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Analysis of Pupillary Data Society for Psychophysiological Research Workshop, Boston, September 14, 2011 Slide 2 Approaches to Pupillary Data Analysis Scale data to mm (can be performed at several points up to statistical analysis, discussed earlier) Identify and correct for blinks and other artifacts Smooth data through filtering algorithms Segregate trials according to conditions (for repeated measures designs) Identify stimulus onset for stimulus-locked analyses Identify time of RT for response-locked analyses Apply signal averaging for repeated measures designs as appropriate Determine critical variables to be used for analyses (absolute diameters, amplitude of change, peak responses, time of critical responses, frequency analyses, etc. Consider more sophisticated approaches including: Principal Components Analysis, Fourier/Wavelet Analyses, waveform comparison models Slide 3 Notes on Pupillary Signal Averaging Signal averaging approaches may be applied to pupillary data in repeated measures experiments just as they are in event-related potential experiments, with all of the same caveats The advantage is that small changes on the order of 0.01 mm or better (sometimes as detailed as several one-thousandths of a mm) can be reliable obtained. The pupil is affected by many types of ongoing changes in visual stimulation, CNS activity, etc., which make signal averaging advantageous, but has a higher signal-to-noise ration than the EEG, so that fewer trials are needed to extract a good averaged response, perhaps with minimums of: 3-5 trials for light reactions 5-10 trials for motor-initiated dilations 5-10 or more trials for other cognitive and emotional events Note that Orienting or highly novel events are limited in the number of trials that can be obtained because of the nature of the task Slide 4 Pupillary Response at Visual Threshold G. Hakerem & S. Sutton, Nature, 1966 Slide 5 Pupillary Light Reactions A single pupillary light reaction is not likely to be a good representation; usually at least 3-5 responses should be averaged The light reaction in darkness is a function of both stimulus characteristics and time of dark adaptation When a series of light stimuli are presented, the first response will start at a larger diameter and show a stronger contraction than subsequent stimuli. Unless standard stimuli and interstimulus intervals are employed, data are likely to be interpretable only through complex non-linear analyses, not simple averaging across all stimuli. At high rates of visual stimulation, pupillary responses become integrated (i.e., individual light reactions not identifiable). Slide 6 (for description of multiple measures, see Steinhauer et al., Psychophysiology, 1992) Slide 7 BaselineStart of Contraction Maximum Velocity Minimum Diameter Redilation Slide 8 Correcting for Artifacts Identify changes in diameter between subsequent points that are too large to be physiologically meaningful, or where pupil diameter is out of range (e.g., scaled value is 0 or 11 mm) If the blink/closure/artifact occurs at a critical time (during a brief visual stimulus, at the minimum of constriction, at the peak of a dilation interval) then it is usually best to discard the trial entirely To apply a linear correction, identify both the last good data point before the artifact (p 1 ) and the first good data point after the artifact (p L ). Take the difference between them (D = p L - p 1 ), divide by the number of points in the artifact plus one (D / (n bad +1)), and add this amount to p 1 incrementally for each of the substituted points. In our laboratories, we have found linear interpretation to be an appropriate and easily implemented correction to most types of data, although several researchers have employed more complex curve fitting to individual data. Slide 9 Filtering Data (1) Why filter if the pupil is a (relatively) slowly changing measure? Except for some light reactions, factors such as magnification, measurement, and signal processing introduce