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.

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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.remotexs.ntu.edu.sg/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.remotexs.ntu.edu.sg/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 ...

event-related-potentials-erps-other-eeg-based-methods-for-extracting

Nanyang Technological University

Research

JoVE Journal

Neuroscience

15.5K Views.  Østfold Hospital Trust. Het vergelijken van EEG spectra en event-gerelateerde potentials, ERPS, met een database kan belangrijke informatie over de hersenfunctie. De voordelen van deze technieken zijn dat ze niet-invasief, betaalbaar, en kan betrekking hebben op een groot aantal wetenschappelijke studies. Het aantonen van deze technieken zal Maria Danielsen, een psychologie kandidaat die heeft gewerkt als technicus in onze kliniek voor ongeveer twee jaar.Als u een bijbehorend EEG-gegevensbestand wilt maken, k...

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

Cortical Source Analysis of High-Density EEG Recordings in Children

EEG is traditionally described as a neuroimaging technique with high temporal and low spatial resolution. Recent advances in biophysical modelling and signal processing make it possible to exploit information from other imaging modalities like structural MRI that provide high spatial resolution to overcome this constraint1. This is especially useful for investigations that require high resolution in the temporal as well as spatial domain. In addition, due to the easy application and low cost of EEG recordings, EEG is often the method of choice when working with populations, such as young children, that do not tolerate functional MRI scans well. However, in order to investigate which neural substrates are involved, anatomical information from structural MRI is still needed. Most EEG analysis packages work with standard head models that are based on adult anatomy. The accuracy of these models when used for children is limited2, because the composition and spatial configuration of head tissues changes dramatically over development3.&#160;
In the present paper, we provide an overview of our recent work in utilizing head models based on individual structural MRI scans or age specific head models to reconstruct the cortical generators of high density EEG. This article describes how&#160;EEG recordings are acquired, processed, and analyzed with pediatric populations at the London Baby Lab, including laboratory setup, task design, EEG preprocessing, MRI processing, and EEG channel level and source analysis.&#160;

In recent years, there has been increasing interest in estimating the cortical sources of scalp measured electrical activity for cognitive neuroscience experiments. This article describes how high density EEG is acquired and how recordings are processed for cortical source estimation in children from the age of 2 years at the London Baby Lab.

EEG is traditionally described as a neuroimaging technique with high temporal and low spatial resolution. Recent advances in biophysical modelling and ...

Behavior

Infant Auditory Processing and Event-related Brain Oscillations

Rapid auditory processing and acoustic change detection abilities play a critical role in allowing human infants to efficiently process the fine spectral and temporal changes that are characteristic of human language. These abilities lay the foundation for effective language acquisition; allowing infants to hone in on the sounds of their native language. Invasive procedures in animals and scalp-recorded potentials from human adults suggest that simultaneous, rhythmic activity (oscillations) between and within brain regions are fundamental to sensory development; determining the resolution with which incoming stimuli are parsed. At this time, little is known about oscillatory dynamics in human infant development. However, animal neurophysiology and adult EEG data provide the basis for a strong hypothesis that rapid auditory processing in infants is mediated by oscillatory synchrony in discrete frequency bands. In order to investigate this, 128-channel, high-density EEG responses of 4-month old infants to frequency change in tone pairs, presented in two rate conditions (Rapid: 70 msec ISI and Control: 300 msec ISI) were examined. To determine the frequency band and magnitude of activity, auditory evoked response averages were first co-registered with age-appropriate brain templates. Next, the principal components of the response were identified and localized using a two-dipole model of brain activity. Single-trial analysis of oscillatory power showed a robust index of frequency change processing in bursts of Theta band (3 - 8 Hz) activity in both right and left auditory cortices, with left activation more prominent in the Rapid condition. These methods have produced data that are not only some of the first reported evoked oscillations analyses in infants, but are also, importantly, the product of a well-established method of recording and analyzing clean, meticulously collected, infant EEG and ERPs. In this article, we describe our method for infant EEG net application, recording, dynamic brain response analysis, and representative results.

