December was a shorter month than the previous ones, as I left for the Christmas break on the 19th. However, there was good progress made in research, collaborations, student supervision, invited talks, and outreach. The reason I called this month connecting to people, is that I got two new students, I have a closer collaboration with Thanos, and we got a lot of resonance from the Maastricht people volunteering for Pint of Science. I even got called “the pint of science lady” once! But, let’s get started.
In December, I got a bit more training into the TMS methodology, by Teresa Schumann. We also filled in the project proposal form for the Non-Invasive Brain Stimulation committee to approve. This made me think about the exact parameters of the stimulation, the equipment and the analysis pipeline of the EEG data. Some formal details:
- NIBS lab required: NIBS1
- Equipment required:
- TMS-compatible EEG caps by EASYCAP (sizes 54, 56, and 58cm)
- MC-B70 coil
- Sham B70 coil
- Number of participants: 40 (20 per group)
- Number and length of sessions per participants:
- Number of sessions: 2 (sham and real stimulation)
- Length per session: 1h in NIBS1, 3 hours in total
- Minimal amount of days between sessions: 1 week
- Stimulation parameters:
- Intensity: cTBS (600 pulses) at max 100% resting motor threshold,
- Site: right pre-SMA
- Localisation: using structural MRI scan, identifying the pre-SMA for each participant individually based on TAL coordinates.
- EEG Recording
- Number of EEG electrodes: 64
- Hardware low pass filter setting (Hz): 0.1 Hz
- Hardware high pass filter setting (Hz) or time constant (s): 100 Hz
- Sampling rate: 500 Hz
- Amplifier gain:1 microV
- Measurement reference electrode location: left mastoid
- Additional electrodes used for off-line rereferencing: right mastoid
- Locations of electrodes recording eye movements and blinks: Fp1, Fp2
- Location ground electrode: AFz
- Software package(s) used: MNE Python
- Procedure for handling eye artifacts : Artifact correction with ICA
- Describe intended analysis (ERP/Time-frequency/frequency bins/MVPA/source localization/connectivity etc): ERP analysis time-locked to the beat onset, and time-frequency analysis to find out which frequencies are more sensitive to beat perception
- Are you intending to do any analysis on data recorded during the stimulation? No
My collaboration with Thanos Lykartsis is going very well. Thanos delivered the first audio analysis of the stimuli. The beat tracking algorithm can classify very well between male vs. female voices and story vs. poem.
I received 4 applications for master thesis internships, of which I accepted 2 and I am very happy to have two new people. These new people brought me hope that I will have help to conduct the experiment and analysis together.
Invited talk in Marburg
On December 13th I gave a talk in the Neurolinguistics Colloquium of Philipps-University Marburg, where I defended my PhD three years ago. It was moving to go back after these few years, and explain what I have learned on the way. My talk was focused on the how to “go natural” in the neurobiology of language. I explained in detail how I created the stories for my PhD research and what were the objectives of our approach at Imperial College London. I also gave a sneak peak into how I am planning to model the responses in NERHYMUS.
Pint of Science is going very well, we have a couple of volunteers as event managers and we sent out a big university-wide call to ask for more volunteers. This call was, to my surprise, presented also in the narrowcasters at Oxfordlaan 55, in the cafe banditos, see picture.
Last, I leave you with my favorite view of my home town in Greece, Heraklion.