noise aspects that are unrelated to true pupillary changes. Filtering eliminates small artifacts that can be seen in the electronically derived continuous pupil waveform. Should filtering be performed before or after signal averaging? Actually, it does not matter computationally, but makes subsequent artifact correction and component identification easier if performed first. Do not filter if your are concerned about very precise time transients (e.g., start of light reaction to nearest 0.016 msec) as filtering smooths any sharp peaks and may shift some peak activity slightly. Slide 10 Filtering Data (2) There are a number of different approaches to filtering. We will describe a simple procedure described by Ruchkin & Glaser (1978). Each point is recalculated beginning with the original data set, using a band of points centered on each original point. The larger the number of points, the greater the effective filter. Assume an initial frequency of 60 Hz (16.7 msec between samples) The frequency of the filter f 0 is characterized as 1/[(2L+1)T], where L is the number of points below and above the center point, and T is the sampling interval (16.7 ms). Ruchkin and Glaser noted that the transfer function has a high secondary peak, so that the first pass is repeated again on the same data, thus compromising a two pass filter. The half power frequency (or -3 dB reduction) yields a gain of 0.707, so that the effective bandpass is 0.44 * f 0. Ruchkin, D.S. & Glaser, E.M.,1978 Slide 11 To calculate filter characteristic: a) multiply sampling rate by 0.44; for 60 Hz, this equals 60 Hz * 0.44 = 26.4 Hz b) divide by the number of points (2L+1) c) Thus, for data recorded at 60 Hz, a 3 point filter -> Low pass of 8.8 Hz a 5 point filter -> Low pass of 5.3 Hz a 7 point filter -> Low pass of 3.8 Hz a 9 point filter -> Low pass of 2.9 Hz Filtering Data (3) Slide 12 To operationalize filter (here for L=1, 3 point filter) 1) set up 3 arrays a, b, c 2) read data into array a 3) for each point x in array a, substitute the mean of x a -1, x a, and x a+1 for x b in array b; that is, x b = (x a -1+ x a + x a +1)/3 (repeat leading and trailing values for first and final points) 4) repeat, calculating array c values from array b This filter results in no phase shifts. Remember that high and low amplitude values will be attenuated Filtering Data (4) Slide 13 Tonic Data In some limited cases, only overall average diameter is needed (is pupil larger for different sustained tasks, is it smaller at end of a session?). Define a specific interval and take the average of points, possibly report variation. Slide 14 Phasic Data (data of D. Friedman et al., EEG Cl. Neuro., 1973) Prestimulus or Baseline: Often use average of at least 1000 msec, minimum 200 msec, prior to stimulus. Peak Dilation: Often seen 1200-1400 msec after critical stimulus. Use either peak or average of several points surrounding peak. During recording in light, earlier peaks or increases in diameter related to inhibition of the parasympathetic system may be seen. Often, integrated activity over much longer durations may be desired Slide 15 Digit Span Task Often, activity over much longer durations may need to be analyzed Slide 16 Probability Effects, Auditory Counting Task Slide 17 Probability Effects, Auditory Choice Reaction Task Slide 18 Principal Components Analysis Data for entire waveforms are entered into PCA, typically each average, each condition, each subject (can also be performed for individual subjects) Factors are based on similar variance effects across conditions, may or may not correspond to peaks or sustained activity periods Factors are extracted in decreasing order of variance explained Factor loadings reflect influence of variables across time points A factor score representing time by loading effects is obtained for each factor for each waveform Factor scores may be used in repeated measures analyses Slide 19 Slide 20 Slide 21 Steinhauer & Hakerem, 1992 Slide 22 Slide 23 Pupil: Time Frequency Analysis Pupillary Sleepiness Test Franzen et al (in