High-density electroencephalography (dEEG) is being used increasingly to study brain development and plasticity in the early years of life. Here we present an application of sophisticated analysis techniques that builds on traditional EEG recording to understand the oscillatory dynamics of rapid auditory processing in the infant brain.

Rapid auditory processing and acoustic change detection abilities play a critical role in allowing human infants to efficiently process the fine spectral ...

Probing the Brain in Autism Using fMRI and Diffusion Tensor Imaging

Newly emerging theories suggest that the brain does not function as a cohesive unit in autism, and this discordance is reflected in the behavioral symptoms displayed by individuals with autism. While structural neuroimaging findings have provided some insights into brain abnormalities in autism, the consistency of such findings is questionable. Functional neuroimaging, on the other hand, has been more fruitful in this regard because autism is a disorder of dynamic processing and allows examination of communication between cortical networks, which appears to be where the underlying problem occurs in autism. Functional connectivity is defined as the temporal correlation of spatially separate neurological events1. Findings from a number of recent fMRI studies have supported the idea that there is weaker coordination between different parts of the brain that should be working together to accomplish complex social or language problems2,3,4,5,6. One of the mysteries of autism is the coexistence of deficits in several domains along with relatively intact, sometimes enhanced, abilities. Such complex manifestation of autism calls for a global and comprehensive examination of the disorder at the neural level. A compelling recent account of the brain functioning in autism, the cortical underconnectivity theory,2,7 provides an integrating framework for the neurobiological bases of autism. The cortical underconnectivity theory of autism suggests that any language, social, or psychological function that is dependent on the integration of multiple brain regions is susceptible to disruption as the processing demand increases. In autism, the underfunctioning of integrative circuitry in the brain may cause widespread underconnectivity. In other words, people with autism may interpret information in a piecemeal fashion at the expense of the whole. Since cortical underconnectivity among brain regions, especially the frontal cortex and more posterior areas 3,6, has now been relatively well established, we can begin to further understand brain connectivity as a critical component of autism symptomatology.
A logical next step in this direction is to examine the anatomical connections that may mediate the functional connections mentioned above. Diffusion Tensor Imaging (DTI) is a relatively novel neuroimaging technique that helps probe the diffusion of water in the brain to infer the integrity of white matter fibers. In this technique, water diffusion in the brain is examined in several directions using diffusion gradients. While functional connectivity provides information about the synchronization of brain activation across different brain areas during a task or during rest, DTI helps in understanding the underlying axonal organization which may facilitate the cross-talk among brain areas. This paper will describe these techniques as valuable tools in understanding the brain in autism and the challenges involved in this line of research.

Neuroimaging techniques, such as functional MRI and Diffusion Tensor Imaging have become increasingly useful in characterizing the cognitive and neural deficits in autism. An examination of brain connectivity in autism at a network level along with adaptations for scanning children with developmental disabilities is presented.

Newly emerging theories suggest that the brain does not function as a cohesive unit in autism, and this discordance is reflected in the behavioral ...

Medicine

Event-related Potentials During Target-response Tasks to Study Cognitive Processes of Upper Limb Use in Children with Unilateral Cerebral Palsy

Unilateral Cerebral Palsy (CP) is a neurodevelopmental disorder that is a very common cause of disability in childhood. It is characterized by unilateral motor impairments that are frequently dominated in the upper limb. In addition to a reduced movement capacity of the affected upper limb, several children with unilateral CP show a reduced awareness of the remaining movement capacity of that limb. This phenomenon of disregarding the preserved capacity of the affected upper limb is regularly referred to as Developmental Disregard (DD). Different theories have been postulated to explain DD, each suggesting slightly different guidelines for therapy. Still, cognitive processes that might additionally contribute to DD in children with unilateral CP have never been directly studied. The current protocol was developed to study cognitive aspects involved in upper limb control in children with unilateral CP with and without DD. This was done by recording event-related potentials (ERPs) extracted from the ongoing EEG during target-response tasks asking for a hand-movement response. ERPs consist of several components, each of them associated with a well-defined cognitive process (e.g., the N1 with early attention processes, the N2 with cognitive control and the P3 with cognitive load and mental effort). Due to its excellent temporal resolution, the ERP technique enables to study several covert cognitive processes preceding overt motor responses and thus allows insight into the cognitive processes that might contribute to the phenomenon of DD. Using this protocol adds a new level of explanation to existing behavioral studies and opens new avenues to the broader implementation of research on cognitive aspects of developmental movement restrictions in children.

