Presentation on theme: "Technical Foundations of Neuronal Dynamics and Z-Scores"— Presentation transcript:
1Technical Foundations of Neuronal Dynamics and Z-Scores Thomas F. Collura, Ph.DBrainMaster Technologies, Inc.January, 2009(c) T. F. Collura, Ph.D.
2NeurofeedbackNeurofeedback is a form of biofeedback training that uses the EEG (Electroencephalogram), also known as the “brain wave” as the signal used to control feedback. Sensors applied to the trainee’s scalp record the brainwaves, which are converted into feedback signals by a human/machine interface using a computer and software. By using visual, sound, or tactile feedback to produce learning in the brain, it can be used to induce brain relaxation through increasing alpha waves. A variety of additional benefits, derived from the improved ability of the CNS (central nervous system) to modulate the concentration/relaxation cycle and brain connectivity, may also be obtained.(c) T. F. Collura, Ph.D.
3Outline Electrophysiology Instrumentation Computerization Signal ProcessingUser InterfacingSystem Overview(c) T. F. Collura, Ph.D.
4First Human EEG Studies - 1924 (c) T. F. Collura, Ph.D.
6ElectrophysiologyNeuronal Potentials – dipoles generation by single cellsPopulation Dynamics – synchrony reinforces strength of signalBrain Physiology & anatomy defines electrical generatorsVolume Conduction to scalp through cerebral fluid and tissueSkin Interface to sensors(c) T. F. Collura, Ph.D.
7Realistic Head Dipole Source (c) T. F. Collura, Ph.D.
8Dipoles - summary All brain dipoles have: Location – can “move”Magnitude – can oscillate and vary in sizeOrientation – can change as sources move among sulci and gyriIt is the population behavior that is “seen” in the EEG(c) T. F. Collura, Ph.D.
9EEG Generation Mechanisms Primary mechanism of brain is inhibitionRhythms generated when inhibition is relaxedAllows thalamocortical reverberationRelaxation at cortical level, and at thalamic levelAllows populations to oscillate in synchrony(c) T. F. Collura, Ph.D.
10Sensor Issues Sensor Type – gold, silver, silver-chloride, tin, etc. Sensor location – at least one sensor placed on scalpSensor attachment – requires electrolyte paste, gel, or solutionMaintain an electrically secure connection(c) T. F. Collura, Ph.D.
11Sensor Types Disposable (gel-less and pre-gelled) Reusable disc sensors (gold or silver)Reusable sensor assembliesHeadbands, hats, etc.Saline based electrodes – sodium chloride or potassium chloride(c) T. F. Collura, Ph.D.
12EEG Instrumentation Sensors pick up skin potential Amplifiers create difference signal from each pair of sensorsCannot measure “one” sensor, only pair3 leads per channel – active, reference, grndEach channel yields a signal consisting of microvolts varying in time(c) T. F. Collura, Ph.D.
16Effect of EEG “blurring” (c) T. F. Collura, Ph.D.
17EEG AmplificationPicks up difference between active & reference via. subtractionCMRR – common-mode rejection ratio measures quality of subtractionHigh CMRR rejects 60 Hz, other common-mode signals, amplifies differenceSensor pair picks up dipoles near sensors, between sensors, and parallel to sensor(c) T. F. Collura, Ph.D.
18Model for Differential Amplifier & EEG Generators (c) T. F. Collura, Ph.D.
19Effect of Reference Placement (c) T. F. Collura, Ph.D.
20Scalp EEG vs. Invasive EEG (1 cm spacing) (c) T. F. Collura, Ph.D.
21Paradoxical Lateralization (c) T. F. Collura, Ph.D.
23Differential Amplifier – “zero” output (c) T. F. Collura, Ph.D.
24Differential Amplifier – nonzero output (c) T. F. Collura, Ph.D.
25Differential Amplifier – nonzero output (c) T. F. Collura, Ph.D.
26Dipole Sensing Sensor pair with differential amplifier picks up: Sources near either sensorSources between both sensorsSources aligned parallel to sensor axis(c) T. F. Collura, Ph.D.
27Region of Maximum Sensitivity (c) T. F. Collura, Ph.D.
28Contralateral Reference (c) T. F. Collura, Ph.D.
29EEG Electrophysiology “Forward problem” – given sources and anatomy, predict surface potentialsSolved & deterministic – 1 solution exists for any set of sources and anatomy“Inverse problem” given surface potentials, find sources and anatomyNon-deterministic - many solutions exist for any surface potential distribution(c) T. F. Collura, Ph.D.
