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A Peagrim’s Progress, or, “Let’s get down to pea-zness”

Hello! There has been some trial and error since my last update. I started my experiment with Monica Gagliano’s protocol (overly simplified!):

  1. Grow seedlings, 48 of them:
  1. Get them used to 8 hours of light, 16 hours dark (circadian rhythm):
  2. Train them under decision covers for 3 days:

  1. Test them

She had 48 of them. Unfortunately, each of those PVC pipes are $16 each:

PVC (above) x 48 = $768

Well, that’s not very practical for a classroom experiment.

So I tried my DIY version.

  1. Take a plant cell box:
  1. Make cardboard covers for each of those 48 cells

Fit the covers over the plant cell box:

Make fan/light circuits for them:

Hook them up:

Here is the schematic:

So in this way, 48 plants are being trained with two circuit boards.

That was in theory.

In reality:

  1. Cardboard is way too flimsy to stay on the appropriate columns.
  2. The fans, 5V, to work with the LittleBits circuits, were way too weak.

Everything kept sliding around, falling apart, as I was supposed to be training them. I was Chi-Fu trying to keep soldiers in line when I needed to be Captain Shang.

The results?

Pea-tiful 🙁 

They grew as straight as sticks, when I was looking for this result:

Plants that grew towards where I presented the fan last, towards the middle of each of the two rows.

The plants also were tall and spindly, meaning they had tried to get to the top as quickly as possible.

So back to the drawing board. We decided to do everything PROPERLY this time. Stick to the protocol. Exactly.

One problem I’d had was everything slipping all over the place so I bolted things down:

We’ll start with a small n of 4. I have another experiment design coming up, but you’ll have to wait until the last post for this and hopefully more exciting data!

Silk Moths, Enter The Arena: Binary Choice Paradigm

Hello, everyone! It’s Jess again. Since I last wrote, I have shifted all my work from cockroaches over to silk moths. I’ve had to make a few modifications to my protocols, but overall the transition has been smooth. Working with the silk moths has been far more enjoyable than the cockroaches (no offense roaches, but you freak me out), and I’ve really settled into my role as a moth mom. The moths will hang out anywhere you put them–sometimes I even walk around with them on my shoulder when I am getting ready for experiments and have to carry other things!

Left: Female silk moth perched on her cup.   Right: silk moth along for a ride



In my last post I mentioned how the experiment has two parts: behavioral observation and electrophysiology. Unlike the cockroach experiment, designing a behavioral paradigm for the moths was fairly simple because the male silk moth’s response to the sex pheromone bombykol is extremely profound. I suppose if you’re only alive for five days reproduction is kind of a huge deal? 

For my assay, I use a binary choice paradigm. The materials are simple, dixie cups (to raise the chemicals off the ground so moths can’t touch them), multiple tupperware containers, and the compounds of your choice.

Three females starting the behavior task


For the experiment, I place a stimulant and corresponding control dixie cups on opposite ends of the tupperware, place a group of same sex moths in the middle and record what half of the arena they are in after 5 minutes have passed. Sometimes, the moths will not move for the full 5 minute trial, so they receive a ‘no choice’ score. All trials are recorded to ensure correct scoring and for the potential to be used in Anastasiya’s awesome tracking program when it’s complete.

In addition to scoring how many moths end up on the stimulant or control side, I also record how many moths are performing reproductive behavior. When the females emit the pheromone from a gland in their posterior, the males begin to rapidly flap their wings and spin around in circles as they orient themselves to the location of the female. It looks like this:


Three males responding to a female emitting bombykol


Males also have this same response when I place synthetic bombykol in the arena. In addition to bombykol, I am testing linalool (plant terpene found in the Mulberry leaves silkworms eat), ethanol (accessible positive control for electrophysiology), de-ionized water (ethanol solvent), mineral oil (linalool and bombykol solvent), in male moths and female moths. I’ve run 40 trails, and it appears that bombykol and female moths are the only things that change behavior in the males, and the females have no response to any of the stimulants. By the next blog post, I will have a visualization of my results.


Observing consistent electrophysiology result has been (and still is) the challenge of this project. The silk moth antenna is significantly more sensitive to mechanosensation than the cockroach and can quickly become overstimulated. Additionally, many of the compounds are oil-based, so they coat the interior of the syringe and make deployment difficult and inconsistent. I’ve come up with a couple solutions to my problems, and now I just need to figure out how work them together.

First, have modified a fan from the BYB office using a milk jug container to direct light airflow on the prep. This reduces the noise from other wind artifacts (literally breathing on the prep gives signal).  Second, using a sponge soaked in DI water, I have humidified the airstream to make the prep last longer.

Modified fan and humidified air set-up


The last, most challenging step has been determining how to deliver the stimulus. Ideally, I would like to inject it into the airstream, but as I mentioned above, the deployment of the syringes does not work well. I have tried blowing air through the syringe onto the prep, and soaking sponges with stimulant and placing them in the airstream. This week I will be prototyping some new ideas. 