prep) seconds Slide 24 Hypothesis: Decreased oscillatory activity in patient groups would be indicative of disruption of central parasympathetic control. Pupil diameter was assessed in darkness for 11 minutes as subjects fixated three small red LEDs. The pupil was digitized 60 times/sec with a resolution of 0.05 mm using an ISCAN ETL-400 system. Initial Data Reduction: The pupil was first corrected for blinks (S1a), then smoothed and detrended (correcting for baseline, S1b). PUI can be computed as the absolute change in diameter across time, essentially a string measure of activity, but this includes all frequencies of change. However, Ludtke et al. (1998) suggested using low frequency slow changes only. A wavelet analysis implemented in MatLab assessed frequencies at each sampling point, expressing PUI as the sum of power in the 0-0.6 Hz frequency range (S1c). Wavelet analysis of Oscillatory Activity S1a S1c S1b Slide 25 Power Derived from Wavelet Analysis Slide 26 MATLAB IMPLEMENTATIONS Thanks to Greg Siegle, Ph.D. Depts. Of Psychiatry and Psychology University of Pittsburgh Slide 27 The environment Matlab is a general purpose language for mathematical and graphical operations All operations can be done interactively or by calling stored functions The basic computational unit is a row x col matrix x=[1 2 3;4 5 6] x = 1 2 3 4 5 6 Scalers (e.g., 3) and vectors (e.g., [3 4 5]) are supported Slide 28 Plotting pts=0:pi/20:2.*pi; x=cos(pts); y=sin(pts); plot(sin(pts)); plot(pts,y); plot(y); plot (x,y); axis on/off, clf xlabel(time); ylabel(dilation); title(my graph); plot([x; y]); plot([x; y]); legend (cos,sin); Specialized plots, e.g., errorbar(pts,y,x); hist(pts) figure; figure(2); subplot(2,2,1); plot(x,y); Slide 29 Functions Each function, accessible as a command, is stored in its own file e.g., storing a function called parabola.m function y=parabola(x) % squares its input % usage: y=parabola(x) y=x.^2; Allows you to say plot(parabola (1:10)) and help (parabola) Slide 30 Operations in functions if a>2 b=4; else b=5; end For ct=1:5 b=b+1; end while ct Raw data After trial segmentation >> p=procsilkkvidexample(1002) found 34280 records in data dropped 1 of 51 trials p = FileName: '1002KVID.isc' header: [1x1 struct] EventTrain: [34280x1 double] RescaleData: [1x34280 double] BlinkTimes: [34280x1 logical] NoBlinks: [34280x1 double] NoBlinksUnsmoothed: [1x34280 double] NoBlinksDetrend: [34280x1 double] EventTicks: [156x1 double] EventCodes: [156x1 double] RescaleFactor: 60 EventTimes: [156x1 double] AllSeconds: [34280x1 double] TrialStarts: [51x1 double] TrialEnds: [51x1 double] TrialLengths: [51x1 double] NumTrials: 51 StimLatencies: [51x1 double] StimTypes: [51x1 double] TrialTypesNoDrops: [51x1 double] PupilTrials: [51x661 double] EventTrials: [51x661 double] BlinkTrials: [51x661 double] DetrendPupilTrials: [51x661 double] NormedPupTrials: [51x661 double] NormedDetrendPupTrials: [51x661 double] TrialSeconds: [1x661 double] Suspect: [1x51 logical] stats: [1x1 struct] drops: [1x51 logical] TrialTypes: [51x1 double] Conditions: [1 2 4] ConditionMeans: [3x661 double] ConditionSds: [3x661 double] condstats: [1x1 struct] The Pupil Toolkit Valence Identification Task Slide 36 p=procvfg(5107,'numorder',2); The Pupil Toolkit Digit Sorting Task (sort 3, 4 or 5 numbers) Slide 37 Reading data from different pupilometers ISCAN readiscan readiscan05text readiscanbehavobstext ASL to use must first install ASL matlab drivers readasl2006 readasl2007 readasl2008 readasltext readasltextlunalab The Pupil Toolkit Slide 38 How the blink elimination routine works data = stublinks(data,graphics,lrtask,manualblink s,lowthresh) Smooths the data using a 3 pt flat filter applied twice Identifies blinks via many criteria (e..g, >.5mm change in 1 sample) Kills single good values between blinks, Interpolates values between blinks Fixes blinks at beginning and end of data collection The Pupil Toolkit Slide 39 Graphs of a single subjects data all trials showpupiltrials(p) plotpupiltrialmatrix(p); threedpupilgraph(p); rqplottrialmatrix(p) pupilautocorrgraph(p) The