30EEG AmplificationPicks up difference between active & reference via. subtractionCMRR – common-mode rejection ratio measures quality of subtractionHigh CMRR rejects 60 Hz, other common-mode signals, amplifies differenceSensor pair picks up dipoles near sensors, between sensors, and parallel to sensor(c) T. F. Collura, Ph.D.
31Model for Differential Amplifier (c) T. F. Collura, Ph.D.
32Model for Differential Amplifier & EEG Generators (c) T. F. Collura, Ph.D.
43Engineering Diagram of the Brain (c) T. F. Collura, Ph.D.From interstitiality.net
44EEG montages Referential – e.g. ear reference Reference is assumed inactiveLinked ears commonly used as referenceBipolar – e.g. T3 active T4 referenceMeasures difference between two sites(c) T. F. Collura, Ph.D.
45Thalamo-Cortical Cycles (c) T. F. Collura, Ph.D.
46Concentration/Relaxation Cycle Discovered by Dr. Barry Sterman in pilots“good” pilots preceded each task item with high-frequency, low-amplitude EEGAlso followed task item with low-frequency, high-amplitude EEG (“PRS”)Poorer pilots did not exhibit control of the concentration/relaxation cycleSlower reaction time, more fatigue(c) T. F. Collura, Ph.D.
47Concentration/Relaxation Cycle (c) T. F. Collura, Ph.D.
48Connectivity (coherence & phase) Coherence: Amount of shared informationPhase: Speed of shared informationThalamocorticalTheta, Alpha, SMRCorticorticalBeta, GammaIntrahemispheric – e.g. languageInterhemisphericFronto-frontal – attention, controloccipito-parietal – sensory integration, aging(c) 2008 Thomas F. Collura, Ph.D.(c) T. F. Collura, Ph.D.
49EEG Analysis Methods Digital Filtering (“IIR” or “FIR”) Fast response, uses predefined bandsLike using a colored lensFast, useful for training or assessmentFast Fourier Transform (“FFT”)Analyzes all frequencies in an “epoch”Like a prismResponse is slower, useful for assessment(c) T. F. Collura, Ph.D.
50Filtering Dynamics Wider filter follows waxing and waning better Need filter bandwidth >= 1 / burst lengthE.g. to see a 250 msec burst, filter must be 4 Hz wideTo see a 100 msec burst, filter must be 10 Hz wide(c) T. F. Collura, Ph.D.
51Filter Order Describes slope of “reject” area outside of main passband Low order = “shallow” skirtsFaster, but less selectiveHigh order = “steep” skirtsSlower, but more selectiveTypical values 2, 3, … 6 order filters(c) T. F. Collura, Ph.D.
52Filter order recommendations Low order (2, 3)High frequency training – SMR, beta, gammaBeginners, children, peak performanceResponse has more “pop”, picks up short burstsHigh order (5, 6)Low frequency training – theta, alphaAdvanced, adults, meditationResponse is more accurate, requires longer bursts(c) T. F. Collura, Ph.D.
53Thresholding Sets amplitude criterion for rewards Compares signal amplitude with set valueCan be constant, or can be varyingPercent time over threshold is indicator of how often signal exceeds threshold(c) T. F. Collura, Ph.D.
54Thresholding Threshold is generally amplitude value Feedback is controlled via thresholds for each trained componentComponent may be “enhance” (“go”) or “inhibit” (“stop”)May use more than 1 component in combination in a protocol“Percent time over threshold” (%TOT)is average time the component is above threshold(c) T. F. Collura, Ph.D.
55Threshold TargetsEnhance – being over threshold allows positive feedbackReward rate = % TOTInhibit – being over threshold inhibits feedbacksuccess is being below thresholdReward rate would be % TOTTotal reward rate is product of individual success rates for each component(c) T. F. Collura, Ph.D.
56Threshold Targets Low inhibit – 20% TOT Midrange enhance – 60% TOT 80% success rate =Midrange enhance – 60% TOT60% success rateHigh inhibit – 10% TOT90% success rate =Expected reward rate:0.8 x 0.6 x 0.9 = 0.43 = 43%SMR enhance is emphasized(c) T. F. Collura, Ph.D.
57Threshold Targets – Example 1 Theta inhibit – 20% TOTSMR enhance – 60% TOTHi Beta inhibit – 10% TOTExpected reward rate:0.8 x 0.6 x 0.9 = 0.43 = 43%SMR enhance is emphasized(c) T. F. Collura, Ph.D.
58Threshold Targets – Example 2 Theta inhibit – 40% TOTSMR enhance – 80% TOTHi Beta inhibit – 10% TOTExpected reward rate:0.6 x 0.8 x 0.9 = 0.43 = 43%Theta Inhibit is emphasized(c) T. F. Collura, Ph.D.