Several children with unilateral Cerebral Palsy seem to disregard the preserved capacity of their affected upper limb. This Developmental Disregard is extensively described in the literature but the involved cognitive processes have not been studied. To study underlying cognitive factors of upper limb control, an event-related potential protocol was developed.

Unilateral Cerebral Palsy (CP) is a neurodevelopmental disorder that is a very common cause of disability in childhood. It is characterized by unilateral ...

Investigating Social Cognition in Infants and Adults Using Dense Array Electroencephalography (dEEG)

Dense array electroencephalography (dEEG), which provides a non-invasive window for measuring brain activity and a temporal resolution unsurpassed by any other current brain imaging technology1,2, is being used increasingly in the study of social cognitive functioning in infants and adults. While dEEG is enabling researchers to examine brain activity patterns with unprecedented levels of sensitivity, conventional EEG recording systems continue to face certain limitations, including 1) poor spatial resolution and source localization3,4,2) the physical discomfort for test subjects of enduring the individual application of numerous electrodes to the surface of the scalp, and 3) the complexity for researchers of learning to use multiple software packages to collect and process data. Here we present an overview of an established methodology that represents a significant improvement on conventional methodologies for studying EEG in infants and adults. Although several analytical software techniques can be used to establish indirect indices of source localization to improve the spatial resolution of dEEG, the HydroCel Geodesic Sensor Net (HCGSN) by Electrical Geodesics, Inc. (EGI), a dense sensory array that maintains equal distances among adjacent recording electrodes on all surfaces of the scalp, further enhances spatial resolution4,5,6 compared to standard dEEG systems. The sponge-based HCGSN can be applied rapidly and without scalp abrasion, making it ideal for use with adults7,8, children9,10,11, and infants12, in both research and clinical4,5,6,13,14,15 settings. This feature allows for considerable cost and time savings by decreasing the average net application time compared to other dEEG systems. Moreover, the HCGSN includes unified, seamless software applications for all phases of data, greatly simplifying the collection, processing, and analysis of dEEG data.
The HCGSN features a low-profile electrode pedestal, which, when filled with electrolyte solution, creates a sealed microenvironment and an electrode-scalp interface. In all Geodesic dEEG systems, EEG sensors detect changes in voltage originating from the participant's scalp, along with a small amount of electrical noise originating from the room environment. Electrical signals from all sensors of the Geodesic sensor net are received simultaneously by the amplifier, where they are automatically processed, packaged, and sent to the data-acquisition computer (DAC). Once received by the DAC, scalp electrical activity can be isolated from artifacts for analysis using the filtering and artifact detection tools included in the EGI software. Typically, the HCGSN can be used continuously for only up to two hours because the electrolyte solution dries out over time, gradually decreasing the quality of the scalp-electrode interface.
In the Parent-Infant Research Lab at the University of Toronto, we are using dEEG to study social cognitive processes including memory, emotion, goals, intentionality, anticipation, and executive functioning in both adult and infant participants.

Investigating Social Cognition in Infants and Adults Using Dense Array Electroencephalography dEEG

Dense array electroencephalography is being used increasingly to study social cognitive functions in infants and adults. Here we present an established methodology that represents a significant improvement on conventional methodologies for studying EEG in infants and adults.

Dense array electroencephalography (dEEG), which provides a non-invasive window for measuring brain activity and a temporal resolution unsurpassed by any ...

