 2 3 4 5Series1
(c) 2009 Thomas F. Collura, Ph.D., QEEGT
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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
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Comodulation (SKIL)
• How similar is the waxing and waning of the amplitudes?(c) 2009 Thomas F. Collura,
Ph.D., QEEGT
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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
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Phase
• Exactly how well do the peaks and valleys line up?(c) 2009 Thomas F. Collura,
Ph.D., QEEGT
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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
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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
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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
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Channel Sum & Difference
(c) 2009 Thomas F. Collura, Ph.D., QEEGT
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Ratio of Sum / Difference
(c) 2009 Thomas F. Collura, Ph.D., QEEGT
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Channel Recombination – BrainScape JTFAF3 and F4
(c) 2009 Thomas F. Collura, Ph.D., QEEGT
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Channel Recombination – BrainScape JTFAC3 and C4
(c) 2009 Thomas F. Collura, Ph.D., QEEGT
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Channel Recombination – BrainScape JTFAT3 and T4
(c) 2009 Thomas F. Collura, Ph.D., QEEGT
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Channel Recombination – BrainScape JTFAO1 and O2
(c) 2009 Thomas F. Collura, Ph.D., QEEGT
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(c) 2008 Thomas F. Collura, Ph.D.
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.)
(c) 2009 Thomas F. Collura, Ph.D., QEEGT
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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
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Live Z Scores – 2 channels (76 targets)
26 x 2 + 24 = 76 (52 power, 24 connectivity)(c) 2009 Thomas F. Collura,
Ph.D., QEEGT
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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
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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
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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
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MINI-Q II Quads
(c) 2008 Thomas F. Collura, Ph.D.
(c) 2009 Thomas F. Collura, Ph.D., QEEGT
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(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
(c) 2009 Thomas F. Collura, Ph.D., QEEGT
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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
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Cyclic normalization
• Initial buildup of amplitudes• Reflects change in activation• Normalization of connectivity follows
(c) 2009 Thomas F. Collura, Ph.D., QEEGT
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Z-score Coherence Range Training(feedback when Z-score is in desired range)
(c) 2009 Thomas F. Collura, Ph.D., QEEGT
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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
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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
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Coherence ranges training with Z Scores(4 coherences in range)
(c) 2009 Thomas F. Collura, Ph.D., QEEGT
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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
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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
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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
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Effect of changing %Z thresholdReduce threshold -> percent time meeting criteria increases
(c) 2009 Thomas F. Collura, Ph.D., QEEGT
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Effect of widening Z target windowWiden window -> higher % achievable, selects most deviant scores
(c) 2009 Thomas F. Collura, Ph.D., QEEGT
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MVP “PzOK” Targeting
(c) 2009 Thomas F. Collura, Ph.D., QEEGT
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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
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Progress of Live Z-Score Training
(c) 2008 Thomas F. Collura, Ph.D.
(c) 2009 Thomas F. Collura, Ph.D., QEEGT
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Progress of MVP Variable
(c) 2008 Thomas F. Collura, Ph.D.
(c) 2009 Thomas F. Collura, Ph.D., QEEGT
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Live Z-Score Selection
(c) 2008 Thomas F. Collura, Ph.D.
(c) 2009 Thomas F. Collura, Ph.D., QEEGT
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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
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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
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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
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“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
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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
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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
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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
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SL - EO Pre and Post
(c) 2008 Thomas F. Collura, Ph.D. Data from Stark & Lambos(c) 2009 Thomas F. Collura, Ph.D., QEEGT
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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
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SL - EC Pre and Post
(c) 2008 Thomas F. Collura, Ph.D. Data from Stark & Lambos(c) 2009 Thomas F. Collura, Ph.D., QEEGT
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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
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Summary of 3 Cases
(c) 2009 Thomas F. Collura, Ph.D., QEEGT
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Summary of 3 Cases
(c) 2009 Thomas F. Collura, Ph.D., QEEGT
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Summary of 3 Cases
(c) 2009 Thomas F. Collura, Ph.D., QEEGT
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Summary of 3 Cases
(c) 2009 Thomas F. Collura, Ph.D., QEEGT
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Recent ASD Case
(c) 2009 Thomas F. Collura, Ph.D., QEEGT
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Recent ASD Case
(c) 2009 Thomas F. Collura, Ph.D., QEEGT
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Recent ASD Case
(c) 2009 Thomas F. Collura, Ph.D., QEEGT
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Recent LD/Impulsive Case
(c) 2009 Thomas F. Collura, Ph.D., QEEGT
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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