59Threshold Targets – Example 3 Theta inhibit – 60% TOTSMR enhance – 100% TOTHi Beta inhibit – 0% TOTExpected reward rate:0.4 x 1.0 x 1.0 = 0.40 = 40%Theta Inhibit is all there is – Theta “squash”(c) T. F. Collura, Ph.D.
60When to adjust thresholds? Never (Lubar)Don’t frustrate traineeAllow to see improvement in scoresOnce for each session (Ayers)Tell trainee new thresholdGoal of consistent number of points per sessionEvery 2-5 minutes (Othmer, Soutar)Optimal rate of rewardShow trainee improvement in EEG scoresContinually (Brown & Brown)Brain is a dynamical systemProvide information regarding emergent variability(c) T. F. Collura, Ph.D.
61Squash Protocol Based on downtraining amplitude Generally directed toward activationLower amplitude -> higher frequency“Bench press” model – work then relaxEasy to learn, especially theta squash(c) T. F. Collura, Ph.D.
62Typical EEG Component Bands Delta (1 – 4 Hz)Theta (4 – 7 Hz)Alpha (8 – 12 Hz)Low Beta (12 – 15 Hz)Beta (15 – 20 Hz)High Beta (20 – 30 Hz)Gamma (40 Hz and above)Ranges are typical, not definitive(c) T. F. Collura, Ph.D.
63Delta (typ. 1 – 3 Hz)Distribution: broad, diffused, bilateral, widespreadSubjective states: deep, dreamless sleep, trance, unconsciousTasks & behaviors: lethargic, not attentivePhysiological correlates: not moving, low-level arousalEffects: drowsiness, trance, deeply relaxed(c) T. F. Collura, Ph.D.
64Theta (typ. 4 – 7 Hz)Low-frequency rhythm associated with internalized thoughtsMediated by subthalamic mechanismsAssociated with memory consolidationGenerally non-sinusoidal, irregularSeen during hypnogogic reverieSeen as precursor, and sequel to sleepEdison’s “creativity” state(c) T. F. Collura, Ph.D.
65Theta (typ. 4 – 7 Hz)Distribution: regional, many lobes, laterlized or diffuseSubjective states: intuitive, creative, recall, fantasy, imagery, dreamlikeTasks & behavior: creative, but may be distracted, unfocussedPhysiological correlates: healing, integration of mind and bodyEffects: enhanced, drifting, trance like, suppressed, concentration, focusTypically 4 – 8 Hz(c) T. F. Collura, Ph.D.
66Three types of “theta” “True” theta Slow alpha Glial theta Subthalamic controlSlow alphaSlowed due to increased processingEvident in adults, meditatorsGlial thetaDC potential, modulated at up to 4 Hz(c) T. F. Collura, Ph.D.
67Alpha (typ. 8 – 12 Hz) Resting rhythm of the visual system Increases when eyes are closedLargest occipital – O1, O2Characteristic waxing and waningGenerally sinusoidal, hemispheric symmetricalIndicates relaxationRole in background memory scanningRound trip thalamus-cortex-thalamus ~ 100 msTypically 8 – 12 Hz, but may be 4 – 20 Hz(c) T. F. Collura, Ph.D.
68Alpha (typ. 8 – 12 Hz)Distribution: regional, evolves entire lobes, strong occipital with closed eyesSubjective states: relaxed, not drowsyTasks & behavior: meditation, no actionPhysiological correlates: relaxed, healingEffects: relaxation(c) T. F. Collura, Ph.D.
69Alpha vs. activationParadox – when alpha appears, brain is less activeActivation is accompanied by high frequency, lower amplitude EEGCan achieve activation by training amplitude down – “squash” protocol(c) T. F. Collura, Ph.D.
70Frontal Alpha Asymmetry Davidson, Rosenfeld, Baehr found:Right alpha should be 10 – 15% > left alphaRequired for positive moodCan train left alpha down to treat depressionActivates left hemisphere(c) T. F. Collura, Ph.D.
71Two Alphas Alpha actually shows 2 bands May wax and wane independently 9 – 12 HzStandard resting rhythmTypical occipital alpha wave7 – 9 HzRelated to emotional processingImportant to frontal asymmetryLonger round-trip may indicate more processing(c) T. F. Collura, Ph.D.
72Low Beta (typ. 12 – 15 Hz) Distribution: localized by side and lobe Subjective states: relaxed, focused, integratedTasks & behavior: relaxed, attentivePhysiological correlates: inhibited motion (when at sensorimotor cortex)Effects: relaxed focus, improved attentive ability(c) T. F. Collura, Ph.D.
