Chronic sleep restriction affects behavioral and ERP correlates of psychomotor vigilance - AKA My First Poster

I suppose this entry is technically late. Tuesday represented my big debut into the fascinating world of sleep research with my first-ever poster session. Sure, I was able to assist in a presentation in January at an aging conference but this was the first work that I've ever presented solo. It was both terrifying and exhilarating. I probably spent the first 15 minutes alternating between terror that someone would ask a question and worry that no one would ask me about my research. But, unsurprisingly, the session went well with polite, friendly people asking a few questions and some even complementing me on my research. And now that I've run through my little spiel in an academic setting, I feel comfortable in sharing it here on the internet. Keep in mind, this isn't an exact replica of my academic-version of the presentation because I know how daunting such technical jargon can be.

The Constellation in Baltimore's Inner Harbpr

My lab was interested in how chronic sleep restriction (a lessening, but not total deprivation of sleep) affects sustained attention, both as a behavioral measure and through recordings of EEG*. Past research has shown that the PVT (psychomotor vigilance task) is an accurate measure of sustained attention. For an abbreviated, modified version of the task, click here. But essentially, participants were asked to watch the screen and hit the spacebar whenever they saw a stopwatch appear on the screen.

Our experimental design began with recruiting 24 undergraduate students with no neurological or psychological problems (including sleep disorders). We had them come into the lab where we recorded resting state EEG (which is sort of a baseline cortical activity) and recorded EEG while they performed the PVT. We then gave them actigraphs (wristwatches that record movement and thereby objective measures of sleep-wake cycles) and asked them to fill out a sleep log entry each day. There were approximately 6 weeks between each of the 3 EEG sessions so we got a span of the full semester.

Based on this data, we found that average daily sleep decreased between session 1 and sessions 2 & 3 (from around 7hrs to 6.5hrs) and this negatively predicted reaction times on the PVT. In other words, sleep went down and time it took to react went up.

My poster

On the EEG side, we found the amplitude of the P3 component significantly decreased from session 1 to sessions 2 & 3. P3 is a change in voltage which has been linked to decision-making in past research. We also found that alpha reactivity amplitude decreased between session 1 and sessions 2 & 3. This component is a little difficult to explain in layman's terms, but essentially a lowered amplitude in alpha reactivity has been linked to dementia, traumatic brain injury, and aging.

The changes in P3 amplitude and alpha reactivity each negatively predicted reaction times both across and within-subjects. What makes this incredible to me is that we saw such clear effects from only avery small change in average daily sleep. We plan to look from here at more specific measures of sleep such as circadian rhythms and sleep latency.

So, that's more or less what I got to present at the conference. It was pretty incredible to talk to so many experts in the field and I look forward to attending next year (hopefully!). For now, I've just settled into my room at Albert Einstein College of Medicine and have started to learn a little about what I'll be doing this summer. More to come on the New York experience next time!

*EEG or electroencephalography is a technique used to record electric potentials created by brain activity and recorded through the scalp. EEG is known for its high temporal (time-based) resolution, but low spatial (location-based) resolution. This makes it good for looking at timing in activation in the brain, but we cannot always locate the origin of the activation.

After a successful, if exhausting, first poster session.

To Infinity, and Beyond!

Ok, so I'm not really going as far as infinity, just New York. Still, that's a pretty big adventure for someone who went to college a scant 30 miles from home. I've never really lived in a big city and just moving to Austin was a drastic lifestyle change for me. But I'm so incredibly excited to have the chance to pursue my neuroscience research at the Albert Einstein College of Medicine in the Bronx.

That's right, this summer I finally nabbed a summer research position! Instead of spending my summer working at a cashier at a grocery store, I'll be out doing science! I really am beyond thrilled. In any case, I thought this research opportunity would be the perfect chance to return to my blogging. I went on an extended hiatus this year thanks to an over-loaded schedule (18 hours plus lab!) and a dearth of creativity. But now I'm back and pumped to share my exploits in the wonderful world of neuroscience.

Just so this entry isn't purely an update on my life, I do have something to share as well. It's a little program called the game of life. It was created by John Conway and relies on 4 simple rules.

1. Any live cell with fewer than two live neighbors dies, due to "loneliness."
2. Any live cell with two or three live neighbors lives onto the next generation.
3. Any live cell with exactly three live neighbors dies, due to "overcrowding."
4. Any dead cell with exactly three live neighbors becomes a live cell through "reproduction."

Although the rules seem incredibly simplistic, they create the building blocks to impressively complex systems. Here's a little taste:

Clearly, certain starting configurations give rise to highly complicated systems. I argue that this is similar to the direction we should be looking to in neural modeling. We know a single neuron receives input from its neighbors and depending on that input, sends a signal of its own. This is very much like the cells in the game of life which change or 'fire' depending on how their neighbors are acting.

While you can't draw an exact 1:1 correlation between the two systems, it still shares a property of emergence whereby cells flickering on and off create complex, interacting grids with an unique output. I don't know if anyone has pursued this kind of modeling in the years since the Game of Life was first developed, but I'd be curious to hear what my readers have to say about this.

And for those who can't get enough of the crazy constructs created in the Game of Life, for you: