Extracting Cortical Inhibition Correlates in ERP–images withinAdult ADHD

J. Kristof Schubert1, Ernesto Gonzalez–Trejo1, Wolfgang Retz3, Michael Rosler3, Tanja Teuber2,Gabriele Steidl2, Daniel J. Strauss1,3,4 and Farah I. Corona–Strauss1

Abstract— ADHD presents a considerable social burdenin adult life, and until recently was thought to be exclusiveof children and adolescents. Techniques for an optimizeddiagnosis (based on electrophysiology) have been provenuseful. In this paper, we present a new method for pro-cessing chirp–evoked, paired auditory late responses withinthe original time–domain, through two–dimensional imageprocessing, using the non–local means approach. Resultsshow effective denoising of cortical inhibition correlates insingle sequences, which leads to an enhanced recognitionof physiological features with fewer trials, as comparedto averaging methods, reducing data loss and acquisitiontime. These results allow to optimize diagnosis by providinguseful pointers regarding cortical inhibition within adultADHD.

I. INTRODUCTION

The prevalence of attention–deficit/hyperactivity dis-order (ADHD) into adulthood has recently been broughtinto attention; while ADHD is a frequent psychiatricdisorder, it was thought to be exclusive of children andadolescents; it is now accepted as a disorder verifiable inadulthood up to 60% as complete or partial symptomatic[1], [2]. It represents a public health burden; subjectswith ADHD have to face both cognitive and socialchallenges in daily activities, normally manageable [3],[4]. The clinical diagnosis of ADHD is mainly based onself–rating tests and interviews, which may be biasedby factors such as IQ, mood of the subject, his or herwill to cooperate, among others. Objective indicatorshave been proposed, such as cortical impairment [5],[6]. A promising approach into an objective pointer

1Systems Neuroscience & Neurotechnology Unit, Saarland Uni-versity, Faculty of Medicine, Neurocenter, Building 90.5, D–66421Homburg/Saar, Germany strauss at snn-unit.de

2Mathematical Image Processing and Data Analysis, Technical Uni-versity Kaiserslautern, Felix Klein Zentrum, D–67663 Kaiserslautern,Germany

3Institute for Forensic Psychology and Psychiatry, Neurocenter,Saarland University Hospital, Building 90.3, D–66424 Homburg/Saar,Germany

4Leibniz–Institut fur Neue Materialien gGmbH, Campus D2 2, D–66123 Saarbrucken, Germany

The work of the authors has been partially supported by DeutscheForschungsgemeinschaft (DFG), Grant STR 994/1-1 and STE 571/11-1, respectively.

for adult ADHD has been the use of event–relatedpotentials (ERPs) [7]. The combination of ERPs andpaired pulse stimulation, using the principle of longinterval cortical inhibition (LICI) [8], [9], allows thestudy of the response to consecutive stimuli and theinhibitory regulation elicited in the cortex. Focusing onauditory evoked potentials (AEP), and more specifically,auditory late responses (ALRs), LICI allows studyingthe elicited components up to 2000 ms post–stimulus.In healthy subjects, a test stimulus (a tone, click, orchirp, for example) following an identical conditioningstimulus, within an inter stimulus interval (ISI) of 500milliseconds, evokes a smaller response in terms ofamplitude, due to intracortical inhibition [10], [11]. Ourworkgroup has already shown that it is possible to aid thediagnostic procedure of ADHD through a phase studyof paired chirp–evoked ALRs within adult ADHD [12].Clinical specialists, however, are used to an originaltime–domain–based analysis that features amplitude andlatency, rather than abstract phase–based features. Whilethe phase study presented an advantage compared witha simple amplitude measurement in time–domain, es-pecially when dealing with short–length measurements(in that case, 40 available 1–second–long sweeps aftereach stimulation in the best case), the amplitude resultscan be additionally processed, in order to improve theinformation given out by single sweeps. Through imageprocessing, we propose a method in order to acquirerelevant information from a reduced number of sweeps,aimed at reducing the processing time and improvingthe overall tools available as a support for the clinicaldiagnosis.

II. MATERIALS AND METHODS

A. Participants

The EEG sweeps employed for the data processingwere taken from a previous study in our workgroup [12].The study was performed on 30 right–handed subjects(16 male, 14 female), with ages ranging from 20 to47 years (mean age, 30.7 ± 9.0), from those, 15 wereADHD patients, recruited from a specialized ADHD

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ambulance and 15 control subjects, with matched ageand gender. ADHD patients were diagnosed accordingto the Diagnostic and Statistical Manual of MentalDisorders (DSM–IV), Wender–Utah Rating Scale re-garding childhood (WURS–k) and self–rating and inter-views (ADHD–SR, ADHD–DC) criteria by a consultantpsychiatrist specializing in adult ADHD. Patients andcontrol subjects did not meet diagnostic criteria for anypersonality disorder.

