Name: event related potentials (erps) and other eeg based methods for extracting biomarkers of brain dysfunction: examples from pediatric attention deficit/hyperactivity disorder (adhd)
Uploaded: 2020-03-12T12:00:00
Duration: 10 min 2 s
Description: Watch this Scientific Journal Video about Event Related Potentials ERPs and other EEG Based Methods for Extracting Biomarkers of Brain Dysfunction: Examples from Pediatric Attention Deficit/Hyperactiv

Ogrim G. has nothing to disclose. Kropotov J.D. was the head of the laboratory at the Institute of the Human Brain, St. Petersburg when and where the WinEEG program was developed. He has no economic interests in the system.

Diagnoses in psychiatry are based on observed behavior. In most cases, a specified number of symptoms must be observed in different settings for 6 months or more. An important part of the diagnostic process is to exclude somatic etiology. In addition, other psychiatric diagnoses must be considered. Quite often the symptoms of interest can be part of another diagnostic category. If several symptoms overlap with other disorders, the clinician must decide if this second disorder is a comorbid or differential diagnosis.
The clinical tools available are diagnostic interviews, rating scales, medical and developmental history, psychological tests and direct observations. Most of these methods are quite subjective; heavily influenced by the informant as well as the professional. Rating scales from parents and teachers usually show quite modest correlations (r = 0.3 - 0.5).
In the representative results, we argue that the underlying mechanisms in ADHD probably differ from patient to patient. Lack of (language) understanding, problems with self-motivation, sensitivity to external distractors, etc. can all lead to symptoms of inattention. The EEG based methods described in this paper can help solve some of these challenges. The problem of subjective interpretations is absent. The ERP methods described seem to reveal underlying psychological operations like working memory, action inhibition, monitoring, response preparation, etc. involving specific brain structures. Deficits in these mechanisms are not limited to specific diagnostic categories. We believe that in the future, treatment (medication, neurofeedback, cognitive training, tDCS, ...) will focus on such cognitive and/or emotional operations and their underlying brain mechanisms and not on the current diagnostic categories.
A purpose of a diagnosis is to determine the best treatments. To evaluate the effects of treatment, self-reported and observed improvements are of course decisive. Such reports can to some degree represent placebo effects, however, and should be supported by (partial) normalization of underlying brain dysfunctions reflected in for example changes in ERP components. This combination of subjective and objective measures of treatment effects are important in both clinic and research.
For reasons as those mentioned above, it is no surprise that people with the same diagnoses often do not respond to the same medical treatments. In personalized medicine diagnoses are supplemented with empirically based measures of response prediction to pinpoint the best treatment for the individual patient. In this paper we have described our research on prediction of stimulant medication response in pediatric ADHD. To find reliable predictors of positive response to antidepressant medication is perhaps even more important as the time needed to evaluate responses is long, as is the titration period. The procedures described in this paper could contribute to the ongoing EEG and ERP based research on prediction of medication effects in depression53.
The EEG based methods described are non-invasive and affordable, and well suited for research as well as for clinical work.

