Day 34: Final Destination

Today is my last day at ASU, and my mentor decided that I practice my presentation in front of people who work in the lab during today's lab meeting. I was the only one who presented which made me feel bad for those who walked all the way to the conference room, but not a lot of people came, mostly because they had actual presentations to do. I was asked only one question, but was unable to answer it. Dr. Parikh answered it in lieu of me, so, if anyone else asks that question at my final presentation at school, I'll be able to answer it. Besides that, they all said they liked my presentation, which made me happy :)

 So seeing as this is my last day in the lab...

I would like to express my gratitude to all who have read this blog and journeyed with me these past 3 months.

I would also like to thank my superiors who have done so much to make this project possible. Specifically, Mr. Paul McClernon, Mrs. Elizabeth McConaghy, Dr. Marco Santello, and Dr. Pranav Parikh.

Day 33: Freaking Out

Here's a glimpse of my presentation that I have been working so diligently on (left). Last week, I mentioned to my mentor how this week will be my last week in the lab, so he suggested that I present my presentation to people in the lab. I seriously thought he was joking. This Friday, I will be giving a presentation to people I have either never seen before or never spoken to before. I am really nervous. Everyone here is really nice and all, but they all will secretly be judging me, and I know it. Luckily, my mentor is willing to help me fix up my slides and my speech. Hopefully that will be enough to compensate for the neuroscience knowledge everyone knows I don't have... I'm freaking out, as you can see. I really should give myself more credit, but everyone in this lab knows so much about the brain and TMS and experiments like mine that I'm afraid I'll appear inept. Everyone here is either a graduate student or a post-graduate scholar, so I'm terrified.

Negativity aside, there's really nothing else I have to say. Just need to add more notes to my presentation to read off of for my trial run Friday. Wish me good luck!

Day 32: The Beginning of the End

This is my last week interning here at ASU. And even though I'm picked on incessantly by Juan (a student graduating from ASU this spring who is sitting right behind me as I type this), my mentor, and everyone else, I'll miss everyone here, especially those who welcomed me so warmly.

Anyway, I just checked the calendar, and it seems I only have a week to finish my presentation. I'm way past halfway done, so no worries there. My final product is coming along as well so I have no complaints nor problems there either. Senior project aside, I have to make posters to advertise Prom, practice for the school concert that's tomorrow, practice for the Symphonette concert taking place Thursday morning, and study for AP Spanish. So I'm pretty busy...

But GOOD NEWS!

My blog has over 1000 views! That is amazing! Thank you to all of my readers, especially the faithful ones, who made this proud moment possible.

Thanks for reading!

Day 31: The End Is Near

Today, I was properly taught how to interpret the data into graphs. Turns out, I've been doing it wrong, so here are now my new, more comprehensible graphs (out of habit, I made the titles as detailed-yet-elegant as possible).

This graph illustrates the difference between the excitability values coming from the FDI (index finger) muscle. The two lines seem to be quite parallel until it reaches 1000 ms (when the Go cue appears). At 1000 ms, there exists a significant difference between the P-MEP values and the P+F-MEP values.





This graph illustrates something similar to what the above graph shows, except this is for the APB (thumb) muscle. It also shows a significant difference at 1000 ms.

• Side note: We had to disregard some data points at 1400 and 1500 ms because data become really noisy. This is due to the fact that movement occurs approximately 300-500 ms after Go cue (which flashed at 1000 ms). 

Lastly, I created a ratio graph to see where the greatest difference between P and P+F occurred. Again, the significant values were at 1000 ms (disregarding 1400 and 1500 once more).







This all indicates that the corticospinal excitability (CSE) is task-dependent at 1000 ms. The comparison of CSE between P and P+F aims at determining whether the sequence of controlling digit position followed by force application is planned similarly to controlling digit position alone, and thus, my project has reached a conclusion.

Day 30: Pizza Party

The Minister of Social Activities in the lab planned out a pizza party for everyone who works in the lab. Everyone here is so kind, and I'm grateful that I was invited to enjoy pizza and conversation with them. Even the Director of the School of Biological and Health Systems Engineering here at Arizona State University was there! I seldom see him, which is disappointing since he is my mentor for this senior research project.

Speaking about my senior research project, I submitted my abstract yesterday to my adviser, Mr. Paul McClernon, who gave me really great feedback. Now I'm working really hard on the edits. The abstract is due tomorrow so wish me luck!

Day 28: Sick Day

I wasn't able to go to the lab today because I offered to stay at home to help my brother, who is at home sick. So I won't be able to attend today's Journal Club meeting.

For today, I've been working more on my abstract. Since the deadline is coming up quickly, I don't have time to finish my article before writing the abstract, so hopefully my abstract will turn out well. I have everything I want to say, but putting it all together elegantly is challenging.

What exactly do I do in this project? You've seen me post about TMS research, data analysis, and...that's basically it. The actually experiment tries to see if there's a difference in excitability of the finger muscles when planning to position the fingers (P) vs. planning to position the fingers and apply a force (P+F). To do this, TMS is used to stimulate the motor cortex and the EMG records the amount of muscle activity from that stimulation. The EMG also records the actual movement of the muscle that takes place approximate 500 ms after the Go cue (Day 25), but we only need the data of the muscle activity while planning for the movement. The objective is to better understand the relationship between the brain and the movement of the fingers. I hope to put something like this paragraph as my abstract.