Pupil Toolkit Slide 40 Single subjects data aggregate waveforms available 1 minute after testing via rqplotaggpupilcondmeans(p) Start with plotpupilcondmeans(p) The Pupil Toolkit Slide 41 Aggregate data available after some work 27362592 2448 2304 2160 2016 1872 1728 158414401296 1152 1008 864 720576 432 288 144 0.4.3.2.1 0.0 -.1 -.2 GRPPR Control - nonpers Control - persrel Depressed - nonpers Depressed - persrel The Pupil Toolkit Slide 42 Statistics available on every trial via p.stats=pupiltrialstats(p) Whole trial: % blinks, blink_in baseline, mean amplitude, slope Trial Segments: mean baseline amplitude Relative to peak: peak, peak latency,, Slope post peak Within a window: mean amplitude, peak, peak latency, max, min, slope Relative to stimulus: Peak amplitude post stimulus, peak latency post stimulus, Relative to rt: peak amplitude post rt, slope post rt The Pupil Toolkit Slide 43 Example the face mask study Question do different masks cause differential light reflexes following faces? Data in facemask\data\fmpall.mat Slide 44 Slide 45 The masks blank pentagonsswishes faceblur shuffledfractal Slide 46 1000 Aggie grand average mask face Slide 47 1000 Aggie condition means mask face p=procfacemask(1000) Slide 48 1001 Greg condition means mask face p=procfacemask(1001) Slide 49 Mean of 13 subjects Metrics: flat & low dip peak-trough and similar median to the face. Winner: swishes maskface fmaggmeans(pall,1) Slide 50 Comparing waveforms Comparing waveforms is not trivial We have implemented functions for computing tests at every sample along the waveform. Unless these comparisons are a-priori I recommend using these only in the context of a group x time or condition x time interaction done on a dimension- reduced dataset. Controlling type I error Guthrie & Buchwalds (1991) technique Guthrie D, Buchwald JS (1991): Significance testing of difference potentials. Psychophysiology 28:240-244. Blair & Karniskis (1993) technique Blair RC, Karniski W (1993): An alternative method for significance testing of waveform difference potentials. Psychophysiology 30:518- 524 The Pupil Toolkit Slide 51 T-tests at every sample, marking intervals significantly long enough to care about via Guthrie & Buchwald (1991) seconds Never Depressed (41) Unmedicated Depressed (47) Regions of significant differences. Yellow is ptype of fit->custom Fit-options = linear least squares or other as I choose Can plot confidence interval around curve function Can output fit params via save-to-workspace Stats regression nonlinear mixed effects The Pupil Toolkit"> Stats toolbox Distribution fitting tool Can have nominal data with nominal class via nominal command reduces space greatly and lets you select, e.g., by category, and make plots by category. W/ curve fitting (cftool) theres a goodness of fit metric and can get quantitative measures of fit for hyp testing And can use custom equation by selecting new fit->type of fit->custom Fit-options = linear least squares or other as I choose Can plot confidence interval around curve function Can output fit params via save-to-workspace Stats regression nonlinear mixed effects The Pupil Toolkit Slide 60 Pupil dilation outside fMRI Pupil dilation during fMRI 024681012141618 0 0.05 0.1 0.15 0.2 0.25 0.3 proportion of maximum dilation seconds 024681012141618 -0.1 -0.05 0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 seconds 3 4 5 Pupil Dilation as a continuous measure of cognitive load Siegle et al, 2003, Neuroimage Slide 61 eye-tracking colored by pupil Courtesy of Greg Siegle, PhD. Slide 62 To Play or Download these presentations: http://www.wpic.pitt.edu/research/biometrics Go to the Lab Publications link Click on the Biometrics Archives Look under SPR 2011 Workshop Select Presentations or References Slide 63 http://www.wpic.pitt.edu/research/biometrics (select Lab Publications) Slide 64 Download Presentations or select Reference List Slide 65 Slide 66 Slide 67 For additional information: http://www.wpic.pitt.edu/research/biometrics sthauer@pitt.edu 412-954-5366