73Sensorimotor Rhythm (SMR) (typ. 12 – 15 Hz) Resting rhythm of the motor systemLargest when body is inactiveIndicates intention not to moveMeasured over sensorimotor strip C3/Cz/C4Round-trip thalamus-cortex-thalamus ~ 80 msTypically 12 – 15 HzAlso called “14 Hz” or “Tansey” rhythm(c) T. F. Collura, Ph.D.
74Beta (typ. 16 – 20 Hz) Distribution: localized, over various areas Subjective states: thinking, aware of self and surroundingsTasks & behavior: mental activityPhysiological correlates: alert, activeEffects: increase mental ability, focus, alertness(c) T. F. Collura, Ph.D.
75High Beta (typ. 20 – 30 Hz) Distribution: localized, very focused Subjective states: alertness, agitationTasks & behavior: mental activities (math, planning, etc)Physiological correlates: activation of mind and body functionsEffects: alertness, agitation(c) T. F. Collura, Ph.D.
76Gamma (“40 Hz”) AKA “Sheer” rhythm Collura (1985) found 6-7 bursts/second in PSI states using FFT techniqueDavidson found sustained gamma in advanced meditatorsShort bursts require wide (35 – 45) filters to detectOthers define:25-30 Hz (Thatcher)32-64 Hz (Thornton)(c) T. F. Collura, Ph.D.
77Gamma (“40 Hz”) Distribution: very localized Subjective states: thinking, integrated thoughtsTasks & behavior: high-level information processing “binding”Physiological correlates: information-rich tasks, integration of new materialEffects: improved mental clarity, efficiency(c) T. F. Collura, Ph.D.
78DC (“Direct Current”) Standing potential, 0.0 – 1 Hz Reflects glial, other mechanismsIncludes sensor offset and driftMay include “injury” potentialDifficult to record, may be unstableRequires Ag/AgCl sensorsSCP is more useful clinically(c) T. F. Collura, Ph.D.
79SCP (“Slow Cortical Potentials”) Typically 0.01 – 2 HzPrimarily glial originAssociated with general brain activation“Bareitschaft” potential evident preceding voluntary motor movementLarge shifts seen preceding seizuresTraining useful in epilepsy, BCI(c) T. F. Collura, Ph.D.
80Coherence Training Coherence reflects similarity between 2 channels Measure of information sharingCoherence may be trained up or down“Goldilocks” effect – may be too high or too low at any given siteAlpha coherence can be trained up bilaterally (occipital or parietal) without adverse reaction(c) T. F. Collura, Ph.D.
81Typical EEG metrics Amplitude (microvolts) Frequency (Hz, peak or modal)Percent energyVariabilityCoherence between 2 channels (percent)Phase between 2 channels (degrees or percent)Asymmetry between 2 channels (ratio or percent)(c) T. F. Collura, Ph.D.
82Effective FeedbackFast – provides timely information to allow temporal bindingAccurate – so brain has good information to work with, not ambiguous or superfluousAesthetic – so brain will respond well to the content of the feedback without undue effort or confusion(c) T. F. Collura, Ph.D.
83Learning Mechanisms Operant Conditioning Classical Conditional Concurrent LearningSelf-EfficacyGeneralization(c) T. F. Collura, Ph.D.
84Instructions to Trainee Allow the sounds to comeDo not “try” to do anythingAllow yourself to learn what it feels like when you get a pointRelax and pay attention to the screenLet the sounds tell you when you are in the desired state(c) T. F. Collura, Ph.D.
85Standard Protocols Alert C3 – beta up; theta, hibeta down Deep Pz – (Penniston) alpha up, theta upFocus C4 – SMR up; theta, hibeta downPeak C3-C4 – alpha coherence upPeak2 C3-C4 – alert and focus combinedRelax Oz – alpha up; theta, hibeta downSharp Fz – broadband squash(c) T. F. Collura, Ph.D.
86Additional Activities ReadingLegosDrawingTetrisColoring bookPuzzlesHomeworkAllow trainee to attain relaxed, focused state even while under a task(c) T. F. Collura, Ph.D.
87Deep States Training Alpha/Theta Training Penniston / Kulkowsky; Bill ScottInduces Hypnogogic StateDe-activates fear mechanismsEngages memory consolidationEffecting internal changeUsed in conjunction with psychotherapyPossibility of abreaction(c) T. F. Collura, Ph.D.
88Low Freq vs. High Freq Training (c) T. F. Collura, Ph.D.
89Low Freq vs. High Freq (cont) (c) T. F. Collura, Ph.D.
90Low Freq vs. High Freq (cont 2) (c) T. F. Collura, Ph.D.