In 2008, initiated by NIMH, the Research Domain Criteria (RDoC) project1 was published, aiming to find a biologically valid framework for the understanding of mental disorders. In 2013, the U.S. Food and Drug Administration (FDA) approved of the first EEG-based biomarker of ADHD to help assess ADHD in patients from 6 to 17 years of age. The Neuropsychiatric EEG-Based Assessment Aid (NEBA) System records EEG for 15-20 min. It is based on the computation of the theta/beta ratio found to be higher in children and adolescents with ADHD than in typically developing children2. Recent publications find that this ratio does not captures all ADHD3.
A large number of publications in clinical neuroscience demonstrate that impaired cognitive control represents a common feature of many psychiatric disorders including ADHD, schizophrenia, depression, and OCD4,5. Theoretically, cognitive control consists of hypothetical operations allowing people to adjust flexibly to goals and contexts. Two different categories of cognitive control, proactive and reactive control, have been described6. Our primary focus is on the reactive mode of cognitive control. Proactive cognitive control includes working memory (i.e., maintaining sensory and motor events for seconds). Reactive cognitive control includes monitoring, detection of conflict7,8, and action inhibition (for review see9,10).
The GO/NOGO paradigm is sensitive to cognitive control11,12,13,14,15. GO stimuli elicit positive fluctuations from parietal brain areas. (P3 GO). The anteriorly distributed positive N2 and P3 NOGO waves, elicited by NOGO stimuli, are associated with detection of conflict and action inhibition16,17,18,19. The N2 wave has been understood as an indicator of inhibition of action, but updated research shows that the N2 wave is associated with infrequent GO stimuli and detection of conflict20. Action inhibition is linked to the P3 NOGO wave at frontal-central sites.
The N2/P3 dichotomy may not be correct. It has been questioned by a view that ERP waves, in particular those representing cognitive control, are sums of several sources that may overlap in locations and time14,21.
To disentangle the sources of ERP waves, several methods of blind source separation have been used15,22,23,24. In studies at the Institute of the Human Brain, St. Petersburg, N2d NOGO wave has been decomposed. Hidden components were detected. These components had distinct topographies and functional meanings. Only one of them was associated with detection of conflict14,15,25,26. In most adult studies of ADHD, P3 NOGO is smaller in comparison to matched healthy controls27,28,29,30,31,32.
The brain operations taking place during tasks of cognitive control do not seem to be correctly explained by the N2/P3 dichotomy when ERPs in GO/NOGO paradigms are analyzed14,15. Several approaches aiming to disentangle hidden components from ERP waves have been used (for review see21). Some studies have used independent component analysis (ICA) for ERPs in patient groups such as patients with schizophrenia29, and adults with ADHD33,34, attempting to discriminate patients from controls without diagnoses.
In (Yeredor, 2010,25 p.75), a new method is suggested and adapted for ERPs. It is a method of blind source separation, based on a procedure of joint diagonalization of cross-variance matrixes. To study the functional meanings of such latent components applying this method in the cued GO/NOGO paradigm, a study from the Institute of the Human Brain was recently implemented26. In this study the action inhibition operations and the conflict detection operations were independently manipulated by modifications of the cued GO/NOGO task. A hidden component, thought to reflect detection of conflict, was found. A N2-like response and frontal topography characterized this component35. In trials requiring inhibition of prepared actions a central topography and P3-like response was seen.
In this publication the studies reported have used the traditional ERP method. Application of ICA, or the procedure of joint diagonalization of cross-variance matrixes25 (page 75) has so far not been done. In general, the results based on the different methods agree with each other, but the methods for discovering latent components seem to be more purely associated with distinct neuropsychological functions. The aim of this paper is to offer a detailed description of the WinEEG method. The focus is on ERPs, but EEG spectra and behavioral data from the GO/NOGO task are also included in the studies described to illustrate the WinEEG method.

<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>amplifier +</td>
			<td><a href="http://www.mitsar-medical.com/" target="_blank">www.mitsar-medical.com</a></td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>Body harness, different sizes</td>
			<td>Electro-Cap International, Inc</td>
			<td>E3 SM; E3 M; E3 L</td>
			<td></td>
		</tr>
		<tr>
			<td>Ear electrodes 9 mm sockets</td>
			<td>Electro-Cap International, Inc</td>
			<td>E5-9S</td>
			<td></td>
		</tr>
		<tr>
			<td>Electrocaps 19 channel different sizes</td>
			<td>Electro-Cap International, Inc</td>
			<td>E1 SM; E1 M; E1 M/SM</td>
			<td></td>
		</tr>
		<tr>
			<td>Electrocaps 19 channel different sizes</td>
			<td>Electro-Cap International, Inc</td>
			<td>E1 L/M; E1 L</td>
			<td></td>
		</tr>
		<tr>
			<td>Electrogel for electrocaps</td>
			<td>Electro-Cap International, Inc</td>
			<td>E9; E10</td>
			<td></td>
		</tr>
		<tr>
			<td>HBi database</td>
			<td><a href="http://www.hbimed.com/" target="_blank">www.hbimed.com</a></td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>Head size measure band</td>
			<td>Electro-Cap International, Inc</td>
			<td>E 12</td>
			<td></td>
		</tr>
		<tr>
			<td>Needle syringe kit</td>
			<td>Electro-Cap International, Inc</td>
			<td>E7</td>
			<td></td>
		</tr>
		<tr>
			<td>Nuprep EEG and ECG skin prep gel</td>
			<td>Electro-Cap International, Inc</td>
			<td>R7</td>
			<td></td>
		</tr>
		<tr>
			<td>Ten20 EEG conductive paste</td>
			<td>Electro-Cap International, Inc</td>
			<td>R5-4T</td>
			<td></td>
		</tr>
		<tr>
			<td>WinEEG program</td>
			<td><a href="http://www.mitsar-medical.com/" target="_blank">www.mitsar-medical.com</a></td>
			<td></td>
			<td></td>
		</tr>
	</tbody>
</table>