In addition to working on the abstract, I've been analyzing the data more, hopefully creating decipherable, more simplified graphs. The graphs I made previously involve the 4 muscles we put electrodes on: FDI, APB, ADM, and FCR. 

But for this project, I only need to look at FDI and APB because the fingers associated to these muscles are the index finger and thumb, which act directly on the object during experiments.

So not much is happening today. Maybe I'll bake something with all this free time! Hopefully though, I'll be able to show more on my next blog post or in my final product and presentation!

Day 27: Someone Stole My Seat...

... and my desk! So now I'm sitting on this too-comfy-to-be-comfy couch. Fortunately, I'm willing to overlook this obstacle and be productive today. I've been working on my abstract for my senior research project which is really quite challenging because of the fact that it should be terse. So to get the most important points of my project, I'm working on my final product which will be a little bit like an article. I remember in Capstone Physics, we wrote articles to get published and we would write the abstracts last because it's easier to remember the thesis once your thoughts have been arranged. So it's kind of a habit for me to write the abstracts last.

• Side note: One of my friends from Capstone Physics, Jeff Milling, has been working really hard doing more research on nuclear fusion on top of his senior research project because his paper (which was co-written by Ryan Hearn) is actually really close to publishing! Absolutely brilliant work! I really hope you guys get published!

In addition, I've been reading another article for Journal Club tomorrow about the contralesional dorsal premotor cortex (cPMd) and what its purpose is after strokes. The study uses TMS and fMRI. TMS has been becoming very popular when being used to study strokes, which continues to intrigue me. The TMS is used for several different studies and treatments that it is becoming more versatile. It could be because the TMS is used to affect the brain, which controls everything in the body. I love the brain.

Thanks for reading my blog! 

Day 25: Organization and Orchestra

Everything is suddenly so clear! I can see the light at the end of the tunnel. We senior-project-doers have less than a month to complete our final product AND presentation, and fortunately, I've completed a detailed outline of both my paper and my presentation--so detailed that really all I have to do is paraphrase a few paragraphs, then copy from my outline and paste into my paper. I'm excited (as you should be) to see the outcome.

On the other hand, I will not be in the lab the next few days because I will be going to Disneyland along with all my friends in our school band/orchestra/instrumental ensemble. But hopefully I'll come up with an update for you tomorrow on this exciting new development!

In the meantime, since I learned more about it today, let me discuss with you a little bit about how trials are conducted.

P and P&F stand for Position and Position AND Force Application, respectively.

The image above contains all the cues needed to conduct our planning/preparation experiment. During these cues, the subject must remain as still possible since the data we need must be collected before they do the action. The cues light up in a similar manner as in the image below.

The location of the yellow dot is a lot more random though

There is only a second between each cue (except in between the Go → Ready cues because we must allow a reparation period). We use TMS between the Ready cue and within 500 ms after the Go cue to measure excitability. We continue collecting data 500 ms after the Go cue to allow the brain to process the visual information from the Go cue. TMS is used, in the case, to help measure the difference of excitability needed to position the fingers (P) and to position + apply force (P&F).

So now... I'm trying to analyze the data I organized yesterday. I'll post my discoveries next time!

Day 24: Numb-er from Numbers

I just finished compiling another set of data, resulting in this pretty graph! In addition, I've been working on my final product, which will either be in the format of a lab report or a research paper. I plan that my final product will consist of many of the topics I've covered in this blog (including TMS), but mainly, it will discuss the conclusion of this project.

So for this post, let me briefly explain what I do with the data. (Don't forget: You can click on images that seem illegible)


Step 1: Run Spike code

During testing, we collect data using the software, Spike. Spike also allows us to write codes that will do something with that data. In this case, we told Spike to create an Excel sheet of all the values it recorded. But what these values tell us is still unclear. In order to clarify all these data, we run these values through a code in Matlab.

Step 2: Run Matlab code

The Matlab code figures out what condition we cued for the subject (Column C: condition) and when we stimulated the subject with TMS (Column D: timings). The first two columns is what Matlab used to comprehend the data to create the "condition" and "timings" columns, but now they become irrelevant to me when finding the averages.

Step 3: Find averages

This image is of one block for one subject. Here, I find the average value of the MEP values for each condition at each TMS time. Then I divide each of the averages by the Baseline (in the image below).

By dividing the values by the Baseline, we find the normalized MEP values. The Baseline value is the threshold MEP value needed for the action. So we base all our data from the Baseline value. So as a result, I collect the image above from each block of each subject. Then I average the average normalized MEP values of each block for each subject to end up with this monstrosity (below)!

The average normalized MEP values of each subject.

Step 4: Make it look pretty

I take the average of all the values of the subjects and end up with this: a beautiful, simplified set of data and graphs. 

Later, I'll be analyzing data and taking standard deviations to find the error margin of all the data I collected. Thanks again for reading my blog!