91MINI-Q External headbox connected to 2-channel EEG Scans sensor pairs sequentiallyUses linked ears referenceUses 12 selected sites2 channels, 6 positionsAllows head scan using 2-channel EEGTake e.g. 1 minute per positionSoftware assists with prompts, organizes dataPrimarily for assessment, can also be used for training(c) T. F. Collura, Ph.D.
92(c) 2007 Thomas F. Collura, Ph.D. MINI-Q Quads1. Fz Cz T3 T4 – Memory / Planning2. F3 F4 O1 O2 – Seeing / Planning3. C3 C4 F7 F8 – Doing / Expressing4. P3 P4 T5 T6 – Perception / Understanding5. Fp1 Fp2 Pz Oz – Attention / Perception5a. T3 T4 Pz Oz – Memory / Perception6. O1 O2 C3 C4 – Seeing / Doing7. F7 F8 F3 F4 – Planning / Expressing8. T5 T6 Fz Cz – Understanding / Doing(c) 2007 Thomas F. Collura, Ph.D.(c) T. F. Collura, Ph.D.
93(c) 2008 Thomas F. Collura, Ph.D. MINI-Q II Quads(c) T. F. Collura, Ph.D.(c) 2008 Thomas F. Collura, Ph.D.
94Purpose of z scores Method to understand a population Method to understand an individualUses statistics to evaluate quantitiesStandard method applicable to any measurementImportant for connectivity, phase, asymmetry measures(c) T. F. Collura, Ph.D.
95Basic Concepts Normative population Normative statistics Database of valuesMethod to quantify any individual(c) T. F. Collura, Ph.D.
96Concepts of z scores Measure a large population Determine population statisticsMeanStandard deviationConvert any single measurement into a z scoreStandard measure of “how normal”(c) T. F. Collura, Ph.D.
97Live versus Static z scores LZ-scores measure instantaneous deviationLZ-scores typically smaller in magnitudeSustained LZ-score results in larger static Z-score“Score on a hole” versus “Score for the game”No standard to convert betweenTypical target is 0 for either(c) T. F. Collura, Ph.D.
98Normal 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) 2008 Thomas F. Collura, Ph.D.(c) T. F. Collura, Ph.D.
100Bell Curve using z scores (c) T. F. Collura, Ph.D.
101Z Scores - equations Standard Normal Distribution: Z score for any sample value x:(c) T. F. Collura, Ph.D.
102What is a z scoreA metric based on any measurement and the associated population statisticsTells “how many standard deviations away from the mean”Defined as:(c) T. F. Collura, Ph.D.
103Z score ranges +/- 4 sigma: +/- 1 sigma: Includes middle 68% of populationFrom 16% to 84% points+/- 2 sigma:Includes middle 95% of populationFrom 2% to 98% points+/- 3 sigma:Includes middle 99.8% of populationFrom .1% to 99.9% points+/- 4 sigma:Forget about it(c) T. F. Collura, Ph.D.
104Z score example Adult height Mean height = 6 feetStandard deviation = 3 inches = .25 ft.Height 6 feet 6 inchesCompute Z = 6.5 – 6.0 / .25 = 2.0Height 5 feet 9 inchesCompute Z = 5.75 – 6.0 / .25 = -1.0Height 5 feetCompute z = 5.0 – 6.0 / .25 = -4.0(c) T. F. Collura, Ph.D.
105Z score training approach Compute ongoing z scoresApply as training variablesEstablish targets and criteriaProvide feedbackUses unique predefined bands, not adjustable in z DLL softwareBands are independent of those used in the main EEG software(c) T. F. Collura, Ph.D.
106Z scores used for EEG Absolute power Relative power Power ratios AsymmetryCoherencePhase(c) T. F. Collura, Ph.D.
108Z scores – 2 channels For each site (2 sites) 8 absolute power8 relative power10 power ratiosFor the connection (1 pathway)8 asymmetry8 coherence8 phase(c) T. F. Collura, Ph.D.
109Live Z Scores – 2 channels (76 targets) 26 x = 76 (52 power, 24 connectivity)(c) T. F. Collura, Ph.D.
110Z scores – 4 channels For each site ( 4 sites) 8 absolute power8 relative power10 power ratiosFor the connection (6 pathways)8 asymmetry8 coherence8 phase(c) T. F. Collura, Ph.D.
111Live Z Scores – 4 channels (248 targets) 26 x x 6 = 248 (104 power, 144 connectivity)(c) T. F. Collura, Ph.D.
112Infiniti with Z-Scores (c) T. F. Collura, Ph.D.