B. Stimulation

Two de Boer chirps [13] (frequency range: 0.1–10kHz, intensity: 80 dB SPL) were employed as stim-ulus, played through isolating headphones (HDA 200,Sennheiser GmbH, Germany) into the right ear of thesubject (left side was muted). The ISI between chirpsused was 500 ms in order to elicit LICI [14], with 8s between each pair of chirps, for a total of 40 pairs.Subjects were asked to relax, close their eyes and trynot to sleep; there were no task–related activities. Thefirst chirp will be referred as conditioning chirp (CC)and the second as test chirp (TC) in this article.

C. Data Acquisition

Ag/AgCl electrodes were used to acquire the EEGsignal over the right mastoid (ipsilateral to stimuli),vertex (reference) and forehead (ground). The left mas-toid signal was acquired as well for future references.Electrode impedances were kept at 5 kΩ or less. Thesignal was digitized by means of a 16–channel, 24–bit biosignal amplifier (g.USBamp, Guger Technologies,Austria) at a sampling rate of 512 Hz, controlled viaSIMULINK (The Mathworks Inc, U.S.A.); the outputdata was stored as a readable MATLAB (The Math-works Inc, U.S.A.) matrix. No online filtering was used,only post–filtering. The chirp file was stereo–recorded,containing in the right channel the chirp sound and inthe left channel (muted for the subject) a trigger signal,used as a time reference for post–processing. This triggersignal was converted to a TTL signal by means of atriggerbox (g.TRIGbox, Guger Technologies, Austria)and also acquired with the amplifier and converted todiscrete values through SIMULINK.

D. Data Processing

After acquiring the EEG, the data was processed inMATLAB. The mean was subtracted to remove theoffset and baseline correction was used to remove thefirst 30 samples of each measurement, as they couldcontain artifacts inherent to the start of the measurement.These artifacts were present only at the beginning of therecording and had no influence on the time interval of

interest. Filtering for the EEG was made with a windowbased FIR bandpass filter (2–30 Hz band). For this study,the signal was segmented into sweeps containing bothchirps (CC and TC), as well as one second (512 samples)segment after TC; this allowed each sweep to be studiedas a one-second post–chirp window. Once segmented,an artifact filter (50 µV) was used to discard segmentswith sudden amplitude peaks caused by movement ofthe subject. Following these steps, only 24 (12 of eachgroup) of the initially 30 subjects showed a sufficientamount of sweeps to be analyzed. The data of the 6 leftsubjects was discarded and neglected.

E. Image Processing

In [15] and [16] we introduced two–dimensionalimage processing techniques for the denoising of single–sweeps by means of the so–called ERP image (single–sweeps in matrix representation). Here, the amplitudeof the sweeps is encoded in a color–scale map. We keptthe idea of the two–dimensional denoising of single–sweeps. Since we showed in [16] that the non–localmeans (NLM) approach outperforms other established2D denoising techniques, this well suited method is usedagain in order to better and faster distinguish betweensubjects suffering from ADHD and healthy subjects.

The Non–Local Means Method

Consider the set A = sn ∈ RM : n = 1, 2, ..., Nof N sampled ERP single sweeps within the timeinterval [0,M/fs] where fs is the sampling frequency.The ERP image S ∈ RN×M can be obtained from Asuch that S = (s1, s2, . . . , sN )T . The two–dimensionalNLM filter exploits the self–similarity in images. Themain feature of the ERP image S is the induced self–similarity over the individual sweeps s due to the use ofevent related experimental paradigms for fixed stimulussettings. The NLM algorithm is characterized by so–called image patch methods, i.e., each pixel si, i =1, . . . , J with J = NM in the image is comparedtogether with its neighborhood to other patches in theimage. For each comparison a weight coefficient ξi,j ∈R (i, j = 1, . . . , J is assigned to the center pixelsi depending on the similarity of the image patches.The denoised pixel qi is the weighted average of allthe surrounding pixels in the ERP image S such thatqi = 1

γi

∑Jj=1 ξi,jsj (1), with γi =

∑Jj=1 ξi,j . Let

si+I and sj+I be two–dimensional patches of the ERPimage S with centers si and sj , respectively, whereI is an appropriate index set. Further, we introduce asampled version of a two–dimensional Gaussian kernelφσ = (φσ,k)k∈I with standard deviation σ. The weightswhich quantify the similarity of si+I to sj+I are now

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given by ξi,j = exp(− 1

λ

∑k∈I φσ,k|si+I − sj+I |2

).

Here, the parameter σ controls the influence of theneighboring pixels on the weights ξi,j . The amount ofdenoising is controlled by λ > 0. Applying (1) to theERP image S yields the denoised version Q with qn

being its rows, i.e., the denoised single sweeps of sn.