An Electrophysiology Protocol to Measure Reward Anticipation and Processing in Children

We present a protocol designed to measure the neural correlates of reward in children. The protocol allows researchers to measure both reward anticipation and processing. Its purpose is to create a reward task that is appropriate for young children with and without autism while controlling reward properties between two conditions: social and nonsocial. The current protocol allows for comparisons of brain activity between social and nonsocial reward conditions while keeping the reward itself identical between conditions. Using this protocol, we found evidence that neurotypical children demonstrate enhanced anticipatory brain activity during the social condition. Furthermore, we found that neurotypical children anticipate social reward more robustly than children with autism diagnoses. As the task uses snacks as a reward, it is most appropriate for young children. However, the protocol may be adapted for use with adolescent or adult populations if snacks are replaced by monetary incentives. The protocol is designed to measure electrophysiological events (event-related potentials), but it may be customized for use with eye-tracking or fMRI.

This protocol is designed to measure reward anticipation and processing in young children with and without autism. Specifically, the protocol is designed to study the neural correlates of reward during social and nonsocial conditions while controlling for reward between conditions.

We present a protocol designed to measure the neural correlates of reward in children. The protocol allows researchers to measure both reward anticipation ...

EEG Mu Rhythm in Typical and Atypical Development

Electroencephalography (EEG) is an effective, efficient, and noninvasive method of assessing and recording brain activity. Given the excellent temporal resolution, EEG can be used to examine the neural response related to specific behaviors, states, or external stimuli. An example of this utility is the assessment of the mirror neuron system (MNS) in humans through the examination of the EEG mu rhythm. The EEG mu rhythm, oscillatory activity in the 8-12 Hz frequency range recorded from centrally located electrodes, is suppressed when an individual executes, or simply observes, goal directed actions. As such, it has been proposed to reflect activity of the MNS. It has been theorized that dysfunction in the mirror neuron system (MNS) plays a contributing role in the social deficits of autism spectrum disorder (ASD). The MNS can then be noninvasively examined in clinical populations by using EEG mu rhythm attenuation as an index for its activity. The described protocol provides an avenue to examine social cognitive functions theoretically linked to the MNS in individuals with typical and atypical development, such as ASD.&#160;

Assessment of the EEG mu rhythm provides a unique methodology for examining brain activity and when combined with behaviorally based assays, can be a powerful tool for elucidating aspects of social cognition, such as imitation, in clinical populations.

Electroencephalography (EEG) is an effective, efficient, and noninvasive method of assessing and recording brain activity. Given the excellent temporal ...

Interictal High Frequency Oscillations Detected with Simultaneous Magnetoencephalography and Electroencephalography as Biomarker of Pediatric Epilepsy

Crucial to the success of epilepsy surgery is the availability of a robust biomarker that identifies the Epileptogenic Zone (EZ). High Frequency Oscillations (HFOs) have emerged as potential presurgical biomarkers for the identification of the EZ in addition to Interictal Epileptiform Discharges (IEDs) and ictal activity. Although they are promising to localize the EZ, they are not yet suited for the diagnosis or monitoring of epilepsy in clinical practice. Primary barriers remain: the lack of a formal and global definition for HFOs; the consequent heterogeneity of methodological approaches used for their study; and the practical difficulties to detect and localize them noninvasively from scalp recordings. Here, we present a methodology for the recording, detection, and localization of interictal HFOs from pediatric patients with refractory epilepsy. We report representative data of HFOs detected noninvasively from interictal scalp EEG and MEG from two children undergoing surgery.
The underlying generators of HFOs were localized by solving the inverse problem and their localization was compared to the Seizure Onset Zone (SOZ) as this was defined by the epileptologists. For both patients, Interictal Epileptogenic Discharges (IEDs) and HFOs were localized with source imaging at concordant locations. For one patient, intracranial EEG (iEEG) data were also available. For this patient, we found that the HFOs localization was concordant between noninvasive and invasive methods. The comparison of iEEG with the results from scalp recordings served to validate these findings. To our best knowledge, this is the first study that presents the source localization of scalp HFOs from simultaneous EEG and MEG recordings comparing the results with invasive recordings. These findings suggest that HFOs can be reliably detected and localized noninvasively with scalp EEG and MEG. We conclude that the noninvasive localization of interictal HFOs could significantly improve the presurgical evaluation for pediatric patients with epilepsy.

