Jan 18, 2011
The media regularly reports on findings based on neuroimaging studies, but rarely do they explain exactly what these techniques are, their benefits or what it’s like to actually participate in these types of studies. Today I’ll describe what a participant goes through when they volunteer for a cognitive neuroscience experiment using a neuroimaging technique called electroencephalography (EEG). Unfortunately, it is exceedingly common for participants to not understand how these techniques benefit previous behavioral findings. Simply stated, if I were a participant, I’d like to know why I needed to wear a weird swim cap and how it benefits the research being done.
EEG is a tool regularly used to view and record the changes in brain activity involved in the various types of cognitive functions while performing a task. Brain cells communicate by producing tiny electrical impulses, and the function of EEG is to record these patterns of electrical activity (as illustrated in Panel A of the figure below; like you’d see with a polygraph machine, but from your head) and then use this data to inform specific behaviors. This activity is recorded by electrodes (small devices that act like microphones listening in on the brain’s spontaneous electrical activity).
Figure legend. Panel A shows the EEG from a participant at 64 different electrodes (along the y-axis) over 16 seconds (x-axis). Panel B highlights the electrical activity from one of the lateral posterior electrodes (PO7) when different colored shapes were presented. Panel C illustrates the event-related potentials (ERPs) observed after averaging all of the segments of data associated with green circles (the red line) and all other shape/color types (black line). The y-axis is indicative of the voltage (positive or negative), while the x-axis shows time in milliseconds (msec): from 200 msec before the colored shapes were presented to 800 msec afterwards.
EEG can be particularly illuminating for researchers trying to better understand how the brain actually changes (for example, after attentional training). For example, a recent study by Eldar and Bar-Haim (2010) examined which functions of attentional processing are affected by attention training in anxious individuals. They found that training helped these individuals change their behavior to divert their attention from threatening stimuli faster. This change could also be seen neurally with changes in their EEG activity over prefrontal electrodes.
So what is it like to actually participate in an EEG study? Here’s the example of someone who recently participated in a study in our lab. Our participant (let’s call her Jenny) had an electrode cap (looks like a swim cap with 64 strategically placed holes in it) placed on her head. A few milliliters of conductive gel were placed at each of the 64 points on her scalp where each electrode was going to rest. This is so that the tip of each electrode would ‘swim’ in this gel and record the neural activity at her scalp. Surprisingly, wearing the cap really isn’t that uncomfortable; in fact, many participants really enjoy the ‘pseudo-scalp massage’ that comes along with adding gel to each electrode site.
For this experiment, we were interested in seeing how attention-related neural activity differed when priming one’s self to respond to a given target image. Jenny was instructed to push a button in response to green circles appearing on a computer screen in front of her, while ignoring all other shapes & colors (see the shapes described at the bottom of Panel B). In the course of the experiment, she saw a total of 100 green circles, and 100 other irrelevant stimuli (the “x 100 events” in the figure). We were particularly interested in neural recordings from the lateral occipital cortex (on the backside of the head) where activity related to visual discrimination (“ooooh, that’s a green circle, hit the button….nope, that’s a green square, he’s obviously trying to trick me, not going to respond…”) is typically recorded. The figure in the middle panel shows the EEG that was recorded over the course of 15 seconds at one of the electrodes where this activity was the greatest.
To see differences between neural activity for green circles vs. everything else, we took small segments of the data around the onset of green circles (and each other shape; see the boxes in the 2nd panel for each target type), and averaged those events together. This averaging led to the figure in Panel C: these are called event-related potentials (ERPs) which require the averaging of many trials to see them (that’s why they are tough to see in the non-averaged activity in the top two panels). Notice that the waveform goes ‘up’ around 100 msec (this is called the P1: p for ‘positive voltage’ and 1 for ‘near 100’ msec) followed by a N1 component (n for ‘negative voltage’…).
So what good are these signals for anyways? Well, certain ERPs can reflect one’s attentiveness: if you’re primed for a certain thing to happen, you can respond to it sooner and more accurately. Notice that the P1 amplitude and N1 amplitude is greater for the green circles as opposed to the ‘non-targets’ blue square. This is especially interesting and important as these markers provide information before the participant even responded! Having a measure that relates to one’s performance before they actually ‘perform’ can be especially informative in figuring out all kinds of things. For Eldar and Bar-Haim, they saw similar markers changed with training; for us, we use these markers to better explain how participants responded quickly to green circles. These types of findings are typical of what an EEG cognitive neuroscience experiment would look for. And yes, they are also the reason that we are thrilled when participants are willing to put up with wearing strange looking head gear for an hour or two.
References: Eldar S & Bar-Haim Y. (2010). Neural plasticity in response to attention training in anxiety. Psychol. Med., 40(4): 667-77.
—- Joaquin A. Anguera is a Postdoctoral Fellow working in Adam Gazzaley’s cognitive neuroscience laboratory at the University of California, San Francisco (www.gazzlab.com). His research focuses on how aging affects different aspects of motor & sensory performance using both behavioral and neuroimaging techniques.
Learn more on neuroimaging techniques (MRI, PET, etc.):