event related potentials (erps) and other eeg based methods for extracting biomarkers of brain dysfunction: examples from pediatric attention deficit/hyperactivity disorder (adhd)

Neuropsychiatric diagnoses like ADHD are based on subjective methods like interviews, rating scales and observations. There is a need for more brain-based supplements. Stimulant medication is the most common treatment for ADHD. Clinically useful predictors of response have so far not been reported. The aim of this paper is to describe the EEG based methods we apply to extract potential biomarkers for brain dysfunction. Examples relate to biomarkers for pediatric ADHD, and prediction of medication response. The main emphasis is on Event Related Potentials (ERPs).
A nineteen channel EEG is recorded during a 3 min eyes-opened task, a 3 min eyes-closed task, and a 20 min cued visual GO/NOGO task (VCPT). ERPs are recorded during this task. The goal of the ERP protocol is to extract biomarkers of assumed brain dysfunctions that significantly differentiate between a patient group and healthy controls. The protocol includes recording during standard conditions and artifact correction. ERP waves can be used or transformed into latent components. The components of the patient group are compared with controls, empathizing components that, when compared, show relatively high effect sizes. Sub-groups of the patients are selected on the basis of the cluster analysis in the space of the components. Treatment procedure (such as medication, tDCS or neurofeedback protocol) can be applied and the changes in components related to treatment in the subgroups are observed, forming the basis for clinical recommendations.
The methods described were applied in a study of 87 pediatric ADHD patients. The index of medication response discriminated significantly between responders and non-responders with a large, and clinically meaningful effect size (d = 1.84). In an ongoing study comparing ADHD children with matched controls, several variables discriminate significantly between patients and controls. The global index will exceed d = .8. The EEG based methods described here could be clinically meaningful.

