The BAS Lab use a multimodal approach that combines non-invasive brain stimulation, neuroimaging, computational modeling, and behavioral approaches. Here we outline some of the techniques and apparatus available to researchers in the lab.
Non-invasive Brain Stimulation
Our laboratory uses a variety of stimulation techniques, with a primary focus on Transcranial Magnetic Stimulation (TMS). TMS allows us to probe the excitability of the human motor system, and to briefly, safely, and reversibly disrupt the normal function of a targeted brain area. We have access to multiple state-of-the-art laboratories equipped for single/dual site/repetitive TMS.
Projects from our group have used TMS to assess the influence of the cerebellum on the primary motor cortex, and to examine how the excitability of the motor system changes when we observe actions performed by other people.
Due to our location next to the Saint Luc Hospital, our group have access to Magnetic Resonance Imaging (MRI), allowing us to conduct structural and functional scans of the brain. We have also made use of online repositories of MRI data for discovery-based neuroscience approaches.
Our work has used MRI to assess the distances required to target different structures within the cerebellum, and to identify target regions for neuronavigated brain stimulation.
Our lab has completed several projects using the Activation Likelihood Estimation (ALE) meta-analysis technique, including advanced applications of Meta-Analytic Connectivity Modeling (MACM). Research in this field is enhanced by Obelix, our dedicated data science workstation (4GHz 18 core processor, 256GB RAM, 48GB Nvidia Quadro RTX 800 GPU, 2TB SSD + 8TB HDD).
Projects have included large scale meta-analyses of the brain regions involved in motor skill learning and the imagination, observation, and execution of movements. We have also used repositories of MRI data to determine the optimal target site when applying non-invasive brain stimulation over the cerebellum.
Motion capture allows the recording and analysis of movement kinematics. Our lab is equipped with multiple setups for motion capture, including state-of-the-art facilities for 3D motion capture (4 x GoPro Hero 8 cameras working in conjunction with DeepLabCut software which allows markerless motion capture based on transfer learning with deep neural networks) and 2D recording (4 x Wacom Intuos Pro tablets).
Prior projects from the lab using motion capture techniques have examined how non-invasive brain stimulation affects motor skill learning, and how observing the actions of other people can interfere with or enhance our own movement performance.
Our group have developed several behavioral tasks to measure and assess participant performance and learning. We have recently developed tasks that can be installed on a participant’s own computer, allowing us to study the effects of learning over much longer time-scales than can be examined in normal laboratory-based studies.
Many of our projects combine behavioural and neuroscientific techiniques. In recent work we have developed an Arbitrary Visuomotor Association (AVMA) Task that, when combined with computational modeling approaches, has allowed us to assess the development and expression of habitual responses. Further work using this task has allowed us to show that minimizing trial-to-trial repetition during practice leads to faster motor skill acquisition.