113Z-Score Targeting Options Train Z Score(s) up or downSimple directional trainingTrain 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 thresholdCombine the above with other, e.g. power training(c) T. F. Collura, Ph.D.
114Z-score Coherence Range Training (feedback when Z-score is in desired range) (c) T. F. Collura, Ph.D.
115Range Function Rng(VAR, RANGE, CENTER) = 1 if VAR is within RANGE of CENTER= 0 elseRng(BCOH, 10, 30)1 if Beta coherence is within +/-10 of 30Rng(ZCOB, 2, 0)1 if Beta coherence z score is within +/-2 of 0(c) T. F. Collura, Ph.D.
116Range 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 rangeCreates new training variable, target = 4(c) T. F. Collura, Ph.D.
117Coherence ranges training with Z Scores (4 coherences in range) (c) T. F. Collura, Ph.D.
118Combined Amplitude and Coherence-based protocol If (point awarded for amplitudes) AND (coherence is normal) THEN (play video for 1 second)(c) T. F. Collura, Ph.D.
119PercentZOK() function PercentZOK(RANGE)Gives percent of Z Scores within RANGE of 01 channel: 26 Z Scores total2 channels: 76 Z Scores total4 channels: 248 Z Scores totalValue = 0 to 100Measure of “How Normal?”All targets have a specified size “bulls-eye”(c) T. F. Collura, Ph.D.
120Z Score “percent” Targeting Strategy Feedback contingency based upon:Size of target bulls-eyes (“range”)Number of targets required (‘target percent hits”)Possibility of biasing targets up or downTargets may be enhances and/or inhibitsWide targets will automatically select most deviant scoresTraining automatically combines and/or alternates between amplitude & connectivity(c) T. F. Collura, Ph.D.
121Z Score training using Multivariate Proportional (MVP) Feedback 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) 2008 Thomas F. Collura, Ph.D.(c) T. F. Collura, Ph.D.
122Effect of changing %Z threshold Reduce threshold -> percent time meeting criteria increases (c) T. F. Collura, Ph.D.
123Effect of widening Z target window Widen window -> higher % achievable, selects most deviant scores(c) T. F. Collura, Ph.D.
124Z-score based targeting Threshold replaced with target sizeFeedback contingency determined by target size and % hits requiredEliminates need for “autothresholding”Integrates QEEG analysis with training in real timeProtocol automatically and dynamically adapts to what is most neededConsistent with established QEEG-based procedures with demonstrated efficacy(c) T. F. Collura, Ph.D.
125Progress of Live Z-Score Training (c) T. F. Collura, Ph.D.(c) 2008 Thomas F. Collura, Ph.D.
126Progress of MVP Variable (c) T. F. Collura, Ph.D.(c) 2008 Thomas F. Collura, Ph.D.
127Live Z-Score Selection (c) T. F. Collura, Ph.D.(c) 2008 Thomas F. Collura, Ph.D.
128Live Z-Score Training Policy EEG deviation(s) should be consistent with clinical presentation(s)EEG normalization should be reasonableConsider coping, compensatory traitsConsider “peak performance” traitsConsider phenotypes & recommendationsMonitor subjective and clinical changes(c) 2008 Thomas F. Collura, Ph.D.(c) T. F. Collura, Ph.D.
129Normalize using Live Z-Scores Excessive Frontal SlowingExcessive Beta or high betaHypercoherence, not left hemisphere (F3-P3)Hypocoherence, not central (C3-C4)Localized (focal) excess or deficit(c) 2008 Thomas F. Collura, Ph.D.(c) T. F. Collura, Ph.D.
130Coping/Compensating Z-Scores Diffuse Low alphachronic pain (barrier)Diffuse high alphachronic anxiety coping mechanismPosterior asymmetriesPTSD, stress coping, cognitive dissonanceSubstance Abuse, AddictionEffects of EEG normalization not well understood(c) 2008 Thomas F. Collura, Ph.D.(c) T. F. Collura, Ph.D.
131“Peak Performance” Z-Scores Left Hemispheric Hypercoherence( F3-P3)Central Intrahemispheric Hypocoherence (C3-C4)“Excess” SMR C4“Excess” posterior alpha“Fast” posterior alphaNote: normalization can be avoided by keeping EEG sensors away from affected sites(c) 2008 Thomas F. Collura, Ph.D.(c) T. F. Collura, Ph.D.