Prediction of medication response in pediatric ADHD 
ADHD is a common neuropsychiatric childhood disorder36. It is characterized by symptoms of inattention accompanied by symptoms of hyperactivity and impulsivity. Impairment in school, home, and leisure settings are common. In school aged children, the estimated prevalence is 5% to 7%. Comorbidities are common. Medical treatment, using stimulants based on methylphenidate (MPH) or dextroamphetamine (DEX), are widely used. Positive effects of stimulant medication (reductions in restlessness, hyperactivity and impulsivity and improved attention) are reported in 70% of the patients. Shifting from medication based on MPH to DEX can increase positive effects to 80%37,38. Frontal-striatal circuits seem to be activated by stimulants39.
There is no generally accepted definition of a medication response that is clinically meaningful. Applying rating scales, comparing baseline scores with scores on medication, is the most commonly used method. In some studies, a 25% or 50% reduction of scores is used as definition of response. In other studies, scores not exceeding 1 SD above population mean are used40,41. Clinically, an overall decision based on all relevant available data is used. To evaluate side effects, such as loss of appetite, insomnia, increased irritability, or anxiety, is important37,42.
The use of rating scales can be criticized for several reasons. Small correlations (0.30-0.50) between teacher and parent scores are reported in several studies48. The search for clinically useful predictors of response is motivated by a large number of non-responders, informants that do not agree, and the fact that everyone can have some modest effects of improved attention when small doses of stimulants are used. Published research on predictors of response include ADHD subtype, demographics, comorbid disorders, gene variables, scores on rating scales, neuropsychological test results, and EEG/ERP variables43,44,45,46. Our 2016 publication47 summarizes studies that have applied ERPs to predict medication response.
In previous studies, we analyze d data from the cued visual GO/NOGO task (i.e., attention test data, EEG spectra, and ERPs). In one study, we found 3 variables contributing significantly to the prediction of side-effects. These variables were combined to an index that was considered clinically meaningful42. In a study on clinical effects, applying the same methods, the prediction index was also considered clinically useful48. The effects of a single dose of stimulant medication on medication responders (REs) and non-responders (non-REs) was investigated in a third study47. The test procedure was completed twice, the first test with no medication, and the second test an hour after having received a trial dose. Based on rating scales and interviews after a 4-week medication trial, the patients were classified as REs or non-REs. Our focus was on changes in cognitive ERPs and attention test scores. We found that the effects on the P3 NOGO component was significantly different in the two groups, with a large effect size (d = 1.76). A significant increase of the component amplitude was seen in REs but not in non-REs. Predictions of response based on two tests was improved compared with predictions based only on test 1.
In our latest study, we developed two global indexes, one for prediction of clinical gains and one for prediction of side-effects. As described above we combined variables that discriminated significantly between compared groups with a modest or large effect size. Each variable was weighted in accordance with the effect size. We examined variables from all three WinEEG domains: EEG spectra, ERPs and behavior. The following variables were combined: Test 1: P3NOGO amplitude and theta/alpha ratio; differences between Test 2 and Test 1: Omission errors, reaction time variability, contingent negative variation (CNV) and P3NOGO amplitude. The effect size of the global scale was d = 1.86. Accuracy was 0.92. Prediction of side-effects was based on 4 variables: Test 1: RT, Test 2: novelty component, alpha peak frequency, and reaction time changes (Test 2 - Test 1). The global scale d was 1.08 and accuracy was 0.7849.
Some preliminary results 
In an ongoing study, we compare a group of 61 ADHD patients age 9-12 years and a group of 67 age-matched healthy controls (HC). The final statistical analyses have so far not been completed. Below we are presenting the preliminary results obtained from WinEEG assessment.
Behaviorally, the ADHD group showed an inattention pattern with statistically (at p&#60;0.001) more omission errors in comparison to the healthy controls (HC) group (13.7% vs. 4.8%) accompanied by an attention lapses pattern expressed in statistically higher (p&#60;0.001) variability of reaction time (151 ms vs. 125 ms).
The main results of comparing ERP waveforms between the two groups are shown in Figure 5 and Figure 6. Figure 5 demonstrates the ERP correlates of dysfunction of proactive cognitive control in ADHD group. Two indexes of proactive cognitive control (P3 cue wave and CNV wave) are reduced in the ADHD group in comparison to the HC group. Figure 6 demonstrates the ERP correlates of dysfunction of reactive cognitive control in the ADHD group. Two indexes of reactive cognitive control (N2 NOGO and P3 NOGO) are reduced in the ADHD group in comparison to the HC group.
<img alt="Figure 5" class="xfigimg" src="/files/ftp_upload/60710/60710fig5.jpg" /> 
Figure 5: Grand average ERP wave patterns (a) and the corresponding maps (b) in proactive cognitive control in ADHD and healthy control (HC) groups. (a) ERPs measured at P3 in ADHD group (green line) and HC group (red line) and their difference (ADHD-HC) wave (blue line). Blue vertical bars below the curves indicate level of statistical significance of the difference (small bars - p&#60;0.05, middle bars - p&#60;0.01, large bars - p&#60;0.001). Arrows indicate the classical waves - P3 cue and CNV (contingent negative variation). (b) Maps at the maximums of amplitudes of P3 and CNV waves for the two groups. <a href="https://www.jove.com/files/ftp_upload/60710/60710fig5large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 6" class="xfigimg" src="/files/ftp_upload/60710/60710fig6.jpg" /> 
Figure 6: Grand average ERP wave patterns (a) and the corresponding maps (b) in reactive cognitive control in ADHD and healthy control (HC) groups. (a) ERPs measured at Fz and Cz ADHD group (green line) and HC group (red line) and their difference (ADHD-HC) wave (blue line). Blue vertical bars below the curves indicate the level of statistical significance of the difference (small bars - p&#60;0.05, middle bars - p&#60;0.01, large bars - p&#60;0.001). Arrows indicate the classical waves - N2 NOGO and P3 NOGO. (b) Maps at the maximums of amplitudes of N2 NOGO and P3 NOGO waves for the two groups. <a href="https://www.jove.com/files/ftp_upload/60710/60710fig6large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
As one can see the ADHD group shows hypo-functioning of multiple operations of cognitive control. These operations occur in different time windows and in different spatial locations. A particular patient might have only one hypo-functioning indicating the source of the individual disorder and the ways of its correction.
Clinical significance 
To compute a clinically useful biomarker for a heterogenous diagnosis such as ADHD, several variables that differ significantly between ADHD and controls need to be combined. The effect size (d) of an index should be above d = .8. An important next step will be applying this index when ADHD is compared with clinical controls.