F. Data Analysis

To objectively evaluate the results of the suggestedimage processing tool, we estimate the relative ampli-tude of the sweeps before and after denoising, i.e., thesweeps si are normalized with respect to their squaredℓ2–norm ∥si∥2ℓ2 . Then, we compare the unprocessed datawith the denoised data in order to quantify the quality ofthe N1–P2 complex in relation to the whole respectivesweep.

III. RESULTS

Fig. 1 shows exemplarily the results of applying theNLM method to the ERP images obtained from CC andTC stimulation for one ADHD and one healthy subject.The denoised ERP images are depicted below theirrespective unprocessed original images. After denoising,the ERP images clearly reveal the known traces of, e.g.,the N1 and P2 component. Those retrieved images aresuitable for an individual analysis of every single sweep.Hence, one individual sweep is exemplarily taken fromthe images in order to show the possibility of singlesweep analysis. The sweeps taken from the denoisedERP images appear to be smoother compared to theircounterparts from the unprocessed ERP images. Thedenoised sweeps recover their morphology and the N1–P2 complex can be easily analyzed by latency and ampli-tude, whereas the N1–P2 complex from the unprocessedsweeps can hardly be recognized. Notice the amplitudedifferences between CC and TC for the control groupand the nearly equal amplitudes for the ADHD subject.In order to objectively show the effectiveness of theNLM algorithm we calculated the relative amplitude.Fig. 2 shows the results for one random subject. Ob-viously a Gaussian–based denoising implies a decreasein amplitude (top left). On the other hand, noise, i.e.,high frequent oscillations, is reduced to a minimumexpressed as a lower ℓ2–norm. Hence, calculating therelative amplitude of the sweeps with respect to theirsquared ℓ2–norm yields an enhanced N1–P2 complex(top right). Extending this procedure to all sweeps yieldsboth ERP images below. The one on the left depicts therelative amplitudes for the unprocessed sweeps, whilethe one on the right shows the relative amplitudes forthe denoised sweeps. Again, the vertical traces of theN1–P2 complex are more visible in the denoised version

Fig. 1. Single–Sweep Analysis: Top (Bottom): Pre–post comparisonof individual sweeps of one subject with ADHD (without ADHD). TheERP images of CC and TC stimulation before and after denoising areshown on the left. One individual sweep is taken from each, CC (bluetrace) and TC (green trace), respectively, in order to compare theirlatencies and amplitudes.

providing a better basis for single sweep analysis. Therelative amplitudes of the NLM processed ERP imagesare larger compared to the original ones.

IV. DISCUSSION

In this paper, we introduced a two–dimensional de-noising process as a supporting tool for ADHD di-agnosis. Although our workgroup has shown that thephase synchronization stability (PSS) represents a ro-bust distinguisher between healthy subjects and ADHDpatients [12], an original time–domain–based analysis isstill common in clinical daily routine. Hence, clinicalexperts could benefit from the denoised illustration ofsingle–sweeps in matrix representation, that offer a fastestimation of the well–established clinical parameterssuch as amplitude and latency in the original time–domain. Besides the PSS, the analysis of the N1–P2amplitude as a marker of reduced intracortical inhibitionand hence as an indicator for ADHD, and especially

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Fig. 2. Pre–post comparison of single sweeps of one randomsubject of the control group. Top left: One individual single–sweep(blue trace) is compared to its denoised version (green trace). Topright: Both sweeps are normalized with respect to its squared ℓ2–norm. The N1–P2 complex of the denoised sweep is enhanced. Thesweep morphology is preserved. Bottom left and right: The relatedERP images of the unprocessed and denoised single–sweeps withtheir relative amplitudes. The latter one reveals clearly visible N1–P2 complex traces.

the analysis of single–sweeps per se, contributes toimprove the information given by the ERP data. Asseen in Fig. 1, analyzing individual sweeps gives a moredetailed view into cognitive processes. The low SNRin the unprocessed sweeps prohibits their direct study.The commonly used averaging increases the SNR byincreasing the amount of data. Apart from the increaseddata acquisition time, in order to get a reasonable SNRby means of averaging, there is a huge information lossduring averaging. The introduction of the ERP image asa two–dimensional illustration of ongoing ERPs, offersthe possibility of studying every individual sweep. Withthe help of suitable image processing tools such asthe suggested NLM algorithm, the amount of acquireddata, i.e., the acquisition time can be reduced to aminimum, making the acquisition procedure much morecomfortable for subjects, which in the case of ADHDpatients becomes an obvious advantage. A performanceanalysis of the proposed NLM method can be found in[16]. The results overall support the hypothesis of re-duced intracortical inhibition as a correlate of disturbedbrain function in adult subjects with ADHD, and showthat NLM denoising can improve single sweep ERPinformation, therefore optimizing original time–domain–based diagnosis.

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