The equipment described in the protocol is ethically approved by the hospital authorities and is used for clinical purposes. The Regional Committee for Medical Research Ethics approved the projects described.
1. Hardware and software for ERPs recording
2. Competence and education
3. Informing patients/participants
4. Creating the EEG data files
NOTE: WinEEG has its own build-in databases separately for storing raw EEG files (extension – .eeg), EEG spectra (extension – .spec), and ERP files (extension – .erp). The databases are created automatically and initially stored in WinEEG/data, WinEEG/spec and WinEEG/erp folders.
5. Preparation of the equipment
6. Registrations of "eyes closed" and "eyes opened"
7. Preparations for EEG recordings in the cued GO/NOGO task
Figure 1: VCPT: Visual Continuous Performance Test. Figure 1 shows the four conditions of the VCPT. One hundred trials of each condition are presented randomly. The total test time is 20 min. Please click here to view a larger version of this figure.
8. EEG and button press recordings in task condition
9. Ending the registration
10. Cleaning
11. Preprocessing the EEG record
NOTE: Three different electrode montages are provided in the HBIdb software. They are: linked ears reference (labeled as Ref), common average reference (labeled as Av), and local average reference (labeled as Aw). Select montage from Montage list in SETUP menu. EEG is recorded in Ref. Change to Av before starting artifact correction.
12. Computing EEG spectra
Figure 2: Computing EEG spectra. To compute spectra: Click Analysis | Spectra. If the settings are correct, the picture shown in Figure 2 appears. Please click here to view a larger version of this figure.
Figure 3: EEG spectra with 19 electrodes. Figure 3 shows EEG spectra in 19 sites. The x-axis is frequency from 0-30 Hz. The y-axis is power in µV2. Please click here to view a larger version of this figure.
13. Computing Event Related Potentials (ERPs)
NOTE: Event Related Potentials (ERPs) are computed by an averaging procedure. Only correct trials are included. ERPs are computed after completion of the preprocessing described above. The gold standard for computing ERPs is to keep the number of the averaged trials above 50.
Figure 4: Parameters of ERP computations. Figure 4 shows ERP components a-a GO (green) and a-p NOGO (red) in 19 sites. The time interval is 1400 ms to 2100 ms. A-a GO is most clearly seen at site Pz and a-p NOGO at Cz. Please click here to view a larger version of this figure.
14. Registration and comparison of behavioral data in VCPT
15. Comparing Event Related Potentials (ERPs) with the reference data base
NOTE: The time interval of interest for comparison is defined by typing the corresponding numbers in the menu: Time interval from (ms), Duration (ms). ERPs can be selectively presented for certain categories of trials (such a-a GO, a-p NOGO, p-p, p-h) by selecting the corresponding graph from the menu Active groups on the top of the ERP window.
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<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<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.
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<0.05, middle bars – p<0.01, large bars – p<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. Please click here to view a larger version of this figure.
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<0.05, middle bars – p<0.01, large bars – p<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. Please click here to view a larger version of this figure.
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.
amplifier + | www.mitsar-medical.com | ||
Body harness, different sizes | Electro-Cap International, Inc | E3 SM; E3 M; E3 L | |
Ear electrodes 9 mm sockets | Electro-Cap International, Inc | E5-9S | |
Electrocaps 19 channel different sizes | Electro-Cap International, Inc | E1 SM; E1 M; E1 M/SM | |
Electrocaps 19 channel different sizes | Electro-Cap International, Inc | E1 L/M; E1 L | |
Electrogel for electrocaps | Electro-Cap International, Inc | E9; E10 | |
HBi database | www.hbimed.com | ||
Head size measure band | Electro-Cap International, Inc | E 12 | |
Needle syringe kit | Electro-Cap International, Inc | E7 | |
Nuprep EEG and ECG skin prep gel | Electro-Cap International, Inc | R7 | |
Ten20 EEG conductive paste | Electro-Cap International, Inc | R5-4T | |
WinEEG program | www.mitsar-medical.com |
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.
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.
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.