Watch this Scientific Journal Video about Event Related Potentials ERPs and other EEG Based Methods for Extracting Biomarkers of Brain Dysfunction: Examples from Pediatric Attention Deficit/Hyperactiv

Event Related Potentials ERPs and other EEG Based Methods for Extracting Biomarkers of Brain Dysfunction: Examples from Pediatric Attention Deficit/Hyperactivity Disorder ADHD

EEG-methods are applied for extracting biomarkers of brain dysfunctions. The focus is on multi-channel event-related potentials (ERPs) recorded in a cued GO/NOGO task. Non-brain artifacts are corrected and ERPs are compared with the normative data. Examples relate to biomarkers for ADHD diagnosis and prediction of medication response.

Event Related Potentials (ERPs) and other EEG Based Methods for Extracting Biomarkers of Brain Dysfunction: Examples from Pediatric Attention Deficit/Hyperactivity Disorder (ADHD)

Neuropsychiatric diagnoses like ADHD are based on subjective methods like interviews, rating scales and observations. There is a need for more brain-based ...

Research

JoVE Journal

Neuroscience

High Density Event-related Potential  Data Acquisition in Cognitive Neuroscience

Functional magnetic resonance imaging (fMRI) is currently the standard method of evaluating brain function in the field of Cognitive Neuroscience, in part ...

Combining Computer Game-Based Behavioural Experiments With High-Density EEG and Infrared Gaze Tracking

Experimental paradigms are valuable insofar as the timing and other parameters of their stimuli are well specified and controlled, and insofar as they ...

Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example

Magneto- and electroencephalography (MEG/EEG) are neuroimaging techniques that provide a high temporal resolution particularly suitable to investigate the ...

Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy

When considering human neuroimaging data, an appreciation of signal variability represents a fundamental innovation in the way we think about brain ...

In vivo Postnatal Electroporation and Time-lapse Imaging of Neuroblast Migration in Mouse Acute Brain Slices

In vivo Postnatal Electroporation and Time-lapse Imaging of Neuroblast Migration in Mouse Acute Brain Slices

The subventricular zone (SVZ) is one of the main neurogenic niches in the postnatal brain. Here, neural progenitors proliferate and give rise to ...

Extracting Visual Evoked Potentials from EEG Data Recorded During fMRI-guided Transcranial Magnetic Stimulation

Transcranial Magnetic Stimulation (TMS) is an effective method for establishing a causal link between a cortical area and cognitive/neurophysiological ...

Somatosensory Event-related Potentials from Orofacial Skin Stretch Stimulation

Cortical processing associated with orofacial somatosensory function in speech has received limited experimental attention due to the difficulty of ...

New Methods to Study Gustatory Coding

The sense of taste allows animals to detect chemicals in the environment, giving rise to behaviors critical for survival. When Gustatory Receptor Neurons ...

Acute In Vivo Electrophysiological Recordings of Local Field Potentials and Multi-unit Activity from the Hyperdirect Pathway in Anesthetized Rats

Acute In Vivo Electrophysiological Recordings of Local Field Potentials and Multi-unit Activity from the Hyperdirect Pathway in Anesthetized Rats

Converging evidence shows that many neuropsychiatric diseases should be understood as disorders of large-scale neuronal networks. To better understand the ...

How to Find Effects of Stimulus Processing on Event Related Brain Potentials of Close Others when Hyperscanning Partners

The partners of each pair must be able to pass the McGill Friendship Questionnaire without communicating. Each partner is then seated in front of a screen ...