Some days my data is beautiful, and some days it looks like shit. A bit of the variation I am seeing is also due to the hardware. I am currently deciding which BYB board will work best to record at the low frequencies I need and this alone can distort the signal from preparation to preparation.


Example of two ethanol trials looking very different just a few minutes apart.


The good news is that I have plenty of time and moths to figure this out. Also, not getting consistent data is frustrating, but this is one of the best part of research- when nothing really makes sense, you kind of hate the procedure, and then one day after you’ve tried it enough times, it miraculously works and you want to do it all over again. I made some good progress today, I’m sure I’ll make good progress tomorrow, and hopefully by the next post I’ll have some consistent data to show you all!

Meditation Data Or Bust

Hello dear reader! It’s Maria again and my meditation project is picking up speed. I’ve now recorded ~brain waves~ from 20 different people! If I’ve learned anything so far, it’s that in science it’s data or bust. I’ve been soliciting people over email and clipboarding more than a Jehovah’s Witness. If this science thing doesn’t work out, at least I’ll have canvassing skills… For the past couple weeks I’ve gone to so many open meditation sessions that I’ve started to make friends! There are weekly “sits” with the Ann Arbor mindfulness community, University of Michigan, and a local Zen Buddhist temple.

The Zen Buddhist Temple

From these communities and around town, I’ve been gathering up people with a large range of meditation experience, from self proclaimed yogis to novices who have never meditated before.

There’s a mind reader in your closet

And so! I decorate the volunteers with sweatbands and wires and let bake for approximately 45 minutes in Greg’s closet. With these headband scalp recordings, the most reliable signal that we can pick up is the alpha wave, a frequency of oscillation that was discovered by Hans Berger in the 1920’s. As a student around my age, he fell off of a horse and in this moment of mortal danger believed that he had spontaneously transmitted his thoughts to his sister. He later became obsessed with proving the physiological basis for “psychic energy” and developed a tool to record the electrical current of the brain from the surface of the scalp using rubber bands and silver foil (not so different from the sweatbands and buttons I’m using this summer).

Electrode setup used during meditation task

I’m recording from the frontal, temporal, occipital, and left parietal areas – locations related to vision, complex object recognition, planning, and tactile attention that I have taken from a corresponding EEG and FMRI studies. The goal is to compare the alpha waves from these areas during different tasks. When the eyes are closed and the visual cortex is not receiving information, and the neurons oscillate slowly at the same rate to produce a summed wave called the alpha frequency. In contrast, when they eyes are open and the visual cortex is active and receiving various types of information, the neurons will fire out of phase and no distinct waveform will be observed (Backyard Brains has a stellar visual explanation of this here). Because alpha waves are thought to correspond to the idling state of neurons, I am curious to see if the waves be affected by more than just visual stimuli. Perhaps meditation is more or less “idle” than other states of rest.

A problem that I’ve run into is what to compare meditation to. What is a consistent control that I can measure meditation against? What the heck is your resting state, the state your brain defaults to when you are not consciously concentrating or directing your thoughts like with meditation or math? So far, I’m testing out what the prompt of “eyes closed rest” will bring about in both the EEG data and what they report to have been thinking about during the open-ended task. So far, over half of all participants have explicitly mentioned “what I need to do today” as the content of their thoughts during the rest task.

I am also testing a more specific contrasting task that is a theoretical opposite to meditation: rumination – a psychological state that is associated with depression. In terms of thinking about yourself, meditation is a kind of experiential self focus in the present moment, whereas rumination is often references past experience in a narrative way. I also leave the electrodes on during the survey questions and ask them questions verbally as another comparative task.

Blinking is not sleeping

I wrote a little bit of code to take calculate the amount of time during each task that the brain waves were in a certain frequency i.e. the amount of time alpha waves are present during meditation.

Positive self reported meditation, mantra technique, 3120 hours of meditation experience

Looking at the data from individual recording sessions so far, you can see that in the frontal region, the delta frequency band has more power on average, but this is actually due to eye blinks and not real delta waves!

A recording of real delta waves and eye blinks that occur at a similar frequency

The FFT algorithm generates a spectrogram (blue boxes) by converting the electrical signal into frequency components of Hz or cycles per second. Spontaneous blinks, which happen at around 0.3 Hz or 15-20 times per minute are often mistaken for delta frequency waves that occur during deep sleep (0.5 to 3 Hz). Although the difference in signal is from eye muscles and not the brain, it suggests that for some reason people blink differently during different tasks. Eye blinks may not be the biomarker that I’m looking for, but it does indicate that there is some difference between the mind states.

PS: here’s a sneak peek of a little side project of portable EEG recordings.

Since I have been struggling with what to use as a control, I figured that I should see if I can measure all mind states at all times!