132Phenotypes and Live Z-Scores Most Phenotypes “map” to live z-scoresDiffuse SlowFocal Abnormalities, not epileptiformMixed Fast & SlowFrontal Lobe Disturbances – excess slowFrontal AsymmetriesExcess Temporal Lobe AlphaSpindling Excessive BetaGenerally Low MagnitudesPersistent Alpha+ Diffuse Alpha deficitExceptions:“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 TrainingInhibits, rewards referenced to normal population or biased for enhance/inhibitPhenotypes do not (currently) consider connectivity deviationsHypocoherent Intrahemispheric (L or R)Hypercoherent Interhemispheric (e.g. frontal)Diffuse Coherence / Phase Abnormalities(c) T. F. Collura, Ph.D.(c) 2008 Thomas F. Collura, Ph.D.
133(c) 2008 Thomas F. Collura, Ph.D. Case of Jack3 YO MaleMild concussive head injuryAtonic, absence, myoclonic seizuresMulti-spike focus, uVInitially used inhibit & coherence trainingTemporarily improved, then declinedThen switched to z-score “all coherences normal” trainingSeizures stopped after 3 sessionsData courtesy of M. L. Smith(c) 2008 Thomas F. Collura, Ph.D.(c) T. F. Collura, Ph.D.
134Jack QEEG pre and post conventional training (c) T. F. Collura, Ph.D.(c) 2008 Thomas F. Collura, Ph.D Data from M.L. Smith
135Jack QEEG pre and post Z-score training (c) T. F. Collura, Ph.D.(c) 2008 Thomas F. Collura, Ph.D Data from M.L. Smith
136(c) 2008 Thomas F. Collura, Ph.D. Case of SL7YO Male, discipline problem, AD/HD, easily excited, aggressiveQEEG Pre and post z-score training21 sessions between QEEG’sPercentZ training at 85% rewardBegin F3 F4 P3 P4, later F3 F4 C3 C4Begin at +/- 2.0 S.D.All scores except 1 within 1.5 S.D. after trainingSignificant clinical improvementData courtesy Drs. C. Stark & W. Lambos(c) 2008 Thomas F. Collura, Ph.D.(c) T. F. Collura, Ph.D.
137SL - EO Pre and Post (c) 2007-9 T. F. Collura, Ph.D. (c) 2008 Thomas F. Collura, Ph.D Data from Stark & Lambos
138SL - EO Loreta Pre and Post (c) T. F. Collura, Ph.D.(c) 2008 Thomas F. Collura, Ph.D Data from Stark & Lambos
139SL - EC Pre and Post (c) 2007-9 T. F. Collura, Ph.D. (c) 2008 Thomas F. Collura, Ph.D Data from Stark & Lambos
140SL - EC Loreta Pre and Post (c) T. F. Collura, Ph.D.(c) 2008 Thomas F. Collura, Ph.D Data from Stark & Lambos
141(c) 2008 Thomas F. Collura, Ph.D. SummaryNew method using normative dataComprehensive whole-head approachNormalizes both activation & connectivityMultiple targeting & biasing capabilityConsistent with QEEG & Phenotype approachesProvides brain with complex informationSimple training formatEffective for assessment & training(c) 2008 Thomas F. Collura, Ph.D.(c) T. F. Collura, Ph.D.
142References (c) 2007-9 T. F. Collura, Ph.D. Thatcher, R.W., Walker, R.A. and Guidice, S. Human cerebral hemispheres develop at different rates and ages. Science, 236: , (This was our first publication with N = 577).Thatcher, R.W. EEG normative databases and EEG biofeedback. Journal of Neurotherapy, 2(4): 8-39, (N = 577 with many details).Thatcher, R.W. EEG database guided neurotherapy. In: J.R. Evans and A. Abarbanel Editors, Introduction to Quantitative EEG and Neurofeedback, Academic Press, San Diego, (N = 577 with many details).Thatcher, R.W., Walker, R.A., Biver, C., North, D., Curtin, R., Quantitative EEG Normative databases: Validation and Clinical Correlation, J. Neurotherapy, 7 (No. ¾): , (61 adult subjects were added so that the N = 625. This is the number currently in use in the database).POSITION PAPER Standards for the Use of Quantitative Electroencephalography (QEEG) in Neurofeedback: A Position Paper of the International Society for Neuronal Regulation Journal of Neurotherapy vol. 8 no. 1 p Contributors: D. Corydon Hammond PhD, Professor, Physical Medicine and Rehabilitation, University of Utah, School of Medicine, Salt Lake City, UT Jonathan Walker MD, Clinical Professor of Neurology, Texas Southwestern Medical School, Dallas, TX Daniel Hoffman MD, Medical Director and Neuropsychiatrist, Neuro-Therapy Clinic, Englewood, CO Joel F. Lubar PhD, Professor of Psychology, University of Tennessee, Knoxville, TN David Trudeau MD, Adjunct Associate Professor, Family Practice and Community Health, University of Minnesota, Department of Psychiatry, Minneapolis, VAMC, Minneapolis, MN Robert Gurnee MSW, Director, Scottsdale Neurofeedback Institute/ADD Clinic, Scottsdale, AZ Joseph Horvat PhD, Private Practice, Corpus Christi, TX(c) T. F. Collura, Ph.D.
