Poster No:
1690
Submission Type:
Abstract Submission
Authors:
Ekaterina Voevodina1, Emily Moore1, John Semmler1, Flavio Frohlich2,3, George Opie1
Institutions:
1Neurophysiology of Human Movement Laboratory, University of Adelaide, Adelaide, SA, Australia, 2Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA, 3Carolina Center for Neurostimulation, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
First Author:
Ekaterina Voevodina
Neurophysiology of Human Movement Laboratory, University of Adelaide
Adelaide, SA, Australia
Co-Author(s):
Emily Moore
Neurophysiology of Human Movement Laboratory, University of Adelaide
Adelaide, SA, Australia
John Semmler
Neurophysiology of Human Movement Laboratory, University of Adelaide
Adelaide, SA, Australia
Flavio Frohlich
Department of Psychiatry, University of North Carolina at Chapel Hill|Carolina Center for Neurostimulation, University of North Carolina at Chapel Hill
Chapel Hill, NC, USA|Chapel Hill, NC, USA
George Opie
Neurophysiology of Human Movement Laboratory, University of Adelaide
Adelaide, SA, Australia
Introduction:
The ability to acquire new motor skills, or motor learning, is an inherent component of daily life. A critical element of motor learning is the ability to adjust to changes in the environment and in our bodies, a phenomenon referred to as sensorimotor adaptation (Krakauer et al., 2019; Reuter et al., 2022). Electroencephalography (EEG) studies revealed that specific patterns of oscillatory brain activity underpin motor function; in particular, modulation of higher frequency amplitude by lower frequency phase, or phase-amplitude coupling (PAC) (Combrisson et al., 2017; Yanagisawa et al., 2012). However, to the best of our knowledge, PAC patterns associated with motor adaptation have not been characterised.
Methods:
We aimed to address this limitation by recording 64-channel EEG as younger adults (n=14, age=18-40, gender-balanced) performed a centre-out reaching adaptation task (CRAT). This involved moving a joystick to shoot an on-screen cursor through targets arranged radially around a central start location. Initially, cursor trajectory followed hand trajectory, allowing initial quantification of performance (baseline stage). Subsequently, cursor feedback was rotated (−30) relative to hand movement, forcing participants to adjust movements to reduce error (adaptation stage).
EEG data was epoched around motor planning (500 ms after the onset of the target) and motor execution (500 ms after the onset of the signal to start the movement) periods and divided into two stages: baseline and adaptation. PAC was quantified using the mean vector length (MVL) (Canolty et al., 2006; Combrisson et al., 2020) (Combrisson et al., 2020) on 30 electrodes of interest located in sensorimotor and frontal areas. The areas of interest (AoIs) can be roughly defined as left sensorimotor (LSM), right sensorimotor (RSM), left frontal (LF), and right frontal (RF), with left side being contralateral to the active hand. Within each condition, the presence of significant PAC was identified using a permutation analysis that compared recorded values to a surrogate distribution (200 permutations), with correction for multiple comparisons applied using the false discovery rate (FDR).
Results:
Average angle error (AAE) during baseline was −1.92° (±6.52). This increased to −21.28° (±8.52) at the beginning of adaptation and reduced to −10.01° (±6.18) by the end of adaptation, demonstrating expected learning effects.
A preliminary assessment of PAC (including theta-, alpha- and beta-gamma coupling) revealed most prominent activity for theta-lower gamma (4-8 Hz/30-50 Hz) coupling. All subsequent analyses therefore focussed on these interactions. During baseline, planning was associated with weak theta-gamma PAC within LSM, whereas execution was associated with increased theta-gamma PAC in RF, LSM and RSM (all p-values < 0.05). In contrast, adaptation was associated with increased theta-gamma PAC in LF, RF and RSM during planning, with further increases apparent in RF, LSM and RSM areas during execution (all p-values < 0.05).
Conclusions:
Irrespective of movement period (i.e., planning or execution) PAC during adaptation tended to be greater than was apparent during baseline. Furthermore, while increases in PAC during adaptation were present across all AoIs, they were more spatially restricted during baseline. These patterns of activity may reflect a requirement for enhanced neural coordination to meet the increased motor and cognitive demands of sensorimotor adaptation. These results provide mechanistic insight that may help inform development of interventions for motor rehabilitation.
This project was funded by ARC DECRA (DE230100022).
Modeling and Analysis Methods:
Connectivity (eg. functional, effective, structural) 2
EEG/MEG Modeling and Analysis
Motor Behavior:
Motor Planning and Execution
Motor Behavior Other 1
Keywords:
Electroencephaolography (EEG)
Other - motor adaptation, phase-amplitude coupling (PAC), theta-gamma PAC
1|2Indicates the priority used for review
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Please indicate below if your study was a "resting state" or "task-activation” study.
Task-activation
Healthy subjects only or patients (note that patient studies may also involve healthy subjects):
Healthy subjects
Was this research conducted in the United States?
No
Were any human subjects research approved by the relevant Institutional Review Board or ethics panel?
NOTE: Any human subjects studies without IRB approval will be automatically rejected.
Yes
Were any animal research approved by the relevant IACUC or other animal research panel?
NOTE: Any animal studies without IACUC approval will be automatically rejected.
Not applicable
Please indicate which methods were used in your research:
EEG/ERP
Behavior
Which processing packages did you use for your study?
Other, Please list
-
MNE Python, Tensorpac, EEGLab
Provide references using APA citation style.
1. Canolty, R. T., Edwards, E., Dalal, S. S., Soltani, M., Nagarajan, S. S., Kirsch, H. E., Berger, M. S., Barbaro, N. M., & Knight, R. T. (2006). High Gamma Power Is Phase-Locked to Theta Oscillations in Human Neocortex. Science, 313(5793), 1626–1628.
2. Combrisson, E., Nest, T., Brovelli, A., Ince, R. A. A., Soto, J. L. P., Guillot, A., & Jerbi, K. (2020). Tensorpac: An open-source Python toolbox for tensor-based phase-amplitude coupling measurement in electrophysiological brain signals. PLoS Computational Biology, 16(10), e1008302.
3. Combrisson, E., Perrone-Bertolotti, M., Soto, J. L., Alamian, G., Kahane, P., Lachaux, J.-P., Guillot, A., & Jerbi, K. (2017). From intentions to actions: Neural oscillations encode motor processes through phase, amplitude and phase-amplitude coupling. NeuroImage, 147, 473–487.
4. Krakauer, J. W., Hadjiosif, A. M., Xu, J., Wong, A. L., & Haith, A. M. (2019). Motor Learning. Comprehensive Physiology, 9(2), 613–663.
5. Reuter, E.-M., Booms, A., & Leow, L.-A. (2022). Using EEG to study sensorimotor adaptation. Neuroscience & Biobehavioral Reviews, 134, 104520.
6. Yanagisawa, T., Yamashita, O., Hirata, M., Kishima, H., Saitoh, Y., Goto, T., Yoshimine, T., & Kamitani, Y. (2012). Regulation of Motor Representation by Phase–Amplitude Coupling in the Sensorimotor Cortex. The Journal of Neuroscience, 32(44), 15467–15475.
No