EEG Mu Rhythm in Typical and Atypical Development

Summary

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.

Abstract

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. 

Introduction

Electroencephalography (EEG) is an effective, efficient, and noninvasive method of assessing and recording brain activity. As neurons fire in the brain, the resulting voltage can be amplified, recorded, and graphically represented. The temporal resolution of EEG allows for the analysis of even brief changes in the oscillation patterns of the brain, as well as the analysis of the brain’s response to specific stimuli.

Despite being the oldest brain imaging technique, dating back to the late 19^th century, EEG still has wide-ranging applicability. While functional magnetic resonance imaging (fMRI) has excellent spatial resolution, it has relatively poor temporal resolution. This represents a major limitation of fMRI assessment given the incredible speed at which processes occur in the brain. EEG has the ability to assess electrical brain activity at the millisecond level, providing potential insight into the phases of the brain’s processing.

Evolving technologies have also expanded the applicability of EEG. An increase in the density of recording systems has allowed for the development of source localization techniques, mitigating some of EEG’s limitations regarding spatial resolution. Additionally, modern systems have reduced the individual participant set-up time significantly, allowing for the assessment of previously unavailable populations, such as infant and clinical samples^1-3,28-30.

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. Mirror neurons were originally identified in monkeys using single neuron recording⁴, evidencing a group of neurons that responded to both the execution and observation of motor actions. This direct recording method of placing electrodes in the brain is rarely utilized in humans, and only in dire clinical cases. EEG has provided a method for assessing the MNS by monitoring the EEG mu rhythm. This oscillation pattern in the 8-12 Hz range has been shown to attenuate EEG power in response to the execution and observation of motor actions, similar to the activation pattern observed in monkeys^5-7. Similarly, stimulation of putative MNS brain regions through Transcranial Magnetic Stimulation (e.g. the inferior frontal gyrus) abolishes EEG mu rhythm⁸ and EEG mu rhythm suppression correlates with BOLD signals from fMRI in putative mirror neuron regions within subjects⁹, providing additional support that this rhythm indexes, at least in part, MNS activity. Assessment of the EEG mu rhythm has allowed for a noninvasive assessment of mirror neuron activity in humans.

EEG provides a unique methodology for examining brain activity and when combined with behaviorally based assays, it can be a powerful tool for elucidating aspects of social cognition, such as imitation, in clinical populations. Further, the applicability of EEG for use with populations with cognitive or language impairments allows for insight into abilities of individuals for whom other imaging techniques or behavioral paradigms may be less successfully utilize. The described protocol provides an avenue to examine social cognitive functions theoretically linked to the mirror neuron system in individuals with typical and atypical development, such as Autism Spectrum Disorder.

Protocol

The following protocol adheres to the guidelines of the University of Washington institutional review board.