143(c) 2008 Thomas F. Collura, Ph.D. References IICollura, T.F. (2008) Whole-Head Normalization Using Live Z-Scores for Connectivity Training. NeuroConnections, April, 2008 and July, 2008Collura, T.F., Thatcher, R., Smith, M., Lambos, W., and Stark, C. (2008) Real-Time EEG Z-Score Training – Realities and Prospects, in Evans, J.,Budzynsky, T., Budzynsky, H., and Arbanel, Quantitative EEG and Neurofeedback, 2nd Edition: Elsevier.Kerson, C., Gunkelman, J., and Collura, T., (2008) Neurofeedback using the Phenotype and Z-Score Modalities, NeuroConnections, July, 2008.Johnstone, J., Gunkelman, J., and Lunt, J. (2005) Clinical Database Development: Characterization of EEG Phenotypes, Clinical EEG and Neuroscience, 36(2);Sterman, M.B., Mann, C.A., Kaiser, D.A. and Suyenobu, B.Y. Multiband topographic EEG analysis of a simulated visuomotor aviation task. Int. J. Psychophysiol., 16: 49-56, 1994.Sterman, M.B. Physiological origins and functional correlates of EEG rhythmic activities: Implications for self-regulation. Biofeedback and Self-Regulation, 21:3-33,1996.Silberstein, R.B., (2006) Dynamic Sculpting of Brain Functional Connectivity and Mental Rotation Aptitude, Progress in Brain Research, Vol. 159,Smith, M. L., (2008) Case Study – Jack, NeuroConnections, April, 2008.Stark, C. (2008) Consistent Dynamic Z-Score Patterns, NeuroConnections, April, 2008Thatcher, R.W. (2008) Z-Score EEG Biofeedback: Conceptual Foundations, NeuroConnections, April 2008Walker, J.E., Kozlowski, G.P., and Lawson, R. (2007) A Modular Activation / Coherence Approach to Evaluating Clinical /QEEG Correlations and for Guiding Neurofeedback Training Journal of Neurotherapy 11(1)(c) T. F. Collura, Ph.D.(c) 2008 Thomas F. Collura, Ph.D.
144Questions1. If you reverse the active and reference leads of an EEG amplifier, which of the following would result?A. The frequency content would shift up or downB. The waveforms would be displayed upside downC. The amplitude of the waveform could changeD. There would be no change in the signals at all(c) T. F. Collura, Ph.D.
145Questions2. CMRR or “common-mode rejection ratio” should be high in order to:A. Reduce the effects of 60 Hz interferenceB. Reduce the effects of motion artifactC. Reduce the effects of electrode imbalanceD. All of the above(c) T. F. Collura, Ph.D.
146Questions3. SMR or “sensorimotor rhythm” can be described as the following:A. A high-frequency alpha wave from the sensorimotor cortexB. A low-frequency beta wave recorded from C3, Cz, or C4C. A brain rhythm associated with the intention not to moveD. All of the above(c) T. F. Collura, Ph.D.
147Questions4. Which of the following apply to Penniston style “alpha/theta” training?A. Alpha is rewarded, and theta is inhibited.B. Alpha is rewarded, and theta is also rewardedC. Alpha is inhibited, and theta is inhibitedD. None of the above(c) T. F. Collura, Ph.D.
148Questions6. As defined, “percent time over threshold” reflects which of the following?A. The proportional amount of time that a signal exceeds the defined thresholdB. The overall size of the signalC. The level of effort of the traineeD. The amount of noise in the EEG(c) T. F. Collura, Ph.D.
149Questions7. Two bandpass filters are being used. They are identical. However, one is set for 8-12 Hz and the other is set for 7-13 Hz. The second filter will be able to do which of the following?A. Reject out of band signals better than the first filterB. Respond faster to component “waxing and waning”C. Have more precise cutoff frequenciesD. All of the above(c) T. F. Collura, Ph.D.
150Questions 8. What is a “Z-Score”? A. A measure of how large a value is B. A measure of how much a value is different from a population meanC. A measure of how healthy an individual isD. None of the above(c) T. F. Collura, Ph.D.
151Questions 8. Which of the following are true of live Z-Score training? A. It depends on a databaseB. It addresses brain connectivityC. It can teach the brain complex patternsD. All of the above(c) T. F. Collura, Ph.D.