High Frequency Oscillations (HFOs) have emerged as presurgical biomarkers for the identification of the epileptogenic zone in pediatric patients with medically refractory epilepsy. A methodology for the noninvasive recording, detection, and localization of HFOs with simultaneous scalp electroencephalography (EEG) and magnetoencephalography (MEG) is presented.

Crucial to the success of epilepsy surgery is the availability of a robust biomarker that identifies the Epileptogenic Zone (EZ). High Frequency ...

Using Brain Activation (nir-HEG/Q-EEG) and Execution Measures (CPTs) in a ADHD Assessment Protocol

Attention Deficit Hyperactivity Disorder (ADHD) is a problem that impacts academic performance and has serious consequences that result in difficulties in scholastic, social and familial contexts. One of the most common problems in the identification of this disorder relates to the apparent over diagnosis of the disorder due to the absence of global protocols for assessment. The research group of School Learning, Difficulties and Academic Performance (ADIR) from the University of Oviedo, has developed a complete protocol that suggests the existence of certain patterns of cortical activation and executive control for identifying ADHD more objectively. This protocol takes into consideration some of the hypothetical determinants of ADHD, including the relationship between activation of selected areas of the brain, and differences in performance on various aspects of executive functioning such as omissions, commissions or response times, using innovative tools of Continuous Performance Testing (based on Virtual Reality CPT and Traditional CPT) and brain activation measures (two different tools, based on Hemoencephalography- nirHEG; and Quantified Electroencephalography --Q-EEG, respectively). This model of assessment aims to provide an effective assessment of ADHD symptomatology in order to design an accurate intervention and make appropriate recommendations for parents and teachers.

Using Brain Activation nir-HEG/Q-EEG and Execution Measures CPTs in a ADHD Assessment Protocol

This work presents a new protocol for the assessment of Attention Deficit Hyperactivity Disorder (ADHD) by providing a more objective diagnostic procedure for this developmental disorder based on the use of innovative tools. It also analyzes the relationship between activation measures and executive function measures.

Attention Deficit Hyperactivity Disorder (ADHD) is a problem that impacts academic performance and has serious consequences that result in difficulties in ...

Studying Brain Function in Children Using Magnetoencephalography

Magnetoencephalography (MEG) is a non-invasive neuroimaging technique which directly measures magnetic fields produced by the electrical activity of the human brain. MEG is quiet and less likely to induce claustrophobia compared with magnetic resonance imaging (MRI). It is therefore a promising tool for investigating brain function in young children. However, analysis of MEG data from pediatric populations is often complicated by head movement artefacts which arise as a consequence of the requirement for a spatially-fixed sensor array that is not affixed to the child's head. Minimizing head movements during MEG sessions can be particularly challenging as young children are often unable to remain still during experimental tasks. The protocol presented here aims to reduce head movement artefacts during pediatric MEG scanning. Prior to visiting the MEG laboratory, families are provided with resources that explain the MEG system and the experimental procedures in simple, accessible language. An MEG familiarization session is conducted during which children are acquainted with both the researchers and the MEG procedures. They are then trained to keep their head still whilst lying inside an MEG simulator. To help children feel at ease in the novel MEG environment, all of the procedures are explained through the narrative of a space mission. To minimize head movement due to restlessness, children are trained and assessed using fun and engaging experimental paradigms. In addition, children's residual head movement artefacts are compensated for during the data acquisition session using a real-time head movement tracking system. Implementing these child-friendly procedures is important for improving data quality, minimizing participant attrition rates in longitudinal studies, and ensuring that families have a positive research experience.

This article introduces a child-friendly research protocol designed to improve data quality by reducing head movement during pediatric magnetoencephalography (MEG). We familiarize families with the MEG environment, train children to remain still using an MEG simulator, and correct for residual head movement artefacts using a real-time head movement detection system.

Magnetoencephalography (MEG) is a non-invasive neuroimaging technique which directly measures magnetic fields produced by the electrical activity of the ...

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

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