1. Electrophysiological Assessment

1. Preparation of Session
   1. Room preparation: place the manipulandum (see Figure 1), a wooden block with a sensor attached, which sends a time-stamped marker to acquisition software when it is grasped, on the table within grasping reach of the participant. Activate EEG acquisition software and begin “new session” (Figure S1).
   2. Net preparation: warm solution of distilled water (1 L), potassium chloride (1 tablespoon), and baby shampoo (1 teaspoon) to 104 °F. Soak 128-electrode dense-array EEG system in the warmed saline solution.
   3. Participant preparation: ensure that the participant is seated comfortably approximately 75 cm from the stimulus presentation monitor and fully in view of the video camera. Find and mark the vertex on the participant’s head with a skin marker. Measure the vertex by finding the intersection of the midpoint between the nasion and the inion and the midpoint between preauriculars.
   4. Net application: Position the EEG cap on the participant’s head such that the vertex electrode is placed directly over the vertex mark. Check impedances and ensure that impedances are below the threshold appropriate for the EEG system in use (Figure S2).
   5. Begin video taping session.
2. Recording setup: Reference signal to the vertex electrode. Analog filter between 0.1 and 100 Hz, amplify the signal, and digitize at 500 samples/sec.
3. Stimulus presentation: present participant with 3 conditions: observe, execute and rest, adapted from the paradigm developed by Muthukumaraswamy and colleagues⁵.
   1. Observe condition: Instruct participant to sit quietly and watch a video of a person grasping the manipulandum. Each trial should last 6 sec. Time the prerecorded video for the observe trials precisely to ensure that the observed grasp occurs at exactly 3 sec. Monitor participant’s visual attention during the task, and mark trials during which they do not attend to the screen to be discarded during post-processing.
   2. Execute condition: Instruct participant to sit quietly with right hand resting just below the manipulandum and, upon hearing a prerecorded auditory cue, to imitate the manipulandum grab from the observe condition video clip. Each trial should last 6 sec. Ensure that the auditory cue is presented at exactly 3 sec by prerecording an auditory track that maintains a consistently timed execute cue and inter-trial interval. Utilize a sensor on the manipulandum to precisely record the time that the participant’s grasp occurs (Figure S3).
   3. Rest condition: Instruct participant to sit quietly with eyes open and passively observe a small crosshair on the stimulus monitor. Record continuous EEG during the rest condition for 3 min.
   4. For both observe and execute conditions, present randomized blocks of ten trials, for a total of forty trials per condition. Ensure that the image of the manipulandum remains on screen throughout the observe and execute blocks, including between trials. Administer the rest condition at the completion of the observe and execute conditions.
4. Data Processing
   1. Following data collection, recheck impedances. Note any changes to impedance levels. End acquisition software recording.
   2. Post-processing: Rereference EEG signal to the average. Segment continuous EEG data into forty 6-sec trials for each condition (Figure S4).
   3. Conduct automated artifact detection. Use automated algorithms to inspect segments for movement artifacts by identifying fast average amplitudes exceeding 200 µV, differential average amplitudes exceeding 100 µV, and zero variance across a given trial (Figure S5).
   4. Conduct manual artifact detection by visually inspecting data and confirming with video review of the session to remove all trials in the observation condition contaminated with any movement artifact and all trials in the execution condition contaminated with any movement artifact unrelated to the grasp gesture. Exclude trials with significant artifact from analysis. Discard any trials that were flagged during acquisition as not attended. Examine and note rate of trial rejection for each diagnostic group under analysis.
5. Data analysis
   1. Per Muthukumaraswamy et al.⁵, segment cleaned trials into 2 sec epochs consisting of 1 sec of data before the grasp and 1 sec after for both the observe (as marked by the photocell) and execute (as marked by the manipulandum sensor) conditions. Segment cleaned 2 sec epochs from the rest condition. 
   2. Fast Fourier transform (FFT) each segment. Select a cluster of eight electrodes on each hemisphere surrounding the standard C3 and C4 positions for statistical analyses (following Muthukumaraswamy et al.⁵ and Bernier et al.³) (Figure 2). For each condition, average the power across the included trials to calculate power spectra.
   3. Calculate mu attenuation by examining the average power during either the execution or observation of a motor action, relative to the average power during the resting condition, across the 8-13 Hz range. Use the log of this ratio to determine degree of attenuation. Note: a negative value represents attenuation during execution or observation, while a positive value represents augmentation. This methodology takes into account variability across individuals, and the non-normality of values expressed in ratio form.
      Note: This protocol was developed using a 128-electrode dense-array EEG system with Net Station software version 4.1. While the basic steps are similar across EEG systems, acquisition and analysis protocols may vary.

2. Sample Characterization

1. Identify potential patient population for participation in paradigm through research registries, previous participant listings, or referrals from area clinics and clinicians.
2. Screen potential participants for likelihood of meeting diagnostic criteria for clinical construct (e.g. Autism Spectrum Disorder) and to identify any exclusionary criteria, such as presence of head injury, tumor, seizure history, or use of anti-convulsant or barbiturate medication which may distort the electrophysiological signal.
3. Confirm diagnostic status of patient population through the use of gold standard diagnostic instruments (e.g. Autism Diagnostic Interview-Revised (ADI-R¹¹) and the Autism Diagnostic Observation Schedule-Generic (ADOS-G,¹²) administered by expert clinician following Diagnostic and Statistical Manual – 5^th Edition (DSM-5) criteria¹³.
4. Identify control sample matched on relevant variables of interest, such as age, gender, cognitive ability, etc.

Results

Typical adults, children and infants have consistently demonstrated mu rhythm during both the execution and observation of actions across a variety of paradigms and stimuli^5,14-30. Attenuation in this frequency band is consistently localized across central electrodes (Figure 3) indicating that this is not reduction of alpha power which is recorded at other scalp regions. Similarly, attenuation in this frequency during the observation of movement is limited to the observat...

Discussion

The successful acquisition, processing, and analysis of electrophysiological data related to the mu rhythm and the application to clinical populations requires 1) the application of EEG methodological tools, 2) careful artifact detection and data reduction, 3) accurate identification of the mu rhythm, and 4) accurate characterization of the clinical population and identification of appropriate control groups.

Appropriate EEG methodology requires properly functionin...

Materials

List of materials used in this article

Name	Company	Catalog Number	Comments
Geodesic EEG System	EGI	N/A	Any EEG system, not only EGI based systems, is applicable for the described study
MATLAB software	MATLAB	N/A	Any mathematical, statistical software that can work with matrices is applicable
Netstation software	EGI	N/A	Any EEG acquisition software is applicable for the described study
Manipulandum	custom	N/A	Any object that is coregistered with data acquisition software to signal a successful grasp